Knowledge in the discovery of market opportunities

Stefan Trzcielinski, Grzegorz Pawlowski
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Abstract

An opportunity is a relationship between a company's objectives, the resources at its disposal, and the situation in the environment, such that it favours the achievement of the objectives with the available resources. Opportunities arise in a changing environment. The dynamics of change causes the life cycle of an opportunity to shorten and companies that discover the opportunity and can exploit it gain a competitive advantage. Opportunity discovery means that opportunities exist but the company does not know about them. Opportunity discovery is the subject of several scientific disciplines. They are dealt with by strategic management using the methods of strategic analysis. They are also of interest to agility theory, which focuses on reacting quickly to market opportunities. In addition, entrepreneurship theory deals with them, which addresses the issue of cognitive traits in their discovery. New possibilities in discovering opportunities are created by artificial intelligence. The article aims to present the opportunity discovery model understood as the relationship between three vectors: the company's goals, its resources, and the external environment in which it operates. The discovery of the opportunity consists in finding such a value of the environment vector at which the desired value of the vector of goals is achieved at a given value of the resource vector. The article answers the question of how the knowledge necessary to find such a relationship between these vectors must be structured so that this relationship reflects the opportunity. It was hypothesized that this structure can be modelled using a network of cause-and-effect relationships leading to the determination of the relationship between demand and supply. This hypothesis was verified using artificial intelligence with the Monte Carlo algorithm, Markov chain, and the Metropolis-Hastings algorithm. The results obtained confirmed the validity of the model structuring the knowledge. The article presents the result of a pilot study. The results concerning knowledge structuring are useful for opportunity discovery both in the case of traditional approaches i.e. using the achievements of strategic management theory, corporate agility and entrepreneurship theory, and in the case of using artificial intelligence. In the first case, the target audience for these results is business executives while in the second case, the research community working on this problem and IT companies.
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知识在于发现市场机会
机会是公司的目标、可支配的资源和环境状况之间的一种关系,这种关系有利于利用现有资源实现目标。机遇出现在不断变化的环境中。变化的动态导致机会的生命周期缩短,发现机会并能利用它的公司获得竞争优势。机会发现意味着机会存在,但公司并不知道。机遇发现是几个科学学科的主题。战略管理使用战略分析的方法来处理这些问题。它们对敏捷性理论也很感兴趣,敏捷性理论侧重于对市场机会的快速反应。此外,创业理论也涉及到这些问题,解决了他们发现过程中的认知特征问题。人工智能创造了发现机会的新可能性。本文旨在将机会发现模型理解为三个向量之间的关系:公司的目标、资源和运营的外部环境。机会的发现在于找到这样一个环境向量的值,在这个值上,目标向量的期望值在给定的资源向量上实现。本文回答了这样一个问题,即必须如何组织发现这些向量之间的这种关系所需的知识,以便这种关系反映机会。假设这种结构可以用因果关系网络来建模,从而确定需求和供给之间的关系。这一假设通过人工智能的蒙特卡洛算法、马尔科夫链和Metropolis-Hastings算法得到了验证。所得结果证实了知识结构模型的有效性。本文介绍了一项初步研究的结果。关于知识结构的结果对于机会发现非常有用,无论是在传统方法的情况下,即使用战略管理理论、企业敏捷性和创业理论的成果,还是在使用人工智能的情况下。在第一种情况下,这些结果的目标受众是业务主管,而在第二种情况下,这些结果的目标受众是研究这个问题的研究团体和IT公司。
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