Sustainability of Facebook-radio convergence for content distribution in Nigeria: Computational content and stakeholders’ perspectives analyses

Abstract

Media convergence is not a new concept in journalism studies, though available evidence indicates that convergence studies have been explored more in the global north than the global south. This study, contextualised in Nigeria, joins the media convergence conversation by exploring the sustainability of Facebook-radio convergence for distributing broadcast programmes by seventeen (17) licenced radio stations in Oyo State, Nigeria. As a computational content analysis study, researchers analysed 85 purposively selected programmes of the stations as broadcast live on their Facebook pages alongside the 9527 likes, 10,314 shares, 7007 comments and 170,681 views the programmes generated. Stakeholders’ interviews were also conducted for a broadcasting expert, presenters of some of the stations, together with audience of the selected stations. The main finding shows that programmes that focussed more on socioeconomic problems and opportunities, and were broadcast in the afternoon, evening and at night received more digital engagement than other programmes’ formats and time belts. Although high cost of Internet data subscription in the country, absence of Internet-enabled mobile phones among many adherents of radio programmes (both in rural and urban areas), epileptic power supply that sometimes leave many people with unpowered mobile phones as well as weak Internet broadband connectivity common to many locations in Nigeria threaten the sustainability of Facebook-radio broadcasting in Oyo State. Deployment of 5G network, installation of more network masts with strong bandwidth and training of radio presenters and radio stations' social media handlers on innovative and audience-participatory programme production are recommended.

Keywords

Digitalisation and technology Facebook-radio convergence media convergence media sustainability oyo state radio stations

Introduction

The dynamic nature of media consumers’ needs alongside the growth of digital technology in non-Western nations have rendered the long-held popular statement that ‘radio is a blind medium’ outdated in the 21st century. In those days of radio broadcasting, the technology of the era made it impossible for radio listeners to neither see the programmers/presenters nor watch them live simultaneously as they presented their live programmes. One vital reason it will not be totally correct to describe radio as a blind medium in this age is that its listeners can both see presenters and watch them present their live programmes on Facebook without necessarily tuning in their radio sets or pick frequencies via their mobile phones. Without technological development otherwise known as technological media convergence (a form of media convergence), this Facebook-radio live streaming would not have become a reality. In fact, with Facebook-radio streaming, listeners do not have to wait for a call on programmes before connecting and engaging with presenters. In as much as the audience are connected to the Internet, their participation in and engagement with presenters’ programmes are accomplished (Bonini, 2014). However, in Nigeria, where people with good quality Internet access are few (41.6%) [Ijaseun, 2022], this may be difficult or impossible to experience. The high cost of Internet subscriptions in a third-world country is also a key element that could affect people’s quality and effective participation in Facebook-radio programmes.

Media convergence describes a merger of different mass media (print, broadcast and online) ‘to the point where they become one medium due to the advent of new communication technologies’ (Chakaveh and Bogen, 2007). Chakaveh and Bogen’s assertion means that media convergence gives a mass medium the opportunity to function as a radio station, a TV channel and a newspaper in terms of content production, distribution or dissemination. That is why digital technology has now made it possible for a print medium to go beyond daily publishing of news stories, editorials and feature stories to creation of a mini digital TV station/studio where short videos of newsworthy events are uploaded, and live programmes of local and national interest are aired. Digital technology, through convergence, has also made it possible for a radio station to live stream its programmes in audio-visual format (as if it were a TV station).

All these have begun to challenge the traditional approach to media content creation and distribution. To actualise the shift to media convergence, especially in transmitting audio-visual contents across to media audiences, many media organisations rely heavily on their Facebook pages (Nwammuo and Nwafor, 2019). From observations, this is the most common approach of media convergence in Nigeria where this study is situated. With Facebook (technology) media convergence, engaging media audiences is facilitated (Drula, 2015). The User-Generated Content (UCG) feature of Facebook and other new media platforms makes this presenters-audience interaction and engagement more robust. That is, media consumers who are also citizen-consumers (Drula, 2015) are able to give comments on the media contents generated, like, share, and watch live or recorded media contents on their (media) Facebook pages. Therefore, these variants of citizen-media engagement are strategies the mass media can use to measure audience feedback in the era of technological media convergence.

Facebook-radio convergence cannot be deeply understood if the theory of technological determinism is not aptly alluded to. This is because technology drives convergence of radio on Facebook. Specifically, McLuhan who was traced as the originator of technological determinism views the society as an entity influenced by its nature of technological innovations (Tiley, 2011). That is, technology enhances globalisation (Hauer, 2017). When the question of whether people are dependent on technology arose, McLuhan argued that people can manipulate technologies based on their societal needs. Hauer (2017) refers to such situational needs as social determinism. An important point McLuhan (undated) and Antecol (1997) argued is that despite that technology determines a lot of human activities, man had conquered it so much that they determine what to use it for and how to use it; thus, turning the world into a global village through it. Does this mean that media convergence through Facebook-radio (a form of technology) in Nigeria would influence radio listeners’ dependence on Facebook-linked radio broadcasting, and view such milestones as a development in the field of broadcast journalism? This is one of the main questions the study answers.

From the foregoing, three main factors are observed to be responsible for driving media convergence. As highlighted by Dave (2011), these include the ever-changing preferences and lifestyle of media consumers, the fragmentation of the media market as well as penetration of technology into man’s day-to-day lifestyles. Specifically, radio stations in Nigeria have started integrating convergence into their broadcast strategy. Kuyucu (2019) describes this strategy as digital convergence of radio. That is, as radio stations broadcast audio contents through their frequency or amplified modulation, the audio-visual formats of their broadcasts are simultaneously transmitted on their digital platforms on social media. With this technology, Kuyucu (2019) argues that such radio stations will be compelled to broadcast in new formats, and contextualise their interactions with listeners within the framework of audio-visual broadcasting. In the long run, such convergence will economically reposition the radio stations while the future of FM (Frequency Modulation) band is threatened.

