Abstract

Access to safe drinking water is essential to healthy living. Thus, investment in rural drinking water points is increasing in Ethiopia. However, little is known about user satisfaction with rural drinking water points. Therefore, this study was undertaken to investigate determinants of the user's satisfaction with rural drinking water points in Ethiopia by considering Woliso District (Woreda) as a case study. A semi-structured questionnaire was administered with 211 randomly selected households from six rural Kebeles (administrative areas), which were selected using a stratified sampling technique. Focus group discussions (FGD) and key interviews (KI) were also held along with observation. The quantitative data were analysed through descriptive statistics and binary logistic regression. The qualitative data were used to augment the results from the regression analysis. The results revealed that location of the water point, availability of guards, queueing time, service reliability, and distance significantly influence the satisfaction of users. Therefore, these significant factors should be addressed when planning water supply projects.

HIGHLIGHTS

  • Conceptual contribution: Highlighting a variable (importance of guards at rural water points) that has never been or rarely assessed before.

  • Practical contribution: Dealing with one of the essential areas (satisfaction with rural drinking water points) which is not given due attention in Sub-Saharan Africa.

  • Methodological contribution: Identifying the factors in their level of dominance in explaining user satisfaction.

INTRODUCTION

Access to safe and sufficient water is essential to healthy living. However, one in three people or 2.2 billion people around the world lack safe drinking water and 4.2 billion people live without access to safe sanitation services (UNICEF and WHO 2019). In rural areas, 8 out of 10 people globally live without improved drinking water sources (WHO 2017a). On top of this, the 2019 United Nations World Water Development report notes that about 4 billion people, representing nearly two-thirds of the world population, experience severe water scarcity. The majority of these people live in developing countries.

Figure 1

Map of the study area (Source: Oromia Bureau of Finance and Economic Development, 2016).

Figure 1

Map of the study area (Source: Oromia Bureau of Finance and Economic Development, 2016).

Water issues in developing countries include scarcity of safe drinking water, poor infrastructure for improved water access, floods, droughts, and the contamination of rivers and large reservoirs. About 827,000 people in low- and middle-income countries die due to inadequate water, sanitation, and hygiene each year, and this figure accounts for 60% of total diarrhoeal deaths (WHO 2017b). In many countries, pollution or rising sea levels are contaminating potable water sources. Water stress and lack of sanitation disproportionately affect women and children. These factors can affect their health, safety and opportunity to engage in economic activities. Millions of women spend many hours every day in collecting water. Their physical and financial burdens concerning water are often greater than those of men (WHO and UNICEF 2005). Children also spend their time in fetching water so that it affects their schooling time. Improving access to potable water reduces these problems.

To improve access to potable drinking water in developing countries, governments have often invested in, and subsidized, water supplies. Governments are attempting to improve access to safe water supply and sanitation facilities in order to achieve social and health benefits for low-income households that comprise a large majority of the rural population (Lammerink 1998; Whittington et al. 1998). This investment is important for improving health, education and livelihoods of the poor. Likewise, the Ethiopian government has been investing in potable drinking water over the last decade. Under GTP (Growth and Transformation Plan) II, the government has planned to achieve 85, 75 and 83% potable water coverage respectively in rural areas, urban areas and the country as a whole by the end of GTP II (2019/20). Towards achieving these goals, a lot of investment was made in the construction of potable water points. The midterm review of GTP II shows that potable water coverage has increased to 68.5% in rural areas, 54.7% in urban areas and an overall coverage of 65.7% (NPC 2018).

However, despite the importance of providing safe drinking water, little is known regarding user satisfaction with the rural drinking water points. Exploring user satisfaction with these services is an important means for improving the performance of drinking water points (Deichmann & Lall 2007), as users' satisfaction indicates whether water points are appreciated and represent a relative improvement to alternative sources previously used by community members (Welle & Williams 2014). Furthermore, users' satisfaction is a determinant for sustainability of water points primarily because users feel a higher sense of ownership, greater confidence in their ability to maintain the water system and promotes a better understanding of how the tariff is used, and also affects their willingness to pay for improvements. How far drinking water points satisfy users’ needs can be a key factor affecting the water points operation and maintenance (O&M) and thereby their sustainability (Welle & Williams 2014).

