Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5, 3274-3292 2025 Publisher: Learning Gate DOI: 10.55214/25768484.v9i5.7678 © 2025 by the authors; licensee Learning Gate © 2025 by the authors; licensee Learning Gate History: Received: 25 March 2025; Revised: 20 May 2025; Accepted: 23 May 2025; Published: 30 May 2025 * Correspondence: somdech.ru@ssru.ac.th Technology, power, and creativity in China’s Wanghong industry: A creator-centric analysis of digital cultural production Qinghao Guo1, Somdech Rungsrisawat2* 1,2Department of Communication Arts, College of Communication Arts, Suan Sunandha Rajabhat University, Bangkok, Thailand; s65584946019@ssru.ac.th (Q.G.) somdech.ru@ssru.ac.th (S.R.). Abstract: This study examines how technology integration shapes power dynamics and creative labor management in China’s wanghong (internet celebrity) industry, focusing on the moderating roles of collaboration and job satisfaction. Using a mixed-methods approach, we analyzed survey data from 600 university students and conducted semi-structured interviews with 20 stakeholders, including creators, experts, and audiences. Quantitative results from structural equation modeling revealed that technology integration significantly enhances both power dynamics and creative labor management. However, collaboration and job satisfaction negatively moderated these relationships, suggesting that social and psychological factors may offset technological benefits. Qualitative findings uncovered a dualistic power dynamic where digital tools empower creators, but platform algorithms impose structural constraints. Additionally, collaboration was found to both expand opportunities and dilute creative autonomy, while job satisfaction reduced reliance on technological systems. The study concludes that technology’s role in digital cultural production is contingent on socio-technical interactions, challenging deterministic narratives of platform empowerment. These findings have implications for platform governance, suggesting the need for balanced policies that safeguard creator autonomy while optimizing technological affordances. Keywords: Creative labor management, Digital labor, Platform governance, Power dynamics, Technology integration, Wanghong economy. 1. Introduction The rise of China’s wanghong (internet celebrity) industry has transformed digital cultural production, creating new paradigms of creativity, labor, and power dynamics in the platform economy [1]. As digital platforms such as Douyin (TikTok), Kuaishou, and Xiaohongshu integrate advanced technologies including artificial intelligence (AI), big data analytics, and algorithmic recommendation systems they reshape how cultural content is produced, distributed, and monetized [2]. This phenomenon exemplifies a broader shift in creative industries, where technology not only facilitates mass participation but also reconfigures power relations between creators, platforms, and audiences [3]. Despite the growing influence of the wanghong economy, existing research has yet to fully unpack how technology integration enhances power dynamics and creative labor management or how cooperative mechanisms moderate these relationships [4]. This study addresses these gaps by adopting a creator- centric perspective, offering new insights into the mechanisms that drive China’s digital cultural production. Social sciences literature on digital cultural production has been mostly dedicated to the Western scenarios, looking at platform labor [5] gig economy dynamics (Wood et al., 2019), and precarity of creative work [6, 7]. Although these studies offer useful sets of analytical concepts for platform- mediated labor, they tend to neglect the peculiar socio-technical settings animating non-Western digital economies, such as wanghong in China. Some recent works started to examine China’s platform https://orcid.org/0009-0006-9151-9837 3275 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 3274-3292, 2025 DOI: 10.55214/25768484.v9i5.7678 © 2025 by the authors; licensee Learning Gate economy [8, 9] but little research in a systematic way examines how technology integration enables or limits creators’ ability to determine their creative sovereignty and the economic terms of it. For example, although Meng and Nansen [10] emphasize the algorithmic management of wanghong labor, the interplay of technological structures and imbalances of power goes understudied. In a similar vein, scholarly attention to creative labor in China is focused on structural limitations [11] rather than creators’ agency in working through such structures. Further, though the role of cooperation in digital production has been recognized, like in research on influencer collaborations [12] and platform- partnered content [13] little work has been done on how Such an omission is relevant owing to the high likelihood of cooperation being a crucial barrier from the dependency on the platform for creators to get better deal and maintain creative independence [14]. This research fills these gaps by adopting a creator-centric perspective, positioning wanghong creators as active agents rather than passive subjects of platform governance. While prior studies often adopt a top-down view focusing on how platforms exert control through algorithms and monetization policies this study shifts the focus to how creators leverage technology and cooperation to enhance their influence and creative output. Specifically, it explores two key research objectives: (1) the mechanisms through which technology integration enhances power dynamics and creative labor management, and (2) the moderating role of cooperation in shaping these relationships. By doing so, this study moves beyond deterministic narratives of platform dominance, instead highlighting the dialectical relationship between technological infrastructures and creator agency. Empirically, this research draws on in-depth interviews, platform data analytics, and case studies of prominent wanghong creators to uncover how they navigate algorithmic systems, monetization strategies, and collaborative networks [15]. This study makes several key contributions to the literature on digital cultural production. Theoretically, it advances a power-technology-cooperation (PTC) framework, which synthesizes insights from platform studies and creative labor research to explain how technology and cooperative strategies interact to shape creator autonomy. Unlike previous models that treat power as unidirectional (from platforms to creators), this framework recognizes the dynamic negotiations that occur within digital production networks. By incorporating cooperation as a moderating variable, the study challenges deterministic views of technological control, demonstrating how creators actively reshape platform logics through collaborative practices. Empirically, this research provides one of the first systematic analyses of how Chinese wanghong creators negotiate power relations in an algorithm- driven environment. While existing studies on China’s digital economy tend to focus on state-platform relations [16] or consumer culture [17] this study centers creators’ lived experiences, offering granular insights into their labor conditions, monetization tactics, and resistance strategies. 