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Book part
Publication date: 10 February 2023

Shivani Agarwal, Apoorv Gupta and Puja Roshani

Introduction: Artificial intelligence (AI) has now become an integral part of every aspect of the corporate sector. AI may be a massive branch of computing connected to building…

Abstract

Introduction: Artificial intelligence (AI) has now become an integral part of every aspect of the corporate sector. AI may be a massive branch of computing connected to building devices smart enough and capable of performing tasks that usually require human intelligence. Integrating AI with human resources (HR) practices will improve organisations, as these applications can analyse, predict, and diagnose to support HR teams for taking better decisions.

Purpose: This chapter throws light upon the current scenario of awareness of AI and machine learning (ML) and their impact on the industry of HR. This chapter tries to describe the usage of AI in our current world and the impact of AI in the field of HRM in organisations.

Methodology: The true possibility of AI and ML in HRM has been analysed with the help of pie charts, bar charts, and histograms with the segmenting of results and interpretations. Various frequently asked questions have been answered, and a sample population has also been surveyed on their viewpoints regarding specific areas.

Findings: This chapter concludes that HR experts see the best potential in analytics, attendance, recruitment, attendance management, and compensation/payroll. AI will significantly diversify the HR sector. HR professionals need to think outside of their function.

Details

The Adoption and Effect of Artificial Intelligence on Human Resources Management, Part A
Type: Book
ISBN: 978-1-80382-027-9

Keywords

Article
Publication date: 20 May 2019

Anastassia Lauterbach

This paper aims to inform policymakers about key artificial intelligence (AI) technologies, risks and trends in national AI strategies. It suggests a framework of social…

4298

Abstract

Purpose

This paper aims to inform policymakers about key artificial intelligence (AI) technologies, risks and trends in national AI strategies. It suggests a framework of social governance to ensure emergence of safe and beneficial AI.

Design/methodology/approach

The paper is based on approximately 100 interviews with researchers, executives of traditional companies and startups and policymakers in seven countries. The interviews were carried out in January-August 2017.

Findings

Policymakers still need to develop an informed, scientifically grounded and forward-looking view on what societies and businesses might expect from AI. There is lack of transparency on what key AI risks are and what might be regulatory approaches to handle them. There is no collaborative framework in place involving all important actors to decide on AI technology design principles and governance. Today's technology decisions will have long-term consequences on lives of billions of people and competitiveness of millions of businesses.

Research limitations/implications

The research did not include a lot of insights from the emerging markets.

Practical implications

Policymakers will understand the scope of most important AI concepts, risks and national strategies.

Social implications

AI is progressing at a very fast rate, changing industries, businesses and approaches how companies learn, generate business insights, design products and communicate with their employees and customers. It has a big societal impact, as – if not designed with care – it can scale human bias, increase cybersecurity risk and lead to negative shifts in employment. Like no other invention, it can tighten control by the few over the many, spread false information and propaganda and therewith shape the perception of people, communities and enterprises.

Originality/value

This paper is a compendium on the most important concepts of AI, bringing clarity into discussions around AI risks and the ways to mitigate them. The breadth of topics is valuable to policymakers, students, practitioners, general executives and board directors alike.

Details

Digital Policy, Regulation and Governance, vol. 21 no. 3
Type: Research Article
ISSN: 2398-5038

Keywords

Article
Publication date: 12 May 2020

Serge-Lopez Wamba-Taguimdje, Samuel Fosso Wamba, Jean Robert Kala Kamdjoug and Chris Emmanuel Tchatchouang Wanko

The main purpose of our study is to analyze the influence of Artificial Intelligence (AI) on firm performance, notably by building on the business value of AI-based transformation…

26289

Abstract

Purpose

The main purpose of our study is to analyze the influence of Artificial Intelligence (AI) on firm performance, notably by building on the business value of AI-based transformation projects. This study was conducted using a four-step sequential approach: (1) analysis of AI and AI concepts/technologies; (2) in-depth exploration of case studies from a great number of industrial sectors; (3) data collection from the databases (websites) of AI-based solution providers; and (4) a review of AI literature to identify their impact on the performance of organizations while highlighting the business value of AI-enabled projects transformation within organizations.

