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Role of Knowledge in Management of Innovation resources Article Role of Knowledge in Management of Innovation Yury Nurulin 1,* , Inga Skvortsova 2, Iosif Tukkel 1 and Marko Torkkeli 3 1 Institute of Computer Science and Technology, Peter the Great Saint-Petersburg Polytechnic University; Politekhnicheskaya Ulitsa, 29, Sankt-Peterburg 195251, Russia; [email protected] 2 Institute of Industrial Management, Economics and Trade, Peter the Great Saint-Petersburg Polytechnic University; Politekhnicheskaya Ulitsa, 29, Sankt-Peterburg 195251, Russia; [email protected] 3 School of Engineering Science, Lappeenranta University of Technology, Yliopistonkatu 34, 53850 Lappeenranta, Finland; marko.torkkeli@lut.fi * Correspondence: [email protected]; Tel.: +7-7812-297-1628 Received: 15 March 2019; Accepted: 23 April 2019; Published: 5 May 2019 Abstract: Knowledge has always been, and still is, a crucial source of economy. However, during the past few years we have seen a growing interest in treating knowledge as a significant organizational resource for innovation. This trend coincides with the rapid development of ICT, indicating the strong influence that ICTs have on the processes of creating, disseminating, and using knowledge. At present, issues of innovation management and knowledge management are studied independently, which creates a certain gap in the systemic understanding of the innovation development processes. The paper proposes an integrated approach to the issues mentioned. The hierarchy and taxonomy of knowledge are considered from the point of view of their influence on decision-making at different stages of the innovation lifecycle. Our proposition complements and contributes to several recent models of decision-making developed in the frame of the innovation process. Keywords: Innovation system; knowledge typology; decision-making; innovation lifecycle 1. Introduction “Whatever a manager does, he does through decision-making” [1]. This catchphrase describes very accurately the nature of an innovation manager’s responsibilities. The concrete content of decisions to be made depends on the problem to be solved, and changes during the innovation lifecycle, but the sequence of actions is invariant and their aggregate forms the framework for technology of decision-making in innovation management. Knowledge is an important and necessary resource for making effective decisions. At the moment, the role of knowledge in innovation management is studied from different points of view. Most studies are devoted to analysis of the general nature of knowledge, and concrete applied areas are considered just to prove and confirm a proposed general approach. Since the results of an object analysis essentially depend on the point of view defined for analysis, we focused this study on the roles played by different types of knowledge when making effective decisions at different stages of the innovation lifecycle. The paper is structured as follows. The first section contains the literature review of the role knowledge plays in decision-making management in general. Knowledge typology and its hierarchical models are studied with in detail with a view toward identifying their role in innovation management. The next section is devoted to analysis of the historical perspective of the transformation of innovation process models. Broader organizational perspective on the innovation systems research is then provided by discussing important gaps that emerge from the review of the literature. The final section provides a summary and presents the discussion of the four general conclusions of the work Resources 2019, 8, 87; doi:10.3390/resources8020087 www.mdpi.com/journal/resources Resources 2019, 8, 87 2 of 12 2. Literature Review 2.1. Classical Decision-Making Model It is widely accepted that the classical model of decision-making process consists of the following logically related sequence of actions [1]: 1. Identification of management problems; 2. Analysis of the problem’s nature; 3. Development of a set of possible alternatives; 4. Selection of the best solution out of the available alternatives; 5. Conversion of selected solution into decision for action; and 6. Ensuring feedback for follow-up. This model was further developed by Herbert Simon on the base of his concept of bounded rationality. He pointed out that the best solution might exist for the problem, but because of bounded cognitive ability of the decision-maker, he is not able to find this solution [2]. In reality, decision-makers often do not have all (or even most) of the information required to identify the problem, do not see all available alternatives, and use incomplete knowledge for decision-making. As a result, the decision usually leads to satisfactory but not optimal consequences. Based on this observation Simon replaced the task of finding the best solution by the task of finding some effective solutions: - Identification of all the alternatives; - Identification of all the consequences for each of the alternatives; - Evaluation of efficiency for each of consequences; - Selection of the most effective alternative which leads to the most effective consequence. We can assert that this upgraded model implicitly reflects the essential dependence of decision-making results on the results of information and knowledge management, since identification and analysis (evaluation and selection) in reality are cognitive procedures. The purpose of decisions are to make our lives better. In the context of business, the purpose of decisions are to create or increase value for the enterprise and all its stakeholders. In short, the final purpose of a decision is the value creation. 2.2. Data–Information–Knowledge–Wisdom (DIKW) Hierarchy Starting from the nineties of the last century, we could see a lively debate in the information and communication technology (ICT) and management literature about the model that describes the nature of the process of human perception of the surrounding world through structural and/or functional relationships—the data–information–knowledge–wisdom (DIKW) model. This model describes human perception as a process of transformation of data to wisdom throughout information and knowledge [3]. The model is based on assumption that data can be used to create information, information can be used to create knowledge, and knowledge can be used to create wisdom. As Rowley pointed out, “Typically information is defined in terms of data, knowledge in terms of information, and wisdom in terms of knowledge” [4]. This allows some authors to declare that there are no significant differences between the levels of the hierarchy. This statement is not supported by ICT specialists, for whom data, information, and knowledge are different categories that require their own tools, methods, and means for storage and processing. Conceptual approaches for defining data, information, and knowledge were analyzed by Zins, who found 130 corresponding definitions formulated by 45 scholars that map the theoretical foundations for information science [5]. This analysis showed a significant dependence of the definitions used when the DIKW model is used in the concrete applied area. So, Gu, and Zhang highlight in the DIKW model the lower level of the hierarchy, which is investigated in the context of large data arrays [6]. Cummins Resources 2019, 8, 87 3 of 12 and Bowden study the explicit and implicit relationships between information and knowledge in analyzing the value of information assets of companies [7]. Swigon explores these same elements of the DIKW hierarchy when developing methods and tools for managing information and knowledge [8]. Kogner and Probst analyze information, knowledge, and wisdom as elements of the DIKW hierarchy as applied to the problem of managing services in the field of information technology [9]. Aven uses the DIKW model in analyzing risk management concepts [10]. Stevenson considers the possibility of using the DIKW model in training [11]. Mushra uses a DIKW model for the cognitive engineering [12]. Pretorius and others study how the DIKW model can assist managers in implementing their deep mine cooling system’s performance [13]. The above literature review shows a fairly high popularity of the DIKW model among specialists in the field of information and knowledge management, who operate with the categories of the virtual world. At the same time, there is an increase in the number of publications on the use of the DIKW model in management, where the categories of the virtual world are inextricably linked with the categories of the real world [14–16]. The need for such a connection was noted in 2009 by Fricke, criticizing the classical DIKW model of Ackoff for its isolation from the real world. Discussing the nature of the data, he emphasized that “The pyramid has no basis” [17]. While arguing with Ackoff about the nature of wisdom as the pinnacle of the DIKW hierarchy, Fricke argued that “Wisdom rather refers to the practical use of know-how to achieve the end results” [17]. It should be emphasized that in the Ackoff classical model there are implicit connections between the higher elements of the DIKW hierarchy and the real world, since it states that “Knowledge...provides the ability to control the system” [3], and management means affecting an object. Some elements of the decision-making model are also present in the works of other authors analyzing the DIKW model [18]. However, the relationships of the elements of the DIKW hierarchy with the real world are not sufficiently represented in the literature. The DIKW model reflects the hierarchic
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