Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy

Abstract

As far back as the industrial revolution, significant development in technical innovation has succeeded in transforming numerous manual tasks and processes that had been in existence for decades where humans had reached the limits of physical capacity. Artificial Intelligence (AI) offers this same transformative potential for the augmentation and potential replacement of human tasks and activities within a wide range of industrial, intellectual and social applications. The pace of change for this new AI technological age is staggering, with new breakthroughs in algorithmic machine learning and autonomous decision-making, engendering new opportunities for continued innovation. The impact of AI could be significant, with industries ranging from: finance, healthcare, manufacturing, retail, supply chain, logistics and utilities, all potentially disrupted by the onset of AI technologies. The study brings together the collective insight from a number of leading expert contributors to highlight the significant opportunities, realistic assessment of impact, challenges and potential research agenda posed by the rapid emergence of AI within a number of domains: business and management, government, public sector, and science and technology. This research offers significant and timely insight to AI technology and its impact on the future of industry and society in general, whilst recognising the societal and industrial influence on pace and direction of AI development.

Benefits of Artificial Intelligence and Machine Learning in Marketing

Abstract

Artificial intelligence (AI) impacts numerous aspects of life in the form of smart devices and smart applications, designed to understand consumer behaviour, needs, and preferences in order to deliver customized experiences. AI has been one of the primary drivers of innovation in marketing. Marketers are already leveraging the advantages of AI to gain valuable insights into customers, competitors and markets. Besides, AI automate tasks, reduce costs, and improve workflows. This paper examines the current and potential applications of AI within marketing by providing comprehensive overview of existing academic research.

Influence of artificial intelligence on technological innovation: Evidence from the panel data of china’s manufacturing sectors

Abstract

This paper analyzes the impact of artificial intelligence (AI) on technological innovation through logic reasoning and empirical modeling. Based on the industrial robot data provided by the International Federation of Robotics (IFR) and the panel data of China’s 14 manufacturing sectors from 2008 to 2017, this paper empirically analyzes the impact of AI on technological innovation. Our analysis shows that the mechanism of how AI affects technological innovation is that the former promotes technological innovation through accelerating knowledge creation and technology spillover, improving learning and absorptive capacities, while increasing R&D and talent investment. Our empirical results indicate that under the condition of controlling intensity of R&D investment, FDI, ownership structure, technical imitation, AI significantly promotes technological innovation. And the impact of AI on technological innovation experiences sector heterogeneity: AI has more significant impact on the technological innovation of low-tech sectors. The higher the level of AI, the greater its impact on technological innovation. Based on our established conclusions, we provide corresponding suggestions and recommendations for managerial decision-making.

Analysis of Collaborative Driving Effect of Artificial Intelligence on Knowledge Innovation Management

Abstract

Artificial intelligence (AI) plays a connotative driving role in cross-border knowledge and factor identification of innovation management. This study explores the collaborative driving effects and influence factors of artificial intelligence on knowledge innovation management. Based on the artificial intelligence application data in knowledge innovation management from 2011 to 2020, this paper adopts the synergy model and the adiabatic elimination to empirically test the influence mechanism and key factors of AI for knowledge innovation management. The results showed that artificial intelligence had a significant impact on dynamic elements, capacity elements, environmental elements of knowledge flow, and stock management. AI made endogenous impacts on the promotion of knowledge flow ability and network diffusion. AI technology promoted the formation of the original technological advantages of innovation and had obvious automatic recognition function for new knowledge, which stimulated the main internal transmission power of knowledge innovation. Furthermore, AI actuated energy level of original innovation and progressive achievements of cutting-edge technology, which mainly came from the deep runoff knowledge of innovation system. Knowledge network affected the transmission efficiency and retention proportion of deep runoff knowledge. The penetration proportion of artificial intelligence determined cooperation space of intelligent industry and application direction of integrated knowledge.