Design/methodology/approach

This study has called on the theory of IT capabilities to seize the influence of AI business value on firm performance (at the organizational and process levels). The research process (responding to the research question, making discussions, interpretations and comparisons, and formulating recommendations) was based on a review of 500 case studies from IBM, AWS, Cloudera, Nvidia, Conversica, Universal Robots websites, etc. Studying the influence of AI on the performance of organizations, and more specifically, of the business value of such organizations’ AI-enabled transformation projects, required us to make an archival data analysis following the three steps, namely the conceptual phase, the refinement and development phase, and the assessment phase.

Findings

AI covers a wide range of technologies, including machine translation, chatbots and self-learning algorithms, all of which can allow individuals to better understand their environment and act accordingly. Organizations have been adopting AI technological innovations with a view to adapting to or disrupting their ecosystem while developing and optimizing their strategic and competitive advantages. AI fully expresses its potential through its ability to optimize existing processes and improve automation, information and transformation effects, but also to detect, predict and interact with humans. Thus, the results of our study have highlighted such AI benefits in organizations, and more specifically, its ability to improve on performance at both the organizational (financial, marketing and administrative) and process levels. By building on these AI attributes, organizations can, therefore, enhance the business value of their transformed projects. The same results also showed that organizations achieve performance through AI capabilities only when they use their features/technologies to reconfigure their processes.

Research limitations/implications

AI obviously influences the way businesses are done today. Therefore, practitioners and researchers need to consider AI as a valuable support or even a pilot for a new business model. For the purpose of our study, we adopted a research framework geared toward a more inclusive and comprehensive approach so as to better account for the intangible benefits of AI within organizations. In terms of interest, this study nurtures a scientific interest, which aims at proposing a model for analyzing the influence of AI on the performance of organizations, and at the same time, filling the associated gap in the literature. As for the managerial interest, our study aims to provide managers with elements to be reconfigured or added in order to take advantage of the full benefits of AI, and therefore improve organizations’ performance, the profitability of their investments in AI transformation projects, and some competitive advantage. This study also allows managers to consider AI not as a single technology but as a set/combination of several different configurations of IT in the various company’s business areas because multiple key elements must be brought together to ensure the success of AI: data, talent mix, domain knowledge, key decisions, external partnerships and scalable infrastructure.

Originality/value

This article analyses case studies on the reuse of secondary data from AI deployment reports in organizations. The transformation of projects based on the use of AI focuses mainly on business process innovations and indirectly on those occurring at the organizational level. Thus, 500 case studies are being examined to provide significant and tangible evidence about the business value of AI-based projects and the impact of AI on firm performance. More specifically, this article, through these case studies, exposes the influence of AI at both the organizational and process performance levels, while considering it not as a single technology but as a set/combination of the several different configurations of IT in various industries.

Details

Business Process Management Journal, vol. 26 no. 7
Type: Research Article
ISSN: 1463-7154

Keywords

Article
Publication date: 22 February 2022

Pooja Goel, Neeraj Kaushik, Brijesh Sivathanu, Rajasshrie Pillai and Jasper Vikas

The purpose of this study, a current systematic literature review, is to synthesize the extant literature on consumers’ adoption of artificial intelligence and robotics (AIR) in…

3676

Abstract

Purpose

The purpose of this study, a current systematic literature review, is to synthesize the extant literature on consumers’ adoption of artificial intelligence and robotics (AIR) in the context of the hospitality and tourism sector (HATS) to gain a comprehensive understanding of it. This study also outlines insights for academia, practitioners, AI marketers, developers, designers and policymakers.

Design/methodology/approach

This study used a content analysis approach to conduct a systematic literature review for the period of 10 years (2011–2020) of the various published studies themed around consumer’s adoption of AIR in HATS.

Findings

The synthesis draws upon various factors affecting the adoption of AIR, such as individual factors, service factors, technical and performance factors, social and cultural factors and infrastructural factors. Additionally, the authors identified four major barriers, namely, psychological, social, financial, technical and functional that hinder the consumer’s adoption of artificial intelligence and robots in the hospitality and tourism industry.

Originality/value

To the best of the author’s/authors’ knowledge, this study is a first attempt to synthesize the factors that drive consumers’ adoption of artificial intelligence and robots in the hospitality and tourism industry. The present work also advances the tourism and consumer behavior literature by offering an integrated antecedent-outcome framework.

