The sixth generation of knowledge management – the headway of artificial intelligence

Bencsik, Andrea

doi:10.14254/2071-8330.2021/14-2/6

Cited by 30 publications

(13 citation statements)

References 35 publications

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“…In other words, AI can extract new knowledge from vast quantities of data, portraying complex mappings as a basis for human decision-making (Paschen et al, 2020). Hence, according to Bencsik (2021), there is a close mutual interaction between KM and AI: the former makes the understanding of knowledge possible, while the latter provides the tools to expand and use knowledge, as well as to create new knowledge in a way that was unimaginable before (Haenlein and Kaplan, 2019;Lu et al, 2018). In this vein, as emphasised by Al Mansoori et al (2021) in their systematic literature KM processes for the effective use of AI review, modern organisations increasingly rely on AI mechanisms to enhance KMPs and performance thanks to their ability to 1) inductively determine relationships and trends in firms' knowledge repositories (i.e.…”

Section: Conceptual Model and Hypothesis Developmentmentioning

confidence: 99%

The mediating role of knowledge management processes in the effective use of artificial intelligence in manufacturing firms

Leoni

Ardolino

Baz

et al. 2022

IJOPM

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PurposeThis paper aims to provide and empirically test a conceptual model in which artificial intelligence (AI), knowledge management processes (KMPs) and supply chain resilience (SCR) are simultaneously considered in terms of their reciprocal relationships and impact on manufacturing firm performance (MFP).Design/methodology/approachIn the study, six hypotheses have been developed and tested through an empirical survey administered to 120 senior executives of Italian manufacturing firms. The data analysis has been carried out via the partial least squares structural equation modelling approach, using the Advanced Analysis for Composites 2.0 variance-based software program.FindingsUsing a conceptual model validated using an empirical survey, the study sheds light on the relationships between AI, KMPs and SCR, as well as their impacts on MFP. In particular, the authors show the positive effects of the adoption of AI on KMPs, as well as the influence of KMPs on SCR and MFP. Finally, the authors demonstrate that KMPs act as a mediator through which AI affects SCR and MFP.Practical implicationsThis study highlights the critical role of KMPs for manufacturing firms that can deploy AI to stimulate KMPs and through attaining a high level of the latter might succeed in enhancing both their SCR and MFP.Originality/valueThis study demonstrates that manufacturing firms interested in properly applying AI to ameliorate their performance and resilience must carefully consider KMPs as a mediator mechanism.

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Section: Conceptual Model and Hypothesis Developmentmentioning

confidence: 99%

The mediating role of knowledge management processes in the effective use of artificial intelligence in manufacturing firms

Leoni

Ardolino

Baz

et al. 2022

IJOPM

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“…ETS methods have an automated ets function in the forecast package. According to Hyndman & Athanasopoulos (2018, 2021 "each model consists of a measurement equation that describes the observed data, and some state equations that describe how the unobserved components or states (level, trend, seasonal) change over time." The ets function is fully automated and selects the most accurate from 11 possible ETS models (Hyndman et al, 2002).…”

Section: Forecast Methods Usedmentioning

confidence: 99%

“…Smaller enterprises can perceive the higher related costs and the need to employ experts as a specific constraint in using more complex methods. This peculiarity is typical for current business activity, even considering the increasing demand for IT products implementation in business management (Balcerak & Woźniak, 2021;Luchko, Arzamasova & Vovk, 2019;Roshchyk et al, 2022;Szeiner et al, 2022), including artificial intelligence-based ones (Bencsik, 2021;Šuleř & Machová, 2020) and other digital tools of modeling and data collection within management systems in enterprises (Adda et la., 2021;Akimova et al, 2020;Bilan et al, 2017;Mura & Hajduová, 2021;Virglerová et al, 2022;Jeza & Lekhanya, 2022). Therefore, we also perform a multicriteria evaluation of the models in this study.…”

