Modelling Inﬂuence of Motivation on Efﬁcient Tasks Distribution

for Given Team-project Correspondence

Valentina Y. Guleva

, Egor N. Shikov

and Klavdia O. Bochenina

National Center for Cognitive Research, ITMO University, Kronverkskii Prospect 49, Saint-Petersburg, Russian Federation

Keywords:

Mathematical Model, Agent-based Model, Personal Productivity, Motivation, Competence, Simulation, Task

Assignment, Project Management.

Abstract:

The mathematical model of project execution, considering motivation effects on personal productivity is sug-

gested in the paper. The main supposed application of the model is the task allocation problem during project

management, which is restricted by the parameters of correspondence between tasks and team competence

and interests, and execution deadline restrictions. In this way, motivation is considered an inﬂuential factor,

and we explore how it affects team productivity and how can it be managed by task allocation strategies on the

basis of personal motivation control. Namely, we explore the effects of personal motivation factors on overall

project success. For this purpose, we consider motivation and competence factors in the model at the agent

level, and take into account the initial abilities of a team, with given competence and motivation, to implement

a project requiring a number of skills. To measure the effect of motivation and its role in project management,

we perform a set of experiments showing (i) optimistic project execution times on the basis of team abilities

against project requirements, (ii) possible effects of motivation on project execution and possibilities of its

management, and (iii) effects of managerial approaches on team productivity (competence and motivational

growth and their effects on tasks execution). The results show that poor correspondence between team and

project competencies and interests can tenfold decrease team productivity, which can be partly eliminated by

task assignment strategies, aimed at motivation control.

1 INTRODUCTION

AI is actively being introduced into everyday life,

in particular, in project management. Personal as-

sistance for professional deals meets dualism in per-

sonal and ﬁrm interests, which criticality is increas-

ing for artiﬁcial professions and science. Motiva-

tion factors may signiﬁcantly decrease personal pro-

ductivity (Vansteenkiste and Ryan, 2013), affect pro-

fessional burnout (Scott, 2020), and overall project

success. In this way, building managerial systems

for tasks distribution, aiming at satisfying both, or-

ganisation and employee interests, is of great impor-

tance. In this case, personal assistants are neces-

sary for employee needs identiﬁcation, and macro-

level algorithms are required for a system of assistants

conﬁguration, dependent on team-project correspon-

dents, ﬁrm deadlines, and other requirements. De-

spite major interest in managerial approaches in en-

https://orcid.org/0000-0002-1555-9371

https://orcid.org/0000-0001-5749-4222

https://orcid.org/0000-0001-6025-0552

terprise and great number of experiments, the existing

conclusions about possible effects on personal pro-

ductivity are weak formalised, in particular, the for-

mal mathematical model of these processes are ab-

sent, for the best of our knowledge. Therefore, the

one is suggested in the current article.

The current study prepares material, which can

be useful for enterprise task management and for the

corresponding systems of personal assistance conﬁg-

uration (Guleva et al., 2020). It provides a minimal

model, reﬂecting 1) effect of motivation and com-

petencies on personal productivity, related to a task

assigned, 2) effects of tasks assigned on personal

competence growth and motivation by measuring the

correspondence between competencies, necessary for

task execution and personal ability, 3) personal contri-

bution to system state variables at the macrolevel, and

4) allows to distribute tasks aiming at personal moti-

vation increase or at quicker project execution. This

dynamics at microlevel allows for exploration of mo-

tivation inﬂuence on project success under different

initial conditions, like ﬁrm deadlines and correspon-

Guleva, V., Shikov, E. and Bochenina, K.

Modelling Inﬂuence of Motivation on Efﬁcient Tasks Distribution for Given Team-project Correspondence.

DOI: 10.5220/0010839400003117

In Proceedings of the 11th International Conference on Operations Research and Enterprise Systems (ICORES 2022), pages 185-192

ISBN: 978-989-758-548-7; ISSN: 2184-4372

185

dence between project requirements and team abili-

ties, which restricts possible best cases in team pro-

ductivity.

The project model contains tasks with a certain

level of necessary competence for their implementa-

tion and a team with their interests and competencies

in various topics. During the simulation, accompa-

nied by the task allocation process, personal motiva-

tion can increase or decrease if the task is interesting

or not, and personal competencies increase depending

on the correspondence between the complexity of the

task and personal abilities. On the other hand, the task

assignment management process affects the dynamics

of the system by taking into account changes in per-

sonal motivation, which affects the task completion

time.