Strategically, when the mass media situate socioeconomic and political programmes in their programme philosophy, they are not only serving as society’s watchdog, but are also setting an agenda for the public sphere. According to Schramm (2014), the mass media, through its political programmes, influences governance, as followers will be encouraged to demand leadership accountability and public trust from their elected representatives. Also, through well-planned and packaged economic programmes, the mass media becomes a respected voice that provides people, including political leaders, with vital information that helps grow the economy, maintain economic prosperity and sail prosperous economic policies. Mass media programmes, especially those that dwell on societal issues, economy and politics, provide people with necessary information that influences their respective opinions (Schramm, 2014).

Mwangi, Biwott, David and Gichuhi’s (2018) study is a typical reflection of Schramm’s argument. According to these authors, the programme contents of vernacular radio stations in Kenya influenced Kenyans’ participation in governance issues because the contents addressed their needs, being community radio stations. Essentially, the stations’ programmes were presented in an interactive technique that engaged the audience on important issues to them such as ‘agriculture, health, roads, education, employment, effectiveness and efficiency of service delivery’ (Mwangi et al., 2018:79) as well as financial issues related to women and youth. Within this context, the current study investigates how licensed radio stations in Oyo State, Nigeria, used Facebook and radio to distribute broadcast contents to their different audiences.

The study specifically joins ongoing conversation by investigating the extent to which stakeholders perceived Facebook-radio integration as new development that enhances rapid dissemination of radio broadcast content in Nigeria. This is imperative as the country’s National Broadcasting Commission (NBC), the body that licenses broadcast organisations, gives approval that limits geographical locations both radio and television stations can reach (Akingbulu, 2021). Apart from the licence-driven geographical coverage of the stations, no researcher (either from Nigeria or beyond) had interrogated the use of Facebook-radio convergence for content distribution from computational content analysis methodological viewpoint. The researchers were also motivated to conduct this study because the use of Facebook to distribute broadcast content by Nigerian radio stations is a current trend that needs to be interrogated, especially interrogating its sustainability in a nation with technological or digital divides. As part of its contribution to scholarship, this study begins the conversation on how Nigerian radio stations can leverage the technological benefits of Facebook-radio convergence to disseminate their broadcast contents, drawing from global conversations on media convergence.

Media convergence and technological determinism

Viewing media convergence from the technological viewpoint, Holliman (2010) describes the concept as integration and interconnectivity of computing networks as well as information and communication technologies that give digitalised information to audiences through an application or a device. The audience then have access to a series of multimodal information (texts, pictures, videos, audios, hyperlinked texts, etc.) which they can customise based on their choices, using their mobile phones. This multiple information the audience can access on a single platform is described by Kopecka-Piech (2011) as hypermedia or multimedia nature of media convergence. To explain media convergence, we need to consider some basic related concepts and the assumption of media convergence theory. As such, Latzer (2013) argues that the digital creative economy can only be understood through the lens of media convergence. Although there are economic and cultural convergence, this study focuses more on the third form of convergence – technological media convergence. According to Kopecka-Piech (2011), technological media convergence occurs when a mass medium (radio in this context) digitalises its broadcasting through different digital platforms. That is, communication technologies (digital platforms like Facebook) converge with computer/mobile networks (Internet) to produce digitalised media content (Sharma, 2021). Thus, media convergence theory states that new technologies integrate different mediums together as one in a way that the media environment is redefined (from its traditional practice) (Chakaveh and Bogen, 2007). Explaining the practicality of the theory, the two researchers submit that:

very soon, there will be no more need for having a television and a computer separate from each other, since both would be able to do the job of the other, ultimately making both extinct and creating a new medium from the synthesis (p. 1).

The second important theory for this work is Technological Determinism. It is a phrase traced to an American sociologist and economist, Thorstein Veblen, who lived from 1857 to 1929. According to him, the theory assumes that the technological advancement in a society defines the nature and culture of that society. That is, technology ‘determines, shapes and influences how people in a society think, believe and behave... Sees the media as the main force behind social and cultural changes in society’ (Emwinromwankhoe, 2020:39). The two main hypotheses of this theory as argued by Langdon Winner are: ‘the technology of a given society is a fundamental influencer of the various ways in which a society exists; changes in technology are the primary and most important source that leads to change in the society’ (Hessi, 2015; Winner, 1977:65).

Facebook-radio convergence: An empirical analysis

Facebook-radio convergence is not a new concept among media scholars and researchers of communication studies in global north and south. However, the concept has received greater scholarly attention in many Western countries than in Africa. For instance, when Freeman et al. (2012) investigated the relationship between Facebook and radio in the United States, Germany and Singapore, they found out that the radio stations had no specific strategy for posting information on their Facebook pages. This could be related to the fact that the stations lacked workers who were familiar with the technicalities of using Facebook’s features that enable radio components to integrate with the features. According to the researchers, the content being posted by the radio stations they studied were only ‘on the basis of trial and error.’ However, the content that got the highest likes and comments (two examples of citizens’ engagement with digital media content) across the 36 radio stations they studied focused on programmes featuring artistes. Then, some radio stations explored the features of Facebook to engage the audience while some did not.

Al-Rawi (2016) investigates the online comments of news items posted on the Facebook pages of Radio Monte Carlo (France24) and Radio Netherlands Worldwide (RNW), two prominent Arabic-language radio channels in the Arab world. After examining over 184,000 comments primarily to understand how radio audiences of the two stations interacted with the stations’ programmes on Facebook through their likes, he noted that programmes that engaged online audiences on broad issues (discussion programmes), and interacted with them using clever quotes, received more likes compared to news programmes that received less engagement. The audiences of the two radio stations were also more interested in active engagement when radio programmes involved contests where they could receive gratification such as winning prizes. One can, therefore, argue that in the current study, programme formats that focus on social interactions would attract more engagement of audiences on Facebook-Radios. Hypothetically, the study asks that:

H₁: Facebook-radio content that focus on socioeconomic issues and challenges receive more likes, shares and comments.