Visual abstract

Figure 2 The objective of the current systematic literature review is to synthesize the extant literature on consumer’s adoption of artificial intelligence and robotics (AIR) in the context of the hospitality and tourism sector (HATS) to gain a comprehensive understanding of it. For that purpose, authors conducted content analysis of extant literature on consumer’s adoption of AIR in HATS from 2011 to 2020. Authors presented an integrated antecedent outcome framework of the factors that drive consumer’s adoption of artificial intelligence and robots in the hospitality and tourism industry.

目的

这篇系统性文献综述的目的是综合现有关于消费者在酒店和旅游部门(HATS)中采用人工智能和机器人(AIR)的文献, 以便全面了解它。这项研究还概述了学术界、从业者、人工智能营销人员、开发人员、设计师和决策者的见解。

设计/方法论/方法

本研究使用内容分析方法对 10 年(2011–2020 年)期间的各种已发表研究进行系统的文献回顾, 主题围绕消费者在 HATS 中采用 AIR。

结果

本研究揭示了四大服务:自动化、定制、信息传播、旅游移动性和导航服务。 此外, 作者确定了阻碍消费者在酒店和旅游业采用人工智能和机器人的四大障碍, 即心理、社会、财务、技术和功能

原创性

本研究首次尝试综合推动消费者在酒店和旅游业中采用人工智能和机器人的因素。本文还通过提供一个综合的前因结果框架, 推进了旅游和消费者行为文献。

Resumen

Objetivo

El objetivo de la actual revisión sistemática literaria es sintetizar la literatura existente sobre la adopción de la inteligencia artificial y la robótica (IAR) por parte de los consumidores en el contexto del sector hotelero y turístico (SHT) para ganar un entendimiento comprensivo del mismo. Este estudio también traza visiones para los académicos, profesionales, comercializadores de AI, desarrolladores, diseñadores, y los elaboradores de las políticas a seguir.

Diseño/metodología/enfoque

El presente estudio siguió un enfoque de análisis de contenido para realizar una revisión sistemática de la literatura durante el período de 10 años (2011–2020) de los diversos estudios publicados y basados en la adopción de IAR en SHT, por parte de los consumidores.

Los hallazgos

Este estudio desvela cuatro grandes servicios: automatización, personalización, difusión de información, movilidad turística y servicios de navegación. Adicionalmente, los autores identificaron cuatro barreras principales, a saber; psicológicas, sociales, financieras, técnicas y funcionales, que impiden la adopción de la inteligenica artificial y la robótica por parte del consumidor, en la industria de la hospitalidad y el turismo.

Originalidad

Este estudio es un primer intento de sintetizar los factores que impulsan la adopción de la inteligencia artificial y la robótica por parte de los consumidores en la industria hotelera y turística. El presente trabajo también fomenta la literatura sobre el turismo y el comportamiento del consumidor, ofreciendo un marco integrado de resultados precedentes.

Book part
Publication date: 18 January 2024

Tulsi Pawan Fowdur, Satyadev Rosunee, Robert T. F. Ah King, Pratima Jeetah and Mahendra Gooroochurn

In this chapter, a general introduction on artificial intelligence (AI) is given as well as an overview of the advances of AI in different engineering disciplines, including its…

Abstract

In this chapter, a general introduction on artificial intelligence (AI) is given as well as an overview of the advances of AI in different engineering disciplines, including its effectiveness in driving the United Nations Sustainable Development Goals (UN SDGs). This chapter begins with some fundamental definitions and concepts on AI and machine learning (ML) followed by a classification of the different categories of ML algorithms. After that, a general overview of the impact which different engineering disciplines such as Civil, Chemical, Mechanical, Electrical and Telecommunications Engineering have on the UN SDGs is given. The application of AI and ML to enhance the processes in these different engineering disciplines is also briefly explained. This chapter concludes with a brief description of the UN SDGs and how AI can positively impact the attainment of these goals by the target year of 2030.

Details

Artificial Intelligence, Engineering Systems and Sustainable Development
Type: Book
ISBN: 978-1-83753-540-8

Keywords

Open Access
Book part
Publication date: 18 July 2022

Agata Leszkiewicz, Tina Hormann and Manfred Krafft

Organizations across industries are increasingly using Artificial Intelligence (AI) systems to support their innovation processes, supply chains, marketing and sales and other…

