Keywords

1 Introduction

In recent times, the audiovisual industry has undergone significant shifts due to technological progress and changing market trends [1]. The rise of digitalization has paved the way for new content creators and transformed the way we consume audiovisual content [2]. The lines separating broadcasters, telecommunication entities, and OTT service providers have become increasingly indistinct, as illustrated by the shift from conventional value chains to platform-centric business frameworks [3]. As audiences diversify their media consumption, the need to establish and execute a coherent business strategy has grown critical. Novel revenue streams, innovative distribution methods, a varied audience base, and cutting-edge technologies have given birth to unique business models that challenge their conventional counterparts [4].

For traditional TV businesses the threat is multidimensional. They have to alter their strategies according to the new category of Internet-based television providers [5] which are the so-called on demand or Over-The-Top (OTT) services such as Netflix, CINOBO, HBO, Apple TV, Disney+ , and Amazon. The objective of this study is to establish the effects of socio-demographic factors on the use of pay tv in Greece, and to construct a forecasting framework for the usage of OTT tv. The lines distinguishing broadcasters, telecommunication firms, and OTT platforms have become increasingly blurred in the evolving marketplace. In essence, the audio-visual sector is now more characterized by its consumers and their content preferences than by the service providers or the technologies they use. For viewers, industry distinctions matter little. Instead, their focus lies on securing premium audio-visual content, considering factors such as cost, user experience, and accessibility [1].

As Yu et al. mention, demographics have been a main issue, as shown by the history of rating firms such as Nielson, founded in 1923 [6]. After decades and as communication technologies have progressed, the role of the individual has been emphasized. In cable TV audience research, Li says better-educated people tend to be early adopters, although demographics vary across studies [7]. Yoon, Kim, and Kim concluded that because digital TV is shared in a household, household attributes, such as household income and location, are more influential than personal attributes [8].

Banerjee et al. outline three main patterns of OTT consumption: co-consumption (combining pay TV with OTT); pure substitution (replacing pay TV entirely with OTT) and first-timer behavior (users who’ve never experienced pay TV). The selection of OTT was influenced by factors like the household’s geographical and demographic traits, device possession, and prior subscription habits [8]. Especially the younger households in the lower-income brackets lead the charge in both shifting entirely to OTT and trying out OTT for the first time. Those who abandon pay TV for OTT platforms are termed ‘cord cutters’ and are majorly younger, tech-oriented users [9]. These individuals, having lesser income yet being tech-oriented, view the internet as an attractive, budget-friendly alternative and thus swap their cable commitments for diverse OTT offerings [10]. This suggests that OTT platforms possibly cater to a broader demographic, including younger individuals and those on tighter budgets. Reinforcing this, McCreery and Krugman indicate that younger demographics are more inclined towards online mediums for video content than conventional TV [11]. Moreover, Medina et al. reveal that gender and education are key variables in the subscriber profile [1].

Lee et al. found that gender and education were the most influential factors in payment decisions based on their study spanning 2008 to 2012 [12]. Notably, telecommunication companies have traditionally banked on bundling as a pricing approach. However, this method faces challenges in the age of internet streaming. As consumers find more affordable and varied avenues to access video content, many are departing from telecom providers and opting to “cut” the cord, as highlighted by Chulkov and Nizovtsev [13].

Understanding the demographic makeup of Greek SVoD users is the primary objective of this study, given that consumers can both resist and embrace innovation for similar reasons. Markets are typically segmented, with each segment containing distinct groups of customers with potential shared attributes and requirements. For example, when examining mobile service adoption, there were identified four unique clusters of mobile internet users, segmented by both demographics (like age, gender, marital status, etc.) and attitudes (like credibility and entertainment value) [14]. Similarly, another research has explored the value of segmentation in understanding the intricate dynamics between demographics, lifestyles, and the adoption of communication tools like pagers and mobile phones in China [15]. A recurring theme in these studies is the reliance on demographic factors to categorize tech users. Generally, market segmentation is brought about through demographic, psychological, and behavioral standards.

2 Methodology

According to the literature review and the maturity of Pay tv services in Greece, the evolution of OTTtv usage is examined through the socio-demographic profile of Greek users. Hence, the research questions are the following: How do demographics factors affect the use of OTT-tv? Which characteristics can forecast the use of OTT-tv? What groups of users are formed for the Greek market?

