Abstract
Purpose
The purpose of this paper is to examine the motivations behind online video content creation on services such as YouTube and Twitch. These activities, performed by private individuals online, have become increasingly monetized and professionalised through the accessible tools provided by video sharing services, which has presented a noteworthy manifestation of the increasing merger of the work and leisure within digital environments and the emergence of a hybrid form of work and play, playbour.
Design/methodology/approach
The data for the study were collected using an online survey of 377 video content creators and it was analysed via structural equation modelling.
Findings
The findings of the study indicate that although the practice of video content creation is becoming more commercialised and professionalised, the extrinsic motivations, often associated with work (e.g. income, prestige), remain less significant drivers for content creation than intrinsic motivations (e.g. enjoyment, socialisation), which are associated with leisure activities.
Originality/value
This study offers insight into how the authors have begun to reorganise the position in the new digital labour culture, where monotonous tasks are increasingly automated, allowing room for intrinsically driven playful labour to develop within the leisure activities.
Keywords
Citation
Törhönen, M., Sjöblom, M., Hassan, L. and Hamari, J. (2020), "Fame and fortune, or just fun? A study on why people create content on video platforms", Internet Research, Vol. 30 No. 1, pp. 165-190. https://doi.org/10.1108/INTR-06-2018-0270
Publisher
:Emerald Publishing Limited
Copyright © 2019, Maria Törhönen, Max Sjöblom, Lobna Hassan and Juho Hamari
License
Published by Emerald Group Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
Introduction
Throughout recent years, we have witnessed the emergence and rapid growth of participatory culture (Chau, 2010; Jenkins, 2006; Kaplan and Haenlein, 2010; Rodrigues and Druschel, 2010) and collaborative consumption, sharing and production (Belk, 2014; Deuze, 2006; Hamari et al., 2016). These developments are exemplified by emerging services of the sharing economy (e.g. Airbnb, Uber), crowdsourcing (e.g. Amazon Mechanical Turk, Wikipedia), and content sharing sites (e.g. YouTube, Twitch). Accelerated by technological advancements, these forms of digital participation and collaboration have transformed the internet into a global stage for self-expression, active discourse, and peer-to-peer collaboration, where the celebrification of private individuals (Jerslev, 2016; Khamis et al., 2017; Marwick, 2015; Senft, 2013) and the monetisation of user-generated content (UGC) (Alexander, 2018a, b) has rapidly developed. This has dramatically changed the legacy conception of the ways in which information and content is internationally created and disseminated. The traditional business-to-consumer value chains seem increasingly irrelevant in digital environments, where consumers or “users” have become the main source of content creation, evolving into “prosumers”; active producers and consumers of digital content (Fuchs, 2014; Kotler, 1986; Ritzer, 2010, 2015; Toffler, 1980).
This prosumerism has reshaped our understanding of labour and leisure, and developed new dimensions of labour practices. Activities that have traditionally been considered as labour are increasingly gamified, whereby game design and the practices of gamer communities are reshaping the way we work (Deterding, 2015; Huotari and Hamari, 2017; Vesa et al., 2017), while work practices are increasingly difficult to distinguish from game-like practices and behaviour. Conversely, leisure activities, such as playing video games, have begun to adopt work-like elements exemplified in activities such as esports (competitive video gaming) (Hamari and Sjöblom, 2017). Hence, next to the gamification movement, we are also witnessing a “playbour” movement that – in contrast to gamification – diffuses professionalised elements to play and leisure activities (Castronova, 2005; Kücklich, 2005; Lloyd, 2017; Scholz, 2013). Due to these developments, individuals seem to be increasingly seeking to transform their work-life into playful, intrinsically motivated activities, beyond the mere pursuit of employment and income. Furthermore, they appear to be turning leisure activities, such as gaming, into productive endeavours.
Perhaps the most prominent manifestation of the notion of playbour is social video content creation. This new wave of content creation and dissemination is undertaken by private individual prosumers, and built around a perceptually playful and social activity: production and distribution of content through one, or many, social media channels and social video sharing services such as Twitch and YouTube. Yet, it has become an increasingly laborious activity, due to both the economic incentive provided by the distribution platforms and the lure of the celebrification of those individuals involved (Jerslev, 2016; Khamis et al., 2017; Marwick, 2015).
The recent rise in popularity of social video content can be attributed to increasing prosumerism, the development of live streaming technologies, and popular social video sharing sites such as YouTube, Twitch, Snapchat and Instagram. The prosumption habits of millennials and Generation Z (Agrawal, 2016) have also made a significant contribution to the rise of social video content, as they increasingly utilise video content to gather and generate information and entertainment.
We argue that the relationship between leisure and work in social video content creation has been blurred by: increased professionalisation (Johnson and Woodcock, 2017); digital celebrification (Driessens, 2013; Jerslev, 2016; Khamis et al., 2017; Marwick, 2015; Senft, 2013); and, the accessibility of different digital revenues provided by video sharing platforms. Therefore, this paper utilises the framework provided by self-determination theory (SDT) (Deci and Ryan, 1985, 2000; Ryan and Deci, 2000) to specifically examine the effects of intrinsic and extrinsic motivating factors in this increasingly professionalised activity. Structural equation modelling (SEM) is used to analyse data gathered from 377 social video content creators via an international survey, in order to answer the question: what drives social video content creation and sharing in an increasingly professionalised ecosystem? The research model utilises SDT and existing research on social video content creation (such as Bründl and Hess, 2016; Kim et al., 2017; Lottridge et al., 2017; Zhao et al., 2018) and research on behaviours in other social digital environments (Hamari et al., 2016; Nov, 2007; Nov and Ye, 2010). By examining the hybrid form of work and play, this research enables us to further our understanding of the possible emerging challenges of labour and play practices in digital environments, and how they can be supported by technological design, work re-organisation and the organisational structures around them.
Background
Peer-to-peer economics and the social content creation culture
In the initial days of the World Wide Web (Berners-Lee et al., 2010), digital content creation still belonged to large conglomerates and functioned within a highly structured ecosystem. However, gradually the digital technologies and cultures have provoked a paradigm shift in the consumer-producer relationship, enabling any online user to become a “prosumer” of content (Fuchs, 2014; Kotler, 1986; Ritzer, 2010, 2015; Toffler, 1980). This has been further emphasised and encouraged by the platform economy, where platforms primarily act as coordinators of content creation and human interaction (Kenney et al., 2016). The impact of these dimensions of digital culture and prosumption can be seen not only through the services individuals utilise for personal purposes or entertainment, but additionally, in the labour-like approaches they utilise to generate income and build professional careers around these new forms of economic coordination, ones which were not available only a few years ago.
