¿EL MÉTODO DE HERÓN ?
EJERCICIO 7: En este enunciado nos referimos siempre a la
Considering that the main focus of this research study is on online communication channel, it was necessary to evaluate the relationship between the chosen channels (Email and Facebook) and the factors that impact older adults use of these particular channels when interacting with the government. Afterwards, a parametric test using the multigroup analysis will be used to assess if there was any level of significance in the difference between the two online communication channels.
5.6.1 Coefficient of Determination
The coefficient of determination (R 2 ) measures the amount of variation in the dependent variable that is explained by the variation in the independent variable which are the constructs in this research study (Keller, 2016). In other words, it describes how well the model fits the data. An r 2 close to 1 implies an almost perfect relationship between the model and the data,
whereas an r 2 close to 0 implies that just fitting the mean is equivalent to the model fitted
(Jackson, 2009). Kohler (2012) suggested that r² values of 0.75, 0.50, or 0.25 resulted in the structural model that are labelled as significant, moderate, or poor, respectively.
5.6.2 Structural equation modelling of the Online Communication Channel (OCC)
For this study the central dependent variable CONF observed an r2 0.892, which demonstrated
that 89.2% of the variability within the older adult’s intention to use a particular online communication channel to communicate with government can be explained by the MOCC model. Satisfactory experience observed that 53.8% of the variability within the satisfaction an older adult gets in using a particular online communication was accounted for by the measures of Satisfaction and KBE. Continuance intention to use explained 46.3% of the variance in continued and intended long term use of a particular online communication channel by older adults when communicating with the government. Overall from the analysis of the entire sample, the r2 value of 53.8% and 46.3% can be interpreted as moderate and the r2 value of 89.2% is significant which could have explained by the direct dependency of CONF on all the constructs. Hence, this implies that the achieved values demonstrated a sufficient explanatory power for the purpose the formed MOCC model.
From this analysis, an overall eight significant relationships were observed within the final MOCC model with regards to online communication channel. Five theoretical constructs had significant influences on the key dependent variable Confirmation. These constructs are decision trust (H4 supported with co-efficient=0.166), performance expectancy (H7 supported with co-efficient= 0.673), knowledge building experience (H1 supported with co-
efficient=0.081), reliability trust (H3 supported with co-efficient=-0.221) and functional service quality (H6 supported with co-efficient=0.155), all at p-values <.05. The three other remaining significant constructs held extremely strong significant paths (p-values <.001) i.e. knowledge building experience (H2 supported with co-efficient=0.620), confirmation (H9 supported with co-efficient=0.158) and satisfactory experience (H10 supported with co- efficient=0.680). These results are now interpreted in the table 5.15.
Table 5.15: Result of the hypothesised online communication channel constructs
Path Coefficient STDEV t-Value p-Value Result
KBE -> CONF =H1 0.081 0.038 2.130 0.033 Supported KBE -> SATEXP= H2 0.620 0.061 10.084 0.000 Supported RELTRU -> CONF=H3 -0.221 0.083 2.673 0.008 Supported DECTRU -> CONF=H4 0.166 0.055 2.985 0.003 Supported TECSEQ -> CONF=H5 0.140 0.144 0.968 0.333 Not supported FUNSEQ -> CONF=H6 0.155 0.068 2.298 0.022 Supported PEFEXP -> CONF=H7 0.673 0.159 4.244 0.000 Supported HAB -> CONF=H8 0.009 0.024 0.392 0.695 Not Supported CONF -> SATEXP=H9 0.158 0.045 3.506 0.000 Supported SATEXP -> CITN=H10 0.680 0.066 10.268 0.000 Supported
Furthermore, a structural model for all participants for the online communication channel developed in the structural equation model analysis was conducted. Thus, figure 5.6 presents the structural model for all the participants.
Figure 5.6: Structural model for all participants
The reason for including data gathered from all the participants to derive the result shown in figure 5.6 is in order to have a general overview of how each construct affects the adoption and continuance use of both online communication channels (Email and Facebook) among the older population. Considering this, it can be observed that figure 5.6 shows the path coefficient result derived for the hypothesised constructs.
