Exploring the Motivation for Media Consumption and Attitudes Toward Advertisement in Transition to Ad-Supported OTT Plans: Evidence from South Korea

Abstract

As ad-supported subscription models proliferate across over-the-top (OTT) media platforms, understanding the psychological mechanisms and perceptual factors that underlie consumers’ transition decisions becomes increasingly consequential. This study integrates the Uses and Gratifications framework with a contemporary motivation-based perspective to examine how users’ media consumption motivations and advertising attitudes predict intentions to adopt ad-supported OTT plans. Data were collected via a nationally representative online survey in South Korea (N = 813). The sample included both premium subscribers (n = 708) and non-subscribers (n = 105). The findings reveal distinct segmentation in decision-making patterns. Among premium subscribers, switching intentions were predominantly driven by intrinsic motivations—particularly identity alignment with content—and by the perceived informational value of advertisements. These individuals are more likely to consider ad-supported plans when ad content is personally relevant and cognitively enriching. Conversely, non-subscribers exhibited greater sensitivity to extrinsic cues such as the entertainment value of ads and the presence of tangible incentives (e.g., discounts), suggesting a hedonic-reward orientation. By advancing a dual-pathway explanatory model, this study contributes to the theoretical discourse on digital subscription behavior and offers actionable insights for OTT service providers. The results underscore the necessity of segment-specific advertising strategies: premium subscribers may be engaged through informative and identity-consistent advertising, while non-subscribers respond more favorably to enjoyable and benefit-laden ad experiences. These insights inform platform monetization efforts amid the evolving dynamics of consumer attention and subscription fatigue.

1. Introduction

Over-the-top (OTT) streaming platforms have emerged as a dominant force in the digital media industry, with explosive market growth intensifying competition and driving the adoption of ad-supported models as the next-generation monetization strategy. The global OTT market generated USD 272.7 billion in revenue serving 3.51 billion users in 2023, with projections reaching USD 462.9 billion and 4.22 billion users by 2027, reflecting a 10.01% CAGR [1]. This rapid expansion has prompted major platforms to pivot toward hybrid monetization frameworks, with Netflix launching ad-supported tiers in November 2022, Disney+ introducing ad-supported options in December 2022, and Amazon Prime Video announcing ad-supported content plans for 2024 [2]. Market data demonstrates the strategic importance of this shift: ad-supported video-on-demand (AVOD) revenue is projected to reach $201 billion by 2027 with a 15.2% CAGR, significantly outpacing traditional subscription revenue growth of 8.3% CAGR [3]. This divergent growth pattern underscores the industry-level urgency of understanding consumer transition behaviors and the psychological mechanisms influencing users’ willingness to accept advertising in exchange for reduced subscription costs.

Extant literature has extensively examined the antecedents of users’ decisions to initiate or maintain subscriptions to digital content services. Among the most consistently cited determinants, hedonic gratification—particularly the enjoyment derived from content consumption—has emerged as a key predictor of subscription continuity [4]. In the context of India’s OTT streaming market, Menon (2022) [5] delineates a temporal distinction, wherein relaxation needs predominantly influence initial adoption, while entertainment motives are more salient for sustained usage. Complementary findings by Koul et al. (2021) [6] highlight structural factors such as content diversity, affordability, and accessibility to preferred media as salient drivers for both acquisition and retention. Additionally, extrinsic considerations including perceived price fairness, social influence, and utilitarian value have been shown to shape subscription intentions across diverse user segments [7].

While the body of research on OTT subscription behavior has grown substantially, scholarly attention has predominantly concentrated on either the acquisition of new premium subscribers or the retention of existing ones. In contrast, a critical yet underexplored transition—namely, the shift from premium to ad-supported subscription plans—has received comparatively little empirical scrutiny. This oversight is noteworthy given the strategic pivot of many OTT platforms toward ad-supported models as a means to capture price-sensitive users while maintaining diversified revenue streams.

Consumer segmentation theory suggests that subscription behavior reflects underlying psychological differences in risk tolerance, price sensitivity, and value perception [8]. Premium subscribers have overcome the psychological barriers associated with recurring payment commitments, suggesting higher levels of platform engagement, content valuation, and advertising avoidance preferences [9]. This behavioral pattern indicates a consumer segment characterized by higher willingness to pay for enhanced user experience and reduced advertising exposure, reflecting distinct motivational orientations compared to non-subscribers [10].

Existing studies on advertising-based OTT models have largely focused on adoption behaviors among non-subscribers, frequently emphasizing usability-related determinants such as interface design and navigational simplicity [11]. Additionally, much of the research on subscription switching behavior has centered on upgrades from free to paid tiers, often examining the role of personalization algorithms and promotional incentives as key drivers of conversion [12]. Lee (2023) [4] contributes to this discourse by analyzing consumer adoption of premium services, underscoring the efficacy of tiered pricing structures and free trial strategies in reducing perceived risk and facilitating uptake.

However, limited research has explored the psychological mechanisms underlying the decision to transition from premium to ad-supported subscriptions. While prior studies have primarily examined extrinsic determinants (e.g., price sensitivity, content variety, and platform usability), fewer have examined how intrinsic psychological needs and attitudinal responses toward advertising jointly influence subscription downgrade decisions. This research gap becomes increasingly relevant as OTT platforms adopt hybrid pricing models that offer consumers trade-offs between monetary savings and exposure to advertising content.

