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Article
Publication date: 13 September 2022

Rohit Bhagat, Vinay Chauhan and Pallavi Bhagat

Technology has been witnessing a rapid growth. The advent of artificial intelligence has further enhanced the satisfaction level of consumers, which makes it even more…

Abstract

Purpose

Technology has been witnessing a rapid growth. The advent of artificial intelligence has further enhanced the satisfaction level of consumers, which makes it even more vital in the current scenario. This paper aims to explore the factors affecting practical implacability of artificial intelligence and its impact on consumers’ online purchase intention.

Design/methodology/approach

This paper has used a technology-based model as the base to explore the different factors affecting consumers’ purchase intention towards e-retailing. This study has formulated a model that demonstrates the integration of artificial intelligence in retailing by the business organizations so as to understand the needs of customers and help them accept technology. This study has further explored faith, subjective norms and consciousness as constructs which enhance the implacability of artificial intelligence.

Findings

This study shows that artificial intelligence positively influences consumers’ buying behaviour. This study through a model also shows that integration of artificial intelligence enhances consumers’ purchase intention.

Research limitations/implications

The study has been focusing on a portion of target population. So there is scope to include the whole set of the population to get closer-to-accurate results.

Practical implications

The study offers useful inputs for academicians as well as marketers for predicting buying behaviour of consumers. Marketing managers can use artificial intelligence–embedded technology to enhance online purchase intention.

Social implications

The study shows that an increase in consciousness towards e-retailing has made consumers keenly analyse and purchase products on the basis of merit and usefulness of the products.

Originality/value

The contribution has been made with the best of knowledge in formulating an integrated artificial intelligence model for consumers’ purchase intention in e-retailing.

Details

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

Keywords

Article
Publication date: 6 March 2020

Pavitra Dhamija and Surajit Bag

“Technological intelligence” is the capacity to appreciate and adapt technological advancements, and “artificial intelligence” is the key to achieve persuasive operational…

4462

Abstract

Purpose

“Technological intelligence” is the capacity to appreciate and adapt technological advancements, and “artificial intelligence” is the key to achieve persuasive operational transformations in majority of contemporary organizational set-ups. Implicitly, artificial intelligence (the philosophies of machines to think, behave and perform either same or similar to humans) has knocked the doors of business organizations as an imperative activity. Artificial intelligence, as a discipline, initiated by scientist John McCarthy and formally publicized at Dartmouth Conference in 1956, now occupies a central stage for many organizations. Implementation of artificial intelligence provides competitive edge to an organization with a definite augmentation in its social and corporate status. Mere application of a concept will not furnish real output until and unless its performance is reviewed systematically. Technological changes are dynamic and advancing at a rapid rate. Subsequently, it becomes highly crucial to understand that where have the people reached with respect to artificial intelligence research. The present article aims to review significant work by eminent researchers towards artificial intelligence in the form of top contributing universities, authors, keywords, funding sources, journals and citation statistics.

Design/methodology/approach

As rightly remarked by past researchers that reviewing is learning from experience, research team has reviewed (by applying systematic literature review through bibliometric analysis) the concept of artificial intelligence in this article. A sum of 1,854 articles extracted from Scopus database for the year 2018–2019 (31st of May) with selected keywords (artificial intelligence, genetic algorithms, agent-based systems, expert systems, big data analytics and operations management) along with certain filters (subject–business, management and accounting; language-English; document–article, article in press, review articles and source-journals).

Findings

Results obtained from cluster analysis focus on predominant themes for present as well as future researchers in the area of artificial intelligence. Emerged clusters include Cluster 1: Artificial Intelligence and Optimization; Cluster 2: Industrial Engineering/Research and Automation; Cluster 3: Operational Performance and Machine Learning; Cluster 4: Sustainable Supply Chains and Sustainable Development; Cluster 5: Technology Adoption and Green Supply Chain Management and Cluster 6: Internet of Things and Reverse Logistics.

Originality/value

The result of review of selected studies is in itself a unique contribution and a food for thought for operations managers and policy makers.

