COVID-19 and connectedness between Sustainable and Islamic equity markets

Because of the increasing importance of and demand for ethical investment, this paper investigates the dynamics of connectedness between sustainable and Islamic investment in nineteen countries that represent developed and emerging financial markets worldwide. To this end, we apply models proposed by Diebold and Yilmaz and Barunik and Krehlik to explore the overall and frequency-based connectedness between selected ethical investments. Our results reveal evidence of a moderate to strong intra country-level connectedness between sustainable and Is-lamic investment and limited cross-country connectedness between ethical investments. The time-varying connectedness analysis suggests enhanced connectedness during periods of market-wide turmoil, such as the European debt crisis, the Chinese financial crisis, and the COVID-19 pandemic. Moreover, the COVID-19 subsample analysis shows an enhanced and idiosyncratic country-level and cross-country connectedness structure between ethical investments, indicating the evolving nature of the relationship between sustainable and Islamic investment. Copyright (c) 2022 Borsa Istanbul Anonim S,irketi . Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Are Exchange Rate Contagions Asymmetric? Evidence from Emerging Market Economies

In view of increasing importance of emerging market currencies in the global foreign exchange markets and the growing concerns regarding the vulnerability of these currencies to global crises, we assess the connectedness of 16 emerging currencies by employing asymmetric domains of time and frequency spanning March 2011 to January 2022. We first notice bidirectional interconnectedness (both positive and negative) among three clusters of sampled exchange rates. The currency contagions follow divergent directions during crisis periods. During US debt selling crisis, there is a short-run negative contagion pointing to the appreciation of currencies. Following the Chinese financial market crisis, emerging market currencies demonstrated devaluation. There is long-run positive contagion (devaluation) in response to European Debt Crisis, Russian Ruble Crisis, Brazilian economic crisis, and Argentinian monetary crisis. The sampled exchange rates demonstrate negative long-run connectedness (appreciation) after COVID-19. The major transmitters to total connectedness are South Africa, Poland, and Mexico and major receivers include Thailand, the Philippines, Malaysia, India, Indonesia, and Egypt. In the long run, China is emerging as a significant transmitter. Our study draws significant policy and practical implications for regulators, investors, and financial market participants. © 2023 Taylor & Francis Group, LLC.

A New Approach for Forecasting Crude Oil Prices Based on Stochastic and Deterministic Influences of LMD Using ARIMA and LSTM Models

Crude oil is one of the non-renewable power sources and is the lifeblood of the contemporary industry. Every significant change in the price of crude oil (CO) will have an effect on how the global economy, including COVID-19, develops. This study developed a novel hybrid prediction technique that depends on local mean decomposition, Autoregressive Integrated Moving Average (ARIMA), and Long Short-term Memory (LSTM) models to increase crude oil price prediction accuracy. The original data is decomposed by local mean decomposition (LMD), and the decomposed components are reconstructed into stochastic and deterministic (SD) components by average mutual information to reduce the computation cost and enhance forecasting accuracy, predict each individual reconstructed component by ARIMA, and integrate the residuals with LSTM to capture the nonlinearity in residuals and help to find the final prediction result. The new hybrid model LMD-SD-ARIMA-LSTM has reduced the volatility and solved the issue of the overfitting problem of neural networks. The proposed hybrid technique is validated using publicly accessible data from the West Texas Intermediate (WTI), and forecast accuracy are compared using accuracy measures. The value of Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) for ARIMA, LSTM, LMD-ARIMA, LMD-SD-ARIMA, LMD-ARIMA-LSTM, LMD-SD-ARIMA-LSTM, and Naïve are 1.00, 1.539, 5.289, 0.873, 0.359, 0.106, 4.014 and 2.165, 1.832, 9.165, 1.359, 1.139, 1.124 and 3.821 respectively. From these results, it is concluded that the proposed model LMD-SD-ARIMA-LSTM has minimum values for MAE and MAPE which assured the superiority of the proposed model in One-step ahead forecasting. Moreover, forecasting performance is also compared up to five steps ahead. The findings demonstrate that the suggested approach is a helpful tool for predicting CO prices both in the short and long term. Furthermore, the current study reduces labor costs by combing the stationary and non-stationary Product Functions (PFs) into stochastic and deterministic components with improved accuracy. Meanwhile, the traditional econometric model can strengthen the prediction behavior of CO prices after decomposition and reconstruction, and the new hybrid forecasting method has better performance in medium and long-term forecasting of the CO price. Moreover, accurate predictions can provide reasonable advice for relevant departments to make correct decisions. © 2013 IEEE.

