TOOLKIT FOR INTEGRATED MANAGEMENT OF BREAKTHROUGH INNOVATIONS IN HIGHER EDUCATION INSTITUTIONS

European Science Economy TOOLKIT FOR INTEGRATED MANAGEMENT OF BREAKTHROUGH INNOVATIONS IN HIGHER EDUCATION INSTITUTIONS Vasyl SHEVCHYK Ph.D in Medical Sciences, Department of Entrepreneurship and Business, Kyiv National University of Technologies and Design, Kyiv, Ukraine ORCID ID: https://orcid.org/0000-0002-3696-1968 Abstract. The article presents the results of research on integrated management of breakthrough innovations in higher education institutions using a functional-structural approach and methodology, which is the basis for the explication and analysis of the organizational-functional structure of innovative companies and the distribution of performers by personality types using the MBTI model. Keywords knowledge economy, high-tech industries, higher education institutions, innovations, innovation process, breakthrough innovations. Introduction “Breakthrough” in the economy means a breakthrough in those of its foundations that form several initial institutional bases (institutions): education (human capital), science, high-tech production (intellectual capital), investments, social values. The level of development of these institutions, their qualitative composition and determine the possibilities of making a breakthrough in the economy. Such a breakthrough is possible only with advanced economic development. The substantial form of existence of the advanced economic development model is an innovative economy. It is in the conditions of an innovative economy focused on the development of high-tech industries that it is possible to ensure the country's transition to a new long-term wave of economic growth, the technological basis of which will be the 5th–6th and 7th technological systems. The aim of the article. is a justification for integrated management of breakthrough innovations in higher education institutions Literature review. «Breakthrough innovations» – are innovations, which open up a new technological cycle, a new cycle of innovative business, since their purpose is not in the development of existing and established basic technology, but to completely change this technology and fundamentally change the market. A «breakthrough innovation» model is the theory of Clayton Christensen [1], which he first described in his book European Science Economy «The Innovator's Dilemma: When New Technologies Cause Great Firms to Fail» in 1997 [2]. This model can be used to describe the impact of new technologies on the functioning of the firm. Clayton Christensen has studied the causes why major companies, global leaders in their branch of industry are rapidly losing their dominant positions and lose their primacy when new technologies are emerging in the market. For the first time such dependence was brought out by J. Schumpeter, believing that radical innovations are accompanied by a creative destruction of the transformation process [3]. Research methodology. The method of structuring the description of functions, described in the literature as a functional-structural approach (FSP), This technique is the basis for the explication and analysis of the organizational-functional structure of innovative companies and the distribution of performers by personality types using the MBTI model. The main provisions of the FSP in a concise form can be expressed in the following provisions: orientation primarily on the range of tasks of the company, taking into account existing processes and patterns of their development; the functions of the company (its subsystems and elements) have a normative nature and a “through-the-counter” functional approach to its analysis and synthesis; the primacy of the functional purpose of the organizational unit in relation to the structure of the innovative company: the function is primary, the structure is secondary; any function is subordinate to the purpose of the existence and functioning of the innovative company; the interaction of the function and the structure of the company in the development process is characterized by a contradictory unity; their consistency and correspondence to each other are temporary, variable and have a cyclical nature. Stage 1. Isolation of the structure of functions from the existing organizational structure. Stage 2. Based on the matrix, the functions are classified for use in ERP systems and for monitoring the consolidation of functions. Stage 3. Analysis of the existing functional distribution of performers. At the same time, the intensity of temperament and professional competencies (TCI) is particularly suitable for distinguishing roles in a team, a project team, since it measures adaptive