A recent work by Laor (2019) where he studied 145 Israeli popular radio programmes shows that radio stations are becoming more active in the digital space, and are adapting to the structure of the digital space. His findings reveal that when multimedia (video or image) is integrated into a radio programme as broadcast on the Facebook page of each radio station, the content generation becomes more popular or goes more viral, while programmes broadcast only in audio format become less popular. Also, programmes with integrated videos or images of prominent individuals or celebrities (news makers) got more audience engagement, thereby leading to popularity of such programmes. Their finding equally indicates that radio programmes that appealed to the emotions of Facebook audiences led to more engagement, high volume of likes as well as the spread of the programmes (shares). To join these conversations, hypothetically, the researchers ask:

H₁: In which of the time belts does liking the stations’ content translate to viral audience engagement?

Laor took a step further in 2020 when he studied the media convergence of 21 public and commercial radio stations in Israel, also paying attention to Facebook convergence. But instead of conducting a quantitative study, the researcher interviewed 65 stakeholders comprising station managers, broadcasters as well as those who did listen to/watch the stations. The interviewees (the audience – viewers – and the broadcasters) stressed the importance of convergence, as more dyadic interactions were facilitated between broadcasters and consumers of broadcast content. Then, viewers felt a sense of connection and authenticity with broadcasters and in their broadcasts when they watch the broadcasters present unlike the old maxim of radio being a blind medium. However, seeing the broadcaster on visual radio (for the first time) might initially lead to dissonance in the audiences’ imagination of the voice (of the broadcaster) they always listen to before the radio station integrates visual radio into its broadcast strategies. To the station managers Laor (2020) interviewed, visual radio which integrates multimedia content with the Internet had come to stay in radio broadcasting. This is because it gives more participatory approach to content distribution and consumption, though some broadcasters did not align with visual radio broadcasting, especially when it does not serve their interest or it goes against their traditional practice. Some similar experiences are earlier shared by Sinton (2018) and later by McMahon (2021b) that visual radio broadcasting on the Internet, using social media applications like Facebook and Twitter had created connectivity, community togetherness, right of engagement and emotional relationship between public radio broadcasters and audiences in New Zealand.

Finally, McMahon (2021a) explores the relationship between radio and Facebook in Ireland from 2011 to 2016 and how radio production practices, audience participation and radio as a medium evolved within these years. Like other previous researchers had found out, McMahon submits that Facebook had become an audio-visual tool Irish radio stations now use to reach new young audiences who are digital natives, for they are growing up in the era of digital explosion. The researcher argues that integrating Facebook technology with media content enables radio stations in Ireland to accumulate socio-cultural and economic capital within their broadcast market sphere. With the evidence, it is pertinent to ask hypothetically:

H₁: Radio presenters prefer to live stream their programmes on Facebook because it offers them more connection with and popularity among radio listeners.

Data, measures and methods

Commercial and campus-based radio stations in Oyo State were the data sources and population. Official Facebook pages, programmes and presenters of the stations were also used as data sources and population. The study also gathered data through an expert interview and in-depth interview with selected audience of the stations. The interviews were conducted using semi-structured written interview guide. The expert is an academic scholar in broadcast media and programme content development at a Public University in the state capital, Ibadan. Selected audiences who reside in Ibadan and other main cities in the state were adherent listeners of the stations. The interview guide was sent to the scholar through email and returned to the researchers through the same approach. This method was adopted because of the busy schedule of the scholar which prevented the use of face-to-face approach. Selected audiences were interviewed using WhatsApp as a platform through which the prepared questions on the guide were posed to the audiences. Each listener was interviewed individually through the platform, having consented to it. WhatsApp was used because of its convenience and as a result of COVID-19 pandemic that restricted people’s movement. Questions that were posed specifically interrogated specific knowledge about media convergence, familiarity with the use of Facebook by the stations and understanding of how social media enhance radio content distribution and bottom-line of the stations.

For a station to be included, three parameters were employed. One, the station must have evidence of convergence on its Facebook page within the last 1 year (2020–2021). The evidence was confirmed by one of the researchers who monitored the Facebook pages for 2 weeks. Two, the station must have evidence of ‘live’ broadcast mode on its Facebook page. This was also confirmed by one of the researchers through monitoring for 2 weeks. Three, it was also necessary that a station had minimum of five live broadcasts within the study period (5 August 2021). Three factors were also considered for excluding some stations from the population: stations with only video uploads, but with no evidence of live broadcasts on Facebook; stations with less than five live broadcasts within the last 1 year and stations with only newspaper-like publishing, but with no live broadcasts. These parameters were conducted on 37 licensed radio stations in the state before selecting those presented in Table 1 that eventually formed channels through which the required data were collected.

Table 1.

Select radio stations based on the inclusion criteria.

S/N	Station	Frequency	Location	Ownership type
1	ACU FM	88.5	Oyo	Private university
2	Agidigbo FM	88.7	Ibadan	Private
3	Space FM	90.1	Ibadan	Private
4	Impact business radio	92.5	Ibadan	Private
5	Royal root FM	92.9	Ibadan	Private
6	J FM	95.5	Saki	Private
7	Iseyin FM	98.5	Ibadan	Private
8	32FM	94.9	Ibadan	Private
9	Oluyole FM	98.5	Ibadan	State government
10	Amuludun FM	99.1	Ibadan	Federal government
11	Jamz FM	100.1	Ibadan	Private
12	Inspiration FM	100.5	Ibadan	Private
13	Parrot FM	101.1	Ogbomosho	Private
14	Splash FM	105.5	Ibadan	Private
15	Fresh FM	105.9	Ibadan	Private
16	Lagelu FM	96.7	Ibadan	Private
17	Diamond FM	101.1	Ibadan	Federal university