5.6.3 Structural equation modelling of Email
Email is one of the chosen online communication channels i.e. the classic one for this research study and the results obtained are as follows; the PLS-SEM result showed that eight of the ten major hypotheses were supported. Both elements of trust i.e. decision trust (H4a supported with co-efficient= -0.246), and reliability trust (H3a supported with co-efficient=0.617), were found to be significant with p<0.005 respectively which shows that older adults have strong
level of trust in Email when using it to communicate with the government. Knowledge building experience (H2a supported with co-efficient=0.074), was also found to be highly significant in its relationship with and satisfactory experience with p<0.005. This shows that the knowledge that an older adult has built over the years using Email to communicate with the government has given him/her a level of satisfaction in using this particular online communication channel. Also, habit (H8a supported with co-efficient=0.155), and technical service quality (H5a supported with co-efficient=0.127), were found to be significant. However, confirmation (H9a supported with co-efficient=0.167), continuance intention to use functional service quality (H6a supported with co-efficient=0.064), were found to be insignificant in determining older adult’s choice and continuance use of Email when interaction with the government. Table 5.16 shows the details of the path analysis of Email and all participants and the result of their hypothesised constructs.
Table 5.16: Summary of the hypothesis testing for Email
Path Coefficient STDEV t-Values p-Value Result
KBE -> CONF =H1a 0.025 0.027 0.935 0.350 Not Supported
KBE -> SATEXP= H2a 0.074 0.035 2.127 0.034 Supported
RELTRU -> CONF=H3a 0.617 0.079 7.847 0.000 Supported
DECTRU -> CONF=H4a -0.246 0.094 2.608 0.009 Supported
TECSEQ -> CONF=H5a 0.127 0.052 2.446 0.015 Supported
FUNSEQ -> CONF=H6a 0.064 0.166 0.387 0.699 Not supported
PEFEXP -> CONF=H7a 0.191 0.069 2.763 0.006 Supported
HAB -> CONF=H8a 0.772 0.191 4.038 0.000 Supported
CONF -> SATEXP=H9a 0.167 0.054 3.076 0.002 Supported
SATEXP -> CITN=H10a 0.720 0.068 10.616 0.000 Supported
Furthermore, the structural model for email was developed and this also produced r2 values.
In terms of r2 values, the key dependent variable CONF observed an r2 0.901, which
demonstrated that 90.1% of the variability within the older adult’s intention to use Email when communicating with government can be explained by the MOCC model. Also, Satisfactory experience observed that 54.6% of the variability within the satisfaction an older adult gets in using Email to communicate with the government was accounted for by the measures of a
Satisfaction and KBE while, continuance intention to use explained 51.9% of the variance in continued and intended long term use. From the analysis of the sample, the r2 value of 54.6%
and 51.9% can be interpreted as moderate and the r2 value of 90.1% is significant which could
be explained by the direct dependency of CONF on all the constructs. Therefore, this result indicates that the MOCC model is adequate for determining the intention to use Email by older adults when communicating with the government.
Figure 5.7: Structural model for with r-squared values for Email with all participants
The reason for including data gathered from all the participants that use only Email to derive the result shown in figure 5.7 is in order to have a general overview of how each construct affects the adoption and continuance use of Email as an online communication channels among the older population. Considering this, it can be observed that figure 5.7 shows result derived for the hypothesised constructs with the inclusion of the path co-efficient.
5.6.4 Structural equation modelling of Facebook
Unlike the Email and online communication in general, the PLS-SEM result showed that seven of the ten major hypotheses were supported. The elements of trust decision trust (H4b supported with co-efficient=0.077) and reliability trust (H3b supported with co- efficient=0.051) were not found to be significant with p<0.005 respectively which shows that older adult do not have much level of trust in the use of Facebook when using it to communicate with the government.
