ARTICLES

 

Reimagining uses and gratifications: integrating media adoption theories for the digital age

 

 

Aimee Viljoen

Department of Communication and Media, University of Johannesburg, Johannesburg, South Africa. Email: aviljoen@uj.ac.za

 

 

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ABSTRACT

This article reimagines the uses and gratifications theory (U&G) by integrating it with the Technology Acceptance Model (TAM) and the diffusion of innovations theory (DOI) to better understand media adoption in the digital age. While U&G has been a foundational framework for explaining audience motivations for media consumption, its individualistic focus and lack of predictive power limit its applicability in today's rapidly evolving media landscape. The Media Acceptance Model (MAM) offers a comprehensive framework that combines the strengths of U&G, TAM, and DOI to address these limitations. MAM incorporates individual needs (from U&G), technological perceptions (from TAM), and social diffusion processes (from DOI) to provide a more holistic understanding of media adoption. The model emphasizes external variables such as socioeconomic status, cultural context and demographic differences, making it particularly relevant for diverse societies like South Africa. By integrating concepts such as perceived usefulness, ease of use, relative advantage and compatibility, MAM enhances predictive capabilities and offers a dynamic tool for analysing contemporary media consumption patterns. This article explores the theoretical foundations, limitations, and synergies of U&G, TAM and DOI, ultimately presenting MAM as a robust framework for understanding and predicting audience behaviour in the digital era.

Keywords: Media Acceptance Model (MAM), audience behaviour, digital media consumption, needs paradigms, Technology Acceptance Model (TAM), diffusion of innovations theory, uses and gratifications theory, predictive frameworks, SDG 9

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INTRODUCTION

The uses and gratifications theory (U&G), first introduced by Katz, Blumler and Gurevitch in 1973, has long been a cornerstone of media and communication studies. Its appeal lies in its simplicity and adaptability. Despite the ever-changing media landscape, it remains a useful tool for understanding why audiences engage with certain media. As a media and film lecturer, I recently led an honours film and television class through the history of audience studies, where U&G played a central role in our discussions. This theory helped us unpack why audiences select specific media to fulfil personal needs, whether choosing pay television over streaming services or turning to social media for entertainment and information. During our discussion, we revisited the seminal need paradigms outlined by Katz et al. (1973). While the theory provides valuable insights, I challenged my students to critically assess its limitations. Scholars such as Ruggiero (2000) argue that U&G is overly individualistic and lacks predictive power. Sundar and Limperos (2013) highlight its struggle to account for the rapid evolution of digital media, while Blumler (1979) points out that the overlapping gratification categories can blur distinctions between motivations. When I asked my students whether they agreed with these critiques, their collective response was clear: while technology changes, fundamental human needs remain the same.

This classroom exchange transported me back to my PhD research (Viljoen-Stroebel, 2022), where I used U&G to analyse and predict television adoption habits during the COVID-19 pandemic. To address the predictive limitations of U&G, I integrated it with the Technology Acceptance Model (TAM) and the diffusion of innovations theory (DOI) to develop the Media Acceptance Model (MAM). Though I had not revisited this research in some time, the classroom discussion reignited my curiosity. When I shared my model with the class, I was struck by how relevant it still appeared. This realisation prompted me to consider refining and expanding the model to create a more robust predictive framework for audience viewing habits and media technology adoption. This article rethinks U&G in the context of today's media environment, enhancing its predictive power by integrating TAM and DOI. I explore the core principles of these frameworks, their limitations, and how they intersect to form a more comprehensive approach to media engagement. By doing so, this study seeks to provide a refined model that better explains how audiences adopt and interact with evolving media technologies.

While originally focused on television, the model's applicability extends to a broad spectrum of media technologies. I have reworked this original model to make it more universally applicable. As media consumption habits continue to evolve, this research offers a fresh perspective on how theoretical models can adapt to contemporary digital landscapes.

 

THE USES AND GRATIFICATIONS THEORY

Audience studies can be separated into two broad categories, namely, message-based studies and audience-based studies. The first focuses on the power of the media text over the audience, who is perceived as being incapable of withstanding the effects of media messages. Message-based studies mainly analyse the content of media messages. The second, being ethnographic in nature and entailing the direct study of audience behaviour, allows for the audiences' active participation in the meaning-making process of the media they consume and not just thinking of them as passive absorbers of information (Tager, 2010; Van der Merwe, 2012).