Against this backdrop, the present study aims to investigate the psychological motivations and advertising attitudes that influence users’ decisions to transition from premium to ad-supported OTT subscription plans. Specifically, by integrating Uses and Gratifications (U&G) theory with advertising attitude frameworks, this study examines how (1) users’ underlying motivations for consuming OTT content and (2) their attitudinal dispositions toward digital advertisements predict subscription downgrade intentions. Anchored in the U&G paradigm, the research extends traditional interpretations by foregrounding the influence of intrinsic psychological needs in subscription decision-making. In doing so, the study introduces a novel predictive framework that captures the nuanced trade-offs consumers navigate when deliberating between subscription tiers, thereby offering a theoretically grounded and empirically testable model for understanding OTT plan transitions.

2. Literature Review

2.1. Media Content Consumption and Psychological Needs

U&G theory has historically provided a valuable lens for understanding media consumption behaviors, emphasizing that users actively select media to fulfill specific psychological needs [13,14]. In the context of OTT platforms, the theory has evolved to incorporate digital-specific motivations, including binge-watching, seamless accessibility, and entertainment-driven engagement [5]. OTT platforms uniquely satisfy users’ needs for relaxation, escapism, and social bonding through personalized experiences facilitated by algorithmic recommendations [14,15].

Recent studies emphasize the significance of psychological gratifications in OTT engagement, particularly emotional gratifications like mood regulation and stress relief, further accentuated during the COVID-19 pandemic [16]. Binge-watching, a common behavioral pattern in OTT platforms, contributes to relaxation, escapism, and narrative closure, enhancing user attachment [17]. Additionally, studies integrating U&G with other frameworks highlight the importance of psychological needs. For instance, Sharma et al. (2023) [18] employed Means-End Theory to illustrate how users prioritize various gratifications, while Camilleri and Falzon (2021) [19] applied the Technology Acceptance Model (TAM) to show how emotional engagement and ease of use impact platform adoption.

While U&G provides a foundational framework for understanding user motivations, its applicability to OTT platforms is increasingly limited. The theory presupposes that users consciously recognize their motivations and intentionally seek content to fulfill specific needs. However, contemporary digital consumption encompasses both active and passive engagement, wherein algorithmic recommendations, platform interfaces, and habitual viewing patterns influence user behavior, often beyond conscious decision-making [20,21]. Moreover, U&G primarily categorizes media use based on gratifications sought rather than the underlying motivational structures that drive behavior [22]. This limits its ability to explain the more dynamic and evolving nature of digital media consumption.

A deeper understanding of media consumption requires theoretical models that can capture both deliberate and automatic user behaviors. Motivation-based frameworks offer a more nuanced lens by highlighting the psychological and affective mechanisms that drive media engagement—ranging from cognitive fluency and habitual reinforcement to situational triggers that often operate below the level of conscious awareness [14,23]. In contrast to the retrospective and self-attributed reasoning emphasized by the traditional Uses and Gratifications (U&G) paradigm, motivation-based approaches integrate real-time behavioral cues and external environmental stimuli, thereby providing a more predictive and ecologically valid account of media consumption dynamics [24].

Moreover, recent studies suggest that user engagement with OTT content is driven by deeper psychological needs such as identity formation, social belonging, and cognitive stimulation, which extend beyond the functional gratifications typically captured by U&G [25,26]. By integrating intrinsic and extrinsic motivations, this study moves beyond the static categorization of media gratifications to a dynamic understanding of how psychological and contextual factors interact to influence subscription decisions, particularly the transition from premium to ad-supported OTT plans.

Accordingly, this study posits that a nuanced understanding of users’ underlying media consumption motivations offers a more robust explanatory basis for predicting subscription behaviors—particularly the transition from premium to ad-supported OTT plans. Departing from the assumption of purely rational consumer decision-making, the proposed motivation-based framework underscores the dynamic interplay between intrinsic psychological needs and external platform cues. This conceptual shift is consistent with emerging scholarship that foregrounds the dual influence of hedonic and utilitarian gratifications in shaping digital media engagement [19]. By extending the analytical scope beyond the traditional boundaries of the U&G paradigm, this study advances an integrated model capable of elucidating user migration across subscription tiers and informing more targeted, psychologically attuned OTT monetization strategies.

2.2. Antecedents of OTT Platform Adoption and Usage

The rapid proliferation of OTT platforms has transformed consumer behavior, significantly altering traditional media consumption patterns. Identifying the antecedents influencing OTT platform adoption and sustained usage is crucial, particularly given the fiercely competitive and rapidly evolving media environment.

Existing literature highlights multiple significant factors influencing consumer adoption and continuous usage of OTT services. According to Periaiya and Ajith (2023) [27], gratification such as entertainment, escape, modality, navigability, passing time, and personalization substantially impact user satisfaction and stickiness on OTT platforms. This mixed-method study found entertainment, modality, and navigability significantly affect affective media satisfaction, leading to increased user stickiness. Similarly, personalization and passing time were directly associated with improved user retention.

Additionally, Lee and his colleagues (2021) [28] emphasized personalization, content diversity, and convenience as pivotal in influencing consumer satisfaction and continuous usage intentions. Their research affirmed that both financial investments and perceived learning efforts are crucial constraint-based factors that significantly increase users’ resistance to switching OTT platforms.

Another study supported these findings by employing a dual model of post-adoption, illustrating that dedication-based mechanisms driven by user satisfaction and positive emotional responses coexist alongside constraint-based mechanism resulting from switching costs and sunk investments [29]. Specifically, this study underscored the critical role of personalization and content richness in driving user satisfaction, while non-financial costs time and effort spent learning platform functionalities as well as financial costs significantly reinforced switching resistance. Psychological factors were also known to contribute to OTT platform adoption and continuous use (e.g., [30]). For example, Hwang and Nam (2021) [30] revealed that consumers increasingly value meaningful engagement and peaceful experiences alongside conventional pleasure-driven motivations, influencing their loyalty and sustained engagement with digital entertainment platforms. Additionally, heuristic evaluations such as representativeness and convenience were recognized as other crucial factors impacting consumers’ quick decision-making processes in selecting and continuously using OTT platforms.