Dombi Bonferroni Mean Operators under Bipolar Complex Fuzzy Environment and Their Applications in Internet World

Internet is almost a necessary facility and tool to solve daily life problems in every field life. Whether at the individual level or national and international level sale purchase of any kind of object has always been of much importance, especially after Corona Pandemic, when online business is at its peak. Because of the enhancement of online sales and purchases, various businessmen are looking for suitable internet websites for their businesses, and the selection of the most suitable internet websites is one of the multi-attribute decision-making (MADM) dilemmas. Thus, in this script, we take benefits of three various concepts that are Bonferroni mean (BM) operator which is a significant technique to catch the interrelatedness among any number of inputs, Dombi operations which are based on Dombi t-norm and t-conorm and the ability to create an aggregation procedure more flexible because of the parameter, bipolar complex fuzzy set (BCFS) which is an outstanding model for tackling two-dimensional information with negative aspect and interpret bipolar complex fuzzy (BCF) Dombi Bonferroni mean (BCFDBM), BCF weighted Dombi Bonferroni mean (BCFWDBM), BCF Dombi geometric Bonferroni mean (BCFDGBM), and BCF weighted Dombi geometric Bonferroni mean (BCFWDGBM) operators. After ward, in this script, for tackling MADM dilemmas in the setting of BCFS, we investigate a MADM procedure based on the investigated operators and solve a MADM dilemma (selection of a suitable internet website for businessmen). Further, to display the superiority and efficiency of our work, we compare our approach and operators with a few current approaches and operators. Author

Interdependence of clean energy and green markets with cryptocurrencies

The adverse effects of the high-power energy consumption by cryptocurrencies on the environment and sustainability have raised the interest of a large body of policymakers and market participants. We apply a network approach to investigate the dependency across clean energy, green markets, and cryptocurrencies from 1 January 2018 to 30 November 2021. Our results indicate that sustainable investments, particularly DJSI and ESGL, play a pivotal role in the network system during the COVID-19 crisis. We find that green bonds are the least integrated with the other financial markets, suggesting their significant role in providing diversification benefits to investors. Rolling windows estimation shows that the dependency across the examined marked increased sharply during the COVID-19 crisis, especially between March 2020 and March 2021, after which it faded and became weak and stable until the end of the sample period. Results of the centrality network are consistent with the dependency network analysis. © 2023

Decision Support System Based on Complex Fractional Orthotriple Fuzzy 2-Tuple Linguistic Aggregation Operator

In this research, we provide tools to overcome the information loss limitation resulting from the requirement to estimate the results in the discrete initial expression domain. Through the use of 2-tuples, which are made up of a linguistic term and a numerical value calculated between (Formula presented.), the linguistic information will be expressed. This model supports continuous representation of the linguistic data within its scope, permitting it to express any information counting received through an aggregation procedure. This study provides a novel approach to develop a linguistic multi-attribute group decision-making (MAGDM) approach with complex fractional orthotriple fuzzy 2-tuple linguistic (CFOF2TL) assessment details. Initially, the concept of a complex fractional orthotriple fuzzy 2-tuple linguistic set (CFO2TLS) is proposed to convey uncertain and fuzzy information. In the meantime, simple aggregation operators, such as CFOF2TL weighted average and geometric operators, are defined. In addition, the CFOF2TL Maclaurin's symmetric mean (CFOF2TLMSM) operators and their weighted shapes are presented, and their attractive characteristics are also discussed. A new MAGDM approach is built using the developed aggregation operators to address managing economic crises under COVID-19 with the CFOF2TL information. As a result, the effectiveness and robustness of the developed method are accompanied by an empirical example, and a comparative study is carried out by contrasting it with previous approaches. © 2023 by the authors.