Artificial Intelligence in Business: From Research and Innovation to Market Deployment

Abstract

Abstract For the last few years, one can see the emergence of a large number of intelligent products and services, their commercial availability and the socioeconomic impact, this raises the question if the present emergence of AI is just hype or does it really have the capability of transforming the world. The paper investigates the wide range of implications of artificial intelligence (AI), and delves deeper into both positive and negative impacts on governments, communities, companies, and individuals. This paper investigates the overall impact of AI – from research and innovation to deployment. The paper addresses the influential academic achievements and innovations in the field of AI; their impact on the entrepreneurial activities and thus on the global market. The paper also contributes in investigating factors responsible for the advancement of AI. For the exploration of entrepreneurial activities towards AI, two lists of top 100 AI start-ups are considered. The inferences obtained from the research will provide an improved understanding of the innovations and the impact of AI on businesses and society in general. It will also provide a better understanding of how AI can transform the business operations and thus the global economy.

Research on the impact of artificial intelligence technology on green innovation

Abstract

Artificial intelligence technology has a far-reaching impact on the way of life and production of human society. It not only deeply integrates with various industries, but also promotes the development of industries. In the era of comprehensively promoting the concept of green development, will artificial intelligence technology promote green innovation? Based on this background, this paper first constructs a theoretical model of the relationship between artificial intelligence technology and green innovation, and then makes an empirical test with data. The results show that AI technology can significantly promote the development of green innovation and enhance the positive effect of government subsidies on green innovation.

An Analytical Approach to the Impact of “Artificial Intelligence” on Business in the Dynamically Changing Era of Disruptive Technology

Abstract

As “Artificial Intelligence (AI)” & automation is growing, strategists are modifying their strategies of business. This encourages the inclusion of Artificial Intelligence into process of business, but the output of such adoption is uncertain & requires more exploration. This research looks at overall impact of AI on organizations, including deployment of market, innovations, research, and prospective business model changes. A 3-dimensional model is created based on “Neo-Schumpeterian Economics” and its 3 forces, namely Entrepreneurship, Knowledge & Innovation to get access to this complete effect. The first component is focusing on artificial intelligence research and development. The second component investigates AI influence on global market and organizational strategic goals, while the third emphasizes on how AI is revolutionizing business settings. Furthermore, the study examined both the positive and negative effects of AI on agents.

Artificial Intelligence in Service

Abstract

Artificial intelligence (AI) is increasingly reshaping service by performing various tasks, constituting a major source of innovation, yet threatening human jobs. We develop a theory of AI job replacement to address this double-edged impact. The theory specifies four intelligences required for service tasks—mechanical, analytical, intuitive, and empathetic—and lays out the way firms should decide between humans and machines for accomplishing those tasks. AI is developing in a predictable order, with mechanical mostly preceding analytical, analytical mostly preceding intuitive, and intuitive mostly preceding empathetic intelligence. The theory asserts that AI job replacement occurs fundamentally at the task level, rather than the job level, and for “lower” (easier for AI) intelligence tasks first. AI first replaces some of a service job’s tasks, a transition stage seen as augmentation, and then progresses to replace human labor entirely when it has the ability to take over all of a job’s tasks. The progression of AI task replacement from lower to higher intelligences results in predictable shifts over time in the relative importance of the intelligences for service employees. An important implication from our theory is that analytical skills will become less important, as AI takes over more analytical tasks, giving the “softer” intuitive and empathetic skills even more importance for service employees. Eventually, AI will be capable of performing even the intuitive and empathetic tasks, which enables innovative ways of human–machine integration for providing service but also results in a fundamental threat for human employment.

Artificial Intelligence and Business Innovation

Abstract

Artificial intelligence technology has been developing for more than 60 years. The innovation of artificial intelligence has brought tremendous impact on business. This paper uses literature analysis method and combines with practice, adopts Osterwalder Alexander’s point of view, puts forward nine paths of AI innovation promoting business innovation, that is, AI will promote the innovation of customer segments, customer relationships, value positions, channels, key resources, key activities, key partnerships, revenue streams and cost structure in the business model, and draws a conclusion that AI innovation promotes business innovation.