This shift from message-based to audience-based research took place during the 1980s when there was a sudden increase in mass communication research, otherwise known as the "ethnographic turn", where scholars attempted to understand the world as it is experienced by media users. These "audience researchers" aim to see and understand the world as it is seen and understood by the participants themselves (Hermes, 2010: 1).

Within the field of audience research, there has been a trend in thinking among scholars, evolving from the belief that media audiences are passive consumers of media texts to the thinking that media audiences play an active role in the understanding and creation of media messages. Roscoe, Marshall and Gleeson (1995: 88) argue that the passive audience paradigm was first challenged by U&G, which assumes that users actively select channels and content for a specific reason, to satisfy a specific personal need, thereby indicating the shift from a passive to an active audience paradigm, where the audience is seen as actively involved in evaluating and choosing what ideologies and messages they take from the media. Livingstone (1998: 193) highlights this shift in thinking about audiences by stating that "audience research is at a crossroads". She (ibid.) argues that audience researchers have moved on from "the semiotics of fixed and given textual meanings, the assertion of linear, casual effect on a passive audience", that audiences are "plural in their decodings" and that the audiences' cultural context plays a key role in understanding how they decode and understand media message and, in turn, how they use and spread those messages. Livingstone (1998: 193) highlights here the crucial change that the active audience paradigm, and eventually ethnography, brought to the field of audience studies. The audience is no longer seen as a "homogeneous, mass audience" but as being constituted of individuals in relation to collective identities, which are also influenced by individual differences in culture, beliefs, and history.

The U&G theory originated in the 1940s when scholars attempted to understand why audiences listen to radio or read newspaper articles (Ruggiero, 2000: 4). Some of the first U&G studies include Herzog (1942) who tried to understand why audiences watch game shows or listen to radio soap operas. Suchman (1942) studied audiences' interest in music and videos, while Wolfe and Fiske (1949) analysed children's interest in comics, and Berelson (1949) studied people's motivations for reading newspaper articles (Katz et al., 1973: 509). The focus of these early studies was to understand and classify audience responses to media into specific categories. Up to the 1970s, studies within this paradigm only focused on gratification sought, "excluding outcomes or gratifications obtained" (Ruggiero, 2000: 5).

This led to criticism of the theory, stating that U&G relied too heavily on self-reports from study participants and did not adequately recognise the social origin of audience's needs (Katz et al., 1973). However, despite this criticism, researchers persevered and attempted to further define the gratifications sought and obtained. This adjusted theoretical framework stated that the social and psychological origins of audience members' needs create expectations of the media which in turn leads to specific media usage choices, which then results in gratification obtained (Katz et al., 1973: 510). Despite further clarification, the theory remained under fire with critics stating that it had a "vague conceptual framework" and a confusing explanatory apparatus (Ruggiero, 2000: 6). Much work has been done to refine the U&G theory; however, scholars are still debating the legitimacy of U&G as a fully-fledged theory. Much of the debate seems to question the legitimacy of the field of communication research as a whole, arguing its lack of concrete theory and evidence compared to other more traditional disciplines (Ruggiero, 2000: 27). However, many scholars agree that U&G provides a cutting-edge theory whenever new media and media technology is released (Ruggiero, 2006: 27; Sundar & Limperos, 2013; Tefertiller, 2017).

Despite the controversy surrounding U&G, many scholars still use this theory to explain user behaviour. Eggermont and Vandebosch (2001) examined television usage among elderly viewers in Belgium by applying the U&G theory to a survey of 284 people over the age of 60. They found that respondents used television to relieve loneliness, to pass time, and to avoid boredom. Many international studies which implement U&G focus on the use of internet technology and social media to satisfy specific needs. Atkin, Jeffres and Neuendorf (1998) conducted some of the first U&G studies on internet adoption. As the internet was still at its infancy at the time, the researchers determined that much is still unknown about the reasons why people use the internet. Later studies would go on to redefine the categories of needs and gratifications based on the effect of internet technology on a user's media use. Stafford, Stafford and Schkade (2004) aimed to bridge the knowledge gap between existing uses and gratifications categories and what needed to be adjusted for U&G to be applied to the internet age. They concluded that social gratification was an important missing category to understand what needs were fulfilled by internet technology, apart from the already presumed content satisfaction. Hanson and Haridakis (2008) studied the needs and gratifications of college students' use of YouTube for watching and sharing news content. Like Stafford et al. (2004), these researchers also highlighted the importance of taking social interaction and increased user input into consideration when studying the gratifications sought and obtained. In South Africa, Tengeh and Udaokpan (2020) made use of the U&G theory to explain South African television viewing behaviours and platform subscription patterns.