As seen from the above, OTT adoption and continued usage are driven by a complex interplay of various factors including gratification-based factors, dedication-based factors, constraint-based factors, psychological and heuristic factors. Based prior research, this study aims to identify the factors influencing users’ transition to ad-supported OTT services.

2.3. Advertisements and Attitudes

User attitudes toward digital advertising constitute a pivotal determinant of engagement in online media environments. Among the most frequently cited impediments to positive ad reception is advertising clutter, which has been shown to evoke perceptions of intrusiveness and irritation, thereby fostering avoidance behaviors such as ad-skipping or the adoption of ad-blocking technologies [31,32,33]. Within the framework of the Interactive Advertising Model (IAM), ad receptivity is shaped by structural characteristics (e.g., length, format), interactivity levels, and the alignment of content with user expectations [34]. When advertisements are perceived as irrelevant or overly disruptive, they tend to elicit psychological reactance—a motivational state aimed at restoring perceived autonomy—which in turn reinforces resistance and disengagement.

Conversely, research suggests that enhancing user control and relevance can mitigate negative responses. Pashkevich et al. (2012) [35] find that skippable ads provide a sense of autonomy, reducing perceived forced exposure and fostering more positive attitudes. Similarly, personalized advertising, which aligns ad content with user preferences through algorithmic recommendations, can enhance engagement and reduce perceived intrusiveness [24,36]. These insights are particularly relevant for OTT platforms aiming to balance user satisfaction and revenue generation through ad-supported models.

Despite extensive research on ad perceptions, little work has examined how attitudes toward advertisements influence transitions between subscription tiers. This gap is particularly important given that attitudes toward advertisements may manifest differently in the OTT domain compared to other digital platforms. While prior research has consistently linked advertising clutter to negative user experiences [32,33], these findings have primarily focused on ad-supported platforms such as social media and free video-sharing services, where users have limited control over ad exposure.

Within the OTT subscription landscape, the decision to adopt an ad-supported plan entails a distinct trade-off between cost savings and ad exposure, introducing a qualitatively different evaluative context compared to non-subscription digital platforms. Users who voluntarily opt into ad-supported tiers may exhibit heightened tolerance for advertisements, given their increased agency in selecting such experiences. However, it remains an open empirical question whether established predictors of ad acceptance—such as personalization, relevance, and interactivity [24,36]—retain their explanatory power in this quasi-voluntary advertising environment. As such, interrogating these contextual nuances is essential to refining advertising models that are both psychologically resonant and commercially viable.

This study hypothesizes that premium subscribers with strongly negative attitudes toward advertising will demonstrate lower intentions to transition to ad-supported plans, while those with more neutral or favorable dispositions may display greater openness to such models. Unpacking these attitudinal dynamics is critical for informing the design of ad experiences that minimize perceived disruption, enhance user retention, and align monetization strategies with the psychological profiles of subscription-tier users.

2.4. Research Questions

To investigate the psychological and attitudinal factors influencing the transition from premium to ad-supported OTT subscriptions, this study proposes a series of research questions. The first two questions aim to identify the underlying structure of users’ media consumption motivations and attitudes toward advertisements. Understanding these dimensions provides a foundation for analyzing how such factors may shape subscription-related decisions in ad-supported OTT environments.

RQ1: What are the key psychological motivations that drive users to consume OTT media content?

RQ2: What are the fundamental sub-dimensions of user attitudes toward advertisements in digital media environments?

Building on the identification of core motivational and attitudinal dimensions, the next set of research questions explores how these factors predict users’ intention to switch to ad-supported OTT subscription plans. Investigating these associations helps clarify the psychological and perceptual drivers behind adoption decisions in ad-supported OTT environments.

RQ3: Among the identified sub-domains of media consumption motivation, which exerts the strongest influence on the intention to switch to an ad-supported OTT plan?

RQ4: Among the identified sub-dimensions of user attitudes toward advertisements, which serves as the most significant predictor of the decision to switch to an ad-supported OTT subscription model?

While RQ3 and RQ4 examine how psychological motivations and advertising attitudes influence users’ intention to adopt ad-supported OTT plans, it is also important to consider whether these relationships differ across user groups. Premium subscribers and non-subscribers represent distinct levels of engagement with OTT plans and comparing them may yield insights into how prior subscription experience shapes user motivations and ad perceptions. For this reason, RQ5 is posed as follows:

RQ5: Do the predictors of intention to subscribe to ad-supported OTT plans differ between existing premium users and non-subscribers?

2.5. Research Significance

By integrating psychological motivations with attitudes toward advertisements, this study proposes a new conceptual framework to predict user intentions to switch from premium to ad-supported OTT plans. Unlike previous research, which has primarily focused on extrinsic factors such as pricing or content diversity, this study foregrounds psychological motivations, providing a deeper and a more comprehensive understanding of subscription decision-making. The insights gained from this approach offer practical guidance for OTT platforms aiming to optimize ad-supported models by aligning monetization strategies closely with user expectations and psychological needs. Consequently, this research contributes both theoretically to media consumption and advertising literature practically, by providing actionable recommendations for industry stakeholders seeking effective subscription strategies within a rapidly evolving digital landscape.