Source: Researchers’ Compilation, 2021

Social, economic and political parameters were used for the selection of programmes of the stations. For a programme to be considered within the social parameter, it must focus on social activities of the people such as love, relationship, conflict within the family and people outside the family structure. Programmes were selected within the economic indicator when it was obvious that such programmes were presented with the intent of informing and educating the audience on economic opportunities and challenges in the state and the country in general. This is not quite different for political-related programmes. They are the programmes that inform and educate the audience about politics, actors and followers’ interactivity within local and international politics. Across the stations, five programmes each that aligned with these parameters were chosen. This resulted in having 85 programmes for analysis with the specific adoption of computational content analysis as the first research method, which was driven by quantitative research approach, while in-depth interview was chosen as the second research method with the consideration of qualitative research approach practices and principles. One of the researchers extracted the programmes’ characteristics from the stations’ Facebook pages into Microsoft Excel sheet. The sheet was developed using station’s name, time belts, programme name, programme synopsis, likes, shares, comments and views as metrics. These metrics were placed across columns on the sheet and the data collector proceeded to the Facebook page of each station for extraction of the needed data according to the arrangement of the metrics. One of the researchers later re-categorised programme synopsis to main programme types and sub-programme types. This was done with the intent of facilitating easy coding and analysis of the data in line with the propositions of the quantitative hypotheses. The re-categorisation led to generation of social, political, socioeconomic and political as the main programme types while newspaper review, issues-focused, sports, entertainment, news, religious, health, people-focused, agricultural, news commentary, educational and road safety constituted sub-programme types. All the categories were validated by the researchers using face validity after watching the programmes independently. In all, 85 programmes were analysed. Nine thousand five hundred and twenty-seven (9527) likes were found from the pages of the stations during the study period, while 10,314 shares, 7007 comments and 170,681 views were discovered from the pages. In the course of coding main and sub-programme types, and time belts, binary option was adopted. The use of binary option was premised on the need to determine the presence or absence of the categories in the programme contents of the stations. When a category is discerned from a programme, Yes was ascribed to it, representing 2, while No was used when the category was not found and one was used to represent it. For analysis, the data were processed using categorical method in the SPSS software. Suffice to note that quantitative data analysis aspect of the study was carried out using two stages. In the first stage, the data were analysed using descriptive approach. This approach generated simple descriptions of what the gathered data represented. In this regard, independent t-test was adopted as a method of analysing the data with the intention of determining dominant socioeconomic and political contents as well as content types along with the identified Facebook’s features. At the second stage of analysis, one-way ANOVA, exploratory descriptive statistics and linear regression were employed as methods. One-way ANOVA was used to pinpoint difference that existed when content categories were analysed along with the engagement features. Exploratory descriptive statistics was employed with the aim of revealing which of the time belts did audience engage with the content categories mostly. Linear regression was used for testing hypotheses of the study. Thematic-diagrammatic approach was used for analysis of the responses of the stakeholders. From the responses, initial and final codes were first generated before forming categories and themes which were represented diagrammatically and supported by textual explication.

Results

In this section, the outcomes of the analyses carried out on the collected data are presented and discussed in line with the earlier propositions and assumptions explained within the theoretical, conceptual and empirical contexts. The section is basically divided into three, using formulated hypotheses as the basis for segmentation.

Socioeconomic issues and challenges-related programmes attract more engagement

It is a common knowledge that media explore and cultivate realities in the society in their quest to provide varied content for the public consumption. Since the realities are the outcomes of the socioeconomic and political inputs contributed by people and organisations, this section explicates nature and characteristics of the programmes being streamed through Facebook by the select radio stations.

Data in Table 2 indicate that the programmes of the stations are more focused on social, socioeconomic and political content. Fifty-four programmes were found as social-oriented while 30 programmes were produced and streamed to inform the audience about socioeconomic and political issues or challenges including opportunities. One programme was found to be politically-inclined. Examination of the programmes across all the engagement using average analysis of features of Facebook shows that social-oriented programmes performed better in three of the features while political-oriented programmes were good in two of the features. In terms of views, the only one political programme was more viewed (M = 6300.00, SD = 0) than social programmes (M = 2235.33, SD = 6623.475). However, when one looks at the zero standard deviation score, it is clear that viewing social programmes are more severe than viewing the political programme. For the share and comment features, analysis also establishes that social programmes also performed better than other categories of the programmes. Similar to the nature of the outcome that emerged for political programme within the view feature, analysis also reveals that political programme is better than social programmes in terms of like, but being liked did not translate to high severity when one considers its zero standard deviation score. Based on these results, individual social programmes are expected to be better by engagement features than political-oriented programmes. This is explored further through Tables 3 –6

Table 2.

Descriptive performance of Facebook select features by content focus.

Engagement feature	Content focus	N	Mean	Std. Deviation
Likes	Social	54	132.17	330.979
	Political	1	233.00	-
	Socioeconomic and political	30	71.90	135.666
Comments	Social	54	110.07	361.954
	Political	1	68.00	-
	Socioeconomic and political	30	33.17	94.477
Shares	Social	54	173.31	512.193
	Political	1	76.00	-
	Socioeconomic and political	30	29.30	-
Views	Social	54	2235.33	6623.475
	Political	1	6300.00	-
	Socioeconomic and political	30	1455.77	3059.583

Source: Facebook Pages of Select Broadcast Stations (2021); Researchers’ Analysis, 2021

Table 3.

Descriptive performance of like by content category.

Content category	N	Mean	Std. Deviation
Newspaper review	18	122.33	166.309
Issues-focused	20	196.75	503.582
Sports	18	122.11	205.708
Entertainment	15	60.27	88.883
News	2	23.00	5.657
Religious	5	26.60	13.315
Health	1	8.00
People-focused	2	19.50	14.849
Agricultural	1	11.00
News commentary	1	9.00
Educational	1	21.00
Road safety	1	21.00

Source: Facebook Pages of Select Broadcast Stations (2021); Researchers’ Analysis, 2021

Table 4.

