1 The Project Management effectiveness of digital marketing channels in the sales of construction materials

1.1 Theoretical basis of Project Management effectiveness in IT innovations implementing for digital marketing channels

The main condition for ensuring the development of an organization is the constant development or search and implementation of innovative projects in its activities. Therefore, now we can observe rising tendency of work organization in frames of  a project. To explain an advantage of project organization over the work on the enterprise we should give the whole comparison between projects and usual work of enterprises. We set different goals in these two cases. Project manager setts unique goals that all members of a team should achieve in certain time period. As a rule, project manager should solve unusual tasks using extremely limited sources. If the result of project activity meets expectations, this project activity becomes repeated routine work of enterprise.

Thus, project activity suits for IT innovations implementing. And the main definitions of this scientific work would be ‘Innovation’ and ‘Project Management’. Projects and innovations are interrelated because people create projects, take part in them to create and suggest society something new. To scrutinize their interrelation, we should first explain the origins of these terms.

Today we use the word ‘project’ as a type of work activity or use a plan to create something. Originally, however, the term draws on the Latin word ‘projicere’ of which the meaning might be derived to throw something forward[1]. A project management is an art, an ability of managing all the aspects of a project from inception to closure using a scientific and structured methodology. Project management is a method for managing large-scale tasks in conditions of time and resource constraints to achieve the stated results and goals. Despite a growing number of publications, there is no unified theoretical basis and there is no universal theory of project management because of its multidisciplinary nature (Smyth and Morris). Project management is quite young direction and it has a more applied nature than other management disciplines.

Turner develops a generic definition of a project: ‘A project is an endeavour in which human, financial and material resources are organized in a novel way to undertake a unique scope of work, of given specification, which constraints of cost and time, so as to achieve beneficial change defined by quantitative and qualitative objectives’. Maylor determines three major stages of the Project Management historical development. The Project Management before the 1950s and our modern Project Management are entirely different. In the 1950s, tools and techniques were developed enough to support the management of complex projects. Managers relied on “one best way” approach, based on numerical methods. The third stage, from the 1990s onwards is characterized by the changing environment in which projects take place. This time, more and more people realize that a project management approach depends on its context. Development of project management is changing from focus on sole project management to the broader management of projects and strategic project management (Fangel, 1993; Morris, 1994; Bryde, 2003). In many ways, project management, as the fields of research, has been kept apart leading to a neglect in the project management area to acknowledge and embrace the unique processes of projects.

In the innovation arena, project management has often been looked upon as a simple implementation endeavour with little problems. An it is a second important term ‘innovation’. Innovation is often used to signify something new, either a new product, service or other output, and/or a new process and method. The word is also traceable to Latin and the word ‘innovo’ which could be translated as to renew[2]. D. A. Endovitsky and S. N. Komendenko characterize innovation as the introduction of innovations in production, marketing, investment and financial activities. From the point of view of management theory, innovation is an adaptive response to changes in environmental conditions, as well as a means of increasing the efficiency of using internal resources. D. Messi, P. Quintas and D. Wild understand by innovation, firstly, a description of the first use of a new product, process or system, and secondly, a process that includes such activities as research, design, development and production organization, a new product, process or system.

V.M. Konovalov connects innovation and an idea that is aimed at economic implementation and the demand for society. In his opinion, innovation is the result of the transformation of ideas, research and development into a new or improved socio-economic or technical solution to achieve social acceptance through practical use.

James P. Andrew and Harold L. Sirkin note that innovation is an outcome in the form of certain benefits, and not just an idea embodied in the proposal of a new or improved product or service, technology or work organization[3]. Layard McLean defines innovation as the successful implementation of a creative idea within an enterprise. Thus, on the one hand, innovation is the result of an activity or a creative process presented as a new or improved product, service or technology. On the other hand, innovation is seen as the process of translating new ideas into new or improved products, services, technologies, strategies, or business models.