Further, we construct an experimental stand and

investigate the dynamics of the project described by

the mathematical model above, which allows us to ex-

plore possible options for task assignment based on

competencies, as well as more complex cases with

motivational effects.

2 RELATED STUDIES

2.1 Competence and Motivation

Growth Mathematical Models

The vast majority of competence and motivation stud-

ies are difﬁcult to formalise, therefore they content

observations from the learning process and are based

on questionaries to small sets of users, allowing for

manual analysis. Mathematical models are few and

concern narrow applied scenarios. This results in the

necessity of model development for competence and

motivation growth for our particular case of project

management scenario and its further calibration and

veriﬁcation. Therefore, we make some observations

from the studies below and incorporate them into our

model for further exploration.

Motivation changes due to task assignments are

explored in a model of educational activity at the

teacher-group level, which is focused on the task of

ﬁlling the “knowledge repository” with tasks of var-

ious topics and complexity (Kusztina et al., 2010).

The motivation function of the teacher and the stu-

dent depends on the complexity of the task and its

similarity on the topic. Similar model is explored for

student projects scheduling aiming at maximising the

number of project teams to perform complex project

tasks (Bakhtadze et al., 2020).(Koeppen et al., 2008)

reviews current issues in competence modelling and

assessment, including an overview of competence ac-

quisition models, where competence is a composition

of cognitive, psychometric, and domain-speciﬁc abil-

ities.Similar decomposition is suggested in other re-

view (Salman et al., 2020), concluding competence

inﬂuence on work visual progress and its quality. The

studies about domain-speciﬁc competence measure-

ment (Chung et al., 2008; Van Der Linden, 2005),

observed in the review (Koeppen et al., 2008), aim

at automated knowledge evaluation like in teaching-

learning process.

Our model for task assignment and project evo-

lution also requires microlevel dynamics of compe-

tence growth, which should reﬂect effects of task

scope and complexity on its execution time, time re-

quired for competence growth, and measure of com-

petence growth. MacGrath et al. (McGrath et al.,

1995) understand a competence in the context of ﬁrms

development as a resource for reaching objectives,

and competence growth as a new source of compet-

itive advantage. They analyse team productivity on

the base of their responses and explore correlations

between comprehension, competence, deftness, cul-

ture, and sector, which a ﬁrm belongs to. Search

for competence growth measurement also results in

sports study (Fransen et al., 2018), where authors

explore personal and team performance dependence

on a leader competence-thwarting or supporting be-

haviour. Performance is measured as a time of task

evaluation for single persons as well as for team.

2.2 Task Assignment

Task assignment (TA) problems are usually formu-

lated in terms of ﬁxed execution times for a pair

(worker, task) given by a cost matrix (detailed ty-

pology of such problems may be found in (Pentico,

2007)). The goal of TA is to ﬁnd a correspondence

between tasks and workers to minimize a total cost.

Although, there are TA problems accounting for agent

qualiﬁcation (by placing additional restrictions) and

allowing for multiple task assignment for the same

agent, a cost is assumed to be static and known in ad-

vance. This assumption is reasonable for highly pre-

dictable environments (job scheduling for a static set

of computing units (Topcuoglu et al., 2002) or in a

multi-robot setting (Luo et al., 2011)), but is not nec-

essarily to be true for human-centric systems. The un-

certainty and the variability of worker’s productivity

bring an additional level of complexity to TA prob-

lems; however, studies of TA in this context are less

numerous.

There are several factors of uncertainty in a

worker’s outcome for a task that are investigated in

ICORES 2022 - 11th International Conference on Operations Research and Enterprise Systems

186

human resource allocation problems (Bouajaja and

Dridi, 2017). Performance may be inﬂuenced by the

personal features of a worker (e. g. competencies ef-

fecting on productivity for the industrial performance

optimization (Boucher et al., 2007)). Authors stress

the performance is inﬂuenced by the level of com-

petencies of workers and competence trajectories, as

well as the ability to allocate and coordinate compe-

tencies along with business processes. Workers may

operate collaboratively, increasing or reducing each

other’s productivity (Younas et al., 2011), which may

contribute to team result as a non-linear function of

its members. R&D process is characterized by a high

level of uncertainty of executions times (Su et al.,

2020), which are predicted as a step of a three-step TA

model by a neural network, which uses estimated sat-

isfactory degree of knowledge for a given team and a

task as an input. In turn, these values are provided by

an additional Task-Knowledge-Team (TKT) graph.

