Andrey I Vlasov
email: vlasov@iu.4.ru

Pavel V Grigoriev

Aleksey I Krivoshein

Vadim A Shakhnov
email: shakhnov@bmstu.ru

Sergey S Filin

Vladimir S Migalin

Smart management of technologies: predictive maintenance of

Keywords: management of technologies, monitoring of technological processes, industrial equipment, predictive repair, smart management concept

published or not. The documents may come

Introduction

Modern conditions imply a new development model based on human capital, innovation and high technologies. Flexibility and speed of innovation are the key success factors. These trends are typical of both social life and industrial production processes. In this context, smart environments perform a special role: in fact, they serve as a frame which will be added some infrastructure that can guarantee new product quality. Smart production involves using machines capable of exchanging information with other production systems and operating with a high degree of autonomy; using the industrial Internet of Things; and utilizing Big Data cloud service and technology. However, the most important link in this chain is integration platforms of an enterprise which are aimed at receiving data from equipment, analyzing and aggregating them. One of the sides of this issue is the system of industrial equipment that requires its condition to be constantly monitored. At present, several methods of servicing equipment have been formed, among which three basic ones can be distinguished: emergency, routine and predictive. The first kind is the maintenance of equipment after its failure [START_REF] Brüel | Monitoring the state of machinery[END_REF]. This approach is justified in the maintenance of simple, cheap equipment, when reservation is available, and replacement will be cheaper than repair work to restore equipment.

The second type of maintenance is planned preventive maintenance of equipment in accordance with the regulations [START_REF] Brüel | Monitoring the state of machinery[END_REF]. In this case, the maintenance is carried out in accordance with the manufacturer's recommendations at regular intervals. The equipment maintenance work is performed with a certain period, which is determined by statistical analysis and, in accordance with the regulatory documents, the period typically equals the time during which at least 98% of the equipment operates without failure [START_REF] Brüel | Monitoring the state of machinery[END_REF].

When servicing according to the regulations, it would seem that the opportunity to use the guarantee of the manufacturer is not lost, at least. However, it should be taken into account that almost 50% of all technical maintenance works according to the regulations are carried out without their factual need. In addition, for many types of equipment, maintenance and repair according to the regulations do not reduce the frequency of their failure. Moreover, the reliability of the equipment operation after maintenance, if the latter involves disassembling the mechanism or replacing parts, is often reduced, sometimes temporarily, until the moment of their running-in, and sometimes this decrease in reliability is caused by the manifestation of installation defects which were missing before the maintenance.

The third type of maintenance, which is discussed in detail in the present paper, is predictive maintenance, also known as maintenance according to the actual technical state [START_REF] Rawi | Machinery Predictive Analytics[END_REF][START_REF] Stone | Introducing Predictive Analytics: Opportunities[END_REF]Vlasov et al, 2017;[START_REF] Mobley | An introduction to predictive maintenance[END_REF]. With this type of service, the state of the equipment is monitored continuously or periodically. Depending on the results obtained, a forecast is made of the technical condition of the equipment and maintenance programs are formed. The predictive maintenance systems can predict the state of the system based on the current state of equipment and determine the necessary maintenance activities. Thus, the probability of an unscheduled system failure is minimized.

That is why in the present research the main attention is paid to the analysis of the model for evaluating the effectiveness of predictive services using wireless sensor networks. Presently, the problem of constructing distributed systems for data collection and monitoring of technological processes and individual equipment is more urgent than ever. This leads to the need to develop methods for constructing intelligent monitoring of technical industrial systems.

Literature review

In connection with monitoring industrial systems in order to prevent malfunctions, the authors will analyze a model of a predictive maintenance system based on a wireless sensor network (Vlasov et al, 2017;Sharapov & Polischuk, 2012;[START_REF] Kozlova | Metrological support of information processing systems: A summary of lectures[END_REF][START_REF] Bogdanov | Prospects and problems of using wireless sensors with autonomous power supply[END_REF][START_REF] Shakhnov | Method of functioning of the wireless sensor network[END_REF]. Sensors are used as the elements of systems recording various data. Sensors are means of measurement designed to generate signals of measurement information in a form that is convenient for transmission, further transformation, processing and/or storage, but which cannot be directly perceived by the observer [START_REF] Kozlova | Metrological support of information processing systems: A summary of lectures[END_REF]. Depending on the signal transmission medium, the sensors can be wired and wireless. The use of wired systems is not always effective due to a high cost of installation and commissioning, as well as technical maintenance. In addition, in some situations the installation of wired sensors is altogether impossible for technological or organizational reasons. Among the advantages of wireless sensors there are minimal restrictions on their placement, the possibility of introducing and modifying the network of such sensors on the operated object without interfering with the operation process, reliability and fault tolerance of the entire system in the event of disruption of individual connections between nodes [START_REF] Bogdanov | Prospects and problems of using wireless sensors with autonomous power supply[END_REF].

The predictive maintenance systems based on the wireless sensor network, in contrast to other types of technical maintenance, save the data obtained in the monitoring process, which allows usingadvanced methods of monitoring the technical state and analyzing the data in real time, as well as making forecasts for the technical condition of the equipment.

For a long time, systems with wired communication channels provided a reliable transmission medium and a high speed with a long service life. Despite many advantages, wired solutions also have a number of limitations, which gradually make them less attractive compared to wireless technologies. Among these limitations,one can identify the following:  spatial arrangement of sensors;  the costs of maintaining communication channels;  the deployment time of wired networks.

