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5.5.3.1: Test 1: Exploring Nigerian (Radio) Broadcast Journalists’ Intention to Use Non- Interactive and Interactive Technologies

As earlier mentioned, a stepwise regression analysis was used to test four models including: controlling for demographic variables (individual characteristics) and participants’ station

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ownership types (measured as tiers of broadcasting). Based on the Model Summary results, not all the models performed well. However, predictors of intention to use non-interactive technologies emerged for journalistic roles and technology adoption variables as they were added into the models.

5.5.3.2 Intention to Use Non-Interactive Technologies as Dependent Variable

To start with, Model 1 with demography as predictors of intention to use non-interactive technologies did not yield a statistical significant model. The four demographic variables (age, gender, job status and job experience), with which the study assessed individual characteristics of the subjects, altogether contributed just three (3) percent of variance that explained broadcast journalists’ intention to use non-interactive technologies (R2 = .030). Given this output, demography alone does not possess sufficient statistical capacity to predict intention to use non-interactive technologies. However, gender approached significance and surfaced as a potential predictor(𝛽𝛽 = −.15, 𝑝𝑝 = .087)19. This is an indication that male broadcast journalists appeared to be more favourably disposed to the use of non-interactive technologies than their female counterparts20.

Model 1 Model 2 Model 3 Model 4

Variable B SE B β B SE B β B SE B β B SE (B) β Gender -0.31 0.18 -.15# _{-0.29 0.16 -.17 -0.30 0.16 -.15* -0.28 0.16 -.13**} Age -0.10 0.22 -.05 -0.13 0.21 -.07 -0.20 0.20 -.10 -0.14 0.21 -.07 Experience -0.00 0.02 -02 -0.00 0.01 -.06 -0.00 0.01 -.05 -0.00 0.01 -.03 Job Status 0.20 0.26 .08 0.15 0.24 .06 0.08 0.24 .03 -0.04 0.24 .02 Disseminator 0.18 0.10 .17# _{0.13 0.10 .13 0.12 0.10 .12} Interpreter 0.18 0.09 .20# _{0.10 0.09 .11 0.11 0.09 .12} Adversary -0.03 0.05 -.06 -0.04 0.05 -.07 -0.04 0.05 -.07 Mobiliser -0.01 0.05 -.02 0.00 0.05 .01 0.01 0.05 .02 Civic 0.15 0.07 .18** 0.10 0.07 .12 0.08 0.07 .10 PUV 0.12 0.11 .11 -0.11 0.11 .11 PHV -0.17 0.11 -.17 0.15 0.11 -.16 PCV 0.35 0.10 .36** 0.35 0.10 .35** PIPC 0.23 0.07 .02 -0.03 0.07 .03 POSA -0.71 0.09 -.06 -0.08 0.10 -.08 Ownership (Broadcast Tiers) 0.13 0.12 .09 IncrementalR2 _{.03 .16** .08* .00} R2 _{.03 .19 .28 29} Total R2 _{.17 .44 .53 .54} Adjusted R2 _{.00 .14 .20 .20} F 1.06 3.57** 3.61** 3.45** F Change 1.06 5.43** 3.15* 1.22

Table 5.8: Summary of Hierarchical Regression Analysis for Variables Predicting Intention to Use Non-interactive Technologies

Note: * p < .05, ** p < .01, # p value above .05 for coefficients that are marginally significant

19_{𝛽𝛽 scores were taken from the models’ Standardized Coefficient matrix.} 20 _{Male = 1, Female = 2; as variable entry in the spreadsheet.}

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But with the introduction of the five role conceptions, technology adoption variables and media ownership into the regressions, Model 2, 3 and 4 were statistically improved, with Models 2 and 3 emerged as highly significant. For instance, Model 2 yields a statistical significant result, which indicate that about 20 percent of the total variance is explained by a combination of all

the five journalistic roles and demography, 𝑅𝑅2 = .196, ANOVA results for Model 2:

𝐹𝐹(9, 132) = 3.571, 𝑝𝑝 < .005. Incremental 𝑅𝑅2 = .166, (from Model Summary Change

