Abdullah Baabdullah
email: baabdullah@kau.edu.sa

Omar Nasseef
email: onasseef@kau.edu.sa

Ali Alalwan

Consumer Adoption of Mobile Government in the Kingdom of Saudi Arabia: The Role of Usefulness, Ease of Use, Perceived Risk and Innovativeness

Keywords: Saudi Arabia, M-Gov, TAM, perceived risk, innovativeness

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

M-Gov deals with the group of services concerning the organised usage of the governmental services and utilisations that are mainly attainable via mobile phones [START_REF] Yfantis | The influential factors of mgov's adoption in the developing countries[END_REF]. The Saudi Government has aimed at increasing the adoption of M-Gov in order to offer citizens more accessibility to governmental services compared with traditional methods in terms of time and cost [START_REF] Alsenaidy | A review of current state m-gov in Saudi Arabia[END_REF]. Nevertheless, the traditional channels for communicating and transacting with the government are still common in Saudi Arabia [START_REF] Al-Busaidi | A model of intention to use m-gov services[END_REF]. Saudi citizens have not adopted it quickly due to the risks associated with the adoption of M-Gov as well as the lack of Saudi infrastructure [START_REF] Ahmad | Current review of ICT and mgovernment services in Saudi Arabia[END_REF]. This study aims to explore the citizens' (potential users) perceptions on the significance of certain factors regarding the issue of M-Gov adoption in the domain of Saudi Arabia.

Theoretically, within the field of IT/IS, researchers have integrated and formulated different theories and models in order to get in-depth knowledge and thoughtful understanding of the customers' behavioural intention towards M-Gov. Examples include the Theory of Reasoned Action (TRA) which was embraced by [START_REF] Althunibat | Modelling the factors that influence m-gov services acceptance[END_REF]. The Theory of Planned Behaviour (TPB) was applied by [START_REF] Susanto | User acceptance of SMS-based e-government services: Differences between adopters and non-adopters[END_REF]. The Technology Acceptance Model (TAM) has also been implemented by [START_REF] Wang | Antecedents and consequences of perceived value in mobile government continuance use: An empirical research in China[END_REF], and it proposed to predict the customers' behavioural intention to use M-Gov. The Unified Theory of Acceptance and Use of Technology (UTAUT) [START_REF] Venkatesh | User acceptance of information technology: Toward a unified view[END_REF] were implemented by [START_REF] Yfantis | The influential factors of mgov's adoption in the developing countries[END_REF]. The low level of M-Gov adoption in the context of Saudi Arabia was referred by [START_REF] Al-Khalifa | Development of m-gov websites: A functional design approach[END_REF]. [START_REF] Alsenaidy | A review of current state m-gov in Saudi Arabia[END_REF] referred that the overt lack of consumers' awareness and the low levels of trust were considered as the main problems when it comes to the actual adoption of the M-Gov service on a larger scale. It is obvious that due to the newly established E-Gov services in Saudi Arabia, the issue of M-Gov has not, so far, been studied comprehensively. Consequently, this matter requires further research to investigate the shortage of M-Gov adoption.

Conceptual model and research hypotheses

TAM has been recognised as the most adopted theory within IT/IS research [START_REF] Venkatesh | User acceptance of information technology: Toward a unified view[END_REF]. In order to solve the matter of inaccuracy in prediction with behavioural intention, the TAM has been extended by a number of researchers. For example, within the context of M-Gov, [START_REF] Al-Busaidi | A model of intention to use m-gov services[END_REF] extended the TAM model to include factors from the IDT model (relative advantage, compatibility, trialability). Likewise, [START_REF] Susanto | Factors influencing citizen adoption of SMS-based egovernment services[END_REF] added elements from the IDT and UTAUT models to the TAM model. [START_REF] Althunibat | Modelling the factors that influence m-gov services acceptance[END_REF] developed their conceptual model relying on factors from TAM, TRA and UTAUT. As Figure 1 shows, this study adopts PU and PEOU from TAM and extended it to include perceived risk (PR) and innovativeness (INN).

Figure 1.

The Proposed Research Model -Adopted from [START_REF] Davis | Perceived usefulness, perceived ease of use, and user acceptance of information technology[END_REF] Perceived Usefulness (PU) is defined as 'the degree to which a person believes that using a particular system would enhance his or her job performance' [START_REF] Davis | User acceptance of computer technology: a comparison of two theoretical models[END_REF]. A considerable number of studies have affirmed the important role of PU over BI within the context of M-Gov (e.g. [START_REF] Hung | User acceptance of mobile e-government services: An empirical study[END_REF], [START_REF] Lu | Determinants of accepting wireless mobile data services in China[END_REF]). Thus, the following hypothesis is: H1: PU positively influences Saudi customers' intention to use M-Gov. Perceived Ease of Use (PEOU) is described as 'the degree to which a person believes that using a particular system would be free of effort' [START_REF] Davis | User acceptance of computer technology: a comparison of two theoretical models[END_REF]. Because of the unique trait of M-Gov which necessitates a specific amount of skill and knowledge, PEOU might have a significant role in influencing the customers' behavioural intention to adopt this service [START_REF] Shareef | Examining adoption behavior of mobile government[END_REF]. Furthermore, PEOU indirectly influences BI through facilitating the influence of PU on BI [START_REF] Zarmpou | Modeling users' acceptance of mobile services[END_REF]. Accordingly, this paper concludes: H2: PEOU positively influences Saudi consumers' BI to adopt M-Gov. H3: PEOU positively influences PU of using M-Gov. [START_REF] Pavlou | Integrating trust in electronic commerce with the technology acceptance model: Model development and validation[END_REF] defines Perceived Risk (PR) as 'the consumer's subjective expectation of suffering a loss in pursuit of a desired outcome.' In M-Gov, the effect of PR on BI was significant [START_REF] Althunibat | Modelling the factors that influence m-gov services acceptance[END_REF], [START_REF] Susanto | Factors influencing citizen adoption of SMS-based egovernment services[END_REF]. Similarly, it can be assumed that: H4: PR has a negative significant impact on consumers' BI to adopt M-Gov. [START_REF] Rogers | Complex adaptive systems and the diffusion of innovations[END_REF] described Innovativeness (INN) as 'the level to which an individual is comparatively early in using novel thoughts than other members of a social system.' [START_REF] Al-Busaidi | A model of intention to use m-gov services[END_REF] referred that increased levels of innovativeness led to a higher positive influence on consumers' adoption of M-Gov services. The following hypothesis can be formulated for the M-Gov context: H5: Innovativeness has a significant and positive relationship with consumers' BI to adopt M-Gov.

Methodology

Since the researchers presupposed as having no list of Saudi M-Gov potential adopters and because the citizens are dispersed in different areas, this research employed convenience sampling (i.e. in Jeddah, Riyadh and Dammam). Twenty-two items of the selected constructs were derived from [START_REF] Agarwal | A conceptual and operational definition of personal innovativeness in the domain of information technology[END_REF], [START_REF] Aldás-Manzano | Exploring individual personality factors as drivers of m-shopping acceptance[END_REF], [START_REF] Davis | Perceived usefulness, perceived ease of use, and user acceptance of information technology[END_REF], [START_REF] Featherman | Predicting e-services adoption: A perceived risk facets perspective[END_REF], [START_REF] Karaiskos | Understanding the adoption of mobile data services: Differences among mobile portal and mobile internet users[END_REF] and [START_REF] Zhou | Understanding users' initial trust in mobile banking: An elaboration likelihood perspective[END_REF]. The level of responses was measured by the seven-point Likert scale.

Results

Response Rate

Table 1 outlines the distributed sample along with the returned responses, incomplete and problematic responses, and finally valid responses.

Data Screening

Data screening was able to check whether the data was clean, reliable, useable and valid with a normal distribution before conducting further multivariate statistical analysis such as Structural Equation Modelling (SEM) [START_REF] Coakes | SPSS: Analysis without anguish: Version 14.0 for Windows[END_REF].

Treatment of Missing Data

The amount of missing data and their distribution pattern has been examined in the data set based on [START_REF] Churchill | Marketing research methodological foundation[END_REF] role, and the amount of missing data per construct was less than 0.05 and in an acceptable level. In addition, the p value of missing completely at random (MCAR) was found to be non-significant in the data set (p= 0.483). This indicates that the missing data over the data set is in a random manner and its values also existed non-systematically. All the missing values have been filled by the variable mean value as recommended [START_REF] Tabachnick | Using multivariate statistics[END_REF].

Outliers

Based on AMOS 22.0 outcomes, Mahalanobis-D squared distance (D 2 ) was used to screen multivariate outliers with a p value of 0.000 over the data set as shown below in Table 2. In fact, the small amount of outliers is not problematic in the large sample size [START_REF] Kline | Principles and practice of structural equation modelling[END_REF]. In addition to this, removing outliers may negatively reflect the generalisability of the results. Accordingly, it was decided to keep these outliers, as they could not improve the multivariate statistical results [START_REF] Hair | Multivariate data analysis[END_REF].

Assessment of Normality

Assessment of normality is meant to ensure that the attained data is normally distributed in a symmetrical shape. Accordingly, the univariate normality of each variable was examined by the skewness-kurtosis approach [START_REF] Byrne | Structural equation modeling with AMOS: Basic concepts, applications and programming[END_REF]. According to [START_REF] West | Structural equation models with non-normal variables: Problems and remedies[END_REF], the cut-off point of skewness and kurtosis values should be 3 and 8 respectively. As seen in Table 3 that represents AMOS 22.0 outcomes, the skewness-kurtosis approach was used over the data set, and all statistical values of this approach were found to be in an acceptable range.

