Hyperautomation as a vital optimization tool in organizations: cognitive approach with the use of Euler circles

Bartosz Niedzielski (International Management Department, Cracow University of Economics, Krakow, Poland)
Piotr Buła (International Management Department, Cracow University of Economics, Krakow, Poland)
Mengxi Yang (School of Economics and Management, University of Chinese Academy of Sciences, Beijing, China)

Journal of Electronic Business & Digital Economics

ISSN: 2754-4214

Article publication date: 11 January 2024

Issue publication date: 16 February 2024

1031

Abstract

Purpose

Hyperautomation is a technological concept whose popularity has been growing continuously since the German manufacturing industry “initiated” the Fourth Industrial Revolution (Industry 4.0), whereas, on the basis of theory, hyperautomation is a term still new and little recognized. This applies equally to scientific studies (articles, conference reports) and empirical studies (quantitative, qualitative). Therefore, this article attempts to fill definition gap that exists in the literature on management and quality sciences on the term hyperautomation.

Design/methodology/approach

The authors use literature review approach to identify the gaps in the existing literature on hyperautomation. They present a nominal definition of hyperautomation, discuss related issues and provide a comparative perspective between hyperautomation and automation.

Findings

The article’s findings include a precise definition of hyperautomation and the problems it raises. The authors point out that the term “hyperautomation” is still relatively new and underutilized in the management and quality sciences literature. It also compares hyperautomation to automation from several angles and emphasizes how it affects businesses, industries and other economic sectors.

Practical implications

Authors emphasize that in order to deploy hyperautomation successfully, enterprises must take a distributed and integrated approach.

Originality/value

This article addresses a gap in the management and quality sciences literature about the definition of hyperautomation. Authors give a thorough explanation of hyperautomation, along with relevant problems, useful implications and a comparison between hyperautomation versus automation.

Keywords

Citation

Niedzielski, B., Buła, P. and Yang, M. (2024), "Hyperautomation as a vital optimization tool in organizations: cognitive approach with the use of Euler circles", Journal of Electronic Business & Digital Economics, Vol. 3 No. 1, pp. 61-73. https://doi.org/10.1108/JEBDE-02-2023-0004

Publisher

:

Emerald Publishing Limited

Copyright © 2023, Bartosz Niedzielski, Piotr Buła and Mengxi Yang

License

Published in Journal of Electronic Business & Digital Economics. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode


Introduction

The development of Industry 4.0 at the dawn of the 21st century ushered in a digital transformation of production and launched new value creation processes in organizations and supply chains on a global scale. The high rate of change, which has been maintained since the beginning of this process, has been further dynamized by the COVID-19 global pandemic, which, a few years ahead of predictions, has changed the way organizations operate in various industries, sectors and branches of the economy. This thesis also seems to be supported by the global research carried out in mid-2020 by McKinsey, a consultancy firm, among nearly 900 senior directors and managers. In their view, as the measurements showed, organizations have accelerated digitalization in the areas of cooperation with customers, supply chain and internal processes, by three to four years. And the mere share of digital products in their portfolio has accelerated by as much as seven years (LaBerge et al., 2020). These changes, however, would not have been possible without—above all—a more sophisticated structure of automation, which on the basis of both practice and theory was assumed to be called hyperautomation (less often: intelligent automation). As a technological concept, it allows the use of advanced and innovative tools, whether in the form of artificial intelligence, machine learning or cloud computing, etc., to detect, analyze, measure, construct, monitor, anticipate and improve complex operational and business processes in the organization. As a result, its capabilities and readiness to implement and adapt state-of-the-art technological solutions are increasing, leading to an enhancement in its digital maturity, which today is considered a key determinant of success for organizations starting their digital transformation. Nevertheless, hyperautomation is a new trend and is not fully identified—especially on a theoretical basis—which determines the need for its conceptualization and, in a further step, also its operationalization.

Purpose of the article

Given the rationale referred to in the introduction, the publication’s primary objective is to fill the cognitive gap that exists in the literature of economic sciences, including management and quality sciences, in the context of the concept of hyperautomation. These are deficiencies that are definitive, exploratory and comparative in nature, the attempt to make up which is always crucial for maintaining continuity and progress in learning. Therefore, the first and cardinal task set by the authors of this publication is to try to present a nominal definition of hyperautomation. This approach will, from the point of view of conceptualizing the subject, allow observation of the further development of hyperautomation in organizations and its proper interpretation. From a synthetic perspective, this is also intended to continuously search for and build tools to measure hyperautomation (i.e. its level or scale) in the real world.

