Determinants of Digital Creator Organizations’ Performance: An Organizational Perspective

Abstract

As digital creators increasingly operate through organized business structures rather than as individual content producers, understanding organizational characteristics associated with digital creator organizations’ performance has become an important research question. This study examines how content production scale, revenue model diversification, and workforce structure are related to the performance of digital creator organizations. Using survey data on the Korean digital creator media industry, we analyze organizational performance in terms of sales volume and sales per employee. The results indicate that content production scale and revenue model diversification are positively associated with organizational performance. The findings also indicate that workforce structure is relevant: the share of permanent employees is positively related to efficiency, whereas the share of production and development employees is negatively associated with performance. Overall, this study suggests that organizational performance in digital creator organizations is associated not only with content production itself, but also with revenue model breadth and workforce structure. This study contributes to the literature by providing an organizational perspective on performance in the creator economy and offers practical implications for the sustainable growth of digital creator organizations.

1. Introduction

The expansion of digital platforms has increasingly transformed content creation from an individual-centered activity into an organized and economically significant domain. In particular, online video platforms such as YouTube, TikTok, and Instagram have enabled the emergence of a digital creator ecosystem in which content planning, production, distribution, advertising, and commerce are tightly interconnected. Within this evolving ecosystem, creators are no longer merely individual content producers; rather, they are evolving into entrepreneurial actors who create value by combining multiple revenue streams. As a result, growing attention has been directed toward the operation and performance of digital creator organizations.

This transformation raises an important question that has received comparatively limited systematic attention at the organizational level: which organizational characteristics are associated with the performance of digital creator organizations? While the creator economy has attracted growing academic attention, existing research has largely focused on three distinct but fragmented perspectives. First, research rooted in the creative industries tradition has emphasized the high uncertainty and hit-driven nature of content markets. Caves described the “nobody knows” property of creative industries, highlighting that the success of content is difficult to predict ex ante [1]. Hesmondhalgh further explained that content production takes place under structural uncertainty and commercial pressure [2], while De Vany demonstrated that content performance follows an extremely unequal distribution [3]. While this line of work helps explain the structural characteristics of content industries, it provides limited insight into how organizations respond to such uncertainty and generate performance at the organizational level.

Second, studies on the platform economy and digital labor have highlighted the extent to which digital creators’ activities are shaped by platform algorithms and revenue-sharing structures. Gillespie argued that platforms act as gatekeepers that determine content visibility and distribution [4], while Srnicek explained how platform capitalism restructures markets through data and algorithms [5]. Cutolo and Kenney further introduced the notion of platform-dependent entrepreneurs and emphasized that creators are exposed to rule changes and power asymmetries imposed by platforms [6]. More recently, Hödl and Myrach and Verwiebe et al. examined how algorithmic control influences creators’ strategic behavior and perceptions [7,8]. Although this literature underscores the importance of the platform environment, its unit of analysis has remained largely at the level of individual creators, thereby offering limited insight into organization-level operations and performance.

Third, research on the creator economy and business models increasingly understands creators as entrepreneurial actors who capture value by combining multiple revenue sources. Cunningham and Craig showed that creators are evolving into independent content businesses [9], while Abidin illustrated how multiple monetization strategies are combined in the influencer economy [10]. Edeling and Wies further emphasized the entrepreneurial activities of creators and the importance of revenue model diversification through the concept of the creatrepreneur [11]. However, this literature has mainly focused on platform mechanisms, monetization structures, or strategic choices themselves and has paid less attention to how internal organizational elements such as production activities and workforce structure are combined with monetization choices to shape performance.

This omission is important because digital creator organizations are not simply collections of individual creators. Rather, they increasingly operate as platform-based digital businesses in which production continuity, monetization breadth, and internal workforce composition may shape organizational outcomes. In this regard, recent electronic commerce research suggests that digital platform capabilities, business digitalization, and operational processes are closely associated with firm performance in digital settings. It also indicates that influencer-centered commercial activities have become an important mechanism through which digital market outcomes are generated [12,13,14]. Yet, despite the growing commercial significance of creator organizations, empirical research that systematically examines how their internal activities and strategic choices are related to performance remains limited. Notably, recent scholarship on the creator economy has itself pointed to this gap [11,15], highlighting the need for more systematic empirical inquiry into how creator-centered entities organize, operate, and generate performance outcomes.

Accordingly, this study examines organizational characteristics associated with digital creator organizations’ performance from the perspective of organizational activities and strategies. Specifically, this study focuses on three dimensions. First, it examines content production scale, which reflects the continuity and accumulated output of self-produced content. Second, it examines revenue model diversification, which captures the breadth of monetization channels actively operated by the organization. Third, it examines workforce structure, which reflects the organizational basis through which content production and monetization are sustained. Using data from the Digital Creator Media Industry Survey conducted by the Ministry of Science and ICT and the Korea Radio Promotion Association, this study analyzes organizational performance in terms of both sales volume and sales per employee.

This study makes several contributions. First, it establishes digital creator organizations as a theoretically distinct organizational form, specifically one in which content production and commercial monetization are performed simultaneously, by the same workforce, within a platform-constrained environment, and demonstrates that this structural configuration has not been systematically theorized in prior organizational research. Rather than simply relocating the unit of empirical inquiry from individual creators to organizations, this study develops an integrative framework that adapts and combines existing organizational concepts to the structurally distinctive context of digital creator organizations. Second, it proposes an integrative capability framework encompassing production capability, monetization capability, and coordination capability, whose architecture is shaped by the structural logic of creator organizations, in which the three capabilities are functionally interdependent and cannot be optimized independently. Third, it offers practical implications for managers and policymakers by identifying organizational conditions associated with stronger and more sustainable performance in creator-centered businesses, while situating these implications within the associational rather than causal nature of the evidence.