Structural Equation Modelling (SEM)

SEM is an approach that is made from two stages. The first stage is the reliability and validity of the constructs and then the fitness of the model are examined via CFA [START_REF] Byrne | Structural equation modeling with AMOS: Basic concepts, applications and programming[END_REF]. The second stage (i.e. structural model) examined the path coefficients between the endogenous variables (i.e. PU, PEOU, PR and INN) and the exogenous construct (i.e. BI) were checked. The relationship was also tested between PEOU and PU.

Measurement Model: Confirmatory Factor Analysis (CFA)

Model Fitness

For the purpose of testing the level of unidimensionality and the model's goodness of fit, a number of fit indices were used as seen below in Table 5 [START_REF] Hooper | Structural equation modelling: Guidelines for determining model fit[END_REF]. All the latent constructs were measured and their own indicators (22 items). The model is fitting the data according to the fit indices. According to [START_REF] Anderson | Structural equation modelling in practice: A review and recommended two-step approach[END_REF], there is a need for a few respecifications and refinements. This process is meant to improve the model fitness by looking at standardised regression weights, modification indices and standardised covariance matrix. It was observed that PR1, 3 and INN1, 5 and 6 had an unacceptable higher value based on the modification indices, and thus all of them were removed. It was also found that the standardised residual values were within the acceptable range of ±2.58 as recommended by [START_REF] Hair | Multivariate data analysis with readings[END_REF]. Finally, and as suggested by [START_REF] Byrne | Structural equation modeling with AMOS: Basic concepts, applications and programming[END_REF], the CFA was tested once more to ensure the goodness of fit of the modified measurement model to see if it was sufficiently enhanced. Accordingly, no extra modifications were conducted regarding the measurement model.

Construct Reliability

Internal consistency (Cronbach's alpha), CR and AVE were employed to check the reliability of each construct after unidimenstionality of the goodness of fit of the measurement model was tested [START_REF] Anderson | Structural equation modelling in practice: A review and recommended two-step approach[END_REF]. Accordingly, and as Table 6 shows, adequate values of internal consistency, CR and AVE were realised.

Construct Validity

The CFA was used to examine the constructs' validity of the conceptual model; both convergent and discriminant. [START_REF] Hair | Multivariate data analysis: A global perspective[END_REF] indicated that all items should have standardised regression weights above the cut-off value of .50. As seen in Table 7, all values were above .50. A threshold that is less than .85 has to be attained for all of the intercorrelation measurement [START_REF] Hair | Multivariate data analysis: A global perspective[END_REF]. According to the correlations table, the measurements of all of the inter-correlations attained this condition. As it can be seen in Table 8, the inter-correlation values with other similar constructs were no more than the square root of AVE given for each latent constructs [START_REF] Fornell | Evaluating structural equation models with unobservable variables and measurement error[END_REF]. Therefore, the conceptual model's estimations achieved a reliable level when it comes to the validity of convergent and discriminant.

Common Method Bias

This study considered Harman's single factor in order to check the common method bias (CMB) in the data set, and the 17 scale items [START_REF] Harman | Modern factor analysis. 3 rd edn[END_REF]. All items in Table 9 were inspected by using an unrotated factor solution after loading the items into the EFA.

According to [START_REF] Podsakoff | Common method biases in behavioral research: A critical review of the literature and recommended remedies[END_REF], the first factor was able to account for less than the cut-off value of 50% as shown in the statistical results (44.864% of variance); otherwise, no factor was able to develop. Thus, the data set is perceived to be reliable, and there are no concerns about the CMB.

Structural Model

The conceptual model was validated and the causal paths between the dependent and independent factors were tested -See Figure 2. The structural model was examined at the second stage of the SEM. The structural model adequately fits the data based on the fit indices (see Table 10). A variance of .50 in BI and of .53 in PU was explained. The standardised estimates of the structural model are detailed below in Table 11.

Discussion

The statistical findings affirmed the predictive power of the adopted conceptual model in interpreting suitable variances explained on the dependent factors (i.e. PU and BI)see Figure 2. In detail, the statistical findings strongly affirmed that PU is a main factor anticipating of BI with (γ=.31). This means that Saudi customers tend to have a stronger behavioural intention to use M-Gov when considering this technology as productive, useful and effective during their routine life. The empirical outcomes asserted that PEOU has significant influence over BI with 17%. As such, the Saudi customers will have a higher behavioural intention to use M-Gov when they perceive the service is easy and only takes a few efforts to use. Indeed, it can be seen that although PEOU has significant influence over BI; yet, it has the least effect amongst other independent variables over BI. This is because the fact that conducting M-Gov requires customers to have some knowledge of using the system which might be difficult in some cases. Interestingly, PEOU significantly influences the PU with (γ=.73). This indicates that customers will perceive M-Gov as a useful service when considering that this service is easy to handle. Accordingly, it is considerably importance to assert the role of INN when pursuing to increase the customers' behavioural intention to use M-Gov. This study shows the significance of this path (γ=.24). This can be explained by the idea that economic prosperity in the Kingdom of Saudi Arabia allows the consumers to experience new technologies and novel products or services such as M-Gov. This study measured the negative importance effect of PR over BI in M-Gov (γ=-.18). Thus, this paper suggests that PR is recognised by Saudi customers as a significant aspect in shaping their behavioural intention to accept or discard the M-Gov in terms of social, financial, performance, and psychological risks.

Contribution

The achievement of this research is estimated to add to the context of M-Gov and to Saudi Arabia's initiatives in developing M-Gov applications as well as citizens' behavioural intention towards using this new promised service. Furthermore, this study theoretically adds to the literature by extending TAM to include two powerful factors to enhance the citizens' intention to shape an actual behaviour.

Conclusion

This study measured the influence of PU, PEOU, INN and PR on BI to use M-Gov within the Saudi Arabian context. The findings indicated that the aforementioned independent factors positively influenced BI; also, PEOU significantly influences PU.

Limitations and Future Research Directions

This study only examines M-Gov which, in turn, adversely reflects on the outcomes' generalisability to other types of mobile services. Future research should consider other contexts and technologies such as the Mobile Internet. As this study considers investigating behavioural intention instead of actual use of M-Gov in Saudi Arabia, future research should provide a complete view about the actual usage in addition to new effective factors to provide a more comprehensive model.

Figure 2 .

 2 Figure 2. Validation of the Conceptual Model

Table 1 .

 1 Response

	Rate
	M-Gov	%
	Sample	600	100
	Returned responses	418	69.67
	Incomplete and problematic responses	22	3.67
	Valid responses	396	66

Table 2 .

 2 Multivariate

		Outliers: Mahalanobis-D squared distance (D 2 )
	Observation number	Mahalanobis d-squared	p1	p2
	61	86.862	.000	.000
	341	70.379	.000	.000
	20	70.017	.000	.000
	225	63.930	.000	.000
	29	63.617	.000	.000
	325	60.903	.000	.000
	158	60.758	.000	.000
	111	52.108	.000	.000
	233	50.717	.000	.000

Table 3 .

 3 Assessment

		of Normality
	Construct		M-Gov
	Perceived Usefulness (PU)	Variable	Skewness	Kurtosis
		PU 1	-1.320	.754
		PU 2	-1.044	.085
		PU 3	-1.025	.034
		PU 4	-1.087	.132
	Perceived Ease of Use (PEOU)	PEOU 1	-.729	-.271
		PEOU 2	-.687	-.370
		PEOU 3	-.733	-.401
		PEOU 4	-.969	.312
	Innovativeness (INN)	INN1	-1.133	1.138
		INN2	-1.287	1.680
		INN3	-.448	-.604
		INN4	-1.038	.584
		INN5	-.861	-.590
		INN6	-.671	-.686
	Perceived Risk (PR)	PR1	.077	1.221
		PR2	.635	2.939
		PR3	.274	.486
		PR4	.072	.378
		PR5	-.037	-.388
	Behavioural Intention (BI)	BI1	-1.415	1.043
		BI2	-1.531	1.420
		BI3	-1.327	.787
	6 Respondents' Profile and Characteristics

Table 4

 4

below shows the demographic characteristics of respondents (i.e. Gender, Age, Education, Occupation, and Monthly Income).

Table 4 .

 4 Demographic Characteristics of Respondents

	Demographic Profile	Number of Respondents (N= 307)	Percentage (%)
	Gender
	Male	228	57.6
	Female	168	42.4
	Total	396	100
	Age
	>=18-20	25	6.3
	21-29	40	10.1
	30-39	230	58.1
	40-49	64	16.2
	50 and above	37	9.3
	Total	396	100
	Education
	Less than High School	7	1.8

Table 5 .

 5 Measurement

	Model

Table 6 .

 6 Constructs

	' Reliability

Table 7 .

 7 Constructs

			' Validity
	Latent Constructs	Items	Factor Loading
	PU	PU1	.915
		PU2	.818
		PU3	.810
		PU4	.835
	PEOU	PEOU1	.803
		PEOU2	.759
		PEOU3	.814
		PEOU4	.813
	PR	PR2	.661

Table 9 .

