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Evolution of public broadcasting: TeleJurnal’s viewership trends and strategic implications for business sustainability

Ramona Săseanu

Săseanu, Ramona

,

Ruxandra Stanomir

Stanomir, Ruxandra

et

Andreea-Alexandra Bădulescu

Bădulescu, Andreea-Alexandra

26 juin 2025

Evolution of public broadcasting: TeleJurnal’s viewership trends and strategic implications for business sustainability's Cover Image

Management & Marketing

Édition 20 (2025): Edition 2 (Juin 2025)

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Catégorie d'article: Research Article

Publié en ligne: 26 juin 2025

Pages: 1 - 10

Reçu: 10 mars 2025

Accepté: 16 juin 2025

DOI: https://doi.org/10.2478/mmcks-2025-0008

Mots clés
Public television, Audience retention, Predictive modeling, TeleJurnal, TVR, Competitive advantage, Digital transformation

© 2025 Ramona Săseanu, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Monthly rating of TeleJurnal (2018–2022) and Forecast for 2023–2025.

TVR forecast based on ARMA model.

Dickey–Fuller test results for stationarity analysis

Null hypothesis: TVR has a unit root
Exogenous: constant, linear trend
Lag length: 3 (automatic based on SIC, MAXLAG = 10)
			t-statistic	Prob.*
ADF test statistic			−4.419714	0.0044
Test critical values:	1% level		−4.130526
	5% level		−3.492149
	10% level		−3.174802

Correlogram of the TVR series_

Date: 08/04/24 Time: 07:58
Sample: 2018:01 2022:12
Included observations: 60
Autocorrelation	Partial correlation		AC	PAC	Q-Stat	Prob
. \|*****\|	. \|*****\|	1	0.689	0.689	29.942	0.000
. \|***\|	.*\|. \|	2	0.432	−0.081	41.930	0.000
. \|**\|	. \|*. \|	3	0.316	0.095	48.430	0.000
. \|*.\|	**\|. \|	4	0.098	−0.270	49.065	0.000
. \|. \|	. \|*. \|	5	0.005	0.098	49.067	0.000
.*\|. \|	.*\|. \|	6	−0.071	−0.150	49.416	0.000
.*\|. \|	. \|. \|	7	−0.124	0.061	50.503	0.000
. \|. \|	. \|*. \|	8	−0.046	0.095	50.657	0.000
. \|*. \|	. \|** \|	9	0.083	0.203	51.158	0.000
. \|*. \|	. \|. \|	10	0.181	0.058	53.588	0.000
. \|**\|	. \|. \|	11	0.245	0.049	58.134	0.000
. \|**\|	.*\|. \|	12	0.233	−0.079	62.343	0.000
. \|*. \|	.*\|. \|	13	0.143	−0.105	63.969	0.000
. \|*. \|	. \|. \|	14	0.091	0.021	64.645	0.000
. \|. \|	. \|. \|	15	0.046	0.026	64.819	0.000
.*\|. \|	.*\|. \|	16	−0.077	−0.101	65.325	0.000
.*\|. \|	. \|. \|	17	−0.166	−0.049	67.696	0.000
.*\|. \|	. \|*. \|	18	−0.132	0.109	69.250	0.000
.*\|. \|	.*\|. \|	19	−0.139	−0.118	70.993	0.000
.*\|. \|	. \|. \|	20	−0.089	0.058	71.728	0.000
. \|*. \|	. \|*. \|	21	0.069	0.142	72.184	0.000
. \|*. \|	. \|*. \|	22	0.156	0.093	74.551	0.000
. \|*. \|	. \|. \|	23	0.188	−0.049	78.116	0.000
. \|**\|	. \|*. \|	24	0.249	0.111	84.504	0.000
. \|*. \|	.*\|. \|	25	0.187	−0.133	88.235	0.000
. \|*. \|	. \|. \|	26	0.083	−0.046	88.993	0.000
. \|. \|	.*\|. \|	27	−0.018	−0.121	89.028	0.000
.*\|. \|	. \|. \|	28	−0.143	0.036	91.392	0.000

Model ARMA (1,11) for TVR_

Dependent variable: TVR
Method: least squares
Variable	Coefficient	Std. error	t-statistic	Prob.
C	0.976146	0.102396	9.533022	0.0000
AR(1)	0.607754	0.103520	5.870912	0.0000
MA(11)	0.910815	0.021739	41.89706	0.0000
R-squared	0.718066	Mean dependent var		1.164407
Adjusted R-squared	0.707997	SD dependent var		0.294058
S.E. of regression	0.158901	AIC		−0.791564
Sum squared residual	1.413970	SC		−0.685926
Log-likelihood	26.35113	F-statistic		71.31404
Durbin–Watson stat	1.986850	Prob(F-statistic)		0.000000
Inverted AR roots	0.61
Inverted MA roots	0.95 + 0.28i	0.95 – 0.28i	0.65 + 0.75i	0.65 – 0.75i
	0.14 − 0.98i	0.14 + 0.98i	−0.41 – 0.90i	−0.41 + 0.90i
	−0.83 + 0.54i	−0.83 – 0.54i	−0.99
Breusch–Godfrey serial correlation LM test
F-statistic	0.340248	Probability		0.713108
Obs*R-squared	0.378654	Probability		0.827516
ARCH test
F-statistic	1.001927	Probability		0.321150
Obs*R-squared	1.019470	Probability		0.312645

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