The paper proceeds as follows. Section 2 reviews the relevant literature and develops the hypotheses. Section 3 describes the research design, sample, and variables. Section 4 presents the empirical findings. Section 5 concludes with a discussion of the main findings, implications, and contributions.

2. Research Background and Hypotheses

Digital creator organizations constitute a structurally distinct organizational form that differs in important ways from traditional media firms, platform companies, and individual creators. Traditional media firms separate content production and distribution across distinct organizational units and operate under relatively stable, institutionalized revenue structures with limited algorithmic dependence [16]. Platform companies function primarily as infrastructure providers rather than content producers, deriving value from network effects and data [5]. Individual creators integrate production and monetization but lack the organizational scaffolding required for systematic coordination and sustained commercialization [9]. Digital creator organizations occupy a different structural position: they are organized entities in which content production and commercial monetization are performed simultaneously, by the same workforce, through the same operational workflow, under conditions of strong platform algorithmic dependence [6,17]. This structural configuration, in which the traditional separation between production, distribution, and monetization activities is collapsed by platform infrastructure, defines creator organizations as a meaningful and previously undertheorized organizational form [18,19].

Table 1. Structural Characteristics of Organizational Forms in the Digital Content Ecosystem.

Dimension	Traditional Media Firms	Platform Companies	Individual Creators	Digital Creator Organizations
Production–monetization separation	Separated across units	Not applicable (infrastructure only)	Integrated but unorganized	Simultaneously integrated within same workflow
Algorithmic dependence	Low	Algorithm is own product	High; limited response capacity	High; organized strategic response
Workforce structure	Large-scale; functionally differentiated	Engineering-centered	None or minimal	Small to medium; production and commercial roles combined
Revenue structure	Advertising and licensing; relatively stable	Commission and data fees	Single-channel dependent; volatile	Multi-channel; actively diversified

Note: Dimensions and organizational form categories are adapted and synthesized by the authors based on [5,6,9,16,18].

summarizes these structural differences across organizational forms.

This structural distinctiveness motivates the integrative capability framework developed in this study. More broadly, the emergence of creator organizations reflects a wider transformation in the digital economy, where platform infrastructures increasingly collapse the traditional separation between production, distribution, and monetization activities [16,18]. Under conditions of algorithmic intermediation, understanding organizational performance therefore requires examining multiple capability dimensions within platform-constrained workflows, rather than treating each in isolation [19,20]. This study conceptualizes the performance of digital creator organizations as a function of three interrelated organizational capabilities: production capability, monetization capability, and coordination capability. The analytical framing of these three capabilities reflects the structural logic of creator organizations: in platform-mediated environments, content production, algorithmic distribution, and commercial monetization occur through the same workflow and are governed by the same platform rules, making functional separation structurally difficult in ways that distinguish creator organizations from other organizational forms. While the present study examines the independent associations of each capability dimension with organizational performance, the framework proposed here suggests that these dimensions need to be considered jointly to develop a fuller understanding of how creator organizations generate performance outcomes under platform-mediated conditions. Accordingly, the following sections develop hypotheses by linking observable organizational characteristics to these underlying capabilities.

2.1. Content Production Scale and Organizational Performance

In digital creator organizations, performance is likely to be less closely tied to the scale of fixed production facilities or the efficiency of producing a single standardized product than to the ability to continuously create, distribute, and monetize content. A defining feature of creative industries is structural uncertainty: audience response is difficult to predict ex ante, and the commercial value of creative output is often revealed only after resources have already been committed [1]. Under such conditions, organizational performance is unlikely to depend solely on one-off hits. Rather, organizations with sustainable production systems characterized by repeated execution, regular production rhythms, and accumulated know-how may be better positioned to perform consistently.

This challenge is particularly acute in the digital creator context, where content functions simultaneously as a production output and a strategic asset. An organization that has produced and distributed a large volume of content has not merely generated more output; it has built an accumulated archive from which audiences can discover past work, advertisers can assess brand fit, and the organization itself can draw on reusable formats, themes, and production templates. Bhargava showed that the scale of content supply on digital platforms is closely tied to creator market position and revenue potential, suggesting that sustained content output is not simply a volume metric but a structural indicator of organizational capability [21]. Similarly, research on multi-channel networks highlights that industrialized content production—characterized by systematic planning, role specialization, and routinized workflows—is a central mechanism through which creator organizations stabilize performance under platform uncertainty [9,22].

From an organizational learning perspective, repeated actions facilitate the accumulation of experiential knowledge, which is subsequently encoded into organizational routines that guide future behavior [23]. These routines, when systematically articulated and refined, evolve into organizational capabilities—particularly dynamic capabilities—that enable firms to adapt and reconfigure resources, ultimately leading to superior performance outcomes [24,25,26,27]. In digital creator organizations, content production scale can be understood not merely as an output measure but as an observable indicator of accumulated production capability developed through repeated production activities. Because such capabilities are inherently difficult to observe directly, prior research has relied on cumulative output and experience as proxies for underlying capability development driven by learning-by-doing and routinization processes [28,29,30,31].