Details

The TQM Journal, vol. 32 no. 4
Type: Research Article
ISSN: 1754-2731

Keywords

Article
Publication date: 1 January 1986

Emerson Hilker

We have long been obsessed with the dream of creating intelligent machines. This vision can be traced back to Greek civilization, and the notion that mortals somehow can…

1527

Abstract

We have long been obsessed with the dream of creating intelligent machines. This vision can be traced back to Greek civilization, and the notion that mortals somehow can create machines that think has persisted throughout history. Until this decade these illusions have borne no substance. The birth of the computer in the 1940s did cause a resurgence of the cybernaut idea, but the computer's role was primarily one of number‐crunching and realists soon came to respect the enormous difficulties in crafting machines that could accomplish even the simplest of human tasks.

Details

Collection Building, vol. 7 no. 3
Type: Research Article
ISSN: 0160-4953

Article
Publication date: 17 October 2022

Kirill Krinkin, Yulia Shichkina and Andrey Ignatyev

This study aims to show the inconsistency of the approach to the development of artificial intelligence as an independent tool (just one more tool that humans have…

Abstract

Purpose

This study aims to show the inconsistency of the approach to the development of artificial intelligence as an independent tool (just one more tool that humans have developed); to describe the logic and concept of intelligence development regardless of its substrate: a human or a machine and to prove that the co-evolutionary hybridization of the machine and human intelligence will make it possible to reach a solution for the problems inaccessible to humanity so far (global climate monitoring and control, pandemics, etc.).

Design/methodology/approach

The global trend for artificial intelligence development (has been) was set during the Dartmouth seminar in 1956. The main goal was to define characteristics and research directions for artificial intelligence comparable to or even outperforming human intelligence. It should be able to acquire and create new knowledge in a highly uncertain dynamic environment (the real-world environment is an example) and apply that knowledge to solving practical problems. Nowadays artificial intelligence overperforms human abilities (playing games, speech recognition, search, art generation, extracting patterns from data etc.), but all these examples show that developers have come to a dead end. Narrow artificial intelligence has no connection to real human intelligence and even cannot be successfully used in many cases due to lack of transparency, explainability, computational ineffectiveness and many other limits. A strong artificial intelligence development model can be discussed unrelated to the substrate development of intelligence and its general properties that are inherent in this development. Only then it is to be clarified which part of cognitive functions can be transferred to an artificial medium. The process of development of intelligence (as mutual development (co-development) of human and artificial intelligence) should correspond to the property of increasing cognitive interoperability. The degree of cognitive interoperability is arranged in the same way as the method of measuring the strength of intelligence. It is stronger if knowledge can be transferred between different domains on a higher level of abstraction (Chollet, 2018).

Findings

The key factors behind the development of hybrid intelligence are interoperability – the ability to create a common ontology in the context of the problem being solved, plan and carry out joint activities; co-evolution – ensuring the growth of aggregate intellectual ability without the loss of subjectness by each of the substrates (human, machine). The rate of co-evolution depends on the rate of knowledge interchange and the manufacturability of this process.

Research limitations/implications

Resistance to the idea of developing co-evolutionary hybrid intelligence can be expected from agents and developers who have bet on and invested in data-driven artificial intelligence and machine learning.

Practical implications

Revision of the approach to intellectualization through the development of hybrid intelligence methods will help bridge the gap between the developers of specific solutions and those who apply them. Co-evolution of machine intelligence and human intelligence will ensure seamless integration of smart new solutions into the global division of labor and social institutions.

Originality/value

The novelty of the research is connected with a new look at the principles of the development of machine and human intelligence in the co-evolution style. Also new is the statement that the development of intelligence should take place within the framework of integration of the following four domains: global challenges and tasks, concepts (general hybrid intelligence), technologies and products (specific applications that satisfy the needs of the market).