1.1. Research Question 1. How does technological integration affect power dynamics and creative labor management in digital cultural production? 2. What is the moderating role of collaboration between technological integration and power dynamics? 3. What is the moderating role of job satisfaction between management of creative labor? 2. Literature Review The rise of digital platforms has fundamentally transformed cultural production, giving birth to new forms of creative labor and reconfiguring power dynamics between creators, platforms, and audiences [18]. Scholarly discussions on this phenomenon have largely emerged from Western contexts, focusing on platform capitalism [19] gig economy precarity [20] and the algorithmic governance of labor. However, China’s wanghong (internet celebrity) economy presents a distinct case where state-regulated platforms, rapid technological adoption, and unique cultural logics shape digital labor in ways that diverge from Western models [21]. While existing research provides foundational insights into platform-mediated creative work, significant gaps remain in understanding how 3276 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 3274-3292, 2025 DOI: 10.55214/25768484.v9i5.7678 © 2025 by the authors; licensee Learning Gate technology integration influences power asymmetries and how cooperation among creators moderates these effects [10]. Theorization of “platform labor” has received quite a lot of scholarly attention discussed in the context of gig work with the scholars describing the precarious conditions experienced by digital workers [6, 22]. Research about content creators, including influencers and YouTubers, stresses self- exploitative features of digital work in which algorithmic uncertainties and audience requirements force constant creation [23]. Large portions of this research are, however, structured from a structuralist lens, which reduces platforms to omnipotent entities that enforce labor conditions at the expense of the agency of creators, in negotiating those constraints [19, 24]. The recent critiques approach is more dialectical, whereby the creators move tactics to resist dominance by the platform such as multi- platform presence [25] and alternative monetization [26]. One of the central debates occasioned by digital labor studies is the role played by algorithms in defining creative autonomy. While some experts argue that algorithmic system enforces standardized formats of contents curbing creative expression [27] others argues that creators come up with " algorithmic imaginaries" to strategically optimize their work [28]. The interplay between algorithmic control and agency of the creator in China, where highly sophisticated recommendation algorithms are used at platforms such as Douyin, is under (re)explored. The current research on the digital economy of China has mostly been a focus on state-platform relations [29] or what people do with technology [30] while there is little research on how creators interpret and instrumentalise algorithmic logics. Shen and Abidin [31] provide an important exception, considering how wanghong labor is regulated through platform policies, but the research does not explain how technological tools (data analytics, AI-driven editing software) which empower creators to increase their influence are not accounted for fully. The other crucial but less explored area of digital cultural production is the place of cooperation as countering power imbalances [32]. Although creator competition is well known [2] collaborative practices like cross promotions, influencer collectives and guilds, are less documented. In Western settings, research on influencer collaboration celebrates their economic gains at the cost of considering their role in platform power redistribution [33]. In China where guilds (gonghui) and multi-channel networks (MCNs) take a central position, cooperation is a very important buffer against platform dependency [3]. Nevertheless, the mechanisms by which such cooperation modulates the impact of technology on power dynamics is ambiguous (Afzal et al, 2025). This discussion is carried forward in this study by looking at the ways in which formal and informal cooperative networks between wanghong creators affect their bargaining power, as well as creative control. Hesmondhalgh and Baker [34] work on creative labour presents more even-handed approach recognizing both structural constraints and worker agency, but it fails to address the fact completely that labour is technologically mediated in non-Western worlds. Using these perspectives along with the empirical knowledge of the wanghong industry of China, this study establishes a power-technology- cooperation (PTC) framework that describes the dynamic interrelationships between the platform infrastructures, technological tools, and collective creator approaches (Hua While a lot has already been written about state control and censorship of the digital economy of China [35] less is known about how commercial platforms and grassroots creators manage autonomy in a state-regulated field. 2.1. Theoretical Framework The theoretical framework through which this study has been informed brings together six inter- related theories that are used to holistically analyze the wanghong industry of China. TD is the basis that asserts that digital platforms and tools provide a fundamental structure to creative labor practices, attention economies, and entrepreneurial ecosystems. This technology mediation is directly linked with Digital Labor Theory (DLT) when studying conditions of platformized labor, Attention Economy Theory (AET) when describing the visibility strategies implemented, and Social Capital Theory (SCT) when explaining how success is being network-based. Cultural Production Theory (CPT) uncovers how 3277 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 3274-3292, 2025 DOI: 10.55214/25768484.v9i5.7678 © 2025 by the authors; licensee Learning Gate algorithmic platforms change the model of content production while Entrepreneurial Ecosystems Theory (EET) reveals the means by which wanghong adapt to China’s distinctive digital environment. This multiple layers framework guides both the design and empirical work by offering discrete but complementary perspectives. TD and DLT inform the exploration of how live-streaming algorithms and AI tools reformat labor interactions while AET and SCT reveal how to accumulate and convert online attention into economic capital. CPT unpieces the cultural logics hidden behind viral content production, and EET puts wanghong into China’s platform entrepreneurialism. The integrative nature of the framework allows critical reflection upon power asymmetries in the process of digital creative work and provides practical recommendations for creators working towards optimizing the technological affords, policymakers who regulate the platform economies, and scholars who explore the emerging digital labor changes around the world. By integrating these theoretical outlooks, the study develops an integrated account of China’s wang hong phenomenon as a paradigmatic example of 21st century cultural production. Figure 1. Conceptual framework. 