2.3 Validation Possibilities – Data Sets

The formulation considered in this article is similar

to one used in various issue tracking systems such

as Jira, Redmine, and others. There are a number of

open repositories collected from data based on these

programs (Ortu et al., 2015; Rahim et al., 2017;

Lenarduzzi et al., 2019; Claes and M

antyl

a, 2020).

They contain a large number of issue reports and

comments, as well as task assignment and resolution

timestamps. However, there are some difﬁculties in

validating the model on these datasets. For instance, it

is often quite difﬁcult to determine interests of users,

as well as their competencies, especially considering

that the latter may change. This can be achieved only

by using additional data sources and competence ac-

quisition methods. Also, text descriptions of tasks are

usually written using task-speciﬁc terms,which makes

it difﬁcult to identify the real values of task compe-

tence vectors.

2.4 Discussion

The goal of current review was to ﬁnd appropriate

mathematical or simulation models, showing compe-

tence and motivation dynamics during project execu-

tion. Despite we have note found actual models, ap-

propriate for our exploration of task assignment pro-

cess and possibilities of digital assistance,one can re-

sume the main patterns, determining personal produc-

tivity, on the base of literature review. They are:

1. task complexity and similarity of a topic affects

personal productivity (Kusztina et al., 2010);

2. competence affect productivity (Koeppen et al.,

2008; McGrath et al., 1995);

3. competence frustration as barrier to motivation

and performance (Fransen et al., 2018);

4. task understanding affects productivity (McGrath

et al., 1995);

5. feeling of competence increase intrinsic motiva-

tion (Fransen et al., 2018);

6. performance is measured as time for a

task (Fransen et al., 2018).

Therefore, personal productivity is measured as time

for a task execution by a user; the time is inversely

proportional to competence and motivation, and mo-

tivation is related to similarity between task topic and

personal interests.

These patterns are reﬂected below in the proposed

mathematical model of project evolution with motiva-

tion changes due to properties of tasks assigned. Nev-

ertheless, we suggest additional parameters, making

model a bit more ﬂexible and personalised to capture

possibilities of validation by means of available data,

and for possibility of exploration with personal assis-

tants.

3 PROJECT EVOLUTION

MODEL WITH

MOTIVATIONAL FEEDBACK

Let consider a project Π as a set of tasks E =

}

k=1

, M ∈ N, and each task is characterised by

competencies in a topic space, necessary for its per-

formance. Then, each user u ∈ U has their interests

∈ R

and competencies u

∈ R

, where Z in the

number of topics forming the interest space, which is

Hilbert space with Euclidean norm. The introduction

of both interests and competence for users allows dis-

tinguishing things they are able to do from ones they

would like to.

3.1 Dynamics

Motivation to do a single task is supposed to be

interest-dependent,

while global user motivation

accumulates their satisfaction of previous assigned

tasks, which results in formulae 1, 2.

m(e, u) = cos



, u



(1)

(t +1) = (1 − ε) ·u

(t) + ε· m(e, u) (2)

For arbitrary number of factors, affecting motivation, a

general form is: m(e, u) =

∑

· f

(e, u), where f

is attrac-

tiveness from the viewpoint of k value, and {γ

} is personal

values vector.

Modelling Inﬂuence of Motivation on Efﬁcient Tasks Distribution for Given Team-project Correspondence

187

Since user satisfaction of tasks assigned is supposed

to reﬂect the vectors collinearity, and we would like

them to be normalised for different time stamps (for

Eq. 2), the cosine function ﬁts these restrictions well.

Here (Eq. 2) we meet the ﬁrst parameter for cal-

ibration, which is related to personal “patience” –

the ability to do routine with minimal motivation de-

crease – the ε-parameter regulates contribution of re-

cent events in contrast to the newest, which can also

be captured by a width of time window, affecting mo-

tivation.

In this way, each task assignment affects motiva-

tion and may increase project efﬁciency by inﬂuenc-

ing task completion time (Eq. 3):

τ(e, u) =

m(e, u)

||c(e)||

||c(u)|| ·cos



c(e), c(u)



. (3)

This implies time decrease with increase in motiva-

tion and user competence. The correspondence of

user competence to task difﬁculty is evaluated as user

competence vector projection on the task competence

vector, after which their norms are compared, which

is reﬂected by the second multiplier in Eq. 3.

