Search

Purpose: Digital health or digital healthcare, is a broad, multidisciplinary concept that includes concepts from an intersection between technology and healthcare. Digital health applies digital transformation to the healthcare field, incorporating software, hardware and services. The usefulness of digital health technologies such as digital contact tracing apps (CTAs) depends deeply on the acceptance to use them. Accordingly, the main purpose of this study is to explore the usage of CTAs by integrating the protection motivation theory (PMT) and technology acceptance model (TAM) in the Jordanian context., Design/methodology/approach: Partial least squares structural equation modelling was employed to test empirically the integrated research model against data collected from 420 respondents, leading to the identification of the important determinants for the usage of CTAs., Findings: The results reveal that perceived severity and perceived vulnerability have a significant positive impact on the perceived need for CTAs, which, in turn, affects the individuals' intention to use CTAs. It also reveals that perceived trust in government, perceived privacy and perceived usefulness have a significant positive impact on individuals' intention to use CTAs, which, in turn, affects the usage of CTAs. In contrast, perceived ease of use did not find any significant relationship, and hence hypothesis H7 was not supported. Lastly, social media awareness has a significant positive impact on perceived usefulness and ease of use., Originality/value: This study is considered among the first studies in the Jordanian context that integrate the PMT and TAM to gain a better understanding of the critical determinants for the acceptance and usage of CTAs in the context of pandemics., (© Emerald Publishing Limited.)

Brand advocates, characterized by their enthusiasm for promoting a brand without incentives, play a crucial role in driving positive word-of-mouth (WOM) and influencing potential customers. However, there is a notable lack of intelligent systems capable of accurately identifying online advocates based on their social interactions with brands. Knowledge Graphs (KGs) offer structured and factual representations of human knowledge, providing a potential solution to gain holistic insights into customer preferences and interactions with a brand. This study presents a novel framework that leverages KG construction and embedding techniques to identify brand advocates accurately. By harnessing the power of KGs, our framework enhances the accuracy and efficiency of identifying and understanding brand advocates, providing valuable insights into customer advocacy dynamics in the online realm. Moreover, we address the critical aspect of social credibility, which significantly influences the impact of advocacy efforts. Incorporating social credibility analysis into our framework allows businesses to identify and mitigate spammers, preserving authenticity and customer trust. To achieve this, we incorporate and extend DSpamOnto, a specialized ontology designed to identify social spam, with a focus on the social commerce domain. Additionally, we employ cutting-edge embedding techniques to map the KG into a low-dimensional vector space, enabling effective link prediction, clustering, and visualization. Through a rigorous evaluation process, we demonstrate the effectiveness and performance of our proposed framework, highlighting its potential to empower businesses in cultivating brand advocates and driving meaningful customer engagement strategies., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Abu-Salih, Alotaibi, Al-Okaily, Aljaafari and Almiani.)

The advent of artificial intelligence and machine learning has enabled robots to serve in consumer market for a better customer experience. Nevertheless, acceptance of robotic technology among consumers is still lacking. Therefore, this study has developed an integrated model with robot appearance, expectation confirmation model, diffusion of innovation and theory of planned behavior and empirically investigates customer intention to use service robot. The research model is empirically tested with 349 responses retrieved from customers visiting retail stores. Statistical results have revealed that customer innovativeness, compatibility, behavioral control, expectation confirmation, service robot appearance and subjective norms explained R 2 80.1 % variance in customer attitude to use service robot. Practically, this research has suggested that policy makers should pay attention in innovativeness, compatibility, perceived behavioral control, expectation confirmation, robot appearance and subjective norms to boost robot service acceptance among customers. This study is original as it develops an integrated model with the combination robot appearance, theory of planned behavior, expectation confirmation and diffusion of innovation theory. In addition to that customer self-identity is conceptualized as moderating factor and hence distinguishing current research with past studies., Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:MANAF AL-OKAILY reports a relationship with Jadara University that includes: board membership. Corresponding Author “Manaf Al- Okaily” is Associate Editor in Heliyon Journal If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors. Published by Elsevier Ltd.)