Knowledge building experience were found to be highly significant both in its relationship with confirmation (H1b supported with co-efficient=0.066) and satisfactory experience (H2b supported with co-efficient=0.529) at p<0.005. This implies that the knowledge that an older adult has built over the years using Facebook to communicate with the government has given them a level of satisfaction in using this particular online communication channel. Interestingly, habit (H8b supported with co-efficient=0.079) was found to be significant with the use of Facebook while interacting with the government. This implies that as older adults get so used to modern online communication channel like Facebook when interacting with the government, it gets easier and they make it habitual. Also, confirmation (H9b supported with co-efficient=0.160), satisfactory experience (H10b supported with co-efficient=0.598), functional service quality (H6b supported with co-efficient=0.184) and performance expectancy (H7b supported with co-efficient=0.414) were also found to be significant in determining older adult’s choice and continuance use of Email when interaction with the government. However, technical service quality (H5b supported with co-efficient=0.175) was not found to be significant also. Table 5.17 shows the details of the path analysis of Facebook and all participants and the result of their hypothesised constructs.
Table 2: Summary of the hypothesis testing for Facebook
Path Coefficient STDEV t-
Values
p-Value Result
KBE -> CONF =H1b 0.066 0.027 2.460 0.014 Supported
KBE -> SATEXP= H2b 0.529 0.067 7.908 0.000 Supported
RELTRU -> CONF=H3b 0.051 0.117 0.435 0.663 Not supported
DECTRU -> CONF=H4b 0.077 0.048 1.601 0.110 Not Supported
TECSEQ -> CONF=H5b 0.175 0.135 1.292 0.197 Not supported
FUNSEQ -> CONF=H6b 0.184 0.069 2.667 0.008 Supported
PEFEXP -> CONF=H7b 0.414 0.157 2.635 0.008 Supported
HAB -> CONF=H8b 0.079 0.034 2.347 0.019 Supported
CONF -> SATEXP=H9b 0.160 0.073 2.171 0.030 Supported
SATEXP -> CITN=H10b 0.598 0.067 8.921 0.000 Supported
Furthermore, the structural model for email was developed and this also produced r2 values.
In terms of r2 values, the key dependent variable CONF observed an r2 0.911, which
demonstrated that 91.1% of the variability within the older adult’s intention to use Facebook when communicating with government can be explained by the MOCC model. Also, Satisfactory experience observed that 41.1% of the variability within the satisfaction an older adult gets in using Email to communicate with the government was accounted for by the measures of a Satisfaction and KBE while, continuance intention to use explained 35.7% of the variance in continued and intended long term use. From the analysis of the sample, the r2
value of 41.1% and 35.7% can be interpreted as between moderate and poor and the r2 value
of 91.1% is significant which could be explained by the direct dependency of CONF on all the constructs. Therefore, this result indicates that the MOCC model is adequate for determining the intention to use Facebook by older adults when communicating with the government.
Figure 5.8: Structural model for with r-squared values for Facebook with all participants
The reason for including data gathered from all the participants using Facebook to derive the result shown in figure 5.8 is in order to have a general overview of how each construct affects the adoption and continuance use of Facebook as an online communication channels among the older population. Considering this, it can be observed that figure 5.8 shows the path coefficient result derived for the hypothesised constructs.
5.6.5 Email versus Facebook
Using multi- group analysis in SEM, Email was compared with Facebook to test for difference in the two online communication channels. The parametric test result indicated that between Email and Facebook, there was significant difference in the way reliability trust impacted in the continuance intention to use online communication when interacting with the government with p<0.05. This means that the continuance intention to use a particular online
communication channel differs between Email and Facebook based on how reliable the online communication is when communicating with the government. On the other hand, all other constructs had no significant difference between Email and Facebook for all the hypothesised constructs with all the derived p-values greater than 0.05. This implies that these other factors or constructs are likely not to have similar impact on both the Email and Facebook use. The result of this comparison is presented in table 5.18.
Table 3: Test for difference between Email and Facebook
Path Coefficients- diff t-Values (Email - Facebook) p-Values (Email - Facebook) Results (Yes/No) KBE -> CONF =H1 0.007 0.152 0.879 NO KBE -> SATEXP= H2 0.088 0.809 0.419 NO
RELTRU -> CONF=H3 0.297 2.030 0.043 YES
DECTRU -> CONF=H4 0.050 0.671 0.502 NO TECSEQ -> CONF=H5 0.111 0.477 0.633 NO FUNSEQ -> CONF=H6 0.007 0.072 0.943 NO PEFEXP -> CONF=H7 0.358 1.355 0.175 NO HAB -> CONF=H8 0.054 1.256 0.209 NO CONF -> SATEXP=H9 0.007 0.082 0.934 NO SATEXP -> CITN=H10 0.123 1.222 0.222 NO
5.6.6 Age difference
As stated earlier in this chapter, the participants were split into four age groups namely: 50-59 (pre-seniors), 60-69 years old (young- old), 70-79 years old (old-old) and 80+ years (very- old). PLS-SEM result showed that confirmation was significant in young old and old-old groups with (p=) respectively. However, the result from pre-seniors and Very-old are not significant.
