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This study investigates the effect of user fees on access and waiting time in Nigeria. For access, the effect of user fees on both preventive and curative care; and the effect of user fees on waiting time at public healthcare facilities were examined. User fees are vital for the fiscal sustainability of healthcare provision for most African economies. Its imposition could debar healthcare access by the poor while its removal can reduce quality of care and induce longer waiting time.

The wave 3 of the Nigerian General Household Survey (2015/16) data was used for users of public health facilities. Access to healthcare was modelled using utilization data in a logistic regression model while waiting time was through the Negative Binomial Regression Model (NBRM).

The analyses showed significant effects of user fees on access to both preventive and curative care and on time spent waiting to make use of healthcare services. Individuals were able to access healthcare services regardless of amounts paid. Also, there was a non-negative effect of user fee imposition on waiting time.

Nigeria should improve healthcare facilities to address the enormous demand for healthcare services when designing policy for health sector.

This paper shows that even with the imposition of user fees, healthcare facilities could still not cater for the rising healthcare needs of the populace but cautioned that its abolition may not be a preferred option.

The purpose of this paper is to assess causal relations between the implementation of the 5S approach and the reduction of patients’ waiting time at out patient departments (OPDs) of hospitals in Tanzania.

Patients’ waiting time was measured under the cluster randomized control trial (c-RCT). In all, 16 hospitals were chosen and divided into treatment and control groups using block randomization. Before the intervention, a baseline study was conducted at OPDs in all 16 hospitals. After one year of the intervention, the end-line study was carried out in both the groups. A comparison of the average waiting time reduction and Difference-in-Difference (DID) analysis was carried out to see the effect of the 5S approach on the reduction of patients’ waiting time.

Statistical significance in reduction of patients’ waiting time was seen in the medical records sections (p=0.002) and consultation rooms (p=0.020) in the intervention group. The same trend was also seen using DID analysis (−15.66 min in medical record, −41.90 min in consultation rooms).

This study has the following limitations in terms of the data. The data were collected for only three days at the time of baseline survey, and again for three days at the time of the end-line survey from 16 hospitals. Moreover, piloted areas for the implementation of the 5S approach vary from hospital to hospital. There might be a bias in the measurement of a patient’s waiting time. Caveats are therefore needed in extrapolating the study results to other settings. Despite these caveats, the findings will provide important insights for implementing quality improvement programs in Tanzania and in other African countries for improvement of time factors.

This study used c-RCT, and has proven the effectiveness of the 5S approach in improving the working environment and reducing patients’ waiting time at OPDs in several hospitals at district level in Tanzania.

Waiting is associated with pain and stress that leads to frustration. However, consumer narratives may help cope with the stress associated with such waiting. This study aims to understand consumer waiting behaviours within online communities.

Data was gathered following a netnographic approach from a Facebook brand community (FBC) by downloading and archiving the threads related to members’ waiting behaviours. This resulted in 91 pages of data, with 438 individual comments and 179 distinct threads.

The data revealed that members of the sampled FBC exercised waiting behaviour. The authors confirm that waiting for a product is associated with both negative outcomes (frustration, boredom, etc.), but positive ones (create stronger ties with the brand and fellow members, etc.). Members of the FBC exhibited reduced consumer anxiety and stress during the waiting period.

This study found 13 waiting behaviours within the FBC and supports the idea that new value-creating behaviours are noticed within the context of FBCs.

This study focuses on waiting within a goods-based context (waiting to be served has been examined predominantly within the service sector). The study explored the behaviours of consumers who use social media to complain about extended waiting periods to receive the product along with other consumer reactions to these waiting crowds to reduce the emotional pain associated with such delays.

La espera se asocia con el dolor y el estrés, lo que lleva a la frustración. Sin embargo, el relato de otros consumidores puede ayudar a hacer frente al estrés asociado a dicha espera. Este trabajo busca entender el comportamiento de espera de los consumidores dentro de las comunidades online.

Los datos fueron recopilados siguiendo un enfoque netnográfico de una comunidad de marca en Facebook (CMF) mediante la descarga de mensajes; sólo los hilos relacionados con los comportamientos de espera de los miembros fueron descargados y archivados. Esto resultó en 91 páginas de datos con 438 comentarios individuales y 179 hilos distintos.

