El papel del BCE en el sistema financiero internacional

In this aspect, regression analysis was conducted to further explore the relationship among individual broadcast journalists’ characteristics (demography), journalistic role conceptions, technology adoption variables and broadcast station ownership types in predicting actual use of non-interactive and interactive technologies. The same analytic procedure of hierarchical regression analyses was followed in exploring the predictive capacities of the aforementioned variables. The results are presented in turn for each of the category of technologies being deployed.

5.5.4.1 Actual Use of Non-Interactive Technologies as Dependent Variable

Based on the Model Summary results for the first sets of regression analyses, not all five models performed well. Three models (Model 2, 3, and 5) appeared significant. For instance, Model 1 with demography as determinant of actual use of non-interactive technologies did not yield any predictors. However, with the addition of role conception variables into the regression equation, Model 2 appeared significant; R2 = .149; ANOVA results of Model 2: 𝐹𝐹(9, 132) =

2.56, 𝑝𝑝 < .001. Role conception variables therefore accounted for 14 percent of variance

which explained broadcast journalists’ actual use of non-interactive technologies. Incremental

R2 = .127 (from Model Summary Change Statistics – Model 2); ANOVA results for

Incremental R2 for Model 2: 𝐹𝐹(5, 132) = 3.94, 𝑝𝑝 < .05 (from Change Statistics in Model 2). Role conception variables alone accounted for about 13 percent of the total variance in the second model. Two (2) journalistic roles; disseminator (𝛽𝛽 = .19, 𝑝𝑝 = .056) and interpreter

(𝛽𝛽 = .19, 𝑝𝑝 = .087) approached significance as potential predictors of actual use of non-

interactive technologies. Journalistic role conception variables can be said to account for a significant amount of variance above and beyond individual characteristics such as gender, age, job status and job experience.

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Model 1 Model 2 Model 3 Model 4 Model 5

Variable B SE B β B SE B β B SE B β B SE B β B SE (B) β Gender 0.03 0.22 .01 0.05 0.21 .02 0.16 0.21 .06 0.20 0.21 .08 0.29 0.20 .12 Age - 0.05 0.27 - .02 -0.07 0.26 -.03 -0.23 0.25 -.09 -0.13 0.26 -.05 -0.09 0.25 -.03 Experience 0.02 0.02 .11 0.01 0.02 .07 0.03 0.23 .20# _{0.03 0.02 .21}# _{0.04 0.02 .22}# Job Status 0.18 0.32 .06 0.14 0.30 .05 -0.01 0.30 -.04 -0.19 0.30 -.06 -0.20 0.29 -.07 Disseminator 0.24 0.12 .19* 0.08 0.12 .06 0.06 0.12 .05 0.02 0.12 .01 Interpreter 0.20 0.12 .19* 0.14 0.12 .13 0.16 0.12 .15 0.12 0.11 .11 Adversary -0.08 0.07 -.11 -0.11 0.06 -.14 -0.11 0.06 -.14 -0.09 0.06 -.12 Mobiliser -0.03 0.07 -.04 0.00 0.07 .00 -0.01 0.07 .02 0.01 0.07 .01 Civic 0.12 0.09 .12 0.03 0.95 .03 0.00 0.09 .00 -02 0.09 -.024 PUV -0.15 0.14 -.12 -0.15 0.14 -.12 -0.19 0.14 -.15 PHV -0.28 0.14 -.02 -0.13 0.14 -.01 0.04 0.14 .03 PCV 0.28 0.13 .23** 0.27 0.13 .22* 0.15 0.13 .12 PIPC 0.23 0.08 .23** 0.23 0.08 .23** 0.22 0.08 .22** POSA 0.18 0.12 .14 0.16 0.12 .12 0.19 0.12 .14 Ownership (Broadcast Tiers) 0.21 0.15 .12 0.17 0.14 .09 Intention to Use 0.34 0.10 .28** Incremental R2 .02 .12 .09 .01 .05 R2 _{.02 .14 .24 .25 .31} R .14 .38 .49 .50 56 Adjusted R2 _{-.00 .09 .16 .17 .22} F 0.74 2.56* 2.97** 2.92** 3.57*** F Change 0.74 3.94** 3.30* 2.01 10.08**

Table 5.10: Summary of Hierarchical Regression Analysis for Variables Predicting Actual Use of Non-interactive Technologies

Note: * p < .10, ** p < .05, *** p < .001, # p value above .05 for marginally significant coefficients