Methodology

The research methodology adopted in this work is qualitative in nature and consists of three stages. In stage 1—from the typology of literature reviews according to the SALSA criteria (SALSA—Search, AppraisaL, Synthesis and Analysis) (Booth, Sutton, & Papaioannou, 2012)—a research method in the form of literature review was selected to analyze the results related to the concept of hyperautomation. However, due to its polymorphic nature, it has been limited to two forms: state-of-art review and integrative review (Creswell, 2014). The first focuses on presenting the current state of knowledge on the definition of hyperautomation, and the second on how the issue of hyperautomation is interpreted and perceived by other authors, researchers and experts. The subject of stage 2 is the presentation of a nominal definition of hyperautomation, the conceptualization of which takes place using direct inference and selected elements of syllogistics, in the form of Euler diagrams (Stapleton, Rodgers, Howse, & Taylor, 2007). Their use allows the identification of not all (as is the case with Venn diagrams, see Gunstone & White, 1986), but only of those important relationships between automation, hyperautomation and (hyper)connectivity. Stage 3—last—is a compilation of the concepts of automation and hyperautomation and an attempt to evaluate them using comparative analysis.

Research/results

At the outset, it should be noted that the term hyperautomation has in the literature of the subject terms which are equivalent to it in the form of intelligent automation or, more succinctly, intelligent process automation (Nunes, Leite, & Pedrosa, 2020; Bornet, Barkin, & Wirtz, 2021). This means that, on the basis of scientific publications, the concept of hyperautomation can be interchangeable with terms intelligent automation or intelligent process automation, provided that the appropriate context is retained. However, hyperautomation is a more complete concept than the two previously mentioned, and its complexity better reflects the essence of the phenomenon of “hyperautomation” or “superautomation.” A literature review conducted by the authors on the number of scientific publications on hyperautomation in general, in the field of management and quality sciences (Buła & Niedzielski, 2021), revealed a significant conceptual, cognitive and research gap in this field. The same is true of the explanation of the meaning of the concept of hyperautomation, which, for the time being, has seen much more perspectives than on the basis of science. Meanwhile, in science, the conceptualization of concepts is an important part of the research process and a starting point for operationalization activities, which in the final stage are intended to lead to the construction of measurement tools intended for quantifying a given phenomenon or trend (Rao & Reddy, 2013). In order to fill this gap, Table 1 presents equivalent definitions (containing: definiendum, hyphen and definiens) of hyperautomation, which reflect the current state of understanding of this concept in scientific literature and provide the starting point for presenting the author’s definition of nominal hyperautomation.

In the context of the authors’ perception of hyperautomation, the analysis presented above leads to conclusions, which can be divided into two groups. The first are conclusions relating to similarities and the second to the differences within the definiens of hyperautomation terms presented in Table 1. To begin with, by focusing on the compatibility between the definiens referred to above, several issues should be noted. First, the authors point out that hyperautomation is inextricably linked to the concept of Industry 4.0, which in practice was initiated with the arrival of the First Industrial Revolution and automation. In a scientific article from March 1959 entitled “Filozofia Automacji,” its author M. Garnysz wrote that as early as 1954, the then President of the American Congress of Industrial Organizations (CIO), founded in The United States (1935), Mr. Reuther states that “the revolution of automation is by no means a matter of the future, but has already happened” (Garnysz, 1959). Second, almost everyone in their definitions, at the center of this phenomenon puts the organization as the main beneficiary of intelligent automation. However, the organization itself—seemingly per se—should be understood much more broadly, i.e. through the prism of its processes, working people or the implementation of strategic objectives (including operational or tactical), to which it has been committed by its stakeholders. Third, it is worth noting that some definiens place clear emphasis on aspects related to connectivity or even hyperconnectivity (for more information on hyperconnectivity see: Carré & Vidal, 2018). This shows that hyperautomation has a direct connection with the transmission of various content, signals or images, mainly in the machine-to-machine system, but also human–machine or human–machine–organization. With this in mind, one can therefore argue that there is no hyperautomation without hyperconnectivity. It is difficult to imagine that hyperautomation could function or develop in isolation from information and communication technology (ICT) systems and applications. Fourth, an important element of all these concepts is that their creators list a number of innovative digital methods and technologies that build hyperautomation, including in particular: robotic process automation (RPA), machine learning (ML), artificial intelligence (AI) or link control protocol (LCP). Nevertheless, in the face of similarities between the hyperautomation terms, there are also some differences (see Table 2). The first is that hyperautomation is variously defined by the authors themselves, in the sense that some consider it a trend or phenomenon, others as a concept or action, and others as a form of strategy or conceptual term. However, even from an etymological point of view, words such as trend or strategy seem distant to each other or at least different. A “trend” refers to the regularity existing within a time framework which is subject to development, while a “strategy” is nothing more than clearly defined rules for the preparation and conduct of targeted activities. The second difference concerns the degree of blurring or imprecision in defining hyperautomation. As an exemplification, it is worth noting, the differences between definiens 1 and 6 (Tables 1 and 2). The third, last, difference is that, given the definiens presented in Table 1, hyperautomation definitions can be divided into both scoped and contextual ones. And, in terms of their number, the latter seem to be towering over the former. Meanwhile, Table 2 presents, in graphic form, the comparative equality definitions of hyperautomation.

