Engaging consumers through artificially intelligent technologies: Systematic review, conceptual model, and further research

While consumer engagement (CE) in the context of artificially intelligent (AI-based) technologies (e.g., chatbots, smart products, voice assistants, or autonomous cars) is gaining traction, the themes characterizing this emerging, interdisciplinary corpus of work remain indeterminate, exposing an important literature-based gap. Addressing this gap, we conduct a systematic review of 89 studies using the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) approach to synthesize the AI-based CE literature. Our review yields three major themes of AI-based CE, including (i) Increasingly accurate service provision through AI-based CE; (ii) Capacity of AI-based CE to (co)create consumer-perceived value, and (iii) AI-based CE's reduced consumer effort in their task execution. We also develop a conceptual model that proposes the AI-based CE antecedents of personal, technological, interactional, social, and situational factors, and the AI-based CE consequences of consumer-based, firm-based, and human-AI collaboration outcomes. We conclude by offering pertinent implications for theory development (e.g., by offering future research questions derived from the proposed themes of AI-based CE) and practice (e.g., by reducing consumer-perceived costs of their brand/firm interactions)

Utilisation Of AI In Nursing Management Within Hospital Settings

The advent of AI in nursing services represents a transformative step in health care technology, and is poised to elevate patient care quality and streamline nursing operations

Antibacterial Evaluation of Gallic Acid and its Derivatives against a Panel of Multi-drug Resistant Bacteria

Background: Infectious diseases are the second leading cause of deaths worldwide. Pathogenic bacteria have been developing tremendous resistance against antibiotics which has placed an additional burden on healthcare systems. Gallic acid belongs to a naturally occurring phenolic class of compounds and is known to possess a wide spectrum of antimicrobial activities. Aims & objectives: In this study, we synthesized thirteen derivatives of gallic acid and evaluated their antibacterial potential against seven multi-drug resistant bacteria, as well as cytotoxic effects against human embryonic kidney cell line in vitro. Methods: 13 compounds were successfully synthesized with moderate to good yield and evaluated. Synthesized derivatives were characterized by using nuclear magnetic resonance spectroscopy, mass spectrometry, and Fourier transformation infrared spectroscopy. Antibacterial activity was determined using microdilution while cytotoxicyt was assessed using MTT assay. Results: The results of antibacterial assay showed that seven out of thirteen compounds exhibited antibacterial effects with compound 6 and 13 being most potent against Staphylococcus aureus (MIC 56 μg/mL) and Salmonella enterica (MIC 475 μg/mL) respectively. On the other hand, most of these compounds showed lower cytotoxicity against human embryonic kidney cells (HEK 293), with IC50 values ranging from over 700 μg/mL. Conclusion: Notably, compound 13 was found to be non-toxic at concentrations as high as 5000 μg/mL. These findings suggest that the present synthetic derivatives of gallic acid hold potential for further studies in the development of potent antibacterial agents

Exploring single-use, multi-use dialysers for patient well-being

Dialysis treatment is a critical lifeline for patients with kidney failure. Patients need to decide the choice between single-use and multi-use dialysers. This plays a vital role in ensuring patient safety and treatment effectiveness

Profiling patterns in healthcare using an ensemble model framework to predict employee health risks

In the current evolution of the digital world, data has become the cornerstone of decision-making processes, shaping industries and societies alike. The exponential growth of data, commonly referred to as big data, has sparked a surge in interest in advanced analytics techniques to harness its potential. Among these techniques, big data analytics, particularly in healthcare, holds immense promise for understanding overall population health and predicting high-risk and high-cost individuals. This thesis delves into the realm of healthcare analytics in Malaysia, focusing on the analysis of extensive medical data to identify patterns and insights that can aid in the identification of high-risk and high-cost individuals. The objectives of this research are: first, to uncover and comprehend usage patterns within healthcare claims data, elucidating factors contributing to the identification of high-risk individuals; second, to propose an innovative ensemble stacking model approach; and third, to demonstrate the efficacy of this approach in enhancing predictive accuracy. The proposed ensemble stacking model integrates the Stacking technique with hybrid feature selection and feature engineering methodologies. By amalgamating multiple predictive models into a cohesive framework, the ensemble model offers superior predictive accuracy compared to traditional single-model approaches. Furthermore, the model's versatility enables its application across various classification tasks within the healthcare domain. Through empirical analysis, this research highlights the enhanced predictive accuracy and efficacy of the ensemble model framework. Notably, key features such as ICD Category, TotalRemainingAmt, and TotalAmtInsured emerge as significant contributors to determining an individual's risk profile based on their medical claim patterns and behaviours. By leveraging big data analytics and ensemble modelling techniques, this research contributes to the advancement of predictive analytics in healthcare, offering valuable insights for decision-makers and stakeholders in the industry

The effect of oil pulling in comparison with chlorhexidine and other mouthwash interventions in promoting oral health: A systematic review and meta-analysis