As creators engage in continuous cycles of content production, they accumulate experiential knowledge and progressively develop routinized processes across the content production pipeline, including ideation, filming, editing, and platform-specific distribution [23,25,32]. In this sense, a larger accumulated content base reflects not only the quantity of output but also the extent to which such routines and production know-how have been developed and refined over time. Moreover, accumulated content can function as a repository of reusable digital assets that can be recombined and redeployed, thereby enhancing production efficiency and supporting subsequent monetization efforts. This argument is particularly relevant under platform conditions characterized by algorithmic opacity and unstable visibility. When exposure and monetization are affected by platform rules that creators cannot fully control, organizations are more likely to rely on regular content supply and routinized production as a means of stabilizing performance. Platform algorithms on major video-sharing services evaluate channels not only on the quality of individual videos but also on sustained upload activity, as consistent content supply provides the system with more behavioral data points from which to assess a channel’s relevance and audience fit, thereby improving the likelihood of recommendation and discovery [4,6]. Critically, because creators cannot directly observe or manipulate distribution logic, the accumulation of content functions as an organizational hedge: organizations with a larger content base maintain a broader surface area for algorithmic exposure, reducing dependence on any single piece of content performing well. This is consistent with the broader logic that, under conditions of environmental uncertainty, organizations with greater accumulated capabilities are better positioned to absorb performance shocks and maintain operational continuity [26,32]. Moreover, recent research in digital platform and electronic commerce settings suggests that internal digital capabilities and process-related factors are important in explaining firm performance in digitally mediated environments [12,14]. Thus, content production scale captures not merely output volume, but also the accumulation of operational capability that may be associated with stronger performance. More specifically, accumulated content may be associated with performance through three conceptual pathways: first, a larger content base increases a channel’s surface area for algorithmic exposure, thereby expanding advertising and sponsorship revenue opportunities; second, a broader content archive enables IP reuse and repackaging, supporting downstream monetization; and third, repeated production cycles drive routinization and cost reduction, improving operational efficiency over time.

Hypothesis 1.

Content production scale is positively associated with the performance of digital creator organizations.

2.2. Revenue Model Diversification and Organizational Performance

Digital creator organizations generate revenue through multiple channels, including advertising, video production, channel management services, digital content or IP distribution, and product sales. Research on the creator economy increasingly views creators as entrepreneurial actors who do not merely produce content, but also design and combine multiple monetization mechanisms in order to appropriate value from their activities [11]. Critically, however, the performance implications of this multi-channel structure depend not simply on whether multiple revenue options exist, but on the extent to which an organization actively operates them. Organizations that simultaneously activate multiple revenue streams generate more diverse demand signals from different market segments—advertisers, brand partners, platform audiences, IP licensees, and direct consumers—each providing independent feedback that can inform content and business strategy. This variety of market contact is itself a form of organizational learning that can enhance the organization’s ability to identify and exploit emerging monetization opportunities [23,26].

Under conditions of uncertainty, heavy dependence on a single revenue source may increase organizational vulnerability. Platform-based advertising income is structurally volatile: it is contingent on content visibility determined by algorithmic ranking, audience engagement rates that fluctuate across content cycles, and broader advertising market conditions that are sensitive to macroeconomic shifts [4,6]. The asymmetric power relationship between platforms and creators—in which platform rules, revenue-sharing formulas, and algorithmic criteria can be altered unilaterally—further amplifies this dependence risk [5,6]. In such an environment, operating multiple revenue models may function as a risk-buffering strategy by reducing dependence on any single channel and by improving revenue stability. This logic draws on the portfolio principle from financial economics, which holds that combining imperfectly correlated income streams reduces aggregate revenue variance even when individual streams remain volatile [33,34]. Applied to creator organizations, revenue streams such as advertising, branded content, IP licensing, and direct product sales are unlikely to covary perfectly, since they depend on different demand drivers and market actors. Activating multiple such streams can therefore reduce the overall volatility of organizational revenue. Prior research in organizational finance similarly suggests that revenue diversification can reduce vulnerability and contribute to greater financial stability [35]. Although the nonprofit context differs from that of digital creator organizations in important ways, the underlying mechanism—that imperfectly correlated revenue streams buffer against income shocks—is structurally analogous and has received empirical support across a range of organizational contexts [36,37]. In a related vein, creators and creator-centered firms operate under asymmetric platform conditions in which rules and competitive conditions may be altered unilaterally, which further strengthens the value of diversification as a strategic response.

Unlike traditional diversification research, however, the present context does not concern diversification across industries or business segments. Instead, digital creator organizations often diversify within a relatively narrow industry space by simultaneously operating several monetization channels. Accordingly, the relevant strategic dimension is not broad corporate diversification, but the extent to which the organization actively operates multiple revenue models. Within this narrower scope, the performance benefits of diversification are more likely to operate through two distinct mechanisms. The first is demand-side stabilization: organizations drawing revenue from multiple customer segments—platform algorithms, brand advertisers, IP licensees, and direct consumers—are less exposed to demand shocks originating in any one segment. The second is supply-side learning: exposure to multiple revenue channels requires the organization to develop complementary capabilities in areas such as brand partnership management, IP commercialization, and direct-to-consumer marketing. These complementary capabilities, once developed, can reinforce each other and strengthen the organization’s overall value appropriation capacity [26,30]. Organizations with a broader set of active revenue streams are likely to be better positioned to stabilize cash flows, experiment with alternative monetization paths, and accumulate market- and customer-related learning. Consistent with this argument, recent evidence from the creator economy suggests that top-earning creator organizations maintain significantly more active revenue streams than lower-earning counterparts, pointing to revenue model breadth as a structural differentiator of organizational performance rather than a mere reflection of organizational size [11,21]. This logic is also consistent with recent electronic commerce research emphasizing the strategic importance of digital business models, platform-enabled market activities, and digitally mediated performance mechanisms [13,14].