These three major shortcomings of wired transmission explain the fact that wireless networks, radio networks are very rapidly gaining momentum. This is explained by the convenience of their use, low cost and acceptable transmission capacity.

When building a wireless sensor network, one needs to choose a data transfer protocol,since, depending on the field of application of the wireless sensor network, one or another protocol or standard can be used to interact with wireless sensors of the reading equipment [START_REF] Vlasov | Methods of proactive prediction of broadband network status[END_REF]. A wireless sensor network consists of a set of spatially-distributed intelligent sensors designed to monitor physical parameters such as vibration, temperature, strain, pressure, etc. Each sensor node in the network performs the function of reading, processing and wireless transmission of data. The use of microelectromechanical system technology (MEMS) provides an opportunity for low-cost production of low-power multifunctional sensors of small size and low weight. The use of wireless sensors with autonomous power supply necessitates a detailed consideration of the principles of their construction, functional filling and used circuit solutions in accordance with the generalized structural scheme in Figure 1. The registration of the monitored characteristics is carried out by the sensing element (SE) of the sensor. At the same time, to measure one and the same quantity, the sensors can be applied that use various physical processes of its transformation into electrical signals and various principles of action [START_REF] Andreev | Silicon pressure transmitters with overload protection[END_REF][START_REF] Freyden | Modern sensors. Reference book[END_REF]Gotra & Tchaikovsky, 1995;[START_REF] Yudin | Project oriented approach in educational robotics: From robotic competition to practical appliance[END_REF][START_REF] Burduk | Evaluation of the risk in production systems with a parallel reliability structure[END_REF]. The processing unit is designed to amplify an electrical signal and convert it to a form convenient for transmission. The functional filling of the processing units depends on the type of output signal. It should be noted that the criteria for achieving a minimum current consumption are contrary to the criteria for achieving maximum performance; therefore, micropower op amps have very modest speed parameters [START_REF] Freyden | Modern sensors. Reference book[END_REF]Gotra & Tchaikovsky, 1995). 493

According to the data presented in Table 1, it is proposed to use wireless sensor networks based on radio frequency identification (RFID) (Vlasov, Grigoriev & Zhalnin, 2017b), since the off-line operation time and the number of simultaneously read labels are the main parameters when choosing a radio interface in the systems of predictive equipment maintenance.

Methods

The concept of a predictive maintenance system based on sensor networks with an intelligent controller

Intelligent controllers are increasingly used in distributed industrial systems of different purposes. Recently, to obtain data on the status of equipment, wireless sensor networks, which consist of various sensors including infrared sensors, acoustic sensors, vibration, shock and acceleration sensors, have been increasingly used (Vlasov, Grigoriev & Zhalnin, 2017b;[START_REF] Freyden | Modern sensors. Reference book[END_REF]Gotra & Tchaikovsky, 1995;[START_REF] Yudin | Project oriented approach in educational robotics: From robotic competition to practical appliance[END_REF][START_REF] Burduk | Evaluation of the risk in production systems with a parallel reliability structure[END_REF]. Figure 2 presents a generalized conceptual scheme for obtaining data and analyzing the state of equipment in real time. In the process of work, continuous monitoring of the status of the modules takes place, as well as comparison of the obtained data from the sensors with readings during normal operation of the equipment, to determine possible malfunctions.

Figure 2 shows a generalized example of a monitoring object (1), on the equipment of which wireless sensor tags are installed. Abandoning wired sensors allows placing sensors in hard-to-reach places, thereby increasing the number of sensors themselves and the accuracy of the parameters obtained during the monitoring process. Sensor data are read and transmitted over the wireless network to external servers, where data are converted, stored and processed (2). All data received from sensors is stored in a database located in the cloud. The data is required to present statistics. There is also a defect database, which includes information related to the defects of various devices, the correspondence of arising defects and the operating time on which such defects usually occur. Data obtained as a 494 result of monitoring is processed and then sorted in the order of frequency of occurrence. This approach allows forming a program of predictive maintenance of equipment, as well as predicting the failure of any of its parts.

Saved data can be obtained at any time in the form of graphs or individual events and displayed on the screen of a computer, tablet or mobile phone (3).

Broad implementation of solutions for the implementation of the concept of "Internet of things" imposes a number of requirements on the approaches to the realization of the functions of predictive maintenance. To achieve this goal, intelligent controllers and multi-agent technologies are needed (Vlasov et al, 2017). To each object in the real world, a software agentis put into correspondence: an object with some degree of intelligence.

Recently, systems have been developed based on KNX and HDL wired buses or Zwave and ZigBee wireless standards. Such solutions often have unused options and, at the same time, do not have priority important functions. The software and hardware complex should interact with the following subsystems: power supply, universal chassis, localization module. In general, the intelligent controller must provide:  monitoring of engineering systems (collection of information with the construction of graphs and temporary reports on current data);  processing of the obtained information (calculation of maintenance, predictive repair and replacement of elements);  timely notification and assistance in decision-making;  remote control of engineering systems in the presence of such functionality;  self-diagnostics of the main nodes, monitoring results should be transferred to a single cloud storage.

Within the framework of the work, the concept of a universal intelligent communication platform for integrated embedded solutions is proposed, which allows connecting the decision module and heterogeneous sensor elements, localization and positioning systems into a single whole. In the "Internet of Things" paradigm, a logical level is added to the hardware subsystems, which allows transferring the computational tasks directly to the execution units themselves, and transmittingonly the processed data to the data collection and storage station. This task can be solved with the help of universal controllers. Such controllers have sufficient computing power to perform tasks not only of processing data received from field devices, but also tasks that require the implementation of complex calculation algorithms. Universal controllers can function as a buffer memory device: all measured and calculated values are put into the controller memory and stored for the required period. Thus, even if communication with the data collection station is lost, they will be buffered, and then transferred to the server when communication is resumed.