Los datos revelan que en Facebook se dan comportamientos de espera. Confirmamos que la espera de un producto no sólo está asociada a resultados negativos (frustración, aburrimiento, etc.), sino también a resultados positivos (crear lazos más fuertes con la marca y los compañeros). Además, se comprueba que los miembros de la CMF reducen la ansiedad y el estrés del consumidor durante el período de espera.

Este trabajo encuentra 13 comportamientos de espera dentro de una CMF

Este trabajo se centra en la espera dentro del contexto basado en los bienes (la espera para ser atendido ha sido examinada principalmente dentro del sector de servicios). Además, no sólo nos centramos en los consumidores que utilizan los medios sociales para quejarse de una espera más larga en la recepción del producto, sino también en la reacción de otros consumidores a estas esperas para reducir el dolor emocional asociado a dicho retraso.

The interruption event will seriously affect the normal operation of urban rail transit lines,causing a large number of passengers to be stranded in the station and even making the train stranded in the interval between stations. This study aims to reduce the impact of interrupt events and improve service levels.

To address this issue, this paper considers the constraints of train operation safety, capacity and dynamic passenger flow demand. It proposes a method for adjusting small loops during interruption events and constructs a train operation adjustment model with the objective of minimizing the total passenger waiting time. This model enables the rapid development of train operation plans in interruption scenarios, coordinating train scheduling and line resources to minimize passenger travel time and mitigate the impact of interruptions. Regarding the proposed train operation adjustment model, an improved genetic algorithm (GA) is designed to solve it.

The model and algorithm are applied to a case study of interruption events on Beijing Subway Line 5. The results indicate that after solving the constructed model, the train departure intervals can be maintained between 1.5 min and 3 min. This ensures both the safety of train operations on the line and a good match with passengers’ travel demands, effectively reducing the total passenger waiting time and improving the service level of the urban rail transit system during interruptions. Compared to the GA algorithm, the algorithm proposed in this paper demonstrates faster convergence speed and better computational results.

This study explicitly outlines the adjustment method of using short-turn operation during operational interruptions, with train departure times and station stop times as decision variables. It takes into full consideration safety constraints on train operations, train capacity constraints and dynamic passenger demand. It has constructed a train schedule optimization model with the goal of minimizing the total waiting time for all passengers in the system.

This paper aims to propose a simulation-based performance evaluation model for the drone-based delivery of aid items to disaster-affected areas. The objective of the model is to perform analytical studies, evaluate the performance of drone delivery systems for humanitarian logistics and can support the decision-making on the operational design of the system – on where to locate drone take-off points and on assignment and scheduling of delivery tasks to drones.

This simulation model captures the dynamics and variabilities of the drone-based delivery system, including demand rates, location of demand points, time-dependent parameters and possible failures of drones’ operations. An optimization model integrated with the simulation system can update the optimality of drones’ schedules and delivery assignments.

An extensive set of experiments was performed to evaluate alternative strategies to demonstrate the effectiveness for the proposed optimization/simulation system. In the first set of experiments, the authors use the simulation-based evaluation tool for a case study for Central Florida. The goal of this set of experiments is to show how the proposed system can be used for decision-making and decision-support. The second set of experiments presents a series of numerical studies for a set of randomly generated instances.

The goal is to develop a simulation system that can allow one to evaluate performance of drone-based delivery systems, accounting for the uncertainties through simulations of real-life drone delivery flights. The proposed simulation model captures the variations in different system parameters, including interval of updating the system after receiving new information, demand parameters: the demand rate and their spatial distribution (i.e. their locations), service time parameters: travel times, setup and loading times, payload drop-off times and repair times and drone energy level: battery’s energy is impacted and requires battery change/recharging while flying.

