Decision support through knowledge management: the role of the artificial intelligence

Metaxiotis, Kostas; Ergazakis, Kostas; Samouilidis, Emanuel

doi:10.1108/09685220310500126

Cited by 38 publications

(7 citation statements)

References 21 publications

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“…One of the most significant problems is the organization and further distribution of business knowledge [27]. As per the findings of [6,28], it is observed that organizational knowledge has nine basic steps for its management either the explicit, tacit or subjective dimensions. These are transforming the information into knowledge, identification and verification of knowledge, capturing and securing the knowledge, organizing and combining the knowledge, creation and learning of knowledge and finally the distribution and selling of knowledge.…”

Section: Introductionmentioning

confidence: 89%

“…Additionally, their study is observed a reasonable contribution in the field of big data and KM for AI. In their study [6], have considered the decision support through KM with the role of AI in computer bases system. Authors explain that both decision support and KM are interdependent to each other in various business organizations.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In recent years, business firms are working to effectively manage their organizational knowledge while looking for some new ways to survive in the marketplace [1][2][3][4]. To effectively manage the organizational knowledge, decision making process seems to be riskier as business firms are moving from domestic market to international arena [5][6][7]. It is a common notion that business firms who can effectively manage the knowledge assets can create values, identify new opportunities and evolve through better financial outcomes [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

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Strategic Management of Organizational Knowledge and Employee's Awareness About Artificial Intelligence With Mediating Effect of Learning Climate

Baslom¹,

Shu²

2019

IJCIS

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This study has aimed to examine the empirically relationship between strategic management of organizational knowledge and awareness about artificial intelligence (AI) through mediating effect of learning climate in service sector of Saudi Arabia. For better understanding, a structural questionnaire was developed, and both questionnaire and interview techniques were applied to collect the data from targeted respondents. Statistical methods like confirmatory factor analysis, structural equation modeling and mediation technique were applied to check the direct and indirect effects with overall valid responses of 390. Findings under factor analysis specify that all the indicators of strategic management of knowledge, learning climate and awareness about AI have demonstrated good factor loadings. Additionally, results under structural equation model for strategic management of knowledge and awareness about AI indicates that knowledge acquisition, knowledge dissemination and knowledge responsiveness have their significant and positive relationship with AI awareness. While mediating effect of learning climate (LC), shows that relationship between knowledge acquisition and AI, between knowledge dissemination and AI is significantly mediated by LC under full sample. While mediating effect of LC between knowledge responsiveness and AI is not significant. In addition, practical implications of this study reveals the fact that management in all three layers of the business firms should consider these findings and positive role by learning climate to promote awareness about AI. However, this study has observed several limitations. At first overall impact of strategic management of knowledge is yet to be explored as this study has examined its impact through knowledge acquisition, dissemination, and responsiveness factors under sperate models. At second, study has a regional limitations and specific organizational implications only for the service sector is observed. At third, earlier studies have raised serious concern over data collection through questionnaire as it covers only one dimensions of employee's attitude. Other instruments like open ended non-structural interview should be reconsidered in more significant manner. Future studies could be reimplemented on similar topic while addressing these limitations.

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Section: Introductionmentioning

confidence: 89%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Strategic Management of Organizational Knowledge and Employee's Awareness About Artificial Intelligence With Mediating Effect of Learning Climate

Baslom¹,

Shu²

2019

IJCIS

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“…In the 21st century, AI has become an important area of research in all fields: Engineering, science, education, medicine, business, accounting, finance, marketing, economics, stock market, and law, among others [12][13][14][15][16][17][18]. The range of AI has grown enormously since the intelligence of machines with machine learning capabilities has created profound impacts on business, governments, and society [19].…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence and Machine Learning Applications in Smart Production: Progress, Trends, and Directions

et al. 2020

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Adaptation and innovation are extremely important to the manufacturing industry. This development should lead to sustainable manufacturing using new technologies. To promote sustainability, smart production requires global perspectives of smart production application technology. In this regard, thanks to intensive research efforts in the field of artificial intelligence (AI), a number of AI-based techniques, such as machine learning, have already been established in the industry to achieve sustainable manufacturing. Thus, the aim of the present research was to analyze, systematically, the scientific literature relating to the application of artificial intelligence and machine learning (ML) in industry. In fact, with the introduction of the Industry 4.0, artificial intelligence and machine learning are considered the driving force of smart factory revolution. The purpose of this review was to classify the literature, including publication year, authors, scientific sector, country, institution, and keywords. The analysis was done using the Web of Science and SCOPUS database. Furthermore, UCINET and NVivo 12 software were used to complete them. A literature review on ML and AI empirical studies published in the last century was carried out to highlight the evolution of the topic before and after Industry 4.0 introduction, from 1999 to now. Eighty-two articles were reviewed and classified. A first interesting result is the greater number of works published by the USA and the increasing interest after the birth of Industry 4.0.

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“…Regarding the decision-making in projects, the knowledge management can be used to take trustworthy decisions in relation to the strategy, competitors, customers, distribution channels and products and services life cycles (DAVENPORT; PRUSAK, 1998). The employment of KM as the support to the decision-making process consists of a subject of innumerable researches (PADMA;BALASUBRAMANIE, 2009;RANTAPUSKA;IHANAINEN, 2008;NICOLAS, 2004;COURTNEY, 2001;METAXIOTIS et al, 2003;HOLSAPPLE, 2001), hence the capacity to make a good decision is very important for the success of a project (AL-HARBI, 2001). Comprehension and response to the risks in a company is a task that depends on the intellectual capital of the company (NEEF, 2005) and one of the most powerful tools in the risk management in projects is knowledge (COOPER, 2003).…”

Section: Knowledge Management and The Decision-making Processmentioning

confidence: 99%

Analytic Hierarchy Process applied to the selection of knowledge management approach in technology-based companies

Neves¹,

Silva²,

Silva³

et al. 2011

PMD

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Within the fields of knowledge management and risk management of projects, the transformation of the existing knowledge in the organization into competitive advantage was proposed in order to make the decisionmaking process possible. The main purpose of this article was to select an approach to knowledge management that gathered the applicable criteria to the decision-making and risk management of projects in technology-based companies. It was intended to describe, analyze and select the approaches to knowledge management through the use of the Analytic Hierarchy Process (AHP) method. The chosen structured lines of research for the selection of the approach were: Nonaka and Takeuchi's; Davenport and Prusak's; Stewart's; Sveiby's; Terra's and Choo's proposals. The selection was based on the geometric average of the judgments accomplished by six specialists, whose data gathering were collected through direct interview. The adopted method of research was modeling research. The study resulted in the selection of Nonaka and Takeuchi's approach as the most adjusted according to the considered criteria.

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Decision support through knowledge management: the role of the artificial intelligence

Cited by 38 publications

References 21 publications

Strategic Management of Organizational Knowledge and Employee's Awareness About Artificial Intelligence With Mediating Effect of Learning Climate

Strategic Management of Organizational Knowledge and Employee's Awareness About Artificial Intelligence With Mediating Effect of Learning Climate

Artificial Intelligence and Machine Learning Applications in Smart Production: Progress, Trends, and Directions

Analytic Hierarchy Process applied to the selection of knowledge management approach in technology-based companies

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