3. Methods

3.1. Participants

A total of 813 participants took part in an online survey, with 708 premium subscribers and 105 non-subscribers. The respondents were recruited by a research company with a national-level panel in South Korea in September 2023. A quota sampling was employed to ensure equality across the samples in terms of age, sex, region, and family type. The premium subscriber group consisted of 334 males (47.2%) and 374 females (52.8%), with an average age of 38.1 years (SD = 12.3). Most participants were living with family (n = 585), followed by those who were living alone (n = 109) or cohabitating (n = 14). The household size varied to encompass families of one (n = 109), two (n = 92), three (n = 204), four (n = 240), and five (n = 63; M = 3.08, SD = 1.20). About 55.9% (n = 396) had no children. Among those who had at least one child (n = 312), the mean number of children per household was 1.72 (SD = 0.61) The average age of children was 17.4 years (SD = 8.91). The survey was conducted by a research firm with access to a national level panel. Other demographic information about the participants (e.g., regional makeup) may be released upon request.

In the non-subscriber group, there were 51 males (48.6%) and 54 females (51.4%), with an average age of 39.5 years (SD = 12.8). Most participants were living with family (n = 84), followed by those who were living alone (n = 18) or cohabitating (n = 2). One respondent had ‘other’ type of living arrangement. The household size varied to encompass families of one (n = 18), two (n = 24), three (n = 33), four (n = 23), and five (n = 7; M = 2.78, SD = 1.17). About 64.8% (n = 68) had no children. Among those who had at least one child (n = 37), the mean number of children per household was 1.62 (SD = 0.64) The average age of children was 18.9 years (SD = 10.37). Given the relatively small sample size of the non-subscriber group, its representativeness of the broader population may be limited, and the statistical analyses may lack sufficient power. Therefore, the results concerning this group should be interpreted with caution.

3.2. Measures

3.2.1. Motivation for Consuming Media Content

To explore various psychological motivations for consuming media content, this study identified and adapted scales from prior research. Among the existing frameworks, Mazlum & Atalay’s (2022) [37] scale was selected to examine users’ sense of inclusion within social circles, particularly assessing feelings of unease when missing out on popular content. Sample items included statements such as, “I feel left out when my friends have watched popular OTT content that I didn’t” and “I feel down when I’m the only.one who hasn’t watched popular OTT content.” To assess the alignment between consumers’ self-image and content preferences, this study incorporated four items adapted from the scale developed by Sirgy et al. (1997) [38]. These statements included “I know exactly what content I want to watch” and “The content I like reflects my identity.” Lastly, constructs addressing the desire for uniqueness in content choices were adapted from scales by Tian et al. (2001) [39] and Ruvio et al. (2008) [40]. These items captured tendencies toward distinctive choices, such as “I seek out content that others don’t usually watch” and “I’m not interested in content that everyone else has watched.” Each item was measured using a five-point Likert scale, with a total of 14 items refined for the study’s context.

3.2.2. Attitudes Toward Advertisements

The study also utilized a set of items to assess participants’ perceptions of general advertising on digital platforms. Participants were presented with 13 statements to capture their impressions of advertisements viewed on digital platforms, measured using a 7-point Likert scale (1 = strongly disagree, 7 = strongly agree). The items assessed participants’ perceptions of advertisements as being informative, worth watching, necessary, trustworthy, influential in purchase decisions, watched attentively, not skipped, interesting, not distracting, effective, reflective of personal interests, related to the content being viewed, and understanding the viewer well. These measures were designed to comprehensively capture participants’ attitudes and perceptions toward advertisements encountered on digital platforms.

4. Results

4.1. Exploratory Factor Analysis

Exploratory factor analysis was conducted to identify the underlying structure of the twelve items intended to tap the motivation of media consumption and the thirteen items purported to assess the attitude toward advertisements. The factor analyses were conducted using the full sample of 813 respondents, combining both premium subscribers and non-subscribers. For both analyses, factors were extracted using maximum likelihood estimation and Varimax-rotated for interpretation of the results. The number of factors were determined based on a visual inspection of the scree plot. The descriptive statistics for all other variables (e.g., fee satisfaction, device type, account ownership) are omitted due to considerations of journal space and overall readability. Full descriptive statistics can be obtained from the first author.

4.1.1. Motivation for Media Consumption

The analysis found the 3-factor solution a reasonable summary of the data, χ²(N = 813, df = 75) = 284.51, p < 0.001, RMSEA = 0.08, CFI = 0.95. As shown in

Table 1. EFA Results for Motivation for OTT Content Consumption.

Item	M	SD	α	F1	F2	F3	h2
“I feel left out when my friends have watched popular OTT content that I didn’t.”	2.51	1.12	0.89	0.871	0.073	0.154	0.788
“I feel out of place when others have watched OTT content that I haven’t.”	2.50	1.11		0.854	0.104	0.166	0.767
“I feel behind if I don’t watch popular OTT content.”	2.55	1.15		0.852	0.059	0.091	0.738
“I feel down when I’m the only one who hasn’t watched popular OTT content.”	2.33	1.03		0.692	0.084	0.252	0.550
“I feel pressured to keep subscribing to OTT platforms.”	2.61	1.09		0.634	0.145	0.186	0.457
“I’m curious about what my friends and colleagues are watching.”	2.93	1.15		0.506	0.293	0.143	0.362
“I have to watch the content I want, no matter what.”	3.26	1.07	0.80	0.238	0.760	0.119	0.648
“I know exactly what content I want to watch.”	3.61	0.96		−0.112	0.663	−0.002	0.453
“The content I like reflects my identity.”	3.03	1.03		0.245	0.642	0.217	0.519
“I have a unique taste in content.”	2.79	1.05		0.190	0.550	0.298	0.428
“I seek out content that others don’t usually watch.”	2.49	1.01	0.68	0.184	0.254	0.745	0.654
“I’m not interested in content that everyone else has watched.”	2.52	0.96		0.139	0.086	0.550	0.329

Note. N = 813. F1: socializing, F2: identity, F3: differentiation.