In the context of the above, having knowledge of contemporary perspectives on hyperautomation, we can now proceed to try to name this term, creating its nominal definition, the design of which will be based on Euler’s diagrams. To begin with, however, it should be emphasized that the nominal definition of hyperautomation presented in this article will result from the analysis and interpretation of the materials which, in tabular form, have been presented in Tables 1 and 2 and from previous literature reviews (Buła & Niedzielski, 2021). Moreover, its content will take into account both linguistic and logical correctness, which in science is decisive for formulating final conclusions from undertaken research. Moreover, perhaps most importantly, the nominal definition of hyperautomation presented must answer the first and simultaneously last question, one cardinal in nature, namely: what does it mean and what is contained within hyperautomation? When answering, three points should be highlighted at the outset. First, it should be made clear that the development of technologies, including so-called modern technologies, is crucial for preserving progress. In other words, there is no progress without technology, just as there is no technology, without scientific knowledge and discovery. They, at the threshold of the First Industrial Revolution, gave rise not only to new solutions, techniques or patents but also to the revolution related to automation. It is automation as a technology, which has started—and continues today—the process of minimizing human contribution to manufacturing or production activities. One of its “products” is technology in the form of RPA, which technicizes work in the field of repeatable business processes, transferring the burden of its execution entirely on robots that simulate human labor. This statement in graphic terms is presented in Figure 1(a) and proves that robotic automation of processes on the basis of subordination relations is a solution that is part of the widely understood automation, which began with the development of the first technologies in the form of: energization, mechanization, automation or—going further—computerization. Second, as is presented in Figure 1(b) graphically, in addition to RPA technology, we have others, even in the form of AI, which complement each other. In practice, their technological coexistence enables the automation of business processes, thus improving their operational efficiency. However, what should be emphasized here is the lack of connectivity or common predefined rules for processes carried out in the tandem of RPA and AI means that separated tools remain in the circle of automation but cannot be directly equated with the concept of hyperautomation. This means that the mere fact that an organization possesses solutions in the form of RPA or AI (or similar) does not yet indicate hyperautomation but only automation (Figure 1(b)). The issue is that the sine qua non condition of the organization’s implementation of the concept of hyperautomation is that two or more modern digital technologies are interconnected, have common elements and there will be interaction between them (see: Figure 1(a)). And third, taking into account the outline by the combined tools within the concept of Euler diagrams of a common core (Figure 2(b)), it should be noted that it is the epicenter of certain elements of all (in the sense of those mentioned here) digital technologies emerging from hyperautomation. In other words, the sum of the partial coincidental actions that has been created by different types of digital technologies belonging to the common circle of automation defines graphically (see: Figure 2(b)) the essence of hyperautomation. This means that hyperautomation as a concept starts with technology, which is entirely subordinated to the issue of automation (Figure 1(a)), which, on the basis of equivalence relations, coincides with the concepts of RPA, AI, low-code development platform (LCPD), virtual assistant (VA), intelligent business process management system (IBPMS), optical character recognition (OCR), and those, as a result of partial coincidence (intersection) and entry of each of them into a different range, form a sum (common field), which we define as intelligent automation (hyperautomation) (Figure 2(b)).