Hypothesis 2.

Revenue model diversification is positively associated with the performance of digital creator organizations.

2.3. Workforce Structure and Organizational Performance

The performance of digital creator organizations depends not only on the creativity of content itself but also on the organizational capacity to continuously produce, coordinate, and monetize content. Given that content production is often project-based and involves the combination of multiple roles and tasks, workforce structure is likely to play an important role in shaping performance [38,39]. In platform-mediated and uncertainty-driven environments, where demand is volatile and algorithmic exposure is unstable, organizational responses to such uncertainty increasingly rely on internal coordination mechanisms and workforce structuring, suggesting that workforce composition may matter for both efficiency and commercialization outcomes [40,41,42,43].

First, the share of permanent employees reflects whether the organization is able to retain core personnel and accumulate knowledge internally. Organizational learning research suggests that performance-relevant knowledge becomes durable when individual experience is embedded in organizational routines [23,44]. Moreover, the knowledge-based view emphasizes that firms create value by integrating and coordinating specialized knowledge, which is facilitated by stable organizational membership and repeated interaction [45,46]. Stable employment relationships facilitate repeated interaction, knowledge sharing, and the development of shared understandings, which enhance coordination and efficiency [47,48]. By contrast, reliance on temporary or project-based labor may fragment knowledge, reduce relational continuity, and weaken the organization’s ability to build cumulative capabilities over time [49,50,51]. A more stable employment base may therefore facilitate collaboration, process standardization, and knowledge retention, thereby supporting stronger organizational performance. This pattern is particularly salient in creative and digital production settings, where tacit knowledge and team familiarity play a critical role in efficient content production and coordination [52]. In this sense, a higher share of permanent employees is likely to strengthen the internal operating basis of the organization.

Second, the share of production and development employees reflects the relative allocation of organizational resources toward content production. While such specialization may strengthen production capability and improve task efficiency [53,54], the digital creator context introduces a structural complication that makes excessive production concentration particularly consequential. Unlike traditional manufacturing or media production settings, where production and distribution are organizationally and temporally separated, digital creator organizations must perform content creation and commercial value capture simultaneously and with the same workforce. Advertising sales, brand partnership management, IP licensing, and direct-to-consumer distribution require sustained relational and commercial effort that cannot be delegated to production staff as a secondary activity, nor outsourced without loss of the audience-specific knowledge that makes these activities valuable. Accordingly, research on organizational design suggests that excessive specialization can create coordination burdens and reduce cross-functional integration, particularly when interdependent tasks must be aligned [55,56]. Teece formalized this logic through the concept of complementary assets: even when an organization possesses strong productive capability, the returns to that capability accrue only if the organization also controls the downstream assets—including commercial, relational, and distributional resources—required to bring output to market [57]. An overly production-heavy workforce structure corresponds, in this framework, to a condition of weak complementary asset coverage, where the capacity to produce systematically outpaces the organizational capacity to monetize. In digital creator organizations, where value creation (content production) must be tightly coupled with value capture (monetization, distribution, and audience management), imbalances in functional allocation may undermine overall performance [58].

In project-based organizations, performance is likely to be shaped not only by the strength of a single function, but also by the coordination and integration of multiple roles [38,59]. Empirical research further suggests that organizations characterized by functional imbalance or over-concentration in a single domain may experience bottlenecks, delays, and inefficiencies due to misaligned resource allocation and coordination failures [60,61]. Accordingly, an excessively production-heavy workforce structure may increase coordination costs, limit the development of commercialization capabilities, and constrain the organization’s ability to convert content into economic value [62,63].

Taken together, these arguments highlight a trade-off between capability accumulation through workforce stability and potential inefficiencies arising from imbalanced functional specialization. While stable employment supports learning and coordination, excessive concentration of human resources in production functions may weaken complementary capabilities required for monetization, ultimately shaping organizational performance in complex ways.

Hypothesis 3a.

The share of permanent employees is positively associated with the performance of digital creator organizations.

Hypothesis 3b.

The share of production and development employees is negatively associated with the performance of digital creator organizations.

3. Research Design

3.1. Sample

This study uses the 2023 and 2024 data from the Digital Creator Media Industry Survey conducted by the Ministry of Science and ICT and the Korea Radio Promotion Association as part of the digital creator media industry infrastructure development project. The digital creator media industry is defined as an industry related to services that plan, produce, distribute, advertise, and support the use of digital creators and digital creator media that generate economic value. In the survey, participation fields are categorized into such areas as video production and production support, advertising and marketing, management, and online video-sharing platforms.

The empirical setting is a single-country study of Korean digital creator organizations observed during 2023–2024. Because the released microdata are anonymized and do not provide organization identifiers, observations from different survey years cannot be linked at the organization level. Accordingly, the analysis uses the 2023 and 2024 survey waves as pooled cross-sectional data.

3.2. Variables

To measure organizational performance, this study uses two dependent variables: sales volume (SV) and sales per employee (SPE). Sales volume is measured as the natural logarithm of annual sales. Sales per employee is measured as the natural logarithm of annual sales per employee, calculated as annual sales divided by the number of employees, in order to capture the efficiency dimension of organizational performance.

