In pursuit of socially-minded data-intensive innovation in banking: A focus group study of public expectations of digital innovation in banking

Aitken, Mhairi; Ng, Magdalene; Horsfall, Dave; Coopamootoo, Kovila; Moorsel, Aad van; Elliott, Karen

doi:10.1016/j.techsoc.2021.101666

Cited by 23 publications

(18 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Sustainability, thus, is one of the major concerns for contemporary organizations and it exceeds the environmental concerns. Consumers have prompted manufacturers to redesign their products for the purpose of reuse and recycling to generate added value (Aitken et al, 2021 ; Qiu et al, 2021 ; Aboramadan et al, 2021 ). Consequently, manufacturers have moved away from the traditional business models to sustainable ones—(Kulkarni, & Anantharama, 2020 ; Shuv-Ami & Shalom, 2020 ; Purnomo et al, 2021 )—models based on the concept of circular economy (Eneizan et al, 2019 ; Al-Abrrow et al, 2021a ; Abdulaali et al, 2019 ; Khaw et al, 2022a ).…”

Section: Introductionmentioning

confidence: 99%

Circular economy of medical waste: novel intelligent medical waste management framework based on extension linear Diophantine fuzzy FDOSM and neural network approach

Chew

Khaw

Alnoor

et al. 2023

Environ Sci Pollut Res

View full text Add to dashboard Cite

Environmental pollution has been a major concern for researchers and policymakers. A number of studies have been conducted to enquire the causes of environmental pollution which suggested numerous policies and techniques as remedial measures. One such major source of environmental pollution, as reported by previous studies, has been the garbage resulting from disposed hospital wastes. The recent outbreak of the COVID-19 pandemic has resulted into mass generation of medical waste which seems to have further deteriorated the issue of environmental pollution. This necessitates active attention from both the researchers and policymakers for effective management of medical waste to prevent the harm to environment and human health. The issue of medical waste management is more important for countries lacking sophisticated medical infrastructure. Accordingly, the purpose of this study is to propose a novel application for identification and classification of 10 hospitals in Iraq which generated more medical waste during the COVID-19 pandemic than others in order to address the issue more effectively. We used the Multi-Criteria Decision Making (MCDM) method to this end. We integrated MCDM with other techniques including the Analytic Hierarchy Process (AHP), linear Diophantine fuzzy set decision by opinion score method (LDFN-FDOSM), and Artificial Neural Network (ANN) analysis to generate more robust results. We classified medical waste into five categories, i.e., general waste, sharp waste, pharmaceutical waste, infectious waste, and pathological waste. We consulted 313 experts to help in identifying the best and the worst medical waste management technique within the perspectives of circular economy using the neural network approach. The findings revealed that incineration technique, microwave technique, pyrolysis technique, autoclave chemical technique, vaporized hydrogen peroxide, dry heat, ozone, and ultraviolet light were the most effective methods to dispose of medical waste during the pandemic. Additionally, ozone was identified as the most suitable technique among all to serve the purpose of circular economy of medical waste. We conclude by discussing the practical implications to guide governments and policy makers to benefit from the circular economy of medical waste to turn pollutant hospitals into sustainable ones.

show abstract

Section: Introductionmentioning

confidence: 99%

Circular economy of medical waste: novel intelligent medical waste management framework based on extension linear Diophantine fuzzy FDOSM and neural network approach

Chew

Khaw

Alnoor

et al. 2023

Environ Sci Pollut Res

View full text Add to dashboard Cite

show abstract

“…AI is utilized to power automated customer service chatbots, to construct efficient systems to spot fraud and money laundering, and to improve automated activities that utilize huge quantities of data (such as risk profiles or credit ratings). Direct consumers and the larger society will experience the effects of this new data-intensive banking technology (Aitken et al, 2021).…”