Descriptive performance of comment by content category

Content Category	N	Mean	Std. Deviation
Newspaper Review	18	55.17	119.052
Issues-Focused	20	195.45	584.414
Sports	18	61.22	83.323
Entertainment	15	58.00	87.046
News	2	10.00	9.899
Religious	5	14.60	19.578
Health	1	.00
People-Focused	2	6.00	7.071
Agricultural	1	13.00
News Commentary	1	1.00
Educational	1	7.00
Road Safety	1	7.00

Source: Facebook Pages of Select Broadcast Stations, 2021; Researchers’ Analysis, 2021

Table 5.

Descriptive performance of share by content category.

Content category	N	Mean	Std. Deviation
Newspaper review	18	44.94	71.673
Issues-focused	20	240.30	688.556
Sports	18	224.56	509.741
Entertainment	15	27.33	37.376
News	2	5.50	2.121
Religious	5	23.60	35.111
Health	1	1.00
People-focused	2	44.00	15.556
Agricultural	1	1.00
News commentary	1	26.00
Educational	1	1.00
Road safety	1	1.00

Source: Facebook Pages of Select Broadcast Stations (2021); Researchers’ Analysis, 2021

Table 6.

Descriptive performance of view by content category.

Content category	N	Mean	Std. Deviation
Newspaper review	18	2576.39	3860.026
Issues-focused	20	3586.55	10,281.422
Sports	18	1456.33	2531.490
Entertainment	15	1349.47	3273.408
News	2	643.50	27.577
Religious	5	714.60	854.180
Health	1	42.00
People-focused	2	372.50	333.047
Agricultural	1	79.00
News commentary	1	70.00
Educational	1	135.00
Road safety	1	188.00

Source: Facebook Pages of Select Broadcast Stations (2021); Researchers’ Analysis, 2021

Like engagement feature is the focus of the data in Table 3 and it clearly reveals that issuesfocused programmes (M=196.75, SD=503.582) received more engagement than other programmes. It is not surprising that newspaper review (M=122.33, SD=166.309) and sports programmes (M=122.11, SD=205.708) nearly closed in terms of like because most newspaper review and sports programmes have similar elements. For instance, in the course of reviewing newspapers, the stations do have some sports news stories in addition to other stories that focused on social activities in relation to economic and political stories. Liking entertainment programmes (M=60.27, SD=88.883) more than people-focused programmes (M=19.50, SD=14.849) resonates with the fact that audience derive similar benefits associated with viewing issued-focused, newspaper review, sports and people-focused programmes.

From Table 4, issues-focused (M = 195.45, SD = 584.414), sports (M = 61.22, SD = 83.323) and newspaper review (M = 55.17, SD = 119.052) programmes are more commented on than other programmes. These programmes fall within socioeconomic and political issues, challenges and opportunities-related contents. While the average rate of commenting on entertainment programmes (M = 58.00) is better than what was found for newspaper review (M = 55.17), analysis indicates that considering the standard deviation score (SD = 119.052) of the newspaper review programmes, the programmes are better than entertainment programmes. On the other hand, entertainment programmes (SD = 87.046) are better than sports programmes (SD = 83.323).

In terms of share engagement feature, Table 5 reveals that issues-focused (M = 240.30, SD = 688.556) and sports (M = 224.56, SD = 509.741) are more shared than newspaper review (M = 44.94, SD = 71.673) and entertainment (M = 27.33, SD = 37.376) programmes. These results also imply that audience have interest in programmes that discuss socioeconomic problems and opportunities more than those that focused on educating and informing them about general happenings packaged by the stations into news, health, agricultural and educational programmes. Sharing issues-focused and sports programmes signify the audience interest in making the programmes go viral more than other programmes of the stations.

Table 6 indicates that issues-focused (M = 3586.55, SD = 10,281.422), newspaper review (M = 2576.39, SD = 3860.026), sports (M = 1456.33, SD = 2531.490) and entertainment (M = 1349.47, SD = 3273.408) are more viewed by the audience than other programmes. Despite their significant view of sports programmes (M = 1456.33), its severity in terms of the standard deviation score (SD = 2531.490) comparable to the score of entertainment programmes (SD = 3273.408) is low, indicating the superiority of the audience interest in entertainment programmes. One of the surprising results in the Table is average sharing of religious programmes (M = 714.60) that is more than what was discovered for news programmes (M = 643.60). It is even more surprising when one examines the standard deviation score, which shows a wide distance between sharing religious programmes more than news programmes. This suggests audience’s significant interest in letting others be aware of the religious-related information more than general news.

Data in Table 7 summarises earlier results with the examination of how different are the engagement features with the programmes. From the data, there is no statistically significant difference in the mean volume of all the indicators (likes, comments, shares and views) of the contents streamed by the select radio stations. However, in terms of F score, views, likes and comments seems to be better than share engagement feature. With the high scores of within groups mean square for views, comments and likes, analysis suggests that these features and content categories differed individually, but not significant as expected. Overall, this section has established individual and collective performance of engagement features of Facebook in relation to the varied content categories of the station. It is obvious that social and economic-related contents have better chance of doing well within the context of the features. It has also become apparent that the level at which all the categories received each of the features is insignificant. Therefore, it is imperative to seek more clarification on the appropriateness of the programme types in the context of time belts which the programmes were aired and live-streamed by the stations. Before finding out this, data in Table 8 reveal connection between the number of likes received by all the programmes and individual engagement indicators (views, comments and shares). The Table also contains the level of variation of likes in each indicator.

Table 7.

One-way ANOVA of content categories versus engagement features.