This definition of ‘innovation’ helps to understand a new phenomenon of management science – ‘management of Innovative Project’. There is a certain procedure of work in management of an innovative project activity. A functional approach to management processes with their subsequent division into jobs, operations, etc. allows us to describe the important aspects of the complete cycle of the management process. As a process of making managerial decisions, management of an innovative project is the implementation of a certain sequence of interrelated stages. As an organizational system, the management of an innovative project is characterized by an organizational structure that includes the composition and interconnection of management bodies, the regulation of their functions, duties, rights and responsibilities, management technology and is built in such a way that all management bodies ensure the achievement of the ultimate goal of the innovative project.

As Anbari (2005, p.101) rightly states “Innovation and project management, … are addressed in the literature generally as separate issues”[4]. However, recently this interrelation between innovation and project management has triggered some academic research. The idea of innovation projects has been pronounced in policy documents as well (e.g. European Commission, 2004, 2006)[5]. Yet, this area still offers opportunities for further research, both in terms of conceptualization and empirics.

There have been several attempts to provide an overview of the state-of-the-art research in PM and outline its trends and future directions[6]. In a recent article, Kwak and Anbari (2009)[7]review relevant academic journals and identify eight allied disciplines, in which PM is being applied and developed. These disciplines include such areas as Operation Management, Organisational Behaviour, Information Technology, Engineering and Construction, Strategy/Integration, Project Finance and Accounting, and Quality and Management.

Departing from the argument that project-based, service-oriented forms of enterprise are not adequately addressed in the innovation literature, Gann and Salter  explore the ways in which these firms manage innovation in construction projects. Based on the case studies, authors examine links between operations at the project level, portfolios and central routine activities.

Keegan and Turner analysed the management of innovation in project-based firms along three dimensions – context supportive for innovation, slack resources and perception of innovation as being useful or not[8]. In other words, the attitude towards managing innovation projects remains mechanical in nature as traditional project management approaches are applied to innovation projects. Keegan and Turner argue in favour of the evolution of the traditional project management towards more informal, organic management of innovation, with a higher tolerance for slack resources and greater levels of redundancy in order to create time, space and creativity for innovation.

Quite often “an innovation project” is equated with new product development, or even left without any definition. One of the few, Anbari explicitly provides a definition for the management of an innovation project, which can be viewed as “…the management of a system that transforms inputs into outputs and has a feedback mechanism to ensure that the project output is consistent with its objectives”[9]. In our view, this definition is a highly generic one, since it can be applied to virtually all categories of projects and it does not underscore the specific nature of innovation.

Project management is the engine for implementing new ideas, and all projects may involve a certain degree of innovation and creative effort, depending on the definition of innovation (product innovation, process innovation, organisational innovation, user innovation, etc). Organisational innovation may emerge as an enabling force contributing to a success of a project, but the project itself might not be innovation one per se.

We follow these definitions, and under an “innovation project” we understand a project dealing with product and service innovation, involving various aspects of innovation and innovativeness. Therefore, an innovation project revolves around certain criteria (and should include at least one of them):

• aimed at development of an innovative (new) product or service (product or service innovation);
• employ innovative methods and approaches (process innovation);
• lead to improvement of innovative and learning capabilities of the project executor (organisational innovation);
• be realised in a close interaction with the project owner (user innovation)[10].

As this brief literature review reveals, the interfaces between innovation studies and (project) management do exist. And it allows us to do further research related to implementation of more effective innovations.

With some notable exceptions, however, the traditional innovation literature largely ignores project management and the intricacies of managing innovation in project-based firms. In addition, the project management literature, considerably expanded in recent decades, largely ignores innovations.

Nonetheless, recently the link between innovation and projects has come under scrutiny as the scholars and practitioners started witnessing a certain degree of convergence between these two research areas. As this brief literature review reveals, the interfaces between innovation studies and (project) management do exist.