The U&G need paradigms outlined by Katz et al. (1973) posit the following: that audiences use media to gratify specific needs. Cognitive needs drive individuals to seek knowledge and information, such as watching a documentary on climate change or reading news articles to stay informed. Affective needs involve emotional fulfilment, where media provides entertainment, joy, or even catharsis, like watching a romantic comedy to feel happy or listening to nostalgic music. Personal integrative needs relate to self-esteem and identity reinforcement, with individuals consuming content that aligns with their aspirations, such as self-help books or lifestyle influencers. Social integrative needs highlight the media's role in fostering connections, whether through discussing popular television shows with friends or maintaining relationships via social media. Lastly, tension-release needs see media as an escape from stress, with activities like playing video games or binge-watching television series offering relaxation and distraction. Figure 1 presents a visualisation of the U&G process.

 

 

Audience members begin with a need that they seek to gratify - whether cognitive, affective, personal integrative, social integrative, or tension release. Once they recognise this need, they actively seek out a media text, television show, or platform they believe will best fulfil the need. If the chosen media satisfies their need, they are likely to continue engaging with it. However, if it falls short, they will return to their original need category and select an alternative media text that better serves their purpose.

This model does not necessarily account for cases where audience members are unaware of their specific needs when they begin their media search. For instance, many people scroll through social media feeds or browse a streaming platform's recommendations until they find something that resonates with them, without consciously identifying their initial need. Additionally, it overlooks the fact that audience members often have multiple needs simultaneously. Someone might watch the news to stay informed (a cognitive need) while also using it as a means to engage in conversations with friends and colleagues (a social integrative need).

The traditional U&G categories are still useful, but they need to be updated to fit today's interactive, personalised, and algorithm-driven media landscape. Researchers like Ruggiero (2000) and Sundar and Limperos (2013) have worked to modernise the U&G framework so that it applies to digital and social media. Rathnayake and Winter (2018) expanded on this by creating a new way to measure how people use and gain satisfaction from social media. Instead of just looking at user needs, they focused on the unique features of social media, such as interactivity, visibility, and persistence; and how these influence behaviour. Similarly, Camilleri and Falzon (2021) explored how combining the Technology Acceptance Model (TAM) with U&G could help explain why people adopt online streaming services. Their research highlights the need to consider both practical features and psychological motivations to improve user experience and engagement.

While the U&G theory provides valuable insights into the psychological motivations behind media consumption, its individualistic focus and lack of predictive power limit its applicability in understanding the broader social and technological factors influencing media adoption. This is where the diffusion of innovations theory (DOI) offers a complementary perspective. DOI shifts the focus from individual needs to the social and systemic processes that drive the adoption of new technologies. By examining how innovations spread through communication channels and social systems, DOI addresses the limitations of U&G by incorporating factors such as relative advantage, compatibility, and observability. Together, these theories provide a more holistic understanding of media adoption, bridging the gap between individual motivations and societal influences.

 

DIFFUSION OF INNOVATIONS THEORY

Diffusion refers to the process whereby an innovation, such as new television platforms and technology, spreads through communication channels among the members of a social system over time (Rogers, 2010: 13). DOI provides a paradigm through which to study how and when an innovation is accepted or rejected by a given society. This includes analysing the process of idea generation, the adoption or rejection of the innovation, the attributes of the innovation, and individuals' beliefs about innovations and their effect on the acceptance or rejection process (Vishwanath & Goldhaber, 2003: 548).

The first diffusion research was conducted by Everett Rogers (1962), who was the leader in the field of diffusion theory. Subsequent research applied diffusion theory to a large variety of new technologies, such as cassette recorders and cable television (LaRosa & Atkin, 1988) and mobile phone technologies (Leung & Wei, 1999). These studies led to a generalised model of diffusion of innovations, which is not bound by the type of innovation being studied, its adopters, or the place and culture of origin. The diffusion model instead represents a "universal micro-process of social change" (Rogers, 2010: 16); thus, making this model ideal for use in a combined model with U&G. Atkin et al. (1998: 625) argue that the current communication revolution allows media users "unprecedented choice", which identifies the study of media technology diffusion as more important than ever. The greatest strength of this model thus lies in is utility across a large scope of fields and innovations.