In this chapter, the author examines the way in which the purchase and delivery infrastructure of darknet cryptomarkets shapes the experience of opiate drug use and dependence. It uses the concept of social time and posits that the illicit drug distribution system reshapes two temporal dimensions shaping the experience of drug users. There is the experience of time located in the pharmacology of the drug and in the body of the drug user, which evokes experiences of withdrawal and dependence. Then there is the socio-technical embedding of the delivery system and governance structures which support or impinge on the autonomy of the user. This ‘drug time’ is both a benefit and a cost of engaging in cryptomarket use. The market infrastructure can give users the opportunities to more carefully manage their drug time, while also creating new risks of non-delivery that can sharpen experiences of dope sickness. The author concludes that the growing professionalisation, digitisation, and commercialisation of the drug market increasingly embed drug time in material infrastructures mediated through technical systems.

Bureaucracy to a large extent entrenches orderliness and productive means of achieving goals in both public and private organisations across the world. However, bureaucracy is not suitable in the management of hospitals due to its peculiar nature of operations. This study investigates the conundrum of bureaucratic processes and health-care service delivery in government hospitals in Nigeria.

The study surveyed 600 outpatients and attendees visiting tertiary and government hospitals in Nigeria using descriptive design to obtained data from the respondents. A research instrument, questionnaire, was used to gather data. Out of the 600 outpatients visiting the 20 hospitals in government and tertiary hospitals, 494 responses were returned from the attendees. The study employed random sampling strategy to collect the information.

The findings of this study were that service delivery in government hospitals were in adverse position on all the four constructs of bureaucratic dimensions as against quality of service delivery in hospitals in Nigeria. It discovered that bureaucratic impersonality cannot impact on the quality of service delivery in government hospitals in Nigeria. Separation and division of labour among health workers have no significant effect on quality service delivery in government hospitals. Formal rules and regulations (administrative procedure, rules, and policies) prevent quality service delivery in government hospitals in Nigeria. Also, patient’s waiting time was not significant to the quality of service delivery in government hospitals.

The results are constrained with dimensions of bureaucratic processes. Thus, the implication of this study is that bureaucracy in the Nigerian public hospitals is an unnecessary marriage which should be carefully separated and de-emphasised for quality service delivery in the hospitals to thrive.

Largely, this study is practical essential as it unearths the irrelevant operations procedure that hinder progress in Nigerian hospitals.

The study accomplishes recognised importance to survey how bureaucracy impedes quality service delivery in government hospitals. This study has provided a vital clue to elements that will bring rapid attention to patients’outcome in Nigerian hospitals and health-care facilities which hitherto has not been emphasised. The study has contributed to the existing body of knowledge associated to healthcare service quality in developing country.

Internet of Things’ (IoT’s) first wave started with tracking services for better inventory management mainly using radio frequency identification (RFID) technology. Later on, monitoring services became one of the major interests, including sensing technologies, and then more actuation for remote control-type of IoT applications such as smart homes, smart cities and Industry 4.0. In this paper, the authors focus on the RFID technology impairment. They propose to take advantage of the mature IoT technologies that offer native service discovery such as blutooth or LTE D2D ProSe or Wifi Direct. Using the automatic service discovery in the new framework will make heterogeneous readers aware of the presence of other readers and this will be used by the proposed distributed algorithm to better control the multiple RFID reader interference problem. The author clearly considers emerging Industry 4.0 use case, where RFID technology is of major interest for both identification and tracking. To enhance the RFID tag reading performance, collisions in the RFID frequency should be minimized with reader-to-reader coordination protocols. In this paper, the author proposes a simple distributed reader anti-collision protocol named DiSim that makes use of proximity services of IoT network and is compliant with the current RFID standards. The author evaluates the efficiency of the proposal via simulation.

In this paper, the author proposes a simple distributed reader anti-collision protocol named DiSim that makes use of proximity services of IoT network and is compliant with the current RFID standards. The author evaluates the efficiency of the proposal via simulation to study its behavior in very dense and heterogeneous RFID environments. Specifically, the author explores the coexistence of powerful static readers and small mobile readers, comparing the proposal with a standard ETSI CSMA method. The proposal reduces significantly the number of access attempts, which are resource-expensive for the readers. The results show that the objectives of DiSim are met, producing low reader collision probability and, however, having lower average readings per reader per time.