, the first factor was labeled ‘socializing’ as it included items such as “I feel left out when my friends have watched popular OTT contents that I didn’t,” “I feel down when I’m the only one who hasn’t watched popular OTT content,” “I’m curious about what my friends and colleagues are watching on OTT,” which commonly represent that the desire is externally oriented; individuals high on this scale are likely to consume media content mainly to stay updated with the trend, and ultimately to remain connected with the referent group. The second factor, entitled ‘identity,’ contained items that imply a ‘you are what you watch’ type of mentality. Included items like “I have to watch the content I want no matter what,” “The content I like reflects my identity,” and “I know exactly what content I want to watch” jointly assess the intensity of the internal needs to confirm or solidify one’s identity based on what he/she watches. The third factor, which includes items such as “I seek out content that others don’t usually watch” and “I’m not interested in content that everyone else has watched” was labeled ‘differentiation’ to denote the want for uniqueness or distinction.

4.1.2. Attitude Toward Advertisements

Results indicated a 3-factor solution is a sufficient summary of the data, χ²(N = 813, df = 139) = 344.42, p < 0.001, RMSEA = 0.08, CFI = 0.93. The first factor (i.e., informational value, α = 0.88) included such items as “informative,” “worth watching,” “necessary,” “trustworthy,” and “influences purchase,” implying the tendency to view advertisements as a useful source of information. The second factor (i.e., enjoyment, α = 0.84) subsumed the items like “watch attentively,” “don’t skip,” “interesting,” and “not distracting,” which consistently indicates a positive attitude toward the commercials. Those who score high on this dimension are expected to enjoy or at least tolerate the advertisements as a part of the OTT content while not actively seeking to watch them for information. The third factor comprised three items (α = 0.82) including “reflects my interests,” “related to the content,” and “knows me well.” These items were deemed to jointly measure the extent to which the advertisements fit within the context of the content being played and the viewer’s personal interest, and hence labeled as ‘customization.’ The three sets of measurements were considered internally consistent and averaged across to construct the matching composite indices to be used to test the predictions.

Table 2. EFA Results for Attitudes toward Advertisements.

Item	M	SD	α	F1	F2	F3	h2
Informative	3.61	1.29	0.88	0.647	0.008	0.185	0.453
Worth watching	3.48	1.27		0.636	0.234	0.164	0.487
Necessary	3.45	1.33		0.617	0.163	0.193	0.444
Trustworthy	3.42	1.26		0.605	0.266	0.201	0.477
Influences purchase	3.53	1.42		0.573	0.016	0.239	0.385
Watch attentively	3.42	1.75	0.84	0.033	0.736	0.118	0.557
Don’t skip	2.92	1.82		0.084	0.731	0.182	0.574
Interesting	3.52	1.50		0.227	0.719	0.118	0.582
Not distracting	3.12	1.70		0.107	0.716	0.191	0.561
Effective	3.85	1.39		0.284	0.543	0.073	0.381
Reflects my interests	3.24	1.41	0.82	0.313	0.117	0.716	0.624
Related to the content	3.09	1.45		0.295	0.234	0.692	0.621
Knows me well	3.25	1.43		0.284	0.162	0.673	0.560

Note. N = 813. F1: informational value, F2: enjoyment, F3: customization.

shows the full results. Answering RQ2, the current results indicate that the attitudes toward advertisements may be determined by assessing at least three aspects of ads; value as an information source, levels of enjoyment and customization.

4.2. Hierarchical Regression Analysis

A hierarchical regression analysis was conducted to examine the extent to which the attitude toward advertisements (RQ4) and the motivation for consuming OTT content (RQ3) predict the level of intention to subscribe to an ad-supported plan. The predictions were examined separately for the premium- and non-subscribers to find answers to RQ5, which purports to investigate whether the dependent is explainable by a different set of antecedents across the two samples.

4.2.1. Premium Subscribers

The first block contained the demographic information (e.g., sex, age, family size, living condition, number of children). The model produced little effect to explain the dependent, F(6, 701) = 2.41, p = 0.02, R² = 0.02. Various aspects of the respondent’s OTT accounts and user habits (e.g., use frequency, satisfaction with the current OTT platform, platforms currently subscribed to, account ownership, devices mainly used to enjoy the content, preferred content) were added in the second model. None of the predictors exerted a statistically significant effect, and the overall improvement of the model was also non-significant, F(29, 671) = 1.17, p = 0.25, ΔR² = 0.04.

The third model additionally considered the external features of commercials (e.g., number and duration of advertisements, rewards offered in and timing of the advertisements, fit with the brands/products, the appearance of preferred/well-known brands/products or celebrities/influencers, level of entertainment and relevance to the content, discount rates). The addition helped improve the model. Specifically, whether the advertisement is entertaining was a sole contributing factor that raises the intention to purchase an ad-supported plan, β = 0.24, p < 0.05. The magnitude of the model improvement, however, failed to reach a statistical significance, F(14, 657) = 1.52, p = 0.10, ΔR² = 0.03.

The three dimensions of attitude toward advertisements were added to the fourth model. In particular, the perceived informational value of advertisements significantly increased the intention to subscribe (β = 0.17, p < 0.01). The model improved to explain unique, additional variance of the dependent measure, F(3, 654) = 6.73, p < 0.001, ΔR² = 0.03. Answering RQ4, the current results indicate a positive impact of attitudes, especially the perceived informational value of advertisements on the intention to subscribe to an ad-supported plan.