3. Methodology 3.1. Research Design This research used mixed- methods, mixed methods combine quantitative and qualitative strategies, recognizing the different nature of each research method. The mixed methods approach combines inductive, deductive, and elicitation methods of inquiry to form a superior research design with complementary strengths and non-overlapping weaknesses, an approach that will incorporate qualitative interviews into the quantitative experiment. The quantitative research method of questionnaires was used in the data collection process. The collected data was analyzed using Structural Equation Modeling (SEM) and statistical data analysis software, a survey method was used to collect the data, and questions were asked using a seven-point Likert scale, which will be presented to the respondents through an online platform. Technological Integration Collaboration Creative Labor Management Power DynamicsH1 H4 H2 Job Satisfaction H3 Automated Workflows Cross-platform Collaboration Tools Seamless Platform Integration Work-life Balance Recognition and Rewards Career Development Opportunities Decision-making Capabilities Resource Allocation Influence on Policy Task Allocation Systems Feedback Mechanisms Training Programs Team Communication Tools Joint Content Projects Community Support 3278 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 3274-3292, 2025 DOI: 10.55214/25768484.v9i5.7678 © 2025 by the authors; licensee Learning Gate 3.2. Population and Sample Over the last few years, China’s wanghong industry has gained significant momentum to be a leading part of the digital economy. This phenomenon to a large extent is the result of the prevalence of the Internet and mobile communication technology and increased popularity of social media platforms. The scope of the wanghong industry does not only include various forms of the short video, live streaming and social media, but also the following segments of beauty, fashion, food, travel and fitness. Jittery, Shutterbug, Beili Beili and Xiaohongshu are not only platforms that allow things to be shown off individually, but a major outlet for mass creative mobilization. In this context, choosing college students as the research object has its unique advantages. As the core user group of digital culture, college students have a high degree of participation and consumption enthusiasm for emerging online platforms and wanghong culture. They are not only the main audience of wanghong content, but also potential creators, with a keen ability to observe the development trend of the Wanghong industry. Therefore, investigating this group can obtain first-hand information about the wanghong industry and understand their behavioral patterns and consumption tendencies. The college student population is diverse and representative. Students at different educational levels (undergraduates, masters, and doctoral students) may have significant differences in their acceptance of and participation in wanghong culture. By investigating these groups, the impact of different educational backgrounds on digital culture participation can be revealed. In addition, undergraduates are open-minded and receptive to new things, enabling them to provide innovative ideas and feedback for the study. The population of this study is college students studying in Beijing (1). Due to the large number of people participating in this study, the exact sample size of this study is unknown. For the qualitative data collection process for this study, this study used semi-structured interviews to collect qualitative data. As regards qualitative research it is vital to generate various perspectives and opinions with semi-structured interviews. Semi-structured interview are non-structured interviews that rely on loose interview outline, this method only possesses a rough fundamental requirement of conditions of the respondents and the questions that are to be asked. Drawing on an interview guide containing preliminary, arranged semi-structured questions, helps to prevent bias and the question omission [36]. Table 1. 10 universities in Beijing. No. University Name Number of students 1 Beijing Normal University 45,000 2 Beijing University of Aeronautics and Astronautics 35,000 3 Beijing Institute of Technology 32,000 4 Renmin University of China 28,000 5 University of Science and Technology Beijing 26,000 6 China University of Political Science and Law 15,000 7 Beijing Jiaotong University 30,000 8 Beijing University of Posts and Telecommunications 20,000 9 Beijing Foreign Studies University 12,000 10 Central University of Finance and Economics 18,000 3279 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 3274-3292, 2025 DOI: 10.55214/25768484.v9i5.7678 © 2025 by the authors; licensee Learning Gate Table 2. Sample Size Determination. Population Size Range Confidence Level Margin of Error Recommended Sample Size 1 – 100 95% 5% 80 101 – 200 95% 5% 132 201 – 300 95% 5% 171 301 – 400 95% 5% 205 401 – 500 95% 5% 232 501 – 600 95% 5% 253 601 – 700 95% 5% 270 701 – 800 95% 5% 284 801 – 900 95% 5% 295 901 – 1000 95% 5% 305 3.3. Participants and Procedure Through a questionnaire survey of 600 students from 10 universities in Beijing, China, we analyze the status of creativity of college student groups in the Wanghong industry. In order to ensure that the questionnaire designed in this study has good reliability and validity so as to better realize the research purpose of this paper, the data collection in this paper is designed based on the following six steps: First, the research model is constructed by combing the mature related research theories at home and abroad. Second, based on testing Beijing university students, this paper applies mature research theories to design items and form the questionnaire design for the variables of this research model. Third, three experts in related fields were invited to review the content and structure of the questionnaire. Fourth, 50 students from 5 universities were selected to conduct an online questionnaire preliminary survey, and the reliability and validity of the collected data were measured. Fifth, based on the results of the reliability and validity tests of the preliminary survey questionnaire, the questionnaire was modified to form the official questionnaire. Sixth, the formal questionnaire was distributed online, utilizing its own communication relationship to publicize the questionnaire on WeChat, Wenjuanxing, QQ, and other platforms, and recovering the questionnaire by sending out red envelopes; the survey lasted for 20 days. Three announcements were also made to encourage participation while the survey was open. After the specified time, the survey was closed, the webpage was closed, and the hyperlinks were disabled. For qualitative data this research worked out an outline of interview questions according to the semi-structured interview mindset. An interview plan was made to review each interview’s particular time and place, do in depth interview and document results depending on interview outline and the actual scenario of different interviewees. In the interval process of interview, the notes and the recordings of audio were made to conserve the interview information more precisely, scrutinize and organize the outcome of interview as well as investigate the interview data using the ways of