Soft computing techniques for forecasting of COVID-19 in Pakistan

Novel Pandemic COVID-19 led globally to severe health barriers and financial issues in different parts of the world. The forecast on COVID-19 infections is significant. Demeanor vital data will help in executing policies to reduce the number of cases efficiently. Filtering techniques are appropriate for dynamic model structures as it provide reasonable estimates over the recursive Bayesian updates. Kalman Filters, used for controlling epidemics, are valuable in knowing contagious infections. Artificial Neural Networks (ANN) have generally been used for classification and forecasting problems. ANN models show an essential role in several successful applications of neural networks and are commonly used in economic and business studies. Long short-term memory (LSTM) model is one of the most popular technique used in time series analysis. This paper aims to forecast COVID-19 on the basis of ANN, KF, LSTM and SVM methods. We applied ANN, KF, LSTM and SVM for the COVID-19 data in Pakistan to find the number of deaths, confirm cases, and cases of recovery. The three methods were used for prediction, and the results showed the performance of LSTM to be better than that of ANN and KF method. ANN, KF, LSTM and SVM endorsed the COVID-19 data in closely all three scenarios. LSTM, ANN and KF followed the fluctuations of the original data and made close COVID-19 predictions. The results of the three methods helped significantly in the decision-making direction for short term strategies and in the control of the COVID-19 outbreak. © 2022 Faculty of Engineering, Alexandria University

Novel Decision Making Methodology under Pythagorean Probabilistic Hesitant Fuzzy Einstein Aggregation Information

This research proposes multicriteria decision-making (MCDM)-based real-time Mesenchymal stem cells (MSC) transfusion framework. The testing phase of the methodology denotes the ability to stick to plastic surfaces, the upregulation and downregulation of certain surface protein markers, and lastly, the ability to differentiate into various cell types. First, two scenarios of an enhanced dataset based on a medical perspective were created in the development phase to produce varying levels of emergency. Second, for real-time monitoring of COVID-19 patients with different emergency levels (i.e., mild, moderate, severe, and critical), an automated triage algorithm based on a formal medical guideline is proposed, taking into account the improvement and deterioration procedures from one level to the next. For this strategy, Einstein aggregation information under the Pythagorean probabilistic hesitant fuzzy environment (PyPHFE) is developed. Einstein operations on PyPHFE such as Einstein sum, product, scalar multiplication, and their properties are investigated. Then, several Pythagorean probabilistic hesitant fuzzy Einstein aggregation operators, namely the Pythagorean probabilistic hesitant fuzzy weighted average (PyPHFWA) operator, Pythagorean probabilistic hesitant fuzzy Einstein weighted geometric (PyPHFEWG) operator, Pythagorean probabilistic hesitant fuzzy Einstein ordered weighted average (PyPHFEOWA) operator, Pythagorean probabilistic hesitant fuzzy Einstein ordered weighted geometric (PyPHFEOWG) operator, Pythagorean probabilistic hesitant fuzzy Einstein hybrid average (PyPHFEHA) operator and Pythagorean probabilistic hesitant fuzzy Einstein hybrid geometric (PyPHFEHG) operator are investigated. All the above-mentioned operators are helpful in design the algorithm to tackle uncertainty in decision making problems. In last, a numerical case study of decision making is presented to demonstrate the applicability and validity of the proposed technique. Besides, the comparison of the existing and the proposed technique is established to show the effectiveness and validity of the established technique.

From sentiment to systemic risk: Information transmission in Asia-Pacific stock markets

This study provides a comprehensive sentiment connectedness analysis in Asia-Pacific. We implement a time-frequency framework and a quantile connectedness approach while analyzing the impact of three crises: the global financial crisis, the Chinese Stock market turbulence (2015-2016), and the COVID-19 pandemic. We find a significant sentiment spillover across markets, though the magnitude is more pronounced in the long run. Although sentiment connectedness is higher during extreme states of the sentiment than in the average state, the systemic risk intensifies further when the sentiment is exceptionally high. Notably, Japan appears to contribute moderately to the sentiment network, while China is the lowest contributor. The three crises strengthened the total sentiment connectedness, while the COVID-19 pandemic had the most substantial impact. Our sentiment network findings have insightful implications on cultural and behavioral factors that drive sentiment systemic risk in Asia-Pacific.

