Determining the effect of Mood on Productivity using Statistical Data Analysis Tool

Abstract

The relationship between mood and productivity has been a topic of interest for researchers and individuals seeking to improve their work output. Numerous studies have shown that mood can have a significant impact on productivity. Positive moods have been found to increase productivity, while negative moods can decrease productivity. Research suggests that positive moods, such as happiness and contentment, can lead to higher levels of productivity by increasing motivation, creativity, and focus. Positive moods have also been linked to higher levels of job satisfaction and better overall performance. On the other hand, negative moods, such as anxiety and stress, can decrease productivity by causing distractions, impairing decision-making ability, and reducing energy levels. Our study seeks to determine the effect of mood on productivity by applying statistical tools on collected data and testing the hypothesis. In our study it is found that employees who experienced positive moods were able to complete tasks faster and with fewer errors than those in a negative mood. Overall, the evidence suggests that mood plays a crucial role in productivity. By recognizing the impact of mood on productivity, individuals can take steps to cultivate positive moods and manage negative ones, leading to better outcomes both in the workplace and in other areas of life.

Full text

Vol-9 Issue-2 2023 IJARIIE-ISSN(O)-2395-4396
19487 www.ijariie.com 1006
Determining the effect of Mood on Productivity
using Statistical Data Analysis Tool.
Mr. Vinay Gadigi1, Dr. Havinal Veerabhadrappa2.
1 Assistant Professor, MBA Department, RYMEC, Ballari, Karnataka, India
2 Professor, Emeritus, Department of Management studies, , Karnataka, India
ABSTRACT
The relationship between mood and productivity has been a topic of interest for researchers and
individuals seeking to improve their work output. Numerous studies have shown that mood can have a significant
impact on productivity. Positive moods have been found to increase productivity, while negative moods can
decrease productivity. Research suggests that positive moods, such as happiness and contentment, can lead to
higher levels of productivity by increasing motivation, creativity, and focus. Positive moods have also been linked to
higher levels of job satisfaction and better overall performance. On the other hand, negative moods, such as anxiety
and stress, can decrease productivity by causing distractions, impairing decision-making ability, and reducing
energy levels. Our study seeks to determine the effect of mood on productivity by applying statistical tools on
collected data and testing the hypothesis. In our study it is found that employees who experienced positive moods
were able to complete tasks faster and with fewer errors than those in a negative mood. Overall, the evidence
suggests that mood plays a crucial role in productivity. By recognizing the impact of mood on productivity,
individuals can take steps to cultivate positive moods and manage negative ones, leading to better outcomes both in
the workplace and in other areas of life.
Keyword: - Mood, Productivity, Statistical Tools Data Analysis and Hypothesis Test.
1. INTRODUCTION
Mood and productivity are closely related concepts, as our mood can have a significant impact on our
ability to get things done. Productivity is a measure of how efficiently we use our time and resources to achieve our
goals, while mood refers to our emotional state at any given time. When we are in a positive mood, we tend to feel
more motivated, energized, and focused. This can lead to increased productivity as we are better able to tackle tasks
and stay on track. On the other hand, when we are in a negative mood, such as feeling anxious, stressed, or
overwhelmed, we may struggle to stay focused and productive. There are several factors that can influence our
mood and productivity, including our physical health, the environment we work in, and our personal habits and
routines. By understanding how these factors impact our mood and productivity, we can take steps to improve both.
Ultimately, by paying attention to our mood and taking steps to improve it, we can become more productive and
achieve our goals more effectively.
Example: practicing regular exercise, getting enough sleep, and maintaining a healthy diet can all help to boost our
mood and energy levels, making it easier to stay focused and productive. Creating a workspace that is free of
distractions and clutter can also help to improve our mood and productivity, as can establishing a regular routine and
setting realistic goals.