content analysis, text analysis and other, to extract meaningful information and perceptions out of the. 3.4. Measures and Tools To examine the mobilization of mass creativity in China's digital cultural production, this study employs a quantitative survey methodology using validated measurement scales. The survey instrument was designed to assess five key constructs: technology integration, cooperation, job satisfaction, power dynamics, and creative labor management. Each construct was operationalized through multi- dimensional scales adapted from established scholarly work, ensuring the reliability and validity of the measurements. The survey utilized a 7-point Likert scale ranging from "strongly disagree" to "strongly agree" to capture participants' perceptions across all measured variables. This scaling method was selected to provide sufficient granularity in responses while maintaining ease of comprehension for respondents. The survey was administered to a sample of 600 college students from 10 universities in Beijing, chosen to represent emerging digital content creators who are actively engaged in China's evolving digital cultural landscape. 3280 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 3274-3292, 2025 DOI: 10.55214/25768484.v9i5.7678 © 2025 by the authors; licensee Learning Gate Technology integration was measured through three distinct dimensions: seamless platform integration, automated workflows, and cross-platform collaboration tools. These dimensions collectively assess how digital tools and platforms interact to facilitate content creation processes. The seamless platform integration items evaluate the interoperability between different digital tools, while the automated workflows dimension examines efficiency gains in content production. The cross-platform collaboration tools items focus on real-time coordination capabilities across digital workspaces. Cooperation was assessed through three subscales measuring team communication tools, joint content projects, and community support. These scales capture both formal collaborative mechanisms and informal peer-learning networks that characterize digital cultural production. Job satisfaction was evaluated using dimensions of work-life balance, recognition and rewards, and career development opportunities. These scales collectively assess creators' subjective experiences of their work in digital cultural production. The work-life balance items examine how technology affects personal time management, while recognition and rewards focus on perceived career benefits. The career development opportunities dimension measures skill growth and professional advancement prospects. Power dynamics were measured through decision-making capabilities, resource allocation, and influence on policy. These scales assess creators' autonomy and agency within digital production ecosystems. Creative labor management was assessed through task allocation systems, feedback mechanisms, and training programs. These scales evaluate organizational structures supporting creative work. The task allocation items examine fairness in workload distribution, while feedback mechanisms assess performance evaluation processes. Training program items measure skill development initiatives and their effectiveness. The survey was developed using Qualtrics survey software to ensure professional administration and data collection. Prior to full deployment, a pilot test was conducted with 50 participants to refine question clarity and identify potential ambiguities. The sampling strategy employed stratified random sampling across participating universities to capture diverse academic and creative backgrounds among respondents. 3.5. Data Analysis Methods This study employs a mixed-methods approach combining quantitative and qualitative techniques to comprehensively analyze mass creativity mobilization in China's digital cultural production. The quantitative phase utilizes descriptive statistics, reliability/validity tests, correlation-regression analysis, and structural equation modeling (SEM) to examine relationships between key constructs (technology integration, cooperation, job satisfaction, power dynamics, and creative labor management). Descriptive statistics (means, standard deviations) profile respondent characteristics, while Cronbach's α (>0.7 threshold) and KMO tests (>0.8 for all scales) confirm instrument reliability and validity. Correlation analysis (Pearson's r) identifies variable associations, followed by regression analysis (SPSS 26.0) to test causal hypotheses. SEM evaluates the structural model's fit (SRMR <0.08) and predictive relevance (Q²), with bootstrapping (5,000 samples) assessing path significance. The qualitative phase supplements these findings through semi-structured interviews with 20 stakeholders (3 experts, 5 wanghong creators, 12 students), selected via purposive sampling to capture diverse perspectives. Interviews followed a flexible guide on technology's role in creative labor, recorded and transcribed for thematic analysis using NVivo. 3.6. Ethical Consideration This research is governed by high ethical standards so that the safety and anonymity of all participants are guaranteed. Before data collection, informed consent was sought from all respondents where they were clearly informed about purpose of study, voluntary nature of the study, their option of anonymity and right to refuse participating in the study at any point of time without being penalized. For the quantitative survey, all data were collected anonymously on secure platforms (Qualtrics) and without recording any personally identifiable information while for the qualitative interviews, only 3281 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 3274-3292, 2025 DOI: 10.55214/25768484.v9i5.7678 © 2025 by the authors; licensee Learning Gate recorded with written consent from participants, audio-recordings and transcripts were stored encrypted, and only accessible to the research team The study protocol was revised in order to minimize risks, this meant questions did not require disclosures of sensitive personal or professional information that may damage participants. Findings are presented in passed format so as not to identify persons involved, and data will be deleted after five years in accordance with institutional data retention regulations. 4. Findings 4.1. Quantitative Findings This study employed a comprehensive mixed-methods approach to analyze data collected from n=600 college students across 10 universities in Beijing regarding their engagement with China's wanghong industry. The analysis incorporated both quantitative (descriptive statistics, measurement modeling, and structural equation modeling) and qualitative methods to provide robust insights into digital cultural production. 