When solving practical problems, special attention is paid to ensuring that intelligent controllers are scalable and expandable to localize solutions for specific parameters. One can identify the following tasks, which should be addressed at the hardware level (controller level):  transfer of signals from the field level devices (sensors) to engineering units and transfer of information to a data collection station;  execution of algorithms for calculating physical quantities to take into account the obtained data;  control of technical systems, taking into account the built-in algorithms, as well as according to the instruction of the head station;  formation and transmission of warning and alarm signals.

The tasks of the data collection station (processing module) can be described in more detail as follows: structuring and formalization of measurement information, accumulation of the dynamics of changes; processing and analysis of incoming data; analysis of the obtained results of processing to identify non-typical behavior and search based 495 on the results of the analysis of a list of possible causes and factors that may lead to the development of contingencies. The intelligent controllers implemented in this concept provide the basic functionality of predictive repair and can be integrated into the complexes implemented within the concept of the "Internet of Things"(IoT).

A model for estimating the effectiveness of predictive repair

Based on the analysis of the solutions presented in the fundamental works on predictive repair (Vlasov et al, 2017a;[START_REF] Burduk | Evaluation of the risk in production systems with a parallel reliability structure[END_REF]Vlasov, Grigoriev & Zhalnin, 2017b;[START_REF] Curcuru | System monitoring and maintenance policies: A review[END_REF][START_REF] Rausand | System reliability theory: Models, statistical methods and applications[END_REF][START_REF] Lu | Using degradation measures to estimate time to failure distribution[END_REF][START_REF] Crowder | On a scheme for predictive maintenance[END_REF][START_REF] Baptista | Forecasting fault events for predictive maintenance using data-driven techniques and ARMA modeling[END_REF], to assess the effectiveness of predictive repair, it is proposed to use the model based on minimizing the costs of maintenance, diagnostics and taking into account the risks of failure of individual components:

      d p p f pr p c S t S F L C S c     , ( 1
)
where pr C is the cost of predictive maintenance; p S is the value of the signal received from the sensors in the process of monitoring the state of the equipment (is a random quantity);   To obtain the average time until the equipment fails, it is necessary to conduct tests that include diagnostic measurements. When analyzing the obtained data, it is necessary to calculate the average time from the moment the signal about the unstable operation of the equipment is received until it completely fails. In this case, applying the equation ( 2), it is possible to calculate the average operating time of the equipment from the moment of receiving the signal about unstable operation until it completely fails. 496

Figure 3 shows a graphical method for determining the input data for calculating the average operating time of the equipment, depending on the diagnostic signals from the sensors in the case of predictive maintenance. As the unit costs for monitoring the state of the equipment for obtaining a diagnostic signal, one can consider the costs of deploying the wireless sensor network and the service; this can be described according to the formula (4):

  r s t d C k C C l c      . (4)
Thus, the model for optimizing predictive repair using wireless sensor networks, based on minimizing the costs of equipment maintenance, can be described according to the formula (5):

            r s t S m i S m n j p j p i p f pr d S m i S m n j p j p i p f pr d p p f pr p C k C C l S t S t n S F L C c S t S t n S F L C c S t S F L C S c p p p p                                          ) ( 1 ) ( 1 ) ( 1 ) ( 1 ) ( ) ( 1 ) ( ) ( 1 , ( 5

Results and Discussion

The proposed module of intelligent monitoring of technical systems was tested in the framework of monitoring the state of electric motors. If the motor is in operation for a long time, various defects may appear in it. If they are not remedied in time, the electric motor will fail. In any electric motor there are rolling bearings or friction bearings. The estimated life of rolling bearings on average does not exceed 8,000-10,000 h, which is slightly more than one year of continuous operation. In practice, rolling bearings often serve more than this period. Analyzing the signals from the vibration sensors installed on the supporting bearing of electric machines, defects in their condition were detected. When choosing defects that are described in this section, the authors proceeded from the following definition: if a defect can be diagnosed by installing vibration sensors on the supporting bearings, then its description is included in this section. Figure 4 shows a histogram of the appearance of defects, depending on the running time of the electric motor. During monitoring, the readings were taken from AC motors. Out of 155 cases of defects, only 3 cases occurred before the operator received a signal about a possible failure of the equipment. As can be seen from the graph The presented concept of a system of predictive maintenance on the basis of sensor networks allows real-time analysis of the state of equipment. According to the data received from sensors, a program of maintenance of equipment is formed. In the future, this approach is supposed to be developed taking into account the microminiaturization of the elements of the intellectual controller, the creation of a specialized element base that allows diagnostics of individual subsystems in real time with intelligent processing functions [START_REF] Whitaker | Application of multivariate data analysis to machine power measurements as a means of tool life predictive maintenance for reducing product waste[END_REF][START_REF] Amruthnath | A research study on unsupervised machine learning algorithms for early fault detection in predictive maintenance[END_REF].

