An Evaluation Model for Dynamic Motivational Analysis

Aluizio Haendchen Filho

1,2

, Simone Sartori

, Hércules Antônio do Prado

, Edilson Ferneda

and Paulo Ivo Koehntopp

Laboratory of Applied Intelligence, University of the Itajaí Valley (UNIVALI), Rua Uruguay, 458, Itajaí, Brazil

University Center of Brusque (UNIFEBE), Brusque, Brazil

Catholic University of Brasilia (UCB), Brasilia, Brazil

Catarinense Association of Educational Foundations (ACAFE), Florianópolis, Brazil

pauloivo@uol.com.br

Keywords: Dynamic Motivational Analysis, Herzberg Theory, Decision-making, Human Resources, Sentiment Analysis.

Abstract: In the past decades, a significant number of researches have sought to determine which factors make a worker

satisfied and productive. Currently, there are intensive efforts to develop efficient systems for motivational

analysis and performance evaluation. Current approaches of measuring motivation are very focused on

questionnaires and periodic interviews. These periods are most often greater than 6 months, and in most cases

performed annually. With today's communication dynamics, employees can be influenced at any time by

external factors of market supply and demand, as well as communications with peers and colleagues in the

device mesh. It is becoming increasingly important to obtain real-time information to take preventive or

corrective measures in a timely manner. This paper proposes a framework for real-time motivational analysis

using artificial intelligence techniques in order to evaluate employee’ motivation at work. The motivation is

evaluated from different groups of indicators: a static and periodic group (interviews and questionnaires), and

two other dynamic groups that collect information in real time. With the results generated by the system, it is

possible to make important decisions, such as understanding the emotional interactions among employees,

improving working conditions, identifying indicators of dissatisfaction and lack of motivation, encouraging

promotions, salary adjustments and other situations.

1 INTRODUCTION

Organizational motivation is a continuous field of

research, given its professional, technical and

personal relevance. There are several criteria that

influence the motivation of employees, including

relations with the leader, working conditions, safety,

personal life, recognition, professional growth,

salary, and benefits. In order to evaluate such criteria,

it is necessary and appropriate to provide a

heterogeneous structure adapted to different

motivational dimensions.

In the past decades, a significant number of

researches have sought to determine which factors

make a worker satisfied and productive, as opposed

to those factors that lead to dissatisfaction and poor

performance (Tay and Diener, 2011; Matei and

Abrudan, 2016; Alharthi et al., 2017 ). The two most

prominent authors in this subject are Frederick

Herzberg and Abraham Maslow. Maslow published

the hierarchy of needs (Maslow, 1943), while

Herzberg developed the theory of the two factors -

hygienic and motivational (Herzberg, 1971).

Motivation is the best potential source of increased

productivity. Thus, employee capabilities will be best

used, leading to job satisfaction and improved

productivity.

Efforts have been made to identify motivational

factors or sentiments based on the support of

Artificial Intelligence (AI) techniques (Toy, 2014,

Medhat, Hassan and Korashy, 2014, Chumkamon,

Masato and Hayashi, 2015), but they are not enough

to provide effective solutions to this matter.

Developers of AI systems turn to the capability of

researchers in achieving goals, performing tasks or

solving problems. This is perhaps more meaningful

than the motivational aspects of the systems (Kelley

and Waser, 2018).

Current approaches of measuring motivation are

very focused on questionnaires and periodic

446

Filho, A., Sartori, S., Antônio do Prado, H., Ferneda, E. and Koehntopp, P.

An Evaluation Model for Dynamic Motivational Analysis.

DOI: 10.5220/0007744304460453

In Proceedings of the 21st International Conference on Enterprise Information Systems (ICEIS 2019), pages 446-453

ISBN: 978-989-758-372-8

interviews. These periods are most often greater than

6 months, and in most cases performed annually.

With today's communication dynamics, employees

can be influenced at any time by external factors of

market supply and demand, as well as

communications with peers and colleagues in the

device mesh. It is becoming increasingly important to

obtain real-time information to take preventive or

corrective measures in a timely manner. On the other

hand, interview responses and questionnaires do not

always faithfully reflect the degree of satisfaction or

dissatisfaction of employees, who often prefer not to

expose their real sentiments.

This paper proposes a conceptual framework for

real-time motivational analysis using artificial

intelligence techniques in order to evaluate

employee’ motivation at work. The motivation is

evaluated from different groups of indicators: a static

and periodic group (interviews and questionnaires),

and two other dynamic groups that collect

information in real time.

2 RELATED WORKS

We reviewed the literature related motivational

analysis using artificial intelligence techniques,

especially the analysis of sentiments and natural

language processing. We found four scientific articles

which deserve to be highlighted.