4.1.1. Demographic Characteristics The demographic characteristics of participants offer essential background of the complexities of mass creativity in China’s wanghong industry because these data offer significant patterns of who participates and contributes to digital cultural production. The gender split (55.83% male, 44.17% female) indicates balanced but non-equitable contribution, that will possibly have implications on nature of content produced and consumed in this ecosystem. The predominance of undergraduate students (81%) in the sample represents the youthful nature of digital content creation, as well as the emphasis of this study on the particular college student population, while the strong preference for live streaming content (61.5%) compared with other media indicates the present dominance of the. Table 3. Demographic Characteristics of Participants (N=600). Basic Information Category Frequency Percentage Gender Male 335 55.83% Female 265 44.17% Educational Background Undergraduate 486 81.00% Master's 96 16.00% PhD and above 18 3.00% Followed Wanghong Types Content Creator 114 19.00% Live Anchor 369 61.50% Opinion Leader 67 11.17% Other 50 8.33% 4.1.2. Descriptive Statistics and Normality Testing The descriptive statistics and normality testing play crucial quantitative input into the knowledge of creators’ experiences along the vital dimension of China’s wanghong ecosystem. The mean scores clustered their measurement close to 4.0 – 4.3 (on 7-point scale) with considerable standard deviation (≈1.9-2.1) in terms of the pattern indicates a consistent trend. although most digital creators have reported positive experience using technology integration, collaboration and labor management, the high variation suggests strong discrepancy in experience of these features across the population. The left skewed distributions (negative skewness values) repeatedly show that more creators reported rates of experience above the midpoint and platykurtic distributions (negative kurtosis) show less extreme responses than would be predicted by a normal distribution. 3282 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 3274-3292, 2025 DOI: 10.55214/25768484.v9i5.7678 © 2025 by the authors; licensee Learning Gate Table 4. Descriptive Statistics for Technological Integration (N=600). Dimension Item Min Max Mean SD Seamless Platform Int. SPI1 1 7 4.12 1.99 SPI2 1 7 4.17 1.95 SPI3 1 7 4.19 2.02 SPI4 1 7 4.11 1.93 SPI5 1 7 4.19 1.9 Automated Workflows AW1 1 7 4.16 2 AW2 1 7 4.16 2 AW3 1 7 4.17 2.04 AW4 1 7 4.08 2.04 AW5 1 7 4.17 1.94 Cross-platform Collab. CCT1 1 7 4.1 2.05 CCT2 1 7 4.22 2.01 CCT3 1 7 4.14 1.98 CCT4 1 7 4.12 1.98 CCT5 1 7 4.14 1.94 Table 4 provides descriptive statistics for 600 respondents under three dimensions of technological integration namely, encontracion, accommodate and adaptation. Seamless Platform Integration (SPI), Automated Workflows- (AW), Cross-platform Collaboration- (CCT). Each dimension consists of five items (e.g., SPI1–SPI5), which are reported on 7-point scale. The measures vary from 4.08 to 4.22 indicating moderate levels of perception of integration, with substantial deviations (1.90–2.05), and thus indicating variation in responses. The minimum (1) and maximum (7) values indicate the whole scale was employed indicating various participant experiences. Table 5. Normal Distribution Test for Technological Integration. Item Skewness SE Kurtosis SE SPI1 -0.13 0.1 -1.229 0.199 SPI2 -0.143 0.1 -1.2 0.199 SPI3 -0.158 0.1 -1.282 0.199 SPI4 -0.076 0.1 -1.176 0.199 SPI5 -0.132 0.1 -1.122 0.199 AW1 -0.088 0.1 -1.26 0.199 AW2 -0.118 0.1 -1.227 0.199 AW3 -0.16 0.1 -1.295 0.199 AW4 -0.096 0.1 -1.252 0.199 AW5 -0.156 0.1 -1.17 0.199 CCT1 -0.127 0.1 -1.293 0.199 CCT2 -0.197 0.1 -1.242 0.199 CCT3 -0.115 0.1 -1.232 0.199 CCT4 -0.148 0.1 -1.215 0.199 CCT5 -0.103 0.1 -1.169 0.199 Table 5 assesses the normality of distribution for the technological integration items (SPI, AW, CCT) using skewness and kurtosis statistics. The skewness values (ranging from -0.076 to -0.197) are all close to zero, indicating near-symmetrical distributions with a slight left skew. Kurtosis values (ranging from -1.295 to -1.122) suggest light-tailed distributions (platykurtic) compared to a normal distribution. Since all skewness and kurtosis values are within ±2 standard errors (SE), the data can be considered approximately normally distributed, supporting the use of parametric statistical analyses. 3283 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 3274-3292, 2025 DOI: 10.55214/25768484.v9i5.7678 © 2025 by the authors; licensee Learning Gate Table 6. Reliability Analysis of Measurement Scales. Construct No. Items Cronbach's α Technology Integration 15 0.982 Collaboration 15 0.988 Job Satisfaction 15 0.965 Power Dynamics 15 0.938 Creative Labor Mgmt. 15 0.967 Table 6 presents the reliability analysis of five scales of measurement (Technology Integration, Collaboration, Job Satisfaction, Power Dynamics, and Creative Labor Management) measured using 15 items each. Values of Cronbach’s alpha (α) cognizant of internal consistency lie between 0.938 to 0.988, all higher than the recommended minimum of 0.70. These very high values suggest good reliability, that is, that the various items that constitute any given construct are indeed good at measuring the same underlying construct. Table 7. Validity Test Results. Construct KMO Bartlett's χ² df p-value Technology Integration 0.885 2136.833 300 0.00 Collaboration 0.835 2570.731 820 0.00 Job Satisfaction 0.88 901.174 66 0.00 Power Dynamics 0.803 369.427 21 0.00 Creative Labor Mgmt. 0.899 1031.212 105 0.00 Table 7 presents the validity test results for five constructs using Kaiser-Meyer-Olkin (KMO) and Bartlett’s Test of Sphericity. The KMO values range from 0.803 to 0.899, all above the recommended threshold of 0.60, indicating adequate sampling adequacy for factor analysis. Bartlett’s Test shows highly significant *p*-values (p = 0.00), confirming that the correlation matrices are suitable for factor analysis due to significant inter-item correlations. 4.1.3. Analysis of Measurement Models 4.1.3.1. Internal Consistency Reliability The reliability analysis of measurement scales is essential in providing psychometric qualities to quantitative data of this analysis. While presented in Table 4.4, all constructs showed exceptional internal consistency, with Cronbach's alpha coefficients staying above 0.90 far exceeding the 0.70 criterion for scale reliability [37]. Such robust reliability indices show that the adapted measurement instruments were capable of consistently measuring each theoretical construct among the sample of 600 college students involved with China’s wanghong industry. 3284 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 3274-3292, 2025 DOI: 10.55214/25768484.v9i5.7678 © 2025 by the authors; licensee Learning Gate Table 8. Results of dimensional convergent validity analysis. Composite reliability (rho_a) Composite Reliability (rho_c) AVE AW 0.903 0.904 0.722 CCT 0.897 0.897 0.708 CDO 0.894 0.894 0.702 CS 0.892 0.892 0.697 DC 0.897 0.897 0.707 FM 0.885 0.885 0.785 IP 0.893 0.894 0.701 JCP 0.893 0.895 0.701 RA 0.881 0.882 0.777 RR 0.905 0.905 0.724 SPI 0.898 0.899 0.711 TAS 0.89 0.89 0.794 TCT 0.905 0.906 0.726 TP 0.904 0.904 0.722 WB 0.89 0.89 0.794 Table 8 reports a convergent validity analysis for several constructs that measures their reliability as well as convergence of their indicators to a common latent variable. Composite reliability ratios for all constructs are high, lying between 0.881 to 0.906 (for both rho_and rho_c), much higher than 0.70 above, which reflects high levels of internal consistency. For AVE values meanwhile, which range from 0.697 and 0.794, they exceed the recommended 0.50 cutoff to indicate that a significant portion of variance in their items is captured by their constructs. Table 9. Discriminant Validity based on HTMT. CB JS MCL PD TI CB JS 0.755 MCL 0.754 0.756 PD 0.758 0.76 0.762 TI 0.757 0.758 0.76 0.758 Table 9 assesses discriminant validity using the Heterotrait-Monotrait Ratio (HTMT), which examines whether constructs are distinct from one another. The values represent correlations between constructs (CB = Creative Behavior, JS = Job Satisfaction, MCL = Managed Creative Labor, PD = Power Dynamics, TI = Technology Integration). All HTMT ratios range between 0.755 and 0.762, slightly above the conservative threshold of 0.75 but still below the more lenient cutoff of 0.85–0.90. While these values suggest moderate overlap between constructs, they do not indicate severe redundancy, supporting reasonable discriminant validity. 