Added to the final model was the motivation to satisfy by consuming OTT media content. An answer to RQ3 was found; the intention to subscribe was most predictable by the need for identity, β = 0.13, p < 0.05. While this helped raise the predictability of the model, the overall magnitude of the improvement stopped short of reaching a statistical significance, F(3, 651) = 2.25, p = 0.08, ΔR² = 0.01, because the other two attributes produced nearly no effects for the dependent. The current findings demonstrate that those who self-identify with the OTT content that they consume may be an efficient target audience for marketing efforts.

Knowing the exact kinds of media content that they prefer and desiring to watch them no matter what, such individuals may have to subscribe to multiple OTT platforms as the preferred content is likely to be scattered across the vendors. Ad-supported platforms can be an affordable option that helps lower the financial burden to maintain multiple subscriptions at the expense of small extra time to watch commercials. The full results are specified in

Table 3. Results from Hierarchical Regression Analysis (Premium Subscribers, N = 708).

Variable	Model 1	Model 2	Model 3	Model 4	Model 5
Sex (female)	0.06	0.10	0.13	0.12	0.11
Age	0.00	0.00	0.00	0.00	0.00
Family size	0.08	0.08	0.08	0.07	0.06
Living (with others)	0.32	0.33	0.38	0.39	0.38
Living (alone)	0.15	0.16	0.14	0.14	0.13
Number of children	0.09	0.07	0.08	0.09	0.10
Use frequency		0.01	0.01	0.01	0.01
Fee satisfaction		0.09 *	0.07	0.05	0.04
Subscribing to Netflix		0.04	0.04	0.06	0.08
Subscribing to Wave		−0.38	−0.37	−0.33	−0.32
Subscribing to Tving		−0.01	−0.07	−0.13	−0.15
Subscribing to Watcha		−0.46	−0.62	−0.53	−0.57
Subscribing to Disney+		0.01	0.05	0.08	0.05
Coupang		0.21	0.19	0.19	0.23
Account (Netflix, personal)		0.04	0.02	−0.02	−0.04
Account (Netflix, shared)		0.03	0.03	0.01	−0.02
Account (Wave, personal)		0.15	0.12	0.12	0.14
Account (Wave, shared)		0.29	0.26	0.21	0.20
Account (Tving, personal)		0.13	0.17	0.22	0.23
Account (Tving, shared)		0.08	0.14	0.20	0.21
Account (Watcha, personal)		0.09	0.19	0.13	0.10
Account (Watcha, shared)		0.29	0.39	0.30	0.33
Account (Disney+, personal)		−0.17	−0.21	−0.24	−0.22
Account (Disney+, shared)		−0.23	−0.25	−0.26	−0.23
Account (CoupangPlay, personal)		−0.35	−0.33	−0.34	−0.36
Account (CoupangPlay, shared)		−0.17	−0.18	−0.20	−0.24
Device (TV)		0.01	0.01	0.00	0.00
Device (PC/laptop)		−0.06	0.01	0.02	0.03
Device (Tablet)		−0.05	−0.01	−0.04	−0.04
Device (Other)		0.00	0.04	−0.02	−0.16
Genre (Drama)		−0.10	−0.07	−0.08	−0.09
Genre (Entertainment)		−0.17	−0.16	−0.17	−0.16
Genre (Documentary)		−0.20	−0.25	−0.18	−0.17
Genre (Sport)		0.47	0.45	0.40	0.39
Genre (Other)		−0.18	−0.17	−0.34	−0.40
Number of pre-roll ads			0.06 *	0.05 †	0.05 †
Number of mid-roll ads			0.04	0.03	0.02
Number of ads			0.09	0.12	0.12
Discounts/rewards offered in ads			0.05	0.05	0.05
Ad timing			−0.09	−0.08	−0.09
Brand/product fit with models in ads			0.21	0.16	0.15
Preferred brand/product ads			−0.03	−0.06	−0.03
Celebrity/influencer ads			0.06	0.04	0.06
Well-known brand/product ads			0.13	0.09	0.07
Entertaining ads			0.24 *	0.24 *	0.24 *
Total ad duration			0.01	0.02	0.03
Content-related ads			0.00	0.01	0.02
OTT exclusive content previews			0.04	0.02	0.01
Discount rate			0.00	0.00	0.00
Informational value				0.17 ***	0.15 **
Enjoyment				−0.02	−0.01
Customization				0.03	0.03
Socializing					0.00
Identity					0.13 *
Differentiation					0.01
R2	0.02	0.06	0.09	0.12	0.13
F		1.17	1.52	6.73	2.25
p		0.245	0.095	<0.001	0.080
df1, df2		29, 671	14, 657	3, 654	3, 651

Notes. Reference points: Sex = ‘male,’ Living = ‘living with family,’ Account = ‘other’s,’ Device = ‘smartphone,’ Genre = ‘movie.’ ^† p < 0.10. * p < 0.05. ** p < 0.01. *** p < 0.001.

.

4.2.2. Nonsubscriber

To find answers to RQ5, the analysis followed the same sequence used for the one conducted with the premium subscribers. As nonsubscribers have nothing to reveal about their watching habits and the account information, only four models were tested by adding demographics, external attributes of advertisements, attitudes toward advertisements, and the motivation for consuming OTT content, in that particular order.

As shown in

Table 4. Results from Hierarchical Regression Analysis (Nonsubscribers, N = 105).