Rogers' (1995) DOI theory outlines five key characteristics that influence the adoption of new technologies: relative advantage, compatibility, complexity, observability, and trialability. These factors help explain why some users readily adopt new technological innovations while others remain hesitant. Relative advantage refers to the extent to which an innovation is perceived as superior to what it replaces. The greater the perceived benefits, the more likely users are to adopt it. For example, the transition from traditional cable television to streaming services like Netflix demonstrated a clear relative advantage - users could watch content on demand without being tied to a broadcast schedule, leading to widespread adoption. Compatibility describes how well an innovation aligns with users' existing values, experiences, and needs. People are more inclined to adopt new technology that feels familiar. For example, the rise of podcasts was aided by their similarity to traditional radio programmes, making them easy for radio listeners to embrace as an alternative or supplementary media format. Complexity refers to how difficult an innovation is to understand and use. The more complicated it seems, the less likely users are to embrace it. For example, early attempts at 3D television failed to gain widespread adoption partly due to the complexity of wearing specialised glasses and setting up compatible devices, discouraging many potential users. Observability is the extent to which people can see an innovation being used by others. The more visible an innovation is within a social group, the more likely people are to adopt it. For example, the popularity of the social media platform TikTok skyrocketed as users saw viral videos being shared across other social media platforms, encouraging them to download the app and participate in trends. And finally, trialability refers to how easily users can experiment with an innovation before committing. Technologies that offer trial periods often see higher adoption rates. For example, music streaming platforms like Spotify and Apple Music offer free trials, allowing users to experience premium features before deciding to subscribe, increasing the likelihood of long-term adoption. It could be concluded that consumers' perceptions about an innovation greatly influence their acceptance of that innovation and in turn the overall adoption of the innovation (Vishwanath & Goldhaber, 2003: 550).

The decision to adopt or reject an innovation is influenced by various other factors. These include adopter-related personality traits, socioeconomic influences, interpersonal channels, mass media, and the perceived attributes of an innovation. Thus, for the purpose of this article the process of innovation adoption is understood as follows: (1) the user will obtain knowledge about the innovation and take into consideration the characteristics of the innovation to (2) form an attitude toward the innovation and then decide if they will (3) adopt or reject it. The user will then (4) implement the innovation and (5) make a final decision. This theory considers many of the traits of the innovation as stimulus for technology adoption or rejection; however, the model does not take users' personal needs and gratifications into account, as does the U&G theory.

By combining U&G, DOI and the Technology Adoption Model (TAM), researchers can gain a more holistic understanding of the factors driving adoption. U&G emphasizes the role of the individual needs, motivations and gratifications sought by users, which is particularly relevant in a diverse and dynamic media landscape like South Africa. DOI provides a framework for understanding how innovations spread through social systems, including the influence of adopter categories, communication channels, and the perceived attributes of the innovation. Moreover, TAM focuses on the perceived usefulness and ease of use of the technology, which are critical for predicting user acceptance. Together, these theories offer a comprehensive lens to analyse not only the technological and social aspects of adoption but also the personal and contextual factors that influence user behaviour. This integrated approach is especially important in South Africa, where socioeconomic diversity, cultural nuances, and varying levels of access to technology play a significant role in shaping media consumption patterns.

While DOI offers a macro-level perspective on how innovations are adopted within social systems, it does not fully account for the individual-level perceptions and attitudes that influence technology adoption. This gap is addressed by the TAM, which focuses on the user's internal beliefs about a technology's usefulness and ease of use. Unlike DOI, which examines the broader attributes of innovations, TAM delves into the psychological and behavioural factors that shape a user's decision to adopt or reject a technology. By integrating TAM with DOI and U&G, we can create a more comprehensive framework that accounts for both the social diffusion of innovations and the individual motivations driving media adoption.

 

THE TECHNOLOGY ADOPTION MODEL

TAM originated with the information sciences and functions as an approach to study users' acceptance and use of technology (Atkin et al., 1998: 631). The model was first implemented in the mid-1980s as part of an evaluation of the market potential of various emerging personal computer-based applications by the computer manufacturer IBM (Davis & Venkatesh 1996: 20). The model quickly gained popularity within emerging internet technology studies (Davis, 1986; Davis, 1989; Davis, Bagozzi & Warshaw, 1989). Davis (1986; 1989) created the original design for TAM, which would later be replicated in multiple technology adoption studies in a wide range of disciplines (Mathieson, 1991; Moore & Benbasat, 1991; Olfman & Bostrom, 1991; Adams, Nelson & Todd, 1992; Trevino & Webster, 1992; Hendrickson, Segars & Grover, 1993; Sjazna, 1994; Chin, Gopal & Salisbury, 1997).