Conclusion

Enhancing the efficiency of industry and infrastructure through the use of smart electronic systemsthe so-called smart environmentsis one of the most well-known avenues for technological development. The distinctive features of smart environments [START_REF] Cook | Smart Environments. Technologies, protocols and applications[END_REF] are remote control, communication between devices, sophisticated device functionality, developed network standards, obtaining information through smart sensory networks and its distribution, predictive and decision-making abilities. Currently, manufacturing companies gaining experiences in production in networks and smart logistics and develop new organisational structures and business models which better benefit from the new technologies and which adapt faster to the rapidly changing network environments [START_REF] Prause | On sustainable production networks for Industry 4.0[END_REF].

Predictive maintenance of equipment is aimed at predicting the place and time of probable breakdown, as well as avoiding downtime and reducing maintenance costs. The use of wireless communication channels in the monitoring system makes it possible to deploy the sensor network in the shortest possible time, regardless of the spatial arrangement of the sensors. A model for optimizing predictive maintenance of equipment using wireless sensor networks based on minimizing the costs of maintenance, diagnostics and deployment of the equipment monitoring system is proposed. The presented concept of a system of predictive maintenance on the basis of sensor networks allows real-time analysis of the state of equipment.

Fig. 1 .

 1 Fig. 1. Generalized block diagram of the wireless sensor Source: compiled by the authors

Fig. 2 .

 2 Fig. 2. Analysis of the state of equipment in the real time modeSource: compiled by the authors.

  function of the probability of failure in relation to the signal from the sensor; f L are losses due to the risks of failure; d c are unit costs for monitoring the condition of equipment to obtain a diagnostic signal; operating time of the equipment before failure, which is determined by the formula (2): of operation of the i-th object with the normal value p S of the signal; the j-th object, which failed before the diagnostic signal from the sensor reached the state p of objects that reach the state p S without failures; n is the total number of sensors for monitoring.

  When calculating the average operating time, it is proposed to apply a simplified model in which the deterioration of the technical state (change of the diagnostic signal) runs along a straight line from the initial state pz S to the limit value of the technical state (to failure) pfi S of the i-th object. The calculation of ) ( ISSN 2345-0282 (online) http://jssidoi.org/jesi/ 2018 Volume 6 Number 2 (December) http://doi.org/10.9770/jesi.2018.6.2(2)

Fig. 3 .

 3 Fig. 3. Graphical method for determining the input data for calculating the average operating time of the equipment, depending on the diagnostic signals from the sensors Source: compiled by the authors.

Fig 4 .

 4 Fig 4. The number of defects that occur depending on the running time of the electric motor Source: compiled by the authors.

Fig. 5 .Fig. 6 .

 56 Fig. 5. The number of defects that occur depending on the running time of the electric motorSource: compiled by the authors.

Table 1

 1

	shows a comparison

Table 1 .

 1 Comparison of RF interface types for wireless tags / sensors

	Indicators	Bluetooth	Wi-Fi	ZigBee	RFID UHF
	Frequency range (Russian Federation)	2.4 GHz	2.4 GHz	2.4 GHz	868 GHz
			5 GHz	868 MHz
	Maximal communication range, m	1/10/100	100	200	50
		(depends onthe
		class
		ofequipment)
	Data transfer rate	721 Kbit/s	450/900/1.3	256 Kbit/s	128-620 Kbit/s
			Gbit/s
	Network topology	Point-to-point	Point-to-point	Point-to-point	Reader-set of
		Star	Star	Star	labels/sensors
				Tree
				Mesh
	Duration of autonomous work from battery power at	Half-year	60 days	1.5 years	2-3 years
	30-second sensor polling period
	Simultaneous reading of many labels	Up to 20	Up to 20	Up to 10	Up to 300
	Software protocols of interactionwith the control	Protocol stack	Protocol stack	Protocol stack	Protocol LLRP
	system	Bluetooth	TCP/IP	ZigBee	(Low Level
					Reader Protocol)
		Source: compiled by the authors

  )where pr C is the cost of predictive maintenance; p S is the value of the signal received from the sensors in the process of monitoring the state of the equipment (is a random quantity);  

				The International Journal
	ENTREPRENEURSHIP AND SUSTAINABILITY ISSUES
	ISSN 2345-0282 (online) http://jssidoi.org/jesi/
		2018 Volume 6 Number 2 (December)
		http://doi.org/10.9770/jesi.2018.6.2(2)
	probability of failure in relation to the signal from the sensor; f L are losses due to the risks of failure; d c are unit
	costs for monitoring the condition of equipment to obtain a diagnostic signal;   p S t	is the average time of
	operation of the equipment before failure, which is determined by the formula (2); l is the number of sensor tags
	with sensors; t C is the cost of one sensor tag; s C is the cost of one sensor; k is the number of readers; r C is the
	cost of purchasing and installing a single reader.
	F	S	p	is the distribution function of the
	497

Volume 6 Number 2 (December) http://doi.org/10.9770/jesi.2018.6.2(2)

Acknowledgments

The research was conducted with the support of the Ministry of Education and Science of Russia within the framework of the project under the Agreement No.14.579.21.0142 UID RFMEFI57917X0142.

* The research was conducted with the support of the Ministry of Education and Science of Russia within the framework of the project under the Agreement No.14.579.21.0142 UID RFMEFI57917X0142.


