Green meets food delivery services: consumers' intention to reuse food delivery containers in the post-pandemic era

Purpose: The coronavirus disease (COVID-19) has increased food delivery service demand, which generates massive amounts of solid waste, specifically plastic material. Therefore, this study aims to examine the determinants of consumers' intention to reuse food delivery containers (ITR) using the extended theory of planned behavior (TPB). Moral obligation was included as an antecedent, while behavioral expectation (BE) ahead of behavioral intention was an immediate predictor of consumers' pro-environmental behaviors. Design/methodology/approach: The hypotheses were tested on 348 food delivery service users in Malaysia and analyzed using the partial least squares structural equation modeling (PLS-SEM). Findings: The findings indicated that consumers' ITR is directly influenced by perceived behavioral control and attitude. Perceived behavioral control and attitude had a positive partial indirect effect on ITR through BE. Meanwhile, subjective norms and moral obligation had a positive full indirect effect on ITR through BE. Research limitations/implications: The findings can be directly applied to practical situations of food delivery companies and environmental protection organizations managing solid waste among food delivery services. Practical implications: Understanding consumers' ITR could promote practical environmental sustainability. Practically, the study provides insights to the food delivery service industry, policymakers and relevant stakeholders to encourage consumer behavior change by reusing food delivery containers in line with Sustainable Development Goal 12. Originality/value: The study enhances the existing literature by extending TPB with two psychological variables: moral obligation (independent variable) and BE (mediating variable). To the best of the authors' knowledge, this study is the first attempt to empirically investigate BE in consumers' pro-environmental behavioral intention in a high-context culture and developing economy. This study could benefit food and beverage merchants, food delivery companies, governments, non-governmental organizations and pro-environmental behavior researchers in this industry. © 2023, Emerald Publishing Limited.

Factors Influencing Intention to Enroll in The Online Academic Year among Undergraduate Students in the Philippines during the COVID-19 Pandemic

The COVID-19 pandemic has resulted in the loss of the number of enrollees at universities in many developing countries, including the Philippines. The current study integrated the Theory of Planned Behavior (TPB) and Technology Acceptance Model (TAM) to determine factors affecting intention to enroll in the online academic year among undergraduate students during the COVID-19 pandemic. 370 Filipino undergraduate students filled the online survey, which contained 71 questions. The Structural Equation Modeling (SEM) indicated that attitude, subjective norms, and perceived behavioral control had significant direct effects on perceived usefulness which subsequently led to an engagement. Regarding the perceived ease of use, SEM indicated that it had a significant direct effect on perceived performance. Furthermore, SEM also indicated that perceived performance and engagement had significant direct effects on perceived effectiveness which subsequently led to the intention to enroll. The integrated TPB and extended TAM can be extended and applied in evaluating the behavioral motives of students to enroll in the online academic year globally that are currently affected by the COVID-19 pandemic. © 2022 ACM.

COVID-19 and sustainable environment: understanding higher education students' willingness to pay more for food delivery containers

Purpose: The Coronavirus disease 2019 (COVID-19) pandemic has increased the use of food delivery containers in the food and beverage industry. Based on the theory of planned behavior (TPB), the aim of this paper is twofold: Firstly, it examines the influence of three elements of TPB (attitude, perceived behavioral control and subjective norm) and time pressure on the intention to reuse reusable food delivery containers (ITR). Secondly, it examines ITR as an antecedent to the willingness to pay more for reusable food delivery containers (WTPM). Design/methodology/approach: Data were collected from 401 higher education institution (HEI) students and analyzed using partial least squares structural equation modelling (PLS-SEM). Findings: The study found that the three elements of TPB influenced ITR. Furthermore, the results revealed that ITR directly influenced WTPM. Surprisingly, time pressure did not influence ITR. Originality/value: The research is one of the earliest studies to investigate HEI students' intention to reuse food delivery containers during the COVID-19 pandemic. The study contributes to TPB by presenting a novel, integrated model to explain the independent roles of time pressure and ITR on ITR and WTPM, respectively. Finally, it contributes to the existing body of knowledge on pro-environmental behavior among HEI students and advances methodologically by establishing the PLS-SEM approach. © 2023, Emerald Publishing Limited.

LONGITUDINAL CHARACTERIZATION OF PRESENTATION CLINICAL TRAITS IN PATIENTS DURING INDEX AND AT LEAST ONE READMISSION POST-ACUTE COVID-19 HOSPITALIZATION