In the Ethiopian context, although the government has invested and continues to invest in rural drinking water points, the service is primarily suffering from non-functionality of the services. Different studies estimate that more than a third of the constructed water points are not functional at a given point in a year (ADF 2005; Anthonj et al. 2018; Gurmessa & Mekuriaw 2019). Different factors are responsible for the non-functionality of the water points, among which the level of user satisfaction is one of the major factors (Sutton et al. 2012; Gurmessa & Mekuriaw 2019). As indicated above, user satisfaction plays an important role in keeping the water points functional (sustainable) as users invest in terms of time and resources to maintain the water points as far as they are satisfied with the services that they get from the points. Sutton et al. (2012) pointed out that sustainability of a drinking water point depends largely on the degree to which users are satisfied with it to be willing to cover the costs of keeping it going. Thus, assessing users' satisfaction with rural drinking water points has a far reaching effect for sustainability of rural drinking water points. Therefore, this study investigates determinants of users' satisfaction with rural drinking water points in Ethiopia by considering the case study of Woliso Woreda, Oromia National Regional State.

RESEARCH METHODS

Description of the study area

Woliso Woreda (district), as shown in Figure 1, is found in South West Shoa Zone of Oromia National Regional State. It is divided into 37 peasant associations (Kebeles – the lowest administrative unit in Ethiopia composed of one or more villages) and Woliso town, the capital of the Woreda. The town is located 114 km west from the capital city of the country, Addis Ababa. The Woreda is the second largest Woreda in the Zone with a land area of 702.38 km2, and it is located between the geographic coordinates of 8°16′N-9°2′N and 37°31′E-38°46′E. The highest and the lowest elevations of the Woreda are 2,800 and 1,500 metres above sea level, respectively.

According to the water sector office of the Woreda (2015), the total population of the Woreda is 179,532 and of this number about 154,501 people do have access to potable water supplies. This shows that the coverage with water points is 86%. As per the the office records, there are 393 drinking water points throughout the rural Kebeles of the Woreda.

Sample size and sampling technique

A multi-stage sampling design was employed to select Kebeles and users (represented by household heads). First, 6 rural Kebeles with relatively higher numbers of drinking water points, medium and lower numbers of drinking water points were selected after classifying all the 37 rural Kebeles of the study area into 3 strata (Kebeles containing higher, medium and lower numbers of drinking water points). Secondly, the users of the drinking water points were picked proportionally from each of the Kebeles through a simple random sampling technique.

The sample size of the study was decided following Godden's (2004) determination formula which is given below: 
formula
where; n is the desired sample size; Z stands for the standard score at 95% confidence level which is 1.96; p is the estimated target population proportion; q is 1-p; and d is the confidence interval, expressed as a decimal, in this case 0.05. Substituting the values into the formula provided a sample size of 196. By assuming 7.5% for contingency, 211 users were selected for the survey.

In addition, two experts from the water sector office of the Woreda were selected for key interviews, and 3 focus group discussions (FGD) comprised of 6 household heads from each of the Kebeles were organized. The participants for the key interviews and focus group discussions were selected based on their knowledge and experience (participation in construction and subsequent management) of potable water points.

Data source and collection instruments

Both primary and secondary sources of data were used. A semi-structured questionnaire was used to collect quantitative data. This tool was used in particular to collect socioeconomic and water point related data (on the variables indicated below in Table 1) from users. The questionnaire was translated into the local language (Oromo language). The qualitative data were also collected from key informant interviews, focus group discussions and observation of the water points. Observation was employed to assess the functionality of sample rural drinking water points. The data collection was carried out with the full consent of the respondents. Upon making the purpose of the data collection (research purpose) clear, data were collected from the respondents with their full knowledge and agreement.

