Shengxing Yang

A systematic literature review on the disruptions of artificial intelligence within the business world: in terms of the evolution of competences

Keywords: Artificial Intelligence, Competences, Firm, Individual, Systematic literature review

The advancement of artificial intelligence has brought both opportunities and challenges to the business world, and its potentially disruptive impact has attracted the research interest of management scholars. This exploratory research applied a systematic literature review approach to explore the nexus between AI and competences to help both firms and individuals better address the disruptions from AI. After reviewing relevant publications from the Business Source Complete database for the past decade (2011-2021), we selected 65 articl debates and issues on AI and perspectives linked with competences. Furthermore, we synthesize two frameworks (RBV framework for firm-level; Key and STEM competences for individual-level) and an overview to gain a holistic understanding of the nexus between AI and competences. We found relatively little empirical evidence in the literature, the implementation of AI was still in its preliminary stages, and the frameworks we aggregated industry and yield richer insights.

1.Introduction

Artificial Intelligence(AI) is indeed a buzzword today, regarded as an umbrella term; it consists of a range of fancy technologies and even be hyped with magic power [START_REF] Helo | Artificial intelligence in operations management and supply chain management: An exploratory case study[END_REF]. It inevitably arouses a broad concern about its potential diverse disruptive impacts on jobs, businesses, and societies. For example, [START_REF] Luo | Frontiers: Machines vs. Humans: The Impact of Artificial Intelligence Chatbot Disclosure on Customer Purchases[END_REF] reported that AI was as effective as proficient workers and even four times more effective than newcomers. And, it enables machines to handle an increasing number of cognitive tasks which used to be exclusively performed by humans [START_REF] Brynjolfsson | How Will Machine Learning Transform the Labor Market?[END_REF].

According to World Economic Forum(2021), most AI systems are only excellent at particular takes a few decades to wait for the emergence of high-level. Nevertheless, AI has received a lot of interest from venture capitalists; shown in Figure 1, the investment in AI has surged since 2013, crossing diverse sectors and industries, especially in mobility and autonomous vehicles, suggesting the great potential and value embedded in AI. However, organizations are still struggling to gain a coherent understanding of AI and take advantage of it in their daily operations [START_REF] Enholm | Artificial Intelligence and Business Value: A Literature Review[END_REF]. Recalling from the highlight of Tambe et al. (2019), AI is coming to be the next General Purpose Technology(GPT), which will require organizations to inject complementary firm-specific investment(e.g., cost of related training, cost of the redesign process and organization structure). Besides, in terms of individuals, AI will continually reshape their working environment, design of occupations, and play an essential role in machine-human collaboration (OECD, 2021). According to one report from [START_REF] Mckinsey | Artificial intelligence as an enabler of B2B marketing: A dynamic capabilities micro-foundations approach[END_REF], in general, there will be more need for the technological, social and emotional, higher cognitive skills in the future, whereas less in terms of physical and manual, basic cognitive skills. Moreover, as World Economic Forum(2018) suggested, along with the acceleration of workforce transition, AI could also link with the issue of skills gaps, inequality, and polarization.

Meanwhile, a significant amount of research has been conducted to understand and demystify AI. Some are trying to figure out the possible impacts on specific occupations: Audit and Accounting [START_REF] Moll | The role of internet-related technologies in shaping the work of accountants: New directions for accounting research[END_REF]; Some are Systematic Literature Reviews(SLR), investigating diverse functions and domains, which include Customer Relationship Management [START_REF] Saura | Setting B2B digital marketing in artificial intelligence-based CRMs: A review and directions for future research[END_REF]; Supply Chain Management [START_REF] Pournader | Artificial intelligence applications in supply chain management[END_REF]Toorajipour et al., 2021); Marketing(Marian ; Purchasing [START_REF] Allal-Chérif | Intelligent purchasing: How artificial intelligence can redefine the purchasing function[END_REF]; Manufacturing [START_REF] Sharma | The interpretive model of manufacturing: A theoretical framework and research agenda for machine learning in manufacturing[END_REF]; Public employment [START_REF] Reis | Influence of artificial intelligence on public employment and its impact on politics: A systematic literature review[END_REF]; Public governance [START_REF] Zuiderwijk | Implications of the use of artificial intelligence in public governance: A systematic literature review and a research agenda[END_REF]; Agriculture [START_REF] Spanaki | Disruptive technologies in agricultural operations: A systematic review of AI-driven AgriTech research[END_REF]. And to our knowledge, there is no related research specifically focused on the nexus between AI and competences, which consists of at least two perspectives: individual and firm. To fill this gap, we conduct this research to answer the main Research Question(RQ):

To what extent does Artificial Intelligence impact profiles of competences for the future?

