Citizen Services Quality (CSQ)

From the descriptive analysis, the relationship between Citizen Service Quality (CSQ) and intention to use m-government services can be interpreted as showing that Citizen Service Quality is a significant factor (see Table 5.28). The computed composite score for the CSQ factor was 2.0852. The hypothesized relationship (H8) between the Citizen Service Quality and intention to use m-government services is therefore supported.

H8: Citizen Service Quality factors (responsiveness, empathy and reliability) positively influence intention to use m-government services.

The correlation between Citizen Service Quality and intention to use m-government services was explored to find out the strength and the direction of the relationship. Each variable was normally distributed, as assessed by skewness and kurtosis (see Table 5.26). The Pearson correlation coefficient assumptions (normality) were considered to assess the relationship between the two factors. The result of the Pearson correlation coefficient indicates that there is a statistically significant, moderate positive correlation between CSQ and ITU, rs =.339, n=1286,

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at 0.01 level. The graph below shows a positive relationship between Citizen Service Quality and citizens’ intention to use m-government since if the score for CSQ increases, so does the score for intention; thus supporting the hypothesis for this factor.

Figure 5-21: The relationship trend line between CSQ and ITU

The positive relationship between the Citizen Service Quality and intention to use services corresponds with other studies in the literature. Alhujran et al. (2013) stated that the service quality dimensions (including responsiveness, reliability and empathy) impact significantly on citizen satisfaction which is one of the important predictors of Jordanian citizens’ intention to use E-government services. Charles K. Ayo et al. (2016) also revealed that perceived e-service quality (responsiveness and reliability) had a significant effect on customer satisfaction and use of e-banking in Nigeria. Sharma (2015) studied the relationship between service quality dimensions and willingness to use E-government services in Oman, and showed that service quality, including reliability and responsiveness, were important determinants of willingness to use E-government services. Moreover, Shareef et al. (2012) investigated the adoption of mobile government among Indians; and found that Perceived Empathy was the most influential factor to predict citizens’ adoption of m-government. Charles K. Ayo et al. (2016) studied the effect of E-Service Quality and E-Loyalty (which is similar to citizen service quality) on online shopping in Jordan, and found that customer service is one of the main explanatory factors of E-Service Quality that influences online customer trust and satisfaction; which impacts directly on E- Loyalty in online Shopping. Also, Ya-Hui Wang (2017) carried out research in Taiwan to study the effect of expectation and service quality on satisfaction and behavioural intention in Taiwan’s medical tourism industry. The results of their study indicated that service quality

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dimensions (including Tangibility, Reliability, Responsiveness, Assurance and Empathy) had a direct effect on both satisfaction and behavioural intention.

5.3.3

Technical Factors (TF)

Based on the research model (MGAUM), there are two technical factors, i.e. System Quality (SQ) and Perceived Mobility (PM). Table 5.27 shows a summary of the findings of the descriptive analysis with interpretations of the results for all factors. The influence of the technical factors was tested statistically, and the findings are discussed below. Both factors were significantly influential on citizens’ intention to adopt and use m-government services.

Table 5-27: Summary of the descriptive analysis of TF from the citizens’ perspective

Factor N Number of items Mean S.D. Interpretation of Results

SQ 1286 7 1.8891 .54653 Influential

PM 1286 3 1.5625 .57052 Very influential

5.3.3.1

System Quality (SQ)

From the descriptive analysis, SQ positively influences citizens’ intention to adopt and use m- government services, and this can be seen clearly in Table 5.29. The composite of the SQ factor was 1.8891 with the result indicating that SQ is influential on citizens’ intention to adopt and use m-government services. The hypothesized relationship (H9) between the System Quality and intention to use m-government services is thus supported.

H9: System Quality positively influences intention to use m-government services.

The correlation was also computed between the variables to investigate the strength and the direction of the relationship. The result of the normality test assessed by skewness and kurtosis for both factors was normally distributed. In this case, the Pearson correlation coefficient assumptions (normality) were considered to assess the relationship between the two factors (see table 5.26). There was a statistically significant, moderate positive correlation between SQ and ITU, rs =.390, n=1286, at the p=0.01 level. Figure 5-22 demonstrates the trendline that illustrates the positive relationship between SQ and ITU.

The graph below illustrates the positive relationship between the two variables. If the SQ score increases, the score for intention also increases; therefore, supporting the hypothesis for this factor.