Table 1

Factors affecting user's satisfaction with the service

VariableVariable TypeVariable descriptions and unit of measurementExpected sign
Dependent (Satisfaction) Dummy User's satisfaction with the service they get from the water points, ‘1’ if satisfied; ‘0’ if not satisfied  
Independent Variables 
Quantity Continuous Daily water consumption from the water point in litres 
Distance Continuous User's distance from the water points, measured by minutes by foot − 
Queueing Continuous Average queueing (waiting) time at the water point measured by minutes − 
Conflict Dummy Occurrence of conflict at water point, ‘1’ if a user has encountered or observed conflict in the water points, otherwise ‘0’ − 
Interruption (Reliability) Continuous Frequency of interruption of the water point services in a year due to mechanical failure − 
Location Dummy The convenience of the location of the water point to the user, ‘1’ if convenient, ‘0’ if not 
Age Continuous  Age of the water point in years since construction − 
Contamination Dummy User's perception of the possibility of contamination at the water point, ‘1’ if yes, ‘0’ if no − 
Guard Dummy Availability of guard at the water points, ‘1’ if yes, ‘0’ if no 
VariableVariable TypeVariable descriptions and unit of measurementExpected sign
Dependent (Satisfaction) Dummy User's satisfaction with the service they get from the water points, ‘1’ if satisfied; ‘0’ if not satisfied  
Independent Variables 
Quantity Continuous Daily water consumption from the water point in litres 
Distance Continuous User's distance from the water points, measured by minutes by foot − 
Queueing Continuous Average queueing (waiting) time at the water point measured by minutes − 
Conflict Dummy Occurrence of conflict at water point, ‘1’ if a user has encountered or observed conflict in the water points, otherwise ‘0’ − 
Interruption (Reliability) Continuous Frequency of interruption of the water point services in a year due to mechanical failure − 
Location Dummy The convenience of the location of the water point to the user, ‘1’ if convenient, ‘0’ if not 
Age Continuous  Age of the water point in years since construction − 
Contamination Dummy User's perception of the possibility of contamination at the water point, ‘1’ if yes, ‘0’ if no − 
Guard Dummy Availability of guard at the water points, ‘1’ if yes, ‘0’ if no 

Method of data analysis

Descriptive statistics (frequency and percentage) and regression analysis were employed to analyse the data. Since the dependent variable is dichotomous (satisfied and not satisfied with the drinking water points), binary models (logit or probit) are used. For simplicity and easy interpretation, the researchers preferred a binary logistic regression model to predict the effects of independent variables on the dependent variable.

Specification of the model

The dependent variable (user's satisfaction with the rural drinking water point) is dichotomous with two values, 1 if a user is satisfied and 0 if not satisfied. Based on Gujarati (2004), the model can take the following equation: 
formula
(1)

In the logistic distribution equation, Pi is the probability of user's satisfaction; Xi is a set of explanatory variables of the ith user; β0 + β1 are the parameters to be estimated.

When β0 + β1Xi in Equation (1) is replaced by Zi, Equation (2) (the probability of user's satisfaction) is obtained: 
formula
(2)
The possibility of user's non-satisfaction (1 − Pi) can be depicted in Equation (3) as follows: 
formula
(3)
From the above two equations, the odds ratio in favour of user's satisfaction could thus be: 
formula
(4)
The logit model uses logarithmic transformation to assume linearity of the outcome variable on the explanatory variables. The logit model could thus be expressed as: 
formula
(5)
If the disturbance term ui is considered in the general logit model with a set of variables, the equation becomes: 
formula
(6)

X1, X2, … Xn are independent variables affecting user's satisfaction with rural drinking water points. These explanatory variables are listed in Table 2 below. The variables were selected based on previous empirical studies in the field.