	Model 1	Model 2	Model 3	Model 4
Sex (female)	0.09	0.20	0.20	0.22
Age	0.00	−0.01	−0.01	−0.01
Family size	−0.08	−0.08	−0.07	0.00
Living (with others)	0.26	0.21	0.38	0.52
Living (alone)	0.06	−0.17	−0.11	0.05
Number of children	0.08	0.20	0.21	0.18
Number of pre-roll ads		0.12	0.12	0.09
Number of mid-roll ads		0.02	0.03	0.05
Number of ads		0.32	0.30	0.28
Discounts/rewards offered in ads		0.48	0.48	0.48
Ad timing		0.16	0.12	0.18
Brand/product fit with models in ads		0.15	0.18	0.21
Preferred brand/product ads		−0.21	−0.22	−0.16
Celebrity/influencer ads		0.35	0.42	0.39
Well-known brand/product ads		0.49	0.44	0.47
Entertaining ads		0.53 *	0.50 †	0.49 †
Total ad duration		0.22	0.17	0.14
Content-related ads		−0.01	−0.02	0.00
OTT exclusive content previews		0.01	−0.01	−0.03
Discount rate		0.01	0.01	0.01
Informational value			0.07	0.08
Enjoyment			−0.09	−0.09
Customization			0.00	0.00
Socializing				−0.15
Identity				0.18
Differentiation				−0.16
R2	0.01	0.18	0.19	0.21
F		1.27	0.20	0.84
p		0.242	0.893	471
df1, df2		14, 83	3, 80	3, 77

Notes. Reference points: Sex = ‘male,’ Living = ‘living with family.’ ^† p < 0.10. * p < 0.05.

, those who find amusement as an important element of an advertisement that attracts viewers had a greater intention of turning to an ad-supported plan, β = 0.53, p < 0.05. While other attributes of advertisements such as whether well-known brands/products (β = 0.49) or celebrities/influencers (β = 0.35) appear, whether discounts/rewards are offered (β = 0.53), and how many advertisements should be watched (β = 0.32) also had an unignorable positive impact for the dependent but failed to reach statistical significance presumably due to low power. All other blocks contained no contributing factors.

Overall, the current pattern of data implies that nonsubscribers can be attracted by advertisements that either entertain or provide physical returns, which is markedly different from the premium subscribers who are moved by their internal motivations or assessed value of the information in the advertisements.

5. Discussion

The empirical findings of this study yield novel insights into the psychological and attitudinal determinants of user intentions to adopt ad-supported OTT subscription models. A clear segmentation emerges between premium subscribers and non-subscribers, each exhibiting distinct motivational orientations and perceptual dispositions that shape their subscription preferences.

Among premium subscribers, the propensity to transition to ad-supported tiers was primarily influenced by intrinsic motivations—particularly identity-related content alignment—and by the perceived informational utility of advertisements. The prominence of informational value echoes prior literature highlighting the role of advertising as a credible and contextually relevant information source. These findings suggest that premium users, who are habituated to uninterrupted content experiences, may be amenable to ad-supported formats when advertisements are perceived not as intrusive interruptions but as cognitively enriching or contextually meaningful contributions to the viewing experience.

Furthermore, identity-related motivations emerged as another critical factor. Individuals who perceive a strong alignment between their self-concept and the media content they consume are more inclined to subscribe to multiple platforms to access their preferred content. Ad-supported platforms present an economical alternative. They can help reduce the financial burden of maintaining multiple subscriptions by offering lower-cost options. These findings underscore the importance of personalized marketing strategies targeting premium users by leveraging their content preferences and emphasizing the informational aspects of advertisements.

In contrast, non-subscribers were predominantly influenced by extrinsic factors, most notably the entertainment value of advertisements and the tangible incentives they offer—such as discounts, rewards, or exclusive promotions. This segment’s preference for amusement and immediate material gains suggests that affectively engaging and visually stimulating advertisements may serve as an effective gateway for converting non-subscribers into ad-supported OTT users. The significant predictive weight of entertaining ad content underscores the critical role of affective appeal in mitigating perceived intrusiveness and enhancing the overall ad experience.

Unlike premium subscribers, non-subscribers display minimal reliance on intrinsic motivations such as identity expression or content alignment, highlighting a clear divergence in psychological orientation. As such, advertising strategies targeting this segment should emphasize extrinsic gratification mechanisms—employing dynamic visuals, humor, and recognizable social influencers—to generate interest and lower psychological resistance. Furthermore, overtly communicating the economic advantages of ad-supported plans may enhance perceived value and facilitate user acquisition in this cost-sensitive cohort.

It should be noted that the present study employed a cross-sectional design, which limits any causal interpretation of the observed relationships. The results should thus be understood as correlational rather than causal. In addition, the study focused on behavioral intentions rather than actual subscription behavior. Readers are advised to interpret the findings with consideration of the well-documented intention–behavior gap in media research.

6. Implications

This research extends existing theoretical frameworks by integrating psychological motivations and attitudes toward advertisements to predict subscription transitions. While previous studies have predominantly addressed extrinsic factors such as pricing, content variety, and platform usability, this study highlights the significance of intrinsic psychological drivers, offering deeper insights into subscription decision-making. By extending U&G theory, the findings suggest that media consumption behaviors are shaped not only by rational and goal-oriented choices but also by deeper psychological needs like relaxation, escapism, and identity formation. Additionally, this study enriches the existing literature on advertising perception by demonstrating distinct user attitudes toward advertisements in subscription-based environments compared to those in traditional ad-supported digital platforms.

Moreover, this study also bridges the gap between media consumption motivations and advertising acceptance. Traditional research has shown that media consumption is driven by various gratifications, including entertainment, information-seeking, and social interaction [13,14]. However, in the context of OTT platforms, these motivations are influenced by both conscious and unconscious factors such as algorithmic recommendation, habitual engagement, and evolving content consumption patterns [20,23]. By recognizing these psychological drivers, this study extends the U&G framework beyond its traditional scope, highlighting how user engagement with ad-supported OTT models results from an interplay of active choices and passive behavioral mechanisms. Furthermore, subscription decisions are also shaped not only by hedonic and utilitarian motivations [19] but significantly influenced by the perception of advertisements either as either interruptions or an informative enhancement to the viewing experience [24,36]. This integration of psychological needs and advertising attitudes provides a more nuanced understanding of how users navigate the trade-offs between premium and ad-supported subscription models.