We have structured this research work into six sections: the first is about the introduction, the subsequent section is about the background of this research. Then the third section is our research methodology. Following is data analysis, we then presented our insights, limitations, and future research recommendations in the discussion. We made our conclusions in the last section. What's more, we attached references and appendixes for more detailed information.

2.Background

Artificial intelligence : typology and definitions

Artificial Intelligence(AI) is not a new conception; it was first espoused in the 1950s along with better computational and storage capabilities whereas less cost (Baum & Haveman, 2020). It is easier to get confused about AI than to understand it. Following, we are trying to make a clear version of AI with: the typology of AI, definitions, and our proposed definition for AI.

Typology of AI

The most effective way to understand AI may be derived from the research of [START_REF] Benbya | Special Issue Editorial: Artificial Intelligence in Organizations: Implications for Information Systems Research[END_REF], which lists AI with three criteria: intelligence, technology, and function. They also highlighted that these technologies may overlap and can hardly be compartmentalized. Following is Table 1 to display the main conceptions and applications motioned.

Algorithmic

The key is about using machine learning algorithms, whether based on labeled or unlabeled training data.

Robotic

Physical robots perform dedicated tasks, now becoming more and more intelligent to solve complex tasks with the help of ML and NLP, such as robotic-assisted surgeries.

Table 1 : Typology of AI

Source: [START_REF] Benbya | Special Issue Editorial: Artificial Intelligence in Organizations: Implications for Information Systems Research[END_REF]

Definitions of AI

The original definition of AI can be traced back to 1950, the famous Turing test: whether a machine could mimic human cognitive capabilities [START_REF] Akter | Transforming business using digital innovations: The application of AI, blockchain, cloud and data analytics[END_REF]. However, there is still no standard accepted definition for AI [START_REF] Mikalef | Artificial intelligence capability: Conceptualization, measurement calibration, and empirical study on its impact on organizational creativity and firm performance[END_REF]. We are trying to review several different definitions of AI in the literature; we summarized their applied context and listed them in Table 2.

Context Definition

Supply Chain Management

« AI is a field in computer science encompassing the development of systems capable of performing tasks that normally necessitate human intelligence » (Pournader et al., 2021, p.2).

Organizational

« AI is the ability of a system to identify, interpret, make inferences, and learn from data to achieve predetermined organizational and societal goals » (Mikalef & Gupta, 2021, p.3).

Management

« Artificial Intelligence (AI) represents a highly capable and complex technology that aims to simulate human intelligence » (Glikson & Woolley, 2020, p.3).

IS research

« We define AI as the frontier of computational advancements that references human intelligence in addressing ever more complex decision-making problems » [START_REF] Berente | Managing Artificial Intelligence[END_REF](Berente et al., , p.1435)).

Organizational

« Defined as technologies that leverage machine-based intelligence and advanced conventional technologies by approximating human cognitive functions to search, analyze, and make decisions based on large-scale data » (Li et al., 2021(Li et al., , p.1605)).

Management and economic

« A set of technologies that can imitate intelligent human behavior » (Clifton et al., 2020, p.5).

Table 2 : Definitions of AI in the literature

As we can perceive in Table 2, the descriptions of AI could have different focuses based on Based on the collection of definitions between 1955 and 2019, through diverse domains, [START_REF] Samoili | AI watch: Defining intelligence[END_REF] summarized four common mentioned characteristics of AI:

Perception of the environment and real-world complexity, Information processing: collecting and interpreting inputs, Decision making, including reasoning, learning, and taking actions Achievement of pre-defined goals.

Considering the organizational and individual context we are investigating, we proposed our initial definition of AI as:

automatedly process inputs(data) to support or make for decision-making, to achieve the pre-defined organizational and More specifically, we argue that AI will help to automate the programmed decision and augmenting(supporting) non-programmed decision, and to transfer the non-programmed decision into programmed decision iteratively. It is inspired by decisionmaking theory, in which programmed decisions are described as already established guidelines and formal patterns, whereas non-programmed decisions are ill-structured and one-time decisions [START_REF] Simon | Administrative behavior: A study of decision-making processes in administrative organizations[END_REF].

Competence : firm and individual level

Competence(also be mentioned as competency, could be used interchangeably with capability in literature, we use competence in this article) is neither a new concept, and there are confusions and debates about its definition and theory. As Le [START_REF] Deist | What Is Competence? Human Resource Development International[END_REF] suggested, it is impossible to generate a coherent theory and definition considering its widely different used contexts. Generally speaking, it is commonly used in two levels: firm and individual.

