When Artificial Intelligence Meets Behavioural Economics

2020 ◽  
pp. 263145412097481
Author(s):  
Girish Balasubramanian
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Human Resources ◽  
Value Chain ◽  
Behavioural Economics ◽  
Dispositional Factors ◽  
Hr Function ◽  
The Impact ◽  
Smooth System ◽  
Applications Of Ai

Behavioural economics has its roots in the problems of rationality and optimising the expected utility, specially the empirical evidence of individuals acting against expected norms. Artificial intelligence (AI), on the other hand, is premised on the dominant idea being that because of the dispositional factors, the human being often might be akin to a disturbance to an otherwise smooth system. Thus, the intersection of both these areas is decision-making under uncertainty. Both these concepts put together have interesting implications for organisations. This article explores the impact of AI and Behavioural Economics on the human resources (HR) function of an organisation. Some of the contemporary applications of AI augmenting decision-making have been presented using the lens of the HR Value Chain. Based on these applications, implications for organisations are discussed. Despite limitations, AI, as a technology, is soon going to be embraced by the firms, leading to hybrid organisations. As a result, organisations need to redesign their processes and policies.

Artificial Intelligence and Inequality in the Middle East

2020 ◽  
pp. 624-649 ◽  
Author(s):  
Nagla Rizk
Keyword(s):  
Artificial Intelligence ◽  
Human Resources ◽  
Young Population ◽  
Mena Region ◽  
Poor Countries ◽  
Current State ◽  
Resource Poor ◽  
Youth Entrepreneurship ◽  
The Arab Spring ◽  
The Impact

This chapter looks at the challenges, opportunities, and tensions facing the equitable development of artificial intelligence (AI) in the MENA region in the aftermath of the Arab Spring. While diverse in their natural and human resource endowments, countries of the region share a commonality in the predominance of a youthful population amid complex political and economic contexts. Rampant unemployment—especially among a growing young population—together with informality, gender, and digital inequalities, will likely shape the impact of AI technologies, especially in the region’s labor-abundant resource-poor countries. The chapter then analyzes issues related to data, legislative environment, infrastructure, and human resources as key inputs to AI technologies which in their current state may exacerbate existing inequalities. Ultimately, the promise for AI technologies for inclusion and helping mitigate inequalities lies in harnessing grounds-up youth entrepreneurship and innovation initiatives driven by data and AI, with a few hopeful signs coming from national policies.

scholarly journals Early Adopters to Early Majority - What's Driving the Artificial Intelligence and Machine Learning Powered Transformation in Financial Services?

2021 ◽  
Vol 12 (4) ◽  
pp. 43
Author(s):  
Srikrishna Chintalapati
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Portfolio Management ◽  
Value Chain ◽  
Financial Services ◽  
Retail Banking ◽  
Early Adopters ◽  
Customer Life Cycle ◽  
Digital Disruption ◽  
The Impact

From retail banking to corporate banking, from property and casualty to personal lines, and from portfolio management to trade processing, the next wave of digital disruption in financial services has been unleashed by the concepts and applications of Artificial Intelligence (AI) and Machine Learning (ML). Together, AI and ML are undoubtedly creating one of the largest technological transformations the world has ever witnessed. Within the advanced streams of research in AI and ML, human intelligence blended with the cognitive reasoning of machines is finally out of the labs and into real-time applications. The Financial Services sector is one of the early adopters of this revolution and arguably much ahead of its leverage compared to other sectors. Built on the conceptual foundations of Innovation diffusion, and a contemporary perspective of enterprise customer life-cycle journey across the AI-value chain defined by McKinsey Global Institute (2017), the current study attempts to highlight the features and use-cases of early-adopters of this transformation. With the theoretical underpinning of technology adoption lifecycle, this paper is an earnest attempt to comment on how AI and ML have been significantly transforming the Financial Services market space from the lens of a domain practitioner. The findings of this study would be of particular relevance to the subject matter experts, Industry analysts, academicians, and researchers focussed on studying the impact of AI and ML in the financial services industry.

scholarly journals Stochastic Fuzzy Algorithms for Impairment of Assets Management

2020 ◽  
Vol 15 (1) ◽  
Author(s):  
Marcel Ioan Bolos ◽  
Victoria Bogdan ◽  
Ioana Alexandra Bradea ◽  
Claudia Diana Sabau Popa ◽  
Dorina Nicoleta Popa
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Fuzzy Numbers ◽  
Cash Flows ◽  
The Other ◽  
Dual Purpose ◽  
Fuzzy Algorithms ◽  
Tangible Assets ◽  
The Impact

The present paper aims to analyze the impairment of tangible assets with the help of artificial intelligence. Stochastic fuzzy numbers have been introduced with a dual purpose: on one hand to estimate the cash flows generated by tangible assets exploitation and, on the other hand, to ensure the value ranges stratifications that define these cash flows. Estimation of cash flows using stochastic fuzzy numbers was based on cash flows generated by tangible assets in previous periods of operation. Also, based on the Lagrange multipliers, were introduced: the objective function of minimizing the standard deviations from the recorded value of the cash flows generated by the tangible assets, as well as the constraints caused by the impairment of tangible assets identification according to which the cash flows values must be equal to the annual value of the invested capital. Within the determination of the impairment value and stratification of the value ranges determined by the cash flows using stochastic fuzzy numbers, the impairment of assets risk was identified. Information provided by impairment of assets but also the impairment risks, is the basis of the decision-making measures taken to mitigate the impact of accumulated impairment losses on company’s financial performance.