 9 Common

				Method Bias Test
				Total Variance Explained
	Component		Initial Eigenvalues	Extraction Sums of Squared Loadings
		Total	% of Variance	Cumulative %	Total	% of Variance	Cumulative %
	1	7.627	44.864	44.864	7.627	44.864	44.864
	2	1.802	10.600	55.464
	3	1.298	7.637	63.101
	4	1.200	7.058	70.158
	5	1.096	6.446	76.604
	6	.649	3.816	80.420
	7	.538	3.163	83.583
	8	.466	2.740	86.323
	9	.441	2.594	88.917
	10	.359	2.111	91.028
	11	.314	1.849	92.877
	12	.308	1.810	94.687
	13	.240	1.412	96.099
	14	.211	1.242	97.340
	15	.180	1.060	98.400
	16	.164	.962	99.363
	17	.108	.637	100.000
			Extraction Method: Principal Component Analysis.

Table 10 .

 10 Structural Model

	Fit Indices	Cut-off Point	Structural Model
	CMIN/DF	≤3.00	1.714
	GFI	≥ .90	.948
	AGFI	≥ .80	.928
	NFI	≥ .90	.958
	CFI	≥ .90	.982
	RMSEA	≤ 0.08	.043

Table 11 .

 11 Standardised Estimates of the Structural Model

	Path	Standardised Estimate	Critical Ratio	P-value	Significance
	PU→BI	.308	4.500	***	Significant
	PEOU→BI	.173	2.418	.016	Significant
	PR→BI	-.184	-3.306	***	Significant
	INN→BI	.243	4.334	***	Significant
	PEOU→PU	.729	13.090	***	Significant


Milan Dordevic
email: milan.dordevic@aum.edu.kw

Fadi Safieddine
email: fadi.safiedinne@aum.edu.kw

Wassim Masri
email: wassim.masri@aum.edu.kw

Pardis Pourghomi
email: pardis.pourghomi@aum.edu.kw

Combating Misinformation Online: Identification of Variables and Proof-of-Concept Study

Keywords: Misinformation, Information, Simulation, Social Media, Authentication

The spread of misinformation online is specifically amplified by use of social media, yet the tools for allowing online users to authenticate text and images are available though not easily accessible. The authors challenge this view suggesting that corporations' responsible for the development of browsers and social media websites need to incorporate such tools to combat the spread of misinformation. As a step stone towards developing a formula for simulating spread of misinformation, the authors ran theoretical simulations which demonstrate the unchallenged spread of misinformation which users are left to authenticate on their own, as opposed to providing the users means to authenticate such material. The team simulates five scenarios that gradually get complicated as variables are identified and added to the model. The results demonstrate a simulation of the process as proof-of-concept as well as identification of the key variables that influence the spread and combat of misinformation online.

Introduction

The process by which information and misinformation travels online and specifically by social media users has been the subject of several publications [START_REF] Lee | Who will retweet this?: Automatically identifying and engaging strangers on twitter to spread information[END_REF][START_REF] Hoang | Virality and Susceptibility in Information Diffusions[END_REF][START_REF] Jin | Epidemiological modeling of news and rumors on twitter[END_REF][START_REF] Budak | Limiting the spread of misinformation in social networks[END_REF][START_REF] Safieddine | Corporate Responsibility in Combating Online Misinformation[END_REF]. The challenges in combating misinformation on social media could be greatly enhanced should researchers be able to simulate the different scenarios of information and misinformation cascades. Specifically here, researchers need to consider the factors involved in the travel of misinformation and the factors involved in combating the spread of misinformation. In this paper, the authors identify the factors that influence the travel of information and misinformation as both theoretically start from one single node and travel across a network of nodes and points. Variables are identified for which the authors develop a fuller picture of what influences the process, speed, and success pace of fighting misinformation online. In the process of identifying these variables, the team simulates five scenarios as new variables are identified with each simulation and added to the model.

Literature Review

Oxford Internet Survey of 2013 results show online social networks as becoming one of the key sources of information and news especially among younger generations [START_REF] Dutton | Cultures of the Internet: The Internet in Britain[END_REF]. Thus, the spread of misinformation has increased as a result of the increase in the number of people using social networks [START_REF]The rapid spread of misinformation online[END_REF]. Due to the lack of accountability of social media users spreading information and not having appropriate filtering techniques similar to reviewing and editing information in traditional publishing, social media have become a significant media for the spread of misinformation [START_REF] Budak | Limiting the spread of misinformation in social networks[END_REF]. Thus, the spread of diverse forms of information, misinformation, and propaganda involves the distribution of false information through an information diffusion process involving users of social networks where the majority of users may not be attentive to the untruth story. In one study, researchers state that the acceptance of misleading information by the people greatly depends on their prior beliefs and opinions [START_REF] Libicki | Conquest in cyberspace: National security and information warfare[END_REF]. In another study [START_REF] Karlova | Plz RT: A social diffusion model of misinformation and disinformation for understanding human information behaviour[END_REF], researchers state that the spread of misinformation in online social networks is context specific with topics such as health, politics, finances, theology, and technology trends are key sources of misinformation. People believe things which support their past thoughts without questioning them [START_REF] Kumar | Detecting misinformation in online social networks using cognitive psychology[END_REF]. We have used the term misinformation to denote any type of false information spreading in social networks.

Considering the dark side of social networks, the environment facilitates the arrangement of groups and campaigns with the intention of undertaking unethical activities as well as mimicking widespread information diffusion behavior [START_REF] Budak | Limiting the spread of misinformation in social networks[END_REF], [START_REF] Kumar | Detecting misinformation in online social networks using cognitive psychology[END_REF]. Consequently, this facilitation of potential misconducts in online environments has encouraged some users to spread misinformation that results in greater support to cult-like views in a variety of topics [START_REF] Safieddine | Corporate Responsibility in Combating Online Misinformation[END_REF]. What is more, those views are sometimes contagious and the individuals behind them make great efforts to spread them to others. The persistence of misinformation in the society is dangerous and requires analysis for its prevention and early detection [START_REF] Kumar | Detecting misinformation in online social networks using cognitive psychology[END_REF][START_REF] Lewandowsky | Misinformation and its correction continued influence and successful debiasing[END_REF][START_REF] De Neys | Biased but in doubt: Conflict and decision condence[END_REF]. The lack of accountability and verifiability however afford the users an excellent opportunity to spread specific ideas through the network while not discouraging freedom of expression and freedom of ideas [START_REF] Budak | Limiting the spread of misinformation in social networks[END_REF].

In online social networks, the enormous distribution of data has resulted in persistent pockets of misinformation. Thus finding reliable information requires sifting-out the different types of misinformation in online social networks which has become a computationally puzzling task [START_REF] Kumar | Detecting misinformation in online social networks using cognitive psychology[END_REF].

Related Work

In a research conducted by Lee et al. [START_REF] Lee | Who will retweet this?: Automatically identifying and engaging strangers on twitter to spread information[END_REF], the authors aimed at identifying and engaging "information propagators" which refers to people willing to help propagate information on social media. By modelling their characteristics and using that model to predict their willingness to propagate information in the future, the authors have been able to identify three characteristics of people willing to propagate information and misinformation online. These characteristics are: (1) personal traits of users such as personality and readiness to share or pass on that information; (2) the wait-time of a user based on the previous time lapses between passing on the information to predict the next time they share that information again; and (3) a recommender system based on the two previous components to select the right set of users with a high likelihood of Re-sharing of information. While the paper focuses on Twitter as an example, parallels could be drawn to other social media applications. In a research conducted by Hoang and Lim [START_REF] Hoang | Virality and Susceptibility in Information Diffusions[END_REF], the authors aimed to identify and model factors that contribute to viral diffusion based on the interrelationships among items, users, and the user-user network. This time the team categorized these factors into two sets. The first set includes external factors such as advertising, while the second set includes internal factors such as: a) Item virality which is the ability of an item to spread the adoptions by users through the follow links; b) the virality of the users diffusing the item which is their ability to spread the adoptions to other users through the follow links; and c) the susceptibility of the user adopting the item, which is the ability of a user to adopt items easily as other neighbouring users diffuse the items to others. The authors then proposed a Mutual Dependency Model that measures all three factors above simultaneously based on a set of principles that help to distinguish each property from others in viral diffusion. In a research conducted by Jin et al. [START_REF] Jin | Epidemiological modeling of news and rumors on twitter[END_REF] the authors applied epidemiological modelling techniques to understand information diffusion on Twitter, in relation to the spread of both news and rumours. Epidemiological models are usually used to better understand how information diffuses by dividing users into several groups that reflect their statuses. The possible groups in which a user has been classified are: susceptible (S), exposed (E), infected (I), and recovered (R). Users could move from one group to another with a certain probability that could be estimated from data. Several models were introduced such as the SI model in which a susceptible (S) user can get infected (I) by one of his neighbours and will stay permanently in this state; SIS model where users can move back and forth between being (S) susceptible and (I) infected; the SIR model where users could move to a recover (R) state which is not really used to in news cascades models; and a model called SEIZ model (susceptible, exposed, infected, sceptic) proposed originally by [START_REF] Bettencourt | The power of a good idea: Quantitative modeling of the spread of ideas from epidemiological models[END_REF] [START_REF] Bettencourt | The power of a good idea: Quantitative modeling of the spread of ideas from epidemiological models[END_REF] which added a new state: exposed (E). [START_REF] Jin | Epidemiological modeling of news and rumors on twitter[END_REF] suggested instead to represent the case where a user may take some time while in the exposed (E) state before believing a rumour (i.e. move to an infected (I) state).