RESULTS AND DISCUSSION

Characteristics of the respondents and their access to the water points

From the total 211 respondents, about 71% were males, while 29% of them were females. The mean age was 43, while the maximum and minimum ages were 85 and 23, respectively. The majority of them (47%) were between 39 and 54 years of age. Sixty five per cent had attended formal education, whereas 29% were illiterate and 6% were exposed to some form of non-formal education. The average household size was 5, with a maximum and minimum size of 10 and 1, respectively.

With an average daily water consumption of 40 litres per day per household, on average users fetch water from the water points twice a day. The average per capita water consumption in the study area was 8.87 litres and this figure is 41% lower than the GTP II's vision to supply water to rural areas at a rate of 15 litres consumption per day per person (l/c/d) within a 1.5 km radius at the end of the Program (2019/20). No more than 9% of users received 15 or more litres of water per day. The majority of users (45.5%) received between 5.1 and 10 litres per day, and 29.39% of users received less than 5 litres per day. These results show that users were receiving much less than the GTP envisaged water consumption per day per person. Similarly, 64.45% of users stated that the amount of water they were getting from the improved water sources (points) is not sufficient.

Users live at an average distance of 14 minutes by foot from the water points. Assuming that an average person walks 4–5 kilometres per hour while carrying water, 73.9% of users are within the radius of 1.5 kilometres. In this aspect, the water points are accessible for the majority of users as envisaged by GTP II (within a radius of 1.5 kilometres). The rest, i.e., about 26.1%, did not have the privilege of accessing the water points within a radius of 1.5 kilometres.

It is not only distance (travel) from the water points that takes up the time of users but also the queueing at the water points is another time consuming daily affair. On average, users queue for 33 minutes at the water points while waiting for their turn to fill the water containers. When the average travel and queueing time are combined, on average it takes 61 minutes (round trip plus queueing time) for a user to fetch water from the water point.

Users in the study area pay on average 4.48 Ethiopian Birr (ETB) per month for the water services. With this payment, 92% of users indicated that they do not have a problem in paying the tariff each month. With regard to the operational functionality of the water points, 62% of the users indicated that the water points that they use were functioning, whereas 38% of them reported that the water points they are using were not functioning at the time of data collection for this study.

Descriptive results

Since the study is about the satisfaction of users with the service that they get from the rural drinking water points (RDWPs), those users where their water points are non-functional were dropped from the analysis as measuring the level of satisfaction for the service that they do not get might distort results. Accordingly, the analysis below considers 130 users that had access to functioning water points. Among these respondents, 48.46% of the users indicated that they were satisfied with the services they get from the water points, whereas 51.54% of them reported that they were not satisfied.

As can be seen from Table 2 below, users' average living distance from the drinking water points varies for both satisfied and unsatisfied users. The mean distance for the satisfied households is 11.8 minutes and this is lower than the mean distance for unsatisfied users (17.2 minutes). An independent sample t-test also shows that there is a statistically significant difference (p < 0.01) between the two groups of users with regard to distance from rural drinking water points. There is also a significance difference (p < 0.1) between satisfied and unsatisfied users with regard to the water fee that they were paying per month. Satisfied users paid an average of 4.86 Birr per month while unsatisfied users paid 4.72 Birr per month. The average age of the water point among the satisfied users was 5.22 years, whereas it was 5.09 years among unsatisfied users, however, the difference is statistically insignificant.

