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One of the targets of sustainable development goal (SDG) 2030 is to reduce maternal mortality ratio to 70 per 100,000 live births and ensure pregnant women attend at least four antenatal visits. In urban Pakistan, it is expected that more women utilize antenatal care (ANC) because urban areas have more resources, higher education and wealthier people. Despite these facilities, the lack of utilization of antenatal care among pregnant women is abysmal—the latest estimate by Pakistan Demographic and Health Survey (PDHS) places the figure at 63%. Therefore, the paper attempts to identify the factors that affect the utilization of ANC in urban areas by using the PDHS 2017–2018.

The study used cross-tabs to determine the socioeconomic characteristics of women, and used the marginal effects from the probit model to evaluate the significance and relationship between socioeconomic determinants and antenatal visits. Finally, the study used Adam Wagstaff's decomposition analysis to identify the magnitude and main determinants of inequality.

The marginal effects show that socioeconomic variables such as education, province of residence, birth of a first child, age, education and consulting a doctor predicted the probability of 4+ antenatal visits. The decomposition analysis shows that women who consulted a doctor, belonged to non-poor class, were more educated and older contributed significantly to the inequality of antenatal care utilization in urban areas.

The study calls for increasing the number of doctors, promoting education, increasing awareness related to pregnancy complications and reducing wealth inequality. Moreover, the study also calls for increasing global intervention by implementing programs similar to ending preventable maternal mortality (EPPM) to increase antenatal coverage.

The distinctiveness of the study can be found in the fact that no study has been conducted that analyses the inequality related to the usage of ANC in urban areas of Pakistan.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-06-2022-0390

Drawing on the mSERVQUAL model and spillover theory, this study aims to examine the mechanism through which food-ordering mobile app service quality influences users’ mobile app satisfaction, food satisfaction and repurchase intentions.

Online surveys were completed by 1,000 customers who used a food-ordering mobile app to order fast food on the day they completed the online survey. Structural equation modelling was then used to examine the proposed mechanism.

Results showed that the effects of food-ordering mobile app service quality on customer satisfaction (i.e. mobile app satisfaction and food satisfaction) and repurchase intention varied widely across service quality dimensions. Mobile app service quality had significant spillover effects on food satisfaction and repurchase intention.

Online food-delivery platforms should find the results insightful to better design their food-ordering mobile app. The findings can also assist restaurateurs and mobile payment companies with supporting the whole online food delivery process.

Rather than examining online food delivery service quality based on the service delivery process in the during-consumption stage or the service outcomes in the post-consumption stage, this study focused on the service quality in the pre-consumption stage to highlight the important role of online food delivery mobile apps. From a longitudinal perspective, this study drew on the associate network theory to explain the spillover effect of mobile app satisfaction in the pre-consumption stage on food satisfaction in the during-consumption stage and repurchase intention in the post-consumption stage.

This paper is specifically developed on the basis of a pragmatic model for the Muslim-friendly medical tourism supply chain industry. The purpose of this study is to investigate the effectiveness and viability of the Muslim-friendly medical tourism supply chain practices in Malaysia.

A framework for the service supply chain management of the Muslim-friendly medical tourism with the effective outcomes was theoretically developed and explored in this study, using a structural equation modelling with the partial least squares technique. A structured questionnaire was developed, distributed and collected from 38 Islamic-friendly hospitals, 9 Muslim-friendly medical agencies and 44 Muslim-friendly hotels that will form the potential relevant associations of the medical tourism supply chain practices in Malaysia.

The findings provide reliable evidence that the effectiveness of the Muslim-friendly medical tourism supply-chain practice has a positive impact on the Muslim-friendly medical tourism supply chain. In addition, the Muslim-friendly medical tourism supply chain practice has also a significant and positive impact on the organizational performance as a whole. The result also demonstrates that collaboration is dependent on the demand management, whilst the productivity is not associated with it.

Practicing and implementing a Muslim-friendly medical tourism service supply chain is important, in the sense that the Muslim patients’ demand on the choice of halal practice of medical treatment and halal food consumption criteria. This investigation is the first to embark on the research on the Muslim medical tourism supply chain production network in Malaysia. This study provides practitioners with the important related data to improve their policies and their performances in the organization. The statistical results provide indispensable valuable information in comprehending the critical medical tourism supply chain practices and most specially to assist the Muslim-friendly medical tourism sector to offer quality medical tourism service that suits and attracts the Muslim medical tourists’ broad needs regionally and abroad.

It is widely accepted that good health and education are important components of human capability. Although China has made encouraging progress in education and health causes, the role of poor health as an intervening factor in education has not been fully recognized. Iron deficiency is the major micronutrient deficiency encountered by children and adolescents. Many medical and social studies have found a negative correlation between iron deficiency and children physical growth and cognitive development. However, few of the existing literatures work to the conclusion of a causal relationship considering the endogenity problem in the estimation of a human capital model. The purpose of this paper is to study the iron status of elementary school children living in the poor countryside of western China and its educational consequence with the application of instrumental variable (IV) estimation.

An analytical framework of academic achievement production is adopted in the paper to empirically identify the relationship between iron deficiency and academic achievement to estimate the impact.

