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Africa has been identified as an area where higher mortality happens due to un-accessibility to health care, drugs and other health facilities. Nigeria, as one of the African countries, is not excluded from such difficulties. This study aims to examine the determinants of efficient last-mile delivery at selected health facilities and the Kaduna State Health Supplies Management Agency (KADSHMA).

The study sourced data from KADSHMA and the health facilities’ staff, with a total of 261 observations used. Likewise, the respondents were picked from warehouses of each health facility and KADSHMA. The data was analysed using the partial least square structural equation modelling analysis to estimate the relationship among the variables of the study.

The study’s findings revealed that all five variables of the study (i.e. determinants) were significantly affecting the efficient last-mile delivery. Four constructs (delivery cost [DC], delivery time [DT], mode of delivery [MD] and facilities technology [FT]) have shown a positive and significant association with efficient last-mile delivery, whereas one variable (product mix [PM]) indicated a negative and significant association with efficient last-mile delivery. The study concludes that DC, DT, MD, FT and PM played significant roles in efficient last-mile delivery.

The study provides that specific means of transportation should always be on standby to transport health supplies. Time schedules should always be prepared and adhered to when transporting health supplies to the facilities, and each facility should network with robust technology to ease communication in terms of order and order planning. Additionally, facilities should try as much as possible to reduce the varieties of products when ordering health supplies, as it will increase the efficiency of the delivery.

To the best of the authors’ knowledge, this study is the first of its kind that considered these five variables (DC, DT, MD, FT and PM) with impact on the last-mile delivery in one model, especially in the Nigerian case. This is a great contribution to knowledge, more importantly, to the last-mile delivery of the health sector. The result confirmed the importance of these determinants (DC, DT, FT and PM) of last-mile delivery efficiency in saving lives.

The purpose of this paper is to investigate and review the impact of the use of statistical quality control (SQC) development and analytical and numerical methods on average run length for econometric applications.

This study used several academic databases to survey and analyze the literature on SQC tools, their characteristics and applications. The surveys covered both parametric and nonparametric SQC.

This survey paper reviews the literature both control charts and methodology to evaluate an average run length (ARL) which the SQC charts can be applied to any data. Because of the nonparametric control chart is an alternative effective to standard control charts. The mixed nonparametric control chart can overcome the assumption of normality and independence. In addition, there are several analytical and numerical methods for determining the ARL, those of methods; Markov Chain, Martingales, Numerical Integral Equation and Explicit formulas which use less time consuming but accuracy. New ideas of mixed parametric and nonparametric control charts are effective alternatives for econometric applications.

In terms of mixed nonparametric control charts, this can be applied to all data which no limitation in using of the proposed control chart. In particular, the data consist of volatility and fluctuation usually occurred in econometric solutions. Furthermore, to find the ARL as a performance measure, an explicit formula for the ARL of time series data can be derived using the integral equation and its accuracy can be verified using the numerical integral equation.

The purpose of this paper is to extend the recent results of Okeke et al. (2018) to the class of multivalued ρ-quasi-contractive mappings in modular function spaces. We approximate fixed points of this class of nonlinear multivalued mappings in modular function spaces. Moreover, we extend the concepts of T-stability, almost T-stability and summably almost T-stability to modular function spaces and give some results.

In this paper, Picard–S hybrid iterative process is defined, which is a hybrid of Picard and S-iterative process. This new iteration converges faster than all of Picard, Krasnoselskii, Mann, Ishikawa, S-iteration, Picard–Mann hybrid, Picard–Krasnoselskii hybrid and Picard–Ishikawa hybrid iterative processes for contraction mappings and to find the solution of delay differential equation, using this hybrid iteration also proved some results for Picard–S hybrid iterative process for nonexpansive mappings.

This new iteration converges faster than all of Picard, Krasnoselskii, Mann, Ishikawa, S-iteration, Picard–Mann hybrid, Picard–Krasnoselskii hybrid, Picard–Ishikawa hybrid iterative processes for contraction mappings.

Showed the fastest convergence of this new iteration and then other iteration defined in this paper. The author finds the solution of delay differential equation using this hybrid iteration. For new iteration, the author also proved a theorem for nonexpansive mapping.

This new iteration converges faster than all of Picard, Krasnoselskii, Mann, Ishikawa, S-iteration, Picard–Mann hybrid, Picard–Krasnoselskii hybrid, Picard–Ishikawa hybrid iterative processes for contraction mappings and to find the solution of delay differential equation, using this hybrid iteration also proved some results for Picard–S hybrid iterative process for nonexpansive mappings.

The study is interested in knowing “the role of the organizational structure as a mediating variable of the relationship between the information technology and organizational communication”.

The study was conducted in several service companies, and the study adopted the questionnaire as a basic tool for the data collection on the practical side, as 267 opinions were surveyed, in addition to conducting personal interviews, and the normal distribution of data was tested, analyzing, describing and diagnosing study variables, testing correlations and determining direct effects.

Findings show that there is no direct and significant statistical impact of information technology on organizational communications. Whereas there was a positive, direct and statistically significant impact of information technology on the organizational structure. There was also a positive, direct and statistically significant effect of the organizational structure on organizational communication.

This paper is restricted to the role of the organizational structure as a mediating variable of the relationship between the influence of information technology on organizational communication.

As part of the practical implication, the paper suggests the need to increase support and attention to the importance of information technology in service organizations in order to increase coordination and organizational communication and achieve a high ability to explore and exploit ideas.

Apart from the fact that several companies were engaged, the organizational structures of these companies were engaged too to examine the impacts of Information technology (ICT) on organizational communication.