Vol-9 Issue-2 2023 IJARIIE-ISSN(O)-2395-4396
19487 www.ijariie.com 1007
1.1 Mood
Mood refers to our emotional state at any given time. It is a subjective experience that can range from
feeling happy and content to sad, anxious, or angry. Our mood can be influenced by a variety of factors, including
our physical health, environment, and personal experiences. Mood can also impact how we perceive and interact
with the world around us. For example, if we are in a good mood, we may be more likely to engage in social
activities and experience positive interactions with others. On the other hand, if we are in a negative mood, we may
be more likely to withdraw and experience conflict in our relationships. Mood can be a complex and multifaceted
experience that can have both positive and negative effects on our lives. While a positive mood can lead to feelings
of happiness, motivation, and productivity, a negative mood can lead to feelings of sadness, anxiety, and a lack of
motivation. It's important to pay attention to our mood and take steps to manage it when necessary. This can involve
engaging in activities that boost our mood, such as spending time with loved ones or engaging in hobbies that we
enjoy. Additionally, seeking help from a mental health professional can be beneficial for managing persistent or
intense changes in mood.
1.2 Productivity
Productivity refers to the measure of how efficiently we use our time and resources to achieve our goals. It
is a measure of how much work we can accomplish in a given amount of time. Productivity is important in many
aspects of our lives, including our personal and professional endeavors. In the workplace, productivity is often used
as a metric for measuring an individual's or a team's performance. There are many factors that can impact
productivity, including our physical and mental health, our environment, and our habits and routines. For example,
getting enough sleep, maintaining a healthy diet, and engaging in regular exercise can all help to boost our
productivity by improving our energy levels and focus? Additionally, having a clear understanding of our goals and
priorities can help us to stay organized and focused, which can increase productivity. Creating a workspace that is
free from distractions and setting realistic deadlines can also help to improve productivity. Effective time
management skills are also important for increasing productivity. This can include prioritizing tasks, breaking them
down into smaller, manageable steps, and using time-tracking tools to monitor progress. Ultimately, by paying
attention to the factors that impact productivity and taking steps to improve them, we can become more efficient and
achieve our goals more effectively.
2. ANALYSIS
The data is collected for a period of one month for 10 samples and 20 samples for one more month. The
data is derived from the computer vision monitoring system that captures and determines the state of mood based on
facial expressions. The data thus derived is compared with the actual work log sheet to tabulate the productivity
using basic formulas. the collected data and segregated into 4 data sets namely 1) happy- ideal productivity 2) sad-
ideal productivity 3) anger- ideal productivity 4) disgust- ideal productivity. The data is put through hypothesis
testing to check for dependencies and variances. the hypothesis is as follows: i) hypothesis for happy mood H0) there
is no difference between the happy productivity and ideal productivity groups with respect to the dependent variable
Ha) there is a difference between the happy productivity and ideal productivity groups with respect to the dependent
variable. ii) hypothesis for sad mood H0) there is no difference between the sad productivity and ideal productivity
groups with respect to the dependent variable Ha) there is a difference between the sad productivity and ideal
productivity groups with respect to the dependent variable. iii) hypothesis for anger mood H0) there is no difference
between the anger productivity and ideal productivity groups with respect to the dependent variable Ha) there is a
difference between the anger productivity and ideal productivity groups with respect to the dependent variable 4)
hypothesis for disgust mood H0) there is no difference between the disgust productivity and ideal productivity
groups with respect to the dependent variable Ha) there is a difference between the disgust productivity and ideal
productivity groups with respect to the dependent variable.
The data is put to mann-whitney u-test where the descriptive statistical analysis of the 1st data set states that the
happy mood productivity values are higher than the ideal mood productivity values; therefore we can determine that
the work done is more in happy mood as values are higher than ideal productivity. The null hypothesis is rejected in
this case as the difference between happy productivity and ideal productivity is statistically significant. The
descriptive statistical analysis of the 2nd data set states that the sad mood productivity values are lower than the ideal
mood productivity values, therefore we can determine that the work done is less in sad mood as values are lower
than ideal productivity. The null hypothesis is rejected in this case as the difference between sad Productivity and
ideal productivity are statistically significant. The Descriptive Statistical analysis of the 3rd data set states that the