The process of adopting new innovations has been studied for over 30 years, and one of the most popular adoption models is described by Rogers in his book, Diffusion of Innovations (Sherry & Gibson, 2002)[11]. Many researches from a broad variety of disciplines has used the model as a framework. We also will rely on this scientific work on the certain stage of research.

1.2 Methods of ranking and implementing innovations into the Project Management team of digital marketing department a company.

However, project management is closely related to the concept of innovation management. Projects represent an organizational framework for the systematic and methodological acquisition of knowledge, ideas and results.

Innovative project management can be viewed according to three positions: as a system of functions, as a process of making managerial decisions, and as an organizational system.

Within the field of project management (PM) the concepts of efficiency and effectiveness are commonly used, but rarely defined. Some researchers apply the concepts when describing how to improve some part of PM (Ward, 1999), some apply it when describing competencies for project execution (Lampel, 2001). In different maturity models, e.g. OPM3 (PMI, 2008b), SPICE (Sarshar, Haigh & Amaratunga, 2004) and (PM)2 (Kwak & Ibbs, 2002), effectiveness is usually listed as one of the reasons for measuring maturity[12]. However, the meaning of PM effectiveness is somewhat unclear. There are some other applications of the concepts discussed in the field of PM, but there is rarely a clear definition of what is meant with efficiency and effectiveness, and they are applied interchangeably.

In the field of quality management (QM), the concepts are applied in a more defined way. In QM, efficiency refers to doing things right, i.e. whatever is performed, it is performed in the most suitable way, given the available resources (high efficiency). Effectiveness, on the other hand refers to doing the right things, i.e. selecting and focusing on producing an output that there is a demand for. In everyday use of the word effective (-ness), it is related to putting something, say “a”, in relation to something else, call it “b”. When comparing these two, “a” can be more or less effective than “b” in terms of for example the outcome. The “a” and “b” can also be the same task, but performed at different times or by different persons, making it possible to compare the two.

If processes can be identified in the a project-based organization, and if the concepts of efficiency and effectiveness can be applied to evaluate processes, it would be possible to evaluate the PM process over time in terms of performance. But in order to discuss efficiency and effectiveness in PM, a common view need to be established between academics and practitioners. Discussing projects from a process viewpoint is particularly interesting within project-based organizations, since they continuously carry out projects. The possibility to learn and improve from one project to the next is something that any project organization should be interested in doing. Dismissing the possibility to compare and learn from one project to the next, in terms of both efficiency and effectiveness, with the argument that every project is unique might just be a pretext for not dealing with overall project organization performance, similar to the reasoning made by Andersen et al. (2006).

In the field of PM, the concepts of efficiency and effectiveness are commonly used, and there are several examples of relevant literature within the field. For example, Wysocki (2011) has previously discussed effective PM, and Hyväri (2006), Lampel (2001), Parast (2011) and Fisher (2011) refer to the concepts in different ways[13]. The book Effective Project Management by Wysocki (2011) book describes different tools, tips and recommendations in order to better manage projects, thus with a focus on one single project. However, effectiveness is not defined but refers to the improvement in managing projects with the goal to improve delivery on time and budget.

IT innovations can be defined as the totality of technical innovations that support information exchange technologies, as a result of which information becomes an important component of the production process, changes the production and market parameters of products, increasing its added value. One of the tasks of IT innovation is to improve the information flows of the organization and improve the quality of information (its efficiency, relevance to the user, reliability, sufficiency).

The origins of project management in the manufacturing and construction industries determine an engineering perspective, viewing a project as a task-focused entity, proceeding in a linear or similar way from the point of initiation to implementation. This view prevailed until comparatively recently. This view is seemingly in stark contrast with the nature of innovation. It is increasingly being acknowledged that the innovation is a complex non-linear process. The earliest view on innovation process as a pipeline model (whereby a given input is transformed to a specific output) has been largely abandoned.