Firm level : view of routines

Highlighting as organizational routines and processes, collective knowledge, the concept of competence is a hit in the literature of management science in the 1990s(Le [START_REF] Deist | What Is Competence? Human Resource Development International[END_REF], gaining mainly concern as the conception of core competences as « Are the collective learning in the organization, especially how to coordinate diverse production skills and integrate multiple streams of technologies » (Prahalad & Hamel, 1990, p. 4).

Especially as we live in the continuing digitalization world, it emerges with the implication of a new routine and process (Volberda et al., 2021). The competition landscape in the business world could change rapidly; it could be vital for firms to prepare for the coming disruption in terms of competence evolution. Also, it sounds like there is more concern about gaining a balanced view(external and internal, trade-off) when facing new challenges [START_REF] Asatiani | Sociotechnical Envelopment of Artificial Intelligence: An Approach to Organizational Deployment of Inscrutable Artificial Intelligence Systems[END_REF].

Individual level : key competences and STEM competences

At the individual level, competences are widely used in education sectors and human resource development as a competence-based approach to highlight the important individual skills, knowledge, and attitudes needed(Le [START_REF] Deist | What Is Competence? Human Resource Development International[END_REF]. Also, competences are mentioned as « more than just knowledge and skills; it involves the ability to meet complex demands, by drawing on and mobilizing psychosocial resources(including skills and attitudes) in a particular context » (DeSeCo, 2005, p.4). Specifically, the investigation into key competences highlights in the research from DeSeCo2 (2005): these competences are not just important in the labor market but also in private relationships, political engagement, these transversal competences are defined as key . They considered virtuous development both for individual and society(economy). [START_REF] Deseco | The definition and selection of key competencies: Executive summary[END_REF] summarized key competences3 into three categories:

1) Use tools interactively (e.g., language, technology)

Using and understanding the languages, symbols, text Knowledge and information management Familiarity with new technologies 2) Interact in heterogeneous groups

Relationship management Teamworking skills Conflicts management 3) Act autonomously

Act within the big picture (Strategic and system thinking) Project management Self-defend and mental maturity Besides, as for a complementary view from Johannessen (2021), the STEM(science, technology, engineering, and mathematics) competences required by robotics and automation in some industries should also make a difference in the current and future situations, in terms of facing the challenges arouse by AI.

Nexus between AI and competences

To our knowledge, there currently is no research trying to draw the nexus between AI and competences from both firm and individual levels, and that's why we are trying to fill this gap by conducting this SLR to investigate the relationships as well as answering our research questions. As [START_REF] Cohen | Absorptive Capacity: A New Perspective on Learning and Innovation[END_REF] suggested from their absorptive capacity theory: the late to invest in new technologies may lead to failure of implementation in the future. It could be necessary for both firms and individuals to notice the need to evolute their competences to prepare for the future, especially facing the challenge aroused by current advancement of AI.

3.Methodology

Following the steps from [START_REF] Xiao | Guidance on Conducting a Systematic Literature Review[END_REF], a "guidebook" of conducting a sound systematic literature review(SLR), this section consists of three components to justify rigorous and scientific of our research, namely: Planning, Conducting and, Reporting.

Planning of review

Research Questions

In order to investigate the nexus between AI and competences, the first step of this research is to formulate the research questions, and it always involves an evolving loop with the protocol [START_REF] Xiao | Guidance on Conducting a Systematic Literature Review[END_REF]. The following is our main research question and subquestions:

The main research question: To what extent does Artificial Intelligence impact profiles of

Sub-questions:

What are the current impacts of AI within the business world, i.e., opportunities, challenges, and risks? What has been investigated by researchers regarding the nexus between AI and competences? How could firms and individuals cope with the evolution of their competences called from AI? What heuristic research directions could this evolution bring to management scholars?

Review Protocol

In order to make the whole review process more rigorous, we first formulated our review protocol, more detail in Appendix B. In general, it consists of seven parts: The purpose of the study; Research questions; Inclusion criteria; Research strategies; Quality assessment criteria and Screening procedures; Strategies for data extraction; Synthesis and reporting, which also stems from the recommendations of [START_REF] Xiao | Guidance on Conducting a Systematic Literature Review[END_REF].

As we are trying to figure out the impact of AI within the business world, we use the most famous database: EBSCO "Business Source Complete". Besides, to guarantee the quality of the article feed in this research, we borrowed the approach from [START_REF] Pereira | A systematic literature review on the impact of artificial intelligence on workplace outcomes: A multi-process perspective[END_REF]. We only considered the peer-reviewed articles and ranked them as 3, 4, or 4* in AJG 4 (Academic Journal Guide).