Sustainable corporate governance and gender diversity on corporate boards: evidence from COVID-19

The unprecedented challenges caused by the COVID-19 pandemic have led to a need to re-examine sustainable corporate governance practices. Within this context, the current study investigates the moderated effect of gender-diverse corporate boards on sustainable corporate governance practices in Malaysian financial and non-financial firms during the period 2011-2020, employing the dynamic estimator (S-GMM). During the COVID-19 pandemic, a negative relationship between ownership constructs and Global Reporting Initiative (GRI) indicators is observed in non-financial firms, whereas the opposite is reported for financial firms. Moreover, the moderated effect of gender-diverse boards is only substantiated in financial firms. The findings reveal that sustainable corporate governance is practised in financial firms but not in non-financial firms. Particularly, we draw significant implications for policymakers and regulatory bodies of Malaysia to carefully monitor the implementation of sustainable corporate governance given uncertain circumstances of COVID-19 pandemic. Further, our study is beneficial for academics, practitioners, and research scholars for their future research endeavours.

Logarithmic cubic aggregation operators and their application in online study effect during Covid-19

The aims of this study is to define a cubic fuzzy set based logarithmic decision -making strategy for dealing with uncertainty. Firstly, we illustrate some logarithmic operations for cubic numbers (CNs). The cubic set implements a more pragmatic technique to communicate the uncertainties in the data to cope with decision-making difficulties as the observation of the set. In fuzzy decision making situations, cubic aggregation operators are extremely important. Many aggregation operations based on the algebraic t-norm and t-conorm have been developed to cope with aggregate uncertainty expressed in the form of cubic sets. Logarithmic operational guidelines are factors that help to aggregate unclear and inaccurate data. We define a series of logarithmic averaging and geometric aggregation operators. Finally, applying cubic fuzzy information, a creative algorithm technique for analyzing multi-attribute group decision making (MAGDM) problems was proposed. We compare the suggested aggregation operators to existing methods to prove their superiority and validity, and we find that our proposed method is more effective and reliable as a result of the comparison and sensitivity analysis.

Good versus bad information transmission in the cryptocurrency market: Evidence from high-frequency data

Using 5-minute high-frequency data, we study realized volatility spillovers in major crypto-currencies, employing generalized forecast error variance decomposition. We also include COVID19 period observations and report time-varying and asymmetric connectedness across various cryptocurrencies using realized volatilities and semi-variances. Our study provides diverse connections after distinctly considering good-and bad volatilities, which is unique in the related literature. Bitcoin and Ethereum are central to the system and dominant transmitters of positive shocks, while Litecoin propagates negative shocks abundantly. Ripple and Stellar are the least connected currencies with others, whereas Cardano and EOS are isolated in the network. This feature makes these currencies suitable diversifiers in a portfolio with other cryptocurren-cies. Further, the majority of these connections are asymmetric in the long-and short-run. The time-varying and asymmetric nature of connections offers potentially unique opportunities for diversification and portfolios strategies. Total volatility connectedness is not only significantly enhanced but also changed in its nature during the COVID19 period. We observe no significant changes in results after the robustness check through varying lengths of the rolling-window. The findings are important to crypto investors and regulatory authorities for better diversification strategies and effective market oversight, respectively.

Lack of Benefit of High Flow Nasal Oxygen Therapy as Ceiling of Treatment for Severe COVID-19 Pneumonitis in Elderly Frail Patients: A Single Centre Observational Study

Background: Severe COVID-19 pneumonitis in elderly frail patients is associated with poor outcomes, and therefore invasive mechanical ventilation is often deemed an inappropriate course of action. Some evidence suggests high-flow nasal oxygen (HFNO) may prevent the need for invasive ventilation in other groups of patients, but whether it is an appropriate ceiling of care for older frail patients is unknown. Method(s): We retrospectively identified patients with severe COVID-19 pneumonitis requiring FiO2 >60% who were deemed inappropriate for invasive ventilation or non-invasive continuous positive airway pressure ventilation (CPAP). Our local protocol based on national guidance suggested these patients should be considered for HFNO. We observed whether the patients received HFNO or standard oxygen therapy (SOT) and compared mortality and survival time in these groups. Result(s): We identified 81 patients meeting the inclusion criteria. From this group, 24 received HFNO and 57 received SOT. The HFNO group was similar in age, BMI and co-morbidities to the SOT group but less frail, as determined by the Clinical Frailty Scale (CFS). All 24 patients that received HFNO died in comparison to 46 patients (80.7%) in the SOT group. Mortality in the HFNO group was significantly higher than in the SOT group. Conclusion(s): Elderly frail patients with severe COVID-19 pneumonitis deemed inappropriate for invasive ventilation and did not benefit from HFNO. Further, HFNO may have been associated with harm in this group. Copyright © 2022 Merchant et al.