The first one presented by Tay and Diner (2011)

analyses a sample from 123 countries. It evaluates the

correlation between the fulfilment of necessities

(Maslow, Deci and Ryan, Ryff and Keyes theories)

and subjective well-being, including life assessment

of positive and negative sentiments. Within the

various cultures studied, using statistical analysis and

regression techniques, they found that the attendance

of the psychosocial needs is adherent to the

conditions of the country. On the other hand,

fulfilment of basic and security needs is not

associated with the conditions of the country.

The second article described by Akdemir and

Arslan (2013) focused to measure of teacher

motivation. For this, they constructed a set of 51

attributes based on the motivational and hygienic

factors of Herzberg. These attributes were evaluated

using a five-point scale (none, small, moderate, very,

and completely). In addition, as a pilot test, the scale

was applied to 150 teachers from different areas of

Zonguldak Province, Turkey. In order to evaluate the

data, the authors used factorial analysis, correlation

tests, and data normalization. The results indicated a

reliable and valid motivational scale that can be used

to measure teacher motivation in four dimensions:

communication, professional growth, institutional

progress and expectations.

In the study published by Medhat, Hassan and

Korashy (2014), the objective was to provide an

overview on algorithms and applications used for the

analysis of sentiments. These was described in 54

recently published articles (2010 to 2013) on this

subject. The authors emphasize that the sentiment

classification algorithms and features selection

techniques are still research fields to be explored. On

the other hand, Naive Bayes and Support Vector

Machines are Machine Learning approach algorithms

most frequently used to solve problems related to

sentiment classification. The main source is the

lexicon WordNet which is available in several

languages besides English.

In the Ravi and Ravi (2015) paper, the authors

worked on opinion mining in 160 papers published

between 2002 and 2015. They used approaches and

applications commonly required for the analysis of

sentiments. The research is organized based on sub-

tasks to be performed, machine learning and natural

language processing techniques. In the literature

review carried out by the authors, seven dimensions

were analysed: subjective classification, sentiment

classification, measurement review utility, lexical

creation, opinion word and aspect of product

extraction, opinion spam detection and several

opinion mining applications. In addition, the

identified approaches involved lexical-based machine

learning, hybrid approaches, ontologies-based

approaches and non-ontologies (considered for

lexical creation and feature extraction).

Considering the above, it was noticed that the

studies analysed aspects related to motivational

analysis from artificial intelligence techniques,

especially analysis of sentiments and natural

language (Akdemir and Arslan 2013, Ravi and Ravi

2015). However, it should be noted that the study by

Ravi and Ravi (2015) deals with a bibliographic

review, whereas the study by Akdemir and Arslan

(2013) uses an approach to analyse the motivation of

teachers in the academic context.

3 PROPOSED APPROACH

In this study we sought to analyse motivation in the

business context. Our approach considers, besides the

commonly used questionnaires, different sources to

obtain information related to motivation of the

employees.

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447

In order to obtain the indicators of motivation, the

main source was Herzberg's two-factor theory and a

brief description of the indicators used in the theory.

Next, we present the data sources used to input the

quantitative and qualitative values of the indicators.

These data sources include the traditional 360-degree

evaluation questionnaires, data obtained from the

Human Resources transactional systems, external

information about the job market, and the main data

sources accessed to obtain the information to perform

the sentiment analysis.

3.1 Herzberg's Two-factor Theory

Indicators

As previously cited, this theory postulates that there

are two groups of factors: hygienic factors and

motivational factors. The first group (extrinsic) is

formed by external elements capable of influencing

people's dissatisfaction but does not guarantee

satisfaction. The second group (intrinsic) elevates the

self-image about the capacity for achievement, thus

promoting motivation.

Absence of hygienic factors creates

dissatisfaction, but their presence will not necessarily

create satisfaction. On the other hand, absence of

motivator factors does not imply dissatisfaction, but

their presence will create satisfaction (Shen and Yu,

2009). Therefore, the two-factor theory considers that

the presence of motivation factors will lead to

satisfaction, while hygienic factors should avoid

dissatisfaction.

A summary of each of the motivation indicators

described by Herzberg (Ruthankoon 2003, apud

Haruna 2013, p.5), is as follows:

 Achievement. An example of positive

achievement might be of an employee who

completes a task or project before the deadline and

receives high reviews, increasing his satisfaction.

However, if that same individual is unable to

finish the project in time or feels rushed and is

unable to do the job well, the satisfaction level

may decrease.

