Defining of necessary number of employees in airline by using artificial intelligence tools

Petrović, Dragan Lj.; Puharić, Mirjana; Kastratović, Edita

doi:10.5937/intrev1804077p

Cited by 3 publications

(1 citation statement)

References 3 publications

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“…In more recent times, with the advancements in IT, several applications of technologies in the selection processes are also being upgraded using artificial intelligence. This is necessary due to the uncertainty and increased risk coupled with the limited time to make the right decision to select the best applicant for the organisation [10]. Communication robots focusing on social innovation are being tested for non-verbal behaviour recognition to predict social interaction outcomes [11].…”

Section: Introductionmentioning

confidence: 99%

Dominant Factors for an Effective Selection System: An Australian Education Sector Perspective

Rozario

Venkatraman

Chu

et al. 2019

Systems

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With the latest advancements in information technologies, many organisations expect systems to provide effective support in the recruitment process and decision making. However, there is a lack of clarity on the dominant factors required for an effective recruitment system which can influence business outcomes. This paper aimed to identify the predominant factors in the employee selection process and the use of a management system for decision support. The empirical study consisted of a qualitative survey of 74 samples and a quantitative survey of 204 individual participants from 17 organisations coming from technical and further education (TAFE)/dual education (higher education and vocational education) sector members of the Victorian TAFE Association in Australia. Using commonly adopted exploratory factor analysis (EFA) of 38 variables, the data triangulation of the qualitative and quantitative analysis resulted in conformance of five dominant factors under three themes. We believe the results of the study offer actionable suggestions in developing an effective recruitment system and furthers the research in this field of study.

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Section: Introductionmentioning

confidence: 99%

Dominant Factors for an Effective Selection System: An Australian Education Sector Perspective

Rozario

Venkatraman

Chu

et al. 2019

Systems

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A Data-Driven Madm Model for Personnel Selection and Improvement

Chuang

Liou

et al. 2020

Technological and Economic Development of Economy

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Personnel selection and human resource improvement are characteristically multiple-attribute decision-making (MADM) problems. Previously developed MADM models have principally depended on experts’ judgements as input for the derivation of solutions. However, the subjectivity of the experts’ experience can have a negative influence on this type of decision-making process. With the arrival of today’s data-based decision-making environment, we develop a data-driven MADM model, which integrates machine learning and MADM methods, to help managers select personnel more objectively and to support their competency improvement. First, RST, a machining learning tool, is applied to obtain the initial influential significance-relation matrix from real assessment data. Subsequently, the DANP method is used to derive an influential significance-network relation map and influential weights from the initial matrix. Finally, the PROMETHEE-AS method is applied to assess the gap between the aspiration and current levels for every candidate. An example was carried out using performance data with evaluation attributes obtained from the human resource department of a Chinese food company. The results revealed that the data-driven MADM model could enable human resource managers to resolve the issues of personnel selection and improvement simultaneously, and can actually be applied in the era of big data analytics in the future.

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Beyond the Horizon

Cingöz,

Durmaz

2024

Advances in Logistics, Operations, and Management Science

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The aviation industry is evolving, driven by advanced techology like autonomous systems, machine learning, and data analytics. Artificial intelligence (AI) applications, including predictive maintenance, flight planning, and air traffic management, are transforming operations and safety. However, integrating these technologies poses challenges and ethical dilemmas explored in this chapter. The authors analyze AI's impact on safety, efficiency, customer service, and cost-effectiveness in the airline industry. Through a systematic examination, the authors seek to offer insights into the pivotal question of whether the preference should lean towards a fully automated AI-driven system, human operation, or a harmonious AI-human partnership within the airline industry. By weighing the pros and cons of each approach, the authors aim to shed light on the path that holds the greatest promise for the future of aviation, ultimately ensuring the industry's continued excellence and sustainability.

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Defining of necessary number of employees in airline by using artificial intelligence tools

Cited by 3 publications

References 3 publications

Dominant Factors for an Effective Selection System: An Australian Education Sector Perspective

Dominant Factors for an Effective Selection System: An Australian Education Sector Perspective

A Data-Driven Madm Model for Personnel Selection and Improvement

Beyond the Horizon

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