This professionalisation of content creation activities can be seen, for example, in the adaptation of specific schedules to content creation activities, as well as in the growing number of individuals converting content creation into their primary source of income. This form of professionalisation of digital content creation can be considered to be related to the concept of “playbour”, where activities regarded as gaming or playing are further infused with aspects of professionalism (Castronova, 2005; Kücklich, 2005; Scholz, 2013; Taylor et al., 2015; Yee, 2006). The concept of playbour can be associated with our understanding of the digital economy, digital labour and free labour (Fuchs, 2014; Lloyd, 2017; Scholz, 2013; Terranova, 2013), but is further exemplified in activities related to professionalisation of play and playful activities. Examples of playbour can be observed in video game economies, such as gold farming and real-money trading (Heeks, 2009; Lehdonvirta and Castronova, 2014), computer game modification (modding) (Kücklich, 2005; Sotamaa, 2010; Taylor et al., 2015), esports (Hamari and Sjöblom, 2017), live video streaming and pre-recorded video broadcasting (Pellicone, 2016; Sjöblom and Hamari, 2017) and UGC.
As an example of playbour, UGC can be examined in reference to a range of services, including Wikipedia (Kaplan and Haenlein, 2014; Nov, 2007) and social media platforms such as Twitter and Facebook (Chen, 2011; Shen et al., 2014). In these cases, the primary content is provided by users and often as a part of a leisure or playful activity. UGC is commonly understood as content that is produced by the end-user of a service or software which expands the original, however, if we consider social video content dissemination through YouTube for example, it cannot merely be viewed as content that expands YouTube as a system. Rather, YouTube is seen as a facilitator of content that is expanded through social interaction and relationships. Therefore, while the term UGC is rather established, it can be seen as outdated in today’s social online environment, where it fails to reflect the interchangeable nature of a prosumer and may limit the understanding of the underlying structures and relationships related to the content itself. Lamb and Kling (2003), who introduced the “user” of ICT as a social actor with multiple affiliations and relationships with the surrounding ecosystem, presented similar arguments. We conceptualise and examine this type of prosumer-generated digital content as social content. That is, that it is produced by independent social actor(s) (Lamb, 2005; Lamb and Kling, 2003) who utilise one or multiple commercial social media channels to disseminate their content and to encourage social interaction with both the content and the content creators.
The production of social video content is often decentralised and independent from the distribution platform (e.g. pre-recorded content broadcasted through video sharing services such as YouTube), but the content can also be directly generated through the distribution platform (e.g. live streaming on services such as Twitch and YouTube live). Although there are various digital video sharing services available for content creators, YouTube and Twitch are currently the leading social video sharing platforms, catering to millions of content creators and their audiences (Coldewey, 2017; Freitas and Albert, 2018; YouTube, 2017), and as such exemplify the growing influence of social video content as a form of media and an activity. YouTube has a prominent role in the market as it was one of the first video sharing platforms to establish market share, but Twitch has rapidly gained popularity as a live streaming platform with an emphasis on video gaming content. See Table I for examples of different types of digital video sharing services.
The business models of video sharing platforms vary, but are primarily constructed around the attention obtained by the content and content creators on the platforms, and the consumption power of the platform users, as explained in the notion of the attention economy (Huberman, 2013). The content generated by content creators and the consumption of said content is monetisation through various strategies and channels, developed to further the economic agenda of the platform, but lately also extended to provide monetary value for the content creators (Welch, 2018). Of these monetisation strategies and channels, the most prominent are the use of direct advertising and offering a paid subscription to specific content or channels. Content creators share of the revenue from these monetisation channels is often provided through affiliate or partner programs, which are accessible to content creators with specified levels of attention on the platform.
These exclusive programs provide a sophisticated way for platforms to further their monetisation strategy as they allow platforms to engage the content creators in further content creation and monetisation of their personal brand and content through e.g. merchandise. In order to access and maintain the advantages of these programs, a content creator is required to maintain a highly systematic approach to their activities, including effective time management, community management, and a rigorous approach to continuous content creation. Out of the 2m unique monthly broadcasters on Twitch, only 27,000 have Twitch “partner” status (Freitas and Albert, 2018), which allows further access and control over monetisation channels such as advertising revenue. However, over 150,000 broadcasters have “affiliate” status on the Twitch platform (Freitas and Albert, 2018), which allows basic access to subscriptions and donations in the form of Twitch supported digital currency, Bits (Twitch, 2018), thereby increasing the ability to earn an income from these activities.
Regardless of these developments in business models and their accessibility, social video content creation is still largely considered a leisure activity, possibly driven by many of the same motivations as playing video games (Hamari and Keronen, 2017; Hamari and Tuunanen, 2014). Therefore, further examination of previous research on motivations can illuminate the underlying behaviours related to these social video content creation activities.
Previous research on social video content creation
Understanding the motivations of individuals can lead to both a better understanding and prediction of human behaviour (Agarwal and Karahanna, 2000; Chen et al., 2014; Deci and Ryan, 1985) as well as to the improvement of service or product design (Bloch, 1995). The motivation behind digital video content production has been the topic of several research efforts in recent years (Table II). It should be noted that the following literature is focussed primarily on production motivations, and may not be exhaustive.
The self-determination theory
The examined literature on video content creation motivation revealed a gap in the understanding of the economic and extrinsic motivation behind the increasingly professionalised video content creation, and its effect on the overall activity. In order to examine this effect of the economic incentive behind video content creation, this research utilises one of the leading schools of thought on human motivation, SDT (Deci and Ryan, 2000, 2002). SDT allows us to analyse the effect of play and labour elements within this activity, utilising the understanding of intrinsic and extrinsic motivation.