Given the above, we can now progress to presenting a nominal definition of hyperautomation. As it will be design-regulatory in nature, the words contained in it will be given a new meaning and the definition itself will be precise. Thus, when offering the meaning of the term “hyperautomation,” it should be said that: “hyperautomation is a technological concept connected to Industry 4.0, relating to activities undertaken in the organization for the development of automation and the improvement of business and technological processes, implemented with the help of a minimum of two integrated digital technologies on the principle of partial coincidence, enabling a construction of the push-button organization” (push-button organizations are organizations that would be started by pressing a button, after which they would perform by themselves all the actions, tasks, processes, etc.). At the same time, it should be added that, taking into account the systemic criterion, hyperautomation can be divided into distributed and integrated. As for the first one, we will consider as “distributed hyperautomation in the organization, a system of loosely coupled digital technologies, such as: RPA, AI, LCPD, VA, IBPMS, OCR, etc., which are connected to each other on the principle of partial coincidence” (Figure 3). On the other hand, we will consider as integrated hyperautomation in the organization, a system of densely coupled digital technologies, such as: RPA, AI, LCPD, VA, IBPMS, OCR, etc., which are interconnected on the principle of partial coincidence (Figure 4). In both cases, the interconnected digital technology systems are organized in a component-based manner and can support all or only selected areas of the organization’s management.

Against the background of the nominal definition of hyperautomation presented above, a question arises as to the existence of differences between it and the concept of automation. Especially since, initially, the two terms sound almost identical from the point of view of articulation phonetics, and what distinguishes them is only the prefix “hyper.” Nevertheless, the distinctness between hyperautomation and automation is much greater and varied and centers on their different attributes or areas. Seven key differences are defined. First, it should be noted that hyperautomation is a new technological trend, inextricably linked to Industry 4.0, while hyperautomation is a concept that, in the context of replacing human muscle work with the energy supplied by machines (Malewski, 1958), has evolved since the birth of the First Industrial Revolution, that is, from the second half of the 18th century. Second, from an etymological point of view, the first part of the complex expression “hyperautomation” shows that, unlike automation, it is characterized by an excess of automation, i.e. the occurrence of its much greater intensity in the organization. In practice, simple automation of tasks and processes in the enterprise, transforms towards high-tech tools (such as: AI, ML, NLP, etc.), which, networked using the Internet, form an intelligent organization. Thirdly, in view of what has been said above, the development of hyperautomation would not have been possible without a network, namely the internet. The heyday of its fifth generation (5G)—and in the near future also the sixth (6G)—makes wireless communication technology able to more precisely create virtual reality or smart cities and organizations. Fourth, hyperautomation—as opposed to automation—can seriously support decision-making processes in the organization, especially those undertaken in conditions of uncertainty, complex problems or risk (Phillips-Wren, 2012; Trunk, Birkel, & Hartmann, 2020). Fifth, from the point of view of freedom and scope of action, all—even the most complex—organizational and business processes can be subjected to hyperautomation, while only those whose data are structured can be automated. However, accuracy, completeness, consistency, reliability and timeliness of data will always be crucial for the organization and its processes, as low-quality data lead to inaccurate analyses, followed by erroneous operational and strategic decisions. Sixth, although technologies connected to hyperautomation contribute to, e.g., reducing the cost of the organization’s fixed costs, enhancing and raising the quality of processes, reducing anomalies and risks or increasing productivity, in practice their development is highly capital intensive (even several times more when compared to automation). A significant part of the costs are mainly generated by: number of functions, manpower resources (man-hours), developer salaries, IT infrastructure, software, administration and organization of work, projects, tests, implementations and servicing related to updating and maintenance of technology. And seventh, the last material effect of the development of hyperautomation in the organization is to build a smart enterprise, of the push-button type. The comparative view of hyperautomation and automation presented above (which is presented in the graphical version in Table 3, below) does not exhaust the wide range of differences but also the similarities between the two concepts. On the contrary, it is intended to encourage other researchers to engage in further, in-depth analyses of their substance, which—according to the authors—will allow researchers to better understand the possibilities, potential and direction of development, especially the idea of hyperautomation in the world of enterprises.