Objectives A meta-analytic review was performed to critically synthesize the evidence of oil pulling on improving the parameters of gingival health, plaque control and bacteria counts against chlorhexidine and other mouthwash or oral hygiene practices. Methods Databases including Medline, Embase and bibliographies were searched from inception to 1 April 2023. Randomized controlled trials (RCTs) with 7 days or longer duration of oil pulling with edible oils in comparison to chlorhexidine or other mouthwashes or oral hygiene practice concerning the parameters of plaque index scores (PI), gingival index scores (GI), modified gingival index scores (MGI) and bacteria counts were included. Cochrane's Risk of Bias (ROB) tool and the Grading of Recommendations Assessment, Development and Evaluation (GRADE) framework were employed to determine the quality of evidence. Two authors independently conducted study selection and data extraction. Meta-analyses of the effect of oil pulling on the parameters were conducted using an inverse-variance random-effects model. Results Twenty-five trials involving 1184 participants were included. Twenty-one trials comparing oil pulling (n = 535) to chlorhexidine (n = 286) and non-chlorhexidine intervention (n = 205) were pooled for meta-analysis. More than half of the trials (n = 17) involved participants with no reported oral health issues. The duration of intervention ranged from 7 to 45 days, with half of the trials using sesame oil. When compared to non-chlorhexidine mouthwash interventions, oil pulling clinically and significantly improved MGI scores (Standardized mean difference, SMD = −1.14; 95% confidence interval [CI]: −1.31, −0.97). Chlorhexidine was more effective in reducing the PI scores compared to oil pulling, with an SMD of 0.33 (95% CI: 0.17, 0.49). The overall quality of the body of evidence was very low. Conclusions There was a probable benefit of oil pulling in improving gingival health. Chlorhexidine remained superior in reducing the amount of plaque, compared to oil pulling. However, there was very low certainty in the evidence albeit the clinically beneficial effect of oil pulling intervention

The rise of solo dining: prediction and consumer profiling

The aim of this study is twofold: first, to investigate the factors that affect consumers’ intentions to dine alone, drawing on the theory of planned behavior (TPB); and second, to segment consumers based on their motives for solo dining. The TPB is chosen as the theoretical framework because it is regarded as a comprehensive social-psychological model that can explain an individual’s intentions well. A total of 207 participants from Malaysia completed an online questionnaire using the convenience sampling method. The collected data were subjected to statistical analyses, including partial least squares structural equation modeling (PLS-SEM) and cluster analysis. Our findings show that only attitudes have a significant positive influence on intentions. Furthermore, entertainment and economic factors are found to be significant factors of attitudes. Findings from cluster analysis show four diverse groups: enjoyers, economical diners, socializers, and relaxers. This study represents the first attempt to explore consumers’ solo dining intentions through the lens of the theory of planned behavior. Moreover, by identifying four distinct segments of solo dining consumers, the findings offer valuable insights for restaurant owners seeking to effectively target this growing market. Overall, this study not only examines the factors influencing consumers’ solo dining intentions but also segments the types of solo diners, extending the utility of the TPB

Effect of roasting in electric oven on oil quality and residue from Cucurbita maxima (Marina di Chioggia) and Cucurbita pepo (Calabaza Mercado Verde) seeds from Morocco

This study aimed to assess the impact of the roasting process, conducted in an electric oven, on the oil quality and residue derived from two pumpkin seed species, namely Cucurbita maxima (C. maxima) and Cucurbita pepo (C. pepo), cultivated in Morocco. The seeds underwent roasting at temperatures of 60, 90, 120, and 150 °C for 45 min. The cold press extracted oils were characterize in terms of fatty acids, phytosterols, tocopherols and pigment content meanwhile phenolic content and antioxidant activity were evaluated in the residues. The heat treatment did not significantly affect fatty acid content; however, it affected sterols, particularly β-sitosterol, which experienced an 8 % decrease in C. maxima. Instead, total sterols significantly increased in C. pepo from 153.84 to 181.71 mg/100g. Moreover, the heat treatment influenced the tocopherol contents, revealing a substantial increase in both species. Phenolic content was significantly affected in C. pepo whereas the variation in C. maxima was statistically nonsignificant. Antioxidant activity exhibited fluctuations during the heat treatment, resulting in an overall increase in the oils. The roasting process influences the composition of bioactive compounds and antioxidant activity in pumpkin seed oil. These findings contribute to a deeper exploration of the functional properties of pumpkin seed products

Modelling claim severity and house price: an enhanced variable selection method

Variable selection is a crucial step in the model-building process in order to construct a suitable forecasting model. Variable selection techniques allow researchers to examine on the importance of every independent variable and generate the best subset of variables for the ultimate predictive model. Previous studies have shown that the filter and wrapper methods usually require greater computational resources. To the author’s best knowledge, the combination of random forest and least shrinkage and selection operator (LASSO)has not been investigated in the actuarial and financial industries, when evaluating the important factors for claims severity and house price. In this study, claims severity and house price data sets are used to build an enhanced variable selection method which combines random forest and LASSO approach. This study also compares various existing variable selection techniques using four data sets. The outcome demonstrates that the combined method (RF-LASSO method) yields superior results. The RF-LASSO method selects lesser independent variables to be integrated into the final forecasting model compared to other methods. Although the independent variables are lessened in the final model, however the R square value is not impacted much. The findings gained from this study might be of assistance to data analysts in insurance and finance industry who are interested in implementing variable selection. The findings are subject to several limitations such as not using discrete dependent variable. Future research should certainly further test whether the method of combining variable selection methods is effective for logistic regression

E-learning Acceptance: The Mediating Role of Student Computer Competency in the Relationship Between the Instructor and the Educational Content

Drawing on the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT), this online cross-sectional study explored the mediating role of computer competencies in the relationship between the instructors’ role and the course's educational content with e-learning acceptance among 403 nursing students in Iran. Based on the results, e-learning acceptance was predicted by students’ computer competency (β=.18, p<.001). Computer competency mediated the association between the instructor's role and course content with e-learning acceptance