As one of the core psychological theories on motivation, SDT (Deci and Ryan, 2000, 2002) distinguishes between intrinsic and extrinsic motivation and proposes that behaviour – such as producing and sharing videos online – is motivated by a host of intrinsic and extrinsic motivations. SDT has been developed to describe three core intrinsic psychological needs that motivate behaviour: competence (the need to display and develop one’s skill); relatedness (the need for human connection and belonging to a group); and autonomy (the need to be independent in one’s own behaviour) (Deci and Ryan, 2000). In addition to these core needs, intrinsic motivations can describe the pursuit of certain behaviours for the sake of the behaviour itself and the psychological value it provides to the individual, for example watching television in the pursuit of enjoyment or relaxation (Baard et al., 2004; Deci and Ryan, 1985, 2002).
SDT also examines extrinsic motivations, that are considered to be separable outcomes from the activity being pursued, for example working in pursuit of a salary or reputation and external approval (Deci and Ryan, 1985, 2000, 2002). Intrinsic and extrinsic motivations often coincide in engagement with work and labour practices (Lepper and Henderlong, 2000), it is, therefore, essential to study both intrinsic and extrinsic motivation in playbour activities where the activities, by definition, combine intrinsic and extrinsic rewards. While intrinsic motivations have previously been studied in the context of playbour, there seems to be a lack of research, which analyses extrinsic motivations in conjunction with intrinsic motivations in the context of playbour.
SDT is a prominent theoretical framework in social psychology and it has also been extensively utilised when examining digital media consumption and production on services such as Wikipedia (Arazy and Nov, 2010; Nov, 2007), YouTube (Cha et al., 2007; García-Rapp, 2017), Facebook (Joinson, 2008) and Twitter (Chen, 2011) to name but a few.
Research model and hypothesis
Previous research on social video content creation has indicated similarities in motivations driving video content production and distribution on different digital platforms (Bründl and Hess, 2016; Lottridge et al., 2017; Zhao et al., 2018). However, drawing on the approach of the SDT, there is a lack of understanding as to whether these increasingly professionalised activities are more prominently motivated by intrinsic or extrinsic motivations. This is primarily a result of the fact that these elements have not yet been analysed in this context.
The nature of social video content creation within the digital environment reflects the core psychological needs described in the SDT, such as: social (relatedness) aspects through the interactive nature of the activity and the distribution platforms; competence through the ability to display and build a unique skillset through content creation; and autonomy through the independent and almost entrepreneurial nature of the activity. Previous research on this topic also indicates that intrinsic motivations, alongside the drive to fulfil the core psychological needs (Ryan and Deci, 2000) such as social interaction, enjoyment, relaxation and self-expression (Bründl and Hess, 2016; Kim et al., 2017; Lottridge et al., 2017; Lu et al., 2018), have been found be associated with the production of video content. Similar research findings on intrinsic motivation, especially social aspects, have also been reported in research related to other forms of digital content creation and media use (Chen, 2011; Joinson, 2008).
As previous research has revealed, various intrinsic motivations, such as social interaction and enjoyment, are positively associated with the creation of digital video content, which is why we argue that the intrinsic motivations examined in this research are also positively associated with the average time invested weekly on content creation (H1). Similarly, we argue, that these intrinsic motivations are also positively associated with the intention to continue video content creation, as also examined in previous research (H2):
The intrinsic motivations examined in this research are positively associated with the average time invested weekly on content creation.
The intrinsic motivations examined in this research are positively associated with the intention to continue video content creation.
However, there also are clear indications that the entrepreneurial aspects of the activity have increased the professionalisation and celebrification of individual video content creators (Driessens, 2013; Jerslev, 2016; Khamis et al., 2017; Marwick, 2015; Senft, 2013). In turn, this has led to increased attention and interest in this activity as a profession and a source of recognition and income (Chambers et al., 2018). This has been further supported by the development of the platform economy (Kenney et al., 2016) and developments in digital labour practices (Lloyd, 2017; Scholz, 2013).
This professionalisation of social video content creation is evident in recent developments on distribution platforms; for example, Twitch (Twitch, 2018) has begun to directly associate higher levels of video content creation activity with their partner and affiliate programs which offer direct monetary and visibility benefits. Due to these developments, we argue that extrinsic motivations such as income, career development and reputation are becoming more prominent in the activity of video content creation, and are positively associated with both the average time invested weekly on content creation (H3) and the intention to continue video content creation (H4):
The extrinsic motivations examined in this research are positively associated with the average time invested weekly on content creation.
The extrinsic motivations examined in this research are positively associated with the average time invested weekly on content creation.
The model used in this research uses nine variables, adapted from previous research, to assess intrinsic and extrinsic motivations in order to better understand the behaviour of social video content creators. Each item was reworded to represent the activity of social video content creation.
Based on the findings of previous research (see e.g. Bründl and Hess, 2016; Kim et al., 2017; Lottridge et al., 2017) the constructs selected to measure intrinsic motivations were: skill development (competence) which is a drive for self-development and actualisation (Nov et al., 2010); social interaction (relatedness), which emerges when an individual feels part of a bigger social group (Lee et al., 2015; Leung, 2001); altruism (relatedness), in which the drive to share and assist others with their lives is expressed (Hsu and Lin, 2008); self-expression (autonomy), which measures an individual’s need to express their personality, attitudes, preferences and lifestyles (Lee et al., 2015); enjoyment, which refers to the positive psychological state individuals experience when they engage with an activity (Nov et al., 2010); and, relaxation, representing the human need to unwind as a means of feeling less tense (Leung, 2001).
The constructs chosen to measure extrinsic motivations were selected based on an understanding of the professionalisation of the activity and extraneous outcomes that drive engagement with it. These are: career development, which describes the drive individuals have to improve their career placement possibilities (Nov et al., 2010); income that represents the psychological perception of receiving a reward for completing a task (Lakhani and Wolf, 2005; Leimeister et al., 2009); and, reputation, the drive to improve an individual’s placement in the hierarchy of the community to which they belong (Hollenbaugh, 2010; Lee et al., 2015). Table III presents how these variables have been utilised in previous research.
The research model in Figure 1 incorporates these nine variables in order to examine how they influence the intention to continue video content creation, and the average time invested weekly on content creation.
Methodology
Participants and procedure
Data were collected via an online survey, directed at social video content creators of different levels of popularity and tenure. Piloting of the survey was undertaken to test the technical functionality of the survey platform as well as to investigate internal consistency of psychometric constructs. After the pilot, two items underwent minor rewording. The survey was distributed during 2017 via social media channels and groups (Reddit, Twitter and Facebook), personal messaging services of social video platforms (Twitch, YouTube), and through an e-mail list of active content creators. The respondents of the survey were offered a chance to win a product valued at $65 as an incentive to participate.