2.1 Data

Data comes from a survey that is conducted every two years in Greece according to European research held by Eurostat and concerns the use of information and communication technologies (ICT) by households and their members. We use datasets for the years 2016, 2018, 2020 and 2022 that refer to Greek households. The datasets were designed to be comparable, collecting representative samples of the respective populations and using the same set of questions. The surveys were conducted on approximately 4500 private households and an equal number of members throughout Greece, with the only prerequisite that there was, at least, one member aged 16–74 in each household.

2.2 Statistical Analysis

To examine the effect of demographic variables (gender, age, education level, employment status, income, number of household members) and the urbanity of the area of residence, a binary logistic regression model was applied to each of the binary dependent variables “have you watched a streaming internet TV program (IUSTV)” and “have you watched video-on-demand (IUVOD)”. For the categorical predictors, all pair-wise comparisons were estimated, using Fisher’s LSD procedure. For each model, the estimates (B), along with the odds ratio (OR) are presented. For the sample segmentation, the methodology of k-prototypes was adopted, that was introduced by Huang [16] and implemented in R by Szepannek [17]. According to Huang [16], the k-prototypes algorithm belongs to the family of partitional cluster algorithms, where the distance function between two points is given by a linear combination of its scalar and nominal characteristics: \(d\left( {x_{i} ,\mu_{j} } \right) = \mathop \sum \nolimits_{m = 1}^{q} \left( {x_{i}^{m} - \mu_{j}^{m} } \right)^{2} + \lambda \mathop \sum \nolimits_{m = q + 1}^{p} \left( {x_{i}^{m} ,\mu_{j}^{m} } \right)\), where m is an index over all variables in the data set where the first q variables are numeric and the remaining \(p - q\) variables are categorical. The distance \(d\left( \cdot \right)\) corresponds to weighted sum of the Euclidean distance between two points in the metric space and simple matching distance for the categorical variables. For the cluster analysis, we included only observations from the latest year of measurement (2022) and 13 variables were included, from which 3 were assumed to be numeric and 10 categorical, and the within-sum-of-squares (WSS) measure was used to assess the optimal number of clusters.

3 Results

3.1 IUSTV-IUVOD Adoption

Both VOD and streaming TV viewing have increased, with the rates in 2016 being 10.5% and 13.7% respectively, while in 2022 the rates increased to 33.3% and 23.2%. The rise in VOD viewing appears to have been rapid between 2018 and 2020, while for STV services it appears smoother and more linear throughout the period under review.

Results of the logistic regression model for the IUSTV variable show that all factors showed statistical significance. Each two-year measurement resulted in a significant increase in streaming internet TV usage. Adjusting for all the covariates, the year 2022 exhibited the highest percentage of streaming TV services, which was 27% higher than 2020, and more than twice the percentage of 2016. The number of household members had a negative relationship with the probability of using the IUSTV service. Age also showed a negative relationship, with each one-year increase in age reducing the probability of use by approximately 2%. Income showed a positive relationship, with a one quintile increase in income increasing the probability of use by 9.2%. Regarding gender, men were 25.7% more likely to use IUSTV services compared to women. Individuals with a moderate level of education were approximately 35% less likely to use IUSTV services, while participants with a low level of education were approximately 55% less likely to use services compared to participants with a high level of education. Participants living in urban areas were 57.9% more likely to use IUSTV services compared to those living in rural areas, while those in semi-urban areas were 15.7% more likely to use IUSTV services compared to those living in rural areas. Finally, regarding employment, students showed the highest odds of using streaming TV services.

In terms of the biennial measurements, 2018 and 2016 showed no significant difference of VOD use. After 2018, VOD use increased substantially, showing a 332% increase between 2020 and 2018, adjusting for the covariates. The number of household members had a non-significant relationship with the probability of using the IUVOD services. Age showed a significant negative relationship, with each one-year increase in age reducing the probability of use by 2.9%. Income also showed a positive relationship here, with a one quintile increase in income increasing the probability of using IUVOD services by 13.8%. In terms of gender, men were 21.4% times more likely to use IUVOD services compared to women.

As with IUSTV services, for the IUVOD variable, individuals with a moderate level of education were 31.8% less likely to use IUVOD services, while participants with a low level of education were approximately 57.5% less likely to use services compared to participants with a high level of education. Participants residing in urban areas were 62.1% times more likely to use IUVOD services compared to rural areas, and 43.6% times more likely to use those services compared to semi-urban areas, however the difference between semi-urban and rural areas was not statistically significant. Employed individuals were 41.3% more likely to use VOD services compared to non-economically active, and 31.7% times more likely compared to unemployed. Students showed the highest percentage of using VOD services, however their percentage was very close to employed participants, as the difference was non-significant. Finally, unemployed individuals and economically inactive participants showed the same rate of VOD use.