Technology adoption variables were added to the regression equation as Model 3. The model appeared significant, R2 = .247; ANOVA results of Model 3: 𝐹𝐹(14, 127) = 2.97, 𝑝𝑝 < .05. This shows that about 25 percent of total variance in actual use of non-interactive is explained by the addition of technology adoption variables to the model. Incremental R2 = .098

(from Model Summary Change Statistics – Model 3); ANOVA results for Incremental R2 for

Model 3: 𝐹𝐹(5, 127) = 3.30, 𝑝𝑝 < .05 (from Change Statistics in Model 3). This shows that technology adoption variables accounted for 9.8 percent of the total variance in Model 3. In this model, perceived institutional policy control (PIPC) emerged as a strong positive significant predictor (𝛽𝛽 = .23, 𝑝𝑝 < .05), while perceived communication value (PCV) was significant as a positive predictor (𝛽𝛽 = .23, 𝑝𝑝 < .05). With these results, technology adoption variables accounted for a significant amount of variance above and beyond role conception variables.

When media ownership types were added to the regression equation, the resultant model (Model 4) proved to be significant with a slight improvement to the model, R2 _{= .259;}

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marginal improvement on the model. However, neither of the three media ownership types surfaced as a significant predictor.

The last model added intention to use non-interactive to the same regression equation which had demography, role conception, technology adoption, and media ownership types as variables for predicting actual use on non-interactive technologies. Model 5 proved to be significant, R2 = .314; ANOVA results of Model 5: 𝐹𝐹(16, 125) = 3.57, 𝑝𝑝 < .001. These results further established the linear relationship between intention and actual use of technology, as reported across literature on technology adoption. As indicated in the results, it means that all the independent variables accounted for 31 percent of the total variance in

predicting actual adoption of non-interactive technologies. Incremental R2 = 0.06 (from

Summary Change Statistics – Model 5); ANOVA results for Incremental R2 for Model 5:

𝐹𝐹(1, 125) = 10.08, 𝑝𝑝 < .05 (from Change Statistics in Model 5). Intention to use non-

interactive technology surfaced as strong positive predictor of broadcast journalists’ actual use of non-interactive technologies, (𝛽𝛽 = .28, 𝑝𝑝 < .05). From the results, marginal significant improvement to Model 5 is recorded (about 6 percent).

5.5.4.2 Actual Use of Interactive Technologies as Dependent Variable

Another series of models were tested in a stepwise regression to explore the predictors of actual use of interactive technology among broadcast journalists. Following the same procedure, Model 1 with demographic variables proved not to be significant. However, with the addition

of the role conception variables, the model (as Model 2) was improved, R2 = .163; ANOVA

results of Model 2: 𝐹𝐹(9, 132) = 2.85, 𝑝𝑝 < .05. Incremental R2 = .123 (from Summary Change Statistics Model – Model 2); ANOVA results for Incremental R2 for Model 2: 𝐹𝐹(5, 132) =

3.86, 𝑝𝑝 < .05 (from Change Statistics in Model 2). This is an indication that role conception

variables accounted for 12 percent of variance in explaining interactive technologies over and beyond demography variables. Disseminator role was highly significant as a positive predictor (𝛽𝛽 = .19, 𝑝𝑝 < .05).

Technology adoption variables were added to the regression equation as Model 3. The model appeared significant, R2 = .244; ANOVA results of Model 3: 𝐹𝐹(14, 127) = 2.92, 𝑝𝑝 < .05. This shows that 24 percent of total variance in actual use of interactive is explained by the addition of technology adoption variables to the model with role conception variables.

Incremental R2 = .081 (from Model Summary Change Statistics – Model 3); ANOVA results

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Model 3). This shows that technology adoption variables accounted for just 8 percent of the total variance in Model 3. In this model, perceived institutional policy control (PIPC) was marginally significant as a predictor(𝛽𝛽 = .17, 𝑝𝑝 = .055) , while job experience also surfaced as a strong positive moderating variable (𝛽𝛽 = .26, 𝑝𝑝 < .05). With these results, technology adoption variables accounted for a significant amount of variance above and beyond role conception variables.