Indicator		Sum of squares	Df	Mean square	F	Sig
Likes	Between groups	305,166.262	11	27,742.387	.331	.976
	Within groups	6,119,436.161	73	83,827.893
	Total	6,424,602.424	84
Comments	Between groups	360,703.133	11	32,791.194	.344	.972
	Within groups	6,955,997.761	73	95,287.641
	Total	7,316,700.894	84
Shares	Between groups	865,986.484	11	78,726.044	.425	.940
	Within groups	13,537,344.622	73	185,443.077
	Total	14,403,331.106	84
Views	Between groups	103,235,059.827	11	9,385,005.439	.271	.989
	Within groups	2,523,728,317.161	73	34,571,620.783
	Total	2,626,963,376.988	84

Source: Facebook Pages of Select Broadcast Stations (2021); Researchers’ Analysis, 2021

Table 8.

Correlation and variation of likes in the number of views, comments and shares for all the content categories.

Model	R	R square	Adjusted R square	Std. Error of the estimate	Change statistics
Model	R	R square	Adjusted R square	Std. Error of the estimate	R square change	F change	df1	df2	Sig. F change
Views	.943	.890	.888	1867.769	.890	670.022	1	83	.000
Comments	.948	.900	.898	94.100	.900	743.305	1	83	.000
Shares	.881	.776	.773	197.147	.776	287.582	1	83	.000

Source: Facebook Pages of Select Broadcast Stations (2021); Researchers’ Analysis, 2021

From the data in Table 8, it is established that significantly, likes induced comments (r² = 0.900, Sig F Change=0.000) more than views (r² = 0.890, Sig F Change=0.000) and shares (r² = 0.776, Sig F Change=0.000). From this data, the F change of the extent to which likes induced comments is higher than how it induced views and shares. This constitutes the most interesting part of the results, which suggests that likes are better in encouraging audience to comment and view than share.

Time belts, likeness and viral audience engagement

As pointed out earlier, this section focuses on seeing usefulness of Facebook-radio convergence in the context of viral audience engagement. This is important since the use of the strategy is predicated on making audience, most importantly digital native listeners, more connected with radio programmes.

As shown in Table 9, audience of the radio stations only engage with the programmes aired in the night and evening when 25% of the entire viral audience engagement is considered within the weighted average parameter. This signifies that at the first quarter of measuring the engagement, audience could only be said to have interest in programmes streamed by the stations in the night and evening. Looking at the half of the total viral audience engagement, the interest in viewing or following night and evening scheduled programmes increased. At the second quarter of the measurement, evening programmes overtook night programmes. Surprisingly, both the evening and night programmes were not viewed or followed mostly when third quarter was considered. Morning programmes were mostly engaged with by the audience. An implication of this is the possibility that audience viewed and followed newspaper review, some socioeconomic and political issues and sports programmes, which are mainly aired in the morning. Using Tukey’s Hinges, further analysis shows that audience engagement with the stations’ programmes in the night and evening increased marginally, while engagement with the afternoon programmes attained radical increase within the first quarter and at the middle of the viral engagement. From the Tukey’s Hinges, it is established that noon and evening programmes were better than the programmes scheduled and streamed in the afternoon and morning. Specifically, all the results indicate that audience engaged with the stations’ programmes during night, evening and noon than in the afternoon and morning. In order to have specific two-time belts, analysis of the data in Table 9 was carried out and the outcomes in Table 10 reveal the dominant time belts that audience engaged with the stations’ programmes most during the study period.

Table 9.

Viral audience engagement by time belt.

	Time belt	Percentiles
Weighted Average		5	10	25	50	75	90	95
	Morning	12.60	34.30	108.25	329.00	1255.00	7785.30	11,270.75
	Afternoon	17.00	37.80	102.00	286.00	1161.00	4383.60
	Night	93.00	118.00	193.00	300.00	1410.75	32,583.50
	Evening	84.00	85.30	112.75	775.00	1704.25	6016.90
	Noon	43.00	43.00	43.00	3649.00
Tukey’s Hinges
	Morning			110.00	329.00	1160.00
	Afternoon			123.00	286.00	1160.00
	Night			196.00	300.00	1104.00
	Evening			118.00	775.00	1536.00
	Noon			43.00	3649.00	7255.00

Source: Facebook Pages of Select Broadcast Stations (2021); Researchers’ Analysis, 2021

Key: Sum of Viral Audience Engagement=188,002, Minimum Viral Audience Engagement=8, Maximum Viral Audience Engagement=51,700.

Table 10.

M-Estimation of all programme categories by time belts.

Time belt	Huber’s M-Estimator^a	Tukey’s biweight^b	Hampel’s M-Estimator^c	Andrews' wave^d
Morning	2.12	2.05	2.19	2.04
Afternoon	3.06	2.78	3.06	2.76
Night	3.70	3.18	3.71	3.17
Evening	3.84	3.60	3.68	3.60

Source: Facebook Pages of Select Broadcast Stations (2021); Researchers’ Analysis, 2021

Key: a. The weighting constant is 1.339. b. The weighting constant is 4.685. c. The weighting constants are 1.700, 3.400, and 8.500. d. The weighting constant is 1.340*pi.

From the data in Table 10, it is apparent that the evening and night are the most appropriate times for the audience to engage with the stations’ programmes. This could be deduced from the four M-estimators presented on the Table. Out of these estimators, Huber and Hampel are more robust in understanding the maximum likelihood of the audience viral engagement with the stations’ programmes in the evening and the night, respectively. In the subsequent section, the outcomes of the stakeholders’ views that were sought are presented and discussed diagrammatically with a view to revealing convergent and divergent points in relation to the insights.