Statistics – Model 2); ANOVA results for Incremental R2 _{for Model 2: 𝐹𝐹(5, 132) = 5.43, 𝑝𝑝 <} .001. This is an indication that role conception variables accounted for a significant amount of variance (16 percent) above and beyond demographic variables. Three (3) journalistic role conceptions approached significance; these are civic role(𝛽𝛽 = .18, 𝑝𝑝 < .05), interpreter

role(𝛽𝛽 = .20, 𝑝𝑝 = .059), and disseminator role (𝛽𝛽 = .17, 𝑝𝑝 = .074) respectively. With civic

role as the strongest predictor, together these were the role conceptions that made significant contributions to predicting broadcast journalists’ intention to use non-interactive technologies. In sum, journalistic roles accounted for a significant amount of variance above and beyond individual characteristics as portrayed by demographic information such as job status, gender, age and job experience.

In Model 3, where technology adoption variables, such as perceived attributes of technologies, perceived organisational support and perceived institutional policy, control were added alongside demography and role conception variables, the model proves to be statistically significant, R2 = .285, ANOVA results for Model 3: F(14, 127) = 3.61, p < .001. This means that 28 percent of variance in broadcast journalists’ intention to use non-interactive technologies could be explained by the trio of demography, role conception and technology adoption variables. Incremental R2 = .089, hence a small but significant contribution (8 percent) is made with the addition of technology adoption variables to demographic and role conception variables. ANOVA results for change in R2 for Model 3: F(5, 127) = 3.15, p < .010 (from Model Summary Change Statistics-Model 3). Gender remained a potential negative predictor

(𝛽𝛽 = −.17, 𝑝𝑝 = .072) of intention to use non-interactive technologies. Technology adoption

variables did make a significant contribution over and above demography and role conception variables as predictors of intention to use non-interactive technologies. Perceived communication value emerged as the single most important predictor of broadcast journalists’ intention to use non-interactive technologies (𝛽𝛽 = .36, 𝑝𝑝 < .001).

When ownership type was entered into the fourth regression as a moderating variable, the resultant Model 4, prove to be statistically significant, but with relative improvement to the

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model, R2 = .292; ANOVA results for Model 4: F(15, 126) = 3.45, p < .001. This shows that 29 percent of the variance is explained by the overall model, including broadcast station ownership. The addition of Ownership in Model 4 lead to an Incremental R2 = .007 (from Model Summary Change Statistics-Model 3), F(1, 126) = 1.22, p = .271. Media ownership such as public, private, and community radio stations made marginal contribution to broadcast journalists’ intention to use non-interactive technologies. Media ownership did not account for a significant amount of variance above and beyond demography, role conceptions and technology adoption variables. From the foregoing, the significance of journalistic role conceptions and technology adoption variables as predictors of intention to use non-interactive technologies in the Nigerian context is established. Altogether they accounted for about 30 percent of variance which explained broadcast journalists’ intention to use non-interactive technologies. Perceived communication value (PCV) emerged as the strongest predictor of intention to use non-interactive technologies (𝛽𝛽 = .35, 𝑝𝑝 < .001).

5.5.3.3 Intention to Use Interactive Technologies as Dependent Variable

The same procedure was followed to determine the predictors of broadcast journalists’ intention to use interactive technologies such as social media platforms of the Internet and mobile phones (SMS and voice calls). Also, four models emerged from testing the predictive powers of demography, role conceptions variables, technology adoption variables and media ownership. Not all the models performed well.