3.2 Market Segmentation

For the household clustering of the year 2022, we utilized a four-cluster solution, as suggested by the scree plot of WSS. Cluster 1 mainly consisted of females (61%), with a median age of 48, with a medium educational level, employed (75.7%) who did not use VOD (82.3%) or STV (93.6%) services. Cluster 2 included younger individuals (Mdn = 23 years), mainly students (51%), that were using mostly VOD (43.5%) and STV services (32.5%). The 3rd cluster included older participants (Mdn = 65 years) that showed very small percentages on using both VOD and STV services. Finally, the fourth cluster members showed the highest VOD (76.4%) and STV (61.6%) usage. The members of this cluster were mostly males, with a median age equal to 45, employed (86.2%), with the highest income and educational level.

4 Discussion

The analysis of the logistic regression results focusing on the IUVOD variable has provided a comprehensive overview of Video on Demand-OTT (VOD) service usage patterns and their influencing factors.

There was a stagnation in VOD usage between 2016 and 2018, but a dramatic surge was witnessed between 2018 and 2020. This implies a swift transformation in consumer behavior and technological adoption within this short period. This can be explained since content usage time depends on people’s places of stay, such as television viewing and newspaper reading, which can mostly be regarded as home-based activities [18]. Also, Sung and Kim state that the total content usage time increased abnormally because of the pandemic [19].

The size of a household did not show a decisive role in influencing VOD service adoption, suggesting that VOD consumption is relatively consistent across various household sizes.

Age continued to be a determining factor, with older individuals to be less likely to adopt VOD services. This finding agrees with Lee et al. who showed that age matters when it comes to adoption of different types of media and that younger consumers are more open to the idea of purchasing online streaming services [12].

Income levels play a pivotal role; higher income brackets indicate a greater inclination towards VOD usage; Income is known to be a variable directly linked to an individual’s ability to pay for media services. This is also in line with the argument that subscribers with high incomes generally prefer the type of media that allows them to choose content that suits their personal preferences [20].

A gender-based disparity is apparent, with males showing a higher propensity towards VOD services compared to females. This can be commented as a general finding in several studies that examine the usage of Internet and technologic application, since females use internet less often and play computer games less frequently than male adolescents do. A clear correlation between education levels and VOD usage is observed. Those with higher education levels are the primary adopters, whereas those with moderate to low education levels have a lesser inclination to engage with VOD services. Empirical research shows that adopters in these services are more likely to be male, younger, and better educated and to have higher incomes than are nonadopters [21].

Urban residents display a significantly higher preference for VOD compared to their rural counterparts. Moreover, urban-centric adoption for STV is significantly higher.

5 Conclusions

In essence, the determinants influencing VOD consumption, ranging from demographic factors to geographical settings and employment status, reflect the evolving nature of media consumption in contemporary society. Such insights are valuable for both industry stakeholders aiming to expand their user base and policymakers looking to bridge digital divides.

Given the distinct growth trajectories and demographic preferences for STV and VOD, content providers and marketers can tailor their promotional campaigns to resonate better with specific age groups, particularly younger demographics who show a higher propensity for adoption. They might need to enhance certain aspects of their offerings to provide cost-efficient, convenient, and high-quality services to their subscribers.

The clear correlation between income levels and service usage suggests a potential for introducing tiered subscription models, ensuring broader accessibility while catering to varied economic capacities.

The gender disparity in both STV and VOD consumption hints at the need for content that appeals to diverse gender preferences, potentially driving up adoption rates among underrepresented groups. The heightened consumption in urban areas underscores the importance of robust digital infrastructure in these locales. Simultaneously, digital outreach programs in semi-urban and rural areas can help narrow the consumption gap.

With employed individuals showing significant media consumption, and with the engagement of younger individuals in Cluster 2, mainly students, presents a fertile ground for introducing student-centric subscription models or promotional deals, tap** into their evolving digital consumption patterns to boost subscriptions and viewer loyalty. Additionally, given Cluster 4’s strong engagement with both VOD and STV services and their higher socio-economic standing, there might be opportunities for upselling premium content or services to this demographic, given their apparent readiness to engage and likely financial capacity.

Limitations: Demographic elements by themselves do not adequately account for the varied and constantly evolving consumer behaviors. This recognition has led to the incorporation of lifestyle considerations in market segmentation to gain a deeper insight into consumer actions.

Future research may take under consideration the general digital profile of TV users, such as the usage of smart TV, the consumption of music through OTT applications, the listening to podcasts etc., aiming to draw a more precise consumers scheme for OTT media paying services.