This systematic review addresses a significant gap in the existing literature by examining the intricate relationship between management practices and Industry 4.0 technologies in shaping supply chain sustainability. While prior studies have explored their individual impacts, this review synthesizes and categorizes findings to identify nuanced trends that contribute to supply chain efficiency, waste reduction, and environmental footprints. To achieve the goal of the study, a rigorous search strategy was employed to select peer-reviewed journal articles focusing on total quality management, just-in-time, vendor-managed inventory, lean, manufacturer-led decentralized systems, blockchain, the internet of things, and big data in the context of supply chain sustainability. The selected studies underwent a thorough evaluation to ensure quality and relevance. The findings highlight key insights: the adoption of management practices, particularly total quality management and just-in-time, significantly contributes to reducing waste, enhancing efficiency, and minimizing environmental footprints across supply chains. Simultaneously, the integration of industry 4.0 technologies like blockchain, the Internet of things, and big data empowers data-driven decision-making, transparency, and traceability, amplifying sustainability efforts. In conclusion, this review contributes a novel perspective by synthesizing, categorizing, and analyzing the impact of management practices and Industry 4.0 technologies on supply chain sustainability. Its findings offer valuable insights for addressing contemporary challenges and advancing sustainable practices amid dynamic global scenarios., Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Manaf Al-Okaily reports a relationship with Jadara University that includes: board membership. Associate Editor in Heliyon Journal If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Since the coronavirus 2019 (COVID-19) pandemic hit the world, many universities have used digital asynchronous learning tools such as Digital Learning Management Systems (DLMS) to continue the educational process. Despite its global usage, only a few studies have investigated its quality in the Jordanian context during the COVID-19 period from a quantitative and qualitative approach perspective. Thus, the current study aims to explore the factors that influence academic performance in Jordanian higher education institutions during the COVID-19 pandemic. A mixed methods research approach was employed to evaluate the quality of the teaching-learning process for Jordanian students in higher education institutions. The triangulated data focused on three core pillars namely if students saw a difference in their grades prior to, during, and after the pandemic, the challenges faced and improvement suggestions. Accordingly, the quantitative approach with an online questionnaire and the qualitative approach with structured interviews were applied to collect the required data from Jordanian students in higher education institutions. The results of the current study revealed that the evaluation of the teaching-learning process quality during the pandemic period affected students' academic performance in different proportions based on their specialization area. In addition, the study results also identified the most important challenges that faced the students during this period and suggested procedures to overcome them and improve the distance learning process. The current study offers empirical evidence on critical success factors underlying digital learning management systems in the COVID-19 era, which can help policymakers in Jordanian universities and the ministry of higher education and scientific research to improve the quality of the teaching-learning process in the Jordanian context., Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Manaf Al-Okaily reports a relationship with Heliyon that includes: board membership. Corresponding Author “Manaf Al-Okaily” is Associate Editor in Heliyon Journal If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

This study investigates the impact of UAE's Green Credit Policy on the non-performing loan. One of the main pillars in the UAE green agenda 2015-2030 is the green finance that has been growing in high acceleration in the Gulf Cooperation Council (GCC) countries and the whole world. Consequently, the main objective of this study is to investigate in the financial risks that associated with green lending and whether an increasing in green lending will decrease the non-performing loans ratio (NPLR) of UAE banks, based on the period 2015-2020 dataset of 23 UAE's banks. To achieve this objective, we have used a regression technique that includes a two-stage least square regression analysis and random-effect regression analysis to test if the increase in green credit ratio can reduce the NPL ratio in a sample of UAE's banks. The current study can be considered the first empirical attempt that conducted on the banking sector in UAE, to discover the variables that might have a direct impact on the NPL ratio. The results reveal that the ratio of green loans has a negative impact on the NPL ratio, as much as the return of equity, while the quality of credit, inefficiency, and the bank size have a positive impact on NPL ratio. But as was not as expected, we found that the impact of solvency ratio has a negative significant on the NPL ratio. Finally, the current study introduces a new value to the current literature about the impact of green lending policies and provides a new perspective which supports the financial sustainability in UAE., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