Presently, however, project management is increasingly recognized as a key generic skill for business management, rather than a planning-oriented technique or an application of engineering sciences and optimization theory, in which project management has its roots. The “management by projects” has emerged as general mode of organizing for all forms of enterprise[14].

This new conceptualisation of project management enables to embrace the non-linear nature of innovation. Even a creative and non-linear nature of innovation is often characterised as an organisational or management process, rather than spontaneous improvisation. Davila et al. state, “Innovation, like many business functions, is a management process that requires specific tools, rules, and discipline”. Hence, a project, with its defined objective, scope, budget and limitations, can be an appropriate setting of innovation.

The non-linear view on innovation stems from seminal works of Joseph Schumpeter, the forefather of innovation studies. Innovation is understood as put by Schumpeter (1934)[15]. This reasoning provides another justification for managing innovation in projects. Project team is made up of specialists of various backgrounds, and it is expected that the separate knowledge residing in individual specialists will be cross-fertilised when working in a project team.

Taking into consideration the way in which innovation emerges, it is worth mentioning that innovation is mostly implemented through projects. However, project management’s focus is on the project itself, and to achieve its aim it is strongly concerned with scientific areas such as strategic management, human resource management, organizational behavior and so on.

To formulate an innovative idea that will allow us to count on business success, you must:

– to open a new way to create some usefulness that is valuable to consumers.

– to create a combination of special abilities to create this utility.

– to create a unique formula with which you can achieve the maximum possible entrepreneurial margin in the value of the created utility and be able to assign this margin in the form of profit[16].

Before choosing the concrete method, we should describe all features that are only related to innovations, because the choice of method depends on the given kind of innovation. The features of IT innovations could be the increased uncertainty regarding the results (it is often difficult to predict the outcome of such a project and to predict the degree of success), the difficulty of determining the customer of the product and the end users on which the project will influence, the use of new practices, the complexity of prototyping the final result of the project, high uncertainty in assessing the timing of the implementation of projects[17].

There are product, process, marketing and organization innovations. Product innovation means creation and realization of a new product. Process and organization innovations are related to the work rules, the way of work in the enterprise. Marketing innovation is an object of our research and we will find suitable projects to implement marketing innovations which will help the marketing department of the company simplify and force their work.

The main method of innovations effectiveness evaluation is evaluation of future costs forecasting. There is a huge variety of marketing researches and every research requires individual approach to it. It means that before choosing definite methods in the researches of innovations we should choose direction of marketing projects, in which sphere we should design, develop innovations. Appendix A represents classification of marketing projects depending on marketing directions[18]. We will choose one direction of marketing, according to our topic. It would be ‘Selection of distribution strategy’, because we will analyze quantity of sold products of one company in different cities, which was recorded with special programme. We will consider this programme as ‘innovation’.

Ranking of innovations helps to select innovations that will be more effective. Now we should scrutinize different methods of research and choose one of them. Methods and criteria for evaluating the effectiveness of innovative programs and projects are usually divided on three types: economic effectiveness, finance effectiveness and budget effectiveness[19]. Economic effectiveness means using such criteria as total sum, annual profit, ROI, payback period of investment. Finance effectiveness includes NPV, internal liquidity. Budget effectiveness depends on the budget revenue, internal budget revenue and its index, and also budget payback period of a project.

Many works that devote to assessing the results of innovative projects mention such problems, as unclear requirements for project results high probability of significant deviations of actual results from expected ones[20]. We will pay more attention to financial approach to evaluate effectiveness of any innovations in the projects. The most common methods of financial evaluation include methods of accounting rate of return, payback period of the project, net present value of the project, internal rate of return[21].

A comparative analysis of the methods described above is aimed to select an approach for the ranking of innovative projects. The analysis of the innovative IT projects features allowed us to form a list of criteria for comparison of approaches that we can see on the appendix B.

With the development of IT and the analysis of innovations in this area, the scientific community has identified a separate area of research into the “adoption” of IT innovations. “Adoption” here means the successful implementation of IT innovation in a company, leading to a qualitative improvement in its operations and performance[22].