BACKGROUND: COVID-19 patients at hospitalization exhibit heterogeneous risk factors putatively associated with hospital mortality. However, stability of risk stratification across readmission (ReAdm) remains unclear. We evaluated longitudinal patterns in presentation clinical traits at index COVID-19 hospitalization and subsequent ReAdm(s). METHODS: Under IRB exemption, discharge electronic medical records underwent extraction of presentation demographics, anthropometrics, laboratory results, and ICD-10 codes. Univariate logistic regression was used to test association (p<.05) of putative clinical traits with COVID-19 hospitalization mortality. Continuous data summarized with median [IQR] were compared using Kruskal-Wallis K statistic. Discrete data summarized as counts or proportions were compared with chi-squared test. Confounders statistically balanced included age, sex, race, comorbidities, and attendant local 4-surges of pandemic. Statistical significance was Bonferroni corrected for multiple contrasts at .017. RESULTS: Among patients discharged alive not to hospice between March 18, 2020 and September 30, 2021, 4430 underwent index COVID-19 hospitalization. Incident with at least one subsequent ReAdm was experienced respectively by 630 and 166 patients. There was no difference in median age 76 [62,85] years among 45% woman distributed across Whites (82%), Blacks (9%) and other races (9%). Time to incident and first subsequent ReAdm respectively was 14 [4,59] vs 21 [6,54] days. Prominent comorbidity prevalence sustained included hypertension (54%), diabetes (34%), chronic pulmonary disease (29%), obesity (24%), and coagulopathy (15%). Significant differential comorbidity prevalence manifested sequentially with deficiency anemias (26%, 32%, 49%) and heart failure (23%, 28%, 38%). Notable at two or more ReAdms and consistent with some comorbidity patterns, is significantly worsening hypoalbuminemia, anemia, neutropenia with increasing creatinine, pBNP and D-dimer. CONCLUSIONS: Patients undergoing ReAdm demonstrated that characterizing trends in SARS-CoV-2 evoked clinical traits may reveal mitigable features of post-acute COVID-19 syndrome. Evaluating linkage between biomarkers and comorbidities across ReAdm patterns can identify those of value for estimating a given outcome.

Complete Blood Count Parameters Outperform Putative Inflammatory Markers in Predicting COVID-19 Mortality: A Multimodal Machine Learning Model

Introduction: SARS-CoV-2 evoked immunodysregulation drives inflammation, morbidity, and mortality across COVID-19 presentation spectrum. We sought to identify baseline cell counts and proportions reported with a complete blood count (CBC) that contribute independent information to a model predicting mortality in hospitalized patients with laboratory confirmed SARS-CoV-2 infection. Such a model may complement or improve presentation risk stratification informed by putative inflammatory markers. Methods: Our retrospective design, analyses and interpretations followed constructs detailed in the Strengthening the Reporting of Observational Studies in Epidemiology reporting guideline. Under IRB exemption, discharge medical electronic health records underwent extraction of administrative and clinical data. Demographics, anthropometrics, vital signs, laboratory test and ICD-10-CM-based Elixhauser comorbidity categories were included. Univariate logistic regression was used to identify CBC parameters and attendant ratios associated (p<.05) with hospital mortality. Generalized regression with adaptive LASSO modeling was used to evaluate explanatory probability while eliminating collinearities in identified CBC parameters (individual and ratio) associated with mortality while controlling age, sex, race, baseline vital signs, Elixhauser comorbidities and COVID-19 epoch quarters / treatment. Additional analysis with Bootstrap Forest (BF) was employed to evaluate aggregated synergies and retain parameters that optimized generalized RSquared representing multivariate prediction accuracy and explained variance proportion (EV%) in mortality provided by each variable. Further BF analysis was used to examine relative magnitude of EV% versus putative COVID-19 inflammatory markers. CBC variables included in final BF model were temporally parsed in 24h intervals then pooled when measured after 120h since first vital sign at hospitalization. Results were averaged when a patient underwent multiple assays within an interval. A two-way ANOVA was employed to compare survival vs. non-survival pathways. Results: Among patients consecutively discharged between March 14, 2020 through May 31, 2021, 208 (10 %) of 2153 died. Survivor vs. non-survivor patient and clinical characteristics are summarized in Table 1. CBC parameters identified as independently associated with hospital mortality included WBC, lymphocytes, bands, segmented neutrophils, monocytes, and RDW-CV. (Table 2) Ratios of CBC parameters associated with mortality included AMC/ALC and APC/ALC (Table 2). Results of BF EF% modeling including CBC parameters respectively without (Rsquare = 0.65) and with (Rsquare = 0.70) inclusion of putative inflammatory markers are illustrated in Figure 1a and 1b. Inflammatory markers alone exhibited lowest Rsquare (0.52) (Figure 1c). Figure 2 illustrates temporal kinetics of modeled CBC parameters across hospitalization. Intergroup differences at baseline were sustained, save for RDW-CV after 5-days. Conclusions: Machine learning approaches identified several CBC parameters measured at presentation that when modeled with putative COVID-19 inflammatory markers, enhanced early prediction of hospital mortality. CBC parameters are usually more often measured compared to other inflammatory markers that show COVID-19 severity and serve as an easily obtainable source of information to determine which patients may require a higher level of care before clinical symptoms follow. This includes progression to critical illness and hospital mortality. We recommend that CBC parameters, especially bands, APC/ALC ratio and AMC/ALC ratio be considered for baseline risk stratification of COVID-19 severity, as these trends are sustained at least 5-days after hospitalization. [Formula presented] Disclosures: No relevant conflicts of interest to declare.

