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Annals in Social Responsibility, vol. 7 no. 2
Type: Research Article
ISSN: 2056-3515

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Artificial Intelligence and Global Security
Type: Book
ISBN: 978-1-78973-812-4

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Article
Publication date: 17 September 2021

Lujie Chen, Mengqi Jiang, Fu Jia and Guoquan Liu

The purpose of this study is to develop a synthesized conceptual framework for artificial intelligence (AI) adoption in the field of business-to-business (B2B) marketing.

Abstract

Purpose

The purpose of this study is to develop a synthesized conceptual framework for artificial intelligence (AI) adoption in the field of business-to-business (B2B) marketing.

Design/methodology/approach

A conceptual development approach has been adopted, based on a content analysis of 59 papers in peer-reviewed academic journals, to identify drivers, barriers, practices and consequences of AI adoption in B2B marketing. Based on these analyses and findings, a conceptual model is developed.

Findings

This paper identifies the following two key drivers of AI adoption: the shortcomings of current marketing activities and the external pressure imposed by informatization. Seven outcomes are identified, namely, efficiency improvements, accuracy improvements, better decision-making, customer relationship improvements, sales increases, cost reductions and risk reductions. Based on information processing theory and organizational learning theory (OLT), an integrated conceptual framework is developed to explain the relationship between each construct of AI adoption in B2B marketing.

Originality/value

This study is the first conceptual paper that synthesizes drivers, barriers and outcomes of AI adoption in B2B marketing. The conceptual model derived from the combination of information processing theory and OLT provides a comprehensive framework for future work and opens avenues of research on this topic. This paper contributes to both AI literature and B2B literature.

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Journal of Business & Industrial Marketing, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0885-8624

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Article
Publication date: 30 August 2021

Hassan Younis, Balan Sundarakani and Malek Alsharairi

The purpose of this study is to investigate how artificial intelligence (AI), as well as machine learning (ML) techniques, are being applied and implemented within supply…

Abstract

Purpose

The purpose of this study is to investigate how artificial intelligence (AI), as well as machine learning (ML) techniques, are being applied and implemented within supply chains (SC) and to develop future research directions from thereof.

Design/methodology/approach

Using a systematic literature review methodology, this study analyzes the publications available on Web of Science, Scopus and Google Scholar that linked both AI and supply chain from one side and ML and supply chain from another side. A total of 388 research studies have been identified through the before said three database searches which are further screened, sorted and finalized with 50 studies. The research thoroughly reviews and analyzes the final lists of 50 studies that were found relevant and significant to the theme of AI and ML in supply chain management (SCM).

Findings

AI and ML applications are still at the infant stage and the opportunity for them to elevate supply chain performance is very promising. Some researchers developed AI and ML-related models which were tested and proved to be effective in optimizing SC, and therefore, the application of AI and ML in supply chain networks creates competitive advantages for firms. Other researchers claim that AI and ML are both currently adding value while many other researchers believe that they are still not fully exploited and their tools and techniques can leverage the supply chain’s total value. The research found that adoption of AI and ML have the ability to reduce the bullwhip effect, and therefore, further supports the performance of supply chain efficiency and responsiveness.

Research limitations/implications

This research was limited in terms of scope as it covered AI and ML applications in the supply chain while there are other dimensions that could be investigated such as big data and robotics but it was found too lengthy to include these additional dimensions, and therefore, left for future research studies that other researchers could explore and pursue.

Practical implications

This study opens the door wide for other researchers to explore how AI and ML can be adopted in SCM and what are the models that are already tested and proven to be viable. In addition, the paper also identified a group of research studies that confirmed the unexploited avenues of AI and ML which could be of high interest to other researchers to explore.

Originality/value

Although few earlier research studies touch based on the AI applications within manufacturing and transportation, this study is different and makes a unique contribution by offering a holistic view on the AI and ML implications within SC as a whole. The research carefully reviews a number of highly cited papers classifying them into three main themes and recommends future direction.

Details

Journal of Modelling in Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1746-5664

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Article
Publication date: 27 March 2020

Dirk Nicolas Wagner

Artificial Intelligence is a general-purpose technology that is bound to affect every industry. To develop successful corporate and business strategies with AI, leaders…

Abstract

Purpose

Artificial Intelligence is a general-purpose technology that is bound to affect every industry. To develop successful corporate and business strategies with AI, leaders have to capture the technological opportunity, understand the economics of AI and lead organizational change.