Model 1 Model 2 Model 3 Model 4 Model 5

Variable B SE B β B SE B β B SE B β B SE B β B SE (B) β Gender 0.01 0.17 .00 0.03 0.16 .01 0.13 0.17 .06 0.13 0.17 .06 0.12 0.16 .06 Age - 0.20 0.22 - .10 -0.24 0.21 -.12 -0.34 0.20 -.17 -0.35 0.21 -.17 -0.30 0.20 -.15 Experience 0.03 0.19 .20 0.02 0.01 .16 0.03 0.01 .26* 0.03 0.01 .26* 0.04 0.01 .28* Job Status .15 0.25 .06 0.11 0.24 .05 -0.08 0.24 -.03 -0.08 0.25 -.03 -0.13 0.23 -.05 Disseminator 0.20 0.17 .19* 0.08 0.10 .08 0.08 0.10 .08 0.01 0.10 .01 Interpreter 0.12 0.20 .14 0.07 0.09 .08 0.07 0.10 .08 0.09 0.09 .10 Adversary -0.7 0.05 -.11 -0.08 0.05 -.13 -0.08 0.05 -.13 -0.04 0.54 -.07 Mobiliser -0.00 0.05 -.00 0.02 0.58 0.04 0.02 0.05 .03 0.02 0.56 .03 Civic 0.11 0.07 .14 0.03 0.07 0.04 0.03 0.07 .04 -0.03 0.07 -.04 PUV 0.10 0.11 .10 0.10 0.11 .10 0.08 0.11 .08 PHV -0.12 0.11 -.12 -0.12 0.12 -.12 -0.10 0.11 -.10 PCV 0.17 0.10 .17 0.17 0.11 .17 0.04 0.11 .04 PIPC 0.14 0.07 .17# _{0.14 0.07 .17}# _{0.13 0.06 .16}# POSA 0.14 0.10 .13 0.14 0.10 .13 0.16 0.09 .15 Ownership (Broadcast Tiers) -0.00 0.11 -.00 Intention to Use 0.35 0.09 .35*** Incremental R2 .04 .12** .08** .00 .08*** R2 _{.04 .16 .24 .24 .32} Total R2 _{.20 .40 .49 .49 .57} Adjusted R2 _{.01 .10 .16 .15 .24} F 1.44 2.85** 2.46** 2.30** 3.80*** F Change 1.44 3.86** 2.71* 0.00 15.54***

Table 5.11: Summary of Hierarchical Regression Analysis for Variables Predicting Actual Use of Interactive Technologies

Note: * p < .10, ** p < .05, *** p < .001, # p value above .05 for marginally significant coefficients

When media ownership types were added to the regression equation, the resultant model (Model 4) proved to be significant with a slight improvement to the model, R2 = .244; ANOVA result of Model 4: 𝐹𝐹(15, 126) = 2.70, 𝑝𝑝 < .05. The model failed to yield a significant predictor in broadcast station ownership, otherwise referred to as tiers of broadcasting. However, job experience surfaced again as a moderating variable pointing to the effect of individual characteristic, (𝛽𝛽 = .26, 𝑝𝑝 < .05). Likewise, perceived institutional policy control (PIPC) emerged as a significant predictor of actual use of interactive technologies, (𝛽𝛽 = .16,

𝑝𝑝 = .05) while perceived organisational support and agenda (POSA) is marginally

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The last model (Model 5) added intention to use interactive technologies as a variable to the same regression equation which had demography, role conception, technology adoption, and media ownership types as variables for predicting actual use on interactive technologies. Model 5 proved to be significant, R2 = .327; ANOVA results of Model 5: 𝐹𝐹(16, 125) = 3.80,

𝑝𝑝 < .001. Again, the results re-established the linear positive relationship between intention

and actual use of technology, as reported across literature on technology adoption. As indicated in the results, the overall model accounted for 32 percent of the total variance in predicting actual adoption of interactive technologies. Incremental R2 = 0.84 (from Summary Change Statistics – Model 5); ANOVA results for Incremental R2 for Model 5: 𝐹𝐹(1, 125) = 15.54,

𝑝𝑝 < .05 (from Change Statistics in Model 5). Intention to use interactive technology surfaced

as a strong positive predictor of broadcast journalists’ actual use of interactive technologies,

(𝛽𝛽 = .35, 𝑝𝑝 < .001). From the results, highly significant improvement to Model 5 is recorded

(8 percent). Job experience also emerged an important variable over and above other demography measuring the effect of individual characteristics (𝛽𝛽 = .28, 𝑝𝑝 < .05).

5.6 Conclusion

So far, the quantitative findings in this chapter have helped in establishing a strong association between perceived technological attributes and journalistic role conceptions. Role conception variables accounted for a significant amount of variance above and beyond individual characteristics (demography) and station ownership with regard to intention and actual use of non-interactive and interactive technologies in the Nigerian broadcast media landscape. Likewise technology adoption variables accounted for a significant amount of variance above and beyond individual characteristics and ownership types in relation to intention and actual use of non-interactive and interactive technologies. Perceived institutional policy control (PIPC) emerged in the data analyses as a significant positive predictor of actual use of interactive technologies while perceived organisational support and agenda (POSA) approached significance. Only gender and broadcast journalists’ job experience approach significance as indices to the marginal effect of individual characteristics in the models.