Vol-9 Issue-2 2023 IJARIIE-ISSN(O)-2395-4396
19487 www.ijariie.com 1008
anger Mood Productivity values are lower than the Ideal mood Productivity values, Therefore we can determine that
the work done is Less in anger mood as values are Lower than ideal productivity. The null hypothesis is rejected in
this case as the difference between Sad Productivity and ideal productivity is statistically significant. The
Descriptive Statistical analysis of the 4th data set states that the disgust Mood Productivity values are lower than the
Ideal mood Productivity values, Therefore we can determine that the work done is Less in disgust mood as values
are Lower than ideal productivity. The null hypothesis is rejected in this case as the difference between Sad
Productivity and ideal productivity is statistically significant. The chart below clearly shows that the happy
productivity is higher than ideal productivity and other moods are lesser than ideal productivity. Over all the
productivity variance when in happy mood is always in positive curve where as in sad, anger and disgust the
variance Is in negative curve.
FIG-1
3. CONCLUSIONS
The data analysis has given clear evidence that the happy mood productivity has got higher values than
ideal mood by which we can determine and conclude that the productivity is higher in happy mood when compared
to other moods i.e., Sad, Anger and Disgust which ave comparatively lower values than the ideal productivity.Hence
we can conclude that an individual employees productivity is high when in happy mood and is less in sad, anger and
disgust moods.

Vol-9 Issue-2 2023 IJARIIE-ISSN(O)-2395-4396
19487 www.ijariie.com 1009
4. REFERENCES
[1]. Burdick, K. E., Endick, C. J., & Goldberg, J. F. (2005). Assessing cognitive deficits in bipolar disorder: Are
self-reports valid? Psychiatry Research, 136(1), 43–50.
[2].Colombo, D., Fernández-Álvarez, J., Patané, A., Semonella, M., Kwiatkowska, M., García-
Palacios, A., Cipresso, P., Riva, G., & Botella, C.. (2019). Current state and future directions of technology-
based ecological momentary assessment and intervention for major depressive disorder: A systematic
review. Journal of Clinical Medicine, 8(4).
[3]. Durand, D., Strassnig, M. T., Moore, R. C., Depp, C. A., Ackerman, R. A., Pinkham, A. E., & Harvey, P.
D. (2021). Self-reported social functioning and social cognition in schizophrenia and bipolar disorder: Using
ecological momentary assessment to identify the origin of bias. Schizophrenia Research, 230, 17–23
[4First, M. B., Williams, J. B. W., Karg, R. S., & Spitzer, R. L. (2015). Structured clinical interview for DSM-5—
Research Version (SCID-5 for DSM-5, Research Version; SCID-5-RV). American Psychiatric Association
[5]. Granholm, E., Holden, J. L., Mikhael, T., Link, P. C., Swendsen, J., Depp, C., Moore, R. C., & Harvey, P.
D. (2020). What do people with schizophrenia do all day? Ecological momentary assessment of real-world
functioning in schizophrenia. Schizophrenia Bulletin, 46(2), 242–251
[6Harvey, P. D., & Pinkham, A. E. (2015). Impaired self-assessment in schizophrenia: Why patients misjudge their
cognition and functioning. Current Psychiatry, 14(4), 53–59.
[7]. Huxley, N., & Baldessarini, R. J. (2007). Disability and its treatment in bipolar disorder patients. Bipolar
Disorders, 9(1-2), 183–196.
[8]. Montgomery, S. A., & Asberg, M. (1979). A new depression scale designed to be sensitive to change. British
Journal of Psychiatry, 134(4), 382–389.
[9]. Schneider, L. C., & Struening, E. L. (1983). SLOF: A behavioral rating scale for assessing the mentally
ill. Social Work Research & Abstracts, 19(3), 9–21.
[10]. Young, R. C., Biggs, J. T., Ziegler, V. E., & Meyer, D. A. (1978 Nov). A rating scale for mania: Reliability,
validity and sensitivity. British Journal of Psychiatry, 133(5), 429–435.
[11]. Oliveri, L. N., Awerbuch, A. W., Jarskog, L. F., Penn, D. L., Pinkham, A., & Harvey, P. D. (2019). Depression
predicts self assessment of social function in both patients with schizophrenia and healthy people. Psychiatry
Research, 284, 112681.