TAM is a useful framework for understanding why people adopt new technologies such as streaming services over older ones like traditional television. Unlike DOI, which looks at factors like a technology's relative advantage, compatibility, complexity, observability and trialability, TAM focuses on two key factors: perceived ease of use and perceived usefulness (Atkin et al., 1998: 637). While DOI is more general and often applied to consumer behaviour, TAM was originally designed to study how people in organisations adopt new technologies. However, it has since been adapted to understand consumer behaviour as well, making it relevant for analysing shifts in media technology and usage.

At its core, TAM aims to explain how external factors influence a person's internal beliefs, attitudes and intentions towards using a technology. As Vishvanath and Goldhaber (2003) explain, TAM identifies a small number of fundamental variables that shape how people think and feel about adopting a new technology. These variables are key for predicting whether someone will accept or reject a technology. For example, in the case of streaming services, TAM helps us understand why people might choose streaming platforms over traditional broadcast television. The model works as follows: acceptance of a technology (like streaming services) is predicted by a user's intention to use it. This intention is influenced by the user's attitude towards the technology, which is shaped by two main factors, namely, perceived usefulness and perceived ease of use. Figure 2 below illustrates the process of adoption as outlined by TAM.

 

 

Firstly, external variables, although unspecified here, need to be taken into consideration as these may influence adoption choices. The next variable that influences adoption behaviour is the perceived usefulness of the technology, which refers to how much a person believes a technology will improve their life or help them achieve their goals. In the context of streaming services, perceived usefulness might include factors such as the ability to watch shows on demand, access to a wider variety of content, and the convenience of watching on multiple devices.

The next factor to consider in the process of technology adoption is perceived ease of use, which refers to how easy it is for someone to learn and use the technology. If a streaming platform is user-friendly, with a simple interface and clear instructions, it is more likely to be adopted. On the other hand, if the platform is complicated or requires technical knowledge, it may discourage adoption. Once a technology has been evaluated in terms of ease of use and perceived usefulness, the user's attitude toward adoption is influenced, either in favour of or against adoption. Behavioural intent (BI) refers to the user's intention to subscribe to a new platform or continue using existing platforms. Finally, system use refers to the actual adoption and usage of the technology. If the user does not find the new technology easy to use or more useful than its predecessor, the technology will not be adopted.

A limitation of this model is the lack of clarification for "external variables". The model alludes to external influences on consumer behaviour; however, factors such as age, education, race, and income level are not overtly taken into consideration. The model also stipulates that external forces such as word of mouth will influence a user's acceptance or rejection of a technology. From an organisational perspective, the model fails to consider that a colleague will have less impact on another's technology acceptance than an employer, for instance. If the company stipulates the use of a specific technology, the opinion of a fellow employee will not hinder a user from having to adopt the newly introduced technology (Ajibade, 2018: 4).

The overlaps between U&G, DOI and TAM reveal significant synergies in understanding technology adoption and media consumption. Firstly, U&G and TAM share common ground in their emphasis on fulfilling user needs. In TAM, perceived usefulness is determined by how well a technology enhances performance, which aligns with the U&G perspective that users select media or technologies based on their ability to satisfy specific needs, such as relaxation, escapism, or information. For instance, in the context of streaming services, users may perceive a platform as useful if it provides personalised content that meets their entertainment needs, resonating with U&G's focus on gratifications sought and obtained. Similarly, TAM's emphasis on perceived ease of use aligns with U&G's concept of ritualised media use, where users adopt technologies that are convenient and effortless to integrate into their daily routines. The ease of streaming on mobile devices contributes to their ritualised use, as highlighted in studies like Camilleri and Falzon (2020).