Emerging Technology and Business Model Innovation: The Case of Artificial Intelligence

Abstract

Artificial intelligence (AI) has been altering industries as evidenced by Airbnb, Uber and other companies that have embraced its use to implement innovative new business models. Yet we may not fully understand how this emerging and rapidly advancing technology influences business model innovation. While many companies are being made vulnerable to new competitors equipped with AI technology, this study attempts to focus on the proactive side of the use of AI technology to drive business model innovation. Describing AI technology as the catalyst of business model innovation, this study sheds light on contingent factors shaping business model innovation initiated by the emerging technology. This study first provides a brief overview of AI, current issues being tackled in developing AI and explains how it transforms business models. Our case study of two companies that innovated their business models using AI shows its potential impact. We also discuss how executives can create an innovative AI-based culture, which rephrases the process of AI-based business model innovation. Companies that successfully capitalize on AI can create disruptive innovation through their new business models and processes, enabling them to potentially transform the global competitive landscape.

Design Thinking and Design Doing: Describing a Process of People-Centred Innovation

Abstract

This chapter outlines the benefits of Design Thinking as a creative framework for innovation that can be applied to projects and organisations across government, business and the public sector. It includes a short history of the Design Thinking approach to set context and define the concept. However, much of the ensuing discussion and evaluation of ideas and methods is based on recent publications, papers and journal articles to give a current view of academic and practitioner activities. At the heart of the approach is a people-centred focus, and the chapter discusses the role of Inclusive Design and design ethnography in underpinning Design Thinking. It outlines five principles: Involve People, Translate Design Thinking into Design Doing, Create Value and Capture Value, Follow the Arc of Design Thinking and Navigate Complexity. It aims to give an overarching view of Design Thinking, demonstrating the value that it can bring to diverse areas of research and application. Challenges and observations for progressing the approach are also noted.

Design Thinking: an Innovative Educational Method in Advertising

Abstract

Design Thinking is one of the most recognised innovation methodologies today. Companies such as Apple, Amazon, Google, IBM, Uber, Deutsche Bank, Procter and Gamble or Nike turn to it to design their products, services, work processes and strategic plans. This methodology runs along a path other than verbal and linear thinking. Moreover, its purpose is not to storage knowledge. Instead, design thinking proposes a more intuitive solution creation model that seeks functionality. It is based on innovation and originality, avoiding preconceived questions that would lead to conventional answers. At the same time, it is a method that requires collaborative work and encourages the creation of multidisciplinary teams. It offers tools to the participants to manage a diversity of opinions and build a global solution enriched by their variety of perspectives. For this reason, design thinking also plays a vital role in forming and managing work teams and solving conflict situations. Regarding the broad range of professional fields in which design thinking has application, the competitive advantage it offers to the organisations and its role in facilitating teamwork, this methodology can play an essential role in the training of future professionals. Hence, experts proposed to insert design thinking techniques into university study plans every day with more frequency. This article incorporates the results of a teaching innovation project based on creating a Design Thinking Workshop as an educational tool applied in the subject of Advertising Strategy within the Communication Degree of a Spanish university centre. Among the motivations that promoted the workshop, we highlighted the applicability of this method in the advertising process; furthermore, the need to strengthen students’ competencies in the face of leadership and cooperation in teamwork. This workshop also means the opportunity to promote multidisciplinary workgroups with other specialities besides Advertising, such as Audiovisual Communication. This workshop, proposed as a pilot project, intends to evaluate the capacity of design thinking as an applied method and its feasibility of implementation within the academic program. Likewise, to measure the perception that students and teachers themselves had of the method. We launched a questionnaire to the students and held a discussion session with the teachers to measure this perception. The results showed an upbeat assessment of the method and its application to the educational field of advertising. Other relevant achievements were a greater motivation in students and their ability to work in a team. Among the main conclusions, we highlight the suitability of this methodology in teaching advertising and its ability to improve students’ skills for future incorporation into the professional field. As ways to expand the study, for future research, we highlight the interest in measuring the perception generated by the application of this method in the teaching of advertising in companies where students do internships.