For simulating spread of misinformation online, Budak et al. [START_REF] Budak | Limiting the spread of misinformation in social networks[END_REF] presented a network algorithm that could be tested in case of two competing campaigns using two variations of the Independent Cascade Model (ICM) termed: (1) Multi-Campaign Independent Cascade Model (MCICM) and (2) Campaign-Oblivious Independent Cascade (COICM) to consider how information and misinformation spreads online. The paper, theoretically, relies on the design of the system itself and the input of 'influential' people to counter 'bad' campaign and limit misinformation. This could potentially be useful during time-sensitive political campaigns or breaking news events. Budak et al. acknowledge a limitation shared by other publications in this area when it comes to lab modelling of information diffusion acknowledging that lab models may not reflect the full extent of influences in real life. Thus, lab simulations will still need to be tested in the real world of social media [START_REF] Budak | Limiting the spread of misinformation in social networks[END_REF] p. 667.

While previous work has provided important literature into the behaviour and challenges of spreading and combating misinformation online, there does not seem to be one uniform method to how the spread of information is modelled. Nor does there seem to be uniformed agreed method for modelling the spread of misinformation. In addition, despite repeated review of the literature, the team could not find any viable or applied proposal on how to combat the spread of misinformation online.

The 'Right-click Authenticate' in combating misinformation online

In a prior publication [START_REF] Safieddine | Corporate Responsibility in Combating Online Misinformation[END_REF], the authors suggested an approach to combating misinformation on social media. The team proposed an automated approach, dubbed as 'Right-click Authenticate' option that could review, rank, and identify misinformation using tools already found online. However, these tools have not been combined together in a setup that helps online users in their pursuit of authentication of the information they view. Three categories of authentication have been identified: textual, imagery, and video misinformation, however the paper focused on the first two: Textual and imagery authentication. In that process, users who are unsure about the content could right-click and select authenticate as conceptualized in figure 1.

Using reverse image search [START_REF] Martin | How to do a reverse Google Image search on Android or iPhone[END_REF], a search that requires the user to upload an image or copy the image's web address to search for matches to that actual image online, users are able to identify the sources and dates of the first appearances of that image online as well as the context in which the image is presented. Some of the highly refined reverse image searches are able to detect even modifications of the image including color tones changes, photo editing, cropping and writing made onto the original image. Second layer is to validate any meta-data linked with the questioned image including the camera used, date it was created, and what photo-editing tools have been used. Meta-data may also help detect if any image editing tools have been used [START_REF] Buchholz | On the role of file system metadata in digital forensics[END_REF][START_REF] Castiglione | A Forensic Analysis of Images on Online Social Networks[END_REF]. The third part is an editorial feedback written in the same format and style Wikipedia operates authentication of information [START_REF]Wikipedia : Version 1.0 Editorial Team. Wikipedia, The Free Encyclopedia[END_REF] with regards to the authenticity of that image. Image editorial feedback is combine with explanations based on the origin, date, meta-data, where the image appears online, or article that dismisses or confirms that image. Finally, a crowdsourcing of feedback is represent the final indication on what the majority of users judging this information. These four sections are combined as: Image Match, Image Metadata, Editorial, and Feedback respectively. The solution is the bundling of these four sections into one single right-click option as conceptualized in figure 2. To ensure the successful results, the same algorithm used for online search engines to be used here. Thus, images that get frequently selected as a match to get higher ranking than those images that do not get selected as a match.

Fig. 2. Conceptualization of the 'Authenticate' outcome [START_REF] Safieddine | Corporate Responsibility in Combating Online Misinformation[END_REF] The right-click search authenticate option can also be used to authenticate a selection of news by title or text since the option to select and search text is already a wellestablished right-click search option on variety of browsers [START_REF] Safieddine | Corporate Responsibility in Combating Online Misinformation[END_REF]. Another benefit for this right-click authenticate for images and text is that copyright infringements on intellectual rights are become more easily detected. In the paper, the authors acknowledge that new images and breaking news to require longer time to be authenticated. This proposal just based on theory and has not been simulated, implemented, or tested.

Research Questions and Methodology

The team acknowledges that the 'right-click authenticate' method for tackling the spread of misinformation needs to be demonstrated and proven as proof-of-concept. This paper attempts to further develop this theory to answer some key questions:

1. What variables are influencing the spread of information and misinformation on social media?

2. By means of simulation, can the process by which information and misinformation be modeled as proof-of-concept?

The team used graph theory computational simulation with observational research method [START_REF] Osmond | Reflective analysis: Techniques for facilitating reflection[END_REF]. In the process of identifying variables, the team used reflective analysis [START_REF] Altmann | Observational study of behavior: sampling methods[END_REF] to review progressively different scenarios in the spread of information and misinformation on social media. This approach is comparable to other approaches identified in the literature [START_REF] Lee | Who will retweet this?: Automatically identifying and engaging strangers on twitter to spread information[END_REF][START_REF] Hoang | Virality and Susceptibility in Information Diffusions[END_REF][START_REF] Jin | Epidemiological modeling of news and rumors on twitter[END_REF][START_REF] Budak | Limiting the spread of misinformation in social networks[END_REF].

In lab conditions, the team observed the different two-dimensional simulations of information as it travelled from the source to a theoretical maximum reach. The two-dimensional simulation represents a slice of what a real-world multi-dimensional simulation of information would likely resemble. Successively analyzing and observing simulations of scenarios, the team subsequently evolved their model of simulation to identify and introduce new variables. With the introduction of new variables, a reflective analysis considered the logical impact of the new variable. Changes to the simulation and the justifications are then considered. While conducting the simulation, the team suggested values for such variables that are not based on any specific scenario or research, but solely for the purpose of facilitating a reflective analysis to re-evaluate the simulation and considering missing factors.

One of the main assumptions agreed at the start of the simulation is that the phenomena by which information and misinformation travels can be simulated despite unpredictability generally dominating human behavior online. This assumption is consistent with other academic publishers in this area of research. Without a preset of simulation scenario or the number of variables, the team developed five simulations and identified a total of ten variables. The demonstration, simulation, and identification of variables presented in this paper will be extended in further research aiming to design a formula by which success rate of combating misinformation online could be used for computational simulation.

Variables and Graph Modelling

Spread of misinformation in social networks can be modeled by using graph theory. The team considered a weighted directed graph 𝐺 = (𝑉, 𝐸) consisting of 𝑉 vertices and edges 𝐸. 𝑉 can be viewed as the users of the social network. Among the vertices in 𝑉, the team distinguishes two types of vertices:

(1) Vertices which belong to set 𝑆, the set of sharing vertices, which represents users that send and receive information;

(2) Vertices which belong to set 𝑅, the set of reading vertices, which represents users who only receive information-accordingly 𝑅 ⊆ 𝑆. A vertice 𝑟 is a neighbor of a vertice 𝑠 if and only if there is 𝑒 𝑠,𝑟 ∈ 𝐸, an edge from 𝑟 to 𝑠 in 𝐺. Furthermore, all vertices from 𝑉 can be divided into subsets (layers) depending on length 𝑙. Where 𝑉 = 𝑉 1 ∪ 𝑉 2 ∪ … ∪ 𝑉 𝑙 from figure 3.

Fig. 3. Graph of Misinformation Modeling

Assuming 𝑖 and 𝑗 are any vertices of the given graph, the vertices 𝑖 and 𝑗 𝑛 are connected by certain chains of edges going through different layers. The main goal of the team's approach is to see the effect of cascade labeling in models that they created. Note that cascade labeling symbolizes pressing the Right-click 'Authenticate'.

As illustrated in figure 3, by selecting edge 𝑒, 𝑤ℎ𝑒𝑟𝑒 𝑒 ∈ 𝐸, the entire sub graph 𝐺 ′ , from 𝑘 1 ∈ 𝑉 2 to 𝑗 1 ∈ 𝑉 𝑙 , where 𝑙 stands for length, is colored in red. This step is known as cascade labeling. Subsequently, this cascade labeling results in coloring some of the vertices from 𝐺 into red. Coloring in red symbolizes the node authentication of the information to be untrue and the exclusion of sharing misinformation. The authors assume that pressing the 'Right-click Authenticate' can happen more than once in a demonstrated model.

Given graph 𝐺 by sequentially repeating the cascade labeling process i.e. pressing the 'Right-click Authenticate' button, the number of vertices colored in red increases while the number of vertices colored in black decreases.

Since selecting an edge 𝑒 results in coloring some vertices of sub graph 𝐺 ′ into red, repeating the same process on any other edge from 𝐸 in a graph 𝐺 will result in coloring some more vertices into red.

Eventually, after 𝑛 repetitions of this process in graph 𝐺, all vertices from subset 𝑉 𝑙 can be colored in red. Therefore, by implying cascade labeling procedure, some of the destination vertices 𝑗 1 , 𝑗 2 , … , 𝑗 𝑛 will be preserved of receiving misinformation.

The model in figure 3 is assuming that only one vertex authenticates the information and passes that information on. The first vertex to authenticate and turn from black to red is modeled as red with black line and labeled as vertex 𝑠 2 ∈ 𝑆 2 , where 𝑆 2 ⊆ 𝑉 2 . The extended version of that model is shown in figure 4.

Fig. 4. Extended Graph of Misinformation Modeling

In the next scenario, the team studied next three variables that need to be considered in combating misinformation online: rate of authentication, rate of sharing, and rate of cross-wire.

The rate of Authentication (𝐴) is a variable that represents the rate of users willing to authenticate the information. This usually occurs when online users are not sure of an information or when they get conflicting information. Thus, these users might decide to authenticate such information to start a correction cascade or at least stop the cascade of misinformation from their part.

The rate of Authentication (𝐴) could be anything between 0 and 100%; although the team acknowledges it is unlikely to be either extreme. For the simulation in figure 5, the team predicts that the percentage of users who will authenticate to be around 30%. Thus for the simulation purposes, the team have assumed the probability of authentication as 𝐴 = 0.3.