Conducting of review

The review process lasted for around one month; we searched the articles in the database of Business Source Complete with nine combinations of keywords index in the abstract(AB). It generally indicates three concerns we were looking for:

1) The direct link with competences and AI, thus we investigated with competence, competency, capability, which also link with individuals;

2) What are the perspectives from management and firms, thus we investigated with management, firm, company, organization, enterprise;

3) Supplement from current SLRs, thus we investigated with other systematic literature reviews concerning AI. Figure 2 summarizes the whole review process. At the first stage, we identified 1824 records, and then we applied the inclusion criteria by screening the title, abstract, and published journal to conduct the further full-text assessment. Around three hundred articles were left for the next stage for eligibility. After thoroughly reviewing the 303 articles, we excluded the papers duplicating, not highly related, not available, etc. We filtered out our sample of articles in the number of 137, we also marked the rank of the paper we chose5 (N=137) from to 5(0 indicates less important, 5 indicates very important), which was based on our judgment.

Then, we applied the second round of review to ensure the quality of the overall articles and the relativeness with our topics, as in the first round we have limited versions of what precisely the articles are talking about. Finally, we included 65 articles in our study 6 .

Reporting of review

Based on the review processing, the reporting of review are the following sectors: data analysis, discussion, and our conclusions. Precisely, the data analysis consists of three parts:

1) General description of our sample(Number, domains, etc);

2) The impact of AI highlighted literature(debates and issues related);

3) AI-related competences.

4.Data Analysis 4.1 General description

First is the general description of the data sample; we have 65 articles. Figure 3 shows that out of 65 are published in 2021, consisting of about 54% of the total sample(excluded three will be published in 2022).

Figure 3 : Years of publications

After we upload the article information into Scopus, based on its categories, as Figure 4 indicates, 48,6% of them are filled into the Business, Management, and Accounting. Following are Social Sciences(14,3%) and Computer Science(13,3%).

The impact of AI: related debates and issues

Through the literature, researchers claim different views about the impacts and potential challenges of AI. There are three primary AI-related debates:

Hype-Fear(Willcocks, 2020); Augmentation-Automation(Replacement) [START_REF] Raisch | Artificial Intelligence and Management: The Automation Augmentation Paradox[END_REF]Tschang & Almirall, 2021); Occupation-Process [START_REF] Fleming | Robots and Organization Studies: Why Robots Might Not Want to Steal Your Job[END_REF][START_REF] Huang | Artificial Intelligence in Service[END_REF].

The Hype-Fear debate mainly concerns two opposite effects implementation: one is about to gain massive benefits and fast adoption, whereas another fears job loss and being replaced by machines, which could lead to aversion and side effects. The second debate is quite similar to the first one; it argues whether AI will augment humans or replace humans with automation. The third debate is about the unit of analysis that should be focused on when considering the potential impact of AI. However, we can claim safely here that Artificial General Intelligence(AGI) which is highlighted to have generalized human cognitive capabilities, does not exist; the current AI is still regarded as Narrow AI [START_REF] Glikson | Human Trust in Artificial Intelligence: Review of Empirical Research[END_REF]. Besides, AI seems even more disruptive if it combines with other advanced technologies, such as the cloud and blockchain [START_REF] Akter | Transforming business using digital innovations: The application of AI, blockchain, cloud and data analytics[END_REF][START_REF] Gregory | The Role of Artificial Intelligence and Data Network Effects for Creating User Value[END_REF]Tschang & Almirall, 2021).

While there is a massive voice about the current and potential business value of AI, a growing body of research equally highlights the issues with AI, which we divide into two broad categories: bias, ethics, ideology, and inscrutability; cost and bounded automation. These issues are relatively interrelated.

1) Bias, ethics, ideology and inscrutability: tagged as black-box effects , researchers alerted that AI is still subject to the datasets it is trained on and the designers who based on their ideologies to develop it, which make it can exist bias and can lead to ethical issues, discrimination, for example [START_REF] Asatiani | Sociotechnical Envelopment of Artificial Intelligence: An Approach to Organizational Deployment of Inscrutable Artificial Intelligence Systems[END_REF][START_REF] Clifton | When machines think for us: The consequences for work and place[END_REF][START_REF] Tambe | Artificial Intelligence in Human Resources Management: Challenges and a Path Forward[END_REF]Vesa & Tienari, 2020). Vesa and Tienari (2020) proposed rationalized unaccountability to highlight the issue of one possible point of accountability of AI implementation, regarding AI as a panacea and the scapegoat when problems emerge. 2) Cost and bounded automation: obviously, the cost related to AI-specific investment, AI talents cannot be out of consideration [START_REF] Fleming | Robots and Organization Studies: Why Robots Might Not Want to Steal Your Job[END_REF][START_REF] Pournader | Artificial intelligence applications in supply chain management[END_REF]. According to [START_REF] Fleming | Robots and Organization Studies: Why Robots Might Not Want to Steal Your Job[END_REF], social concerns, which consist of the price labor, the power relationship within the organization, and the nature of the task, need to consider and raise bounded automation , indicating firms replace manual labor economically.