Jurate Cerneviciute
email: jurate.cerneviciute@vda.lt

Rolandas Strazdas
email: rolandas.strazdas@vgtu.lt

TEAMWORK MANAGEMENT IN CREATIVE INDUSTRIES: FACTORS INFLUENCING PRODUCTIVITY

Keywords: collective creativity, teamwork, efficiency in creativity, creative industries

The 'experience' economy, characterizes by the growing needs for cultural identity and social empowerment, and aided by technologies of knowledge generation, information processing and communication of symbols, further reinforce this. The creative industries involve the concretization of an image, through whatever medium for some form of economic return. However, the nature of experience goods makes demand pattern unpredictable and production process difficult to control. The uncertainty of demand for the creative product, pose managerial and organizational challenges. The structure and staffing of creative projects are often temporary, as are capital investment. Success is dependent on the composition of projects teams with individuals and groups working in a highly interactive and adaptive fashioning of the product: Despite this fact, a great deal of research conducted in the area of group dynamics suggests that groups are often much less creative and productive than they are usually assumed. The important question of how to manage creative teams to achieve a high productivity with limited resources and time arises in innovation management both from the theoretical and practical points of view. There is still no clarity which factors affecting productivity of teamwork are more important than others. The study was aimed at the identification most important factors for the productivity of teamwork. The survey of 113 student creative teams in 8 counties (Lithuania, Poland, Canada, China, France, Italy, Russia, and Denmark) was performed. Based of the findings the hierarchy of the significance of the factors influencing the productivity of teamwork is established and described in the article.

Introduction

The 'experience' economy, characterizes by the growing needs for cultural identity and social empowerment, and aided by technologies of knowledge generation, information processing and communication of symbols, further reinforce this [START_REF] Stepaniuk | Visualization of expressing culinary experience in social network, memetic approach[END_REF][START_REF] Strielkowski | Heritage, screen and literary tourism[END_REF] 504 symbolic creativity being organized around, and for, the market: the industrialization of culture and its commodification for the mass audience [START_REF] Hesmondhalgh | The Cultural Industries[END_REF][START_REF] Mitkus | Analysis of state aid policy to stimulate innovation and internationalization in Lithuanian animation industry[END_REF].

The creative industries involve the concretization of an image, through whatever medium for some form of economic return. However, the nature of experience goods makes demand pattern unpredictable and production process difficult to control. The uncertainty of demand for the creative product, pose managerial and organizational challenges. The structure and staffing of creative projects are often temporary, as are capital investment [START_REF] Defillippi | Paradox in project-based enterprise: the case of film making[END_REF]. Success is dependent on the composition of projects teams with individuals and groups working in a highly interactive and adaptive fashioning of the product: the necessary skills for a film project are specialized into narrow creative, technical, and managerial realms that they are held by individual experts: creatives (writers, designers, choreographers, actors, directors), technicians (editors, cinematographers, lighting technicians) and managers (producers, line producers, controllers, accountants, financiers, and marketers), working concurrently and successively on a film [START_REF] Wasko | How Hollywood works[END_REF][START_REF] Eliashberg | The Motion Picture Industry: Critical Issues in Practice, Current Research and New Research Directions[END_REF]. A film project is typically set up and controlled by a production company where the producers, marketers, and sometimes the financiers, are long-term employed.

Creative industries and creative-based organisations need to cope with challenges whether and how to control of the creative process; how should be mediated relationship between producer and consumer. Organizational practice exemplified by the creative industries emphasizes coping with problems, managing in states of uncertainty and unknowability, and thus challenges traditional thinking on managing people, production and marketing to the consumer. The challenge for managers and policymakers is to choose the most efficient organization in order to profit from the creativity -in the case of creative industries, the key problem is how firms, labour markets, and industries are built around the raw material constituted by "creatives", i.e., individuals with cultural (as well as technical) skills.

The creative act is an act of managing self, managing others and managing objects. It is the activity of organizing, co-ordinating and directing. Success in both areas often involve collaborative practice, especially as the basis for innovation. Although emanating from different sources, there are pressures to innovate in both management and art. The artist is obliged to innovate from that which went before in order to claim an individual voice. Within business, innovation of product or process is equally a form of 'market' imperative. Both may be seen as a form of practice. While this is largely recognized for the artist, the manager does not often see him as so engaged. A process of reflection 'what it is that they do' and 'how they do what they do' is not traditionally part of a recognized discourse of management activity. The introspective nature of the artistic process as the individual 'struggles' in order to express a creative intent might on first glance place it well outside the norms of management activity.

A greater awareness of management as practice opens up discussion as to the importance of skill and craft in the practice. Breaking from the traditional view of management as a role, or the the disciplinary of industry, management as practice and the need for reflexivity and self-critique in achieving good performance have been emphasized [START_REF] Gabriel | On the paragramatic uses of organizational theory[END_REF][START_REF] Bogdanović | Dark triad characteristics between economics & business students in Croatia & Slovakia: what can be expected from the future employees?[END_REF][START_REF] Borisov | Destructive motivation of personnel: a case study of Russian commercial companies[END_REF], Mura et al. 2017;[START_REF] Vlacsekova | Effect of motivational tools on employee satisfaction in small and medium enterprises[END_REF].

It is important to focus on the nature of the relationship between creativity and management and does not privilege an 'artistic' or 'managerial' perspective. The disciplinary divide between creative programmes and management and the tension that arises between creative or creative endeavour and commercial constrains management in being able to realize creative endeavours, there is a lot that management can learn from the creative arts in terms of the way they manage their activities and the lessons that this might have for business (Lampel et al.2000;De Fillippi 1998;[START_REF] Sutton | The weird rules of creativity[END_REF][START_REF] Tvaronavičienė | Towards competitive regional development through clusters: approaches to their performance evaluation[END_REF][START_REF] Peterlin | The strategic process of developing social aspects of sustainability through the vision reflection in business education[END_REF].