Using a sample of 45,549 school children from Gansu, the paper finds that around 86 per cent of the sample children are iron deficient and 40 per cent of the sample children are suffering from iron deficiency anaemia. The use of IV is innovative in identifying the endogenous health variable, namely iron deficiency.

This paper enriches existing studies on children health and education. IV estimation indicates that iron deficiency has a significant adverse impact on school children academic achievement. The paper suggests health intervention, such as iron intake fortification, as a possible measure for education cause.

Despite mounting evidence that job insecurity triggers counterproductive work behavior (CWB), the underlying mechanism within the association in hybrid employment remains unknown. This study aims to investigate turnover intention as a mediator in the association between job insecurity and CWB, as well as the differences between permanent and contract workers concerning the effect of job insecurity on employees’ CWB.

Dyadic data were collected from 213 workers (103 contract and 110 permanent workers) and their supervisors, who were employed in one of the three branches of a Chinese state-owned enterprise, respectively, located in Hunan, Shenyang and Xinjiang.

This study found that job insecurity was positively related to CWB, in which turnover intention acted as a partial mediator. Employment status acted as a moderator between job insecurity and CWB.

First, this study extends the theoretical knowledge concerning how job insecurity activates CWB by identifying turnover intention as a mediating mechanism. To the best of the authors’ knowledge, this is the first study to explore the mediating role of turnover intention in terms of job insecurity and CWB. Second, this research expands the understanding of the relation between job insecurity and CWB by investigating this link in the case of contract workers versus permanent workers. Finally, this paper aims to provide an understanding of why contract workers and permanent workers may differ in their reactions to job insecurity.

It has been widely documented that investors and analysts underreact to information in past earnings changes, a fundamental performance indicator. The purpose of this paper is to examine whether managers’ voluntary disclosure efficiently incorporates information in past earnings changes, whether analysts recognize and fully anticipate the potential inefficiency in management forecasts and whether managers’ potential forecasting inefficiency entirely results from intentional disclosure strategies or at least partly reflects managers’ unintentional information processing biases.

Archival data were used to empirically test the relation between management earnings forecast errors and past earnings changes.

Results show that managers underreact to past earnings changes when projecting future earnings and analysts recognize, but fail to fully anticipate, the predictable bias associated with past earnings changes in management forecasts. Moreover, analysts appear to underreact more to past earnings changes when management forecasts exhibit greater underestimation of earnings change persistence. Further analyses suggest that the underestimation of earnings change persistence is at least partly attributable to managers’ unintentional information processing bias.

This study contributes to the voluntary disclosure literature by demonstrating the limitation in the informational value of management forecasts. The findings indicate that the effectiveness of voluntary disclosure in mitigating market mispricing is inherently limited by the inefficiency in management forecasts. This study can help market participants to better use management forecasts to form more accurate earnings expectations. Moreover, our evidence suggests a managerial information processing bias with respect to past earnings changes, which may affect managers' operational, investment or financing decisions.

Previous knowledge base question answering (KBQA) models only consider the monolingual scenario and cannot be directly extended to the cross-lingual scenario, in which the language of questions and that of knowledge base (KB) are different. Although a machine translation (MT) model can bridge the gap through translating questions to the language of KB, the noises of translated questions could accumulate and further sharply impair the final performance. Therefore, the authors propose a method to improve the robustness of KBQA models in the cross-lingual scenario.

The authors propose a knowledge distillation-based robustness enhancement (KDRE) method. Specifically, first a monolingual model (teacher) is trained by ground truth (GT) data. Then to imitate the practical noises, a noise-generating model is designed to inject two types of noise into questions: general noise and translation-aware noise. Finally, the noisy questions are input into the student model. Meanwhile, the student model is jointly trained by GT data and distilled data, which are derived from the teacher when feeding GT questions.

The experimental results demonstrate that KDRE can improve the performance of models in the cross-lingual scenario. The performance of each module in KBQA model is improved by KDRE. The knowledge distillation (KD) and noise-generating model in the method can complementarily boost the robustness of models.

The authors first extend KBQA models from monolingual to cross-lingual scenario. Also, the authors first implement KD for KBQA to develop robust cross-lingual models.

The purpose of this paper is to introduce and test a model for numerical evaluation of the effectiveness of green practices implemented in two industrial supply chains (SCs). Two real case applications were made: footwear and metal-mechanics industry.

The research method is quali-quantitative modeling. By literature review, a model based on three constructs was proposed (green strategy, green innovation, and green operations), organized in 16 categorical indicators, prioritized with analytic hierarchy process. Three practitioners of each focal companies assessed the indicators fulfilling scales (very good to very bad).

The overall performance reached 51 and 57 percent, respectively of the maximum possible. The indicators that most jeopardized the performance were complexity management and communication, barriers to green supply chain management, green products, and green market (first case) and innovation in processes, and green market (second case).

The model cannot be generalized or extended to other SCs. Further refinement and testing are required.

Managers and practitioners can improve the eco-efficiency of SC, focusing on the green practices that should be prioritized in greening strategies for the entire chain.

Improvement of eco-efficiency is positively correlated with corporate social responsibility.

The model can produce a numerical overall value that represents the level or degree of implementation of green practices in the context of a SC management.