Using Design Thinking to Drive Human-centred Innovation in Marketing

Abstract

The purpose of this chapter to introduce the principles of design thinking and its role in creativity and marketing as well as some of the organisations that are using it in order to spur innovation and how they are using it effectively. The chapter examines some of the results and best practices for how organisations, and their creative leaders, can use design thinking effectively. Last, the chapter provides some insight into the future trends of design thinking.

OPEN INNOVATION: A RESEARCH FRAMEWORK AND CASE STUDY OF HUAWEI

Abstract

Open innovation (OI) has received significant attention from practices and theories over the past decades. This paper investigates the role of OI and personalized patterns in firms at home and abroad, and then measures the risks involved. Firstly, this paper reviews the definition of OI, the business model innovation characteristics, and the facing problems in practice. Based on the existing literature, we illustrate the openness and challenges of business OI. By introducing bibliometrics, this paper presents a whole research framework. Based on keywords cooccurrence analysis and clustering analysis, we locate the current research hotspots and potential research opportunities from a comprehensive perspective. According to the analysis results, five clusters are obtained, including resource management and value creation; collective innovation and form sustainability; innovation management, intellectual property management, and crossborder cooperation; knowledge management and knowledge sharing; innovation ecosystem, big data, and policy-level innovation. Taking Huawei as an example, its typical business OI model is studied from the perspectives of organizational, project-related, marketing and consumer-based, and summaries the facing challenges and risks. We illustrate its financial performance, innovation performance, and development prospects. We found that, during the implementation of OI practical activities and theoretical exploration, the risks and opportunities facing small and medium-sized enterprises (SMEs) are multiple dimensional.

The Review of SMEs Open Innovation Performance

Abstract

Open innovation has provided more opportunities and broader space for Small- and Medium- sized Enterprises (SMEs for short) in innovative practices, but also brought new challenges to SMEs. Focusing on the performance of SMEs in open innovation, this paper conducts a literature commentary from several aspects including innovation performance, constraints, impact and strategy of improving open innovation performance of SMEs on the basis of systematic domestic and foreign literature review. The paper also points out the direction for future research.

Using Social Media in Open Innovation: Opportunities and Challenges

Abstract

Open innovation is relatively a new category in organizations. Organizations are encouraged to share their R&D infrastructure in open innovation approach and attach other’s R&D and innovation to their own value chain through creating technical platforms or joint ventures. Social media is one of the tools of communication in the current business world. Social media creates a platform for cooperating and encouraging people for social activities. Considering the expanded role of social media in encouraging participation and gaining external knowledge in an organization, research is missing in this relationship. Papers dealing with social media and open innovation are really limited. So, in the current research, we study the results of using social media in open innovation. As there are limited researches, it is difficult to base a research project on available studies. So we study this area by deductive approach, Delphi method. In order to do so, we collected ideas of 12 experts from fintech industry and prioritized and analyzed them with the Delphi method and finally reached to 16 opportunities and 21 challenges. From experts’ point of view, the most important opportunities are increasing the number and quality of received ideas. The most important challenges are creating new methods for receiving ideas to decrease unrelated content and information validation, and legislation. Finally we categorized the opportunities and challenges and presented them as a framework and model.

Enabling Open Innovation Process Through Interactive Methods: Scenarios And Group Decision Support Systems

Abstract

To be able to utilise opportunities in a radically changing business environment, various organisations are transforming their practices towards open innovation processes. Openness does not, however, mean any kind of looseness in innovation management but calls for coordination and facilitation. The challenge for the management in the open innovation process is to find out the appropriate methods and practices for the utilisation of external knowledge resources. One of the first attempts, this study brings two widely utilised innovation management methods — scenarios and the group decision support system (GDSS) — to the open innovation context and suggests guidelines for the management of the open innovation process in an inter-organisational context.