AI-related competences

Through the literature, researchers proposed different views into the competences related with AI, and currently we found more from firm-level than individual-level.

Firm-level

Based on different contextual and potential impact of AI, researchers proposed several interesting AI-related competences. For example, AI innovation capability [START_REF] Lou | AI on Drugs: Can Artificial Intelligence Accelerate Drug Development? Evidence from a Large-scale Examination of Bio-pharma Firms[END_REF]; AI capability [START_REF] Mikalef | Artificial intelligence capability: Conceptualization, measurement calibration, and empirical study on its impact on organizational creativity and firm performance[END_REF]; Data governance capability [START_REF] Janssen | Data governance: Organizing data for trustworthy Artificial Intelligence[END_REF].

Individual-level

In contrast to different views within firm-level competences, there are only two papers directly investigated individual-level competences in our sample.

One is based on the interviews with managers in multinational corporations in IT sector, [START_REF] Jaiswal | Rebooting employees: Upskilling for artificial intelligence in multinational corporations[END_REF] highlighted five critical skills employees need to upskill within the AI context: data analysis, digital, complex cognitive, decision making, and continuous learning skills.

The other one formulized the "job replacement theory", specifically in the context of service sector, which consists four ordinal and parallel intelligences: Mechanical; Analytical; Intuitive; Empathetic [START_REF] Huang | Artificial Intelligence in Service[END_REF]. [START_REF] Huang | Artificial Intelligence in Service[END_REF] argue the advancement of AI could gradually substitute or replace these intelligences.

5.Discussion

Result of data analysis

General descriptions

We can easily see that the topics related to AI are getting more and more concerning from researchers as the publications are surging. Also, considering the articles we flited are from excellent journals, this trend indicates that more and more AI-related excellent academic work is conducting. Besides, it seems several domains attract more attention from researchers than others, such as marketing and SCM. And the implementation of AI in the firm is fairly in its early stage. Considering the variety of AI technologies and complexities within different businesses, there actually is less empirical evidence related to the real-world impact of AI. Hopefully, we can have a deep and holistic understanding of the impact of AI in the coming future.

Evolving competences: to harness the potential of AI

We can perceive that AI is associated with both risks and opportunities through related debates and issues. In order to harness the potential of AI, evolving related competences could be the key. We are trying to draw a fundamental overview based on our SLR, into twolevel: firm-level and individual-level:

1) Firm-level Based on our literature review, RBV seems to be appropriate to formulate the competences building of AI at the firm level. This framework shows a generalized formulation to build competence and reflects the need aroused by AI. Based on the framework from [START_REF] Mikalef | Artificial intelligence capability: Conceptualization, measurement calibration, and empirical study on its impact on organizational creativity and firm performance[END_REF]and other capabilities and insights we come across through literature, which are listed in Table 3, we modified and summarized this framework, see Figure 5. Source: [START_REF] Grant | The Resource-Based Theory of Competitive Advantage: Implications for Strategy Formulation[END_REF][START_REF] Mikalef | Artificial intelligence capability: Conceptualization, measurement calibration, and empirical study on its impact on organizational creativity and firm performance[END_REF][START_REF] Janssen | Data governance: Organizing data for trustworthy Artificial Intelligence[END_REF]Li et al., 2021;[START_REF] Lou | AI on Drugs: Can Artificial Intelligence Accelerate Drug Development? Evidence from a Large-scale Examination of Bio-pharma Firms[END_REF][START_REF] Sjödin | How AI capabilities enable business model innovation: Scaling AI through co-evolutionary processes and feedback loops[END_REF] Notes: the parts in red are modified and extended based on our review More specifically, we added infrastructure into the tangible block to cover some specific equipment needed for AI usage [START_REF] Lou | AI on Drugs: Can Artificial Intelligence Accelerate Drug Development? Evidence from a Large-scale Examination of Bio-pharma Firms[END_REF]. In the human resource block, inspired by the risk and bias within current AI, we highlighted the need of intuition within ethics, bias, and the explanation within the AI technologies implemented [START_REF] Lou | AI on Drugs: Can Artificial Intelligence Accelerate Drug Development? Evidence from a Large-scale Examination of Bio-pharma Firms[END_REF]. Last, for the intangible block, we added the need to build the common language across departments [START_REF] Lou | AI on Drugs: Can Artificial Intelligence Accelerate Drug Development? Evidence from a Large-scale Examination of Bio-pharma Firms[END_REF]; we highlighted the importance of having external networks to create value co-creation (Li et al., 2021;[START_REF] Sjödin | How AI capabilities enable business model innovation: Scaling AI through co-evolutionary processes and feedback loops[END_REF], learn from past experience [START_REF] Lou | AI on Drugs: Can Artificial Intelligence Accelerate Drug Development? Evidence from a Large-scale Examination of Bio-pharma Firms[END_REF], and have personalized data governance(their own rules and styles) [START_REF] Janssen | Data governance: Organizing data for trustworthy Artificial Intelligence[END_REF][START_REF] Sjödin | How AI capabilities enable business model innovation: Scaling AI through co-evolutionary processes and feedback loops[END_REF].