To verify or not to verify: using partial least squares to predict effect of online news on panic buying during pandemic

Purpose Coronavirus disease (COVID-19) pandemic has given rise to different dimensions of uncommon human behavior, and panic buying is one of them. Interestingly, panic buying research has not been given much attention. The purpose of this paper is threefold. Firstly, it examines the influences of the theory of planned behavior (TPB) elements (subjective norm, attitude and perceived behavior control (PBC)) on panic buying. Secondly, it investigates online news and the perceived likelihood of being affected (PLA) as antecedents to the TPB constructs. Finally, to examine online news verification as a moderator on the relationship between the TPB constructs and panic buying. Design/methodology/approach Data were collected from 371 respondents and analyzed using the partial least squares method structural equation modeling (PLS-SEM). PLS predict was applied to determine the predictive power of the model further. Findings This study found that subjective norms and attitude influence panic buying. The results further revealed that online news has a direct influence on the PLA and attitude. However, PBC has no such effect on panic buying. Surprisingly, online news verification also has no moderating effects on the relationships between the TPB elements and panic buying. Originality/value This research helps to understand consumer panic buying behavior, especially during shock events such as the COVID-19 pandemic. This study is the first that extends the TPB incorporating both online news and PLA as antecedents to panic buying in the same model. Furthermore, the study serves as an initial attempt to investigate online news verification as a moderator between the link of three constructs of TPB and panic buying, contributing to existing literature. Lastly, it advances the body of knowledge on consumer behavior and contributes methodologically by introducing the PLS approach.

Direct versus indirect admission of COVID-19 patients to intensive care from emergency department

INTRODUCTION: Approximately 20% of COVID-19 patients require hospitalization including 5% needing intensive care. We compared outcomes among patients admitted to an academic community hospital undergoing ICU admission for COVID either directly from emergency department or from general medical units. METHODS: Data were collected after IRB exemption from APACHE IV database and electronic medical records including demographics;administrative data;baseline inflammatory, metabolic and coagulation biomarkers significantly associated with primary outcome;ICU day 1 APACHE IV score and Acute Physiology Score (APS);length of stay (LOS);ICU readmission within same hospital visit;and ICU and hospital mortality. Primary outcome was hospital mortality. Continuous data were summarized with mean [?SD] or median [IQR] respectively compared using t-test or Kruskal-Wallis Test. Discrete data were summarized as counts (proportions) compared with chi-square. Two-tailed p<.05 was significant. RESULTS: Of 244 COVID patients discharged between March 23 and July 28, 2020, respectively 26 and 40 underwent direct (2.6 [1.0-3.4] hrs), or indirect admission (49.8 [19.2 - 136.3] hrs) to ICU. Intergroup similar baseline data were pooled (63±2 years, 58% male, 61% White, 14% Black, 25% Other);temperature 99.0±1.2 °F;SpO2 95±3%;C-reactive protein 14.7±8.5 mg/dL;ferritin 1292±1741 ng/mL;lactate dehydrogenase 455±195 U/L;D-dimer 3.9±6.9 mg/L;prothrombin time 13.4±5.1 seconds;hemoglobin 13.0±2.3 gm/dL;APACHE IV score (66±29);APS (52±27);mechanical ventilation days 14.7±10.9 days;ICU LOS 11.6±10.7 days;and hospital LOS 20.9±14.6 days. There was no difference in number of comorbidities (3.6±1.6) or Elixhauser comorbidity score (9.9±9.8) across ICU admission pathways. Chronic condition prevalence included hypertension (56%), diabetes (28%), obesity (28%) and hypothyroidism (16%). COVID directed drug class treatment was similar. Six patients readmitted to ICU were indirect admissions. ICU (39.4%) and hospital (42.4%) mortality were independent of ICU trajectory. CONCLUSIONS: Outcomes were similar in patients admitted to the ICU directly versus those who decompensated and required transfer from medical floors. Given equivalent biomarker risk at presentation, this finding evinces care quality depth of our safety net.

Facing the unknown: pandemic and higher education in Malaysia

Purpose: The purpose of this paper is to provide commentary on the challenges and impacts of the pandemic crisis to higher education institutions (HEIs) in Malaysia. It also outlines mitigation plans, innovative strategies adopted and implications and recommendations to HEIs. Design/methodology/approach: The research study uses a discourse analysis to examine the higher education challenges and impacts of the pandemic in Malaysia. Findings: The findings reveal that the coronavirus disease 2019 (COVID-19) pandemic has affected the higher education sector in Malaysia significantly. Both lecturers and students in HEIs are faced with multiple challenges in teaching and learning. The effect was more severe in East Malaysia due to poor Internet connectivity for online learning. Nevertheless, COVID-19 also provides an opportunity to HEIs to reconstruct the education system and establish updated programmes and assessments. The success of online learning to mitigate the impacts is still unknown. Research limitations/implications: The review of the literature and news are not comprehensive as the current pandemic crisis is not over. Originality/value: This paper is presumably representing a frontier review with more empirical research studies to be conducted to investigate the extent of the current crisis affecting teaching and learning in HEIs in Malaysia. Though there were some commentaries on how the pandemic affects education, to the best of the authors’ knowledge, this is the first commentary paper in higher education realm in Malaysia. © 2020, Emerald Publishing Limited.