Overall, the survey gathered data from 377 respondents from 30 different countries, of whom most were from Finland (38.6 per cent), and the USA (32.3 per cent). Almost 70 per cent of the respondents reported either having a full-time job or being students. Although the survey did not ask the participants to specify whether video content creation was considered as their full-time job, 45.8 per cent of the respondents reported generating income through their video content creation activities. The respondents were also asked to assess their activity as constitutes work or play on a seven-point Likert scale, over 60 per cent of the respondents reported a value higher than 4, indicating the activity to be considered primarily as play. This further exemplifies the merger of work and play within this activity. More detailed demographic information can be found in Table IV.
Measurements
The questionnaire items were extracted from existing measurement instruments, used in previous research on social and digital media behaviour. Relaxation and social interaction constructs were created by combining items from different scales, thereby increasing the depth of the relevant scales. The social interaction construct included items from sense of community (McMillan and Chavis, 1986), socialisation (Lee et al., 2015) and sociability (Leung, 2001), while the relaxation construct adopted additional items from escape (Leung, 2001). Reputation was a combination of personal status (Lee et al., 2015) and exhibitionism (Hollenbaugh, 2010), while income was a combination of a construct measuring the perception of financial rewards (Lakhani and Wolf, 2005; Leimeister et al., 2009) and the actual income of the content creator, which was measured based on the income estimates provided by respondents.
The dependent variables measured in this study were average time invested weekly on content creation, and intention to continue video content creation. Average time invested weekly on content creation was measured using the estimated hours per week spent on video content creation and dissemination through different services (e.g. YouTube and Twitch), as well as the average hours spent on promoting the video content and channel on different social media (e.g. Facebook, Twitter, Snapchat). Average time invested weekly on content creation is something the content creator themselves has clear control over and it was measured in hours spent, as many additional activities related to social video content creation (such as editing video, preparing for a live stream and promoting the content) should also be taken into consideration when assessing the laborious nature of this activity. Intention to continue video content creation was adapted from behavioural intention to use a system (Bock et al., 2005; Venkatesh, 2000), in order to highlight the intention to continue video content creation in the future.
The extracted items were amended to emphasise the activity of social video content creation, by utilising the term “streaming” which was introduced to respondents as producing, sharing and posting video content online. All of the items, except estimated income and average time invested weekly on content creation, were measured on a seven-point Likert scale (1 indicating, “Strongly disagree” and 7 indicating, “Strongly agree”). A list of all items and the sources from which they were adapted can be found in the Appendix. Three individual items were removed during the analysis as they showed poor loading with other items in their corresponding constructs. These three items were from the self-expression, reputation and community scales. These deletions are reflected in the listing of items and their sources the Appendix.
Validity and reliability
Model-testing for this research was conducted through component-based partial least squares structural equation modelling (PLS-SEM) (Chin and Newsted, 1999). In order to ensure the validity and reliability of measurement, specific measures were taken in the construction of the survey and in the analysis of the data. The order of the survey items in the online survey was randomised to ensure that respondents were unable to detect patterns between the items (Campbell and Cook, 1979), also serving to decrease the potential effect of common method bias (Podsakoff et al., 2003). Analysis was conducted using SmartPLS 3.2.6 software (Ringle et al., 2015). Accepted thresholds for internal consistency and convergent validity were exceeded across the standard measures of Cronbach’s α, composite reliability (CR) and average variance extracted (AVE) (Fornell and Larcker, 1981; Nunnally, 1967) (see Table V). Furthermore, accepted thresholds for discriminant validity were exceeded across the standard measure of square roots of the AVE for each of the constructs being higher than the correlation for any other construct, as well as each item having the highest loading with the construct to which it belongs (Fornell and Larcker, 1981; Joreskog and Sorbom, 1993; Joreskog and Yang, 1996). The validity calculations are displayed in Table V. In conjunction with validity measurements, the sample size (n=377) satisfies multiple different criteria for the lower bounds of sample size for PLS-SEM (Anderson and Gerbing, 1988; Chin and Newsted, 1999).
Results
Data were analysed at the levels of: overall intrinsic and extrinsic motivations modelled as second-order constructs (constructs that combine all items of constructs regarded as either intrinsic or extrinsic motivations); and individual constructs across intrinsic and extrinsic motivations.
Analysis of overall intrinsic and extrinsic motivations
The model accounted for 37.9 per cent of the variance for intention to continue video content creation, as well as for 2.1 per cent of variance in the average time invested weekly on content creation. Results show that neither of the second-order constructs of intrinsic (p=0.310) or extrinsic (p=0.154) motivation were statistically significantly associated with average time invested weekly on content creation (H1 and H3 rejected). However, a strong, and statistically significant, positive association exists between intrinsic motivations and intention to continue video content creation (H2 not rejected, p=0.000). Extrinsic motivations did not have significant associations with intention to continue video content creation (H4 rejected, p=0.052). Results are summarised in Table VI and Figure 2.
Analysis of specific motivations across intrinsic and extrinsic motivations
The more detailed analysis accounted for 40.9 per cent of the variance for intention to continue video content creation, and 5.5 per cent of variance in the average time invested weekly on content creation. The analysis showed that perceived enjoyment (p=0.001), skill development (p=0.038) and social interaction (p=0.000) are positively associated with the intention to continue video content creation. Moreover, social interaction was positively associated with the average time invested weekly on content creation (p=0.043) and relaxation was negatively associated with the average time invested weekly on content creation (p=0.037).
From the individual constructs reflecting extrinsic motivations, both career development (p=0.048) and income (p=0.046) had a significant association with the average time invested weekly on content creation. None of the items from extrinsic motivations correlated with the intention to continue video content creation. Results are summarised in Table VII and Figure 3.
Discussion and conclusion
The findings of this research reveal similarities with previous research into social video content creation (see, e.g. Bründl and Hess, 2016; Kim et al., 2017; Lottridge et al., 2017) and research into content production and consumption on different digital media platforms (Arazy and Nov, 2010; Cha et al., 2007; Chen, 2011; Joinson, 2008; Nov, 2007). Intrinsic motivations such as enjoyment and social interaction (relatedness) have a positive bearing on continued engagement in video content production. However, this study additionally identified a significant relationship between specific extrinsic motivations and the weekly activity levels of a content creator. These findings between extrinsic motivations and weekly activity levels of a content creator are significant to this study as they reflect the change towards the more professionalized aspects of this activity, and therefore will be examined in the discussion of this paper.