functioning better than other modern cadastres [10]. Unlike the MBTI model, the TCI model measures seven parameters of adaptive functioning, each of which is related to functioning and adaptability as a performer in such critical roles as decision-making, human relations and emotional self-regulation [14]. To implement “breakthrough innovations”, the composition of the HEI team must meet certain requirements for creativity, innovative thinking and the ability to work with information and implement bold ideas in life. Therefore, the MBTI model is more suitable for this. According to this criterion, sixteen psychotypes can be divided into four groups of subtypes: EN, IN, ES, IS. It is also important to distinguish subtypes that describe the relationship of leadership and subordination, since there is always a certain hierarchy in the project team: TE, SI, NE, FI. It is believed that the combination of the psychological type of constructs N – Intuition (intuition) and E – extraverted (extroversion) is a necessary condition for the fruitful European Science Economy implementation of “breakthrough technologies” [15]. That is, for the psychotype “EN – Brainstormer” (brainstorming), as a rule, the form of conduct is an extroverted process [16]. Sensory and intuition, for this subtype (EN), are usually balanced, since EN lives in a world of abstract reasoning; nothing is a dogma for him; in his reality everything is interconnected and everything is possible; he easily combines facts known to others, however, the relationship between which was not obvious [16]. Let us consider the interaction of members of the ZVO project team of different psychotypes when solving problems using the brainstorming method. At the first stage, the ENTP, ENFP, ENTJ, ENFJ, INTJ, ESTJ, ESTP psychotypes should participate in the collective discussion process (Table 1). They put forward ideas and discuss them. It is advisable to involve ESFP and ESFJ psychotypes in the discussion to create a creative atmosphere. INTP, INFP, INFJ psychotypes can put forward ideas, but collective discussion can be an obstacle for them. For these psychotypes, individual, written brainstorming, brainstorming using e-mail, etc. are more suitable. ESTJ, ESTP psychotypes rarely put forward their own ideas, but they can pick up and develop the ideas of other members of the project team. The ideas that were later selected for implementation are mainly put forward by ENTP, ENFP, ENTJ, ENFJ, INTP, INFP, INTJ, INFJ psychotypes. In the case of individual, written, email-based brainstorming, it is advisable to involve only these team members. Table 1 The optimal role of project team members – representatives of certain psychotypes – in the process of finding breakthrough solutions The role of team members Psychotype Communicator Generator Critic Solver Scheduler Organizer Moderator Finisher ENTP + + + + + ENFP + + + + ENTJ + + + + + + + ENFJ + + + INTP + + + INFP + INTJ + + + + + INFJ + ESFP + + ESFJ + + ISTJ + ISFJ + ISTP + + + ISFP + + ESTJ + + + + + + + ESTP + + + + In the process of selecting ideas, it is advisable to involve the psychotypes ENTP, ENTJ, INTP, INTJ, ISTP, ISFP, ESTJ. Naturally, team members who participated in the nomination of ideas should not participate European Science Economy in their selection. However, exceptions to this rule are possible for a team of four or fewer members. It should be noted that with a small number of project team members, there is a certain hierarchy of advantages in using team members in a certain role. Thus, it is more advisable to use the extroverted types ENTP, ENTJ when nominating ideas, and the introverted types INTP, INTJ when selecting ideas. After the preliminary stage of selecting ideas, it is advisable to involve ISTJ and ISFJ team members who can analyze in detail the shortcomings of ideas and obstacles to their implementation. In addition, ISTJ are good candidates for preparing and recording the results of brainstorming. The best moderators of the discussion are the psychotypes ENTJ, ESTJ, ESTP. In a team of several people, it is advisable to appoint an ESTP as a moderator, since ENTJ and ESTJ can also perform other roles. ENTJ can participate in the nomination of ideas, and ESTJ - in their selection. Stage 4. Mathematical substantiation of the model for optimizing the organizational structure. The mathematical substantiation of the sequence of using the MBTI model for optimizing the organizational structure was carried out using cluster analysis for the Kyiv National University of Technologies and Design. This university recently successfully implemented 2 innovative projects in the field of digitalization of organizational process management. To