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The purpose of this paper is to present a framework that captures the strategic value of artificial intelligence (AI) systems. Although AI has become a crucial component of digital transformation efforts tied to organizational strategy, many firms struggle to derive strategic value from emerging AI systems.

The analytical framework in this paper is based on a learning-centered approach. Specifically, by building on the knowledge-based perspective, this paper elaborates on how AI can contribute to organizational learning to create a competitive advantage in knowledge-intensive contexts.

This paper argues that the power of AI as a strategic resource lies in its self-learning capacities. Such learning capacities are only realized in partnership with humans through mutual learning. This paper formulates the concept of artificial capital and the ways artificial and human capital can together drive routinization and strategic learning processes that connect internal and external environments of the organization.

This is a timely contribution as many organizations are considering adopting AI technologies for strategic purposes. This paper translates the proposed framework into several practical implications for managing and developing AI to meet strategic business goals.

The purpose of this article is to determine the future proportion and variants of usage of human intellect and artificial intelligence (AI) in entrepreneurship of industry 4.0 that fits social entrepreneurship the most. It could be convergence (simultaneous utilization during the same entrepreneurial processes with the emphasis on unique features by the terms of the competition) or divergence (usage during different business processes by the terms of labor division).

The authors determine the influence of usage of human capital and AI on the efficiency of social entrepreneurship. The authors identify the perspective directions of usage of AI in social entrepreneurship and evaluate the readiness and interest in the implementation of these directions of concerned parties. The authors also model the optimal proportions and the variant of usage of human intellect and AI in social entrepreneurship in the conditions of Industry 4.0 in the future (until 2030).

It is found that social entrepreneurship will use the opportunities of Industry 4.0 for optimization of its activities until 2030, but will refuse from full automatization, using human intellect and AI at the same time.

The most perspective directions of application of AI at social companies are a collection of social goods and services, marketing studies and promotion of social goods and services. Neither convergence nor divergence of human and artificial intellectual capital does not fully conform to the interests of concerned parties. The most preferable (optimal) variant of usage of human intellect and AI in social entrepreneurship in the Industry 4.0 is human intelligent decision support.

To protect against the increasing environmental degradation many agents choose today to replace consumption of depleted environmental goods with that of privately produced substitute goods. The present paper aims to highlight how this “self‐protective” behaviour that is increasingly frequent in modern societies may affect the welfare of the individuals.

The paper presents a combination of narrative with argument and analysis. It first provides several examples of self‐protective choices to give a heuristic view of this phenomenon and then examines their effects through a simple evolutionary model that leads the reader beyond a purely intuitive understanding of the substitution mechanism described in the first part.

The paper shows that replacing environmental goods with artificial substitute goods may give rise to an “undesiderable growth” process, that is, a vicious circle between environmental degradation and private consumption which contributes to economic growth but may have negative effects on the welfare of the agents.

The paper investigates an aspect of the link between environmental degradation and economic growth that has been mainly ignored in the literature so far. While most contributions have underlined that self‐protective choices can boost economic growth, the paper goes one step forward and shows that they can actually give rise to a self‐reinforcing growth process in which environmental degradation increases economic growth and viceversa, leading the economy on a welfare‐reducing path.

The purpose of the research is to determine the perspective directions of state regulation of competition between human and artificial intellectual capital in Industry 4.0 and to develop scientific and methodological recommendations for their implementation. For this, the directions of state regulation of competition between human and artificial intellectual capital are described, monitoring of competition between human and artificial intellectual capital by the example of modern Russia (2019) is performed and scientific and practical recommendations for state regulation of competition between human and artificial intellectual capital are developed, with their approbation by the example of modern Russia (2019).

A method of expert evaluation is used for collection of the information and empirical data. The method of comparative analysis is used for comparing the successfulness of implementing the distinguished directions of state regulation of competition between human and artificial intellectual capital (4.1) according to the official statistics to their current evaluation according to the interested parties. Also, future evaluation (forecasts) according to the interested parties (until 2045) is determined.

It is substantiated that during evaluation of state regulation of competition between human and artificial intellectual capital in Industry 4.0, one cannot use only the official statistics, as these data are fragmentary and indirect. Fuller and more precise data are provided by assessment according to the interested parties. They allow determining the current and the future state of affairs and, based on it, compiling a forecast and developing a long-term strategy of state regulation of competition between human and artificial intellectual capital in Industry 4.0.

The perspective directions of state regulation of competition between human and artificial intellectual capital in Industry 4.0 are as follows: stimulation of competition in the market of intellectual capital, social risk management of the market of intellectual capital, managing international competition in the market of intellectual capital and ecological risk management of the intellectual capital market. As the experience of modern Russia shows, even at the initial stage of transition to Industry 4.0, the measures of state regulation of competition between human and artificial intellectual capital are not enough, but their deficit is moderate. In the course of development of Industry 4.0, the necessity for the measures of regulation will grow, and their deficit will increase. That's why there's a need for strategic approach to their implementation, which envisages their systemic reconsideration and supplementing. An author's algorithm is offered for this.

The purpose of the research is to study the consequences of total (comprehensive) automatization of entrepreneurship for interested parties through the prism of competition human and artificial intellectual capital in production and distribution in Industry 4.0.

The research is conducted with application of scenario analysis, regression analysis, imitation modeling, forecasting and non-linear multi-parametric optimization with the simplex method.

The authors perform scenario modeling of competition between human and artificial intellectual capital in production and distribution in Industry 4.0 and offer recommendations for pro-active management of competition between human and artificial intellectual capital in production and distribution in Industry 4.0.