During the ranking of several innovative IT projects we should follow the methods of innovative IT projects effectiveness.

Whereas ranking of projects is directly related to a project manager, first of all we should define the role of a project manager in a project activity and the structure of project activity. A project manager should control all the process that are included into 4 phases of project life cycle. All project life cycles consist of 4 phases: initiation, planning, execution and determination. Project initiation means a stage of defining with aims, scope and tasks of a project with a client. All information is gathered together. When the scope of the project has been defined the project enters the second stage – detailed planning stage. On this stage the project creates different plans. Table 1 contains all plans and their functions.

Table 1 – Type of plans on the 2^nd phase of a project life cycle[23]

The second phase is project institution which involves all the projects which were created on the previous stage. As a series of management processes are undertaken don’t want to draw and controls of the deliverables have been output by the project. On the stage the manager identifies change risks and issues, the manager measures each deliverable produced against the acceptance criteria.

The last stage is project closure. It releases the final deliverables to the customer, a project manager undertakes a post implementation review to quantify the level of project success and identifies any lessons learned for future projects. What is characterizes a project is that the project should be completed on time within budget and to deliver a quality product that satisfies users and meets requirements. However project management for IT professionals and the usual project arrangement to our little bit different. Project management for IT professionals includes planning, scheduling, monitoring and controlling and also reporting on information system development. We will observe project management within the framework of IT definitely, because all further calculations are associated with the development of electronic commerce and the creation of IT products to improve work with clients of one company that produces and provides building materials – KNAUF KUBAN.

We would like to scrutinize mechanisms of project work on the example of marketing department of this company. In case of marketing department of KNAUF KUBAN it was a task to create a loyalty programme. If any company or department has daily routine problems there is no reason to create a special project team. The second feature is the consequence of the previous one because projects unique results of any projects cannot be constant and have time limits. KNAUF marketing department had time limit equaled a year[24]. Marketers had to make the whole project life cycle during a year.

On the first phase of project managers were working on determining of quality and quantity features of staff, calculating costs, forming of responsibility division matrices, job descriptions of project team members. On the second phase they were deciding who would play any roles in the project, selecting project staff and appointing project managers. Execution means that chosen staff started to work on project, they were analyzing the situation with a product of a company in the market in general, scrutinizing work principles of analogical loyalty programme that are used in competing companies or companies that produce another type of goods. IT specialists offered new different variants of loyalty programmes that can differ from other ones with unique competitive features. On the last phase when project is done members of project demonstrate their result and receive a reward for their work. In this case, the result was a creation of new loyalty programme targeting on brigadiers and all workers of construction field. Members of this programme have an opportunity to receive some discounts and many other advantages. Therefore, the results of the loyalty programme are used to create an econometric model to forecast future data. This forecasting is also a proof of this programme effectiveness and the right choice of a project.

1.3 Methodology of ranking and implementing innovations into the Project Management team of digital marketing department a company using econometric modeling.

Innovations in the projects are objects of innovativeness (I) and competitiveness (K). The simplest method to solve the task is related to determination of average values of expert ratings for each criterion of innovativeness and competitiveness. Common assessment criteria are defined as:

Where  is the meaning of criteria i for project j for innovativeness index;  is the meaning of weight coefficient for innovativeness index; n is a number of criteria for innovativeness index;  is the meaning of criteria i for project j for competitiveness index;  is the meaning of weight coefficient for competitiveness index; j is a number of projects; I_min, I_max, K_min, K_max are minimal and maximal meanings of innovativeness and effectiveness indexes. Annex B has a list of criterion for innovativeness and competitiveness indexes that effect the choice of a project [9][25].