Is FinTech providing effective solutions to Small and Medium in ASEAN countries?

With the continuous boom of FinTech, the similar features of different platforms provide effective solutions for small and medium enterprises. This study examines whether FinTech offers useful business mechanisms for SMEs in selected ASEAN countries. The ASEAN countries included in the study are Indonesia, Malaysia, Philippine, Singapore, and Thailand. The study employed factor analysis and segregated the FinTech-SME nexus into five factors. The responses of 300 SME owners were collected through interview questionnaires and surveys. We find that new FinTech and SMEs 'collisions' (our term for new utilization) during COVID-19 are the most important factors in the growth of FinTech and the strength of SMEs. Further, we utilized the Kruskal-Wallis test to validate our results and for ranking the factors alongside the ASEAN countries. We present useful implications for policymakers, regulatory bodies, ASEAN countries, and SMEs for welcoming FinTech solutions to facilitate digital transactions. (c) 2022 Economic Society of Australia, Queensland. Published by Elsevier B.V. All rights reserved.

Impact of COVID-19 on chronic respiratory disease outcome survey in Pakistan

Introduction: Pakistan has high prevalence of chronic respiratory diseases, especially bronchial asthma and chronic obstructive pulmonary disease (COPD). Objectives: The main objective of the study is to find the impact of COVID-19 on chronic respiratory disease in Pakistan. Material and methods: This cross sectional study was conducted in King Edward Medical University, Mayo Hospital Lahore during January 2021 to July 2021. Before and after the COVID-19 period were defined by a predetermined set of criteria in the form of a questionnaire. Results: The data was collected from 314 respondents. According to the respondents, the survey also queried about the presence of respiratory comorbidities in patients who had tested positive for COVID 19. COPD was mentioned as a comorbidity by more than a third of those who responded, and several others, including bronchial asthma, ILD, and tuberculosis (TB), were also mentioned by many respondents. Conclusion: It is concluded that because of the lockout's efficacy and the widespread use of masks outside the facility, the air was probably rather clean. As a result, both the number of people visiting an asthma outpatient clinic and the number of people being admitted to the hospital with acute severe asthma dropped.

The Implications of COVID-19 Pandemic on Dollar Exchange Rate of Pakistan

The Coronavirus disease (COVID-19) most likely began in an animal species and subsequently transmitted to humans in Wuhan, China, a city of 11 million people, on December 29, 2019, when the first case was recorded. The Coronavirus then transmitted from person to person by infected droplets from a sick person's coughing, sneezing, or contaminated hands. Hence, the purpose of the study is to see the impact of the outbreak of COVID-19 daily tests on the Pakistani rupee against the US dollar exchange rate using Vector Autoregressive approach. The data is gathered from February 26, 2020 to March, 2021. This period was selected, because the pandemic expanded, and the first case was observed in Pakistan on Feb 26th 2020. To verify this effect, a Vector Autoregressive Model was developed. A generalized version of the Autoregressive Model is a Vector Autoregressive (VAR) model. As a result of the COVID-19 pandemic, the Pakistani rupee devalued against the US dollar throughout the abovementioned period. When analyzing the Pakistani rupee vs. the US dollar exchange rate using a Vector Autoregressive Model, the values of the lags (1, 4, 6, and 7) of the explanatory variable have a significant impact. Besides, under the VAR model, the IRF (Impulse Response Function) asserted the actual impact of the daily COVID-19 tests, as well as Decomposition of Variance was shown to provide for the daily COVID-19 tests just a small part in understanding the volatility of the Pakistani rupee against the US dollar exchange rate. The Granger Causality suggests that the short-term and long-term changes in the Pakistani rupee against the US dollar exchange rate are caused by daily COVID-19 tests. Copyright © 2022 Akhtar, Abiad, Mashwani, Aamir, Naeem and Khan.