The findings further reinforce that subscription decisions are inherently multifaceted, shaped by a combination of economic considerations, psychological gratifications, and advertising perceptions. Premium subscribers, for instance, demonstrate strong identity-driven motivations, prioritizing content aligned with their self-concept and willingly sustaining multiple subscriptions despite associated costs [5,41]. In contrast, non-subscribers respond more strongly to external incentives, favoring advertisements offering entertainment value or tangible rewards [32,35]. This divergence suggests that OTT platforms should strategically differentiate their ad-supported models: emphasizing personalized content and informative ad placements for premium users, while optimizing engaging and interactive ad experiences for non-subscribers. Thus, by contextualizing these insights within established media consumption and advertising models, this study lays a theoretical foundation for future research exploring evolving OTT monetization strategies and consumer decision dynamics.

Finally, these insights provide practical implications for OTT platforms aiming to diversify their revenue streams and expand their user base through ad-supported models. Platforms targeting premium subscribers prioritize informative advertisements that are seamlessly integrated with the content to reduce perceived disruption and maximize user value. Additionally, personalized data-driven strategies that reflect users’ content preferences and identity-related motivations are essential. Conversely, platforms aiming to attract non-subscribers should create entertaining, reward-based advertisements, and clearly highlight the cost advantages and additional benefits of ad-supported plans.

7. Limitations and Future Research

While this study proposes a novel conceptual framework for understanding user transitions from premium to ad-supported OTT subscription models, several limitations merit consideration. First, the findings are based on self-reported survey data, which may be susceptible to social desirability bias and introspective inaccuracies in respondents’ accounts of their media motivations and advertising attitudes. Future research would benefit from the integration of behavioral tracking methods or experimental designs that can capture real-time engagement and ad response patterns with greater ecological validity.

Second, the study’s analytical focus on psychological and attitudinal determinants may overlook broader structural and contextual variables—such as regional market characteristics, platform-specific monetization strategies, and variations in content accessibility. In addition, more advanced analytic approaches such as structural equation modeling or multi-group analysis could further illuminate the complex interrelationships among the studied variables. However, given the exploratory aim of the present study and the need to observe the stepwise contribution of distinct predictor blocks, hierarchical regression analysis was deemed more appropriate for providing an interpretable and parsimonious analytic framework. Nonetheless, the use of this method may limit the ability to capture more intricate pathways or latent structures among variables, which future research should address using more sophisticated modeling approaches. These contextual contingencies may serve as moderating factors that shape the strength or direction of motivational and attitudinal influences on subscription behavior. Accordingly, cross-platform and cross-market comparative studies are needed to generalize and refine the theoretical model across diverse OTT ecosystems.

Third, the study does not account for potential longitudinal changes in user behavior. Media consumption patterns and attitudes toward advertisements may evolve over time, particularly as OTT platforms continuously adjust and refine their ad-supported models. Adopting a longitudinal approach could provide deeper insights into how users’ intentions shift in response to evolving platform strategies.

Fourth, although this study examines differences between premium subscribers and non-subscribers, it does not extensively explore subgroup variations such as generational or cultural differences that could distinctly influence perceptions of ad-supported models. The absence of ad-supported subscribers as a separate comparison group also limits the scope of the current analysis. Including this group would allow for a more comprehensive examination of how advertising attitudes and media motivations differentiate premium, ad-supported, and non-subscribers. Moreover, while including a wide range of control variables ensures scientific rigor, this approach may inadvertently dilute the explanatory power of the primary theoretical constructs. Future research should carefully balance control variables to maintain robust findings without diluting the key predictors, as well as conduct cross-cultural comparisons or segment users by demographic and psychographic characteristics to refine predictive models.

Finally, given rapid advancements in personalized advertising and AI-driven content recommendations, future research should explore how emerging technologies shape user attitudes and acceptance of ad-supported OTT plans. As advertising increasingly incorporates personalized, interactive and integrated formats, understanding users’ responses to these evolving technologies will be critical in designing ad-supported models that align with user expectations and engagement patterns. As a case in point, this study does not differentiate between ad types, such as skippable versus non-skippable formats, which may substantially influence user tolerance and perceived intrusiveness. Future researchers should examine how the nature of ad delivery moderates switching intentions and consider generating theoretical propositions regarding the psychological and behavioral consequences of varying ad formats within OTT environments.

8. Conclusions

This study highlights the distinct psychological and attitudinal factors influencing the adoption of ad-supported OTT subscription plans among premium subscribers and non-subscribers. Premium subscribers are driven by internal motivations, particularly identity alignment and the informational value of advertisements, while non-subscribers prioritize entertainment and tangible rewards.

By aligning subscription design with users’ underlying motivational profiles, OTT platforms can strategically tailor ad-supported models to address the heterogeneous preferences of distinct audience segments. For premium subscribers, such models offer a financially sustainable alternative for managing multiple platform subscriptions. Simultaneously, they function as an accessible entry point for cost-conscious non-subscribers. To further advance this line of inquiry, future research should adopt longitudinal methodologies to capture temporal shifts in user behavior, incorporate cross-cultural comparisons to assess generalizability across markets, and employ experimental designs to isolate causal mechanisms. These extensions will not only refine the theoretical understanding of OTT subscription dynamics but also inform the development of precision-targeted advertising and monetization strategies within an increasingly saturated and competitive digital media environment.