generate these innovations, it is necessary to optimize the personnel of performers who were previously tested using MBTI psychotype tests. To conduct hierarchical agglomerative clustering, the Ward linking method and the Half Square Euclidean distance were used as a measure of similarity for project team members (18 people) who have similar MBTI psychotype assessments. The typical representatives of each cluster are calculated as the average value within the cluster, which is presented in Fig. 2. Fig. 1: Identification of psychotype clusters by optimal roles in the project team In Fig. 1, clusters 1–5 are arranged in sequence from top (cluster 1) to bottom (cluster 5). The optimal number of 5 clusters was calculated using the Davis–Bouldin index [17]. The degree of overlap between two clusters European Science Economy was estimated by calculating the pairwise probability of intersection between them based on the hypergeometric distribution: where p observations belong to the set Pi of size h and also belong to the set Gj of size n; and g is the total number of observations. Therefore, the lower PIhyp, the higher the degree of overlap. Here, the p–value of the hypergeometric test is used as a measure of the strength of commitment/ability to perform a particular role in the team. The distribution of MBTI scores across the sample is shown in Table 2. On average, project team members were exceptionally extreme, being very high in goal persistence. However, there was considerable variability around these mean values, which was examined by cluster analysis. Table 2 Team member role ratings, according to MBTI psychotype Type of activity in the team Descriptive descriptor Percentage average Evaluation value Standard deviation of the estimate Generator optimistic 38,6 –0,29 1,20 Communicator orderly 46,0 –0,10 1,20 Critic separate 21,2 –0,80 1,00 Solver confident 88,5 +1,20 1,00 Scheduler responsible 65,5 +0,40 0,97 Organizer helpful 59,9 +0,25 1,20 Moderator altruistic 55,2 +0,13 0,99 Finisher stubborn 47,2 +0,11 0,88 Clusters of team members with similar MBTI psychotype profiles were identified using Ward’s clustering method without considering the current role in the project. MBTI psychotype dimensions were recorded from low to high, clustering at the mean value of the Likert scale for each trait, as shown by color coding in Fig. 2 for ease of visual review and pattern recognition. The optimal number of personality clusters identified was five according to the Davis–Budin validity index (Fig. 2). Each of these five clusters was characterized by a separate personality profile. European Science Economy Fig. 2: Profile of average values of MBTI psychotype clusters compared to mappings to current roles in the project Then, five personality clusters were tested for the correspondence of the role in the project and the personal psychotype. Cluster 1 (“investigator”) is inherent in the project function “Collective discussion”, 22% (4/18) of team members correspond to this cluster. The profile of individuals in this cluster is purposeful, highly collegial and skeptical, which allows them to solve organizational and planning tasks. Cluster 2 (“command”) is inherent in the project function “Criticism, decision selection”, 28% (5/18) of respondents correspond to this cluster with high indicators of “independence”, autonomy, self-direction, low levels of shyness and self-criticism. Cluster 3 (“trustworthy”) is inherent in the project function “Moderator”, 17% (3/18) of respondents, they have a trustworthy character, they are persistent, controlling, but friendly and encouraging. Cluster 4 ("breakthrough"), inherent in the project function "Nomination of ideas", in 11% (2/18) of respondents. They are persistent, ambitious, inventive. People of this cluster are impulsive with low responsibility and high novelty of search. Cluster 5 ("finishers"), in 22% (4/18) of respondents. Respondents of this cluster have a sociable temperament, are responsible, scrupulous, persistent and hardworking. When analyzing the scores of the obtained clusters and their relationship with psychotypes, a correlation was found, typical for the general situation of the need to form an organizational structure for the development of breakthrough innovations. The choice of the optimal organizational structure was carried out using the ANOVA toolkit. Calculations showed that clusters were moderately correlated (p <0.01) with self-direction in the search for breakthrough innovations (r=0.62), the stability of the new organizational structure (r=0.38) and the reduction of project failure risks (r=-0.27), but significantly correlated with cooperativeness (r=0.18) and dependence on reward (r=0.16). The