ORIGINAL ARTICLE

Aim: To assess the impact of synchronous learning on medical students of the SKZMC. Place and duration of study: The study was carried out at SKZMC during the month of May 2021. Methodology: A questioner was developed on Google and sent to the medical students of each year of the SKZMC. Data of the 101 duly filled forms was analyzed by using SPSS software. Results: Results of the study reveal that online learning is a new entity for 82.2% medical students and they have not experienced it before. 53.4% participants felt headache during synchronous learning. 42.6% students are of the opinion that online learning method provides an opportunity for real time discussion, 18.8% thinks that it is cost effective, 24.8% for immediate feedback. 77.2% medical students were unable to study during Covid 19. 24.8% students opined that students show misbehavior during online learning. Conclusion: Having real time teacher-students discussion in cost effective manner and knowledge clarity due to immediate feedback enhances the applicability of this technology bound learning methodology. Inability of synchronous learning to develop clinical skills in future physicians with time constraints along with student misbehavior are some pitfalls in this newly implemented learning strategy. Online learning is not only more effective than traditional classroom learning but it is also significantly more cost effective than the tradition way of learning.

Parenting Practices during COVID-19 in Pakistan

Objective: The current study intends to look at how COVID-19 pandemic affected parenting practices during COVID-19 in Pakistan and if the children were exposed to more abuse and neglect at home. Study Design: A quantitative design survey. Place and Duration of the Study: Data was collected from parents visiting outpatient departments (OPDs) in four hospitals of Lahore, Peshawar, and Karachi in three months from July to September 2020. Material and Methods: A quantitative design survey was used, and data (N=923) were collected using a self-administered COVID-19 Parenting Response Scale (α = 0.74). Results: The primary responsibility of taking care of children rested with mothers in most of the cases. Ratio of severe disciplinary practices like shouting, yelling, cursing, and slapping children was increased significantly during the lock down as the anger and frustration in the parents also mounted. This effect was more pronounced in families from lower socioeconomic groups as well as for those who suffered income loss during COVID-19. Conclusion: Financial and emotional stress caused by COVID-19 exacerbated the already difficult parenting practices. Ultimately children suffered more violence at the hands of parents. In Pakistani society there is little awareness on building one‟s capacity on good parenting and little availability of such trainings. There is a need to understand implications for good parenting and create awareness of positive parenting methods.

Effect of COVID-19 on the Screen Time of the Children in Pakistan

Objectives: This study intended to look at how people were receiving information on COVID-19, how children spent time during the COVID-19 pandemic and if the screen time for children had increased during the pandemic. Study Design: The study was descriptive in nature and a quantitative Place and Duration of the Study: Data was collected from parents visiting OPDs in four hospitals of Lahore, Peshawar and Karachi in three months from July to September 2020. Material and Methods: Quantitative survey design was used, data (N=923) were collected using self-administered scale and checklist. Results: Most of the information about COVID-19 was received either through TV (50.8%) or through online social media (33.6%). Most children were spending their time either playing (32.2%) or watching cartoons/movies (19.6%). Screen time of children in the lockdown period was increased. 87.4% children used screens for at least 2 hours per day during the lock down whereas 69.1% used screen for more than 2 hours before COVID-19. Most often used devices were mobile/tablet/PC in 64.2% whereas TV was viewed 34.9% of the time. Conclusion: Most COVID-19 related information was gathered online, and a large proportion of children spent their time in front of screens. There is a need to understand the serious implications of increased screen time for children and to develop effective strategies to reduce screen time of the children.

Understanding the accessibility of retail mobile banking during the COVID-19 pandemic

Purpose: This study offers an understanding of vulnerable populations' experiences of actual use of mobile banking and their expectations of mobile banking (MB). Design/methodology/approach: Data were generated from MB customers and bankers using online reviews, focus groups and semi-structured interviews, as a mix of methods and sources can provide rich and in-depth understanding. Findings: The affordance of MB for vulnerable populations is explained in four concepts: meaning, material, competency and usability. Recommendations that could further engage and improve the service quality of MB apps for vulnerable populations include customization and personalization of services, access to the digital health data of members of vulnerable populations, audio-based option selection and touchscreen options, and enhancement of service and performance standards. Research limitations/implications: It is suggested that retail bankers should improve the service quality and performance of their MB apps by considering the recommendations drawn from vulnerable people's experiences. This study discusses implications for retailers. Originality/value: This study applied social practice theory and affordance of technology theory to understand how those in vulnerable populations experienced MB apps;the results could be used to improve the accessibility, performance and service quality of MB apps. © 2022, Emerald Publishing Limited.