The passing on Rate (𝑃) is a variable that represents the ratio of users who read the information and then perform an action of actively disseminate it further. Thus, the ratio shows the probability that vertex which authenticate will pass that correct information to anyone else as well as the ratio of those vertex that pass on misinformation.

The synonyms used for passing on rate are average of forwarding, liking, and sharing rate. We assume that the rate of willingness to share is probably the same for those who believe the misinformation. To demonstrate this scenario, the team assumed the probability of sharing information by online users regardless they believe it or not to be 𝑃 = 0.5. Although if the research determines differences in sharing between those who believe and those who do not believe the information, variations of this variable could be created as 𝑃 1 for those who believe the information to be true and 𝑃 𝑜 for those who do not believe it.

The Cross-Wire (𝐶𝑤) is a variable that represents the probability that user who received different information from different sources will react to validate. In such a case, online users exposed to misinformation are sufficiently skeptical to question it and use the 'Right-click Authenticate' to validate it. In figure 5 vertex 𝑐 1 received different information from sources 𝑎 and 𝑏 and accept the information received from 𝑎 while discard the misinformation received from 𝑏. For the purpose of simulation, the team assumed 𝐶𝑤 = 0.3. The simulation in figure 5 shows the usage of variables 𝐴, 𝑃 and 𝐶𝑤. Moreover, the figure shows how the speed of misinformation spread is slowed down compared to the scenario in figure 4 and again for those who authenticate the information.

As demonstrated in figure 5, providing means of authentication can have important impact on the spread of misinformation online. Red nodes are shown to be playing a role in limiting the spread of misinformation.

For the next simulation, the team considers Same Level Communication (𝑆𝑙) as a variable that represents the probability that users who authenticate information and leave feedback encourages other users from the same level also to authenticate. That includes passing on vertices on the same level thus turning several of these vertices from black to red. For example, in figure 6, vertices 𝑐 1 and 𝑐 2 have validated the misinformation. Provided 𝑐 1 or 𝑐 2 left a feedback, this turns the remaining vertices from black to red. The same happens to 𝑐 3 , 𝑐 4 , 𝑎𝑛𝑑 𝑐 5 .

The team assumes that all other variables and assumptions are kept in place.

A vertex that is red or just turned red with probability 0.5 will alert other online users that the information is not true. In such scenario, the team assumes that other vertices will, in turn, discard the misinformation and turn into red. The reason behind this assumption is that the first online user to authenticate has a greater impact on subsequence online users. So the team assumed that probability of Same Level Communication is 𝑆𝑙 = 1.

Considering the rate of authentication 𝐴, passing on information rate 𝑃, average crosswire rate 𝐶𝑤, success rate of Same Level communication rate 𝑆𝑙, where 𝐴 = 0.3, 𝑃 = 0.5, 𝐶𝑤 = 0.3, and 𝑆𝑙 = 1 respectively, excluding misinformation does not extend beyond 𝑉 4 . The simulation of this scenario specific lab scenario, demonstrated in figure 6, where at level 𝑉 4 all vertices are red. 𝑅𝑣 represents a probability that the user who initially believed the misinformation, while being informed by other users through their feedback that the information is not true, either removes the post or rectify the post, thus turning red node themselves. However, to differentiate them from other red node, the team decided to label such node green. This is a backflow to a previous or source vertex. The output of applying the 𝑅𝑣, is shown in figure 7 as green vertices. The team considered this final variable at probability that the source vertex will take action to rectify the misinformation as 𝑅𝑣 = 0.5.

5

Results and Limitations

The combinations of all these variables and the assumptions that the team made to understand how combating misinformation works has resulted in identifying some key variables where 𝑖 is the first vertex and 𝑗 𝑛 is the last vertex of the given simulation. 𝑉 1 represents the first phase of spread of misinformation and 𝑙 represents the maximum possible reach of information through the network. The authors conclude that combating misinformation online is also be influenced by the following variables: rate of authentication 𝐴, passing on information rate 𝑃, average cross-wire rate 𝐶𝑤, success rate of Same Level communication rate 𝑆𝑙, and Reverse Validation rate 𝑅𝑣. Thus the paper demonstrates by means of simulation how misinformation travels online. The paper also shows how 'right-click authenticate' process can reduce the spread of misinformation online. Thus suggesting a viable solution for combating misinformation online by identifying and demonstrating key variables and factors.

The proof-of-concept has been constrained with assumptions that are based mostly on observations of computer simulation and reflective analysis subjective to individual experiences of the team. However, the approach has been backed by similar observations done in other academic publications [START_REF] Lee | Who will retweet this?: Automatically identifying and engaging strangers on twitter to spread information[END_REF][START_REF] Hoang | Virality and Susceptibility in Information Diffusions[END_REF][START_REF] Jin | Epidemiological modeling of news and rumors on twitter[END_REF]. The team acknowledges that the proposed variables may not be exclusive, and that further research may reveal additional factors influencing the travel of information and the means of combating misinformation online. Furthermore, the identification of the variables is lab based and further proof should be drawn from examples from existent event observations once the formula is developed. This is a limitation acknowledged in the literature when it comes to lab modeling as opposed to real life simulation [START_REF] Budak | Limiting the spread of misinformation in social networks[END_REF]. The 'right-click authenticate' process has two key limitations in application and implementation [START_REF] Safieddine | Corporate Responsibility in Combating Online Misinformation[END_REF]. In application, the authors acknowledge that the 'authentication' option has little or no real impact at authenticating breaking news. For the process to work, time is needed for the information or image to be authenticated and a review written. For the implementation limitation, the building of the 'right-click authenticate' option requires authorization and collaboration from a reverse image search engine, which may not be forth coming.

Conclusion

The team set out to demonstrate a proof-of-concept and identified the variables involved in the travel of information and the 'Right-click Authenticate' idea suggested in a previous publication [START_REF] Safieddine | Corporate Responsibility in Combating Online Misinformation[END_REF]. The team believes that some headway has been achieved but that still work to be done to develop the formula and conduct simulations to further validate the concept. Two parallel lines of further research are expected to follow. First, the team will be working towards developing the formula and run computational simulations of the formula using MATLAB and BioLayout Express for three dimensional simulation. Second and equally important, the team intend to develop a prototype browser based on an existing open source applications that allows demonstration of the concept and the running of actual simulations thus allowing lab and field simulations.

Fig. 1 .

 1 Fig. 1. Conceptualizing a right-click 'Authenticate' option[START_REF] Safieddine | Corporate Responsibility in Combating Online Misinformation[END_REF]

Fig. 5 .

 5 Fig. 5. The Authenticate, Passing on rate, and Cross-Wire rate simulation

Fig. 6 .

 6 Fig. 6. Reverse Validation (𝑅𝑣). Fig 7. Same Level Communication (𝑆𝑙) For the final simulation, the authors have considered Reverse Validation (𝑅𝑣) Variable.𝑅𝑣 represents a probability that the user who initially believed the misinformation, while being informed by other users through their feedback that the information is not true, either removes the post or rectify the post, thus turning red node themselves. However, to differentiate them from other red node, the team decided to label such node green. This is a backflow to a previous or source vertex. The output of applying the 𝑅𝑣, is shown in figure7as green vertices. The team considered this final variable at probability that the source vertex will take action to rectify the misinformation as 𝑅𝑣 = 0.5.


Prof Pedro Manuel
email: manuelp@ugr.es

Rodríguez Bolívar

Political Factors for the Adoption of Different Governance Models in the Provision of Public Services under Web 2.0 Technologies

Keywords: Web 2.0 technologies, governance models, public services, political factors

This paper contributes to the current debate on Web 2.0 technologies and their implications for local governance through the analysis of the perceptions of policy makers of local governments about the governance model to be adopted in the management of Web 2.0 applications for the delivery of public services. Also this paper analyses political factors as attributes that could explain the governance patterns to be adopted by municipalities. To achieve this aim, an e-survey has been performed by policy makers in Spanish municipalities. Findings indicate that policy makers are mainly prone to implement Web 2.0 technologies under the "Bureaucratic Model" framework, keeping the leading role in this implementation. Nonetheless, right-wing ideologists and majority governments are prone to implement collaborative models of governance, whereas left-wing ideologists and minority governments are in favor to implement non-collaborative models of governance.

Introduction

The use of Web 2.0 technologies in the public sector could be relevant to undertake the transformation of the roles played by citizens, who will no longer be mere 'endusers', but will become partners and co-creators of information and services [START_REF] Johnston | Design lessons for smart governance infrastructures[END_REF],

with the aim at obtaining more citizen-oriented services, which has led to a blurred distinction between production and consumption. Nonetheless, the use of social media introduces a number of policy problems, such as the interpretation of the information shared in networks or the loss of significant control over the content and applications [START_REF] Graells-Costa | Administración colaborativa y en red[END_REF].

So, the implementation of Web2.0 technologies in public administrations is forcing a reconsideration of the administrative structures of governments and the fostering of open, user-driven governance [START_REF] Bertot | Crowd-sourcing transparency: ICTs, social media, and government transparency initiatives[END_REF]. In fact, a lively debate in the political arena is the governance model to be used in managing Web 2.0 technologies, which has led to the service-focused uses to be the holy grail of social computing, possibly the most difficult to implement successfully but most impactful if successful [START_REF] Chang | Leveraging Web 2.0 in Government[END_REF].