This method allows only to select one project among several proposed projects, according to required innovativeness and competitiveness criterion. Forecasting of indexes, which the selected projects can suggest, can also prove the right choice of selected project. In our case of projects in distribution strategy, we can suppose that our goal would be to increase a quantity of sales and create a system of their recording using some modern, innovative technologies. We will suppose that we already have one innovative technology that can record sales and probably increase them at the same time. This innovative technology has recorded the quantity of products sales in different cities during the definite period. We have quantitative data per month of that period. It means that we have time series data. According to the given information, we can create a dynamic econometric model to forecast future sales that can be recorded using mentioned innovative technology.

We should notice that our innovative technology should work in such spheres, us marketing and project management. Both spheres could be interrelated and identified as social spheres. Social spheres have specific factors that influence on the choice of econometric model. In this case, it would be more reasonable to choose dynamic econometric model that is more helpful in forecasting human behavior, choice. Because the main component of dynamic model is lag ad the reason of lag is mostly anthropogenic. The reasons of lag are:

– psychological – inertia in the people’s behavior (the habit of a certain lifestyle);

– technological – replacing large computers with personal computers took a long time;

– tools – for example, contracts between firms and labor contracts;

– mechanisms for the formation of economic indicators – for example, inflation.

We have chosen this model with distributed lag and now we explain all variables of it.

Before analyzing the principles of dynamic econometric model, we should define the meaning of dynamic econometric model. An econometric model is called dynamic if this model reflects the dynamics of subsequent variables at each moment in time, i.e. if at a given moment in time t, it takes into account the values of the variables included in it, relating to both current and previous time instants[26].

There are two kinds of dynamic econometric models: autoregressive models and autoregressive distributed lag models. Equations of autoregressive model include meanings of dependent variables as lagged variables. The example of this equation is

,                                              (1)

Where

t – current period;

_t-1, I = 1, 2, 3  –  the total value of the influencing factor during the period t-i.

_I, I = 1, 2, 3 – coefficients of influence of the I time lag,  – error.

₀ – short-term or impact multiplier.

Autoregressive distributed lag model has only explanatory variables as lagged variables. It is the difference between these two models.

For many applications, a widely used solution to the problem of including dynamics in a model while mitigating the problem of multicollinearity is to employ an autoregressive distributed lag model, often written ADL (p, q). The ‘autoregressive’ part of the name refers to the fact that lagged values of the dependent variable are included on the right side as explanatory variables p is the maximum number of lags of the dependent variable used in this way q is the maximum lag of the X variable, or variables if there are several.

Models of lagged dependent variables are dynamic econometric models that contain both current and lag values of factor variables. This model shows the effect of x_t factor change in a factor at some point in definite time period on an effective attribute during the following time periods[27].

Y_t = a + b₀x_t + b₁x_t-1 + b₂x_t-2 + … + b_Lx_t-L + e_t,                          (1.1)

Where

t – current period;

_t-1, I = 1, 2, 3  –  the total value of the influencing factor during the period t-i.

_I, I = 1, 2, 3 – coefficients of influence of the I time lag,  – error.

₀ – short-term or impact multiplier.

We will start with the simplest model of all, the ADL (1,0) model where the only lagged variable is the lagged dependent variable. Given the continuity of many time series processes, the value of a time series at time t – 1, is often a good guide to its value Y, at time t. When this is the case, it makes sense to include Y_t-1 explicitly in the model as an explanatory variable[28]:

 _,                                                   (1.2)

Where

t – current period;

_t-1, I = 1, 2, 3  –  the total value of the influencing factor during the period t-i.

_I, I = 1, 2, 3 – coefficients of influence of the I time lag,  – error.

₀ – short-term or impact multiplier.

There is no universal way to identify the length of a lag. But we observe some of them to determine lag length.

The first way is determining of lag length by statistical significance.

An obvious way to choose the length of a lag is to start with a long lag test the statistical significance of the coefficient at the longest lag—the “trailing lag”—and shorten the lag by one period it if we cannot reject the hypothesis that the effect at the longest lag is zero. We continue shortening the lag until the trailing lag coefficient is statistically significant.