When comparing TAM and DOI, the key difference lies in their scope and focus. TAM was initially created to understand internet technology implementation in organisational settings; thus, primarily focusing on perceived usefulness and ease of use (Vishvanath & Goldhaber, 2003). While it allows for external variables, it sometimes overlooks subjective factors. In contrast, DOI considers a broader range of adoption factors, including subjective and social variables, making it more comprehensive in explaining innovation adoption (Atkin et al., 1998). Despite these distinctions, notable similarities exist. For example, perceived usefulness in TAM closely resembles the relative advantage variable in DOI, as both assess the performance and benefits of a technology. Similarly, ease of use in TAM aligns with complexity in DOI, both addressing the perceived difficulty of adoption. Olushola and Abiola (2017: 73) confirm that TAM remains a robust and statistically reliable model, particularly when integrated with other frameworks like DOI.

U&G and DOI also exhibit conceptual overlaps. Relative advantage in DOI, which refers to the perceived superiority of an innovation over alternatives, aligns with U&G's gratifications obtained. Users are more likely to adopt technologies that provide greater benefits or fulfil their needs more effectively than existing options. The relative advantage of over-the-top (OTT) platforms over traditional television, for instance, lies in their ability to offer personalised, on-demand content, directly addressing users' entertainment and relaxation needs. Compatibility in DOI, which refers to how well an innovation aligns with users' values and experiences, similarly overlaps with U&G's focus on selecting media that suits individual needs and preferences. Millennials, for example, favour streaming services due to their compatibility with mobile and personalised viewing habits (Lee et al., 2019).

TAM has been widely applied beyond internet technology adoption, including research on television technologies. Liu (2017) studied consumer intention to continue using web-based television (now known as streaming television), identifying key external variables such as interactivity, split-screen functionality, personalisation, and global community. These findings closely resemble Li's (2020) research on diffusion, which also highlights interactivity as a major attraction for OTT television users. Personalisation, a key differentiator of OTT platforms from traditional television, allows users to curate content, create personalised watchlists, and manage multiple user profiles within a single account. The accessibility and mobility of OTT platforms further influence adoption, making these a more attractive alternative to traditional television (Tengeh & Udaokpan, 2020). Lee et al. (2019) expanded TAM by identifying price value, quality, and entertainment as key factors influencing OTT adoption. Similarly, Camilleri and Falzon (2020) combined U&G and TAM to examine streaming media consumption among Southern European university students, finding that streaming technologies were both easy to use and ritualised. The participants indicated that mobile streaming was the most preferred choice due to its convenience and mobility. The study also found that emotional gratifications, such as relaxation and escapism, were central to streaming adoption.

The overlaps and synergies between U&G, DOI and TAM highlight the need for an integrated model that combines their strengths while addressing their limitations. This study presents the Media Acceptance Model (MAM) as such an integrated model. MAM builds on these theories by incorporating individual needs (from U&G), social diffusion processes (from DOI), and user perceptions of technology (from TAM). By integrating these elements, MAM offers a more robust and predictive framework for understanding media adoption in the digital age.

 

MEDIA ACCEPTANCE MODEL

The aim of MAM is to be both a predictive tool for media adoption as well as a tool for audience studies researchers to better understand audience choices and viewership behaviour. Inspiration for MAM was drawn from Vishvanath and Goldhaber (2003) who investigated the adoption of mobile phones in order to test their own theory combining the diffusion theory and the technology adoption theory. They conducted 611 telephonic interviews in Buffalo, NY to establish why respondents had not adopted (purchased) mobile phones. The responses were analysed using their adapted technology adoption model and the results indicated that respondents' attitudes had a significant effect on the adoption of mobile devices. Prior diffusion research does not include attitude in its analysis of media adoption. The advantages of combining the two theories therefore warrants its inclusion in future diffusion research. They created a new model, The Research Model, which is illustrated in Figure 3 below.

 

 

The Research Model indicates that the negative belief variables (perceived complexity, relative disadvantage, incompatibility, and lack of observability) strongly affect a user's attitude towards technology adoption. The researchers take "perceived usefulness" and "ease of use" from the TAM model and equate it with "relative advantage" and "complexity" from diffusion theory. The Research Model considers some of the subjective variables previously overlooked by TAM research, such as age, gender and occupation. These variables, along with media use (the extent to which media exposure affects one's decisions to adopt), media ownership (similar to compatibility in DOI) and change agent contact (similar to observability in DOI), form the external stimuli which affect users' cognitive responses to technology. The cognitive response is influenced by the external stimuli and takes into consideration the following negative belief variables: "relative disadvantages" of the technology (perceived usefulness), the "perceived complexity" of the technology, the "lack of observability", "perceived incompatibility", and "compatibility". These cognitive responses influence the affective response, which is the users' attitude toward the technology, which in turn affects the behavioural intent (BI). Figure 4 below illustrates the MAM.