To examine organizational characteristics associated with the performance of digital creator organizations, the following independent variables are constructed. First, content production scale (CPS) captures the intensity of self-produced content activity and is measured as ln(self-produced and distributed content count + 1). Because the underlying raw content-count variable is highly right-skewed, the logarithmic transformation is used to reduce skewness and to limit the influence of extremely high-output organizations. CPS can therefore be interpreted as an observable production-activity indicator rather than as a direct measure of content quality or audience response. Second, revenue model diversification (RMD) measures the breadth of monetization activity. We classify revenue items into five broad categories—video production, advertising, channel management services, digital content/IP distribution, and product sales/other sources—and count the number of categories in which annual revenue is greater than zero. Thus, RMD captures the number of active revenue streams rather than the balance or relative importance of revenue across streams. Third, workforce structure is captured using two variables. Share of permanent employees (PERM) is measured as the number of permanent employees divided by the total number of employees. Share of production and development employees (PDEV) is measured as the number of employees engaged in production and development tasks divided by the total number of employees. Finally, firm size (SIZE) and firm age (AGE) are included as control variables, along with year, business segment, and organization-type fixed effects.

Table 2. Descriptive Statistics.

Variable	Min	Max	Mean	Std. Dev.
SV	1.1	12.7	5.79	1.29
SPE	0.51	9.05	4.61	0.95
CPS	0	9.21	2.34	1.7
RMD	0	5	1.32	0.73
PERM	0	1	0.68	0.33
PDEV	0	1	0.32	0.3
SIZE	0.69	7.22	1.51	0.63
AGE	0	26	6.66	4.63

Note: N = 1488.

presents the descriptive statistics of the variables used in the analysis, and

Table 3. Correlation Matrix.

	1	2	3	4	5	6	7	8
1 SV	1.00
2 SPE	0.73 *	1.00
3 CPS	0.19 *	0.08 *	1.00
4 RMD	0.16 *	0.13 *	0.23 *	1.00
5 PERM	−0.13 *	0.05	−0.05 *	0.06 *	1.00
6 PDEV	−0.22 *	−0.08 *	0.17 *	0.07 *	0.33 *	1.00
7 SIZE	0.76 *	0.11 *	0.20 *	0.12 *	−0.22 *	−0.22 *	1.00
8 AGE	0.13 *	0.09 *	0.04	0.05	0.02	−0.01	0.11 *	1.00

Note: * p < 0.05.

reports the correlations among the main variables. As shown in Table 2, the mean of SV is 5.79, whereas the mean of SPE is 4.61. The mean of CPS is 2.34, suggesting substantial variation in the scale of self-produced and distributed content across digital creator organizations. In addition, the mean of RMD is 1.32, indicating that many organizations operate a relatively limited number of active revenue models.

As reported in Table 3, the main independent variables show correlations with organizational performance largely in the expected directions. CPS and RMD are positively correlated with both SV and SPE, whereas PDEV is negatively correlated with both performance measures.

3.3. Regression Model Specification

This study measures the performance of digital creator organizations from two perspectives— SV and SPE—and examines how organizational activities and strategic factors are associated with performance through regression analysis.

$${\mathrm{SV}}_{\mathrm{i},\mathrm{t}}= {\mathsf{\beta }}_0+{\mathsf{\beta }}_1{\mathrm{CPS}}_{\mathrm{i},\mathrm{t}}+{\mathrm{Controls}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\epsilon }}_{\mathrm{i},\mathrm{t}}$$

(1)

$${\mathrm{SV}}_{\mathrm{i},\mathrm{t}}= {\mathsf{\beta }}_0+{\mathsf{\beta }}_1{\mathrm{RMD}}_{\mathrm{i},\mathrm{t}}+{\mathrm{Controls}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\epsilon }}_{\mathrm{i},\mathrm{t}}$$

(2)

$${\mathrm{SV}}_{\mathrm{i},\mathrm{t}}= {\mathsf{\beta }}_0+{\mathsf{\beta }}_1{\mathrm{PERM}}_{\mathrm{i},\mathrm{t}}+{{\mathsf{\beta }}_2{\mathrm{PDEV}}_{\mathrm{i},\mathrm{t}}+\mathrm{Controls}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\epsilon }}_{\mathrm{i},\mathrm{t}}$$

(3)

$${\mathrm{S}\mathrm{V}}_{\mathrm{i},\mathrm{t}}={\mathsf{\beta }}_0+{\mathsf{\beta }}_1{\mathrm{CPS}}_{\mathrm{i},\mathrm{t}}+ {\mathsf{\beta }}_2{\mathrm{RMD}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\beta }}_3{\mathrm{PERM}}_{\mathrm{i},\mathrm{t}}+{{\mathsf{\beta }}_4{\mathrm{PDEV}}_{\mathrm{i},\mathrm{t}}+\mathrm{Controls}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\epsilon }}_{\mathrm{i},\mathrm{t}}$$

(4)

where i and t denote firm and year, respectively. ${\mathrm{Controls}}_{\mathrm{i},\mathrm{t}}$ is a vector of control variables. All models include year, business segment, and organization-type fixed effects. ${\mathsf{\epsilon }}_{\mathit{i},\mathit{t}}$ is the random error term. To account for potential heteroskedasticity, robust standard errors were employed in the regression analyses.

4. Empirical Results

4.1. Main Analysis

Table 4. Regression Results for Sales Volume.