we also found some issues aroused in the validation of the framework from the work of [START_REF] Mikalef | Artificial intelligence capability: Conceptualization, measurement calibration, and empirical study on its impact on organizational creativity and firm performance[END_REF]; for example, the following two questions are not validated: « D2. We integrate data from multiple internal sources into a data warehouse or mart for easy access; T1. We have explored or adopted cloud-based services for processing data and performing AI and machine learning » (Mikalef & Gupta, 2021, p.16).

We believe this is because there is no universal approach to implementing AI in the enterprise. Not to mention the variety of AI technologies, it is reasonably reflecting the specific needs of firms based on their stage(industries, size, etc.). framework we summarized could also be sensitive to the different firms according to their position and approaches. But at this stage, this framework may offer some insights for the firm to evolve its competences related to AI.

2) Individual-level

Similar to the firm level, for the individual level, the competences calling from AI could be sensitive to the need required from their position. Based on our SLR, we can see that AI could potentially drive the skill transition, but fewer insights we obtained. However, we found what we mentioned in the background: key and STEM competences could generate a kind of fundamental framework for the individual level.

1) Dimension

Actively using the tools and technologies to investigate the world.

Linkage: fundamental need to access the AI.

2) Dimension 2:

To link with others and boost cooperation. Linkage: .

3) Dimension 3

To understand the rules within the system and also align within personal needs. Linkage: have a big picture within the ethical usage within AI.

In general, these key competences could help the individual be agile in most situations they could experience in both work and life when they come across AI consciously or unconsciously. However, they may also need to build STEM competences to help formulate common languages for coordination within AI(deployment and development). We claim this framework could be a fundamental overview to further formulize specific competences for individual, considering the requirement of industry, firm, and position.

An overview of nexus between AI and competences

In this section, we are trying to draw an overview of the nexus between competences and AI. As Figure 7 shows, the impact of AI and the evolution of firms' and individuals' competences are interrelated. For example, AI could lead to the evolution of firms, and could further guide to the evolution of the individual. We should also mention that environment will also have huge impacts in terms of policy, economy, society, technological advancement, and others. It also reflected the AI debates and issues we summarized from the literature.

Research limitations

This research obvious exited a range of limitations, which are summarized into the following categories:

Researcher bias: the primary research conductor from a business-orientated academic background has less deep knowledge of the inscrutability of AI technologies, and he is not a native English speaker; Process limitation: with limited time and could still pass some interesting papers related(Especially based on the rank of AJG, could possibly pass some important articles), and the backward and forward researching is not available for this research;

Source limitation: obviously, more sources can take into consideration, for example, the conference paper and consulting documents; also, the language could include more than English;

Manual bias: as some part of this research consist manual parts which could exist some errors, for example, overlooking some articles through screening;

Business secrets: this may lead to bias in business research. For example, the responders can avoid telling the truth and, even worse, say something wrong or opposite, besides, it may make it more difficult for management researchers to conduct related research;

Theory bias: this research investigated less into the theory of competence and may need a complementary literature review of competence.

Future research recommendations

Concerning the future research, as the frameworks we summarized in this article are still at the early stage, they may generate more value if researchers could apply and modify them to a specific industry to gain more empirical evidence (Case studies, interviews, etc.) as well as in more detail senior, middle managers) need of AI competences could also be valuable.

6.Conclusions

It's fair to say that there still exists a significant number of questions and curiosities surrounding the definition, impact, and "magic of AI. Our research is trying to contribute in one direction, specifically within competences.