The findings of this study may indicate that involvement in a creative activity, such as social video content creation, requires an intrinsic motivation that drives the continuity of the activity (hence the rejection of H4). However, the willingness to input more systematic effort into the content creation activities, is influenced by the addition of specific extrinsic incentives such as fame and fortune, which may partially explain the rejection of H1. These findings related to more specific extrinsic motivations, may have been diluted in the higher-level analysis of this research, which would explain the rejection of H3.
The lure of fame and fortune – extrinsic motivations to create video content
The significant relationship between income and weekly time invested on content creation may indicate that the developments in platform specific monetisation structures are becoming more accessible and appealing for content creators, which is beginning to affect their behaviour. A prominent example of this type of development is the gamified affiliate programme introduced by Twitch in 2017. The affiliate programme is a step towards the Twitch partner programme but is a separate monetisation scheme that is directed at entry level/beginner streamers on Twitch. It utilises strategic gamified techniques (Siutila, 2018), in the form of specific tasks and challenges, to motivate the content creators to generate more content as well as an audience. As the incentive for a specific activity level of video content creation, the streamers are promised access to the revenue of certain monetisation features, such as subscriptions, as well as other privileges offered by the platform (Twitch, 2018). Similar uses of gamification are utilised in more traditional working environments, as well as digital applications, in order to increase motivation and productivity (Warmelink et al., 2018; Werbach and Hunter, 2012).
In practice, similar strategies could also be utilised in other video sharing platforms to promote the accessibility and availability of these monetisation schemes to beginners, which may add an extrinsic motivator to their activities and increase the activity levels of the content creators. This approach could also further the business models of these video sharing platforms. Interestingly these types of programs utilise familiar elements from our working environment such as clear goals, which emphasise the idea of playbour in this context.
Further examination of the significant relationship between career development and average time invested weekly on content creation, seems to indicate that content creators are also viewing their activities professionally (anonymized for review). It may be that, as a result of the increasing spread of the celebrity culture related to digital content creation exemplified in the concepts of micro-celebrity (Khamis et al., 2017; Marwick, 2015; Senft, 2013) and the influencer culture (Abidin and Ots, 2015), many have come to attempt to attain these celebrity statuses themselves. This relationship may also have been influenced by the rise of more organised digital professions, such as esports players and broadcasters (see, e.g. Bayliss, 2016; Salo, 2017).
The celebrity culture associated with digital content creation has developed through the popularity and rise of individual content creators. Particularly notorious video content creators such as PewDiePie and Paul Logan or Twitch streamers Dr DisRespect and Ninja, are prime examples of online celebrities. Their rise to fame has garnered a substantial amount of commercial and media attention, giving the overall profession of content creation a sense of ease and access (Fagan, 2018; Grundberg and Hansegard, 2014). In addition to this, the development of esports and its growing synergy with video content creation, especially live streaming on Twitch, has opened new professional opportunities related to video content creation. The allure of these digital professions is already highlighted in the future aspirations of the younger population (Chambers et al., 2018), but in practice, such professions are still relatively unstructured and unsupported, and could benefit from further research efforts related to labour laws and structures.
Although activities related to esports are already somewhat supported through organisations and teams (Funk et al., 2018; Holden, Kaburakis and Rodenberg, 2017; Holden, Rodenberg and Kaburakis, 2017; Paravizo and de Souza, 2018), there exists a dire need for more globally structured regulations and systematic support to further motivate this activity as a realistic career. In practice, this could be enforced by the video sharing platforms themselves or organised entities at a more national level.
It should be noted that the apparent playfulness in this type of playbour might obscure the labour involved in creating a career and income, which is why the content creators may not recognise these extrinsic motivations in their behaviour, as reflected in our results. Similar elements of playbour are evident in the game-modding culture (Kücklich, 2005; Sotamaa, 2007, 2010), where gamers (often fans of specific games) modify and share digital game content, thereby creating new value for the game industry. In these activities the “modder” is often driven by motivations such as self-expression or community involvement (Sotamaa, 2010), which may mask the laborious aspects of the activity. In this way, the playbourer may not be compensated for their activities, or aware of their impact and value creation. To elevate our understanding of playbour and its effects on economics and our current perceptions of labour, further research should be targeted at the behaviours related to playbour and its effects on the digital economy, regulations and labour laws.
The social hedonists of video content creation – intrinsic motivations
Although the commercial structures around this activity are developing, the findings of this study indicate that the overall continuity of current social video content creation is still primarily driven by intrinsic motivations. Of these, social interaction was also found to have a positive association with the average time invested weekly on content creation, and has been observed to motivate these activities in previous research (see, e.g. Bründl and Hess, 2016; Zhao et al., 2018) and on other social media channels (Matikainen, 2015; Pai and Arnott, 2013; Sjöblom and Hamari, 2017). The importance of social interaction (relatedness) as a motivator in social video creation may not be surprising as the activity is highly social in nature, but the multifaceted aspects of social interaction require further examination.
The community that consumes, and otherwise interacts, with both social video content and the content creator, may directly influence the topic and nature of content being prosumed. Through active discourse, the audience allows, and engages in, global dissemination of knowledge and information about numerous topics and themes (Chang and Chuang, 2011). Furthermore, active participation in a community affects the social placement of the participant within the community itself (García-Rapp, 2017; Welbourne et al., 2013) and the popularity of a content creator (Törhönen et al., 2018). These aspects of social interaction and its importance may be reflected in the association between social interaction and the average time invested weekly on content creation. In practice, this finding accentuates the importance of integrating social interaction in the centre of content creation. Social interaction and engagement have already become the focus of various live streaming platforms, such as Twitch, but there is a need to incorporate and enhance community-building tools on video sharing platforms in order to support creators’ motivations related to social interaction.
A culture of learning and teaching is also evident in social video content creation, which may partly explain the association between skill development and the intent to continue content creation. This culture is a part of “creator” communities, where social video content creators support each other’s activities through discourse and collective effort. These communities nurture the democratic side of online autonomy and self-expression by forming communities around even niche topics in a global environment. This type of active, two-way communicative environment is also seen in peer-to-peer networks (Rodrigues and Druschel, 2010) and collaborative work such as crowdsourcing (Nov et al., 2010). To utilise this motivation of skill development, video-sharing services could further enhance their instructive/help resources through the utilisation of their own community. To an extent this is already done on specific forums and through videos, but this could be further developed through organised collaborative efforts.