The

505

It is thus important to frame an approach that does not favour one discipline over the other, but develops a common vocabulary privileging neither management nor creative endeavour.

Factors influencing productivity of teamwork: theoretical aspects

The considerable theoretical advancements have been made in linking contextual factors with intra-individual factors is [START_REF] Amabile | A model of organizational innovation[END_REF][START_REF] Amabile | Motivating creativity in organizations[END_REF] Creative ideas need to be new and unique relative to others, and to what is currently available within the team or organization. Creative ideas have to be of value or useful to the team or organization. This focus on the mix of novelty and usefulness ensures that the definition of creativity is never static, but rather bounded within a social, cultural, or historical precedent (Perry-Smith, Shalley 2003). In the creative industries, where originality or the generation of something new is usually considered the currency of the realm [START_REF] Florida | The Rise of the Creative Class: And How Its' Transforming Work, Leisure, Community and Everyday Life[END_REF], there is continued pressure to look for ways to combine what already exists into something different or unique (Hargadon, Bechky 2006), to experiment with new, different, and existing ideas, as well to completely break from the known and venture into unknown territories.

Team creativity has been described as a collective phenomenon where members behaviorally, cognitively, and emotionally attempt new things, take novel approaches to their work; or generates products, processes, or procedures that are both novel and useful [START_REF] Gilson | A little creativity goes a long way: an examination of team's engagement in creative processes[END_REF]. Team creativity can originate with the idea or suggestion of a single individual that is then developed, worked on, and elaborated upon by the team. In many teams there is a lead creator whose ideas are the seed for the projects that the team will subsequently develop.

Creativity can also emerge from the collective -an idea emanates from the unit and the unit as a team or as a collective works to refine and develop its ideas. Ideas also can be developed by subgroups, and then moved to the large groups who will further develop the idea and take the process forward. The work at Pixar can be considered as an exemplar in that small groups of individuals are encouraged to generate and refine ideas as well as give feedback and communicate with others inside and outside of the team. [START_REF] Catmull | How Pixar Fosters Collective Creativity[END_REF]. Creative individuals and artists are often labelled as eccentric, sensitive, self-confident, introvert, and intuitive (MacKinnon 1975)characteristics that are not count as good team members. Enterprises in the creative industries need to continually balance their need to tap into the creative potential of both individuals and the teams as a unit and therefor it becomes critically important that the environment be one where opinions and divergent thinking are encouraged (i.e., Pixar) and self-confidence and at times aggression is regarded as passion rather than the deviance. [START_REF] Gilson | Creativity in Teams: Processes and Outcomes in Creative Industries[END_REF].

There is no doubt that some teams can excel in the production of creative ideas or products. Despite this fact, a great deal of research conducted in the area of group dynamics suggests that groups are often much less creative and productive than they are usually assumed (Rietzschel E. F et al. 2010), [START_REF] Dreu | Hedonic tone and activation level in the mood-creativity link: Towards a dual pathway to creativity model[END_REF], [START_REF] Camacho | The role of social anxiousness in group brainstorming[END_REF]. 506

In many cases a manager responsible for innovation development has limited resources to take care of all the factors influencing productivity of teamwork. The important question of how to manage creative teams to achieve a high productivity with limited resources and time arises in innovation management both from the theoretical and practical points of view. There is still no clarity which factors affecting productivity of teamwork are more important than others.

The study on group creativity as a multifaceted, multistage phenomenon (Rietzschel E. F et al., 2010) revealed that creativity is a multistage a process. The performance in one stage of the creative process does not automatically carry over into the next stages. Different stages of the creative process may be affected by completely different variables.

Many studies have been carried out in order to identify the factors that influence creativity in team [START_REF] Rietzschel | The selection of creative ideas after individual idea generation: Choosing between creativity and impact[END_REF], [START_REF] Amabile | The power of small wins[END_REF][START_REF] Dreu | Hedonic tone and activation level in the mood-creativity link: Towards a dual pathway to creativity model[END_REF], [START_REF] Stroebe | Why groups are less effective than their members: On productivity losses in idea-generating groups[END_REF], [START_REF] Camacho | The role of social anxiousness in group brainstorming[END_REF]. The fact that people produce fewer ideas when they work in a group as compared to when they work individually is partly due to some social factors, such as social loafing [START_REF] Stroebe | Why groups are less effective than their members: On productivity losses in idea-generating groups[END_REF]) and social inhibition [START_REF] Camacho | The role of social anxiousness in group brainstorming[END_REF].

The research on motivation of teams of knowledge workers (Amabile 2011) revealed two groups of factors "the catalyst" and "the nourishment" relevant to the progress of team performance. The study on the influence of hedonic tone (i.e., positive vs. negative moods) on creative performance (De [START_REF] Dreu | Hedonic tone and activation level in the mood-creativity link: Towards a dual pathway to creativity model[END_REF]) revealed that both positive and negative activating moods lead to higher creative performance than deactivating moods. There are studies which are focused on how to increase the innovation efficiency not at team level, but at meso or macro levels [START_REF] Bae | Efficiency and effectiveness between open and closed innovation: empirical evidence in South Korean manufacturers[END_REF].

Empirical research methodology on the factors influencing productivity of teamwork

Usually creative teams differ in terms of their composition, the task being carried out, and the resources available, etc. Very often neither the task assigned to the team, nor the composition of the team nor the time allotted to carrying out the tasks can be changed, or can be changed only insignificantly. It has been noticed that some teams can achieve better results even when they have the same resources, carrying out the same or similar task and having the same amount of time allotted to them as other teams. The urgent question of how to manage creative teams to achieve a high performance of the team arises in creative team management both from the theoretical and practical points of view.