Section: Literature Reviewmentioning

confidence: 99%

Demand forecasting: AI-based, statistical and hybrid models vs practice-based models - the case of SMEs and large enterprises

Kolková¹,

Ključnikov²

2022

Economics &Amp; Sociology

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Demand forecasting is one of the biggest challenges of post-pandemic logistics. It appears that logistics management based on demand prediction can be a suitable alternative to the just-in-time concept. This study aims to identify the effectiveness of AI-based and statistical forecasting models versus practice-based models for SMEs and large enterprises in practice. The study compares the effectiveness of the practice-based Prophet model with the statistical forecasting models, models based on artificial intelligence, and hybrid models developed in the academic environment. Since most of the hybrid models, and the ones based on artificial intelligence, were developed within the last ten years, the study also answers the question of whether the new models have better accuracy than the older ones. The models are evaluated using a multicriteria approach with different weight settings for SMEs and large enterprises. The results show that the Prophet model has higher accuracy than the other models on most time series. At the same time, the Prophet model is slightly less computationally demanding than hybrid models and models based on artificial neural networks. On the other hand, the results of the multicriteria evaluation show that while statistical methods are more suitable for SMEs, the prophet forecasting method is very effective in the case of large enterprises with sufficient computing power and trained predictive analysts.

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“…21st-century organisations must be aware of the importance of non-material factors in building a position or creating a competitive advantage. Knowledge as an intangible resource is of great importance in creating the organisation's value (Rosińska-Bukowska, 2020;Bencsik, 2021). Effective knowledge management allows organisations to gain many benefits, such as improving organisational quality or quickly transferring and using the right resources in an appropriate manner (Sagan, 2019).…”

Section: Systematic Literature Reviewmentioning

confidence: 99%

“…Their study presents considerations regarding the possibility of including knowledge management in integrated management systems, treating knowledge as a basic element connecting the system (Skuza and Prusak, 2018). Knowledge management combined with the future of artificial intelligence is shown in an article by Bencisk (Bencsik, 2021). Radziszewska writes about the role of social media in the context of sustainable development.…”

Section: Systematic Literature Reviewmentioning

confidence: 99%

Knowledge management and corporate social responsibility interactions in theory and practice

Krawczyk

2022

ECKM

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Knowledge management (KM) in the context of corporate social responsibility (CSR) is a new and challenging topic. The possibilities of effectively introducing this process within an organisation and the global trend of its implementation are signals that the topic is worth taking a closer look at. Knowledge management brings the benefits necessary to effectively conduct business. When designed consciously and appropriately, they can increase the effectiveness of business activities and strengthen a company's competitive position. On the other hand, many companies have made the principles of sustainable development a part of their strategy. Knowledge management in the area of CSR is related to important challenges. One of them is a commitment of the board of directors. Particular attention should also be paid to the needs of people in various positions and different company departments. Companies spend huge amounts of money to accumulate knowledge in various forms. However, they cannot turn this knowledge into concrete actions. This article aims to identify common areas of KM and CSR. How the application of both of them will enhance company performance. It utilizes the methods of literature analysis and company reports analysis, as well as own observations. The research was conducted in two stages. Stage 1: literature analysis and synthesis. Stage 2: Analysis of non-financial reports of selected entities. The research question is, how to use knowledge management in the context of CSR. The goal of this article is to show the resonance between knowledge management and corporate social responsibility. The conclusions presented in the article can be valuable for business professionals, small business owners, entrepreneurs, academics, researchers, and business students.

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The sixth generation of knowledge management – the headway of artificial intelligence

Cited by 30 publications

References 35 publications

The mediating role of knowledge management processes in the effective use of artificial intelligence in manufacturing firms

The mediating role of knowledge management processes in the effective use of artificial intelligence in manufacturing firms

Demand forecasting: AI-based, statistical and hybrid models vs practice-based models - the case of SMEs and large enterprises

Knowledge management and corporate social responsibility interactions in theory and practice

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