scholarly journals HR analytics a roadmap for decision making: case study

2019 ◽  
Vol 15 (2) ◽  
pp. 979 ◽  
Author(s):  
Brahim Jabir ◽  
Noureddine Falih ◽  
Khalid Rahmani
Keyword(s):  
Operating System ◽  
Decision Making ◽  
Literature Review ◽  
Human Resources ◽  
Management Model ◽  
Amount Of Information ◽  
Vast Amount ◽  
The People ◽  
The Impact

<p>In the socio-economic world, the human resources are in the most top phase of the enterprise evolution. This evolution began when the arithmetic, statistics are applicable over a vast of opportunities and used to identify problems and support decision. However, analytics has been emerged to provide predictions and understand the people performance based on available data.</p>In light of this vast amount of information, human resources services need to deploy a predictive management model and operating system of analytics that can be an efficient and an instead solution that can respond to the gaps of the traditional existing ones and facilitate the decision making. In this paper, we present a literature review of this HR analytics concept and a case study concerning the impact of interventions using an analytics solution.<p> </p>

Artificial Intelligence Inroads Into HR

2022 ◽  
pp. 231-246
Author(s):  
Swati Bansal ◽  
Monica Agarwal ◽  
Deepak Bansal ◽  
Santhi Narayanan
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Human Resources ◽  
Generation Y ◽  
Industry 4.0 ◽  
Career Aspirations ◽  
Work Life ◽  
Human Decision ◽  
The Right ◽  
Talent Acquisition

Artificial intelligence is already here in all facets of work life. Its integration into human resources is a necessary process which has far-reaching benefits. It may have its challenges, but to survive in the current Industry 4.0 environment and prepare for the future Industry 5.0, organisations must penetrate AI into their HR systems. AI can benefit all the functions of HR, starting right from talent acquisition to onboarding and till off-boarding. The importance further increases, keeping in mind the needs and career aspirations of Generation Y and Z entering the workforce. Though employees have apprehensions of privacy and loss of jobs if implemented effectively, AI is the present and future. AI will not make people lose jobs; instead, it would require the HR people to upgrade their skills and spend their time in more strategic roles. In the end, it is the HR who will make the final decisions from the information that they get from the AI tools. A proper mix of human decision-making skills and AI would give organisations the right direction to move forward.

scholarly journals Artificial Intelligence: The Ambiguous Labor Market Impact of Automating Prediction

2019 ◽  
Vol 33 (2) ◽  
pp. 31-50 ◽  
Author(s):  
Ajay Agrawal ◽  
Joshua S. Gans ◽  
Avi Goldfarb
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Labor Market ◽  
Market Impact ◽  
Recent Advances ◽  
The Impact ◽  
Decision Tasks

Recent advances in artificial intelligence are primarily driven by machine learning, a prediction technology. Prediction is useful because it is an input into decision-making. In order to appreciate the impact of artificial intelligence on jobs, it is important to understand the relative roles of prediction and decision tasks. We describe and provide examples of how artificial intelligence will affect labor, emphasizing differences between when the automation of prediction leads to automating decisions versus enhancing decision-making by humans.

Factors explaining the Impact of Performance Measures in European Local Governments

2015 ◽  
Vol 13 (2) ◽  
pp. 229-248 ◽  
Author(s):  
Lourdes Torres ◽  
Vicente Pina ◽  
Caridad Martí
Keyword(s):  
Decision Making ◽  
Human Resources ◽  
Performance Measures ◽  
Local Governments ◽  
Monetary Incentives ◽  
Senior Managers ◽  
Hr Management ◽  
Perceived Impact ◽  
The Impact

This paper seeks to identify the drivers of the variations in the impact perceived by managers of the implementation of performance measures (PM) across European local governments. We argue that insights from the use of PM for managerial processes and human resources (HR) management features condition the perceived impact of PM systems. The data was collected through a questionnaire answered by local governments of 16 European countries. The results show that the higher the use of PM in performance-oriented budgeting processes, the higher the impact of PM on improvements in the quality of decision-making. Monetary incentives linked to performance better explain the actual results in PM implementation than the kind of HR system of the cities and the academic backgrounds of their senior managers.

Exclusive books: applying a behavioural economics perspective

2020 ◽  
Vol 10 (3) ◽  
pp. 1-30
Author(s):  
Thiroshnee Naidoo ◽  
Charlene Lew
Keyword(s):  
Decision Making ◽  
Learning Outcomes ◽  
Consumer Choice ◽  
Behavioural Economics ◽  
Choice Overload ◽  
Consumer Decision Making ◽  
Consumer Decision ◽  
Content Type ◽  
The Impact

Learning outcomes The learning outcomes are as follows: understanding of the principles of choice overload and the impact of consumer choice overload on company sustainability and growth prospects; understanding of how several heuristics inform consumer decision-making; applying nudge theory to interpret and clarify the impact and consequences of nudges on consumer decision-making; and considering the challenge of a newly appointed CEO to influence consumer choice. Case overview/synopsis The case study and teaching note offers insights into the use of behavioural economics principles in consumer choice. The case study methodology was used to design, analyse and interpret the real-life application of behavioural economics in the retail sector. The case demonstrates how choice overload, dual process theory, decision heuristics and nudge theory play a role in consumer decision-making. The case offers insights into the application of behavioural economics to support the sustainability of a company in an emerging market context. Managers can use the findings to consider how to use behavioural economics principles to drive consumer choice. The application of behavioural economics to an industry facing challenges of sustainability offers new insights into how to design spaces and cues for consumer choice. Complexity academic level The case study is suitable for course in business administration, specifically at postgraduate level. Supplementary materials Teaching notes are available for educators only. Subject code CSS 8: Marketing

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