The present investigation is aimed at establishing the factors that determine the high performance of creative teams. Having elucidated the major factors of organising teamwork that determine a high performing team, it is possible to focus attention on those factors and thus increase the efficiency of collective work. A questionnaire survey of team members, which they filled in upon completion of the task involving the relevant teamwork, was applied to an empirical investigation. The questionnaire was devised according to the factors that have an effect on a team established by Teresa Amabile [START_REF] Amabile | The power of small wins[END_REF]. Teresa Amabile (Amabile 2011) divides the factors that influence teams into catalysing factors, which have an effect on the performance-progress of the team and supporting factors, which have an effect on inner work life. According to Amabile, the catalysing factors have an effect on the inner work life of a creator too.

507

In carrying out the survey of the teams, each member of the team was asked to evaluate the following factors: whether the task of the team was clear/interesting; whether personal tasks were clear/interesting; team/personal autonomy; team/personal time; team/personal knowledge; the leader's help; the team's openness to ideas; learning from experience; respect within the team; team unity; job satisfaction in teamwork; leadership assessment; evaluation of the results achieved. The 7-point Likert scale was used for the evaluation. The higher the value of the point was, the stronger the factor under assessment manifested itself in the opinion of the team member. A total of 487 team members from 113 teams in 8 countries were interviewed: in Denmark (22 members from 6 teams), in Italy (21 members from 9 teams), in Canada (131 members from 20 teams), in China (39 members from 7 teams), in Lithuania (161 members from 38 teams), in Poland (70 members from 19 teams) in France (23 members from 8 teams) and in Russia (20 members from 6 teams).

These countries were chosen seeking to ensure a cultural and geographical variety of creative teams (relatively large and small countries, countries of Western and Oriental culture, etc.). Student creative teams were studied, that is, these teams had to find/generate some creative solution. The team member being interviewed had to evaluate the factors that had an effect on the team and its performance. According to the evaluation of performance, three groups were distinguished: of high performance (6-7 points), of average performance (5 points) and of low performance (1-4 points). Further the evaluations of only high and low performance teams calculating the averages of the evaluation of the factors of high and low performance teams in different countries were considered and compared. Though the tasks assigned to the teams and the duration of performing them were different, the teams of the same country had the same task and the same time allotted to perform it, and the same resources, therefore calculating the averages of these countries and comparing them was meaningful.

Evaluation of the performance factors of creative teams

The average of the evaluation of the performance factors of creative teams of all the countries under study (Denmark, Italy, Canada, China, Poland, Lithuania, France, and Russia) shows that high performance teams valued (see Fig. 1) respect most in the team (6.30 points), team autonomy (6.16 points), the team's openness to ideas (6.13 points), and team unity (6.09 points). The criteria of whether the task was interesting and teamwork satisfaction were evaluated at 5.98 points (Figure 1). The average of all the evaluation of performance of creative teams of all the countries under study (Denmark, Italy, Canada, China, Poland, Lithuania, France, and Russia). It shows that low performance teams (see Fig. 2) gave the lowest valuation to whether the team task was interesting (3.91 point), the team's knowledge (4.03 points), learning from experience (4.04 points) and personal knowledge (4.19 points) (Figure 2). Low performance teams conditionally gave the highest valuation to respect in the team (5.51 points), team autonomy (5.34 points) and personal autonomy (5.22 points).

When comparing the differences in the evaluation of the factors of high and low performance teams, we see that high performance teams valued all the factors of the effectiveness on average at a higher level. The greatest differences are in the evaluation of whether the team task was interesting (difference of 2.08 points), teamwork satisfaction (difference of 1.71 point), and team unity (difference of 1.70 point) (see Fig. 3).

The The least differences between high and low performance teams are in the evaluation of whether the team task was clear (difference of 0.53 points), the evaluation of whether the personal task was clear (difference of 0.54 points), the evaluation of personal autonomy (difference of 0.75 points) and the evaluation of respect in the team (difference of 0.79 points).

The hierarchy of factors influencing the teamwork productivity

As it was already mentioned, according to Teresa Amabile [START_REF] Amabile | The power of small wins[END_REF], knowledge team motivation factors can be divided into catalysing factors, which influence the performance-progress of the team and supportingnourishing factors, which have an impact on inner work life. Catalysing factors also influence the inner work life of the creator. The group of catalysing factors consists of the following: clear meaningful goals, autonomy/freedom, sufficient resources, help at work, learning from problems and achievements, an open flow of ideas, and sufficient amount of time. The group of nourishing factors consists of the following: help and recognition, incentive, emotional support, belonging and friendship.

Investigations carried out into 487 team members (113 teams) from 8 countries (Lithuania, Poland, Canada, China, France, Italy, Russia, and Denmark) showed that the team's members' satisfaction is directly related to the 511 results achieved by the team). The conducted investigations confirm the so-called Progress Principle by Teresa Amabile, which states that progress achieved by the team has a positive impact on the creator's inner work life, and the creator's positive inner work life has a positive effect on the result/progress of the team. The conducted investigations supplement Teresa Amabile's Progress Principle in revealing the impact of both catalysing and supporting factors on the results of the team. The hierarchy of the significance of the factors is established on the basis of the impact of the factors on the team's result/progress. Two groups of factors i.e. basic micro factors and additional micro factors are identified: The basic micro factors are (see Fig. 4):

-an interesting team task; -the team's openness to ideas; -learning from experience; -team autonomy; -respect in the team; -team unity.