 

 

MAM incorporates ideas from TAM, DOI, U&G and The Research Model to provide a more detailed explanation of media adoption and viewership behaviour. It posits that all behaviour is influenced by need, and that all decisions to use one media technology over another derives from having a basic need that needs to be fulfilled. Similar to U&G, it starts with the audience, referring to individuals who make decisions about which programmes to watch or which media technologies to adopt. The next stage involves external variables, as identified in TAM and DOI. While these models do not specify these variables, MAM refers to the key factors outlined in The Research Model (Vishvanath & Goldhaber, 2003), namely, age, income, occupation, education, and gender. These factors shape adoption behaviour by influencing affordability, awareness, needs, and preferences. For example, younger, well-educated individuals with higher incomes are generally more inclined to adopt new technologies, whereas older or lower-income groups may be more resistant due to financial constraints or a preference for familiar media. In South Africa, socio-economic inequalities and cultural norms further impact these adoption patterns, affecting both the speed and extent of media and technology uptake.

Once the audience member and their external variables are established, the model moves into the cognitive response phase. This phase begins with a fundamental need that the media technology or programme must fulfil. Here, U&G need paradigms come into play. A media user, shaped by personal background and external influences, has a core need that must be met by the new technology. These needs can be straightforward, fitting into just one U&G paradigm, or complex, combining multiple paradigms such as cognitive, affective, personal integrative, social integrative, or tension release. This step is critical because it is the initial need that drives the user to seek out new media or technology.

Once the need paradigm is identified, the user actively searches for new media technologies or programming to satisfy that need. This brings us to the next part of the cognitive phase: media sought. Media choice is not solely driven by need. It is also influenced by key factors from TAM and DOI. These include perceived usefulness, which determines whether the technology or programme aligns with the identified need; perceived ease of use, which relates to how simple or complex the platform or programme is; and compatibility, which assesses how well it fits into the user's existing habits and needs. Observability and trialability also play a role, as users may want to test the technology before committing. Additionally, change agent contact is important, as opinion leaders, influencers, family, and friends can all impact decision-making.

In this model, a basic need must be met and new media technology is sought, but before a choice is made, it must pass what I refer to as the compatibility checklist. If the new technology or programme successfully meets the need, is easy to use, trailable, or has strong reviews, the process moves into the affective response phase: attitude influenced. This stage determines whether the initial need was truly met. If the answer is yes, the user will continue engaging with the technology or media. If not, the model resets at the need paradigm stage, prompting a reassessment of what was lacking in fulfilling the user's need.

Here is an example scenario to better explain the practical application of MAM: when choosing a movie to watch for the evening, Sarah, a 30-year-old marketing professional in South Africa, goes through a complex decision-making process shaped by MAM. Her external variables (age, education, income, and cultural influences) affect her adoption behaviour, making her more inclined to explore different streaming platforms like Netflix, Showmax, or DStv Stream. As a middle-class individual with a stable income, she can afford luxury items such as multiple streaming subscriptions, a smart television and high-speed internet, which broaden her entertainment choices. Her decision starts with a core need, in this case, tension release, as she wants to unwind after work, although social integrative and affective needs may also play a role. She then actively seeks media that align with these needs, but her choice is further shaped by perceived usefulness (will the movie help her relax?), perceived ease of use (which platform is easiest to navigate?), and compatibility (does the movie match her mood and preferences?). Observability and trialability influence her as she checks reviews and social media trends, while recommendations from friends (change agent contact) further impact her decision. After narrowing her options to two films - one on Netflix and one on Showmax - she chooses Netflix due to its convenience, personalised recommendations, and wider selection of content. If the movie satisfies her need, she continues watching, reinforcing her engagement with the platform. If not, she restarts the process, reassessing her needs and exploring alternative options. This example illustrates how media adoption and viewership are driven by a combination of personal needs, external influences, platform usability, and social factors.

 

CONCLUSION

MAM marks an advance in understanding media technology adoption and audience behaviour. By integrating three well-established theories, namely the U&G theory, the DOI theory, and TAM, MAM offers a more comprehensive framework for analysing media consumption patterns.