Table 2

Descriptive statistics of continuous variables that affect user satisfaction with rural drinking water points

Independent variablesSatisfactionNMeanSDIndependent sample t-test
t-valueP-value
Age of the water point (years) yes 63 5.22 3.71 − 0.2310 0.818 
no 67 5.09 2.80 
Distance from the water point (minutes) yes 63 11.8 6.9 2.6155** 0.01 
no 67 17.2 15.1 
Queueing time (minutes) yes 63 18.6 23.6 3.407****** 0.001 
no 67 32.6 23.2 
Daily water consumption (litres) yes 63 36.43 15.92 0.1034 0.918 
no 67 36.72 15.80 
Interruption in the water point service (frequency) yes 63 0.540 0.91 1.8517** 0.066 
no 67 1.54 4.18 
Independent variablesSatisfactionNMeanSDIndependent sample t-test
t-valueP-value
Age of the water point (years) yes 63 5.22 3.71 − 0.2310 0.818 
no 67 5.09 2.80 
Distance from the water point (minutes) yes 63 11.8 6.9 2.6155** 0.01 
no 67 17.2 15.1 
Queueing time (minutes) yes 63 18.6 23.6 3.407****** 0.001 
no 67 32.6 23.2 
Daily water consumption (litres) yes 63 36.43 15.92 0.1034 0.918 
no 67 36.72 15.80 
Interruption in the water point service (frequency) yes 63 0.540 0.91 1.8517** 0.066 
no 67 1.54 4.18 

Source: Own survey data.

NB: ***, ** and * indicate the level of significance at 1, 5 and 10%, respectively.

The mean average waiting (queueing) time at the drinking water points differs between the two groups (satisfied and unsatisfied users). The mean average waiting time of satisfied users (18.6) minutes) was lower than the average waiting time of unsatisfied users (32.6 minutes) and this difference is statistically significant (p < 0.001). Similarly, the t-test value (1.8517) with a p-value of 0.066 shows that there is an association between user satisfaction and interruption in the water point services. The average annual interruption in the water point services among satisfied users was 0.54 while the average interruption among unsatisfied users was about three times this figure at 1.54 interruptions per year.

As indicated in Table 3, there is a statistically significant association (Χ2 = 32.099, p = 0.000) between user satisfaction and the location of the water point. A higher proportion of users, i.e., 63.83% of users who had a conveniently located water point were satisfied with the services, and conversely 36.17% of users were not satisfied although they indicated that their respective water points are in a convenient location. The very large proportion of users (91.67%) who were unsatisfied indicated that the water points that they use are in an inconvenient location. Similarly, the availability of guards at the water points has a significant association (Χ2 > =18.782, p = 0.000) with the satisfaction of users. While 64.47% of users who had guards at their respective water points were satisfied with the water point services, 35.53% of users who are dissatisfied with the services did not have guards at their respective water points. As shown in Table 3, the incidence of conflict at the water points and perception of possibility of contamination did not show a statistically significant association with satisfaction of users.

Factors affecting users' satisfaction with rural drinking water points

Before examining the results further, the model was diagnosed for econometric assumptions. The model was tested for misspecification (omission of relevant variables) and the linear predicted value (_hat) is significant (P = 0.000) while the corresponding linear predicted value squared (_hatsq) is insignificant (P = 0.946) and this shows that the relevant variables have been included in the model and the functional form is correct, and thus the model has no problem with regard to omission of relevant variables. The model has also passed the test of multicollinearity with a VIF (variance inflation factor) value of less than 10 for all the variables with the condition index of less than 15 (the minimum threshold for detecting collinearity). Hosmer and Lemeshow's goodness-of-fit statistics is insignificant with a p value of 0.4839 and this shows that the model fits the data well.

As shown below in Table 4, the overall model with a chi-square value of 64.15 and a probability of P < 0.000 indicates that the set of explanatory variables have a significant effect on user satisfaction with the service. The variables in the model accounted for 35.62% (Pseudo R2) of the variation in user satisfaction. The regression estimation result shows that out of the eight variables considered in the regression, five factors were found to be statistically significant in influencing the satisfaction of users with the rural drinking water points in the study area. The coefficient of these variables is different from zero at 1, 5 and 10% levels of significance.