Changes in competence are also involved in per-

sonal dynamics evaluation, and proportional to the

difference between personal and task competence

(Eq. 4). The γ multiplier provides restriction on com-

petence increase for similar tasks evaluation, and it is

the second parameter for calibration, which reﬂects

speeds of obtaining and forgetting knowledge. The γ

can also be interpreted as / related to “adaptability”.

(t +1) − u

(t) =

c(e) − c(u)

. (4)

In this way, the model contains two drivers of em-

ployee activity, two kinds of feedback links, and re-

sult of tasks assignment at task level as completion

time, which is observed at system state variable after

aggregation over project.

Therefore, task attractiveness for a user, to be con-

sidered by a personal assistant and for project man-

agement, should be based on the balance between ﬁrm

and employee satisfaction, which is implemented as

the dependence on current motivation level:

Φ(u, e) = u

· cos(u

, e

) +(1 −u

)· cos(u

, e

), (5)

where cos(u

, e

) = m(e, u) generally. In this way,

higher motivation corresponds to more energy to do

any task, therefore, a task, appropriate for ﬁrm neces-

sity can be assigned as attractive enough. At the same

time, low motivation is a sign of burnout and a reason

to increase it by an interesting task, which is captured

by Eq. 5.

3.2 Personal Assistance and Project

Requirements

Each person demonstrates different critical values of

patience, after which low motivation does not allow

for efﬁcient project execution. Personal efﬁciency for

project goals can be managed by a personal assistant.

On the other side, a project may have strict deadlines,

which restricts number of possible managerial scenar-

ios, or it may contain time gaps to increase people

competence and motivation for more efﬁcient further

work, which also can be managed by a project assis-

tant to contribute to further ﬁrm development. The

third side is restrictions in project implementation,

arising from the correspondence of its topic and dif-

ﬁculty to team’s abilities and interests. This restricts

the range of project states for any quality of manage-

ment.

Maintaining constant productivity of employee is

more efﬁcient for project execution performance than

sprint-strategy with longer recovery time intervals

(e. g. there are time limits, required for recovery, ob-

tained for sport competitions (Moxnes and Moxnes,

2014)). Therefore, digital personal assistants aim

at maximisation of user satisfaction, control project

state, and at minimization personal productivity ﬂuc-

tuations at the same time. Therefore, a personal as-

sistant is supposed to monitor motivation changes in

order to minimise ﬂuctuations in productivity. In con-

trast, urgency of project execution requires minimal

consideration of motivation, which is captured by spe-

cial α parameter. Eq. 5 is adapted by scalar α, s. t.

zero-alpha focuses on motivation, α = 1 uses com-

petencies for task assignment, and α = 0.5 uses opti-

mal task preferences depending on current motivation

level:

Φ(u, e) = α · u

· cos(u

, e

) + (1 − α) · (1 − u

) · cos(u

, e

)

(6)

α should be tuned by a personal assistant depending

on current requirements for project evaluation and an

observed user state. This modiﬁes task attractiveness

function by potentially optimal parameter of personal

efﬁciency α

∗

From the global viewpoint, short- and long-term

planning restricts α-s at system level. Planning

restrictions are combined with people’s ability to

project execution. Therefore, the global α averages

personal α-s, and its minimal and maximal values of

motivation consideration should be stated on the basis

of requirements and system conﬁguration restrictions.

ICORES 2022 - 11th International Conference on Operations Research and Enterprise Systems

188

3.3 Global State Variable

Project success is estimated as a number of tasks com-

pleted (Eq. 8), or as time for all tasks completion

(Eq. 9). In this way, the main managerial goal is to

distribute tasks between users to minimize project re-

alization time and maximize users’ satisfaction and

competence increase to provide further ﬁrm develop-

ment.

s(e,t) =

(

1, complete

0, otherwise

(7)

s(Π,t) =



e|s(e,t) = 1



#E (Π)

(8)

T (Π) = max

j=1:N

∑

k=1

k j

τ(e

, u

)

, (9)

where s(e, t) determines a task state at time t, {b

k j

} =

B is a task assignment matrix, having ones, if task is

assigned to a user, and zeros – otherwise.

The system explored and project success are re-

stricted by the correspondence a between tasks top-

ics and team competence (from one side) and by the

correspondence b between user interests and task top-

ics at the system level. In this way, we will observe

project success s(Π, t) and T (Π) in the connection to

restrictions like a and b, and show, how much project

execution times can be regulated by means of em-

ployee motivation control for different conﬁgurations

of these parameters.