This paper is focused on the field of public sector management and the influence that political variables could have in the perceptions of policy makers to choose the governance model if public administrations embrace Web 2.0 technologies in public sector delivery. To achieve this aim, a questionnaire has been designed and sent to policy makers of local governments in Spain. The findings indicate that policy makers want to play the leading role in the implementation of Web 2.0 technologies using the Bureaucratic model of governance.

This paper is structured as follows. Section 2 discusses the governance models under the Web 2.0 era and the influence of political attributes. Section 3 describes the empirical research and, finally, the discussion and conclusions bring the paper to an end.

2 Web 2.0 technologies. A new way for co-production of public services and public sector governance

Web 2.0 Technologies in delivering public services and public governance

The implementation of Web 2.0 technologies has emphasized the idea of coproduction in public sector services [START_REF] Emerson | Ethical Attitudes of Accountants: Recent Evidence from a Practitioners' Survey[END_REF]. Four citizen co-production initiatives in the age of social media can be identified, termed "Citizen Sourcing" (G2C), "Government as a Platform" (C2G) and "Do it Yourself Government" (C2C) and "Bureaucratic Model" (G2G), in accordance with the interaction between citizens and government and with how citizens are involved in the co-production of public services [START_REF] Linders | From e-government to we-government: Defining a typology for citizen coproduction in the age of social media[END_REF][START_REF] Bolívar | Governance Models for the Delivery of Public Services Through the Web 2.0 Technologies A Political View in Large Spanish Municipalities[END_REF].

Under the "Bureaucratic Model" of governance (G2G), the government designs the strategy for the use of Web 2.0 technologies, produces the content and manages the network. Governments only seek to provide an innovative channel for its online representation and for broadcasting of government information about public services via social media sites [49] in order to reach a wider spectrum of citizens, what has been called as "screen-level bureaucracy" [START_REF] Bovens | From Street-Level to System-Level Bureaucracies: How Information and Communication Technology is Transforming Administrative Discretion and Constitutional Control[END_REF].

In the model termed "Citizen Sourcing" (G2C), citizens help governments improve public sector delivery in a participative framework [START_REF] Nam | Suggesting frameworks of citizen-sourcing via Government 2.0[END_REF] and share their opinions with government through social media channels [START_REF] Linders | From e-government to we-government: Defining a typology for citizen coproduction in the age of social media[END_REF]. Citizen-sourcing is, therefore, conducive to civic learning and may change the government's perspective on the public to "makers and shapers" of policies and decisions [START_REF] Lukensmeyer | Citizensourcing: Citizen participation in a networked nation[END_REF].

Under the label "Government as platform" (C2G), the government urges citizens to actively collaborate in the design and creation of public services. Social media channels are used to increase interactivity with citizens or to work in collaboration with government stakeholders on innovative ideas to fulfil the mission of government [START_REF] House | The Open Government Initiative[END_REF].

Finally, under the "Do It Yourself Government" (C2C) environment, citizens govern themselves with little or no interference from the government. Citizens are interconnected through social media applications [START_REF] Dunleavy | The second wave of digital era governance[END_REF] and form an important addition to the government-centric form of public service provision, by fostering both the exchange of experiential information and social-emotional support [START_REF] Meijer | Networked of Public Services in Virtual Communities: From a Government-Centric to a Community Approach to Public Service Support[END_REF].

All previous governance models should not be viewed as mutually exclusive. By contrast, governments can experiment some of these governance models over time as if they were a continuum in a line of development of governance of Web 2.0 technologies for providing public sector services. It determines the role played by local governments as a continuum of top-down to bottom-up processes from "executor (commissioner)" to "initiator (facilitator)" [START_REF] Span | The relationship between governance roles and performance in local public interorganizational networks: A conceptual analysis[END_REF] -Figure 1-.

Fig 1.

Classifying citizen co-production initiatives and role of government in the age of social media (Source: 19, 36).

Models for governance Web 2.0 Technologies in delivering public services and political attributes

Political structures could affect both the likelihood and nature by which e-government activity enhances citizen participation [START_REF] Ahn | Politics of E-Government: E-Government and the Political Control of Bureaucracy[END_REF] and the decisions taking regarding public services [START_REF] Ya Ni | The Decision to Contract Out: A Study of Contracting for E-Government Services in State Governments[END_REF]. According to [START_REF] Bel | Why do local governments privatise public services?. A survey of empirical studies[END_REF] and [START_REF] Ya Ni | The Decision to Contract Out: A Study of Contracting for E-Government Services in State Governments[END_REF], politicians have preferences for some policies over others according to their ideological attitudes. Indeed, political Ideology refers to a set of political beliefs or a set of ideas that characterize a particular culture. Right-wing parties have been linked to more pro-private business values (privatization) and to adopt e-government for collaboration with third parties to achieve efficiency and a cost reduction [START_REF] Tolbert | Institutions, policy and e-government in the American States[END_REF][START_REF] García-Sánchez | The Relationship between Political Factors and the Development of E-Participatory Government[END_REF], whereas left-wing organizations are conventionally associated with public values (public production). Under this framework, leftwing ideologists see the "do-it-yourself-state" as an instrument for empowerment whereas right-wing ideologists do not see a need for empowerment since this would challenge the existing order [START_REF] Meijer | The Do It Yourself State. The future of participatory democracy[END_REF]. Therefore, our first political hypothesis is derived:

H1: Governments that operate in a more conservative political environment (rightwing governments) are more likely to adopt collaborative governance models of Web 2.0 technologies in the delivery of public services for ideological reasons.

On the other hand, [START_REF] Smith | Delegating direct democracy: Interparty legislative competition and the adoption of the initiative in the American states[END_REF] argue that political competition plays a key role in the decision of politicians to devolve institutional power to citizens. Political competition is the rivalry for the capacity to influence or determine official governmental decisionmaking and action on questions of public policy. Majority governments are those with increased political leadership of the government team and robustness in the face of managing public services [START_REF] García-Sánchez | The Relationship between Political Factors and the Development of E-Participatory Government[END_REF], whereas minority governments are in the opposite situation. By contrast, prior research demonstrates that a high degree of political competition can create a favourable environment for technological reforms [START_REF] Tolbert | Institutions, policy and e-government in the American States[END_REF] and egovernance [START_REF] Sriramesh | E-government in a corporatist, communitarian society: The case of Singapore[END_REF]. Thus the following hypothesis is derived:

H2: Minority governments are less likely to adopt non-collaborative models of governance of Web 2.0 technologies for political reasons because of political checks and balances.

Another political incentive of the development of e-government is the political stability [START_REF] García-Sánchez | The Relationship between Political Factors and the Development of E-Participatory Government[END_REF][START_REF] Krishnan | Moderating effects of governance on information infrastructure and e-government development[END_REF]. Although political stability is not a well-defined concept, it means the durability and integrity of a current government regime. In accordance with prior research, technological infrastructure and political stability are crucial factors for ICT-Non-collaborative Executor (commissioner) Partner (co-producer) Initiator (facilitator) "Bureacratic Model" (G2G) X "Citizen Sourcing" (G2C) X "Government as a Platform" (C2G) X "Do it Yourself Government" (C2C) X Collaborative Co-production initiatives/Role of Governments led development because its payoff will become apparent only in the medium to long term [START_REF] García-Sánchez | The Relationship between Political Factors and the Development of E-Participatory Government[END_REF]. In addition, [START_REF] Meso | Information infrastructure, governance, and socio-economic development in developing countries[END_REF] emphasized that the level of political stability has the potential to influence the level of engagement by local citizens in productive economic activity. Therefore, the following hypothesis is derived: H3: Governments in a more stability environment of the ruling party are more likely to adopt e-participatory government and, therefore, collaborative models of governance of Web 2.0 technologies for providing public sector services.

Finally, another political incentive to adopt participatory governments is the political strength, which can be defined as the party fragmentation of the local council. According to [START_REF] Roubini | Political and Economic Determinants of Budget Deficits in the Industrial Democracies[END_REF], coalition governments may experience some kinds of weakening due to internal conflicts. These governments show some problems of coordination and are less effective in undertaking budgetary reforms, which can affect to the implementation of e-government technologies. Therefore, the following hypothesis is derived: H4: Governments with a high political strength are more likely to adopt eparticipatory government and, therefore, collaborative models of governance of Web 2.0 technologies for providing public sector services.

3

Policy makers' perceptions on Web 2.0 implementations in Spanish local governments

Sample selection

Large cities have been at the forefront in the adoption of e-government innovations [START_REF] Moon | The evolution of e-government among municipalities: Rhetoric or reality?[END_REF][START_REF] Ho | Reinventing local governments and the e-government initiative[END_REF] and use mechanisms that permit direct citizen involvement [START_REF] Peters | The future of governing[END_REF] due to the tradition of citizen participation at the local level [START_REF] Oakerson | Governing local public economies: Creating the civic metropolis[END_REF]. Thus, this paper focuses on large Spanish local governments due to the managerial devolution process implemented in Spain in the 1990s [START_REF] Gallego | Public Management policymaking in Spain: The Politics of Legislative Reform of Administrative Structure, 1991-1997[END_REF] and their rapid introduction of new technologies to provide e-services [START_REF]eEspaña. Informe anual 2014 sobre el desarrollo de la sociedad de la información en España[END_REF]. Sample Spanish municipalities are those with a population of over 50,000 inhabitants, together with those which, although smaller in terms of numbers are provincial capitals, regional capitals or in which the headquarters of regional institutions are located. In total, 148 Spanish municipalities meet these conditions, and account for over 50% of the total population of Spain [37].

