Revolution Impact on Drinking Water Consumption: Real Case of Tunisia

Water is considered a major social and political issue. This article stands out by its quantification of the impact of socio-political disturbances on social behavior regarding the consumption of drinking water. It also allows the monitoring of fluctuation of the economic pattern through the main economic sectors during these disturbances by the study of a revolution impact—case of the revolution in Tunisia in January 2011, on the spatial, temporal and sectorial variation of the drinking water consumption. Six water users covering the whole consumption are considered in this study: connected and non connected household users, tourism, industries, collective and various other users. Among the results, in this coastal country, the relation between drinking water consumption by tourism, the touristic activities and the national economy are clearly established. All these components are similarly affected by the socio political disturbances but longer lasting fallouts hit only the most vulnerable regions of the country. Industrial activities that used drinking water seemed to be affected only when the socio political disturbance is at its paroxysm due to strikes, sit-ins of unemployed people, job seekers protests and companies closure. The revolution impacts such as weakening of the State’s authority when facing inappropriate and illegitimate social desires, anarchy, people displacement and refugees are the major causes of non-connected household drinking water waste or over-consumption (up to 89 %) and an alarming increase of the induced breakages (+445 % in 2012 compared to 2010).     

Quality Control Chart for the Mean using Double Ranked Set Sampling

In this paper, an attempt is made to develop Quality Control Charts for monitoring the process mean based on Double Ranked Set Sampling (DRSS) rather than the traditional Simple Random Sampling (SRS). Considering a normal population and several shift values, the performance of the Average Run Length (ARL) of these new charts was compared with the control charts based on Ranked Set Sampling (RSS) and SRS with the same number of observations. It is shown that the new charts do a better job of detecting changes in process mean compared with SRS and RSS.     

Regression estimators in extreme and median ranked set samples

The ranked set sampling (RSS) method as suggested by McIntyre (1952) may be modified to come up with new sampling methods that can be made more efficient than the usual RSS method. Two such modifications, namely extreme and median ranked set sampling methods, are considered in this study. These two methods are generally easier to use in the field and less prone to problems resulting from errors in ranking. Two regression-type estimators based on extreme ranked set sampling (ERSS) and median ranked set sampling (MRSS) for estimating the population mean of the variable of interest are considered in this study and compared with the regression-type estimators based on RSS suggested by Yu & Lam (1997). It turned out that when the variable of interest and the concomitant variable jointly followed a bivariate normal distribution, the regression-type estimator of the population mean based on ERSS dominates all other estimators considered.     

Support vector regression based residual control charts

Control charts for residuals, based on the regression model, require a robust fitting technique for minimizing the error resulting from the fitted model. However, in the multivariate case, when the number of variables is high and data become complex, traditional fitting techniques, such as ordinary least squares (OLS), lose efficiency. In this paper, support vector regression (SVR) is used to construct robust control charts for residuals, called SVR-chart. This choice is based on the fact that the SVR is designed to minimize the structural error whereas other techniques minimize the empirical error. An application shows that SVR methods gives competitive results in comparison with the OLS and the partial least squares method, in terms of standard deviation of the error prediction and the standard error of performance. A sensitivity study is conducted to evaluate the SVR-chart performance based on the average run length (ARL) and showed that the SVR-chart has the best ARL behaviour in comparison with the other residuals control charts.     

The Effect of Attitudinal and Behavioral Commitment on the Internal Assessment of Organizational Effectiveness: A Multilevel Analysis

Using a multilevel analysis including 207 volunteers and paid workers nested within 51 nonprofit organizations (NPOs), this study examines the effect of individual and group attitudinal and behavioral commitment on their assessment of organizational effectiveness. Drawing on classical attitude theories, our results indicate that individuals with higher affective organizational commitment tend to assess their NPO’s effectiveness higher, while individuals staying because of the lack of alternatives assess it lower. However, in line with behavioral commitment theories, both relationships are mediated by the effect of teamwork behavioral commitment. We also found a negative effect of normative attitudinal commitment partially nested at the group level. Overall, our results suggest that encouraging volunteers and paid workers to participate in concrete teamwork behaviors on a daily basis constitutes a twofold benefit: it adds to the effect of affective attitudinal commitment at the individual level, while counter balancing the negative effects related to normative individual and collective resistances.     

Application of the “Benefit-Of-the-Doubt” Approach for the Construction of a Digital Access Indicator: A Revaluation of the “Digital Access Index”

The use of composite indicators as a tool for ranking and making decisions is ever increasing in a world marked by the inequalities and competition in all domains. However, the dependence of countries ranking on the weighing scheme used to aggregate individual indices or sub-indicators, most of the time set by experts/stakeholders may weaken the credibility of composite indicators. One method which is able to overcome these limits is the “Data Envelopment Analysis” approach, particularly named “Benefit-Of-the-Doubt” in the context of composite indicators’ construction. We propose a revaluation of the Digital Access Index given that Information technology is the most important factor driving improvement in a wide array of areas critical for the quality of life for individuals as well as societies. We have shown that this method is more suitable for identifying trends and drawing attention to particular issues and also for setting policy priorities. In fact, the weights of the individual indices, used to compute the composite indicators are based on the data itself and are proper for the country under consideration. These weights contain adequate information in order to help policy-makers to better understand the nature of the new innovation economy and the types of public policies needed to drive innovation, productivity and broad-based prosperity for its citizens.     

Statistical quality control based on ranked set sampling

Different quality control charts for the sample mean are developed using ranked set sampling (RSS), and two of its modifications, namely median ranked set sampling (MRSS) and extreme ranked set sampling (ERSS). These new charts are compared to the usual control charts based on simple random sampling (SRS) data. The charts based on RSS or one of its modifications are shown to have smaller average run length (ARL) than the classical chart when there is a sustained shift in the process mean. The MRSS and ERSS methods are compared with RSS and SRS data, it turns out that MRSS dominates all other methods in terms of the out-of-control ARL performance. Real data are collected using the RSS, MRSS, and ERSS in cases of perfect and imperfect ranking. These data sets are used to construct the corresponding control charts. These charts are compared to usual SRS chart. Throughout this study we are assuming that the underlying distribution is normal. A check of the normality for our example data set indicated that the normality assumption is reasonable.