In sum, there is a strong mutual, rather than linear relationship between journalists’ role conceptions and technology use behaviour in the Nigerian broadcast technology adoption context. Certain technology adoption variables such as perceived communication value and perceived institutional policy support when considered within the broadcast journalism setting coalesce with civic, disseminator and interpreter roles to predict intention and actual use of

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interactive and non-interactive technologies with gender and experience as moderators. Broadcast station ownership types or tiers of broadcasting have no statistical significance in the models. These quantitative findings are further investigated in the next section within the paradigm of qualitative data analytics using a series of semi-structured interviews. It is hoped that this procedure will enrich the focus of this study while at the same time complement the methodological shortcomings associated with each of the approaches.

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CHAPTER 6

PRESENTATION AND ANALYSIS OF QUALITATIVE INTERVIEW DATA

6.1 Introduction

This chapter presents the results of qualitative study used as a part of mixed methods research designed to explore relevance of new media technologies and their relationship with broadcast journalists’ role conceptions in the Nigerian context. The qualitative results in this chapter serve to complement the quantitative findings presented in the previous chapter. The aim is to further shed light on adoption and use of new media in the context of participatory programming which may have been overlooked in the methodological and analytical approaches to the quantitative research. As discussed in chapter 5 of this thesis, semi-structured interview data were collected from 18 broadcast journalists drawn purposively from three tiers of broadcasting and located in 4 out of 6 southwest states of Nigeria. These broadcast journalists belonged to public or state-owned broadcasting corporations, commercial/private FM radio stations and a campus-based community FM radio station. This representation included a caste of broadcast professionals (5 females and 13 males) who held various managerial and mid-level cadre roles in the stations. Scheme of thematic coding was developed and used to analyse the transcribed interview data. The Nvivo 11 (Pro version) software was engaged to map out the emergent referents of themes in the interviewees’ responses.

Thematic analyses were routed through a constructionist theoretical orientation of qualitative interviews (see Chapter 3 & 4 for details), which involved a combination of extant literature operationalised under the study’s conceptual framework without losing track of the research questions (Braun & Clarke, 2006). Based on this approach, two broad areas manifest in Nigerian broadcast journalists’ discussions on the relevance of new media technologies and their relationship with journalistic role conceptions. The bifurcation tallies with the universal perceptions of interactive technologies as a “double-edged sword:” one with perceived utilitarian value and dysfunctional uses. The dysfunctionalities are however traceable to the attributes of technologies on the one hand and individual broadcast journalist’s ethical responsibility on the other hand. The double-edged perception is contextualised to uniquely

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understand new technology adoption scenario in the Nigerian setting. Three broad themes were identified in line with the conceptual framework of this study. These themes were used to explore broadcast journalists’ perceptions of new communication technologies, bearing in mind the research questions. Together, the themes and sub-themes establish that Nigerian broadcast journalists, just like their Western counterparts, considered new technologies, and in particular interactive technologies of the Internet and mobile phones, as being very relevant to contemporary journalistic practices. But this relevance is underscored by situated challenges observable in the interviewees’ responses to the emergent themes and sub-themes.

The chapter begins with a short presentation of the interview data and proceed to create a clear background for the analyses of the qualitative interviews. The analyses open with a presentation of general findings on pattern of new communication technologies use in Nigerian broadcasting considering its significance in setting the right context for understanding new digital technology adoption in the Nigerian setting. To this end, themes and sub-themes are used to ensure mutual exclusivity and exhaustiveness of the underlining categories. The chapter later concludes with a paragraph which summarises the key findings.

6.2 Exploring the Extent of New Technologies Use for Participatory Programming in the Nigerian (Radio) Broadcasting

As earlier mentioned in the review of literature, different types of system had been examined in studies on technology adoption. Lee et al. (2003) group these technologies into four categories: communication systems, general-purpose systems, office systems, and specialised business systems. In view of adoption pattern in journalism, these technologies are grouped into two: interactive and non-interactive technologies. Given the research focus on participatory programming and having focused on these two dimensions with the quantitative study, special attention is paid to new interactive technologies of the Internet and mobile phones alone in this qualitative part. This would make exclusive Lee et al. (2003) “general- purpose” and “communication systems” which has both Internet and systems primarily used for communications, such as mobile-based technologies, instant messaging, under different categories.