Data were obtained by sending a link to perform an e-survey, which was sent to the policy makers of sample local authorities via email. The contact details were obtained from the Spanish central government's website. 7 sample municipalities had not yet introduced social networks, and thus neither had experience of Web 2.0 nor dedicated human resources to this area. Therefore, the questionnaire was sent to 141 local governments and 46 complete replies were received from policy makers -response rate, 32.62%-. Nonetheless, there were 107 incomplete responses to the questionnaire and, in consequence, for some questionnaire items, the response rate exceeded the abovementioned minimum -Table 3 in Annex-.

The official web pages of sample municipalities of the respondents were re-visited to obtain information regarding the use of Web 2.0 technologies. These technologies are being used by governments for the delivery of public services but in different ways.

The blogs are used to provide updated information and to debate about a topic. Face-book is used to promote social and personal relations, which makes this tool useful for projecting a communicative image of governments. YouTube is more addressed to broadcasting, which allows governments to give full access to videos. And, finally, Twitter has a more hybrid character. So, the objective of each one of these tools and the level of citizen engagement are mainly different according to the technology used.

Methodology of Research

To address the research questions established, a questionnaire was designed and sent to all policy makers in the sample municipalities with the aim at capturing their perceptions on the issues that are analysed in this paper. Policy makers were addressed in this survey taking into account not only their significant role in the policy-making process within local government, but also their direct involvement in the possible implementation of Web 2.0 technologies in public sector delivery.

The questionnaire was made up of 16 questions covering the role that local governments must play in delivering public services with the implementation of Web 2.0 technologies -Table 3 in Annex-. Two draft versions of the survey were pre-tested on a selected group of stakeholders, to refine the design of the questionnaire items. First, the research team drafted a preliminary version based on the conclusions of previous work in the field of Web 2.0 technologies [START_REF] Span | The relationship between governance roles and performance in local public interorganizational networks: A conceptual analysis[END_REF][START_REF] Dunleavy | The second wave of digital era governance[END_REF][START_REF] Linders | From e-government to we-government: Defining a typology for citizen coproduction in the age of social media[END_REF]. Then, the initial text was presented to two specialists on Web 2.0 technologies and to ten policy makers, to ascertain their opinions on the understandability and clarity of the questionnaire and the possible inclusion of other questions relevant to the study aims. Comments and suggestions made were analysed and incorporated into the text of the questionnaire. The link to the second version of the questionnaire was provided to the policy makers of each sample local government. Also, they were offered the possibility of clarifying any remaining doubts before completing the questionnaire.

Based on prior studies on attitude analysis [START_REF] Emerson | Ethical Attitudes of Accountants: Recent Evidence from a Practitioners' Survey[END_REF], respondents were asked to describe their degree of agreement with each statement on a five-point Likert scale (from strongly disagree, "1" to strongly agree, "5"). Although the Likert scale has some limitations [START_REF] Hodge | Phrase completions: An alternative to Likert scales[END_REF], they do not invalidate conclusions about the numbers [START_REF] Norman | Likert scales, levels of measurement and the "laws" of statistics[END_REF] and the results obtained have proven to be robust, reliable and valid [START_REF] Norman | Likert scales, levels of measurement and the "laws" of statistics[END_REF].

After the questionnaire was completed, each item was analysed separately using the median and the mode of the responses, because the mean can have scale problems [START_REF] Bertram | Likert scales[END_REF]. Also, the tests of the hypotheses were performed using the multi-regression analysis and cluster analysis. In this regards, we estimate the following OLS regression:

y " = α + β ( POL IDEOLOGY + β 1 POL COMPETITION + β 6 POL STABILITY + β : POL STRENGHT + ε "
where yi is the dependent variable (total score for "Do it Yourself", "Citizen Sourcing", "Government as a Platform" and "Bureaucratic" models of governance"), α is the constant of the equation, each of the independent variables analysed in this paper is represented by their name (Political ideology, Political Competition, etc.), βi is the parameter to be estimated and εi is the error term.

Besides, based on [START_REF] Rapkin | Cluster Analysis in Community Research: Epistemology and Practice[END_REF], cluster analyses were performed using the k-means algorithm with the aim at identifying homogenous groups of attributes. Variables' description and basic statistics of political incentives are displayed in Table 2 in Annex.

Analysis of results

Although the policy makers who responded to the questionnaire believe local governments could play any of the roles identified in prior research (initiator, partner or executor), the results obtained reflect their preference to act as executor (or commissioner) (mean scores for items 1.13, 1.15 and 1.16 -Table 3-). Nonetheless, the respondents also showed their preference for greater openness in the information exchanged (median score: 4.5; mode score: 5) as well as for sharing government knowledge, infrastructure and other assets for use by the public (median and mode scores: 4). Thus, Information and Nudging is also a model in which respondents could be interested, while the municipal authorities would retain a leading role in the design and execution of public services delivery. This could be a means of enhancing edemocracy and at the same time help improve the government's image.

By contrast, the least-favoured model of governance of Web 2.0 technologies in providing public services is that of the Do-it-yourself Government model (C2C) (the median and mode scores for items 1.1, 1.2, 1.3 and 1.4 -Table 3-) and policy makers are opposed to the creation of associations to represent citizens in managing the provision of public services (median and mode scores: 1). The co-production model of governance, too, obtained scores lower than those for the Bureaucratic model and the Informing-and-Nudging model. Respondents believe that, although effective collaboration between citizens and local governments should be encouraged (median and mode scores: 4), this collaboration should be conducted mainly in terms of enhancing the quality of information (median score: 4; mode score: 5) rather than via the coexecution of public services (median score: 3; mode score: 4).

Regarding the hypothesis testing, table 4 in Annex shows OLS coefficients and Table 4 in Annex depicts the clusters analysis for each one of the governance models. Based on the OLS models, results indicate that the political ideology is a determinant factor for the model chosen to manage Web 2.0 technologies in providing public services ("Do it Yourself" and "Bureaucratic Model" -Table 4-). By contrast, no significant association has been found regarding political competition, political stability and political strength. In Table 4, clusters analysis seem to indicate that right-wing parties are prone to implement the "Do it Yourself" whilst the left-wing parties are prone to implement "Bureaucratic Model" of governance. Thus, H1 is supported and we can conclude that countries ruled by conservative parties are more likely to adopt collaborative governance models of Web 2.0 technologies in the delivery of public services, than countries ruled by progressive parties.

The political competition is not significant for choosing a model of governance of Web 2.0 technologies for the delivery of public services. Therefore, H2 is not supported by the data. Nonetheless, it seems that majority governments are more prone to collaborative models of governance whereas minority governments are in favour of the Bureaucratic Model of governance (Table 4). This result does not support prior research which indicates that governments with broad electoral majorities are not motivated to remain cued to citizens' feedback [START_REF] García-Sánchez | The Relationship between Political Factors and the Development of E-Participatory Government[END_REF].

Finally, regarding political stability and political strength, results indicate that these variables do not impact on the governance model to manage Web 2.0 technologies in providing public services. Therefore, H3 and H4 are not supported. This result does not confirm prior research that indicates that the higher level of political stability and the higher political strength have the potential to influence the level of engagement of citizens, promoting participatory governments [START_REF] Roubini | Political and Economic Determinants of Budget Deficits in the Industrial Democracies[END_REF][START_REF] Meso | Information infrastructure, governance, and socio-economic development in developing countries[END_REF]. Also, no differences exist in the cluster analysis performed regarding these variables (Table 4).

As for the cluster analysis, results in Table 4 in Annex indicate that political ideology and political competition could have influence on the perception of policy makers regarding the model of governance when Web 2.0 technologies are used for e-services delivery. Collaborative models are characterized for being supported by right-wing ideologists and majority governments (clusters 1, 3 and 5 in Table 4), whilst noncollaborative models are preferred by left-wing ideologists and minority governments (cluster 7 in Table 4). In fact, right-wing ideologists think that "Government as a platform", "Citizen Sourcing" and "Do it Yourself" models of governance are the best models of governance with a focus on the "Do it Yourself" model of governanceclusters 1, 3 and 5 in Table 4 in Annex-. By contrast, left-wing ideologists seem to prefer the "Bureaucratic Model" of governance (cluster 7, Table 4).

Discussions and conclusions

Although social networks are forcing governments to foster open and user-driven governance [START_REF] Bertot | Crowd-sourcing transparency: ICTs, social media, and government transparency initiatives[END_REF], findings indicate that the main goal pursued by governments is the representation of the agency function through all available online channels. In fact, sample policy makers show a preference for the "Bureaucratic Model" and the "Government as a Platform" models, and wish to retain a predominant role in the implementation of Web 2.0 technologies for the delivery of public services, monitor and manage the Web 2.0 technologies directly, and are less favourable to the inclusion of citizens in the generation of content and information. This model of Web involvement shows that social media services are by no means immune to government censorship or government-sponsored censorship [START_REF] Mackinnon | Flatter world and thicker walls? Blogs, censorship and civic discourse in China[END_REF].

Also, findings indicate that the "do it yourself" model is preferred by right-wing ideologists, which indicate their preference for privatizing public services and leaving them to the market [START_REF] Bel | Why do local governments privatise public services?. A survey of empirical studies[END_REF]. By contrast, the left-wing ideologists think that government must design and provide the e-services. So, senior officials are responsible for the coordination of all municipal Web 2.0 activities, which is agree with the findings of prior research regarding open communication [START_REF] Harrison | A new public service communication environment? Public service broadcasting values in the reconfiguring media[END_REF]. Nonetheless, employee training could be a highly significant factor for networking and network management [START_REF] Ferro | Policy making 2.0: From theory to practice[END_REF].