We conducted a systematic literature review to demystify the nexus between AI and competences in this research. After reviewing 65 papers, we summarized two related frameworks (RBV framework for firm-level; Key and STEM competences for individuallevel) and an overview to have a preliminary understanding the nexus between AI and competences. We figured out there was currently less empirical evidence of the value of AI and how could firms and individuals harness the potential benefits and minimum risks. More possible interesting topics worth investigating regarding the evolution of competences calling from AI. It could be valuable if the research involves a specific industry or sector.

Toorajipour, R., Sohrabpour, V., Nazarpour, A., Oghazi, P., & Fischl, M. (2021) 2) If the results and methodology are validated, then access the full text to have a further judgement.

Plus: Making the categories and coding for different topics, e.g. the methodology them use, industries they focus, also lists the key articles.

Strategies for data extraction

Combining with Dropbox and Excel to do the extraction, make them align with the notes.

Synthesis and reporting

Making metrics, other visualization tools to investigate the possible gaps and conduct further insights. The author proposed "rationalized unaccountability" to claim the ideology behind the AI technologies("black box" effects), using AI as an undoubtful approach to justify the decision can be a problematic future of humanity, as we should notice that the current AI is still subjective to data and its designers, which consists bias and ideologies. The analyses are on the core qualities of intelligent agent programs: autonomy, adaptability, and sociality Title

Appendix C. Notes for key articles

Figure 1 :

 1 Figure 1 : Venture capital in AI worldwide(2012-2020) Source : OECD AI Policy Observatory 1

Figure 2 :

 2 Figure 2 : Processing of reviewSource:[START_REF] Enholm | Artificial Intelligence and Business Value: A Literature Review[END_REF][START_REF] Xiao | Guidance on Conducting a Systematic Literature Review[END_REF]

Figure 4 7

 4 Figure 4 7 : Domain of publications

Figure 5 :

 5 Figure 5 : A modified RBV framework based on the work from Mikalef & Gupta(2021)

Figure 6 :

 6 Figure 6 : Key and STEM competences Source: (DeSeCo, 2005; Johannessen, 2021) Showing in Figure 6, we concluded key c and summarized the linkages between AI:

Figure 7 :

 7 Figure 7 : An overview for nexus between AI and competences

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  explanations named"data as a strategic resource" about why digital corporations are engaging in the fundamental AI research: 1. they are lead users of advanced AI, they can profit most from the advancement of this field. Thus they have huge incentives; 2. Conducting AI research needs access to massive datasets; owning these datasets can also attract complementary scientists; 3. Data can play as complementary assets to ensure an appropriate large share of value creation. They argued the data could secure firms' competitive advantage. the three famous books about AI robotic and the workforce to conduct the insights, as (Books) the ideas promoted in books are organizations should focus on augmentation rather than automation. They conclude that augmentation and automation are interdependent in terms of time and space; only focusing on one part could lead to a vicious circle for the organization; thus, one must try to make the balance of taking advantage of them. risk, the complexity of AI, and the need to ensure AI orientation, link with overall strategic directions and goals to guide the investment, adoption, practices of AI technologies. Based on upper echelons theory, their empirical results indicated that CIO presence impacted AI orientation, which could also be positively moderated through board characteristics(educational diversity; governance capability", setting data governance as organizations' personal usage of data into accountability, the life-circle view across enterprises. They also offer 13 principles for the data governance opportunities of AI in terms of four business capabilities: automation; engagement; insight/decision making, and innovation. They raised great questions, for example, automation vs augmentation; automation of work or process; internal and external engagement; accountability issues. They also offered three typologies for AI based on intelligence, technology, function, they offer the first conceptualized AI capability, and use empirical case to investigate the link between organizational creativity and performance(the results indicates a positive impact)The role of Artificial Intelligence and Data Network Effects for Creating User Value[START_REF] Gregory | The Role of Artificial Intelligence and Data Network Effects for Creating User Value[END_REF] They introduced a model of Data Network Effects(more value that users could obtain if more of the data they used are collected by the platform they used) as the extent of network effect theory, in which they defined platform AI capability: "the ability of a platform to learn from data to continuously improve its products and services for each user." AI innovation capability" (AIIC): the ability to develop, use, and manage AI resources in combination or copresent with other resources and capabilities to effectively conduct scientific discovery and R&D. Also highlighted the combination view of AI skills and domain knowledge, listed some pros and cons in implementing of AI. the manufacturing industry to display the AI capabilities: data pipeline, algorithm development, and AI democratization, then they introduced 1. without a strategy and top managers' support, could lead to the failure of AI; 2. Need a robust technological infrastructure and supported data; 3.Huge costs related to AI technics and talents. Also listed are great AI real-world examples, linked AI methods with the