A number of new digital technologies are associated with interactivity and participatory programming in broadcast journalism the world over. An examination of how Nigerian broadcast journalists perceive these technologies was carried in a bid to understand trends and pattern of adoption in the context under investigation. Based on the interview responses, these interactive technologies can be grouped into two; whether it is text-based or whether it is audio- based. For the text-based interactive technologies, these are synchronous or quasi-synchronous

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social network sites of the Internet and the short message service (SMS) of the mobile phone. In this category, Facebook, Twitter, Email, WhatsApp and mobile phone SMS are popular among Nigerian broadcast journalists. Based on the literature and initial exploration of the study’s qualitative interview data, using these technologies for participatory programming is both universal and context-specific.

While studies have established the fact that technological artefacts are not always universal in their adoption (e.g. Caroll, Howard, Peck & Murphy, 2002a, 2002b; Isaac, Besseyer, Des Horts & Leclercq, 2006), the analyses here is hinged on the assumption that technology adoption is both an open or universal and closed or context-dependent concept. Appropriation is about technology use in circumstances. Appropriation signifies how technology is used, adapted and fitted in the users’ daily activities (DeSanctis & Poole, 1994; Ansari, Channar & Syed, 2011). Given this background, the context of use and the perceptions toward attributes of the technologies and outcome of use are considered under the broad concept of adoption. The rational to investigate this broad perspective is borne out of the blurriness in journalists’ adoption scenario, where no clear cut demarcation exists between individual and media organisation’s use of interactive technologies, especially in relation to deploying communication system such as mobile devices and Internet technologies (general- purpose system) for journalistic roles.

Tiers of Broadcasting Total

Interactive Technologies Public Private Community Total Sources Total References

Mobile Phone 61 21 19 18 101

Facebook 29 19 12 18 59

Twitter 16 15 11 14 42

WhatsApp 11 00 2 5 13

Email 06 01 00 5 7

Table 6.1: showing word query results of interactive technologies mentioned in the interview data

The data in Table 6.1 show the significance of interactive technologies as indicated in the interviewees’ responses on the subject. The two channels of mobile phone (SMS and voice call) appeared to be the predominant technology for interactivity in the Nigerian context. This is followed by Facebook and Twitter which are both social network sites popular among Nigerian broadcast journalists. At least a mention of “mobile phone” and “Facebook” appeared in the responses of all the 18 interviewees with varied degrees of frequency and in relation to the interview questions. While 101 references were made by the interviewees in connection to the word mobile phone, 59 instances of the word Facebook were recorded in their transcribed

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interview data. Email appeared not to be popular as a technological tool for interactivity among Nigerian broadcast journalists. Using the NVivo software, the relevance of Facebook as the most adopted social media platform was explored by visualising the pattern of discussion as expressed by the interviewees on the subject of interactive technologies (see chapter 5). Table 7.1 also shows that government-owned broadcasting corporations preferred mobile phone interactivity to social media interactivity with social media platforms more popular among private/commercial broadcast stations and campus-based community radio stations.

6.3 Interpreting the Scheme of Thematic Coding for the Study

This section introduces the theoretical framework of the study and how the framework connects with the qualitative interview data to drive thematic analyses. The study recognises that attributes of technologies in relation to broadcast journalism are premised on anecdotal reference to the “double or two-edged sword” (see also Lee, 2015). The double-edged description means that new digital technologies possess both utilitarian and detrimental/disruptive values for journalism practice. In the light of this duality of purpose, analysing the relevance of new communication technologies in the Nigerian broadcast journalism would mean to consider the perceived utilitarian value of the technologies alongside the “situated” challenges mentioned by the interviewees. Perceived utilitarian value is a core construct in technology adoption in workplace. In the framework of analyses it underscores the interviewees’ beliefs about new media technologies’ capabilities to enhance productivity (and role-play) in broadcast journalism as a profession and business. Central to the theoretical interpretation of this construct are extant theories of adoption such as perceived usefulness, outcome expectations, extrinsic motivation, relative advantage, and summarily in performance expectancy construct of the UTAUT (see Chapter 2). As the strongest and the most consistent predictor of technology usage intentions in the work setting, this construct is thematised to include effort expectancy construct by focussing on “external goals” such as work performance rather than “user-system interaction” as defined in TAM (see van der Heijden, 2004; Sun & Bhattacherjee, 2014, p. 3). Hence, central to this first theme are technologically-motivated improvements associated with journalism practice such as newsgathering and interactivity. It also extends to influences of technologies on journalistic roles such as gatekeeping and agenda setting.

The second theme addresses perceived communication value that represents another important construct in relation to technology use in the workplace. The significance of this theme can be interpreted against the Core IT model. The model suggests that communication