A Study of Personality influence in building Work life balance using Induced Bi-directional Associative Memories (IBAM)

A Study of Personality influence in building Work life balance using Induced Bi-directional Associative Memories (IBAM)

A.Victor Devadoss1 and J. Befija Minnie2

1Head & Associate Professor, Department of Mathematics, Loyola College, Chennai-34.

2Ph.D Research Scholar, Department of Mathematics, Loyola College, Chennai-34.

Abstract:

The personality plays an important role in the work life balance irrespective of the organizational setups and other factors. It has become a subject of concern in view of the contemporary demographic, technological, market and organizational changes associated with an individuals personality. The concept of personality and its qualities used in this study were derived from the Big Five Personality traits. In this study an attempt is made to study the holistic picture of the personality influence on the work-life balance using Induced Bi-directional Associative Memories (IBAM) on the basis of experts opinion.

Keywords: Personality, Work Life balance, Induced Bi-directional Associative Memories

1. Introduction

   1. Personality

      Personality is made up the characteristic patterns of thoughts, feelings, and behaviors that make a person unique. In different situations personality and our responses are generally stable. Personality is psychological, but is influenced by biological needs and processes. Personality of an individual is a set of qualities that make the person distinct from another and assume a role or manner of behavior. Within ones personality the complex of all the attributes such as behavioral, temperamental, emotional and mental are considered. Guest (2002) defined the personality as the extent to which family or work is a central life interest influences the perceptions of balance of every individual [3].

      Personality is the entire mental organization of a human being at any stage of his development. It embraces every phase of human character: intellect, temperament, skill, morality and every attitude that has been built up in the course of one's life, (Warren & Carmichael, 1930, Elements of human psychology). Personality is a result of interaction between the individual and the environment. (B. F. Skinner, 1953, Science and Human Behavior). An individual's pattern of psychological processes arises from motives, feelings, thoughts and other major areas of psychological function. Personality is expressed through its influences on the body, in conscious mental life, and through the individual's social behavior. (Mayer 2005, Comprehensive handbook of personality and psychopathology CHOPP Vol. 1: Personality and everyday functioning.

   2. The Big Five Dimensions of Personality

      a) Extraversion:	This	trait includes
      characteristics	such	as excitability,
      sociability,		talkativeness

      a) Extraversion:	This	trait includes
      characteristics	such	as excitability,
      sociability,		talkativeness

      Today, many researchers believe that they are five core personality traits. Evidence of this theory has been growing over the past 50 years, beginning with the research of D. W. Fiske (1949) and later expanded upon by other researchers including Norman (1967), Smith (1967), Goldberg (1981), and McCrae & Costa (1987). The "big five" are broad categories of personality traits are Extraversion, Agreeableness, Conscientiousness, Neuroticism, and Openness [4]. While there is a significant body of literature supporting this five-factor model of personality, researchers don't always agree on the exact labels for each dimension. However, these five categories are usually described as follows

      assertiveness and high amounts of emotional expressiveness.

      1. Agreeableness: This personality dimension includes attributes such as trust, altruism, kindness, affection, and other pro social behaviors.

      2. Conscientiousness: Common features of this dimension include high levels of thoughtfulness, with good impulse control and goal-directed behaviors. Those high in conscientiousness tend to be organized and mindful of details.

      3. Neuroticism: Individuals high in this trait tend to experience emotional instability, anxiety, moodiness, irritability, and sadness.

      4. Openness: This trait features characteristics such as imagination, inventive and insight and those high in this trait also tend to have a broad range of interests.

      It is important to note that each of the five personality factors represents a range between two extremes. For example, extraversion represents a continuum between extreme extraversion and extreme introversion. In the real world, most people lie somewhere in between the two polar ends of each dimension.

   3. Work Life Balance

      By definition work life balance is about people having measure of control over when, where and how they work. There is a view that work- life balance only in the framework of what the company does for the individual. However, work-life balance is a two-pronged approach. The other prong of work-life balance, which many individuals overlook, relates to what individuals do for themselves. The core of work life balance could also be summed as achievement with enjoyment. Achievement and enjoyment at work is a critical part of work-life balance. Furthermore, achievement and enjoyment in the other three quadrants of one's life (e.g. family, friends and self) is critical as well. Work cultures have often demanded a transformation from inflexibility to flexibility. The underlying principle perhaps is the increasing realization that certain issues pertaining to the imbalance in working life and personal life of an individual are being overlooked. With globalization becoming the norm of the day, these issues seem to have taken a back seat for quite a while. Work-life 'imbalance' has over a period of time attracted concern

      because of increasing problems related to employee health, monotony at work place, declining levels of productivity and efficiency at the employee level. The imbalance also has a negative impact in the personal life of working people-some of which have even become social hazards- increasing number of divorces, infertility due high stress levels, advent of nuclear families etc.