In addition, decision trust was found to be significant with young adults and very- old but not with the pre-seniors and old- old age group. Reliability trust was only found significant within the old-old compared to all other group categories.
Equally, functional service quality was found to be significant amongst the pre-seniors and where not supported by other group categories. On other hand, no significant result was found for the technical service quality.
Furthermore, knowledge building experience and satisfactory experience were found to be significant in all age groups. Performance expectancy was found significant within three age groups namely; pre-seniors, young old and old-old. Habit was only found to be significant amongst the very-old age group. Table 5.19 shows the details of the path analysis of all age categories and the result of their hypothesised constructs.
Table 5.19; Summary of hypothesis testing all age groups
Path Pre-seniors Young old Old -old Very - old
KBE -> CONF =H1 Supported
KBE -> SATEXP= H2
RELTRU -> CONF=H3 Supported Supported Supported Supported
DECTRU -> CONF=H4 Supported
TECSEQ -> CONF=H5 Supported Supported
FUNSEQ -> CONF=H6
PEFEXP -> CONF=H7 Supported
HAB -> CONF=H8 Supported Supported Supported
CONF -> SATEXP=H9 Supported Supported
SATEXP -> CITN=H10 Supported Supported Supported Supported
Note: Empty cells denotes not supported
In addition, a parametric test was conducted using the multi-group analysis in SmartPLS 3 to check for significant difference within the groups. Furthermore, the structural model for each segment was developed and this also produced the r² values. The detailed result of these can be found on the appendix section of this thesis.
5.6.3 Moderating effect of the demographic variables
According to Venkatesh et al. (2012), the moderating variables such as gender, age, experience and voluntariness of use sometimes affect the relationship between the independent variables and the dependent variable. Bearing this in mind, the demographic variables age, gender,
education and health status were selected in this study as moderators for confirmation and satisfactory experience in determining the dependent variable continuous intention to use which is consistent with the UTAUT 2 model. These moderating variables were selected based on the aim of this research as well as the suggestion of previous studies (Venkatesh et al., 2012; Pheeraphuttharangkoon, 2015). The following provides the details of the test for moderating effect.
Based on the findings, result showed that age had negative moderated relationship with confirmation and satisfactory experience which in turn affects continuance intention to use. This implies that age does not strengthen the effect of confirmation to continuance intention to use and also, satisfactory experience to intention to use. Moreover, gender and education were also used but were found to be negatively moderating the effect confirmation and satisfactory experience just like age.
However, health status was also selected as a moderator for the relationships between satisfactory experience and continuance intention which was found to be significant moderator for the relationship. Result also found health status to have a positive significance between confirmation and satisfactory experience. This implies that health status strengthens the effect of confirmation on satisfactory experience which in turn will affect continuance intention positively. The table 5.20 presents the result of the test for the moderating effect of age.
Table 5.20: Testing the moderating effect of age, gender, education and health
status
Paths Coefficients (STDEV) t-values p- Values Result Age* sat -> CITN -0.009 0.146 0.220 0.826 Not supported Age*conf -> SATEXP -0.009 0.020 0.499 0.618 Not supported Edu * satexp -> CITN -0.071 0.088 0.826 0.409 Not supported Edu * conf -> SATEXP -0.012 0.022 0.558 0.577 Not supported Gender * satexp -> CITN 0.174 0.223 0.991 0.322 Not supported Gender * conf -> SATEXP 0.060 0.044 1.444 0.149 Not supported Health * satexp -> CITN 0.154 0.069 2.265 0.024 Supported Health * conf -> SATEXP -0.089 0.047 1.965 0.050 Supported