And the second way is determining of lag length by information criteria.

Information criteria are designed to measure the amount of information about the dependent variable contained in a set of regressors. They are goodness-of-fit measures of the same type as R²or  , but without the convenient interpretation as share of variance explained that we give to R² in an OLS regression with an intercept term. The two most commonly used criteria are the Akaike information criterion (AIC) and the Schwartz/Bayesian information criterion (SBIC)[29].

AIC is criteria used to select one model from several statistical models.

(1.3)

Where

k – a number of parameters in statistic model,

L is maximized meaning of a model likelihood function.

Furthermore, we will assume that the model errors are normally and separately distributed.

(1.4)

n is a quantity of observations.

(1.5)

,                                                   (1.6)

In this equation T is sample length, K is the total number of estimated coefficients, and  are the residuals. The “main ingredient” in both information criteria is the sum of squared residuals, which we want to make as small as possible[30]. Thus, we want to minimize the criteria and choose the model with the smallest AIC or SBIC value.

When we use the information criteria to choose lag length, all candidate models are estimated over exactly the same sample period. The first term of the information criteria (common to both) is the log of the standard error of the estimate, uncorrected for degrees of freedom. It shows how well the model explains the dependent variable. The second term is a “penalty term” that depends positively on the number of estimated parameters.

We can determine lag length by residual autocorrelation, adding lags of x and/or y to the right-hand side of a distributed-lag regression usually lessens the degree of autocorrelation in the error term. Estimators of some models (especially those with lagged dependent variables) are sensitive to autocorrelated errors, so another criterion that is sometimes used for choosing lag length is the elimination of autocorrelation in the residuals.

When using residual autocorrelation to determine lag length, one adds lags until the residuals appear to be white noise. After running the distributed-lag regression, one extracts the residuals and uses a Breusch-Godfrey LM test or a Box-Ljung Q test to test the hypothesis that the residuals are white noise[31]. Rejecting the white-noise hypothesis means that more lags should be added to the regression according to this criterion.

We have chosen this model with distributed lag and now we explain all variables of it (1.7).

(1.7)

Where

t – current period;

_t-1, I = 1, 2, 3  –  the total value of the influencing factor during the period t-i.

_I, I = 1, 2, 3 – coefficients of influence of the I time lag,  – error.

₀ – short-term or impact multiplier.

According to this formula, we can conclude that the regression coefficient ₀ is a short-term multiplier that shows the average absolute change in the result of Y_t when the factor X_t changes per unit of measure at some fixed period t, without taking into account the effect of lag values X_t[32].

The regression coefficient ₁ shows an absolute change of result y_t while factor X_t is changing per unit of measurement in the period t -1.

The sum (₀ + ₁) is an interim multiplier. It shows a cumulative effect of factor X_t on a result y_t at the moment (t + 1). This impact will be characterized with a sum (₀ + ₁ + ₂) at the moment (t + 2). If we want to explain this impact at the moment (t + 3) we analogically write (₀ + ₁ + ₂) and add ₃, etc.

A long-term multiplayer is a sum of all :

= b₀ + b₁ + b₂ + b₃ … + b_p,                                                (1.8)

It means the common absolute changing of the result y_t at the moment (t + p)[33]. Changing of factor x_t effects on changing of the result y_t per unit of measurement in period t.

We will begin by investigating the dynamics implicit in the model graphically. At time t, Y, is given by (1.8). Y_t-1 has already been determined at time t, so the term b₃ Y_t-1 is fixed. Thus, it may be viewed as giving the short-run relationship between Y, and X, for period t. For period t, it is represented by the bottom line in picture 1, the value of Y, indicated by the marker being that corresponding to the actual value of X_t. (b₁ + b₃ Y_t-1) is effectively the intercept and b₂, the slope coefficient, gives the short-run effect of X on Y.

Picture 1 – Short-run and long-run dynamics in a model with a lagged dependent variable[34]

Now we can count all relative correlation coefficients.