Design/methodology/approach

What to do with AI? How to set up for AI? Where is the value? Initial answers to these three fundamental questions are derived based on an interdisciplinary and integrated literature review.

Findings

AI leaders pursue competitive advantage by taking up the technological offer to predict-prescribe-automate, they enact machine learning by entering into a portfolio of experiments within a modular strategic framework, they organize the business to form of man-machine teams following the concept of human-agent collectives and they avoid falling victim to exponential growth bias.

Practical implications

In a business environment where the cognification of everything will lead to exponential growth of both, volume of data and intelligence of machines, it will be paramount for strategic leaders to have a robust AI strategy in place and to develop the competencies required for managing the Artificially Intelligent Firm.

Originality/value

This overview enables strategic leaders to prepare themselves and their organizations for AI. It spearheads making required connections between technology, economics and management of AI.

Details

Strategy & Leadership, vol. 48 no. 3
Type: Research Article
ISSN: 1087-8572

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Article
Publication date: 29 July 2021

Jillian Carmody, Samir Shringarpure and Gerhard Van de Venter

The purpose of this paper is to demonstrate privacy concerns arising from the rapidly increasing advancements and use of artificial intelligence (AI) technology and the…

Abstract

Purpose

The purpose of this paper is to demonstrate privacy concerns arising from the rapidly increasing advancements and use of artificial intelligence (AI) technology and the challenges of existing privacy regimes to ensure the on-going protection of an individual’s sensitive private information. The authors illustrate this through a case study of energy smart meters and suggest a novel combination of four solutions to strengthen privacy protection.

Design/methodology/approach

The authors illustrate how, through smart meter obtained energy data, home energy providers can use AI to reveal private consumer information such as households’ electrical appliances, their time and frequency of usage, including number and model of appliance. The authors show how this data can further be combined with other data to infer sensitive personal information such as lifestyle and household income due to advances in AI technologies.

Findings

The authors highlight data protection and privacy concerns which are not immediately obvious to consumers due to the capabilities of advanced AI technology and its ability to extract sensitive personal information when applied to large overlapping granular data sets.

Social implications

The authors question the adequacy of existing privacy legislation to protect sensitive inferred consumer data from AI-driven technology. To address this, the authors suggest alternative solutions.

Originality/value

The original value of this paper is that it illustrates new privacy issues brought about by advances in AI, failings in current privacy legislation and implementation and opens the dialog between stakeholders to protect vulnerable consumers.

Details

Journal of Information, Communication and Ethics in Society, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1477-996X

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Article
Publication date: 18 August 2021

Sheshadri Chatterjee, Sreenivasulu N.S. and Zahid Hussain

The applications of artificial intelligence (AI) in different sectors have become agendas for discussions in the highest circle of experts. The applications of AI can help…

Abstract

Purpose

The applications of artificial intelligence (AI) in different sectors have become agendas for discussions in the highest circle of experts. The applications of AI can help society and can harm society even by jeopardizing human rights. The purpose of this study is to examine the evolution of AI and its impacts on human rights from social and legal perspectives.

Design/methodology/approach

With the help of studies of literature and different other AI and human rights-related reports, this study has taken an attempt to provide a comprehensive and executable framework to address these challenges contemplated to occur due to the increase in usage of different AI applications in the context of human rights.

Findings

This study finds out how different AI applications could help society and harm society. It also highlighted different legal issues and associated complexity arising due to the advancement of AI technology. Finally, the study also provided few recommendations to the governments, private enterprises and non-governmental organizations on the usage of different AI applications in their organizations.

Research limitations/implications

This study mostly deals with the legal, social and business-related issues arising due to the advancement of AI technology. The study does not penetrate the technological aspects and algorithms used in AI applications. Policymakers, government agencies and private entities, as well as practitioners could take the help of the recommendations provided in this study to formulate appropriate regulations to control the usage of AI technology and its applications.

Originality/value

This study provides a comprehensive view of the emergence of AI technology and its implication on human rights. There are only a few studies that examine AI and related human rights issues from social, legal and business perspectives. Thus, this study is claimed to be a unique study. Also, this study provides valuable inputs to the government agencies, policymakers and practitioners about the need to formulate a comprehensive regulation to control the usage of AI technology which is also another unique contribution of this study.

Details

International Journal of Law and Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1754-243X

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Article
Publication date: 16 August 2021

Aslıhan Ünal and İzzet Kılınç

This paper aims to examine the feasibility of artificial intelligence (AI) performing as chief executive officer (CEO) in organizations.