Sustainable manufacturing is one of the most important and most challenging issues in present industrial scenario. With the intention of diminish negative effects associated with cutting fluids, the machining industries are continuously developing technologies and systems for cooling/lubricating of the cutting zone while maintaining machining efficiency. In the present study, three regression based machine learning techniques, namely, polynomial regression (PR), support vector regression (SVR) and Gaussian process regression (GPR) were developed to predict machining force, cutting power and cutting pressure in the turning of AISI 1045. In the development of predictive models, machining parameters of cutting speed, depth of cut and feed rate were considered as control factors. Since cooling/lubricating techniques significantly affects the machining performance, prediction model development of quality characteristics was performed under minimum quantity lubrication (MQL) and high-pressure coolant (HPC) cutting conditions. The prediction accuracy of developed models was evaluated by statistical error analyzing methods. Results of regressions based machine learning techniques were also compared with probably one of the most frequently used machine learning method, namely artificial neural networks (ANN). Finally, a metaheuristic approach based on a neural network algorithm was utilized to perform an efficient multi-objective optimization of process parameters for both cutting environment.

This study aims to examine the impact of habitual Facebook use (HFU) on life satisfaction and psychological well-being. In addition, the study examined the impact of life satisfaction on psychological well-being. Moreover, the study investigates the impact of social safeness in moderating the relationship between HFU and life satisfaction.

The study employed a quantitative survey design, using a sample of 261 Generation Y students based in Mthatha, in the Eastern Cape Province of South Africa. Scales for data collection were operationalized from prior studies. The collected data were analyzed using structural equation modeling.

The study’s results disclosed that HFU positively and significantly impacts life satisfaction and psychological well-being. In addition, life satisfaction positively and significantly impacted psychological well-being. Moreover, the results showed that social safeness had a positive and significant moderating effect on the nexus between HFU and life satisfaction.

This study contributes to understanding HFU as a precursor to life satisfaction and psychological well-being among Generation Y students. Also, evaluating the moderating effect of social safeness contributes to a more thorough understanding of the link between HFU and life satisfaction. Furthermore, this research aims to add to the body of knowledge in Africa’s communication psychology and social media literature, a field that has received little academic attention in developing countries.

Leaders have been facing serious challenges in managing organizations during COVID-19, which has brought the need for implementing sudden technological change across the globe. Hence, it was important to identify effective leadership styles to successfully manage the transformational process during the period. Therefore, the current study aims to explore and compare the effectiveness of transformational and ethical leadership (EL) in terms of achieving organizational goals during COVID-19 in public and private sector organizations in Pakistan.

Comparative research was carried out to find out the effectiveness of transformational and EL during and pre-COVID-19 in public and private sector organizations using the lens of social exchange theory. Data was collected from 214 respondents representing 67.6% of public and 32.4% of private sector organizations of Pakistan at two different points in time. Detailed comparative analyses were conducted in AMOS version 24 to assess the effectiveness of leadership styles before and during COVID-19 times.

On the whole, transformational leadership (TL) was found to have a greater impact on organizational effectiveness (OE) in comparison with EL in both pre-and during COVID-19 situations. Moreover, the effectiveness of TL significantly increased and the same decreased for EL during COVID-19. Additional analyses indicated that TL was effective for the private sector and EL for public sector organizations during COVID-19.

The study has not considered the mediating mechanisms of employee motivation, engagement and performance in the relationship between transformational and EL styles and OE, which can be explored in the future.

These results have important implications for private and public sector organizations and suggest that the adoption of a TL style will generate better results in the private sector and an EL style in public sector organizations to achieve OE in uncertain situations such as COVID-19.

The study shows that leadership with more care and concern for humanity tends to perform better in terms of generating results for OE. Therefore, both transformational and EL are based on individualized consideration for employees and are effective during COVID-19 in private and public sector organizations in Pakistan.

The study has carried out the comparative analyses in three different ways, including leadership styles (transformational and ethical), type of organization (private and public) and time frames (pre and during COVID-19), which is a true contribution of the research in the Pakistani context.

Considering the continuous rise in the public debt stock of developing countries (particularly Ghana) with the unstable economic growth rate for the past decades and the recent borrowing because of the impact of COVID 19, this paper aims to examine the causal relationships between public debt and economic growth over time.

The paper uses a dynamic multivariate autoregressive-distributed lag (ARDL)-based Granger-causality model to test the causal relationships between public debt and economic growth [gross domestic product (GDP)]. Annual time-series data that spanned 1978–2018 were sourced from the World Bank Development Indicator database and the IMF fiscal Affairs Department Database and WEO.

The results reveal that public debt has no causal relationship with GDP in the short-run but there is unidirectional Granger causality running from public debt to GDP in the long run. Again, investment spending has a negative bi-directional causal relationship with GDP in the short-run but they have a positive bi-directional causal relationship in the long run. Conversely, no short-run causal relationship exists between government consumption expenditure and GDP but long-run Granger causality runs from government consumption expenditure to GDP. Finally, public debt has a positive impact on the inflation rate in the short run.

The findings imply that government(s) must ensure high fiscal discipline to serve as a precursor for the effective and efficient use of recent borrowing, that is, the loans should be used for highly prioritized projects (preferably investment spending) that are well evaluated and self-sustained to add positively to the GDP.

This paper provides contemporary findings to augment extant literature on public debt and economic growth by using variables and empirical models, which prior studies could not sufficiently cover in a developing country perspective and affirms that public debt contributes to GDP only in the long run.