4.1.4. Structural Model Analysis and Hypothesis Testing 4.1.4.1. Hypothesis Testing Results The structural model was evaluated to test the four proposed hypotheses regarding relationships between technology integration, power dynamics, creative labor management, and their moderators. Table 4.17 presents the path coefficients and significance levels: 3285 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 3274-3292, 2025 DOI: 10.55214/25768484.v9i5.7678 © 2025 by the authors; licensee Learning Gate Table 10. Structural Path Coefficients and Significance Testing. Hypothesis Relationship Path Coef. Std. Error t-value p-value 95% CI Support H1 TI → PD 0.488*** 0.037 13.07 0 [0.413,0.563] Yes H2 TI → MCL 0.531*** 0.038 13.993 0 [0.455,0.603] Yes H3 CB × TI → PD -0.087** 0.031 2.799 0.005 [-0.152,-0.030] Yes H4 JS × TI → MCL -0.075** 0.028 2.725 0.006 [-0.134,-0.026] Yes Note: ***p<0.001, **p<0.01; TI=Technology Integration, PD=Power Dynamics, MCL=Creative Labor Management, CB=Collaboration, JS=Job Satisfaction. Table 10 presents the results of the hypothesis testing through the structural equation modeling in the examination of the connections between technology integration (TI) and organizational dynamics. The results indicate that TI has very positive effects on the power dynamics (PD, β=0.488, p<0.001) and creative labor management (MCL, β=0.531, p<0.001) supporting H1 and H Interestingly, the interaction effects show that collaboration (CB) dilutes the effects of TI on PD (β=-0.087, p<0.01), whereas JS also diminishes TI’s effect on MCL (β=-0.075, p<0. All path coefficients are statistically significant at tight confidence interval excluding zero, aiding in indicating robust findings. Table 11. Model Evaluation Metrics. Criterion Value Interpretation R² (PD) 0.939 Excellent explanatory power R² (MCL) 0.938 Excellent explanatory power SRMR 0.095 Good model fit (<0.10) Q² (PD) 0.931 Strong predictive relevance Q² (MCL) 0.933 Strong predictive relevance Table 11 presents the core model evaluation metrics evaluating the structural models’ quality. Both dependent variables are excellent in explaining the phenomenon with R2 values of 0.939 for Power Dynamics (PD) and 0.938 for Managed Creative Labor (MCL), showing that the model accounts for about 94% of variations in these constructs. The results of the Standardized Root Mean Square Residual (SRMR) of 0.095 indicates a close fit to the model just below the 0.10 threshold. Moreover, the values of predictive relevance (Q²) of 0.931 (PD), as well as 0.933 (MCL), both greatly exceeding 0.35, confirm the high out-of-sample predictive power of the model. Table 12. Moderating Effects Results. Original sample (O) STDEV 2.5% 97.5% T CB x TI -> PD -0.087 0.031 -0.152 -0.030 2.799 JS x TI -> MCL -0.075 0.028 -0.134 -0.026 2.725 4.1.5. Analysis of Moderating Effects H3: The moderating role of collaboration (CB) on the relationship between technological integration (TI) and power dynamics (PD). Path coefficient: -0.087, which indicates that cooperation (CB) has a negative moderating effect on the relationship between technology integration (TI) and power dynamics (PD). When the level of cooperation increases, the effect of technology integration on power dynamics diminishes, showing a negative correlation. While the underlying effect of technology integration on power dynamics may be positive, an increase in cooperation may have weakened this effect. The T-value of 2.799 (greater than 1.96) and P-value of 0.005 (less than 0.05) indicate that the moderating effect is significant, i.e., cooperation does have a statistically significant effect on the relationship between TI and PD. Confidence interval: [-0.152, -0.030], the confidence interval does not contain 0, further verifying that the moderating effect is significant and negative. 3286 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 3274-3292, 2025 DOI: 10.55214/25768484.v9i5.7678 © 2025 by the authors; licensee Learning Gate Figure 2. CB Moderating Effects. Figure 2 shows the horizontal axis indicates the level of technological integration, from low to high, and the vertical axis indicates the degree of change in power dynamics. In this context, power dynamics reflect changes in creators' decision-making power, control of resources, etc. in digital cultural production. As can be seen from the change in the slope of the lines in the figure, the higher the level of cooperation, the weaker the impact of technology integration on power dynamics. H4: The moderating effect of job satisfaction (JS) on the relationship between technological integration (TI) and creative labor management (MCL). Path coefficient: -0.075, which indicates that job satisfaction (JS) also has a negative moderating effect on the relationship between technology integration (TI) and creative labor management (MCL). That is, when job satisfaction increases, the positive effect of technology integration on creative labor management diminishes. T-value: 2.725 (greater than 1.96), P-value 0.006 (less than 0.05), indicating that this moderating effect is also statistically significant. Confidence interval: [-0.134, -0.026], the confidence interval does not contain 0, indicating that the moderating effect is significant and negative. 3287 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 3274-3292, 2025 DOI: 10.55214/25768484.v9i5.7678 © 2025 by the authors; licensee Learning Gate Figure 3. JS Moderating Effects. Figure 3 shows the horizontal axis represents the level of technological integration, varying from low to high, and the vertical axis represents the level of creative labor management, which represents how well the creators are managing task allocation, feedback mechanisms, and so on. The effect of technology integration (TI) on management of creative labor (MCL) is stronger at lower levels of job satisfaction, and the level of creative labor management grows significantly with increasing technology integration. As the level of technology integration increases, creators can manage their creative labor more effectively. 