Business Strategy through Decision Support Systems: A Case Study of Best Employee Selection in Indonesia DOI Open Access

Hening Nakuloadi,

Nur Wening,

Rianto Rianto

et al.

WSEAS TRANSACTIONS ON SYSTEMS, Journal Year: 2024, Volume and Issue: 23, P. 490 - 498

Published: Dec. 31, 2024

his research aims to design a decision support system (DSS) modeling for predicting the best employees in Company. This will use SAW (simple additive weighting) method. has helped facilitate process of assessing and selecting at can minimize injustice employees. It even saves an HRD manager's time determining ranking is expected provide effective efficient solution employee candidates who deserve rewards. specific this case study not necessarily suitable other organizations that may have their own assessment criteria (apart from SMART: Specific, Measurable, Achievable, Relevant, Timely.

Language: Английский

AI-Powered Risk Assessment DOI
Wasim Khan, Mohammad Ishrat, Syed Mohd Faisal

et al.

Advances in computational intelligence and robotics book series, Journal Year: 2025, Volume and Issue: unknown, P. 59 - 92

Published: April 8, 2025

Artificial Intelligence (AI) is transforming financial risk management, employing sophisticated computational methods to assess, forecast, and reduce risk. This chapter discusses the potential of AI change how institutions manage risk, its applications, advantages, challenges implementation. While enables precision in assessment decision-making, adoption introduces complex ethical, technical, regulatory challenges. The discussion focuses on practical implications, new approaches, forward-looking aspects for AI-based management systems.

Language: Английский

Citations

0

Transforming the Service Sector With New Technology DOI

C. V. Suresh Babu,

Sahil Alam Shaikh,

P. Kirubakar

et al.

Advances in hospitality, tourism and the services industry (AHTSI) book series, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 18

Published: Jan. 17, 2025

This study aims to explore the impact of artificial intelligence (AI) and automation on service industries, focusing their role in enhancing operational efficiency decision-making processes. Utilizing a qualitative methodology, research incorporates expert interviews, case studies, secondary data reviews gather comprehensive insights into current applications challenges associated with AI adoption. The findings reveal that significantly improves customer engagement, streamlines workflows, enables predictive analytics, thereby fostering better business outcomes. However, also highlights concerns regarding ethical implications workforce displacement. conclusions emphasize necessity for organizations balance technological integration human oversight reskilling initiatives, ensuring sustainable transition an AI-driven landscape. contributes deeper understanding how can be effectively leveraged sectors while addressing potential challenges.

Language: Английский

Citations

0

AI-Driven Decision Support System for Workplace Wellbeing DOI
T. Venkat Narayana Rao, Gargi Patel,

S. Bhavana

et al.

Advances in human resources management and organizational development book series, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 18

Published: Dec. 27, 2024

This chapter focuses on decision support systems (DSS) based artificial intelligence to improve work related well-being. Concerning well-being, organizations have begun appreciate the need care for employees' mental and physical health, AI technologies provide way of approaching problem. The system includes predictive models, feedback, machine learning note stress causes mood changes. Collect health sentiment data from communication interfaces, human-wearable devices, engagement surveys. They detect stress, also recognize possible problem areas before climax problem, so that necessary measures can be taken. DSS may promote general well-being employees increase organizational performance development. efficient use analytical techniques enables ensure they adapt strategies improving standards among while at workplace. Standards

Language: Английский

Citations

1

It was(n’t) me: vignette experiments on managers’ responsibility attribution in AI-advised decision-making DOI
Nina Passlack, Teresa Hammerschmidt, Oliver Posegga

et al.

Behaviour and Information Technology, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 30

Published: Nov. 26, 2024

The increasing integration of artificial intelligence (AI) in managerial decision-making organisations raises questions regarding responsibilities. We explore how managers attribute responsibility joint scenarios where they receive advice from either AI agents or human consultants. Our empirical approach combines a mixed-design vignette study, providing insights into managers' attribution, with qualitative data on manager perceptions agent capabilities. This enhances our understanding processes involving agents. findings highlight that agency are not harmony when there negative consequences. gap between being responsible and assuming is critical to understand given perceived be their decisions greatly affect individuals. work also presents three propositions guide future research the discrepancies feeling responsible.

Language: Английский

Citations

0

Business Strategy through Decision Support Systems: A Case Study of Best Employee Selection in Indonesia DOI Open Access

Hening Nakuloadi,

Nur Wening,

Rianto Rianto

et al.

WSEAS TRANSACTIONS ON SYSTEMS, Journal Year: 2024, Volume and Issue: 23, P. 490 - 498

Published: Dec. 31, 2024

his research aims to design a decision support system (DSS) modeling for predicting the best employees in Company. This will use SAW (simple additive weighting) method. has helped facilitate process of assessing and selecting at can minimize injustice employees. It even saves an HRD manager's time determining ranking is expected provide effective efficient solution employee candidates who deserve rewards. specific this case study not necessarily suitable other organizations that may have their own assessment criteria (apart from SMART: Specific, Measurable, Achievable, Relevant, Timely.

Language: Английский

Citations

0