 Recognition. When the employee receives the

acknowledgement, he deserves a complimentary

for a well-done job, and the satisfaction will

increase. If the employee’s work is overlooked or

criticized, it will have the opposite effect.

 Work Itself. This involves the employee’s

perception of whether the work is too difficult or

challenging, too easy, boring or interesting.

 Responsibility. Is the degree of freedom an

employee has to take his/her own decision and

implement his/her ideas. The more liberty he has

on that responsibility, the more inclined the

employee is to work harder on a project and

increase his satisfaction with the result.

 Advancement. This refers to the expected or

unexpected possibility of promotion. An example

of negative advancement would be if an employee

did not receive an expected promotion.

 Possibility of Growth. This includes the chance

one might have for advancement within the

company. This could also include the opportunity

to learn a new skill. This could have a negative

effect on the satisfaction the employee feels with

his job and position.

The following are the hygienic indicators, which

work in the same way with positive or negative

attributes. However, these factors can only have an

effect on dissatisfaction.

 Company Policy or Administration. The

employee’s perception of whether the policies in

place are good, bad, fair or not, may change the

level of dissatisfaction.

 Personal or Working Relationships. This

indicator refers to relationship of the employee

with his supervisors, his peers, as well as his

subordinates. The way someone feels about

interactions and discussions that take place within

the work environment can also affect satisfaction.

 Salary. This factor is straightforward. Increase or

decrease in wages has a great impact on

satisfaction or dissatisfaction.

 Personal Life. Although people try to separate

work from personal life, it is inevitable that one

will affect the other.

 Feeling of Job Security. This is a significant

factor. The sense of job security in the company,

as well as a position within the organization is

very important regarding the level of satisfaction.

According to Lundberg et al. (2009), to achieve

employee’s motivation, managers must give

responsibilities to their employees and create

platforms for feedback.

3.2 Data Sources for Indicator

Quantification

The three main sources for obtaining data for the

quantification of indicators are: (i) 360º evaluation;

(ii) data sources of the human resources department;

and (iii) data collected from social networks and

communication channels.

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3.2.1 360-degree Evaluation

Modern evaluation systems include a more balanced

and holistic approach, conveying the performance of

everyone in the organization. This proposal provides

a flexible 360-degree evaluation framework where

executives, managers and employees (with peer

review) can express their judgments in different

domains. The results can be expressed linguistically,

numerically or in intervals. Figure 1 (adapted from

Espinilla et al. 2013) presents the proposed 360-

degree assessment.

Figure 1: 360-degree evaluation proposal.

Sources of information work with hygienic and

motivational factors, which together lead to the

aggregation of results. That is, the result of the

evaluation process occurs after the aggregation of the

indicators on a Likert scale, given the importance

attributed to the indicator and to the dimension.

Therefore, the evaluation considers the interaction

between the evaluated criteria, its relevance and the

importance of all of those involved in the process.

3.2.2 Human Resources ERPs

The main data to be obtained from the Human

Resources are the hygienic and motivational

indicators, which include the following: (i) Position;

(ii) Salary; (iii) Health / Insurance Plan; (iv) faults and

delays; (v) medical absence.

Such data can normally be obtained in ERP

systems. For the purpose of motivational analysis, the

ideal is to organize this data in specific files, so that

they can generate information about the hygienic and

motivational factors. Historical data from employees’

such as positions held over time, salary changes,

awards, faults and delays can be stored and updated

regularly, thus becoming a valuable source for

motivational analysis.

Figure 2 presents some complementary data, such

as information about salary paid in the market, based

on the Brazilian Occupation Code. In addition to this

data, other information can be obtained from the

controls of HR and external sources. As above

mentioned, ideally, this information should be

organized in a DataMart, where the load with

updating of the data is regularly done. The HR

Department can provide information about the

employee profile, salary history and attendance,

opportunities for promotion, policies and benefits,

among others.

Figure 2: Data sources from HR Department.

Existing methods range from machine learning

methods, exploiting patterns in vector representations

of text for lexicon-based methods. This is done taking

into account the semantic orientation of individual

words. These words are matched with a sentiment

lexicon, extracting their associated sentiment.

With this information it is possible to check the

history of positions held, salary increases, received

awards, attributed responsibilities during the career,

salary positioning in relation to job market, among

other indicators.

3.2.3 Analysis of Sentiments

In order to use analysis of sentiments, it is important

to identify the domain related to the text. For

example, to perform analysis of sentiments related to

indicators such as Company Policy and Management,

Relationship with the Chief, Work Conditions, and so

on, it is first necessary to identify the domain relates

to such indicators. This can be done using Latent

Semantic Analysis (LSA) techniques. In this way it is

possible to identify the domain and to carry out

analysis of sentiments, selecting the word sense with

the highest semantic similarity to the context.