The additional micro factors are: -team task clear; -team knowledge; -team time available for the task; -personal autonomy; -personal knowledge; -personal time available for the task; -personal task clear. In order to achieve a higher result/performance of a creative team the greatest attention must be devoted to the basic micro factors. A task should be interesting for the team. The team members should be open to the ideas and respect each other. For the high productive team a sufficient level of autonomy have to be granted. It includes financial, decision making, teamwork management and other autonomy. Management of the team also has to stimulate sense of team unity. It is also very important to establish Leaning from the experience/continuous improvement system in order to constantly analyze the performance and satisfaction of the team. Key performance and key satisfaction indicators have to be established as well as regular monitoring procedures. The above mentioned basic micro factors and additional micro factors could be used for development of key satisfaction indicators. Our findings also revealed that result/performance of the team has direct impact on satisfaction of the team. High result/performance of the team stimulates better satisfaction of the team which could lead to the establishment of positive loop i.e. high result/performancebetter satisfaction, better satisfactionhigh result/performance. The opposite to the positive loop it could be a negative loop when poor result/performance could lead to dissatisfaction and then dissatisfaction lead to even worst result/per high result/performance of the team. Attention is to be paid to the fact that micro factors are not only factors which has impact to the performance/result of a team. Our research revealed that a team could achieve very different final result/performance with the same or very similar micro factors (task, time available, composition of the team and etc.). A poor performance of the team could be influenced by increase competition, negative political, economic, social or technological changes. This shows that not only micro but also other factors (mega, macro and meso) factors could have a big impact on the results/performace of the teams.

It means that in order to achieve high performance of the team meso, macro and mega factors should be taken into consideration. 513

Conclusions

The research findings confirmed the so-called "Progress Principle" by Teresa Amabile [START_REF] Amabile | The power of small wins[END_REF] in revealing the impact of both catalysing and nourishment factors on the results of the teams. The hierarchy of the significance of the factors influencing the productivity of teamwork is established.

The hierarchy factors influencing the productivity of teamwork consist of two groups of factors: Basic micro factors. This group includes 6 factors (interesting team task, the team's openness to ideas, learning from experience, respect in the team, team unity and team autonomy). These factors are crucial for productivity and satisfaction of a team;

Additional micro factors. This group of factors includes such factors as time allocate to the team, team knowledge, team task, personal time of the team members, personal knowledge of the team members, personal autonomy, and clear and interesting personal tasks. These factors are important for productivity and satisfaction of a team.

The finding of the study shows that in order to achieve higher productivity of a creative team the greatest attention must be given to the factors of higher hierarchical level. Such focus would help to achieve not only higher productivity of teamwork, but would also increase the efficiency of innovation development process. The findings are relevant not only for creative industries, but also for innovation development in other sectors as the higher productivity of a creative team would affect the efficiency of innovation development in all sectors.

Despite the fact that all the analysed teams were given conditionally the same task, the same goal, same time and resources, some of the teams managed to achieve better results than others. This shows that not only micro factors had an influence of the productivity of the teams, but also external environmental (mega, macro and meso) factors had also an impact on the results of the teams. Mega, macro and meso factors can have a crucial impact on productivity of teamwork in creative industries, despite existence of all necessary positive micro factors. The influence of mega, macro and meso factors have to be additionally investigated.

Limitation of the study. The paper presents conclusions from students teams involved in development of creative projects, but not from the teams working in creative industries, assuming that factors of productivity in students teams involved in creative projects are similar to the teams working in creative projects in creative industries. To identify factors influencing productivity of teams working in creative industries are difficult due uniqueness of each projects (task, allocated resources, timing and etc.). These factors usually are out of direct control of the creative teams and are determined by meso, macro and mega factors (customers, CEOs, competitors, market and etc.) Research of students teams involved in development of creative projects allowed us to partially eliminate these factors and concentrate on the factors that are in direct control of creative teams. It implies that the presented research was conducted on closed to reality environment, but in order to verify the results, further research should be done in real environment i.e. by analysing teams in creative industries.

Fig. 1 .

 1 Fig. 1. Overall evaluation of the factors of high performance (6-7 points) teams

Fig. 2 .

 2 Fig. 2. Overall evaluation of low performance (1-4 points) teams

Fig. 3 .

 3 Fig. 3. Difference in evaluation high and low performance teams

Fig. 4 .

 4 Fig.4. The hierarchy of factors influencing the teamwork productivity

  . The term 'creative industries' reflect the progression of

	The International Journal
	ENTREPRENEURSHIP AND SUSTAINABILITY ISSUES
	ISSN 2345-0282 (online) http://jssidoi.org/jesi/
	2018 Volume 6 Number 2 (December)
	http://doi.org/10.9770/jesi.2018.6.2(3)

  The group of catalysing factors is made up of the following: clear meaningful goals, autonomy/freedom, sufficiency of resources, help at work, learning from problems and achievements, an open flow of ideas, and sufficiency of time. The group of supporting factors consists of the following: respect and recognition, encouragement, emotional support, a feeling of belonging and friendship/a sense of community.

	The International Journal
	ENTREPRENEURSHIP AND SUSTAINABILITY ISSUES
	ISSN 2345-0282 (online) http://jssidoi.org/jesi/
	2018 Volume 6 Number 2 (December)
	http://doi.org/10.9770/jesi.2018.6.2(3)