On the other hand, although political competition is not found a statically significant variable in governance models. Results indicate that majority governments are prone to implement collaborative models of governance whereas the minority governments are not. This result does not support prior research which has demonstrated that political competition plays a key role to devolve institutional power to citizens [START_REF] Smith | Delegating direct democracy: Interparty legislative competition and the adoption of the initiative in the American states[END_REF].

Finally, political stability and political strength do not impact on the governance model to manage Web 2.0 technologies for e-services delivery. This finding is confirmed in both statistical methods used (OLS and cluster analysis) and it is not agree with prior research which indicates that political conditions must be stable for egovernment success [START_REF] Krishnan | Moderating effects of governance on information infrastructure and e-government development[END_REF]. Therefore, future studies should examine the relationship between the political stability and the use of e-participation as a medium of citizen participation, especially regarding e-services.

In brief, although the political value of e-participation applications has been seen as instruments of bureaucratic reform [START_REF] Ahn | Politics of E-Government: E-Government and the Political Control of Bureaucracy[END_REF], our findings confirms other recent research findings according to which social media have their own logic, but it is only manifest when it encounters fertile ground within a government bureaucracy [START_REF] Meijer | Social media strategies: Understanding the differences between North American police departments[END_REF]. Only rightwing ideologists and policy makers of majority governments have indicated the collaborative models of governance, as the "Citizen Sourcing" or the "Do it yourself" models, and the role of co-producer as mainly relevant. But, are these findings influenced by the public administration style of countries?; Will the introduction of Web 2.0 technologies widen the digital divide?. Can we use different modelling techniques to analyse this topic?. For example, data panel techniques could be used if we can collect multiple data over time and its application could help to know trends in the topic analysed in this paper. All these questions remain to be addressed in future research.

ANNEX. RESULTS OF THE RESEARCH

Table 1

 1 shows that the Web 2.0 applications most commonly used for providing public services by the local governments sampled are RSS channels (73.91%), Facebook (65.22%), Twitter (58.70%) and YouTube (43.48%). In addition, the 93.47% of the municipalities use up to 5 Web 2.0 applications at the same time in order to be communicated with citizens and they usually use 3 different Web 2.0 applications at the same time to be in touch with citizens (median, media, mode and the low value of the standard deviation in Table1). Despite previous comments, data indicate that whereas some local authorities use eight different Web 2.0 applications (for example, Vitoria-Gasteiz), others only use one of them (for example, Alcobendas or Manresa).

Table 1 .

 1 Characterization of the sample and Web 2.0 applications embraced by the local governments that answered all the e-survey questions.

Table 2 .

 2 b) Web 2.0 applications embraced by the local governments that answered all the e-survey questions

			Descriptive Statistics
	Web 2.0 application	Frequency Number of % Total municipalities sample (N)* municipalities	Number of Web 2.0 applications used/municipality	Frequency	Main Statistics
	RSS	34	73.91	1	7	Mean	3
	Facebook	30	65.22	2	10	Median	3
	Twitter	27	58.70	3	11	Mode	3
	YouTube	20	43.48	4	6	Maximum	8
	Official Blogs	10	21.74	5	9	Minimum	1
	Flickr	11	23.91	6	2	deviation Standard	1.64
	Tuenti	6	13.04	7	0
	Friend Feed	5	10.87	8	1
	Linked in	5	10.87	TOTAL	46
	Delicious	1	2.17
	Slice	0	0.00
	Formspring	0	0.00
	TOTAL	149

NOTE:

* This column indicates the number of local governments that use each one of the Web 2.0 applications. In this regard, one local government can use more than one Web 2.0 application at the same time, as noted in the following columns of this table. The column named "%Total sample municipalities" is calculated using the number of municipalities that use the Web 2.0 application and the total number of municipalities that responded the e-survey (46 municipalities).

Table 2 .

 2 Definition of variables and descriptive statistics.

		Variable	Description	Calculation	Source	Hypothesis	Cases	Mean	Median	Mode	Standard deviation	Min	Max
	(1)	Total	Total score in favour	#$%& 1.4		Based on data collection		46	12.76	13.00	12.00	3.11	5.00	20.00
		it Yourself"	of this model of	!	" #
		Total	governance Total score in favour	#=#$%& 1.1 #$%& 1.8		Based on data collection		46	13.87	15.00	15.00	3.59	6.00	20.00
		"Citizen Sourcing"	of this model of	!	" #
		Total	governance Total score in favour	#=#$%& 1.5 #$%& 1.12		Based on data collection		46	16.17	17.00	17.00	2.76	7.00	20.00
		"Government as a	of this model of	!	" #
		Platform" Total	governance Total score in favour	#=#$%& 1.9 #$%& 1.16		Based on data collection		46	14.34	15.00	16.00	2.82	5.00	20.00
		"Bureaucratic Model"	of this model of	!		" #
			governance	#=#$%& 1.12
	(2)	Political Ideology	Political Ideology of	0-Right wing	Spanish Ministry of Public	H1	46	0.61	0.00	0.00	0.74	0.00	2.00
			the ruling party	1-Left wing	Administrations database
				2-Others
		Political	Majority vs. minority	1-Majority	Spanish Ministry of Public	H2	46	0.65	1.00	1.00	0.48	0.00	1.00
		Competition	governments	0-Minority	Administrations database
		Political Stability	Numerical variable	Difference in percentage	Spanish Ministry of Public	H3	46	0.23	0.19	0.12	0.15	0.04	0.56
			that proxies for the	of votes of the ruling	Administrations database
			popularity of the party	party with respect to the
			in office	second most-voted party
		Political Strength	Numerical variable that reflects the local governments' level of	∑ 3 (∑ #=1 3 #=1	2 # 2 2 # )	2	(maximum fragmentation) to 1 Herfindahl index is used, from 0 Based on data collection.	H4	46	0.42	0.42	0.38	0.07	0.29	0.56
			political strength.				(minimum fragmentation)
		Source: Own elaboration.

Key: (1) dependent variables; (2) independent variables.

Table 3 .

 3 Results for items related to the governance model and the role of local governments in the use of Web 2.0 technologies. For Web 2.0 technologies to be efficient as a means of interaction between local governments and citizenry, citizens should be represented by associations but they should not interact directly with the local government as individuals. Social networks and blogs are suitable spaces in which discussion topics and issues of interest for citizens can be put forward. The local government opens up a problem or activity for resolution or co-execution by citizens in order to tap into the unique skills, talents, and knowledge of the population.

		Questionnaire	Frequency	Response Rate	Mean	Median	Mode	Standard Deviation	Min	Max
	RQ1. Governance model and role of local governments in the use of Web 2.0 technologies
	a)	Initiator (Facilitator) (C2C). "Do it Yourself" model
	1.1. 46	32.62%	1.73	1.00	1.00	1.56	1.00	5.00
	1.2. The creation of public/private/user/academic communities for the delivery of public	53	37.59%	3.58	4.00	4.00	0.97	1.00	5.00
	services should be encouraged and developed.
	1.3. The implementation of Web 2.0 in providing public services gives an essential role to	54	38.30%	3.72	4.00	4.00	1.00	1.00	5.00
	users of these services.
	1.4. 48	34.04%	3.83	4.00	4.00	0.95	1.00	5.00
	b)	Partner (Co-producer) (G2C) Co-production. "Citizen sourcing" model
	1.5. Web 2.0 technologies foster effective collaboration between citizens and government.	54	38.30%	3.67	4.00	4.00	0.95	1.00	5.00
	1.6. Citizens may participate in the generation of content and information.	53	37.59%	3.15	3.00	4.00	1.20	1.00	5.00
	1.7. 54	38.30%	3.13	3.00	4.00	1.20	1.00	5.00
	1.8. User contributions enhance the quality of information, and Web 2.0 describes these	53	37.59%	3.96	4.00	5.00	1.14	1.00	5.00
	contributions.
	c)	Partner (

Co-producer) (C2G) Informing and Nudging. "Government as a platform" model

  1.9. The local government uses Web 2.0 technologies to openly share government knowledge, infrastructure, and other assets for use by the public. The local government uses Web 2.0 technologies to disclose information pro-actively, to open up the inner workings and performance of government and thus empower citizens to hold their government to account. Local governments should designate a senior official responsible for the coordination of their Web 2.0 activities.

			55	39.01%	4.20	4.00	4.00	0.85	2.00	5.00
	1.10. Public sector information is released for use by citizens.	50	35.46%	3.70	4.00	4.00	0.99	1.00	5.00
	1.11. Greater openness of all kinds of information is obtained.	48	34.04%	4.31	4.50	5.00	0.88	1.00	5.00
	1.12. 48	34.04%	3.92	4.00	4.00	0.85	2.00	5.00
	d)	Executor (Commissioner) (G2G). "Bureaucratic Model"
	1.13. A plan is created to document security controls, and this plan is reviewed at regular	46	32.62%	4.26	5.00	5.00	0.95	1.00	5.00
	intervals.
	1.14. For Web 2.0 management, external advisors are hired to monitor government policies	46	32.62%	1.96	1.00	1.00	1.73	1.00	5.00
	and procedures with respect to external communications activities.
	1.15.

Acknowledgments

This research was carried out with financial support from the Regional Government of Andalusia (Spain), Department of Innovation, Science and Enterprise (Research project number P11-SEJ-7700).

(1) Political Ideology: 2.00 (Others), 1.00 (Left wing) and 0.00 (Right wing).

(2) Political Competition1: 1.00 (Majority Governments) and 0.00 (Minority Governments).