For instance, the first regression with demography (as individual characteristics) did not yield significant predictors. But addition of role conceptions variables significantly improved the regression model, 𝑅𝑅2 = .213; ANOVA results for Model 2: 𝐹𝐹(9, 132) = 3.97,

𝑝𝑝 < .001. With this result, 21 percent of the total variance in intention to use interactive

technologies can be attributed to journalistic roles conception variables. Incremental 𝑅𝑅2 =

.201, (from Model Summary Change Statistics – Model 2); ANOVA results for Increment in

R2 for Model 2: 𝐹𝐹(5, 132) = 6.73, 𝑝𝑝 < .001 (from Model Summary Change Statistics – Model 2). Two (2) journalistic role conceptions emerged as strong positive predictors of broadcast journalists’ intention to use interactive technologies; these are civic role (𝛽𝛽 = .32, 𝑝𝑝 < .05), disseminator role (𝛽𝛽 = .24, 𝑝𝑝 = .010). Adversary role also emerged as a significant negative predictor (𝛽𝛽 = −.17, 𝑝𝑝 < .05). These journalistic role conceptions altogether accounted for a significant amount of variance (20 percent) that explained intention to use interactive technologies, above and beyond individual characteristics.

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Model 1 Model 2 Model 3 Model 4

Variable B SE B β B SE B β B SE B β B SE (B) β Gender 0.02 0.17 .05 0.63 0.16 .03 0.03 0.15 .01 0.03 0.16 .01 Age 0.21 0.22 .01 -0.47 0.20 -.02 -0.12 0.19 -.06 0.12 0.20 -.06 Experience -0.00 0.01 -00 -0.00 0.01 -.06 -0.00 0.18 -.04 0.00 0.01 -.04 Job Status 0.25 0.25 0.11 .0.20 0.23 .08 0.16 0.23 .07 0.16 0.23 .07 Disseminator 0.25 0.09 .24** 0.03 0.15 .19* 0.20 0.09 .19** Interpreter 0.04 0.09 .05 -0.12 0.19 -.05 -0.05 0.09 -.05 Adversary -0.10 0.05 -.17* -0.10 0.01 -.16* -0.10 0.05 -.16** Mobiliser -0.00 0.05 -.01 0.00 0.23 .01 0.10 0.05 .01 Civic 0.25 0.07 .32** 0.19 0.09 .24** 0.19 0.07 .24** PUV 0.05 0.11 .05 -0.05 0.11 .05 PHV -0.06 0.11 .06 -0.06 0.11 -.06 PCV 0.37 0.10 .39*** 0.39 0.10 .39*** PIPC 0.01 0.06 .01 0.10 0.06 .01*** POSA -0.06 0.09 -.06 -0.70 0.09 -.06 Ownership (Broadcast Tiers) 0.00 0.11 .00 IncrementalR2 _{.01 .20*** .10*** .00} R2 _{.01 .21 .31 .31} Total R2 _{.11 .46 .56 .56} Adjusted R2 _{-.01 .15 .24 .23} F 0.42 3.97*** 4.25*** 3.94*** F Change 0.42 6.73*** 3.96** 0.00

Table 5.9: Summary of Hierarchical Regression Analysis for Variables Predicting Intention to Use Interactive Technologies

Note: * p < .10, ** p < .05, *** p < .01

Model 3 had technology adoption variables added to the regression equation. The model was significantly improved, R2 _{= .319; ANOVA results for Model 3: 𝐹𝐹(14, 127) = 4.25, 𝑝𝑝 <}

.001. The overall model, including technology adoption variables in the regression equation contributed 31 percent of the variance that explained broadcast journalists intention to use interactive technologies. Technology adoption variables accounted for a significant amount of variance (10 percent) above and beyond demography, and role conception variables (Incremental R2 = .106; (from Model Summary Change Statistics – Model 3), 𝐹𝐹(5, 127) =

3.96, 𝑝𝑝 < .005. Perceived communication value (PCV) surfaced as a strong positive predictor

of intention to use interactive technologies (𝛽𝛽 = .39, 𝑝𝑝 < .001).

When ownership type was added into the fourth regression, the resultant Model 4, prove to be statistically significant, but with no improvement to the model, R2 _{= .319; ANOVA results} of Model 4: 𝐹𝐹(14, 127) = 4.25, 𝑝𝑝 < .001. Media ownership types or tiers of broadcasting to which a journalist belong do not contribute in any way toward explaining broadcast journalists intention to use interactive technologies.

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5.5.4. Test 2: Exploring Nigerian (Radio) Broadcast Journalists’ Actual Use of Non-