The motivating effect of enjoyment on social video content creation, observed in this study, may indicate that the roles of the consumer and the producer have merged further within social video content creation. A central aspect of the nature of these new forms of social economic coordination, information creation and dissemination (such as piracy and the sharing economy) is that individuals increasingly partake simultaneously, both as producers and consumers of content (Belk, 2014; Hamari et al., 2016; Nov et al., 2010). Therefore, it may be conceived that the experiences, practices, and activities of production and consumption become intertwined in such a way that it may be increasingly difficult for an individual to discern and separate their roles and activities. Accordingly, a spillover-effect may exist where gratification derived from consumption, such as enjoyment (Hamari and Tuunanen, 2014; Hamilton et al., 2014; Lin and Lu, 2011; Sjöblom and Hamari, 2017), may also be attached to production and vice versa. However, the crossover of these activities is further accentuated in social video content creation activities since the majority of the content is related to the content creators consumption of hedonic products such as games (Ryan et al., 2006). This also adds a dimension of multimediality, as the communicative abilities of media products have begun to merge and transform (Schrock, 2015).
As such, the majority of the activity is characterised by “playbour”, where professional-like activities are undertaken under the drive of intrinsic motivation. The results of the current study give credence to these interpretations as they show that the motivations important in playing games such as achievement/skill and competence development, relatedness/community and enjoyment (Hamari and Keronen, 2017; Yee, 2006) also seem to be those that predict social video content creation. In practice, the utilisation of these playbourous activities gives the video sharing platforms power over large creative efforts and therefore the responsibility for fair and justified treatment of the content and the content creators. As the digital environment provides a global stage and domain for these activities, research on the current structures and working conditions of these digital producers is needed. The intrinsic drive to create content has already revealed problems related to growing “working” hours and burnout associated with the activity (Alexander, 2018c) and the need for further support for content creators and their mental health is growing. In the future, the role of the supporting services such as commercial platforms, networks and agencies should become even more central in respect to these issues.
Limitations and future research
The data collected in this study were gathered using an online survey, which can be associated with specific limiting factors. The online survey was filled out in a non-supervised environment, where respondents can be exposed to different distractions. This may affect their ability to respond to the survey and therefore lead to common-method bias (Straub et al., 2004). This research aimed to alleviate this issue by using various distribution sources for the survey, and a randomized order of the construct items in the survey.
As social video content creation is a digital activity and, therefore, a global phenomenon, we also acknowledge the differences between cultures and countries that may limit the study. The demographic of this study is also predominantly male, which may limit the findings. This may be the result of the gender division on the largest video sharing platforms, where the users are predominantly male, for example Twitch reports that 81.5 per cent of Twitch users are male (Twitch, 2017). A study into different practices of social video production among a variety of cultures might provide fruitful in investigating how inherent cultural differences are reflected upon contemporary forms of mass communication.
The research findings reveal various interesting aspects for future research. In particular, that prosumption related to social video content creation and playbour in social video content creation could benefit from closer examination in the future. Analysing the current economic structures of social video content creation and their effect on the activity and information and knowledge sharing would also be important in the future.
The continuous development and diffusion of technology, services and cultures surrounding social video content creation would benefit from more multidisciplinary research in order to understand the delicate relationships and structures of social video content creation. This type of multidisciplinary research would require the development of consistent terminology related to this activity. Although this research defines the social video content creator as a social actor (Lamb, 2005; Lamb and Kling, 2003) and aims to alleviate the diffusion and confusion of terminology, the fragmented terminology in social video content creation is divided by different technologies and developments that have occurred during recent years.
Figures
Examples of different types of digital video sharing services
| Example | Content creator | Type of content | Dissemination |
|---|---|---|---|
| YouTube, Twitch | Independent/commercial | Independent | Commercial |
| Netflix, HBO Now | Commercial | Commercial | Commercial |
| View.ly (in development) | Independent/commercial | Independent | Independent |
Previous research in social video content creation motivations
| Study | Theoretical background | Summary of topic | Key findings |
|---|---|---|---|
| Why do users broadcast? Examining individual motives and social capital on social live streaming platforms (Bründl and Hess, 2016) | Social capital theory | Twitch broadcasting motivations | Information dissemination, monetary incentives and commitment associated with content contribution. Enjoyment, Information dissemination, social interaction, commitment and shared vision associated with user’s intention to continue content contribution |
| Determinants of live streamers’ continuance broadcasting intentions on Twitch: a self-determination theory perspective (Zhao et al., 2018) | Self-determination theory | Twitch intentions | Performance expectancy and perceived website attractiveness subsequently affect streamers’ continuance of broadcasting intentions on Twitch |
| You watch, you give, and you engage: a study of live streaming practices in China (Lu et al., 2018) | n/a | Live streaming practices in China | Social aspects primary motivation for content creation |