Discussion

From the audience to presenters of some of the selected radio stations, four latent and observed constructs emerged (Figure 1). Analysis of the responses reveals internal and external forces that drive impeding and facilitating conditions for Facebook-radio convergence. It also establishes usefulness and negativity as observed constructs, and how they resonate with the categories of beneficiaries of the alliance and, internal and external-inclined negativities latent constructs, which have the possibility of denying the beneficiaries sustainable benefits expected from the convergence. High cost of data subscription is the greatest concern of the audience. Though, the audience identify poor Internet broadband connectivity, it is more of concern to the presenters than the audience. This pattern is also discerned when the presenters believe that non-availability of data affects successful convergence, whereas the audience’s concern is the cost of the data. This is similar to what the data reveal in terms of good electricity. The audience consider poor electricity as a big concern that affects being hooked up for programmes on Facebook, also a potential factor for denying followers of the stations to listen or watch various programmes. Apart from this, the lack of Internet-enabled mobile phones, which is described by the majority of the audience as digital inequality, could limit the extent to which the convergence would be effective. These findings are in consonance with the earlier position that technical hitches and financial materials could be the stumbling blocks for audiences’ effective convergence of Facebook and radio within the context of broadcast media content distribution in Nigeria.

Figure 1.

Emerged hypothetical model. Source: Researchers’ formulation, 2021

Both audience and presenters agreed that Facebook-radio convergence is a great addition to conventional radio broadcasting in the areas of listenership base increase, turning Facebook to visual platform for audience, where they can see the presenters and participants which was not possible before. This finding aligns with the proposition of media convergence theory that new technologies integrate different mediums together as one in a way that the media environment is redefined (Chakaveh and Bogen, 2007). As this was described as a good development in radio broadcasting, a broadcasting academic at the University of Ibadan argued that ‘with Twitter, Facebook, Instagram and other social media, the local radio stations have their reach and responses from people in Nigeria and Diaspora more’ unlike before when ‘they were restricted by technology’. However, presenters were of the view that it defeats the principle of anonymity associated with radio broadcasting.

To the audience and the presenters, beyond the fact that the stations derive benefits from the convergence, it has also become an opportunity for the presenters to get more followers on personal accounts on the social networking site. Hence, the need to improve their presentation skills and styles very well before broadcasting time. This is mostly linked to the fundamentals of creating valuable programmes that address the needs and demographics of the audience as one of the factors that would enhance sustainability of the convergence. Theoretically, these findings are more appreciated from the propositions of technological determinism which stress that technology determines a significant part of human activities and helps in conquering territories towards rapid changes in a society (McLuhan, undated; Winner, 1977; Antecol, 1997). Having valuable programmes is not sufficient, consistency in broadcasting with constant innovation from the presenters and social media handlers of the stations remain imperative. The data suggest that if the presenters and other employees in the stations are up to the task, and the stations lack inclusive technical know-how such as good transmitters and studio environment, the convergence would not be sustainable. This insight is corroborated by the expert interviewee when he submitted that radio stations can sustain their convergence strategies that attract more audiences if they produce quality content, good programmes and employ good and creative presenters who are not averse to training and retraining. In other words, audience would be more interested in exploring live stream programmes when they have the right conditions and most likely to derive substantial benefits from media content (Al-Rawi, 2016). Specifically, the tested hypotheses reveal that the facilitating conditions would continue to enhance benefits being captured by the audience, presenters and stations. However, the inherent impeding factors and identified negativity would have moderate impact on the expected sustainability level or degree.

Limitations of the Study

There are some limitations to this study, pointing to areas that should be investigated further. Consideration of one academic scholar indicates that the views expressed were limited and not robust enough to substantiate them with the numerous quantitative results of the study. The ideas offered are narrow and not robust enough to be substantiated by the study’s various quantitative outcomes. As a result, future study should include more academics as well as broadcasters, including experts in digital media. The study’s focus on Oyo State necessitates additional research into other Nigerian states, particularly Lagos, which has a significant number of radio stations. Because only radio stations were addressed in this study, future research should include television stations as well as broadcast media convergence in the context of online newspaper production, which has been facilitated by advent of the Internet.

Conclusion and Recommendations

Technological media convergence through Facebook-radio broadcasting has crept into the media landscape of more than 15 licenced radio in Oyo State, Nigeria to the extent that the technology keeps influencing the pattern of engagement digital audiences want to have with radio presenters and their programmes. This insight is aptly supported by the main arguments of technological media convergence and technological determinism theories highlighted by Chakaveh and Bogen (2007) alongside Emwinromzwankhoe (2020) respectively. To them, ‘new technologies integrate different mediums together as one in a manner that the media environment is redefined’ and technology ‘determines, shapes and influences how people in a society think, believe and behave’ in a way that the media is seen ‘as the main force behind social and cultural changes in society’.

Thus, our study has shown that the broadcasting format in which a radio station produces its content and transmits such via its Facebook page, as well as the time belt of broadcasting, greatly determine the level of interactions its digital audiences have with the station and its presenters. Measured in terms of likes, shares, comments and views, we conclude that programmes that focussed more on socioeconomic problems and opportunities, and broadcast in the afternoon, evening and at night received more digital engagement than other programmes’ formats and time belts. Empirically, Laor (2019) had earlier revealed that Facebook-radio programmes with multimedia content (video) and that appealed to people’s emotions were more popular among and more engaged by the audience.

As revealing as this study is, there are challenges that threaten the sustainability of technology media convergence of radio stations in Oyo State, Nigeria. Basic among these are high cost of Internet data subscription in the country, absence of Internet-enabled mobile phones among many adherents of radio programmes (both in rural and urban areas), epileptic power supply that sometimes leave many people with unpowered mobile phones as well as weak Internet broadband connectivity common to many locations in Nigeria. Then, technological media convergence has challenged presenters and radio stations on the need to produce and sustain innovative audience-catchy programmes.

To solve these problems threatening the sustainability of this convergence of Facebook-radio broadcasts in Oyo State, we provide three recommendations. One, the Nigerian Communications Commission (NCC) and the Federal Ministry of Communications and Digital Economy should be swift in their plan to deploy 5G network across the country, and liaise with the major Nigerian telecommunications companies to increase installation of masts (as NCC recommends) which will strengthen the country’s Internet connectivity and reduce digital divides. Two, radio stations that are unrelenting about technological media convergence should keep training their young and veteran presenters and social media handlers on creative strategies to deploy in order to give their audiences what they actually want, and produce engaging multimedia contents. That is a practical way participatory broadcasting can be enhanced. Lastly, presenters who currently use Facebook-radio streaming technique should inculcate more audience research into their contents while those presenters who are yet to be indoctrinated into Facebook-radio convergence should join the trends.