   4. Personality influence on work life balance

      An individual should be able to strike a proper balance between work and personal life, there are many factors which are influencing the work life balance, and however an individuals personality also plays a vital role in balancing the work life. Aspects of personality including the need for achievement and propensity for work involvement belong among important individual factors. The approach of psychology of individual differences may be also fruitful for research of Work Life Balance due to the fact that studying aspects of different personality types can enhance our understanding of perceptions of work life balance. Therefore it can be realized that the Personality of an individual can have effects on an individuals balance between work and life.

2. Bi-directional Associative Memories (BAM)

   Bi-directionality, forward and backward information flow, is introduced in neural networks to roduce two-way associative search for stored stimulus-response associations (Ai,Bi).

   A group of neurons forms a field. Neural networks contain many fields of neurons. Fx denotes a neuron field which contains n neurons and Fy denotes a neuron field which contains p neurons.

   Neuronal Dynamical Systems The neuronal dynamical system is described by a system of first order differential equations that govern the time evaluation of the neuronal activations or membrane potentials.

   Xi gi X ,Y ,…, Yj hj X ,Y ,…

   Where xi and yj denote respectively the activation time function of the ith neuron in Fx and the jth neuron in Fy. The over dot denotes time differentiation, gi and hj are functions of

   X, Y etc., where X(t) = (x1(t),..,xn(t), Y(t)

   = (y1(t),..,yn(t)) define the state of the neuronal dynamical system at time t. Additive Bivalent Models describe asynchronous and stochastic behaviour. At each moment each neuron can randomly decide whether to change state, or whether to omit a new signal given its current activation. The BAM is a non-adaptive, additive, bivalent neural network.

   2.1. Bivalent Additive BAM

   In neural literature, the discrete version of the earlier equations is often referred to as the Bidirectional Associative Memories or BAMs. A discrete additive BAM with threshold signal functions, arbitrary thresholds and inputs, an arbitrary but a constant synaptic connection matrix M and discrete time steps K are defined by the equations.

   i j j ij i

   i j j ij i

   p

   xk 1 S yk m I

   j

   j i i ij j

   j i i ij j

   n

   yk 1 S yk m I

   i

   Where, mij M, Si and Sj are the signal

   1. Bidirectional Associative Memories

      When the activation dynamics of the neuronal fields Fx and Fy lead to the overall stable behavior, the bi-directional networks are called as Bi-directional Associative Memories or BAM. A unidirectional network also defines a BAM if M is symmetric

      i.e. M = MT.

   2. Additive Activation Models

   An additive activation model is defined by a system of n+p coupled first-order differential equations that interconnects the fields Fx and Fy through the constant synaptic matrices M

   and N described earlier. Si(xi) and Sj(yj) denote respectively the signal function of the ith neuron in the field Fx and the signal function

   of the jth neuron in the field Fy. Discrete

   additive activation models correspond to neurons with threshold signal functions. The neurons can assume only two values ON and OFF. ON represents the signal value +1 and OFF represents 0 or -1 (-1 when the representation is bipolar). The bipolar version of these equations yield the signal value -1

   when xi<Ui or yj<Vj.

   j

   j

   functions. They represent binary or bipolar . p

   threshold functions. For arbitrary real-valued thresholds U = (U1,U2,Un) for Fx neurons and V = (V1,V2,Vn) for Fy neurons. The

   xi Ai xi + Sj(yk )mji Ii

   j

   i

   i

   threshold binary signal functions corresponds . n

   neurons.

   1. Synaptic Connection Matrices Let us suppose that the field Fx with n neurons is synoptically connected to the field Fy with p

      neurons. Let mij be a synapse where the axon from the ith neuron in F terminates, mij can be positive, negative or zero. The synaptic matrix

      M is a n p matrix of real numbers whose

      entries are the synaptic efficacies mij. The matrix M describes the forward projections from the neuronal field Fx to the neuronal field

      Fy. Similarly, MT, a p n synaptic matrix and

      describes the backward projections Fy to Fx.

   2. Unidirectional Networks

      These kinds of networks occur when a neuron synoptically interconnects to itself. The matrix

      N is n n square matrix.