| Third-wave livestreaming: teens’ long form selfie (Lottridge et al., 2017) | Uses and gratifications | The live streaming behaviours and motivations of American teens | Enjoyment, thrill and social aspects significant motivators for content creation activities |
| It is more than just sharing game play videos! Understanding user motives in mobile game social media (Kim et al., 2017) | Uses and gratifications | Use motivations of GameDuck | Enjoyment and social recognition significant motivators for content creation activities |
| Framing and praising Allah on YouTube: exploring user-created videos about Islam and the motivations for producing them, (Mosemghvdlishvili and Jansz, 2013) | Uses and gratifications | Exploring user created videos about Islam and the motivations for producing them | Communication/interaction, self-expression and social recognition significant motivators in content creation activities |
| Meerkat and periscope: I stream, you stream, apps stream for live streams (Tang et al., 2016) | n/a | Use of Meerkat and Periscope for live streaming video | Social interaction and career development significant motivators in content creation activities |
| Live-streaming mobile video: production as civic engagement (Dougherty, 2011) | n/a | The streaming practices related to the production of civic content | Social aspects, communication/educative power of the medium significant motivators in content creation activities |
| Motivations and stake management in producing YouTube “bro-science” videos for baldness treatment, (McNeill and Sillence, 2018) | n/a | Motivations and stake management in producing YouTube “bro-science” videos | Information sharing, social interaction/community, altruism significant motivators in content creation activities |
| Older people’s production and appropriation of digital videos: an ethnographic study (Ferreira et al., 2017) | n/a | Older people’s content production and appropriation of digital videos | Sharing moments/information, maintaining memories, skill development, and social inclusion significant motivators in content creation activities |
Summary of previous research in motivations for using digital services
| Construct | Previous research | Theoretical framework | Topic/Service | Results |
|---|---|---|---|---|
| Intrinsic motivations | ||||
| Enjoyment | Nov et al. (2010) | Self-determination theory | Flickr | Enjoyment not related to tagging and sharing photos |
| Hamari et al. (2016) | Self-determination theory | Collaborative consumption | Perceived enjoyment has a significant positive effect on attitude and the behavioural intention | |
| Relaxation | Sjöblom and Hamari (2017) | Uses and Gratifications | Twitch consumption | Tension release (relaxation) has a positive effect with hours of streams watched |
| Leung (2001) | Uses and Gratifications | ISQ (I Seek You) | Relaxation has a positive effect on the use ISQ (I Seek You) | |
| Self-expression | Matikainen (2015) | Social media content generation | Self-expression has a positive effect on social media content creation | |
| Lee et al. (2015) | Self-expression has a positive effect on Instagram use | |||
| Social interaction | Matikainen (2015) | Social media generation | Social interaction has a positive effect on social media content creation | |
| Lee et al. (2015) | Social interaction has a positive effect on Instagram use | |||
| Altruism | Hsu and Lin (2008) | Theory of reasoned action | Blogging | Altruism positively related with attitude towards blogging |
| Skill development | Nov et al. (2010) | Self-determination theory | Flickr | Skill development has a positive effect on tagging images and the social aspects of photo sharing |
| Extrinsic motivations | ||||
| Career development | Lakhani and Wolf (2005) | Self-determination theory | Free/Open Source Software Projects | Extrinsic motivations (career) has an effect on hours per week dedicated to Free/Open Source Software projects |
| Income | Lakhani and Wolf (2005) | Self-determination theory | Free/Open Source Software Projects | Extrinsic motivations (income) has an effect on hours per week dedicated to Free/Open Source Software projects |
| Reputation | Nov et al. (2010) | Self-determination theory | Flickr | Reputation has a positive effect on tagging images and the social aspects of photo sharing |
| Hsu and Lin (2008) | Theory of reasoned action | Blogging | Reputation positively related with attitude toward blogging | |
Demographic information
| n | % | n | % | ||
|---|---|---|---|---|---|
| Gender | Employment | ||||
| Male | 280 | 74.3 | Part-time | 51 | 13.5 |
| Female | 92 | 24.4 | Full-time | 129 | 34.2 |
| Other | 5 | 1.3 | Student | 131 | 34.7 |
| Age | Unemployed | 61 | 16.2 | ||
| <17 | 33 | 8.8 | Retired | 5 | 1.3 |
| 18–24 | 160 | 42.4 | Video content type | ||
| 25–34 | 126 | 33.4 | Pre-recorded | 122 | 32.3 |
| 35–44 | 37 | 9.8 | Live streamed | 24 | 6.4 |
| 44> | 21 | 5.6 | Both | 231 | 61.3 |
Results of validity and reliability testing
| α | CR | AVE | AVGHW | ALT | ENJ | RLX | SELF-E | SOCI | SDEV | IC | CDEV | INCOME | REP | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| AVGHW | 1.000 | 1.000 | 1.000 | 1.000 | ||||||||||
| ALT | 0.823 | 0.883 | 0.654 | 0.069 | 0.809 | |||||||||
| ENJ | 0.859 | 0.904 | 0.702 | 0.074 | 0.432 | 0.838 | ||||||||
| RLX | 0.772 | 0.848 | 0.538 | −0.041 | 0.173 | 0.569 | 0.734 | |||||||
| SELF-E | 0.707 | 0.831 | 0.627 | 0.057 | 0.377 | 0.534 | 0.420 | 0.792 | ||||||
| SOCI | 0.857 | 0.891 | 0.542 | 0.125 | 0.426 | 0.619 | 0.492 | 0.547 | 0.737 | |||||
| SDEV | 0.809 | 0.875 | 0.638 | 0.103 | 0.561 | 0.558 | 0.336 | 0.514 | 0.559 | 0.798 | ||||
| IC | 0.794 | 0.866 | 0.619 | 0.172 | 0.340 | 0.527 | 0.312 | 0.427 | 0.555 | 0.499 | 0.787 | |||
| CDEV | 0.897 | 0.927 | 0.760 | 0.135 | 0.290 | 0.309 | 0.190 | 0.358 | 0.354 | 0.496 | 0.359 | 0.872 | ||
| INCOME | 0.934 | 0.950 | 0.794 | 0.151 | 0.100 | 0.172 | 0.106 | 0.140 | 0.255 | 0.160 | 0.176 | 0.464 | 0.891 | |
| REP | 0.796 | 0.855 | 0.544 | 0.049 | 0.301 | 0.371 | 0.316 | 0.397 | 0.483 | 0.414 | 0.350 | 0.599 | 0.444 | 0.738 |
Notes: AVGHW, Average time invested weekly on content creation; ALT, altruism; ENJ, enjoyment; RLX, relaxation; SELF-E, self-expression; SOCI, social interaction; SDEV, skill development; IC, intention to continue video content creation; CDEV, career development; INCOME, income; REP, reputation. The diagonal numbers in italic are the square root of AVE