Table 4

 4

	Domain/Industry	Qualitative	Quantitati ve	Mixed	LR	SLR	N/A	Grand Total
	Accounting				1			1
	Agriculture					1		1
	Auditing		1				1	2
	Customer relationship Management		1			1	1	3
	Healthcare	1	1				1	3
	Human Resource Management		1				1	2
	Information system	1					2	3
	IT sector	1						1

displa methods and the domains(industries). N/A represents the most significant amount, which economy, and social environment. Excluding N/A items, Marketing and Supply Chain Management are the two most frequently investigated domains. Besides, various research methods have been applied, with slightly more qualitative than quantitative research.

7 Four articles are not available in scopus, so the figure is based on N=61

Table 3 : Method and Domain(Industry) of selected articles

 3

Table 4

 4

	Context	Capability	Detail
			Tangible resources, Personnel-based resources,
		AI innovation capability(AIIC):	and AI-enabled intangible resources;1. Tangible:
		« the ability to develop, use, and	data, infrastructure, and algorithms used for R&D;
	Drug discovery industry; RBV; IT capability	manage AI resources in combination or copresent with other resources and capabilities to conduct scientific discovery and	2. Human resource: having AI skills, as embodied in the employees, is important to have common language , balance and combination within AI skills and domain expertise, cross-disciplinary
		R&D effectively» (Lou & Wu,	skills needed. 3. Intangible: firm practices and
		2021, p.1453).	knowledge assets that can empower and foster the
			use of AI
		AI capability: « An AI capability is	1. Tangible resources: data, technology, and basic
	Organizational; RBV	the ability of a firm to select, orchestrate, and leverage its AI-specific resources » (Mikalef &	resources; 2. Human skills: technical and business skills; 3. Intangible resources: inter-department coordination, organizational change capacity, and
		Gupta, 2021, p.4).	risk proclivity.
	Big Data Algorithmic	Data governance capability	personalized style to define, apply and monitor rules and permissions to guide and
	Systems		ensure accountability for the proper functioning of

displaces the main capabilities that occurred in our review, and it should be noted that the first two are both based on the resource-based view(RBV) 8 to formulate the AI capabilities. The other focuses are mainly on data governance and the issue related to value co-creation.

Table 4 : AI capabilities

 4

For more detail: https://oecd.ai/en/data?selectedArea=investments-in-ai&selectedVisualization=vcinvestments-in-ai-by-industry; screenshot on 10/02/2022

Full name: Definition and Selection of Competencies(DeSeCo), for more information, please visit their official website: https://www.deseco.ch/bfs/deseco/en/index.html

For the full version of this taxonomy(a summary), please see in Appendix A.

Based on this, we made key notes for 19 articles, the notes see in Appendix C, and records of

articles in Appendix D.6 As for time and labor limited, and considering the numer of eligible article at the first review step, we did not apply backward and forward search in our research.

For more detail and a historical overview of resource-based view theory, one possible source: https://www.valuebasedmanagement.net/methods_barney_resource_based_view_firm.html

More detail of these definitions please see in their paper, they also offer a detail framework.(In order to make hin this capability)

Use language, symbos nd texts interactively Effective use; spoken abd written language skills; computation and other mathematical skills

Use knowledge and information interactively

It consists sevel requirements: 1. recognise and map what is not known; 2. Could identify, locate and access to the appropriate information source; 3. Could evaluate the value of information and its source; 4. Can organise the knowledge and information

Use technology interactively

The awareness of the new ways of the technologies could be used into their daily lives(own circumstances and goals), to incorporate technologies into their own practices, producing familiarity to further extended usage. (Link with technologies innovations and advaces)

Interacting in Hetergeneous Groups

The aim is to learn, live and work with others

Reasons : 1. to deal with diversity in pluralistic societies; 2. the important of

Relate well to others

Enable individuals to initiate, maintain and manage personal relationships; being able to respect and appreciate the value, beliefs, cultures and histories of others( to create an environment where they feel welcome, included and thrive)

Co-operate, work in teams

To balance the commitment and their own priorities, share leadership and supporting for others. Can be concluded as: The author proposed "bounded automation", highlighted social considerations (the price labor, the power relationship within organization, and the nature of the task) over technologies; He claimed that the economic views from firms to replace manual power and related cost for in AI equipment need taking into consideration.

He forecasted that there would be more low-skilled and low-paid jobs in the future.

When machines think for us: the consequences for work and place [START_REF] Clifton | When machines think for us: The consequences for work and place[END_REF] A historical review of the definition of AI and its trend, applications; Claimed