DiSim is evaluated with the ETSI standard LBT protocol for multi-reader environments in several environments with varied levels of reader and tag densities, having both static powerful RFID readers and heterogeneous randomly moving mobile RFID readers. It effectively reduces the number of backoffs or contentions for the RFID channel. This has high reading success rate due to the avoided collisions; however, the readers are put to wait, and DiSim has less average readings per reader per time. As an additional side evaluation, the ETSI standard LBT mechanism was found to present a good performance for low-density mid-coverage scenarios, however, with high variability on the evaluation results.

To show more results, the author needs to do real experimentation in a warehouse, such as Amazon warehouse, where he expects to have more and more robots, start shelves, automatic item finding on the shelve, etc.

Future work considers experimentation in a real warehouse equipped with heterogeneous RFID readers and real-time analysis of RFID reading efficiency also combined with indoor localization and navigation for warehouse mobile robots.

More automatization is expected in the future; this work makes the use of RFID technology more efficient and opens more possibilities for services deployment in different domains such as the industry which was considered not only in this paper but also in smart cites and smart homes.

Compared to the literature, the proposal offers the advantage to not be dependent on a centralized server controlling the RFID readers. It also offers the possibility for an existing RFID architecture to add new readers from a different manufacturer, as the readers using the approach will have the possibility to discover the capabilities of the new interaction other RFID readers. This solution takes advantage of the available proximity service that will be more and more offered by the IoT technologies.

During rush hours, many passengers find it difficult to board the first train due to the insufficient capacity of metro vehicles, namely, left behind phenomenon. In this paper, a data-driven approach is presented to estimate left-behind patterns using automatic fare collection (AFC) data and train timetable data.

First, a data preprocessing method is introduced to obtain the waiting time of passengers at the target station. Second, a hierarchical Bayesian (HB) model is proposed to describe the left behind phenomenon, in which the waiting time is expressed as a Gaussian mixture model. Then a sampling algorithm based on Markov Chain Monte Carlo (MCMC) is developed to estimate the parameters in the model. Third, a case of Beijing metro system is taken as an application of the proposed method.

The comparison result shows that the proposed method performs better in estimating left behind patterns than the existing Maximum Likelihood Estimation. Finally, three main reasons for left behind phenomenon are summarized to make relevant strategies for metro managers.

First, an HB model is constructed to describe the left behind phenomenon in a target station and in the target direction on the basis of AFC data and train timetable data. Second, a MCMC-based sampling method Metropolis–Hasting algorithm is proposed to estimate the model parameters and obtain the quantitative results of left behind patterns. Third, a case of Beijing metro is presented as an application to test the applicability and accuracy of the proposed method.

In Kenya, gaps exist in health service provision to slum residents, especially service availability and access to quality care. There is also little information on the health status of people living in slums other than in Nairobi. The purpose of this paper is to generate evidence for use in designing interventions to improve health services in four mid-sized slums in Embu, Nyeri and Thika, Kenya.

A cross-sectional survey of clients receiving services in health facilities was conducted in the targeted slums. Data were collected through face-to-face interviews. Factor scores were generated using the Rasch model; simple and multivariate logistic regression analyses were done using the R statistical software.

Overall, 81 per cent of the 203 participants reported being satisfied with health services. Most clients (89 per cent) reported that health facility staff greeted them warmly; 82 per cent said their consultation was private. The facility type, waiting time and client experience with service providers determined their satisfaction (p < 0.05).

Healthcare managers can improve client satisfaction levels by understanding the client flow in their facilities and addressing causes of client dissatisfaction, such as long waiting times, while at the same time promoting facilitating factors.

The authors use latent variable modelling to compute client satisfaction scores, which were dichotomised into two categories and fitted into a logistic regression model to identify factors that influence client satisfaction. Health facility clients in the four slums are satisfied with services and have confidence the providers will serve them in a friendly and professional manner that promotes respect and quality care. The paper recommend healthcare managers in similar settings carry out client flow analysis and institute remedial measures to address long waiting times. Qualitative studies are recommended to determine the reasons behind the high satisfaction levels reported in this study.