Details

Kybernetes, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0368-492X

Keywords

Book part
Publication date: 15 September 2022

Wolfgang H. Schulz, Oliver Franck, Stanley Smolka and Vincent Geilenberg

Climate change requires society to focus more strongly on sustainability. This requires an adjustment both on the demand side and on the supply side. Consumers must be…

Abstract

Climate change requires society to focus more strongly on sustainability. This requires an adjustment both on the demand side and on the supply side. Consumers must be given incentives to optimise their consumption according to sustainability aspects. In the supply of capital goods and consumer goods, firms must do their part to ensure that environmental savings are made possible and cost-efficiency. However, there must be doubts that a more resource-efficient production technology leads to the desired environmental effects. Policymakers ignore the Jevon’s paradox. The Jevon’s paradox states that an improved technology that leads to resource savings disproportionately increases the intensity of use. In absolute terms, there is a higher consumption of resources after the technology is introduced. This effect is currently ignored, for example, by all forecasts on demand for lithium for electromobility. Regardless of this, it is fundamentally better to optimise the technologies. However, this raises the question of whether the Jevon’s paradox cannot be undermined by artificial intelligence. Artificial intelligence applied to production promises the possibility to replace partial optimisations with total optimisations. By pursuing an absolute maximum (maximum maximorum), the intensity of use is limited. Therefore, this chapter is concerned with understanding the primary effects of artificial intelligence in production and highlighting the potential effects on sustainability.

Purpose: Increasing the sustainability in industrial production is getting more and more important. Furthermore, the technology of artificial intelligence is getting more and more important as well. For this reason, it is time to understand how artificial intelligence and sustainability are linked with one another in the context of production.

Need for the study: This chapter aims to deliver a solid argumentation regarding the prospects and the relevance of the usage of artificial intelligence in the context of production. Moreover, it specifically aims to show how artificial intelligence affects the sustainability of production.

Method: Literature analysis.

Findings: The findings are that artificial intelligence does enforce cooperative action within the industry via the effects on productivity variables, transaction costs, and production elasticities. Furthermore, the Jevon’s paradox does not seem to apply to artificial intelligence. Therefore, it is suggested that more empirical research has to focus on this topic.

Practical Implications: This chapter highlights the importance of artificial intelligence for the topic of sustainability.

Details

The New Digital Era: Digitalisation, Emerging Risks and Opportunities
Type: Book
ISBN: 978-1-80382-980-7

Keywords

Article
Publication date: 17 June 2019

Jeannette Paschen, Jan Kietzmann and Tim Christian Kietzmann

The purpose of this paper is to explain the technological phenomenon artificial intelligence (AI) and how it can contribute to knowledge-based marketing in B2B…

10775

Abstract

Purpose

The purpose of this paper is to explain the technological phenomenon artificial intelligence (AI) and how it can contribute to knowledge-based marketing in B2B. Specifically, this paper describes the foundational building blocks of any artificial intelligence system and their interrelationships. This paper also discusses the implications of the different building blocks with respect to market knowledge in B2B marketing and outlines avenues for future research.

Design/methodology/approach

The paper is conceptual and proposes a framework to explicate the phenomenon AI and its building blocks. It further provides a structured discussion of how AI can contribute to different types of market knowledge critical for B2B marketing: customer knowledge, user knowledge and external market knowledge.

Findings

The paper explains AI from an input–processes–output lens and explicates the six foundational building blocks of any AI system. It also discussed how the combination of the building blocks transforms data into information and knowledge.

Practical implications

Aimed at general marketing executives, rather than AI specialists, this paper explains the phenomenon artificial intelligence, how it works and its relevance for the knowledge-based marketing in B2B firms. The paper highlights illustrative use cases to show how AI can impact B2B marketing functions.

Originality/value

The study conceptualizes the technological phenomenon artificial intelligence from a knowledge management perspective and contributes to the literature on knowledge management in the era of big data. It addresses calls for more scholarly research on AI and B2B marketing.

Details

Journal of Business & Industrial Marketing, vol. 34 no. 7
Type: Research Article
ISSN: 0885-8624

Keywords

Article
Publication date: 10 December 2020

Faten F. Kharbat, Abdallah Alshawabkeh and M. Lynn Woolsey

Students with developmental/intellectual disabilities (ID/DD) often have serious health issues that require additional medical care and supervision. Serious health issues…

1048

Abstract

Purpose

Students with developmental/intellectual disabilities (ID/DD) often have serious health issues that require additional medical care and supervision. Serious health issues also mean increased absence and additional lags in academic achievement and development of adaptive and social skills. The incorporation of artificial intelligence in the education of a child with ID/DD could ameliorate the educational, adaptive and social skill gaps that occur as a direct result of persistent health problems.