	(1)	(2)	(3)	(4)
CPS	0.058 *			0.053 +
	(0.037)			(0.063)
RMD		0.102 *		0.083 *
		(0.014)		(0.043)
PERM			0.137	0.128
			(0.178)	(0.205)
PDEV			−0.254 *	−0.311 *
			(0.029)	(0.010)
SIZE	1.297 ***	1.318 ***	1.318 ***	1.286 ***
	(0.000)	(0.000)	(0.000)	(0.000)
AGE	0.013 *	0.012 *	0.014 *	0.013 *
	(0.021)	(0.026)	(0.014)	(0.024)
Year/Segment/Org. Type FE	Controlled	Controlled	Controlled	Controlled
Constant	3.527 ***	3.522 ***	3.622 ***	3.495 ***
	(0.000)	(0.000)	(0.000)	(0.000)
Observations	1488	1488	1488	1488
R-squared	0.461	0.461	0.461	0.467
F-value	122.8	141.2	130.6	105.1

Note: p-values in parentheses. *** p < 0.001, * p < 0.05, + p < 0.10.

and

Table 5. Regression Results for Sales per Employee.

	(1)	(2)	(3)	(4)
CPS	0.057 *			0.048 +
	(0.039)			(0.085)
RMD		0.104 *		0.084 *
		(0.012)		(0.041)
PERM			0.225 *	0.214 *
			(0.025)	(0.031)
PDEV			−0.234 *	−0.285 *
			(0.042)	(0.017)
SIZE	0.087 +	0.106 *	0.122 **	0.093 *
	(0.053)	(0.013)	(0.005)	(0.043)
AGE	0.014 *	0.013 *	0.014 **	0.013 *
	(0.012)	(0.016)	(0.009)	(0.016)
Year/Segment/Org. Type FE	Controlled	Controlled	Controlled	Controlled
Constant	4.159 ***	4.152 ***	4.175 ***	4.053 ***
	(0.000)	(0.000)	(0.000)	(0.000)
Observations	1488	1488	1488	1488
R-squared	0.058	0.058	0.059	0.069
F-value	8.047	8.986	8.192	7.623

Note: p-values in parentheses. *** p < 0.001, ** p < 0.01, * p < 0.05, + p < 0.10.

present the estimation results using market performance and operational efficiency as two different dimensions of organizational performance.

In the market performance models shown in Table 4, Models (1) through (4) examine how content production activities, revenue model operations, and workforce structure are related to sales volume. First, content production scale shows a positive relationship with market performance, providing support for Hypothesis 1, although the evidence is weaker in the fully specified model. In Model (1), the coefficient is positive and significant at the 5% level (β = 0.058, p < 0.05), and in Model (4), the positive direction is maintained but becomes marginally significant at the 10% level (β = 0.053, p < 0.10). This finding suggests that the accumulation of self-produced and distributed content is associated with higher sales in digital creator organizations.

Second, revenue model diversification shows a positive and significant coefficient in Model (2) (β = 0.102, p < 0.05), and this positive and significant relationship remains in Model (4) (β = 0.083, p < 0.05). This suggests that digital creator organizations operating a broader range of revenue streams tend to achieve higher sales, thereby supporting Hypothesis 2.

With regard to workforce structure, the share of permanent employees shows a positive coefficient, but it is not statistically significant in the market performance models. Thus, Hypothesis 3a is not supported in terms of sales volume. In contrast, the share of production and development employees shows a consistently negative and significant coefficient in Models (3) and (4) (Model (3): β = −0.254, p < 0.05; Model (4): β = −0.311, p < 0.05), supporting Hypothesis 3b. It suggests that a relatively production-heavy workforce structure may be associated with weaker monetization balance or higher coordination burdens compared with a more functionally diversified staffing structure.

Among the control variables, firm size shows a strongly positive coefficient in the market performance models (p < 0.001), and firm age is also positive and significant. This indicates that larger organizations tend to achieve higher sales and that organizations with longer industry participation also tend to generate stronger market performance. The explanatory power of the models (R²) is approximately 0.46–0.47, indicating that market performance is substantially explained by structural and strategic characteristics of the organization.

Table 5 presents the results for operational efficiency measured as sales per employee. First, content production scale again shows a positive relationship with efficiency. In Model (1), the coefficient is positive and significant (β = 0.057, p < 0.05), and in Model (4), the positive relationship remains and is marginally significant at the 10% level (β = 0.048, p < 0.10). This suggests that accumulated content production is positively related to both sales volume and sales per employee, although the evidence is comparatively weaker in the full model. The weaker statistical significance of this variable in the efficiency models may indicate that productivity is influenced by a wider range of unobserved factors, including platform exposure, content format, advertising rates, and revenue-sharing structures.

Second, revenue model diversification shows a consistently positive and significant relationship in the efficiency models as well, again supporting Hypothesis 2 (Model (2): β = 0.104, p < 0.05; Model (4): β = 0.084, p < 0.05). This finding suggests that organizations operating a broader set of revenue streams tend to report higher sales per employee as well as higher sales volume.

Third, the workforce structure variables show a more distinct pattern in the efficiency models. The share of permanent employees has a positive and significant coefficient (Model (3): β = 0.225, p < 0.05; Model (4): β = 0.214, p < 0.05), supporting Hypothesis 3a. This suggests that employment stability is associated with higher organizational productivity. A higher share of permanent employees may help organizations retain core personnel, establish routines for collaboration and division of labor, and standardize production and operating processes, thereby improving output per unit of labor input. In contrast, the share of production and development employees again shows a significant negative relationship (Model (3): β = −0.234, p < 0.05; Model (4): β = −0.285, p < 0.05), supporting Hypothesis 3b. This suggests that increasing the relative share of production personnel does not necessarily improve productivity and may instead generate inefficiencies in commercialization or coordination.