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Figure 5-22: The relationship trend line between SQ and ITU

The positive relationship between System Quality and intention to use services corresponds to findings in other studies in the literature, i.e. whenever the system quality of the provided m- government services rises, so will citizens’ intention to adopt and use them. AlaaAldin A. AL Athmay et al. (2015) investigated factors that impact the adoption of UAEE-government services from the citizens’ perspective. Their findings revealed that E-government system quality had a strong impact on the intention to use E-government services. Moreover, Fadi Taher Qutaishat (2013) studied the relationship between Jordanian citizens’ perceptions of website quality and how this affected intention to use E-government services. The results of his study indicated that system quality positively impacted user intention to use E-government services(Qutaishat, 2013). Abdullah M. Baabdullaha et al. (2019) conducted a study in Saudi Arabia to explore the main factors that could affect the use of mobile banking in Saudi Arabia. They found that system quality (including ease-of-use, access speed, visual appeal and navigation) was one of the main factors that significantly affected actual use behaviour. Also, Xiao Jiang and Shaobo Ji (2014) examined Chinese citizens’ adoption and continuance intention (CI) of an E-government web portal from the perspective of service level and service quality. They found in their study that the Web portal’s service quality dimensions (including information quality, design and function, and the system’s reliability) significantly affect user’s adoption and continuance intention and that the effect differed among different types of user groups. Bakar and Melan (2018) studied the impact of System Quality criteria on continuous intention to use a tax e-filing system in Malaysia. Moreover, Abdullah M. Baabdullaha et al.(2019) found that system quality was one of the key factors that significantly impacted on

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actual use behaviour. The result of their study revealed that the system’s usability and availability, functionality and navigation facility had an impact on continuous intention to use government e-services.

5.3.3.2

Perceived Mobility (PM)

The descriptive analysis in Table 5.29 shows that PM has a high positive influence on citizens’ intention to adopt and use m-government services. The composite of the PM factor was 1.5625 which indicates PM is a very influential in affecting citizens’ intention to adopt and use m- government services. The hypothesized relationship between the Perceived Mobility factor and intention to use m-government services (H10) is therefore supported.

H10: Perceived Mobility positively influences intention to use m-government services.

The correlation was also computed between the two variables to investigate the strength and the direction of the relationship. The result of the normality test, assessed by skewness and kurtosis for both factors was normally distributed. The Pearson correlation coefficient assumptions (normality) were considered to assess the relationship between the two factors. There was a statistically significant, strong positive correlation between PM and ITU, rs =.548, n=1286, at the p=0.01 level. Figure 5-23 presents the trendline that shows the positive relationship between SQ and ITU.

The graph below illustrates that there is a positive relationship between these variables; as when the score for Perceived Mobility increase, so does the score for intention; which supports the hypothesis for this factor.

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The positive relationship between perceived mobility and intention to use services is reflected in other research. Yung-Shen Yen & Feng-Shang Wu (2016) carried out a study in Taiwan, to investigate factors that could affect the continued use intention for mobile financial services (MFS). They stated in their research that perceived mobility significantly affected continued usage intention for MFS. Moreover, Jen-Hung Huang et al. (2006) carried out research in Taiwan universities to explore and identify predictors for the acceptance of mobile learning (m- learning). Their research showed that perceived mobility value was one of the predictor factors of user intention to use and accept m-learning. Also, Changlin Wang (2014) stated that continued use of mobile government in China is strongly influenced by mobility. Moreover, TM Faziharudean and Tan Li-Ly (2011) confirmed that Perceived Mobility has a significant positive effect on consumers’ intention to use mobile data services in Malaysia.

5.4

Conclusion

Table 5.28 presents all the relationships between the MGAUM’s constructs and the intention to use m-government services that were hypothesized. It is clear that all the factors, except Awareness, were revealed to have significant positive and direct relationships with citizens’ intention to adopt and use m-government services (See Figure 5-24).

Table 5-28: Summary of the results of the hypothesized relationships from the citizens’ perspective Hypothesized Relationship Direction of Relationship Hypothesis Test Relationship Strength

PEOU  ITU Positive Supported Strong (rs = .635 **) sig

PU  ITU Positive Supported Strong (rs = .757 **) sig

CULT  ITU Positive Supported Moderate (rs = .398 **) sig

PT  ITU Positive Supported Moderate (rs = .376 **) sig

SI  ITU Positive Supported Strong (rs = .621 **) sig

PCOM  ITU Positive Supported Strong (rs = .651 **) sig

AW  ITU Positive Supported Weak (rs = .270 **) sig

CSQ  ITU Positive Supported Moderate (rs = .339 **) sig

SQ  ITU Positive Supported Moderate (rs = .390 **) sig

PM  ITU Positive Supported Strong (rs = .548 **) sig

* . Correlation is significant at the 0.05 level. **. Correlation is significant at the 0.01 level.

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Figure 5-24: The Updated MGAUM model from the citizens’ perspective

The findings of this study imply that from Saudi citizens’ perspectives all factors in the MGAUM, with the exception of Awareness, need careful consideration to successfully implement m-government services. Doing so would increase citizens’ intention to make use of these services, thus achieving a higher level of satisfaction and actual use. Nevertheless, according to the descriptive analysis Awareness was close to being moderately influential and still needs consideration although it is suggested that the other factors should be given a higher priority.

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