Assessing the potential of plastic waste management in the circular economy: a longitudinal case study in an emerging economy.

Unlabelled: Plastic waste management represents a fundamental challenge in terms of environmental pollution and health in many emerging countries. Yet, some firms believe improved plastic waste management could lead to value creation and capture, especially from a circular economy perspective. This study draws on a longitudinal research approach that involved 12 organizations in assessing plastic waste management's contribution to Cameroon's circular economy.

Extending artificial intelligence research in the clinical domain: a theoretical perspective.

Academic research to the utilization of artificial intelligence (AI) has been proliferated over the past few years. While AI and its subsets are continuously evolving in the fields of marketing, social media and finance, its application in the daily practice of clinical care is insufficiently explored. In this systematic review, we aim to landscape various application areas of clinical care in terms of the utilization of machine learning to improve patient care.

Adoption of Industry 4.0 technologies by organizations: a maturity levels perspective.

This study employs a structured literature analysis considering Industry 4.0 technologies and their adoption stages (intention, adoption, implementation, routinization, continuance, and diffusion). We identify the technology adoption stage for each technology type, which in turn supports a maturity level categorization, as well as future research suggestions and challenging open research questions.

COVID-19 vaccine hesitancy: a social media analysis using deep learning.

Hesitant attitudes have been a significant issue since the development of the first vaccines-the WHO sees them as one of the most critical global health threats. The increasing use of social media to spread questionable information about vaccination strongly impacts the population's decision to get vaccinated. Developing text classification methods that can identify hesitant messages on social media could be useful for health campaigns in their efforts to address negative influences from social media platforms and provide reliable information to support their strategies against hesitant-vaccination sentiments.

Artificial intelligence in E-Commerce: a bibliometric study and literature review.

This paper synthesises research on artificial intelligence (AI) in e-commerce and proposes guidelines on how information systems (IS) research could contribute to this research stream. To this end, the innovative approach of combining bibliometric analysis with an extensive literature review was used. Bibliometric data from 4335 documents were analysed, and 229 articles published in leading IS journals were reviewed.

Supply chain resilience in the UK during the coronavirus pandemic: A resource orchestration perspective.

The COVID-19 pandemic caused significant disruptions to global operations and supply chains. While the huge impact of the pandemic has nurtured important literature over the last couple of years, little is being said about the role of resource orchestration in supporting resilience in highly disruptive contexts. Thus, this study aims to this knowledge gap by proposing an original model to explore supply chain resilience (SCRE) antecedents, considering supply chain alertness (SCAL) as a central point to support resilience.

A Framework Based on Blockchain, Artificial Intelligence, and Big Data Analytics to Leverage Supply Chain Resilience considering the COVID-19.

In the global supply chains era, firms are more connected, integrated, and interdependent, bringing along a set of benefits and a number of risks. It is clear that the singular COVID-19 epidemic outbreak has led to unparalleled disruptions and considerable challenges for supply chains (SCs). For example, the sluggish economic environment provoked by the COVID-19 has negatively impacted the flow of goods, generating shortages and interruptions through the SCs.

A structured literature review on the interplay between emerging technologies and COVID-19 - insights and directions to operations fields.

In recent years, emerging technologies have gained popularity and being implemented in different fields. Thus, critical leading-edge technologies such as artificial intelligence and other related technologies (blockchain, simulation, 3d printing, etc.) are transforming the operations and other traditional fields and proving their value in fighting against unprecedented COVID-19 pandemic outbreaks.

Infection vulnerability stratification risk modelling of COVID-19 data: a deterministic SEIR epidemic model analysis.

Basic Susceptible-Exposed-Infectious-Removed (SEIR) models of COVID-19 dynamics tend to be excessively pessimistic due to high basic reproduction values, which result in overestimations of cases of infection and death. We propose an extended SEIR model and daily data of COVID-19 cases in the U.S.

Responsible Artificial Intelligence as a Secret Ingredient for Digital Health: Bibliometric Analysis, Insights, and Research Directions.

With the unparallel advance of leading-edge technologies like artificial intelligence (AI), the healthcare systems are transforming and shifting for more digital health. In recent years, scientific productions have reached unprecedented levels. However, a holistic view of how AI is being used for digital health remains scarce.

Impacts of epidemic outbreaks on supply chains: mapping a research agenda amid the COVID-19 pandemic through a structured literature review.

The coronavirus (COVID-19) outbreak shows that pandemics and epidemics can seriously wreak havoc on supply chains (SC) around the globe. Humanitarian logistics literature has extensively studied epidemic impacts; however, there exists a research gap in understanding of pandemic impacts in commercial SCs. To progress in this direction, we present a systematic analysis of the impacts of epidemic outbreaks on SCs guided by a structured literature review that collated a unique set of publications.

'Too-much-of-a-good-thing'? The role of advanced eco-learning and contingency factors on the relationship between corporate environmental and financial performance.

Inspired by the natural-resource-based view (NRBV) theory, we attempt to shed light on a controversy which has been persistent over the last decade, concerning the relationship between corporate environmental performance (CEP) and corporate financial performance (CFP). Using the 'too-much-of-a-good-thing' (TMGT) concept, which suggests that "too much can be worse than too little," we link mixed results and consider the roles of advanced eco-learning and contingency factors in influencing the CEP-CFP relationship. Based on a sample composed of ISO 14001 certified companies in Indonesia, and analyzing the data using consistent Partial Least Squares (PLSc), we found that: the CEP-CFP relationship follows an inverted U-shape; advanced eco-learning is a significant predictor of the CEP-CFP relationship, meaning that organizations able to develop higher eco-learning capability will be better able to identify the ideal boundaries of investment in environmental performance without reducing their financial performance; and that contingency factors such as environmental strategy and firm size have a significant role in influencing the CEP-CFP relationship.

RFID-enabled healthcare applications, issues and benefits: an archival analysis (1997-2011).

In this paper, a comprehensive review of articles published between 1997 and 2011 in the Journal of Medical Systems (JMS) on RFID technology is presented. A total of 22 papers are analyzed using a classification framework that has three dimensions: RFID-enabled healthcare applications, RFID-enabled healthcare issues, and RFID-enabled healthcare benefits. In addition, an invitation sent to all authors of the papers accepted for the special issue on RFID for the JMS allows them to position their various papers within the classification framework.