Table 3

Descriptive statistics of dummy variables that affect user satisfaction with rural drinking water points

Independent variablesResponseSatisfaction (Frequency(%))
Chi2 test
YesNoChi2P-Value
Convenience of the location of the water point Convenient 60 (63.83) 34 (36.17) 32.099****** 0.000 
Inconvenient 3 (8.33) 33 (91.67) 
Availability of guard at the water point Yes 49 (64.47) 27 (35.53) 18.7816****** 0.000 
No 14 (25.93) 40 (74.07) 
Incidence of conflict in the water points Yes 9 (50.00) 9 (50.00) 0.0198 0.888 
No 54 (48.21) 58 (51.79) 
Perception of possibility of contamination Yes 38 (55.07) 31 (44.93) 2.5731 0.109 
No 25 (40.98) 36 (59.02) 
Independent variablesResponseSatisfaction (Frequency(%))
Chi2 test
YesNoChi2P-Value
Convenience of the location of the water point Convenient 60 (63.83) 34 (36.17) 32.099****** 0.000 
Inconvenient 3 (8.33) 33 (91.67) 
Availability of guard at the water point Yes 49 (64.47) 27 (35.53) 18.7816****** 0.000 
No 14 (25.93) 40 (74.07) 
Incidence of conflict in the water points Yes 9 (50.00) 9 (50.00) 0.0198 0.888 
No 54 (48.21) 58 (51.79) 
Perception of possibility of contamination Yes 38 (55.07) 31 (44.93) 2.5731 0.109 
No 25 (40.98) 36 (59.02) 

Source: Own survey data.

NB: *** indicates the level of significance at 1%.

Table 4

Logistic regression estimation result of user satisfaction with the water point service

VariablesOdds RatioStd. ErrorP > |z|Standardized Domin. StatRank (Relative Importance)Marginal effect (dy/dx)
Location (Convenient) 13.92561 10.05253 0.000****** 0.4321 0.4430179 
Guard (Yes) 2.980215 1.497678 0.030**** 0.1819 0.1751036 
Queueing 0.9776426 0.010592 0.037**** 0.1289 −0.0032587 
Interruption (Reliability) 0.728761 0.1243687 0.064** 0.1045 −0.0456 
Distance 0.9528467 0.0248862 0.064** 0.0905 −0.006961 
Logistic regression statistics Number of obs = 130 
LR chi2(6) = 64.15 Prob > chi2 = 0.0000 
Log likelihood = −57.971957 Pseudo R2 (McFadden's) = 0.3562 
Hosmer-Lemeshow chi2 = 7.50 Prob > chi2 = 0.4839 
_hat coef 0.9962685 P > z = 0.000 
_hatsq coef −0.0068119 P > z = 0.946 
Mean VIF = 1.29 (minimum 1.09 and maximum 1.67, and condition index ranging between 1.000 and 143.8889) 
VariablesOdds RatioStd. ErrorP > |z|Standardized Domin. StatRank (Relative Importance)Marginal effect (dy/dx)
Location (Convenient) 13.92561 10.05253 0.000****** 0.4321 0.4430179 
Guard (Yes) 2.980215 1.497678 0.030**** 0.1819 0.1751036 
Queueing 0.9776426 0.010592 0.037**** 0.1289 −0.0032587 
Interruption (Reliability) 0.728761 0.1243687 0.064** 0.1045 −0.0456 
Distance 0.9528467 0.0248862 0.064** 0.0905 −0.006961 
Logistic regression statistics Number of obs = 130 
LR chi2(6) = 64.15 Prob > chi2 = 0.0000 
Log likelihood = −57.971957 Pseudo R2 (McFadden's) = 0.3562 
Hosmer-Lemeshow chi2 = 7.50 Prob > chi2 = 0.4839 
_hat coef 0.9962685 P > z = 0.000 
_hatsq coef −0.0068119 P > z = 0.946 
Mean VIF = 1.29 (minimum 1.09 and maximum 1.67, and condition index ranging between 1.000 and 143.8889) 

NB: ***, ** and *indicate the level of significance at 1, 5 and 10% respectively.

Source: Own survey data.