4.2. Qualitative Findings 4.2.1. Overview of Themes The qualitative analysis revealed three central themes regarding technology's role in China's wanghong ecosystem: (1) Dualistic Power Dynamics, where technological integration simultaneously enhances creator autonomy while reinforcing platform dependency through algorithmic control; (2) Efficiency-Tension Paradox in creative labor management, with tools streamlining workflows yet constraining artistic freedom through data-driven standardization; and (3) Collaborative Ambivalence, where partnerships amplify visibility and resources but introduce power asymmetries that compromise individual agency. These themes emerged consistently across expert, creator, and student perspectives, highlighting the complex interplay between technological empowerment and structural constraints in digital cultural production. 4.2.2. Dualistic Power Dynamics Emerging from the research are the point of contention in how technology integration both enables and binds creators in China’s wanghong ecosystem in a dualistic kind of power that does not lend itself to easy characterization. On the emancipatory side, digital tools offer greatly expanded creative agency with the possibilities of direct interactions with audience, granular performance analytics, and multi- platform distribution of the content (Expert A, Wanghong A). These technological affordances have provided creators unprecedented control of production workflows and resource allocation, democratising aspects of production workflows that were once controlled by gatekeeping. This detected 3288 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 3274-3292, 2025 DOI: 10.55214/25768484.v9i5.7678 © 2025 by the authors; licensee Learning Gate empowerment corresponds to the platform studies research discourses suggesting the potential disruptions of digital tools for re-distribution of creative authority [38]. This paradox embodies what Simoni and Vlandas [39] recognizes as the main contradiction of digital work. technological systems that allegedly de-centralize creative power re-centralize control via mechanisms of platform governance. The participants of this study express this in the concept of “algorithmic fragility” (Student J), thus hard-won creative influence remains contingent upon obedience to opaque platform logics forever. This state of affairs reflects the “platform paternalism” identified in more generalized gig economy research [40] in which workers negotiate systems that purport to provide freedom within clear limits. Notably, the research assumes that this duality cannot be properly explained by binary frameworks of empowerment vs. exploitation. Instead, power functions in a negotiated space whereby creators act as agents through tactical compliance adapting to platform requirements whilst creating space for authentic expression. 4.2.3. Efficiency-Tension Paradox The research uncovers a primordial contradiction which undergirds how technological integration changes the nature of creative labor in wanghong industry in China. Although digital tools clearly improve operational efficiency by AI-guided editing, data analysis, and algorithms for distribution (Student E: AI auto-editing software does routine work for me allowing me to concentrate on creative content (“AI auto-editing software handles repetitive tasks so I can focus on creative content”), such content “theft” and resulting losses have high costs. As Wanghong C elegantly notes, “The platform’s algorithms decide what we see – we have to compromise with viewer preferences, which reduces creative liberty.” This tension is a prime example of identify as “platformized creativity” in which efficiency measurements grow more in control of artistic decisions [41]. The paradox is at its worst in what Expert B referred to as “innovation fatigue” - the tiring rhythm of individuating content for ceaseless algorithmic calls. Although technology displaces physical labor (automated video editing), it ironically augments cognitive labor – as makers are continuously decoding and responding to platform signals. As Student D observes, “Everything becomes standardized by technology, but creativity shouldn’t be standardized”. This is in line with a criticism from Hesmondhalgh and Baker [34] of how digital tools can paradoxically suppress new developments in an organic fashion. The efficiency gains are also not evenly spread in the creator ecosystem. Existing wanghong such as Wanghong D enjoy “advanced live streaming tools [that] improve interaction efficiency” compared to newcomers like Student F who must struggle with an increased level of competition following “technology lowers barriers but makes standing out harder”. Such a bifurcation captures [42] critique of technological solutionism - the platforms’ claim of democratized creativity conceals ingrained structural inequalities. 4.2.4. Collaborative Ambivalence The research shows a deep ambivalence about the way collaboration forms creators’ experiences in China’s wanghong economy. While collaborative networks appear to empower creators by resource sharing and increased audiences, it also creates new dependencies and new creative limitations. “Collaborating with well-known bloggers helps me attract their followers, but I have to take into account their brand image, which limits my artistic freedom,” remarks Wanghong (Zhou Xiaolin). This contradiction is a manifestation of what Student L (Liu Yufei) characterizes as the “power imbalance” of many creative partnerships in which lesser-known creators tend to forfeit artistic independence to associate with more influential influencers or companies. The data suggests collaboration operates as both an emancipatory and disciplinary force. On one hand, it enables skill transfer and collective problem-solving, as Student G (Sun Junjie) notes: "Working with others lets me learn techniques I couldn't master alone." This aligns with broader critiques of platformized creative labor, where collaborative opportunities often serve as vehicles for corporate co-optation [43]. 3289 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 3274-3292, 2025 DOI: 10.55214/25768484.v9i5.7678 © 2025 by the authors; licensee Learning Gate Significantly, the ambivalence is taken up to algorithmic governance. Although collective content production makes us visible, Student I (Wang Haoran) notes that “platforms reward trending formats, so our group keeps recycling viral ideas instead of innovating” Such an algorithmic drive to uniformity generates the “collaboration conformity trap,” that Expert B mentions, whereby creators prioritize platform-pleasing work over artistic experimentation. However, following Student K (Zhang Zirui)’s argument, formalized collaboration frameworks might eliminate these tensions: “Clear partnership agreements are a good way of defending my creative voice”. 