The techniques are applied in two phases: pre-

processing and processing. In the first one, the

algorithm changes text words to lowercase and

performs removal of accents. In the second one,

stopwords removal techniques and lemmatization is

applied.

Figure 3 (adapted from Hogenboom et al., 2013)

shows a schematic view of the analysis of sentiments

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449

from input documents, and returns results based on

word scoring. The presented method first splits a

document into paragraphs, sentences, and words

using n-grams techniques. Then, for each sentence,

the Part-of-Speech (POS) and lemma of each word is

determined. In lemmatization, text words are reduced

to their radical, eliminating effects of verbal times in

sentiment interpretation, as well as gender and

number variations.

Figure 3: Schematic view of sentiments analysis.

The word sense is subsequently disambiguated using

an unsupervised algorithm. It iteratively selects the word

sense with the highest semantic similarity to the word’s

context. The sentiment of each word, associated with its

particular combination of POS, lemma, and word sense,

is then retrieved from a sentiment lexicon like

SentiWordNet.

Figure 4 presents analysis of sentiments, showing

part of data collection, classification, summary and

results.

Figure 4: Collect and process data for the analysis of

feelings.

This data can be collected from social networks

such as Twitter, LinkedIn, Facebook, Instagram (as

long as the data is public). Social networking sites are

considered a good source of information because

people freely share and discuss their opinions on a

given topic. Such information can be positive,

negative or neutral, as well as portraying happiness,

well-being, sadness, bipolarity, among other

motivational characteristics.

In order to obtain a good analysis, it is necessary

to use the sentence level or keywords, with the

purpose of classifying the domain of sentiment

expressed in each sentence.

4 EVALUATION MODEL

The objective of the proposed approach is to provide

information about the work as a whole in order to

obtain a balanced and holistic view of motivational

evaluation. For dynamic motivational analysis, we

create a framework composed by three dimensions:

i. 360-degree Evaluation. The enquiry will be

conducted through the use of questionnaires. The

objective of this process is to obtain an evaluation

of each employee in relation to the organization as

a whole.

ii. Human Resources. The results of the model will

indicate different dimensions of employees’

motivation with a direct influence on increasing

human resources performance.

iii. Sentiment Analysis. Data collected from social

networks and communication channels for

analysis of sentiments. Here, we want to analyze

sentiments related to indicators such as company

policy, relationship with the chief, work

conditions, and so on.

4.1 Indicators Evaluation

The evaluation of the indicators considers the Likert

scale of five points: (5) excellent, (4) good, (3)

neutral, (2) poor and, (1) bad. Thus, response to

higher levels corresponds to greater satisfaction with

the organization or the indicator, or even one area

compared to another. For each indicator, a weighting

factor can be used when assigning a given

quantitative value.

Some methods of weighting are derived from

statistical models such as factor analysis, data

development analysis and unobserved component

models (UCM). Budget allocation processes (BAP),

analytic hierarchy processes (AHP), and conjoint

analysis (CA) (OECD, 2008) are other methods.

Regardless of the method used, weight is an

essentially valuable judgment.

This research proposes the use of the linear

aggregation method, in which the value attributed to

the indicator by the Likert scale is multiplied by the

respective weight, thus obtaining the relative

importance of the indicator within the analysis set.

In addition to analysing indicators individually,

organizations can use statistical models such as

Principal Component Analysis (PCA) or Factor

Analysis. In this way, it is possible to group

individual indicators according to their degree of

correlation, and then proceed with correlation

analysis of indicators.

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Table 1 shows the three-dimensional framework.

Data were considered to be fictitious of the evaluation

result for an employee. The approach uses a set of

indicators that are punctuated in a three-dimensional

framework, considering a dynamic system of

assigning vertical and horizontal weights. The table

identifies the two main categories of indicators,

according to Herzberg's Two Factor Theory.

Table lines show the indicators, which are scored

in each of the three dimensions according to the

Likert scale. The indicators that correspond to the

hygienic factors in Herzberg's theory are located in

the top of the table, while the motivational indicators

are at the bottom in darker tones.

Table 1: Three-dimensional matrix for dynamic evaluation.

In the example, weights can be assigned to the

indicators (column 3). Therefore, the calculation to

find the degree of motivation will depend on the

situation of the employee in relation to each indicator

plus its development in every dimension. In addition,

some indicators do not have data (supervision,

Relationship with the leader, colleagues and

subordinators, work itself, and so on), because these

data is not obtainable in the Human Resource

dimension. The last column shows the resultant score

after the calculation and weighting of the horizontal

and vertical weights.