4 EXPERIMENTAL SET UP

The experiment is aimed to explore effects of moti-

vation consideration (α) during tasks assignments B

to a team, according to their interests u

and compe-

tence u

, in order to decrease projects execution time

T (Π) and increase team ability and motivation poten-

tial for further projects. Possible project success is

restricted by the correspondence between task topics

and difﬁculty and team interests and competence, de-

termined by a and b parameters (Table 1).

In this way, we consider three sets of experiments,

aimed at determining (i) optimistic project execution

times, (ii) possible effects of motivation on project

execution and possibilities of its management, and

(iii) effects of managerial approaches on team produc-

tivity (competence and motivational growth and their

effects on tasks execution).

Table 1: Motivation-sensitive project model parameters

at global scale.

Restrictions Parameters Observations

a corres-

pondence

between

project

and em-

ployee

compe-

tence

α motivational

necessity

(from task

urgency

vs. further

efﬁciency

focus)

s(Π,t)number of

tasks com-

pleted

T (Π) project

execution

time

b correspondence

between

project

topic

and em-

ployee

interests

∆u

competence

increase

per time

∆u

motivation

increase

4.1 Optimistic Project Execution Times

Since initial correspondence between team abilities

and suggested tasks restricts the best execution times,

we ﬁrstly explore them for ﬁxed motivation and as-

sign task according to competencies only. That

is task execution time τ(e, u) (Eq. 3) is evaluated

with m(e, u) = 1, and task attractiveness Φ(u, e) =

cos(u

, e

) (Eq. 5), since u

= 1, α = 1. In this way,

we vary a and obtain average times and deviations

of project completion for the cases of correspondence

between tasks and team competence (a = 1) and for

the opposite case (a = 0).

4.2 Effects of Motivation

When certain values of project execution times are

known for the case of the highest motivation (u

= 1),

we explore the effect of motivation-sensitive dynam-

ics on project success. For this case, the initial mo-

tivation values are ﬁxed for all users and then they

are changed during simulations depending on tasks

assigned. Tasks are assigned a) according to compe-

tencies (u

= 1, α = 1 in Eq. 5), b) in order to in-

crease motivation if it is low (u

(t) 6≡ 1, α = 1, Eq. 5).

For both cases we explore how the correspondence

between tasks and user interests affect project execu-

tion times. The resulting times are compared to times,

obtained from the ﬁrst experiment.

Modelling Inﬂuence of Motivation on Efﬁcient Tasks Distribution for Given Team-project Correspondence

189

4.3 Effects of Managerial Approaches

on Team Productivity and Growth

The goal of this experiment is to understand how

quickly the project can be evaluated and how it can

affect further team efﬁciency. In this way, we vary α

for task attractiveness evaluation and measure project

execution time, motivation increase and competence

change. Generally, the efﬁciency notion should be in-

troduced in order to measure current team state and

its ability to execute tasks. This issue also leads to

question of optimal “efﬁciency” level determination.

Algorithm 1: Task assignment method with motivation ef-

fects on execution time and task attractiveness and user

states dynamics.

1 U - set of users

2 E

E - set of tasks

E - set of not completed tasks

4 t = 0

5 while

E is not empty do

6 for u in U do

7 for e in

E do

8 Φ(u, e) = α · u

· cos(u

, e

) +

(1 − α) · (1 − u

) · cos(u

, e

)

9 end

10 e

= argmax(Φ(u, e))

11 u

(t +1) = (1−ε)·u

(t)+ε·m(e

, u)

12 u

(t +1) − u

(t) =

c(e)−c(u)

13 τ(e

, u) =

m(e

,u)

||c(e

)||

||c(u)||·cos



c(e

),c(u)



14 busy

= busy

+ τ(e

, u)

15 if busy

< 0 then

E =

E \ {e

}

17 end

18 t = t + 1

19 end

5 RESULTS AND DISCUSSION

5.1 Optimistic Project Execution Times

Project and team are initialised with different corre-

spondence of their competence (a change). At the

same time, different concentration values of Dirichlet

distribution results in uniform or peak values in vec-

tors, which affects difference between task and user

competence vectors for low a values. In this way,

high concentration (conc = 1.00) demonstrates sim-

ilar project states for various a (Fig. 1). At the same

time, low concentration values results in T(Π) ≈ 500

Figure 1: Dependence of project execution times on the cor-

respondence between task and user competencies. T(Π)(a)

shows certain values of execution times (for 90 % of all

project tasks) and deviations for a ∈ [0; 1]. Concentration

values of Dirichlet distribution reﬂect difference between

peak and the lowest values of competence in user and task

distributions (low conc. for high difference and majority

of equal values for conc. = 1). Equal values of task and

user competencies (conc. = 1) correspond to their better ﬁt,

therefore, for a = 1, conc = 1 gives the best execution times.

for completely different competence of project and

team and to T (Π) = 30 for similar competencies.