In order to see the relative importance of these variables, a dominance analysis was carried out following the procedure provided by Azen & Traxel (2009). Accordingly, location of the water points is found to be the most important (dominant) factor while distance is the least important of the significant explanatory variables. The detail is presented in Table 4.

As indicated in Table 4, location of the water point, presence of guards at the water point, queueing time, service interruption (reliability) and distance from the user's home to the water point are found to significantly influence satisfaction of users with the water point service. Whereas the frequency of travel to the water point per day to fetch water, age of the water point and the user's perception of the possibility of contamination of the water were found to be insignificant in determining the likelihood of satisfaction of households.

Convenience of location

As expected, the convenience of the water point location to users is found to be a significant factor that positively affects the satisfaction of users with the water point service, with an odds ratio of 13.92561 and a p-value of 0.000. The odds ratio result indicates that the likelihood of satisfaction with a convenient location is 13.92 higher than where there is an inconvenient location. The marginal effect of this variable is 0.4430, implying that the probability of user satisfaction with a convenient location is higher by 43.30% as compared to users with an inconvenient location. The result of this study is consistent with empirical studies of Abebe et al. (2013) and Bhandari & Grant (2007) that found an inconvenient location decreased the satisfaction of users. Other studies also reported that location is an important determinant factor of user satisfaction because of its implication for the distance to the water point and the time to collect water (WB & IFPRI 2010; Masanyiwa et al. 2014). It has to be noted that location might not necessarily refer to proximity and time taken to travel, but it is also linked with the social and location specific attributes of the water source regardless of its nearness to the user’s home. In a study conducted in Tanzania, Mwamaso (2015) linked the location of the source with isolated areas, where some locations were perceived insecure for women and children in particular. They further reported that water location near to cemeteries might not be in a convenient location as it might hamper women's access to the water outlet, especially during evening to night-time, for security reasons. In this current study, it was found that housewives and children were the most likely household members to fetch water at 73.08% and 26.15%, respectively. There was only one case where a husband was reported to fetch water frequently. Given such a burden (of fetching water) on women, who are quite busy with household chores, the appropriate location of water points that can save time and energy, and at the same time provide them with a sense of security is crucial. The burden also rests on children who spend their time in fetching water, and this in turn affects their schooling time. Hence, a convenient location in terms of both distance and security plays an important role in influencing the satisfaction of users.

Presence of guards at the water points

The presence of guards at the water points is another important factor found to be statistically significant (p < 0.05) that boosts the satisfaction of users with the rural drinking water points with an odds ratio of 2.98. The marginal effect of the variable is positive with a magnitude of 0.175, and this implies that the probability of satisfaction among users increases by 17.5% when the water points do have guards. The issue of guards is a neglected aspect in the study of sustainability of water points and user satisfaction. This study demonstrated that influence of guards on user satisfaction. The plausible reasons might be that guards might protect the water points from livestock, keep order in queues, report breakdowns and other water point related issues to the water users’ committees on time, and provide security for women and children.

Queueing time

Average queueing (waiting) time at the water point is also found to be a negative and statistically significant factor that affects user satisfaction. This variable has an odds ratio, p-value, and marginal effect of 0.978, 0.037, and −0.0033, respectively. The odds ratio of 0.978 of average waiting time at the water point shows that the probability of satisfaction decreases by 0.978 for a one minute increment in the average waiting time at the water points. The marginal effect of −0.0033 for the average waiting time at the water point also indicates that the probability of satisfaction decreases by 0.33% with a one minute increment in the average waiting time at the water point. This study is similar to previous works of Belachew (2014) and Abebe et al. (2013) which found that an increase in the average queueing time at the water source decreased the satisfaction of households. Long queueing times at the water points would have several implications. The crowding due to the long queue lines might be a cause of conflict; the longer users wait for their turn might result in a higher probability of conflict occurrence. It was also revealed in focus group discussions that conflicts usually occur during the queueing time, particularly at the water points where the number of households using drinking water from the same water points is high. This in turn leads to dissatisfaction among users as conflict creates disorder and temporal interruption in fetching water. In addition, the longer users stay at the water points while waiting their turn, the more time is lost that would have been spent in other household and economic activities, and this in turn could lead into user dissatisfaction (with the water points that have long queue lines). One of the researchers also noticed at the time of data collection that the queueing time at the water point was long when compared to unimproved water sources.