; j = 0; L,                                                 (1.9)

If all relative correlation coefficients have the same indexes, it means:

(1.10)

If we know  we can count

(1.11)

It is a middle lag or middle period. The result of y_t changes under the influence of a factor X_tin the moment t during this period. If the meaning of a middle lag is not big it means that factor Y_trapidly reacts to changings of factor X_t. If the meaning of middle lag is big it means that the reaction of result Y is slow[35].

Median lag  (L_me) is the period of time during which a half of the total impact of the factor on the result will be realized.

(1.12)

(1.13)

Where

t – current period;

_t-1, I = 1, 2, 3  –  the total value of the influencing factor during the period t-i.

_I, I = 1, 2, 3 – coefficients of influence of the I time lag,  – error.

₀ – short-term or impact multiplier.

There is no universal way to identify the length of a lag. But we observe some of them to determine lag length.

According to this formula, we can conclude that the regression coefficient ₀ is a short-term multiplier that shows the average absolute change in the result of Y_t when the factor X_t changes per unit of measure at some fixed period t, without taking into account the effect of lag values X_t [7. P.26]. Fixed period is lag, it should be calculated before using formula 1.13.

Median lag  (L_me) is the period of time during which a half of the total impact of the factor on the result will be realized (1.14).

(1.14)

If we know lag length we can know data set of what period we can use in the formula with distributed lag. And all further calculations will be according to the first linear correlation formula.

According to these formulas, we can see that the quality of innovation is determined by the effect of its commercialization, the level of which can be determined by assessing the competitiveness of products. Thus, innovative relations are the result of competitiveness, which allows us to consider competitiveness as a function of innovation – K% 3D f (). Therefore, innovativeness and competitiveness are the most significant indicators for assessing the quality of innovative projects[36]. The adequacy of the criteria used with respect to the complex indicator is formed by giving each criterion the coefficients and weighted applications of the additive multiplicative calculation method. It is advisable to carry out an evaluation of an innovative project using a graphic model for assessing innovativeness and competitiveness of a project in three stages: selection of optimal criteria; determination of weights; positioning of projects in the matrix. Innovative projects are the objects of two interacting segments: science and business. Therefore, it is advisable to formalize them as two-dimensional objects: innovativeness and competitiveness. To calculate these criteria, the following method is proposed. The easiest way to solve this problem is to determine the average values ​​of expert estimates for each criterion of innovation and competitiveness.

The model research was held according this scheme: firstly, we tested the Gauss-Markov theorem, then tested a value of the given indexes using t-test and f-test, and the last stage is checking of model adequacy.

This model allowed us to make less mistakes on the stage making calculations to select innovations and to select more suitable regions which data we can use for making an econometric model and forecasting.

Appendix A

Table 1

Classification of marketing projects according to marketing direction

Appendix B

Table 2

Innovativeness and Competitiveness criterion

1. Ссылки на источники литературы должны быть на каждой странице 1 главы.
2. Определение эффективности по теме также хорошо добавить.
3. П. 1.2 – исправить междустрочный интервал.
4. Таблицу 1 не следует отделять от заголовка таблицы.
5. П. 1.2 сделать более экономикоориентированным, на

сравнительный анализ экономических методов решения

исследуемой проблемы.

1. Все формулы оформить согласно методическим

указаниям.

1. Частично, требуемое содержание п. 1.2 у Вас находится в

п. 1.3.

1. В п. 1.3 сразу рисунок 4, а где рисунки 1, 2,3 и, где ссылка

на источник в заголовке рисунка 4?

1. На стр. 20 по тексту Формула 4, где она?
2. К п. 1.3 надо добавить абзац выводов по данной части 1-

ой главы.

1. Приложение A оформите по методическим указаниям,

шрифт 12, междустрочный интервал.

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Вообще-то уже с 69 года существует Институт управления проектами (Project Management Institute) и на базе их работ сейчас все и основывают