Contrary to the existing approach to studying competition between human and artificial intellectual capital in Industry 4.0, automatization of distribution, not production, is most preferable. This shows increase of the value of human intellectual capital in distribution during its automatization based on AI. This is an unprecedented and breakthrough conclusion for the modern economic science. It allows creating a completely new direction of research of competition between human and artificial intellectual capital in production and distribution in Industry 4.0, in which optimization of social consequences is achieved not by means of restraint of automatization but by means of its stimulation. The key condition is stimulation of automatization of distribution with limited automatization of production. Based on this conclusion, it is recommended to continue research in continuation of the presented work.

The purpose of the paper is to develop a concept of organization of the system of science and education in the form 4.0 based on human and artificial intellectual capital.

The methods of regression and correlation analysis are used. The role of human and artificial intellectual capital for provision of effectiveness and competitiveness of the system of science and education is determined, as well as its correspondence to the modern challenges. Analysis of perspectives and limitations of automatization of the system of science and education based on AI is conducted; a model of organization of the system of science and education in the form 4.0 based on human and artificial intellectual capital is compiled.

It is established that in the system of science and education, the decisive production factor is intellectual capital, but human intellect does not necessarily have to dominate in its structure. AI is one of the most popular technologies of Industry 4.0 in the system of science and education, which has wide perspectives of practical implementation. As experience of the leading world universities, which had the highest level of digitization in 2018, showed, foundation on non-breakthrough digital technologies (computer equipment and Internet) does not allow opening the potential of increase of indicators of effectiveness and competitiveness of the system of science and education and bringing it in correspondence to the modern challenges based on digitization. However, correlation of activity of application of artificial intellectual capital with these indicators is four times higher (0.2), as compared to correlation of these indicators with activity of application of human intellectual capital (0.05). This shows demand for digitization of the system of science and education, but based on breakthrough digital technologies, of which AI should become the key one.

It is substantiated that higher education could be automatized based on AI by 85% and science by 63%. Therefore, on the whole in the system of science and education, the share of AI in the structure of intellectual capital could reach 74% ((85 + 63)/2). It is recommended to use the developed model of organization of the system of science and education in the form 4.0 based on human and artificial intellectual capital.

This research examines the relationship between the green version of intellectual capital (IC) (measured through green versions of human, structural and relational capitals (GHC, GSC and GRC)), co-creational capital (CC), green innovation (GI), technological innovation (TI) (measured through artificial intelligence) and start-up competitive advantage (SCA).

An online questionnaire collected data from 275 participants. To test the hypotheses, the data were analyzed using SmartPLS.

The results confirmed the positive influence of GSC and CC on TI and GI, GRC with GI and that of GI and TI with SCA. The results also reveal that IC can influence innovation and describe how innovation can drive the competitive advantage (CA) of start-ups.

This self-report study examines the associations by collecting data at one point in time, which results in methodological limitations regarding the generalization of the results. The second limitation is that the findings are limited to start-ups.

This research work examined a model that combined three components of green IC, customer capital, two forms of innovation and CA. These associations have not been previously examined yet can provide useful insight into what drives green and TIs and how they further influence competitiveness. This study provides unique inferences that improve the value of the literature on IC and innovation, using start-ups as context.

This study aims to investigate about the use of artificial intelligence (AI) (man machine relationship) regarding organizational behavior. The aim of this research paper is to analyze whether the current AI is used also to replace man in “creative” activities.

This study is based on a qualitative and explorative approach. It is made a review of the literature with “Scopus” and “Web of Science” databases. The research fields are AI, organizational behavior, man-machine relationship and creativity.

Analyzing whether the intensive use of AI in organizational behavior can replace human work in creative activities.

The connection of AI with creative activities within the organization is only just beginning. For this reason, other sources, like Harvard Business Review, public reports and professional papers found on the internet have been considered. The most important limitation of this paper is that all the results presented here do not concern a single case study.

In this paper, there are some examples that can show the use of AI in creative activities; however, this does not complete the situation facing companies in any sector because the AI technologies used within enterprises are constantly evolving. It is possible to continue to do research in this field.

The paper is meaningful because highlights the development of AI toward creative activities typically of human resources. It is also interesting because it analyzes the exploratory use of AI in increasingly human work, generating positive and negative externalities.

This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies.

This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context.

This conceptual paper proposes a model for growing company competitive advantage into the future by integrating a knowledge management strategy with progressive insights from Big Data and artificial intelligence. The ultimate strategic aim here is to create and codify intellectual capital that adds business value.

The briefing saves busy executives, strategists and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.

This study empirically examines the relationship between human capital ecosystems and entrepreneurial ecosystems. The study also investigates the mediating roles of digital knowledge capability and innovation capability and the moderating role of technology turbulence in the entrepreneurial ecosystem.

The study takes inputs from existing literature and resource-based view (RBV) and absorptive capacity theories and develops a theoretical model. Later, the model is validated using the structural equation modeling technique with data collected from India-based startup enterprises.

The authors found a nexus between human capital and entrepreneurial ecosystems. The study also highlights that enterprises' digital knowledge capability and innovation capability significantly influence the entrepreneurial ecosystem. The study also found that technology turbulence has a significant moderating impact on the relationship between digital knowledge capability and innovation capability in entrepreneurial ecosystems.

The study shows the importance of developing human capital for improving enterprises' innovation capability and digital knowledge capability, which are also important to develop the entrepreneurial ecosystem. Entrepreneurs can use the proposed model in their enterprises for appropriately developing the entrepreneurial ecosystem.

The authors propose a theoretical model for entrepreneurial ecosystems, from the human capital perspective, which entrepreneurs could use to improve the overall organizational performance. Moreover, the model uses technology turbulence as a moderator, which is also a novel approach. The study also adds valuable knowledge to the overall body of literature of intellectual capital.