Results of the analysis on overall intrinsic and extrinsic motivations
| Average time invested weekly on content creation | Intention to continue video content creation | |||||
|---|---|---|---|---|---|---|
| 0.021 | 0.379 | |||||
| R² | β | CI | P | β | CI | P |
| Intrinsic motivations | 0.046 | −0.035–0.143 | 0.310 | 0.568*** | 0.491–0.661 | 0.000 |
| Extrinsic motivations | 0.117 | −0.047–0.275 | 0.154 | 0.076 | −0.006–0.169 | 0.052 |
Notes: *p < 0.05; **p < 0.01; ***p < 0.001
Analysis of specific motivations across intrinsic and extrinsic motivations
| Average time invested weekly on content creation | Intention to continue video content creation | |||||
|---|---|---|---|---|---|---|
| 0.055 | 0.409 | |||||
| R2 | β | CI | P | β | CI | P |
| Intrinsic motivations | ||||||
| Enjoyment | 0.044 | −0.112–0.190 | 0.567 | 0.244*** | 0.107–0.381 | 0.001 |
| Relaxation | −0.139* | −0.276–0.021 | 0.037 | −0.059 | −0.151–0.04 | 0.230 |
| Self-expression | −0.008 | −0.13–0.134 | 0.908 | 0.054 | −0.036–0.149 | 0.260 |
| Social Interaction | 0.145* | 0.000–0.294 | 0.043 | 0.293*** | 0.182–0.402 | 0.000 |
| Altruism | −0.009 | −0.127–0.104 | 0.848 | −0.012 | −0.137–0.108 | 0.885 |
| Skill development | 0.032 | −0.073–0.144 | 0.552 | 0.143* | 0.015–0.279 | 0.038 |
| Extrinsic motivations | ||||||
| Career development | 0.098* | −0.011–0.186 | 0.048 | 0.102 | −0.013–0.226 | 0.092 |
| Income | 0.123* | 0.020–0.258 | 0.046 | −0.026 | −0.121–0.097 | 0.609 |
| Reputation | −0.114 | −0.275–0.051 | 0.190 | −0.001 | −0.093–0.103 | 0.984 |
Notes: *p < 0.05; **p < 0.01; ***p < 0.001
Research constructs and items
| Construct | Item | Cronbach’s α | Adapted from |
|---|---|---|---|
| Enjoyment | |||
| ENJ 1 | I find my streaming activities interesting | 0.826 | Nov et al. (2010), Van der Heijden (2004). |
| ENJ2 | I find my streaming activities enjoyable | 0.875 | |
| ENJ3 | I find my streaming activities exciting | 0.813 | |
| ENJ4 | I find my streaming activities fun | 0.837 | |
| Relaxation | |||
| RLX1 | My streaming activities are a pleasant rest | 0.816 | Leung (2001) |
| RLX2 | My streaming activities help me relax | 0.809 | |
| RLX3 | My streaming activities make me feel less tense | 0.832 | |
| RLX4 | My streaming activities take me away from my pressures and responsibilities | 0.693 | |
| RLX5 | I stream to role-play or try things with my identity | 0.445 | |
| Self-expression | |||
| SELF-E1 | My streaming activities allow me to express who I really am | 0.884 | Lee et al. (2015) |
| SELF-E2 | I can express my identity through my streaming activities | 0.869 | |
| SELF-E3 | I share personal details of my life through my streaming activities | 0.588 | |
| SELF-E4a | I show off through my streaming activities | ||
| Altruism | |||
| ALT1 | I like helping other people through my streaming activities | 0.733 | Hsu and Lin (2008) |
| ALT2 | It feels good to help other people through my streaming activities | 0.850 | |
| ALT3 | I believe that my streaming activities help other people | 0.838 | |
| ALT4 | I like that other people can benefit from my streaming activities | 0.809 | |
| Income | |||
| INCOME1 | My streaming activities benefit me financially | 0.949 | Lakhani and Wolf (2005), Leimeister et al. (2009) |
| INCOME2 | My streaming activities enhance my economic situation | 0.903 | |
| INCOME3 | My streaming activities lead to getting financial gains | 0.918 | |
| INCOME4 | I gain extra income from my streaming activities | 0.914 | |
| INCOME5 | On average, how much income do you make from video sharing related services/activities per month? Please answer in US dollars | 0.757 | |
| Skill development | |||
| SDEV1 | I think my streaming activities develop my skills | 0.835 | Nov et al. (2010) |
| SDEV2 | I learn new things through my streaming activities | 0.714 | |
| SDEV3 | I gain experience from my streaming activities | 0.817 | |
| SDEV4 | I can improve my personal abilities through my streaming activities | 0.821 | |
| Career development | |||
| CDEV1 | My streaming activities provide me with a means of developing my career | 0.865 | Lakhani and Wolf (2005) |
| CDEV2 | My streaming activities can have a positive impact on my career options | 0.875 | |
| CDEV3 | I am perceived better in the job market because of my streaming activities | 0.861 | |
| CDEV4 | I have a better chance of finding a job because of my streaming activities | 0.886 | |
| Reputation | |||
| REP1 | I am known because of my streaming activities | 0.607 | Lee et al. (2015), Hollenbaugh (2010) |
| REP2 | I feel that my streaming activities improve my status | 0.827 | |
| REP3 | I feel that my streaming activities improve my reputation | 0.784 | |
| REP4 | My streaming activities bring me fame | 0.735 | |
| REP5 | My streaming activities make me feel important | 0.717 | |
| REP6a | People like to watch my streaming activities because of me | ||
| Social interaction | |||
| SOCI1 | I keep in contact with people through my streaming activities | 0.602 | Lee et al. (2015), Leung (2001), McMillan and Chavis (1986) |
| SOCI2 | I interact with people through my streaming activities | 0.715 | |
| SOCI3 | I meet new people through my streaming activities | 0.799 | |
| SOCI4 | I make new acquaintances through my streaming activities | 0.789 | |
| SOCI5 | I feel a sense of belonging through my streaming activities | 0.749 | |
| SOCI6 | I feel like I am surrounded by friendly people within my stream community | 0.646 | |
| SOCI7a | I feel like I share values with my stream community | ||
| SOCI8 | I feel sense of community through my streaming activities | 0.829 | |
| Intention to continue video content creation | |||
| IC1 | I intend to stream at least as much in the next months as I have previously | 0.713 | Venkatesh (2000), Bock et al. (2005) |
| IC2 | I predict I will increase my streaming activities in the next months | 0.834 | |
| IC3 | I plan to continue streaming in the next months | 0.788 | |
| IC4 | I intend to stream more frequently in the near future | 0.808 | |
| Average time invested weekly on content creation | |||
| AVGHW | On average, how many hours per week do you spend to produce and post videos? | ||
Note: aMarked items were removed from the analysis
Appendix
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Acknowledgements
This work was supported by Business Finland (5479/31/2017 and 40009/16), Finnish Foundation for Economic Education (Grants Nos 12-6385 and 14-7824) and participating partners, Satakunnan korkeakoulusäätiö and its collaborators, Academy of Finland (Center of Excellence – GameCult).