The explanatory power of the efficiency models (R²) ranges from 0.06 to 0.07, which is substantially lower than that of the sales-volume models. We acknowledge that this is a low explanatory power and that interpretation of the results is correspondingly cautious. The structural and strategic variables we measure account for only a small share of variation in sales per employee, indicating that efficiency in digital creator organizations is shaped to a much greater degree by factors that are not captured in the survey than is sales volume. Nevertheless, the consistent signs of the main coefficients provide useful descriptive evidence about how organizational characteristics are associated with both performance dimensions.

4.2. Additional Analyses

To further assess the robustness of the empirical findings, we conducted additional analyses focusing on the measurement of revenue model diversification and selected diagnostic checks for model specification.

4.2.1. Alternative Measures of Revenue Model Diversification

To further assess the measurement validity of revenue model diversification, we conducted additional analyses using three alternative revenue-share-based measures: the Herfindahl–Hirschman Index (HHI) [64,65], Shannon entropy of revenue shares [66], and dominant revenue share. Our primary RMD measure captures the breadth of active revenue streams by counting the number of monetization channels operated by the organization, but it does not reflect how revenue is distributed across streams. The alternative measures are calculated from the revenue shares of the same monetization categories used to construct RMD. Let $s_j$ denote the share of total revenue accounted for by revenue stream $j$. HHI is calculated as ${\sum }_j{s_j}^2$, with larger values indicating greater revenue concentration. Shannon entropy is calculated as $-{\sum }_j{s}_j\mathrm{l}\mathrm{n}(s_j)$, with larger values indicating a more dispersed revenue distribution across streams. Dominant revenue share is calculated as ${\mathrm{m}\mathrm{a}\mathrm{x}}_j\left(s_j\right)$, capturing the proportion of total revenue accounted for by the organization’s largest revenue stream. We therefore re-estimated the full sales and SPE models under seven specifications: the original RMD measure only, each alternative measure entered alone, and each alternative measure entered jointly with RMD. The full results are reported in Appendix A 

Table A1. Alternative Measures of Revenue Model Diversification. Panel (A). Dependent Variable: Sales Volume (SV). Panel (B). Dependent Variable: Sales per Employee (SPE).

(A)
Variables	(1) RMD Only	(2) HHI	(3) Entropy	(4) Dominant Share	(5) RMD + HHI	(6) RMD + Entropy	(7) RMD + Dominant Share
RMD	0.077 + (0.086)	—	—	—	0.262 *** (0.001)	0.330 *** (0.000)	0.209 ** (0.002)
HHI	—	0.017 (0.918)	—	—	0.738 ** (0.010)	—	—
Entropy	—	—	0.017 (0.871)	—	—	−0.616 ** (0.005)	—
Dominant share	—	—	—	0.062 (0.775)	—	—	0.692 * (0.032)
Observations	1415	1415	1415	1415	1415	1415	1415
R-squared	0.468	0.467	0.467	0.467	0.472	0.472	0.471
(B)
Variables	(1) RMD Only	(2) HHI	(3) Entropy	(4) Dominant Share	(5) RMD + HHI	(6) RMD + Entropy	(7) RMD + Dominant Share
RMD	0.082 + (0.067)	—	—	—	0.264 *** (0.001)	0.329 *** (0.000)	0.210 ** (0.002)
HHI	—	−0.000 (0.998)	—	—	0.726 ** (0.009)	—	—
Entropy	—	—	0.030 (0.777)	—	—	−0.602 ** (0.006)	—
Dominant share	—	—	—	0.038 (0.857)	—	—	0.672 * (0.034)
Observations	1415	1415	1415	1415	1415	1415	1415
R-squared	0.060	0.057	0.057	0.057	0.066	0.067	0.065

Note: p-values are reported in parentheses. + p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001. All models include CPS, PERM, PDEV, SIZE, AGE, and year, segment, and organization-type fixed effects.

. Because the revenue-share-based measures require positive total revenue and valid revenue-component information, the sample size for these analyses is N = 1415, slightly smaller than the main models (N = 1488).

As reported in Appendix A Table A1, none of the three revenue-share-based measures are statistically significant when entered alone in either performance dimension. The explanatory role of the count-based RMD measure is therefore not displaced by the alternative measures. The joint specifications show a consistent sign pattern: HHI and dominant share enter positively, whereas entropy enters negatively. This pattern is consistent with the possibility that, conditional on monetization breadth, a clearer dominant revenue base may be associated with stronger performance. However, because these revenue-share-based measures are correlated with RMD, we interpret the joint results as measurement-sensitivity evidence rather than as evidence of an independent concentration effect.

Substantively, the across-specification pattern is most consistent with a breadth interpretation: the count-based RMD measure remains positive in every specification in which it is included, whereas the revenue-share-based measures (HHI, entropy, dominant share) are not statistically significant when entered alone. We therefore retain RMD as the focal measure of monetization structure and treat the joint specifications as a measurement-sensitivity check. Overall, the additional analyses suggest that the main RMD result is not overly sensitive to the particular count-based operationalization.