This discussion explores how MAM addresses the gaps in previous models, its unique contributions, and its potential limitations. One of the key ways in which MAM improves upon earlier models is through its integration of multiple theoretical frameworks. U&G focuses on individual motivations for media use, but it lacks predictive power, and it does not fully consider technological or social influences on adoption. TAM, on the other hand, prioritises perceptions of usefulness and ease of use, yet it does not adequately address psychological or social factors. DOI explains the spread of innovations, but it does not account for individual needs and motivations. MAM bridges these gaps by combining elements from all three models, creating a more integrated approach that considers individual needs (from U&G), technological perceptions (from TAM), and the role of social diffusion processes (from DOI).

Another improvement MAM brings is its response to the static nature of U&G. Traditional U&G theory has been criticised for not adapting to the rapidly evolving digital media landscape. MAM modernises U&G by incorporating elements such as algorithm-driven media choices, personalisation and interactivity, all of which play a key role in contemporary media consumption. This dynamic approach makes MAM more applicable to digital environments, where users continuously navigate evolving media ecosystems. MAM also advances previous models by incorporating external variables that influence media adoption. While TAM and DOI often overlook factors such as socioeconomic status, cultural context and demographic differences, MAM explicitly includes these as external variables. By recognising the impact of age, income, education and gender on media adoption, MAM becomes more relevant to diverse societies, particularly in a context like that of South Africa, where access to media technology is shaped by deep socioeconomic disparities.

A key strength of MAM is its predictive power. Unlike U&G, which primarily describes media use, MAM enhances predictive capabilities by integrating TAM's focus on perceived usefulness and ease of use with DOI's emphasis on relative advantage, compatibility, and observability. By combining these elements, MAM provides a more accurate way to forecast media adoption trends, making it particularly valuable for researchers and industry professionals seeking to anticipate audience behaviour. MAM further acknowledges that media consumption is driven by multiple, overlapping needs - an area where traditional U&G models fall short. Whereas U&G tends to categorise needs into rigid groups (e.g., cognitive, affective, or social integrative), MAM recognises that users often engage with media for a variety of reasons. For example, a person might use a streaming platform to seek entertainment, stay informed and connect with peers, simultaneously. This dynamic need paradigm makes MAM more reflective of real-world media use. Additionally, MAM highlights the importance of trialability and observability - two key concepts from DOI that are particularly relevant in the digital era. By factoring in these aspects, MAM provides a more nuanced understanding of how and why people adopt new media technologies. What sets MAM apart is its comprehensive framework, which integrates individual motivations, technological perceptions, and social diffusion processes into a single model. This makes it applicable across various media technologies, from traditional television to digital streaming services and social media platforms. Unlike earlier models that often treated users as passive recipients of technology, MAM takes a user-centric approach, placing individual needs, attitudes, and behaviours at the heart of the adoption process.

Despite its strengths, MAM is not without limitations. One of the biggest challenges is its complexity. While integrating multiple theories makes it more comprehensive, it also makes it harder to apply in practice, especially for researchers unfamiliar with U&G, TAM and DOI. Another potential issue is the overlap between constructs. While MAM attempts to clarify concepts like perceived usefulness (from TAM) and relative advantage (from DOI), there is still a risk of redundancy or confusion in how these terms are operationalised. A further limitation is MAM's limited empirical validation. As a newly proposed model, it has yet to be tested across different contexts, particularly between developed and developing countries. Future research will need to assess its predictive power and applicability across various cultural and technological environments. Additionally, MAM may place too much emphasis on individual agency, despite its improvements over U&G's individualistic focus. While it does consider social influences, it may still underplay the role of structural factors such as corporate control of media platforms and algorithmic biases, which significantly shape media consumption. Finally, while MAM acknowledges cultural differences, its applicability across vastly different cultural contexts requires further adaptation and testing. Media adoption patterns vary significantly across regions, and certain external variables may carry more weight in some societies than in others.

In conclusion, MAM represents a step forward in media and communication research by addressing the limitations of U&G, TAM and DOI. Its integration of individual needs, technological perceptions, and social diffusion processes provides a more nuanced and predictive framework for understanding media adoption in the digital age. However, its complexity and lack of empirical validation highlight the need for further research to refine and test the model in different contexts. If developed further, MAM has the potential to become a foundational framework in audience studies and media technology research.

 

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Date submitted: 17 March 2025
Date accepted: 2 October 2025
Date published: 31 December 2025