   3. Bidirectional Networks

   A network is said to be a bidirectional network if M = NT and N = MT.

   yj Ajyj Si (yk )mij I j i

   The bivalent signal functions allow us to model complex asynchronous state-change patterns. At any moment different neurons can decided whether to compare their activation to their threshold. An each moment any of the 2n subsets of Fx neurons or the 2p subsets of Fy neurons can decide to change state. Each neuron may randomly decide whether to check the threshold conditions in the equations given above.

   At each moment each neuron defines a random variable that can assume the value ON (+1) or OFF (0 or -1). The network is often assumed to be deterministic and state changes are synchronous i.e. an entire field of neurons is updated at a time. In case of simple asynchrony only one neuron makes a state change decision at a time. When the subsets represent the entire fields Fx and Fy synchronous state change results.

   In a real life problem the entries of the constant synaptic matrix M depends upon the investigators feelings. The synaptic matrix is given a weight age according to their feelings.

   If xFx and yFy the forward projections

   from Fx to Fy is defined by the matrix M.:{F(xi,yj)} = (mij)=M, 1<i<n, 1<j<p. the backward projection is defined by the matrix M T. :{F(yj,xi)} = (mji) = M T, 1<i<n, 1<j<p.

   2.7. Bidirectional Stability

   All BAM state changes lead a fixed-point stability. This property holds for synchronous as well as synchronous state changes.

   A BAM system (Fx, Fy, M) is bi-directionally stable if all inputs coverage to fixed point equilibrium. Bidirectional stability is a dynamic equilibrium. The same signal information flows back and forth in a bidirectional fixed point.

   Let us suppose that A denotes a binary n- vector and B denotes a binary p-vector. Let A be initial input to the BAM system. Then the BAM equilibrates a bi-directional fixed point (Ai,Bj) as

   , yp be the attributes associated with the characteristics of work life balance. The connection

   matrix M of order n X p is obtained through the experts opinion. Let A be the initial input vector. A particular component is kept in ON state and all other components in OFF state and we pass the state vector A through the connection matrix M. To convert the resultant vector as a signal function, choose the positive values to ON state and other values to OFF state with 1 and 0 respectively. Denote this

   process by the symbol B. The resulting vector is multiplied with MT and the thresholding yields a new vector A1. This vector is related with the connection matrix and that vector which gives the highest number of attributes to ON state is chosen as A2. That is, for each positive entry we get a set of resultant vectors; among these vectors the one which contains maximum number of 1s is chosen as A2. If there are two or more vectors with equal number of 1s in ON state, choose the first occurring one as A2. Repeat the same procedure till a fixed point or a limit cycle is

   A A A A

   .

   .

   M B M B M B MT B

   obtained. This process is done to give due importance to each vector separately as one vector induces another or many more vectors into ON state. Get the hidden pattern by the limit cycle or by getting a fixed point. Next we choose the vector with its second component in ON state and repeat the same to get another cycle. This process is repeated for all the vectors separately. We observe the hidden

   pattern of some vectors found in all or many

   .Af M

   Bf

   cases. Inference from this hidden pattern

   f

   f

   f

   f

   A ¬ MT ¬ B

   where A', A",.. and B', B" .. represents intermediate or transient signal state vectors between A and Af , B and Bf. respectively. The fixed point of a bidirectional system is time dependent. The fixed point for the initial input vectors can be attained at different times which are illustrated later. Based on the synaptic matrix M which is eveloped by the investigators feelings, the time at which bidirectional stability is attained also varies accordingly.

   2.8 Induced Bi-directional Associative Memories

   Suppose that there are n attributes, say x1, x2,

   , xn, where n is finite, associated with the five dimensions of personality and let y1, y2,

   highlights the causes.

3. Description of the Study

   The Big five personality traits are taken as the attributes of the domain space and the characteristics of work life balance are taken as the attributes of the range space. The attributes of the domain space are described as

   P1 Openness

   P2 Conscientiousness P3 Extraversion

   P4 Agreeableness P5 Neuroticism

   The attributes of the range space are described as

   W1 Planning and organizing priorities

   W2 Good in coping skills

   W3 Able to enjoy work and have career progression

   W4 Good at doing household activities W5 Time for child care/ elder care/ family

   W6 Time to spend with friends and relatives

   W7 Adequate time for personal care like s ing/eating on time etc,

   W11 Doing things creatively W12 Good performance at work

   W	W	W	W	W	W	W	W	W	W	W	W
   0	1	3	0	0	0	2	4	3	0	5	2
   5	-1	4	0	0	-1	0	-2	0	0	0	4
   -1	0	1	0	0	4	3	3	1	4	0	1
   -4	-1	0	4	3	3	-2	0	0	2	0	-1
   -5	-2	-2	0	0	0	1	-4	-5	0	-4	-3