This study examines nurses' Continuance Intention (CI) to use electronic health records (EHRs) through a combination of three conceptual frameworks: the Unified Theory of Acceptance and Use of Technology (UTAUT), the theory of expectation-confirmation (ECT), and the Five-Factor Model (FFM). A model is developed to examine and predict the determinants of nurses' CI to use EHRs, including top management support (TMS) and the FFM's five personality domains. Data were collected from a survey of 497 nurses, which were analyzed using partial least squares. No significant relationship was found between TMS and CI. The study revealed that performance expectancy significantly mediated the influences of two different hypotheses of two predictors: agreeableness and openness to testing CI. A significant moderating impact of conscientiousness was found on the relationship between performance expectancy and CI and the relationship between social influence and CI. The findings of this study indicated that rigorous attention to the personality of individual nurses and substantial TMS could improve nurses' CI to use EHRs. A literature gap was filled concerning the mediating effects of performance expectancy on the FFM-CI relationship, and the moderation effects of Conscientiousness on UTAUT constructs and CI are another addition to the literature. The results are expected to assist government agencies, health policymakers, and health institutions all over the globe in their attempts to understand the post-adoption use of EHRs., Competing Interests: The authors declare no conflict of interest.

Peer-to-Peer (P2P) energy trading has gained much attention recently due to the advanced development of distributed energy resources. P2P enables prosumers to trade their surplus electricity and allows consumers to purchase affordable and locally produced renewable energy. Therefore, it is significant to develop solutions that are able to forecast energy consumption and generation toward better power management, thereby making renewable energy more accessible and empowering prosumers to make an informed decision on their energy management. In this paper, several models for forecasting short-term renewable energy consumption and generating are developed and discussed. Real-time energy datasets were collected from smart meters that were installed in residential premises in Western Australia. These datasets are collected from August 2018 to Apr 2019 at fine time resolution down to 5 s and comprise energy import from the grid, energy export to the grid, energy generation from installed rooftop PV, energy consumption in households, and outdoor temperature. Several models for forecasting short-term renewable energy consumption and generating are developed and discussed. The empirical results demonstrate the superiority of the optimised deep learning-based Long Term Short Memory (LSTM) model in forecasting both energy consumption and generation and outperforms the baseline model as well as the alternative classical and machine learning methods by a substantial margin., Competing Interests: The authors declare no conflict of interest., (© 2022 The Author(s).)

The COVID-19 pandemic has produced an unprecedented change in the educational system worldwide. Besides the economic and social impacts, there is a dilemma of accepting the new educational system "e-learning" by students within educational institutions. In particular, universities students have to handle several kinds of environmental, electronic and mental struggles due to COVID-19. To catch the current circumstances of more than two hundred thousand Jordanian university student during COVID-19. The students have been randomly selected to respond on an online survey using universities' portals and websites between March and April 2020. At the end of the data gathering process, we have received 587 records. The dataset includes 1) Demographics of students; 2) students' perspectives concerning the factors influencing their intention to use e-learning system within the Jordanian universities context. Data were analyzed using Partial Least Squares - Structural Equation Modelling (PLS-SEM). Next, the result has confirmed the positive of direct effect variables (subjective norm, perceived ease of use, and perceived usefulness) on the students' intention to use e-learning system. Next, the result has also confirmed the mediating effect of perceived usefulness and perceived ease of use between subjective norm and the behavioral intention to use the e-learning system with partially supported., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships which have, or could be perceived to have, influenced the work reported in this article., (© 2020 The Author(s).)