Service interruption (un/reliability)

Service interruption measured the frequency of disruption of the water services due to mechanical failure per year. In this aspect, service interruption per year was found to be statistically significant but negatively affecting user satisfaction with an odds ratio, p-value and marginal effect of 0.729, 0.064 and −0.0456, respectively. The odds ratio of service interruption shows that the probability of satisfaction decreases by 0.729 times for one unit increment in the frequency of the interruption of the water services in a year. In other words, the probability of user satisfaction decreases by 4.56 per cent when the number of water service interruptions increases by one per year. This result is obvious in that any breakdown at the water points hampers users from getting an adequate amount of water at a time. When the incidence of breakdown increases, it lowers user satisfaction.

Distance from water point

The distance of users from the water points is another important factor found to affect user satisfaction. Similar to the waiting time, distance is found to be a negative and statistically significant factor that affects the satisfaction of users of the rural water scheme services. The odds ratio of the variable is 0.953 with a p-value of 0.064, and this indicates that user satisfaction decreases by 0.953 units with an increment of distance between the user's house and the water point of one minute. The marginal effect of −0.0070, in other words, indicates that the probability of user satisfaction decreases by 0.70% as the distance increases by one minute. Similar findings were reported by Abebe et al. (2013) and Belachew (2014). They reported that an increase in the distance of the water point from the user's home decreases the satisfaction of users. One of the probable reasons is that having to go a longer distance distance takes more energy and time. Because of the time taken to travel coupled with the queueing time, a longer distance might also compete with household chores of women and school time of children, who are the most likely members of the household to fetch water as noticed in the study area.

CONCLUSION

Despite investment in the provision of safe drinking water points, little is known about user satisfaction with rural drinking water points in Ethiopia. With this observation, this study investigated the factors that affect user satisfaction with rural drinking water points in six rural Kebeles of Woliso district. It was found that 48.46% of the users indicated that they were satisfied, whereas 51.54% of them reported that they were not satisfied with the water points they were using. It was also found that location of the water point, availability of guards, queueing time, service reliability, and distance between the user’s house and the water point significantly influence the satisfaction of users.

Location is a dominant factor that could be interpreted in terms of distance, security and other communal attributes of a rural drinking water point. In addition, hiring guards for the water points also contributes to the satisfaction of users. Although neglected in other studies, guards are particularly crucial in safeguarding the water points from livestock, keeping order in queues, and reporting interruptions, breakdowns and other water point related issues to the water users’ committees on time. A long queueing time at the water points, on the other hand, might cause conflict, and consumes the time of women and children, and this in turn leads to user dissatisfaction. It is therefore crucial to envisage queues when installing water points and thus the water discharge volume should be commensurate with the number of the possible users, and if not, mechanisms that minimize queues should be put in place. Service interruption as a sign of unreliability, and distance between the user's home and water point are also notable concerns for users of rural drinking water points. Recurrent breakdowns of the water points hampers users from getting an adequate amount of water at a time. When the incidence of breakdowns increases, it apparently lowers user satisfaction. Similarly, distance of the water point from the user's home influences satisfaction. On top of the time and energy demand of distant water points, a longer distance to walk also competes with the household chores of women and the school time of children, who are the most likely to fetch water as noticed in the study area. It is therefore essential to address the issue of location and distance when physically installing the water points, and put mechanisms in place to maintain service reliability, ensure shorter queues and safeguard the water points with guards.

DATA AVAILABILITY STATEMENT

Data cannot be made publicly available; readers should contact the corresponding author for details.

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