Footnotes

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Umar O Ajetunmobi

Author biographies

Umar O Ajetunmobi is an Applied Communication Researcher who has a special interest in development, media, health and digital communication issues. He is with Infoprations Limited, Lagos and Department of Communication and Language Arts, University of Ibadan, Nigeria.

Mutiu I Lasisi is a Research and Communications professional with a special bias for combining academic and industrial approaches for issues and needs exploration towards personal and societal growth. His research and industry interests include entrepreneurship development, media, communication, public governance, big data, computational and data journalism. As a co-author of articles on media, journalism and communication in reputable national and international journals, he currently works with Infoprations Limited, a data-driven management consulting company, Lagos, Nigeria.

References

Al-Rawi

(2016) Understanding the social media audiences of radio stations. Journal of Radio & Audio Media 23(1): 50–67.

Akingbulu

(2021) Public Policies on Radio Broadcasting in Nigeria. John Archers Ltd, pp. 1956–2006.

Antecol

(1997) Understanding McLuhan: television and the creation of the global village. ETC: A Review of General Semantics 54(4): 454–473.

Bonini

(2014) The new role of radio and its public in the age of social network sites. First Monday. Available at: https://firstmonday.org/article/view/4311/4093 (accessed 15 September 2021).

Chakaveh

Bogen

(2007) Media convergence, an introduction. In: JA

Jacko

(ed), International Conference on Human-Computer Interaction. Heidelberg: Springer, pp. 811–814.

Drula

(2015) Forms of media convergence and multimedia content–A Romanian perspective. Comunicar. Media Education Research Journal 23(1).

Dave

(2011) Media convergence: different views and perspectives. IMS Manthan.

Emwinromwankhoe

(2020) Media convergence and broadcasting practice in Nigeria: three broadcast stations in focus. Covenant Journal of Communication 7(1): 2354, 354X.

Freeman

Klapczynski

Wood

(2012) Radio and Facebook: The Relationship between Broadcast and Social Media Software in the US, Germany, and Singapore. First Monday.

10.

Holliman

(2010) Media convergence. Available at: https://www.researchgate.net/publication/46285112_Media_convergence (accessed 15 September 2021).

11.

Hess

Vanderbilt University (2015) Power, ideology, and technological determinism. Engaging Science, Technology, and Society 1: 121–125.

12.

Hauer

(2017) Technological determinism and new media. International Journal of English Literature and Social Sciences 2(2): 239174.

13.

Ijaseun

(2022) Nigeria’s Broadband Penetration Rises to 41.6% in January. Available from https://businessday.ng/news/article/nigerias-broadband-penetration-rises-to-41-6-in-january/(Business Day, March 25accessed 2 May 2022).

14.

Kuyucu

(2019) Digital convergence of radio: Effects of digitization on media radio. In:6th Communication Days Digital Transformation Symposium, Turkey, 2–3 March 2019. Faculty of CommunicationÜsküdar University.

15.

Kopecka-Piech

(2011) Media convergence concepts. Media Studies 46(3): 1–19.

16.

Latzer

(2013) Media convergence. SSRN Electronic Journal. DOI: 10.2139/ssrn.2271508

17.

Laor

(2020) How does it ‘sound'? Audiences, broadcasters, and managers on visual radio in Israel. Convergence: The International Journal of Research Into New Media Technologies 27: 1038–1054. DOI: 10.1177/1354856520942404

18.

Laor

(2019) “Hello, is there anybody who reads me?” Radio programs and popular facebook posts. International Journal of Interactive Multimedia and Artificial Intelligence 5(7): 80–87.

19.

McMahon

(2021a) The Economic, Social & Cultural Impact of the Social Network Site Facebook on the Irish Radio Industry 2011-2016. United Kingdom: University of Derby.

20.

McMahon

(2021b) In tune with the listener: how local radio in Ireland has maintained audience attention and loyalty. Online Journal of Communication and Media Technologies 11(3): e202112.

21.

Mwangi

Biwott

Gichuhi

(2018) Content of vernacular radio stations programs and public participation in devolved governance in Nyeri county, Kenya. Journal of Development and Communication Studies 5(2): 70–81.

22.

McLuhan

(). Technological determinism. Available at: https://masscommunicate.files.wordpress.com/2010/04/technological-determinism-marshall-mcluhan.pdf (accessed 1 May 2022).

23.

Nwammuo

Nwafor

(2019) Convergence of traditional and new media of communication. Available at: researchgate.net/publication/333616547_CONVERGENCE_OF_TRADITIONAL_AND_NEW_MEDIA_OF_COMMUNICATION (accessed 15 September 2021).

24.

Sharma

(2021) Media convergence. Available at: https://leverageedu.com/blog/media-convergence/(accessed 15 September 2021).

25.

Sinton

(2018) No longer one-to-many: how web 2.0 interactivity is changing public service radio’s relationship with its audience. Journal of Radio & Audio Media 25(1): 62–76. DOI: 10.1080/19376529.2017.137071

26.

Schramm

(2014) Media Development’s Role in Social, Economic, and Political progressWinner L (1977) Autonomous Technology. Cambridge: Media Development Investment FundMIT Press.

27.

Tilley

(2011) New Culture/old Ethics: What Technological Determinism Can Teach Us about New mediaThe Ethics of Emerging Media: Information, Social Norms and New Media Technology. The Continuum International Publishing Group.

28.

Winner

(1977) Autonomous Technology: Technics-Out-Of-Control as a Theme in Political Thought. MIT Press.