   W	W	W	W	W	W	W	W	W	W	W	W
   0	1	3	0	0	0	2	4	3	0	5	2
   5	-1	4	0	0	-1	0	-2	0	0	0	4
   -1	0	1	0	0	4	3	3	1	4	0	1
   -4	-1	0	4	3	3	-2	0	0	2	0	-1
   -5	-2	-2	0	0	0	1	-4	-5	0	-4	-3

   The following connection matrix M is given on the basis of experts opinion

   1 2 3 4 5 6 7 8 9 10 11 12

   P

   P

   P

   leep

   W8 Hobbies/leisure time events/ refreshing activities/ fun

   W9 Time for health care like doing meditation Yoga/ Physical exercise

   W10 Time for social activity

4. Results and discussions

   Now let us take the input vector as A1 = (1 0 0 0 0) where the personality openness is kept in ON state and the rest of the nodes in OFF state.

   A1M = (0 1 3 0 0 0 2 4 3 0 5 2)

   (0 1 1 0 0 0 1 1 1 0 1 1) = B1

   B1MT = (20 5 9 -4 -19)

   (1 1 1 0 0) = A2

   A2(1) = (1 0 0 0 0)

   A2(2) = (0 1 0 0 0)

   A2(3) = (0 0 1 0 0)

   A2(1)M = (0 1 3 0 0 0 2 4 3 0 5 2)

   (0 1 1 0 0 0 1 1 1 0 1 1) = B2(1)

   2

   2

   B (1) MT = (1 1 1 0 0)

   The sum is 3

   A2(2)M = (5 -1 4 0 0 -1 0 -2 0 0 0 4)

   1

   2

   P3

   P

   P

   4

   P5

   2

   2

   (1 0 1 0 0 0 0 0 0 0 0 1) = B (2)

   2

   2

   B (2) MT = (5 13 1 -5 -10)

   (1 1 1 0 0)

   The sum is 3

   A2(3)M = (-1 0 1 0 0 4 3 3 1 4 0 1)

   2

   2

   (0 0 1 0 0 1 1 1 1 1 0 1) = B (3)

   2

   2

   B (3) MT = (14 5 17 2 -13)

   (1 1 1 1 0)

   The sum is 4

   Therefore, A3 = (1 1 1 1 0)

   A3M = (0 -1 8 4 3 6 3 5 4 6 5 6)

   (0 0 1 1 1 1 1 1 1 1 1 1) = B3

   B3MT = (19 5 17 9 -17)

   (1 1 1 1 0)

   Thus the binary pair {(0 0 1 1 1 1 1 1 1 1 1 1),

   (1 1 1 1 0)} represents the fixed point.

   Input vector	Limit point	Triggering pattern
   (1 0 0 0 0)	(0 0 1 1 1 1 1 1 1 1 1 1),(1 1 1 1 0)	A A A
   (0 1 0 0 0)	(0 0 1 1 1 1 1 1 1 1 1 1),(1 1 1 1 0)	A A A
   (0 0 1 0 0)	(0 0 1 1 1 1 1 1 1 1 1 1),(1 1 1 1 0)	A3 A A
   (0 0 0 1 0)	(0 0 1 1 1 1 1 1 1 1 1 1),(1 1 1 1 0)	A4 A A
   (0 0 0 0 1)	(0 0 1 1 1 1 1 1 1 1 1 1),(1 1 1 1 0)	A5 A A

5. Conclusion

   The result clearly indicates that the personality type Extraversion is striking a proper balance between work and life outside work. The results also represents that personality traits always have influence in the work life balance, Openness and Conscientiousness comes next after Extraversion. To conclude, this study has provided a clear evidence that the personality

   traits plays a vital role in work life balance and the Extravert people are managing both work and life outside work properly.

6. Future direction

A broad area of personality type behavioral difference at work and outside of work still left unstudied. It is noticeable that each personality behavior is not unique at work and outside of work. This is making the analysis of

psychometric tests to be more complex than it appears. This area can be studied to find out which personality tends to show more difference at professional and personal life and which personality shows less difference.

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