Abstract

Organizations across industries are increasingly using Artificial Intelligence (AI) systems to support their innovation processes, supply chains, marketing and sales and other business functions. Implementing AI, firms report efficiency gains from automation and enhanced decision-making thanks to more relevant, accurate and timely predictions. By exposing the benefits of digitizing everything, COVID-19 has only accelerated these processes. Recognizing the growing importance of AI and its pervasive impact, this chapter defines the “social value of AI” as the combined value derived from AI adoption by multiple stakeholders of an organization. To this end, we discuss the benefits and costs of AI for a business-to-business (B2B) firm and its internal, external and societal stakeholders. Being mindful of legal and ethical concerns, we expect the social value of AI to increase over time as the barriers for adoption go down, technology costs decrease, and more stakeholders capture the value from AI. We identify the contributions to the social value of AI, by highlighting the benefits of AI for different actors in the organization, business consumers, supply chain partners and society at large. This chapter also offers future research opportunities, as well as practical implications of the AI adoption by a variety of stakeholders.

Details

Smart Industry – Better Management
Type: Book
ISBN: 978-1-80117-715-3

Keywords

Abstract

Details

The Emerald Handbook of Computer-Mediated Communication and Social Media
Type: Book
ISBN: 978-1-80071-598-1

Article
Publication date: 26 July 2023

James W. Peltier, Andrew J. Dahl and John A. Schibrowsky

Artificial intelligence (AI) is transforming consumers' experiences and how firms identify, create, nurture and manage interactive marketing relationships. However, most marketers…

3043

Abstract

Purpose

Artificial intelligence (AI) is transforming consumers' experiences and how firms identify, create, nurture and manage interactive marketing relationships. However, most marketers do not have a clear understanding of what AI is and how it may mutually benefit consumers and firms. In this paper, the authors conduct an extensive review of the marketing literature, develop an AI framework for understanding value co-creation in interactive buyer–seller marketing relationships, identify research gaps and offer a future research agenda.

Design/methodology/approach

The authors first conduct an extensive literature review in 16 top marketing journals on AI. Based on this review, an AI framework for understanding value co-creation in interactive buyer–seller marketing relationships was conceptualized.

Findings

The literature review led to a number of key research findings and summary areas: (1) an historical perspective, (2) definitions and boundaries of AI, (3) AI and interactive marketing, (4) relevant theories in the domain of interactive marketing and (5) synthesizing AI research based on antecedents to AI usage, interactive AI usage contexts and AI-enabled value co-creation outcomes.

Originality/value

This is one of the most extensive reviews of AI literature in marketing, including an evaluation of in excess or 300 conceptual and empirical research. Based on the findings, the authors offer a future research agenda, including a visual titled “What is AI in Interactive Marketing? AI design factors, AI core elements & interactive marketing AI usage contexts.”

Abstract

Details

Marketing in Customer Technology Environments
Type: Book
ISBN: 978-1-83909-601-3

Article
Publication date: 24 July 2023

José Arias-Pérez, Juliana Chacón-Henao and Esteban López-Zapata

Digital technology is increasingly important in enhancing organizational agility (OA). Institutional theory and resource-based view were harmonized to analyze firms' adoption of…

Abstract

Purpose

Digital technology is increasingly important in enhancing organizational agility (OA). Institutional theory and resource-based view were harmonized to analyze firms' adoption of digital technologies. However, previous studies on OA have revealed that external pressures imply the imposition of barriers or technological standards that ultimately restrict OA. This study employs this double theoretical lens to investigate the mediation role of business analytics capability (BAC) in the relationship between co-innovation (CO), i.e. open innovation in digital platforms, and OA, as well as the negative moderating effect of external pressure for artificial intelligence adoption (EPAIA) on this mediation.

Design/methodology/approach

Structural equation modeling was used to test the moderated mediation with survey data from 229 firms.

Findings

The main result indicates that 72% of OA variance is explained by the effect of CO that is transmitted by the mediator (BAC). However, contrary to the authors' expectations, EPAIA only has a positive moderating effect along the path between BAC and OA.

Originality/value

This work contradicts the prevalent notion of the negative consequences of external pressures for artificial intelligence adoption. Specifically, this study's findings refute the notion that institutional pressures are the source of technical problems that disrupt CO and BAC integration and reduce OA. In contrast, the unexpectedly positive effect of EPAIA may indicate that this type of external pressure can be viewed as a significant sign and an opportunity for the company to adopt the industry's most advanced and effective digital transformation practices.

Details

Business Process Management Journal, vol. 29 no. 6
Type: Research Article
ISSN: 1463-7154

Keywords

21 – 30 of over 26000