Artificial Intelligence potential within airlines: a review on how AI can enhance strategic decision-making in times of COVID-19

Pérez-Campuzano, Darío; Ortega, Patricio Morcillo; Andrada, Luís Rubio; López-Lázaro, Antonio

doi:10.3926/jairm.189

Cited by 9 publications

(6 citation statements)

References 40 publications

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“…Crisis Management Frameworks, such as AI-based systems, are designed for preparedness and effective communication during crises (Agbaji, 2021;Essien & Petrounias, 2022;Pérez-Campuzano et al, 2021;Saha et al, 2023). They are essential for sectors prone to emergencies.…”

Section: Crisis Management Framework Vs Strategic Thinking and Decisi...mentioning

confidence: 99%

“…Crisis Management Frameworks are indispensable in high-risk sectors like healthcare, emergency services, and natural resource management. These frameworks enable organizations to respond effectively to unforeseen events, minimizing risks and damages (Essien & Petrounias, 2022;Pérez-Campuzano et al, 2021).…”

Section: Crisis Management In High-risk Sectorsmentioning

confidence: 99%

“…The potential of AI applications in strategic decisionmaking, especially in times of crisis like the COVID-19 pandemic, is a notable trend. Airlines and other sectors are exploring AI to enhance strategic decisions during challenging times (Pérez-Campuzano et al, 2021).…”

Section: Ai In Strategic Decision-making During Crisesmentioning

confidence: 99%

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Strategic Management in the Digital Age: A Review of Decision-Making Frameworks

Choori,

Kazemi

2023

IJIMOB

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Objective: This article aims to critically review and synthesize the evolution of strategic management frameworks in the digital age, focusing on how these frameworks have adapted to the challenges and opportunities presented by digital technologies. Method: The review employs a narrative approach to review scholarly articles and industry reports. It categorizes decision-making frameworks into groups such as IT governance mechanisms, digital transformation strategies, and predictive models. Each framework is examined in detail regarding its application, strengths, weaknesses, and real-world examples. Comparative analysis is used to assess the suitability of these frameworks across different business contexts, and emerging trends are identified. The method also involves analyzing challenges in implementation and predicting future developments in strategic management frameworks. Results: The findings reveal that strategic management in the digital age is characterized by a shift towards more innovative, technology-driven approaches. Key trends include the integration of AI and machine learning, an increased focus on sustainability and ESG factors, and the importance of agility and flexibility in decision-making. Challenges in implementation range from aligning technological capabilities with strategic objectives to managing cognitive biases and ensuring cybersecurity. Future predictions suggest a continued evolution of these frameworks, with a greater emphasis on data-driven insights, sustainability, and technological integration. Conclusion: The review concludes that evolving strategic management practices are crucial for businesses to navigate the complexities of the digital age effectively. Organizations must embrace technological advancements, integrate sustainability into their core strategies, and maintain agility and flexibility in their decision-making processes.

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Section: Crisis Management Framework Vs Strategic Thinking and Decisi...mentioning

confidence: 99%

Section: Crisis Management In High-risk Sectorsmentioning

confidence: 99%

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Strategic Management in the Digital Age: A Review of Decision-Making Frameworks

Choori,

Kazemi

2023

IJIMOB

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“…The research object of this paper is the sales volume of the restaurant industry; however, the large differences in the dishes of stores in different business formats may seriously affect the generalizability of the model; at the same time, the daily sales volume of various dishes in most restaurant industries is proportional to the sales volume, and the sales volume of dishes can be further estimated from the daily dish sales; therefore, the sales volume of the restaurant industry is selected as the basic research object of this paper [ 18 ].…”

Section: Neural Network For Cross-border E-commerce Sales Dynamic Pre...mentioning

confidence: 99%

A Dynamic Prediction Neural Network Model of Cross-Border e-Commerce Sales for Virtual Community Knowledge Sharing

Tian

Wang

2022

Computational Intelligence and Neuroscience

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In this paper, a neural network algorithm is used to conduct in-depth research and analysis on the sales dynamics prediction of virtual community knowledge sharing in cross-border e-commerce. Both the expected returns and the social network structure are analyzed, and both have positive effects on knowledge sharing in the actual development process, but the degree of them also possesses certain variability. A model of the factors influencing the quality of knowledge shared by users is constructed to explore the relationship between the dimensions in social capital and how they affect the community users’ perceptions of knowledge quality. Exploring the strong influencing factors of product repurchase rate has key implications for promoting product sales and sales forecasting. The scale of this paper has undergone several minor revisions, and the content validity is very good. Criterion validity is generally reflected by Person correlation coefficient, and construct validity includes exploratory factor analysis and confirmatory factor analysis. First, the values were clustered, and the optimal variables were selected using stepwise regression and fitted with Poisson regression models to explore the relationship between the repurchase rate of different products and the factors that strongly influence the repurchase rate in the case. To predict the sales of goods, the advantages of the BP neural network, LSTM neural network, and Verhulst gray model are combined: BP neural network can predict sales by combining the current data corresponding to independent variables, LSTM neural network can explore the influence of historical data on sales, and Verhulst model can predict sales based on the growth trend of variables. A BP-LSTM-Verhulst nested neural network based on the AP algorithm is constructed to predict sales volume, and the accuracy of this method is proved by example. Finally, it is found that the proposed sales prediction method has higher accuracy than exponential regression and shallow neural networks. The deep learning prediction method combining unstructured data such as images proposed in the paper not only provides a more accurate sales prediction method for short life cycle products in e-commerce but also provides an effective deep learning method for management practices. The KMO value obtained after the test is less than 0.5, which is not suitable for factor analysis. Using SPSS22.0 to expand the KMO value and Bartlett’s sphericity test, the KMO value is significantly greater than the minimum requirement of 0.5, the KMO value of self-efficacy is 0.826, and the KMO value of expected return is 0.870.

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“…A couple of works that have tackled the practical application of ML algorithms during the pandemics are ( Pérez-Campuzano et al, 2021a ), which gathers and proposes a handful of strategic AI applications that could be implemented during these times of crisis, and ( Tsa and Hung, 2021 ), where a model is modified to predict enterprise performance and compared against different engines such as Neural Networks.…”

Section: Literature Reviewmentioning

confidence: 99%