This states the restrictions for project success, de-

pending on initial conditions of competence for a con-

sidered system conﬁguration.

5.2 Effects of Motivation

Let initialise motivation for all users as u

(0) = 0.5.

This will increase tasks execution time, but will al-

low for implementation of different task assignment

strategies. The motivation is changed depending on

tasks assignment, and task attractiveness uses moti-

vation changes by a) α = 1 (Fig. 2a) or b) α = 0.5

(Fig. 2b). The resulting execution times are greater

for all a values, in contrast to the ﬁrst experiment,

since motivation affects execution time, but it is not

used during tasks assignment process. As a conse-

quence, the consideration of motivation during task

assignment decrease execution times (Fig. 2b).

In this way, if motivation affects project success

it can be managed. At the same time, correspon-

dence between interests and project affects more in

the case of skill-oriented tasks assignment for high

competence correspondence, while it affects more for

low competence correspondence.

In addition, one can see signiﬁcant difference in

project execution times for combinations of a and b.

This demonstrates at global scale, that lack of corre-

spondence between interests and skills may ten times

decrease team productivity, while changes in man-

agerial approaches increase productivity less signiﬁ-

cantly.

ICORES 2022 - 11th International Conference on Operations Research and Enterprise Systems

190

(a) Competence-based tasks assign-

ment, urgent case, α = 1.

(b) Motivation increase during tasks

assignment, α = 0.5 .

Figure 2: Effects of motivation changes due to tasks assign-

ment on project execution times.

5.3 Effects of Managerial Approaches

on Team Productivity and Growth

In order to analyse inﬂuence of managerial ap-

proaches (during project implementation) on team

productivity we observe competence increase de-

pending on tasks assignment approach (α variation).

Figure 3 shows average competence increase is higher

for the cases of planning controlling motivation, while

urgent task assignment, based on competence corre-

spondence, demonstrate the lowest competence in-

crease. This is accompanied by time dynamics, show-

ing better performance for middle values of motiva-

tion share (α ∈ {0.55; 0.78}), while the worst results

are for competence-centered and motivation-centered

edges.

6 CONCLUSION

Motivation is an important factor for project success,

which may compensate lack of competence in a pos-

itive case, or may contribute negatively in the case of

demotivation. This complicates a problem of tasks

assignments by additional feedback links in a project

organization system, affecting project state dynamics

(a) Competence increase.

(b) Times for motivation-sensitive task assignment.

Figure 3: Motivation-sensitive tasks assignment inﬂuence

on team competence growth and motivation.

by microlevel effects. In order to manage motivation

at personal scale, we develop a model, incorporat-

ing motivation and competence feedback links at mi-

croscale of user-task interaction, which is connected

to macrolevel by task execution times, inversely pro-

portional to user motivation and competencies, based

on theoretical results from psychological and man-

agement studies.

The analysis of project execution process model is

performed for different team-project correspondence

in competences and interests. Results demonstrate

how much team productivity depends on this corre-

spondence and how can it be managed by task assign-

ment strategies, which take motivation factors into ac-

count.

The results can be applied to create digital per-

sonal assistant systems and to select effective task as-

signment strategies for this correspondence between

the team and the project.

The developed models can be used for project evo-

lution simulation or employee behaviour after ε and γ

tuning. In addition, the model is well suited for de-

scribing the issue tracking task in software develop-

Modelling Inﬂuence of Motivation on Efﬁcient Tasks Distribution for Given Team-project Correspondence

191

ment, therefore it can be adapted for corresponding

tasks. Nevertheless, absolute ﬁt requires additional

methods of data preprocessing, to extract motivation

and competence information.

ACKNOWLEDGEMENTS

The authors are grateful to Alexander V.

Boukhanovsky for criticism and discussion of

the proposed model. This work was supported by the

Ministry of Science and Higher Education of Russian

Federation, Goszadanie no. 2019-1339.

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