Managing Uncertainty in AI-Enabled Decision Making and Achieving Sustainability

Wu, Junyi; Shang, Shari S. C.

doi:10.3390/su12218758

Cited by 19 publications

(7 citation statements)

References 55 publications

(161 reference statements)

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“…They influence perception (Cavlek, 2002; Rittichainuwat and Chakraborty, 2009). Decision-making (Wu and Shang, 2020) and attitude (Ajzen et al , 2011; Daugherty et al , 2009; Tooher et al , 2013). When tourists have inadequate knowledge about the actual conditions, the information they obtained from the media will become knowledge that influences the perceived risk regarding their trips (Cavlek, 2002).…”

Section: Literature Reviewmentioning

confidence: 99%

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Applying knowledge, social concern and perceived risk in planned behavior theory for tourism in the Covid-19 pandemic

Rahmafitria

Suryadi

Oktadiana

et al. 2021

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Purpose The paper aims to examine the effect of physical distancing control on the intention to travel during the pandemic and to assess the influence of knowledge, social concern and perceived risk on the theory of planned behavior (TPB). Design/methodology/approach A total of 1,068 Indonesian respondents through a purposive sampling approach, filled out online questionnaires during the pandemic. The collected data were analyzed using partial least squares-structural equation modeling. Findings The results indicated that subjective norm as the external factor of the TPB has a stronger effect than the internal factors; attitude and behavioral control. It appears that in a collectivist society, prohibition enforced by family and friends, as well as the government’s sanctions have a stronger influence on one’s decision to travel. The findings also suggested that knowledge, perceived risks and social concern effect tourists’ behavior. Uncertainty and inadequate knowledge will decrease the level of perceived risk, which leads to lower control in practicing physical distancing and increasing intention to travel during the pandemic. Research limitations/implications The research has limitations in its use of a convenient sampling method. This method may not represent the whole population, causing the results to be difficult for generalization. There is also the need for extending the TPB model with different variables in the context of tourism and pandemic. This study enriches the existing tourism literature by applying TPB to examine tourists’ behavior during the Covid-19 pandemic, focusing on knowledge, social concern and perceived risk theory. Practical implications This paper offers useful insights for tourism planners in the government and private destination management levels. It is crucial for a destination management organization to understand the relationship between knowledge, perception and social concern with tourists’ behavior while traveling during pandemics. The understanding of tourist’s behavior when traveling during the pandemic will assist in developing and creating activities and designing health protocols at tourist attractions. Originality/value This study extended the TPB to analyze tourists behavior during the pandemic by applying knowledge, social concern and perceived risk elements.

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Section: Literature Reviewmentioning

confidence: 99%

“…Uncertainty makes effective making decision problematic. Sources of uncertainty include incomplete information (information uncertainty), inadequate understanding of a situation or environment uncertainty (lack of knowledge) and intentional uncertainty of individual (Wu and Shang, 2020).…”

Section: Literature Reviewmentioning

confidence: 99%

Applying knowledge, social concern and perceived risk in planned behavior theory for tourism in the Covid-19 pandemic

Rahmafitria

Suryadi

Oktadiana

et al. 2021

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“… 1 The decision-making process in AI involves learning from the environment and the information it gathers. 2 AI encompasses different types, among them machine learning (ML)—an algorithm that learns from data to make predictions. 3 Another category is Natural Language Processing (NLP), which utilizes algorithms to comprehend and generate human-like conversations.…”

Section: Introductionmentioning

confidence: 99%

A Systematic Review of the Application of Artificial Intelligence in Nursing Care: Where are We, and What’s Next?

Ruksakulpiwat,

Thorngthip,

Niyomyart

et al. 2024

JMDH

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Background Integrating Artificial Intelligence (AI) into healthcare has transformed the landscape of patient care and healthcare delivery. Despite this, there remains a notable gap in the existing literature synthesizing the comprehensive understanding of AI’s utilization in nursing care. Objective This systematic review aims to synthesize the available evidence to comprehensively understand the application of AI in nursing care. Methods Studies published between January 2019 and December 2023, identified through CINAHL Plus with Full Text, Web of Science, PubMed, and Medline, were included in this review. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines guided the identification, screening, exclusion, and inclusion of articles. The convergent integrated analysis framework, as proposed by the Joanna Briggs Institute, was employed to synthesize data from the included studies for theme generation. Results A total of 337 records were identified from databases. Among them, 35 duplicates were removed, and 302 records underwent eligibility screening. After applying inclusion and exclusion criteria, eleven studies were deemed eligible and included in this review. Through data synthesis of these studies, six themes pertaining to the use of AI in nursing care were identified: 1) Risk Identification, 2) Health Assessment, 3) Patient Classification, 4) Research Development, 5) Improved Care Delivery and Medical Records, and 6) Developing a Nursing Care Plan. Conclusion This systematic review contributes valuable insights into the multifaceted applications of AI in nursing care. Through the synthesis of data from the included studies, six distinct themes emerged. These findings not only consolidate the current knowledge base but also underscore the diverse ways in which AI is shaping and improving nursing care practices.

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“…Globalization and the Fourth Industrial Revolution (4IR) drive businesses toward integrated multinational operations, optimizing processes through digital tools. This shift, from decentralized to hybrid centralized systems, enables global alignment while preserving local standards (Patalas-Maliszewska & Losyk, 2020;Wu & Shang, 2020;Garc ıa, Irisarri, P erez, Est evez, & Marcos, 2018;. Multinational operations, exemplified by Saudi Aramco, leverage diverse yet similar systems globally, such as crude oil distillation, fostering competitiveness through production optimization and customization.…”

Section: Introductionmentioning

confidence: 99%

Industry 4.0 implementation for multinationals: a case study

Buhulaiga,

Telukdarie

2024

DTS

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PurposeMultinational business deliver value via multiple sites with similar operational capacities. The age of the Fourth Industrial Revolution (4IR) delivers significant opportunities for the deployment of digital tools for business optimization. Therefore, this study aims to study the Industry 4.0 implementation for multinationals.Design/methodology/approachThe key objective of this research is multi-site systems integration using a reproducible, modular and standardized “Cyber Physical System (CPS) as-a-Service”.FindingsA best practice reference architecture is adopted to guide the design and delivery of a pioneering CPS multi-site deployment. The CPS deployed is a cloud-based platform adopted to enable all manufacturing areas within a multinational energy and petrochemical company. A methodology is developed to quantify the system environmental and sustainability benefits focusing on reduced carbon dioxide (CO2) emissions and energy consumption. These results demonstrate the benefits of standardization, replication and digital enablement for multinational businesses.Originality/valueThe research illustrates the ability to design a single system, reproducible for multiple sites. This research also illustrates the beneficial impact of system reuse due to reduced environmental impact from lower CO2 emissions and energy consumption. The paper assists organizations in deploying complex systems while addressing multinational systems implementation constraints and standardization.

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Managing Uncertainty in AI-Enabled Decision Making and Achieving Sustainability

Cited by 19 publications

References 55 publications

Applying knowledge, social concern and perceived risk in planned behavior theory for tourism in the Covid-19 pandemic

Applying knowledge, social concern and perceived risk in planned behavior theory for tourism in the Covid-19 pandemic

A Systematic Review of the Application of Artificial Intelligence in Nursing Care: Where are We, and What’s Next?

Industry 4.0 implementation for multinationals: a case study

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