4.2.2. Diagnostic Checks for Multicollinearity and Functional Form

We also conducted selected diagnostic checks for multicollinearity and functional form. Although Table 3 shows a relatively high bivariate correlation between sales volume and firm size, variance inflation factors (VIFs) for all covariates in the fully specified model remain below conventional concern thresholds, with the maximum VIF equal to 3.04. This suggests that multicollinearity is unlikely to materially distort the estimated coefficients.

In addition, we tested quadratic specifications for CPS and RMD to examine whether the baseline linear specification masks non-linear relationships, including a potential inverted-U relationship for revenue model diversification. The squared terms are not statistically significant in either the sales-volume or sales-per-employee models. These diagnostic checks do not alter the substantive interpretation of the main findings.

5. Discussion and Conclusions

Taken together, the results lead to several conclusions. First, content production scale shows a positive relationship with both market performance and operational efficiency, although the evidence is comparatively weaker in the fully specified models, supporting the argument that the accumulation and continuity of content production are associated with stronger performance in digital creator organizations. Interpreted through organizational learning and dynamic capability perspectives, sustained content production may reflect the routinization of production activities and the accumulation of experiential knowledge through repeated content creation [23,28]. Second, revenue model diversification is consistently positive and statistically significant across both performance measures, indicating that the extent to which an organization actively operates multiple revenue streams is closely related to better performance. This suggests that reducing dependence on a single revenue source and securing multiple monetization paths may be an effective strategy for digital creator organizations. More specifically, this finding is consistent with complementary asset logic [57] and the portfolio stabilization mechanism [33], as multiple monetization channels may support both demand-side stabilization and the development of complementary commercial capabilities. Third, the effects of workforce structure differ across performance dimensions. The share of permanent employees shows a significant positive association with efficiency but not with market performance, whereas the share of production and development employees shows a significant negative relationship with both market performance and efficiency. These findings, consistent with the knowledge-based view [45,46], suggest that workforce stability may support internal coordination and knowledge retention, while an overly production-heavy workforce structure may weaken the balance between content creation and monetization.

These findings provide several implications for policy and practice. First, the fact that the key factors associated with performance are not one-time inputs such as equipment ownership, but rather the continuity of content production and the breadth of monetization channels, suggests that support policies may need to move beyond simple production subsidies or facility support and instead be designed to strengthen operational capabilities that enable continuous production and the expansion of monetization paths. Second, the positive association between the share of permanent employees and productivity suggests that the competitiveness of digital creator organizations does not lie solely in expanding short-term freelance-based employment, but also in the stable retention of core personnel and the accumulation of expertise. Finally, the negative association of the share of production and development employees suggests that increasing production staff does not always translate into better performance and highlights the importance of balancing production with distribution, sales, and marketing functions. Accordingly, future policy discussions should consider support measures that facilitate not only the strengthening of production capability but also the integration of monetization and operational functions. However, these implications may also have distributional consequences: if sustained content production scale and revenue model diversification are systematically associated with stronger performance, smaller or younger creator firms that lack the resources or capabilities to develop these dimensions may face structural disadvantages. This possibility suggests that policy discussions should also consider potential distributional consequences within the industry, rather than focusing only on aggregate industry growth.

More broadly, this study also speaks to the emerging electronic commerce literature that emphasizes how digitally mediated performance is shaped not only by platform conditions, but also by firms’ internal capabilities, digitalization processes, and commercially oriented organizational structures. In this sense, digital creator organizations may be understood not simply as cultural producers, but as platform-based digital businesses whose performance is associated with how they organize production, monetization, and workforce composition under conditions of platform uncertainty [12,13,14]. The findings are also broadly consistent with studies of creator organizations and platform-dependent entrepreneurs in other national contexts, which have emphasized the importance of content output continuity and revenue model diversification [6,9,11]. However, the workforce-structure findings, particularly the null association of permanent employment with sales volume and the negative association of production concentration with both performance dimensions, may reflect features specific to the Korean creator industry. The Korean setting therefore provides an analytically useful context for examining digital creator organizations, but the findings should not be read as formal cross-country evidence, leaving systematic cross-national comparison as an important agenda for future work.

This study has several limitations. First, the analysis uses cross-sectional survey data from Korean digital creator organizations observed during 2023–2024, which limits the range of observable information. Because the released microdata do not contain organization identifiers, the same establishment cannot be tracked across survey waves. As a result, firm fixed effects, lagged specifications, dynamic panel models, or other panel-based approaches are not feasible. Reverse causality and omitted-variable bias therefore cannot be ruled out; for example, higher-performing organizations may have greater capacity to expand content production or diversify revenue models. Second, important factors such as content quality, audience engagement, platform visibility, and the detailed composition of monetization channels are not fully captured in the data. The survey also does not fully observe management capability, team competencies, content specialization, or platform-specific monetization conditions. Third, the findings are derived from a limited period and a single national context, which may constrain their generalizability. Survey-based measures of creator organizations are not directly comparable across jurisdictions because of differences in industry classification, monetization-channel categorization, platform composition, and labor market conditions.

Future research may build on this study by combining survey data with platform-level and content-level indicators, constructing panel datasets that allow temporal ordering to be examined, and conducting cross-country comparisons using harmonized measures. Future studies could also examine whether revenue model diversification mediates the relationship between content production, workforce structure, and subsequent organizational performance. Finally, although this study focuses on organizational-level performance differences, industry-wide consequences such as winner-take-most dynamics and uneven distributional outcomes across creator organizations remain important directions for future research.