Discussion/conclusions

This article, devoted entirely to the issue of intelligent self-automation, presents a nominal definition of hyperautomation, which was developed mainly through a research methodology based on Euler diagrams (Figures 1(a, b) and 2(a)). As a result of the defining process, the definiens was specified for the hyperautomation expression, which takes into account the unification rule and the principle of partial coincidence, so important for Euler’s inference scheme (Figure 2(b)). The research instrument used in the form of Euler diagrams, literature review or integrative review—including a comparative view of the equal definition of hyperautomation (Table 1)—has created a much broader research perspective that has contributed to the development of definiens, also for the terms “distributed hyperautomation” (Figure 3) and “integrated hyperautomation” (Figure 4). Consequently, this contributed to a more precise definition of the basic term, which was the subject of this article, namely “hyperautomation.” However, given the consideration presented in this work, both theoretical and practical terms, of various types of: properties, characteristics, equivalences, partial adjustments, theories or also facts about hyperautomation (but also automation) that have been examined, we can undertake to formulate several original and reasonable conclusions and diagnoses of an explication and/or discriminatory and evaluatory nature. First, it should be noted that the term “hyperautomation,” as evidenced by literature studies conducted by Buła and Niedzielski (2021), is a phrase still new and little recognized, especially on the basis of theory. According to Google Trends analysis, the popularity of the term hyperautomation in the Internet began to grow from July 1, 2019 (for more information see: https://trends.google.com). Second, hyperautomation is a technological concept created as a result of the development of Industry 4.0 and represents a hyperdimensional level of automation, which started with the development of Industry 1.0. Third, the nominal definition of design and regulatory hyperautomation presented in this work attempts to fill the conceptual gap in the literature on management and quality sciences. Fourth, conceptualizing the term hyperautomation, but also, taking into account the systemic criterion, distributed and integrated hyperautomation, is an important starting point for operationalization activities, which in the final stages are intended to lead to the construction of measurement tools for quantitative hyperautomation in relation to the sciences of organization and management. Fifth, in the absence of scientific studies on the concept of hyperautomation, scientific value and usability will be reduced not only of itself but also indirectly of the management and quality sciences. Sixth, the comparative approach between automation and hyperautomation shows that, despite the similarity resulting from the articulation phonetics between these concepts, they are concepts that show differences in many fields. Seventh, it should be made clear that the main determinants of the development of hyperautomation are the internet and the fifth and subsequent generations of mobile networks. But it should also be noted that at the same time these technologies set the limits for its possible progress. Eighth, the final result of the development of hyperautomation in the organization is to build an intelligent enterprise, of the push-button type. And lastly, it should be assumed that hyperautomation will become the most important source of competitive advantage of the organization in the first half of the 21st century.

Figures

Definition of automation—graphic approach using Euler diagrams (a) example I (b) example II

Figure 1

Definition of automation—graphic approach using Euler diagrams (a) example I (b) example II

Definition of hyperautomation—graphic approach using Euler diagrams (a) example I (b) example II

Figure 2

Definition of hyperautomation—graphic approach using Euler diagrams (a) example I (b) example II

Distributed hyperautomation—graphic approach using Euler diagrams

Figure 3

Distributed hyperautomation—graphic approach using Euler diagrams

Integrated hyperautomation—graphic approach using Euler diagrams

Figure 4

Integrated hyperautomation—graphic approach using Euler diagrams

Hyperautomation - selected equality definitions

DefiniendumHyphenDefiniensAuthor/source
Intelligent Automation (Hyperautomation)
(1)
this isone of the most modern trends in the field of widely understood artificial intelligence. In practice, it is a novel combination of methods and technologies including: humans, organizations, machine learning (ML), low-code platforms (LCP), robotic process automation (RPA), and other tools, techniques, solutionsetcBornet et al. (2021)
Hyperautomation (2)these areholistic activities relating to automation throughout the organization, which is closely linked to Industry 4.0 and the digital twin concept (DT)Jacoby and Usländer (2020)
Hyper-automation (3)this isa strategic initiative of the organization consisting of the continuous development of automation and improvement of business results, using a combination of technologies, the foundation of which is robotic process automationJOLT Advantage Group (2021)
Hyperautomation (4)this isa technological term that involves the automation of knowledge-based work with a wide range of business and technology, integrated into a responsive workforce, mainly linking RPA with ML and/or other AI functionsLasso-Rodriguez and Winkler (2020)
Hyper automation (5)this isa concept that, in the era of the Fourth Industrial Revolution, expands the range of tasks that can be subjected to automation and includes not only highly repetitive low-skilled work, but also highly routine, medium-skilled work. In practice, hyperautomation and hyperconnectivity enable the construction of intelligent enterprises supported by key technologiesPark (2018)
Hyperautomation (6)this isa phenomenon characteristic of the Fourth Industrial Revolution and closely related to hyperconnectivity, which contributes to the rapid development of innovation and the transformation of the competitive environment of the organizationChih-Yia and Bou-Wenb (2021)