Abstract

Purpose

This paper aims to examine the feasibility of artificial intelligence (AI) performing as chief executive officer (CEO) in organizations.

Design/methodology/approach

The authors followed an explorative research design – classic grounded theory methodology. The authors conducted face-to-face interviews with 27 participants that were selected according to theoretical sampling. The sample consisted of academics from the fields of AI, philosophy and management; experts and artists performing in the field of AI and professionals from the business world.

Findings

As a result of the grounded theory process “The Vizier-Shah Theory” emerged. The theory consisted of five theoretical categories: narrow AI, hard problems, debates, solutions and AI-CEO. The category “AI as a CEO” introduces four futuristic AI-CEO models.

Originality/value

This study introduces an original theory that explains the evolution process of narrow AI to AI-CEO. The theory handles the issue from an interdisciplinary perspective by following an exploratory research design – classic grounded theory and provides insights for future research.

Details

foresight, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1463-6689

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Article
Publication date: 23 August 2021

José Arias-Pérez and Juan Vélez-Jaramillo

Artificial intelligence (AI) will be performing 52% of the tasks in companies by 2025. The increasing adoption of AI is generating technological turbulence in the business…

Abstract

Purpose

Artificial intelligence (AI) will be performing 52% of the tasks in companies by 2025. The increasing adoption of AI is generating technological turbulence in the business environment. Previous studies have also shown that employees are aware of the high risk of losing their jobs when being replaced by AI. The risk of employees engaging in opportunistic behaviors, such as knowledge hiding, is thus fairly high. Therefore, the aim of this paper is to analyze the mediating effect of employee’s AI awareness on the relationship between technological turbulence generated by AI and the three types of knowledge hiding: evasive hiding, playing dumb and rationalized hiding.

Design/methodology/approach

Structural equations by the partial least squares method were used to test the proposed research model.

Findings

The most interesting finding is that employee’s AI and robotics awareness fulfills almost all mediating functions in the relationship between technological turbulence generated by AI and the three types of knowledge hiding.

Originality/value

The results show that knowledge hiding in the digital age is first and foremost a strategy by employees to sabotage and induce failure in process automation, to reduce the risk of being replaced in the workplace by AI. This study indicates that employees are willing to hide knowledge in all possible ways when perception that AI is a threat to their job increases. In other words, technological turbulence generated by AI and employee’s AI awareness are the two great new triggers of knowledge hiding in the digital age.

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Article
Publication date: 28 July 2021

Ahmad Arslan, Cary Cooper, Zaheer Khan, Ismail Golgeci and Imran Ali

This paper aims to specifically focus on the challenges that human resource management (HRM) leaders and departments in contemporary organisations face due to close…

Abstract

Purpose

This paper aims to specifically focus on the challenges that human resource management (HRM) leaders and departments in contemporary organisations face due to close interaction between artificial intelligence (AI) (primarily robots) and human workers especially at the team level. It further discusses important potential strategies, which can be useful to overcome these challenges based on a conceptual review of extant research.

Design/methodology/approach

The current paper undertakes a conceptual work where multiple streams of literature are integrated to present a rather holistic yet critical overview of the relationship between AI (particularly robots) and HRM in contemporary organisations.

Findings

We highlight that interaction and collaboration between human workers and robots is visible in a range of industries and organisational functions, where both are working as team members. This gives rise to unique challenges for HRM function in contemporary organisations where they need to address workers' fear of working with AI, especially in relation to future job loss and difficult dynamics associated with building trust between human workers and AI-enabled robots as team members. Along with these, human workers' task fulfilment expectations with their AI-enabled robot colleagues need to be carefully communicated and managed by HRM staff to maintain the collaborative spirit, as well as future performance evaluations of employees. The authors found that organisational support mechanisms such as facilitating environment, training opportunities and ensuring a viable technological competence level before organising human workers in teams with robots are important. Finally, we found that one of the toughest challenges for HRM relates to performance evaluation in teams where both humans and AI (including robots) work side by side. We referred to the lack of existing frameworks to guide HRM managers in this concern and stressed the possibility of taking insights from the computer gaming literature, where performance evaluation models have been developed to analyse humans and AI interactions while keeping the context and limitations of both in view.

Originality/value

Our paper is one of the few studies that go beyond a rather general or functional analysis of AI in the HRM context. It specifically focusses on the teamwork dimension, where human workers and AI-powered machines (robots) work together and offer insights and suggestions for such teams' smooth functioning.

Details

International Journal of Manpower, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0143-7720

Keywords

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