conducted studies proved that the success of the implementation of a breakthrough innovation project directly depends on the qualitative and quantitative composition of the project team and their personality profile. The level of satisfaction with the team role may be associated with minor differences in personality that were not fully captured by the five average cluster profiles. European Science Economy Conclusions The conducted study demonstrates the methodology of the MBTI model of psychotypes for substantiating the optimal structure of invariant management of HEIs as a tool for implementing "breakthrough innovations". The initiation and successful implementation of the project directly depends on an optimally balanced team of performers, the selection of which is advisable to carry out depending on their identified psychotypes and the functions performed. In addition, studies have proven that project team members are functional people who are usually adaptable and elastic, as evidenced by their high self-direction, cooperativeness, perseverance and life satisfaction. Here we found that there is significant heterogeneity among these basic features, and this heterogeneity in personality profiles is strongly related to individual differences in the choice of role in the project. References 1. Dyer, J., Gregersen, H. & Christensen C. (2011). The Innovator's DNA: Mastering the Five Skills of Disruptive Innovators. Boston, MA: Harvard Business Press. 2. Christensen, C. (1997). The innovator's dilemma: when new technologies cause great firms to fail, Boston, Massachusetts, USA: Harvard Business School Press. 3. Schumpeter, J. (1989). Essays: on entrepreneurs, innovations, business cycles, and the evolution of capitalism. Paris: Transactions Publishers. 380 p. 4. Foster, R. & Kaplan, S. (2001). Creative Destruction: Why Companies That Are Built to Last Underperform the Market and How to Successfully Transform Them, New York, NY: Currency/Doubleday. 5. Ryan, L. (2013). Leading change through creative destruction: how Netflix's self–destruction strategy created its own market. International Journal of Business Innovation and Research (IJBIR), Vol. 7, No. 4. https://doi.org/10.1504/ijbir.2013.054868. 6. Arthur, M. & Diamond, Jr. (2007). Schumpeter’s Creative Destruction: A Review of the Evidence. Econ Journal Watch, 4(3). 15 р. 7. Hanushchak–Efimenko, L., Shcherbak, V. & Kaplun, V. (2017). Resource–innovative model of raising university autonomy. Scientific bulletin of Polissia, 1(9), Р. 2. рр. 61–69. DOI: 10.25140/2410–9576– 2017–2–1(9)–61–69. 8. Allan, J. & Lawless, N. (2005). Learning through online collaboration by SME staff а scoping investigation into likely team–role stressors. Education + Training, 47 (8/9). рр. 653–664. https://doi.org/10.1108/00400910510633189. 9. Briggs Myers, I. (1962). The Myers–Briggs Type Indicator: Manual. https://doi.org/10.1037/14404–000. 10. Myers–Briggs, I., McCaulley, M. & Quenk, N. (1998). MBTI Manual (A guide to the development and use of the Myers Briggs type indicator). Consulting Psychologists Press, 131 p. https://doi.org/10.1037/t01943– 000. European Science Economy 11. Hogan, R. & Blake, R. (1999). John Holland's Vocational Typology and Personality Theory. Journal of Vocational Behavior, Vol. 55, Iss. 1, P. 41– 56. https://doi.org/10.1006/jvbe.1999.1696. 12. Staggs, G. D., Larson, L. M. & Borgen, F. H. (2007). Convergence of Personality and Interests: Meta–Analysis of the Multidimensional Personality Questionnaire and the Strong Interest Inventory. Journal of Career Assessment, Vol. 15, Iss. 4, P. 423–445. https://doi.org/10.1177/1069072707305760. 13. Grucza, R. A. & Goldberg, L. R. (2007). The Comparative Validity of 11 Modern Personality Inventories: Predictions of Behavioral Acts, Informant Reports, and Clinical Indicators. Journal of Personality Assessment, Vol. 89, Iss. 2, P. 167–187. https://doi.org/10.1080/00223890701468568. 14. Keirsey, D. & Bates, M. (1978). Please Understand Me. Character and Temperament Types. Del Mar, CA: Prometheus Nemesis Book Co. 15. Walter, J. (2003). Neurolinguistic programming: temperament and character types. BMJ, Vol. 326, Iss. 7394, P. 132–133. https://doi.org/10.1136/bmj.326.7394.s133a. 16. Sievert, M., Zwir, I., Cloninger, K. M., Lester, N., Rozsa, S. & Cloninger, C. R. (2016). The influence of temperament and character profiles on specialty choice and well–being in medical residents. PeerJ, 4:e2319. https://doi.org/10.7717/peerj.2319. 17. Zwir, I., Huang, H. & Groisman, E. A. (2005). Analysis of differentially– regulated genes within a regulatory network by GPS genome navigation. Bioinformatics, Vol. 21, Iss. 22, P. 4073–4083. https://doi.org/10.1093/bioinformatics/bti672.