Design/methodology/approach

The literature regarding the use of artificial intelligence in education for students with ID/DD was collected systematically from international online databases based on specific inclusion and exclusion criteria. The collected articles were analyzed deductively, looking for the different gaps in the domain. Based on the literature, an artificial intelligence–based architecture is proposed and sketched.

Findings

The findings show that there are many gaps in supporting students with ID/DD through the utilization of artificial intelligence. Given that the majority of students with ID/DD often have serious and chronic and comorbid health conditions, the potential use of health information in artificial intelligence is even more critical. Therefore, there is a clear need to develop a system that facilitates communication and access to health information for students with ID/DD, one that provides information to caregivers and education providers, limits errors, and, therefore, improves these individuals' education and quality of life.

Practical implications

This review highlights the gap in the current literature regarding using artificial intelligence in supporting the education of students with ID/DD. There is an urgent need for an intelligent system in collaboration with the updated health information to improve the quality of services submitted for people with intellectual disabilities and as a result improving their quality of life.

Originality/value

This study contributes to the literature by highlighting the gaps in incorporating artificial intelligence and its service to individuals with ID/DD. The research additionally proposes a solution based on the confounding variables of students’ health and individual characteristics. This solution will provide an automated information flow as a functional diagnostic and intervention tool for teachers, caregivers and parents. It could potentially improve the educational and practical outcomes for individuals with ID/DD and, ultimately, their quality of life.

Book part
Publication date: 7 October 2020

Ali B. Mahmoud, Shehnaz Tehseen and Leonora Fuxman

This chapter attempts to provide answers to the following questions:

  • What is artificial intelligence (AI)? Moreover, what is AI-based retail innovation?
  • How does AI work?

Abstract

Learning Outcomes

This chapter attempts to provide answers to the following questions:

  • What is artificial intelligence (AI)? Moreover, what is AI-based retail innovation?

  • How does AI work?

  • What are the applications of AI in retail services innovation?

  • What are the ethical aspects, considerations and issues regarding the employment of AI in retail?

What is artificial intelligence (AI)? Moreover, what is AI-based retail innovation?

How does AI work?

What are the applications of AI in retail services innovation?

What are the ethical aspects, considerations and issues regarding the employment of AI in retail?

Article
Publication date: 30 August 2022

Milan Zorman, Bojan Žlahtič, Saša Stradovnik and Aleš Hace

Collaborative robotics and autonomous driving are fairly new disciplines, still with a long way to go to achieve goals, set by the research community, manufacturers and…

Abstract

Purpose

Collaborative robotics and autonomous driving are fairly new disciplines, still with a long way to go to achieve goals, set by the research community, manufacturers and users. For technologies like collaborative robotics and autonomous driving, which focus on closing the gap between humans and machines, the physical, psychological and emotional needs of human individuals becoming increasingly important in order to ensure effective and safe human–machine interaction. The authors' goal was to conceptualize ways to combine experience from both fields and transfer artificial intelligence knowledge from one to another. By identifying transferable meta-knowledge, the authors will increase quality of artificial intelligence applications and raise safety and contextual awareness for users and environment in both fields.

Design/methodology/approach

First, the authors presented autonomous driving and collaborative robotics and autonomous driving and collaborative robotics' connection to artificial intelligence. The authors continued with advantages and challenges of both fields and identified potential topics for transferrable practices. Topics were divided into three time slots according to expected research timeline.

Findings

The identified research opportunities seem manageable in the presented timeline. The authors' expectation was that autonomous driving and collaborative robotics will start moving closer in the following years and even merging in some areas like driverless and humanless transport and logistics.

Originality/value

The authors' findings confirm the latest trends in autonomous driving and collaborative robotics and expand them into new research and collaboration opportunities for the next few years. The authors' research proposal focuses on those that should have the most positive impact to safety, complement, optimize and evolve human capabilities and increase productivity in line with social expectations. Transferring meta-knowledge between fields will increase progress and, in some cases, cut some shortcuts in achieving the aforementioned goals.

Details

Kybernetes, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0368-492X

Keywords

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