Source(s): Own study

Hyperautomation equality definitions—comparative approach


DefiniensDefiniens 1Definiens 2Definiens 3Definiens 4Definiens 5Definiens 6
Attribute
Refers to the concept of Industry 4.0 noyesnonoyesyes
Refers to the phenomenon of automation noyesyesnoyesno
Refers to digital tools, e.g. RPA, ML, AI, OCR etc yesnoyesyesnono
Uses the word connectivity, connection, hyperconnectivity yesnonoyesyesyes
Presents convergent expressions (synonyms) to hyperautomation yesnonononono
Refers to the organization yesyesyesnoyesyes
Refers to people yesnonoyesnono
Refers to machines nononononono
Refers to processes nonoyesnonono

Source(s): Own study

Automation vs. hyperautomation—a comparative perspective

AutomationHyperautomation
Etymologyfrom English: automationfrom English: hyperautomation or hyper-automation
Place of originUnited States of AmericaUnited States of America
Time of inception2nd half of 18th century1st half of 21st century
Concept typeOldNew
Industry conceptIndustry 1.0Industry 4.0
Development thanks toMechanizationInternet
Trend typeEvolutionaryRevolutionary
TechnologyRobotic automation of tasks and processesEcosystem of high-tech tools and links (sensors, etc.)
Type of technologySimpleComplex and highly complex
ToolsRPAAI, ML, NLP, OCR, chatbots
Results of the actionEfficient operations and processesEfficient and intelligent operations, processes, systems
Automation rangeSimple tasks and processesEverything that is possible (no formal restrictions)
AreaCarried out mostly using a single platformMost frequently carried out using an ecosystem of platforms, systems, technologies and tools
ConstraintsStructured dataNo restrictions
ManagementDoes not participate in the decision-making processSupports decision-making
EffectsAutomationHyperautomation, megaautomation
Characteristic ofAutomated organizationsDigital organizations and push-button organizations
CostsSmall/mediumHigh
Human resourcesBypasses/eliminatesEngages
BenefitsFast RETURN on investment and increased productivityIntegration of digital technologies and the ability of employees to focus - thanks to more time - on tasks with greater added value for the organization

Source(s): Own study

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Corresponding author

Mengxi Yang is the corresponding author and can be contacted at: yangmengxi@ucas.ac.cn

About the authors

Bartosz Niedzielski - Assistant Professor at the Krakow University of Economics. He studied and conducted scientific activities, among others, at: the University of Greenwich, University of Oxford, London School of Economics, Deggendorf Institute of Technology and Jagiellonian University. He has over 10 years of professional experience gained in global corporations in BPO/SCC sector (mainly at HSBC). His research interests focus on aspects related to the application of artificial intelligence in organizations, management, and internal audit as well. He is the author or co-author of more than 50 scientific publications from economics and management.

Piotr Buła - Associate Professor, Head of International Management Department at Krakow University of Economics (KUE), Vice-Rector for Projects and Cooperation KUE, Senior Research Professor in Business Management Department at University of Johannesburg, Grand Valley State University scholarship holder. His research interests focus on aspects related to the application of artificial intelligence and neuro-management, risk management, internal audit, international management and logistics. A member of international organizations associating professors and business practitioners in the management area: EFMD, CEEMAN, IMDA, GBATA, EIBA, GSSI, IAMB, EECSA, SAIMS. He is the author or co-author of more than 170 publications from economics and management.

Mengxi Yang - Associate Professor of the School of Economics and Management at the University of Chinese Academy of Sciences, China. He is also an Associate (Visiting) in Organizational Behavior at Judge Business School, University of Cambridge. His research interests include artificial intelligence and organization management, creativity and innovation. His work has been published in various academic journals.

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