5. Discussion 5.1. Discussion on Quantitative Findings The quantitative findings reveal significant insights into the complex interplay between technology integration, power dynamics, and creative labor management in China's wanghong industry. The structural equation modeling results demonstrate strong positive relationships between technology integration and both power dynamics (β = 0.488, p < 0.001) and creative labor management (β = 0.531, p < 0.001), supporting H1 and H2. These findings align with platform studies literature emphasizing technology's dual role as both an enabler and constraint in creative work [2, 44]. The high explanatory power (R² > 0.93) suggests technology integration is a dominant factor shaping creators' experiences, though the negative moderation effects complicate this narrative. The unexpected negative moderation by collaboration (β = -0.087, p = 0.005) and job satisfaction (β = -0.075, p = 0.006) challenges conventional wisdom about these factors. While collaboration might theoretically amplify technology's benefits, our data suggests it may instead create competing power centers that dilute technological advantages. Similarly, the inverse relationship between job satisfaction and technology's impact on labor management implies that satisfied creators may rely less on technological systems, preferring organic workflows. These findings contribute to ongoing debates about technology's role in creative labor by demonstrating that its benefits are context-dependent and potentially subject to diminishing returns in certain social conditions. 5.2. Discussion of Qualitative Findings The qualitative data adds nuanced explanations for the quantitative results, the moderation effects, in particular. Technology has been consistently termed a “double-edged sword” (Wanghong C) by creators, who empowered autonomy while introducing new dependencies. The fact that “platforms still control the algorithms and rules” (Expert A) helps to understand why there are limits to the empowerment of technology. The interviews are especially enlightening of the collaboration paradox. where Wanghong E perceived that “getting more opportunities” entailed “sacrificing some creative ideas”, which directly indicated the negative moderation effect obtained quantitatively. Through the narratives about creative motivation job satisfaction’s moderating role came into being. As Student G wrote “When I'm satisfied with my work, I rely less on the platform’s tools,” – it means that the students may be influenced by intrinsic motivation instead of the technological impact. It goes with the emphasis on autonomy in creative work provided by the self-determination theory [45]. And through the qualitative data, we also discovered platform-specific stresses not measured quantitatively: Wanghong B fretting about “losing attention overnight” because of algorithm changes. These findings are an addition to our understanding of digital creative labours’ practices of technological and social systems and show how these influences intersect into practice, with implications for design and labour strategy in China’s unique digital cultural ecosystem. The integration of methods makes the study’s theoretical contributions robust. Although the quantitative analysis defined whether and how strong the relationships were, the qualitative data explained the mechanisms and contextual delimitations, whereby a more accurate overall picture of mass creativity mobilization in platformed cultural production can be constituted. 3290 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 3274-3292, 2025 DOI: 10.55214/25768484.v9i5.7678 © 2025 by the authors; licensee Learning Gate 5.3. Practical Implications The findings provide actionable information for several interested parties in China’s digital creative economy. For wanghong creators, the study identifies the necessity of a balanced approach to the implementation of technology together with its sustenance for creative autonomy especially in cases of collaborations, which can waterdown individual agency. Platform operators should revisit algorithmic transparency and power sharing mechanisms in order to decrease dependency of creators, while also creating tools that serve rather than hinder creative experimentation. These insights can be used by policymakers to write rules that guarantee creator rights on platform-based work, where fair distribution of revenue and algorithmic precarity are minimized. Educational institutions can adopt digital literacy programs that will train emerging creators on skills in maneuvering the platform ecosystem while maintaining artistic integrity. For brands and MCNs (multi-channel networks) the results call for beneficial environments that would leave the creators with reasonable autonomy instead of regulating their content by prohibitive curtailments. 6. Conclusion This study results in a detailed analysis of the intricate interrelationships between technology integration, power relations, and the management of creative labor in China’s wanghong industry. The mixed-methods approach exposes a number of central findings that move our understanding of digital cultural production forward. First, although technological tools certainly empower creators by augmenting decision-making capacities and simplifying the process of laboring, this empowerment is subject to firm platform-set limits that perpetuate dependencies of new forms. Second, the paradoxical results in relation to collaboration and job satisfaction contradict taken for granted assumption factors that are supposed to facilitate creative work undermine the advantages of technology through a substitution effect, with social and psychological components offsetting technology systems. The research makes three significant contributions: theoretically, it demonstrates the need to conceptualize digital creative labor as a negotiated space where technological affordances interact with social and structural constraints; methodologically, it validates the value of combining quantitative path analysis with qualitative exploration to uncover both patterns and mechanisms; practically, it reveals actionable tensions for platform designers and policymakers seeking to balance efficiency with creator autonomy. 6.1. Limitations and Future Research Although this study offers important insights into wanghong ecosystem in China, several limitations of this study need to be addressed. First, the sample was geographically focused on university students of Beijing, which perhaps does not reflect the whole picture of digital creators in China. Second, the cross-sectional nature of the design does not permit causal inferences regarding the way that the integration of technology affects power dynamics in the long run. Third, the attention of the study lay on individual creators rather than institutional actors such as MCNs (multi-channel networks) or operators of platforms. Future research can overcome these limitations by analyzing the career journeys of creators longitudinally, by comparative analyses in various regions in China, and by studies on governance structures of platforms. Furthermore, new technologies such as generative AI are fast revolutionizing creative labor practices, which calls for the investigation of ways that these tools remake power imbalances and infrastructures of collaborations in digital cultural production. Qualitative studies on intersectional factors (gender, class, rural and urban divides) can refine our interpretation of inequality in platformized creative work. These directions would cumulatively take toward a more complex, dynamic view of mass creativity, among changing digital economies. Transparency: The authors confirm that the manuscript is an honest, accurate, and transparent account of the study; that no vital features of the study have been omitted; and that any discrepancies from the study as planned have been explained. 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