Section: Literature Reviewmentioning

confidence: 99%

Scientometric Analysis on Digital Banking Innovation Model

Rusydiana,

Kanz

2024

AMS

View full text Add to dashboard Cite

This study examines the development of research trends in digital banking innovation-related articles published in reputable journals indexed by Scopus between 1996 and 2022. The analysis focuses on trends in keywords, journals and research paths for 350 publications on digital banking innovation. The results of the systematic review analysis mapping are then reviewed in considerable detail to examine digital banking development strategies that can be utilized by the banking industry in Indonesia. This study constructs bibliometric maps using descriptive statistical approaches and bibliometric analysis with the biblioshiny R program. The study presents nine research paths based on the results of MCA (Multiple Correspondence Analysis) keyword cluster mapping, which can be used as a conceptual structure map. In addition, to negotiate the changing competitive landscape, three strategies were provided in this research: 1) Omni-Channel; 2) Open Bank and 3) Smart Banking. This includes incumbents addressing customer retention, challengers pursuing a low-cost digital experience, niche companies providing banking as a service, and social media platforms developing payment platforms. Banks must be capable of adapting their business innovation model, organizational structure, work culture, and infrastructure in order to perform a digital transformation. Digital channels must be utilized to provide services and innovations within banks. The main thing is how to find the right balance between efforts to optimize opportunities and carry out various digital innovations in an effort to increase market share in the future.

show abstract

“…Therefore, the developed countries focus on securing supply chains in line with the medical devices and equipment industry (Solomon et al, 2020;Iyengar et al, 2020). In addition, environmental standards and requirements from consumers have prompted manufacturers to redesign the production processes to offer new solutions and to obtain additional pro ts (Aitken et al, 2021;Qiu et al, 2021;Aboramadan et al, 2021). Consequently, manufacturers have shifted from traditional business models to sustainable models (Kulkarni, & Anantharama, 2020; Shuv-Ami & Shalom, 2020; Purnomo et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Circular Economy of Medical Waste: Novel Intelligent Medical Waste Management Framework Based on Extension Linear Diophantine Fuzzy FDOSM and Neural Network Approach

Chew

Khaw

Alnoor

et al. 2022

Preprint

View full text Add to dashboard Cite

The COVID-19 pandemic has caused overwhelming levels of medical waste, resulting in constant threats to environmental pollution. Furthermore, many environmental issues related to medical waste have emerged. This study aims to propose an application that allows the identification and classification of hospitals that generate overwhelming levels of medical waste aftermath of the COVID-19 pandemic by using Multi-Criteria Decision-Making methods (MCDM). MCDM was designed on the integration of the Analytic Hierarchy Process (AHP), linear diophantine fuzzy set-fuzzy decision by opinion score method (LDFN-FDOSM), and Artificial Neural Network (ANN) analysis. Ten hospital managers were interviewed to determine the volume of medical waste generated by the hospitals they manage. Five types of medical waste were identified: general waste, sharps waste, pharmaceutical waste, infectious waste, and pathological waste. Among these five types, pharmaceutical waste is appointed as one that most impacts the environment. After that 313 experts in the health sector with experience in sustainability techniques were targeted to determine the best and worst technique for the Circular Economy to manage medical waste using the neural network approach. Findings also revealed that incineration technique, microwave technique, pyrolysis technique, autoclave chemical technique, vaporised hydrogen peroxide, dry heat, ozone, and ultraviolet light were the most vital and effective methods to dispose of medical waste during the pandemic. Additionally, ozone was ranked first as the most Circular Economy-related method for medical waste disposal. Among the implications of this study for governments, policymakers, and practitioners identify actions that hospitals may consider regarding the Circular Economy concept. Another implication is the supportive role of policymakers in transitioning most pollutant hospitals to becoming more sustainable.

show abstract

In pursuit of socially-minded data-intensive innovation in banking: A focus group study of public expectations of digital innovation in banking

Cited by 23 publications

References 24 publications

Circular economy of medical waste: novel intelligent medical waste management framework based on extension linear Diophantine fuzzy FDOSM and neural network approach

Circular economy of medical waste: novel intelligent medical waste management framework based on extension linear Diophantine fuzzy FDOSM and neural network approach

Scientometric Analysis on Digital Banking Innovation Model

Circular Economy of Medical Waste: Novel Intelligent Medical Waste Management Framework Based on Extension Linear Diophantine Fuzzy FDOSM and Neural Network Approach

Contact Info

Product

Resources

About