The company can choose a weight of 1, 2 or 3 for

each indicator (in the example, a default value of 1 is

shown). The same can be used for the dimension’s

evaluation. For example, in the Dimension 360

degree a weight of 20 was assigned. The dimensions

Human Resources and Analysis of Sentiments

received weight 30 and 50, respectively.

The weights can be changed (that is, they are

variable) according the company`s external or

internal factors. External factors refer to the supply

and demand of labor, the economic context (inflation,

exchange rate, employment level, exports) and so on.

4.2 Clusters Identification

The results obtained in the evaluation of the

indicators will be used to identify clusters. Among

similarity metrics, Euclidean distance is one of the

most commonly used (Carvalho et al. 2006).

According to Moita Neto and Moita (1997), in cluster

analysis the similarity between two samples can be

expressed as a function of the distance between the

two points represented in n-dimensional space. The

most usual way of calculating the distance between

two points a and b in the n-dimensional space is

known as the Euclidean distance.

According to Kaufman and Rosseeuw (2009),

Euclidean distance is the most common metric and

can be combined with weights in the variables,

depending on the importance of each attribute in the

description of an object. The formula is as follows:

(1)

Where:

• d (a, b) = similarity metric between object "a" and

object "b", where the closest to 0, the more similar the

objects;

• w = weight of each attribute;

• ax = attribute value x, from object a, on the x-axis;

• bx = attribute value x, of object b, on the x-axis

In the problem proposed for this work, an

employee is a point on the n-dimensional Cartesian

plane, he/she is represented by: a = {x, y, z, ..., n},

that is, each plane (x, y, z, ..., n) represents an attribute

of the Herzberg factor (ex. Realization, Recognition).

The distance will always be calculated between two

employees, so that the closest to 0 score, the more

similar is the motivation degree among then.

5 RESULTS OVERWIEW

The graphical user interface applied to present the

results is based on the calculation and presentation of

clusters for an overview. It is possible to visualize the

motivational score of a employee, a department, or a

section, using drill-down techniques.

In the context of this work, clusters are formed by

employees who obtained similar indices, as shown in

Figure 5. The vertical axis represents the number of

employees, while the horizontal axis represents the

degree of motivation resulting from the matrix. The

points in the graphic represent the scores presented in

the Results column in Table 1.

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451

Figure 5: Presentation of results in clusters.

Each point presented in Figure 5 refers to the

degree of motivation of each employee. As can be

seen, about 25% of the employees are poorly

motivated (ranging from 1 to 2.9 on the Likert scale);

60% are motivated (ranging from 3 to 4 on the Likert

scale), and 15% are highly motivated (ranging from

4.1 to 5 on the Likert scale).

Figure 6 shows details of the drill-down

technique. The idea is that by clicking on a cluster or

point, a detailed representation of that point can be

visualized. In the same way, each representation can

offer the vision of the indicators of an employee.

Figure 6: Detailing an employee motivation indicator.

Important accentuate that results obtained

individually by employees can be consolidated by

section, department, area, rising in the hierarchy

using drill-up technique. Likewise, the indicators can

be viewed at the highest levels of the functional

hierarchy and go down the hierarchy to examine the

lower levels using the drill-down technique.

6 DISCUSSION AND

CONCLUSION

For decades, job motivation has been shaped under

static approaches, neglecting the dynamics of the job

market and the ability of employees to obtain

information. As a consequence, the collected

evaluations did not allow to gather the richness of the

information coming from several sources. Our

approach considers that there is a improvement of

motivational analysis when the evaluation system

works with a wide range of assessments from several

sources.

During observation phase and data gathering

work, it was verified that, in practice, the motivation

assessment was mostly based on questionnaires,

which occurred in long frequency periods, averaging

6 to 12 months. For the most part, they do not reflect

the current situation because in long times periods

many indicators can change. In fact, the majority of

indicators are constantly changing, either by internal

or external influences.

The great advantage of working with a dynamic

approach is the possibility of predicting situations that

may affect the motivation of an employee or a group,

and then take preventive measures. The analysis of

sentiments can disclose new characteristics of the

evaluation process, at any moment. In this way,

employees can be evaluated using several domains of

expression.

The presented approach allows evaluation of the

degree of employees’ motivation. It was developed in

conjunction with the HR team of a large company,

including researchers in the areas of Enterprise

Administration and Computing. Therefore, the main

contribution of this paper is the development of a

framework for dynamic assessment of employee’s

motivation, as opposed to current static approaches.

We could not find an approach with such

characteristics in literature, which makes the

approach innovative.

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