Group work by Sitie Ajmal, Monida Sieng, and Scott Whiteman. Submitted as final research project for the course BUS 235B (Business Research) Spring 2020 session at San Jose State University. Disclaimer: The results reported are from a class project conducted during the BUS 235B course at SJSU; this data should not be considered as or used in any official/academic publication.

1. Qualtrics Survey Results Overview:
Pandemic Stress
BUS 235B | Spring 2020
Sitie AJMAL
Monida SIENG
Scott WHITEMAN
Disclaimer: The results reported are from a class project conducted during the BUS 235B course
at SJSU; this data should not be considered as or used in any official/academic publication.
Effect of the COVID-19 Shelter-In-Place Situation on Job Satisfaction, Well-being,
Workload, and Productivity among San Jose State University Students

2. How is COVID-19 affecting people?
Business impact:
● Lost sales due to closed operations or
unavailable resources
● Increased concerns regarding well-being of
employees
● Productivity, utilization declining
● Environment changes from office setup to
working from home
● Possible employee shortage
● Heavy demand on services e.g. delivery
services, restocking supplies etc
Impact on individuals:
● You are no longer able to dine out at your
favorite restaurant
● Potential losses in wages or income if
temporarily placed on leave without pay
● Limitations to doing what you enjoy due to
fear of getting COVID-19
● Concerns for friends’ and family’s health
● Added workload of homeschooling, looking
after family, in addition to getting work done
remotely
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3. ● Hypothesis by induction: Supporting
literature**
● Hypothesis by deduction:
Premise 1a: Job satisfaction is positively
associated with productivity
Premise 1b: Well-being is positively
associated with productivity
Premise 1c: Job satisfaction and
well-being are positively associated with
each other
Premise 2: Stress reduces both job
satisfaction and well-being
Therefore, stress reduces productivity by
affecting job satisfaction and well-being.
Effect of Stress on Productivity
**Arends, Prinz, & Abma (2017); Brough et al. (2014); Brunner et al. (2019); Caesens,
Stinglhamber, & Luypaert (2014); Ekienabor (2016); Felstead & Henseke (2017); Halkos &
Bousinakis (2010); Kern et al. (2015); Khojamli et al. (2014); Rawat et al. (2014); Schall
(2019); Smith (2018) 3

4. Methodology and Limitations
● Non-random sample: convenient + snowball
○ SJSU students enrolled in Spring 2020 session
○ Majority from Lucas College (Business)
○ Thus limitations: non-representative, possible bias, recall
limitations, reduced generalizability (external validity)
■ However we can still draw insightful data for further
exploration and future studies
● Online self-administered survey questionnaire assessing
stress, job satisfaction, well-being, workload
○ Items all based on previously validated instruments: WSQ, PSQ,
JSS, SWEMWBS
○ Repeated (paired) measures taken for current situation (April
2020) vs pre-pandemic situation recall (before March 2020)
○ High internal consistency (Cronbach’s ɑ ranging 0.718-0.842)
○ Hosted by SJSU-Qualtrics, survey link:
https://sjsu.qualtrics.com/jfe/form/SV_8IeSLGDZskkoEfz
● N = 103 respondents
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Our Sampling Frame

5. Summary of Results
● SJSU students have perceived
○ Increased stress
○ Decreased job satisfaction
○ Decreased overall well-being
during the current pandemic situation (April 2020) compared to before March
2020.
● Higher stress levels have negatively affected both job
satisfaction and well-being among SJSU students.
Inference: Based on these results, thus productivity is also expected to
decrease during this period. 5

6. 6
Insights and Recommendations
Regarding workload: Given the overall workload seems statistically unchanged, thus SJSU can continue to maintain
the current amount of workload given to students, whether via fully online or hybrid teaching modes. We still
discourage increasing the amount of workload should the assumption arise that remote working will allow people
more free time; this may be an erroneous thinking as other factors (especially stress) can contribute towards
productivity and quality of work delivered.
Regarding stress and well-being: Perhaps students could make use of SJSU’s Counselling and Psychological
Services. The department could also reach out, send updates, and provide continued support to both
undergraduates and graduates. This will not only help with students’ general well-being but may improve their
productivity as well.
Regarding online learning delivery (i.e. “remote” option): Given workload amount was perceived as unchanged
regardless the method of delivery, perhaps future (non-pandemic) teaching modes can be transitioned to
fully-online or hybrid modes. This flexibility provides more convenience, avoids unnecessary commutes, and
perhaps could even contribute towards better productivity under normal circumstances.

7. Details: Exploratory Data Analysis
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Note: Gender distribution difference not significant
(χ2
= 3.505, df = 1, p = 0.061)
Note: 10 missing data
(N = 93 out of 103)

8. Details: EDA (contd.)
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9. Details: EDA (contd.)
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Note: Significant difference in employment status distribution
(χ2
= 13.157, df = 2, p = 0.001)

10. Details: STRESS Results
Scores ranging from 1 to 5 were averaged from the respondents’
5-point Likert scale ratings on the STRESS items, with higher
scores indicating higher perceived stress levels. Thus,
paired-samples t-test demonstrated significantly increased stress
score means in the current pandemic situation (3.25 + 1.02),
compared to pre-pandemic situation (2.97 + 0.78).
t(102) = 2.095, p = 0.039
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11. Details: JOB SATISFACTION Results
Scores ranging from 1 to 5 were averaged from the respondents’
5-point Likert scale ratings on the JOB SATISFACTION items, with
higher scores indicating higher overall job satisfaction. Thus,
paired-samples t-test demonstrated significantly decreased job
satisfaction score means in the current pandemic situation (3.38 +
0.78), compared to pre-pandemic situation (3.57 + 0.76).
t(102) = -3.022, p = 0.003
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12. Details: WELL-BEING Results
Scores ranging from 1 to 5 were averaged from the respondents’
5-point Likert scale ratings on the WELL-BEING items, with
higher scores indicating higher overall well-being. Thus,
paired-samples t-test demonstrated significantly decreased
well-being score means in the current pandemic situation (2.96 +
0.71), compared to pre-pandemic situation (3.50 + 0.67).
t(102) = -6.135, p < 0.001
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13. Results: Effect of
STRESS on JOB
SATISFACTION
Adjusted R2
= 0.043, i.e. 4.3% of the
variability in job satisfaction can be
explained by regression model.
Model significant for current
pandemic situation only [F(1, 101) =
5.579, p = 0.02].
Stress significantly predicted job
satisfaction.
β = -0.229, t(101) = -2.362, p = 0.02
i.e. higher stress is more likely to
reduce job satisfaction.
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Model Summary
Predictors: (Constant), Stress “Now”
R R2
Adj. R2
Std. Error of Estimate
0.229 0.052 0.043 0.76030
ANOVA
Dependent Variable: Job Satisfaction “Now”
Predictors: (Constant), Stress “Now”
Model 1 Sum of Squares df Mean Square F Sig.
Regression 3.225 1 3.225 5.579 0.020
Residual 58.384 101 0.578
Total 61.609 102
Coefficients
Dependent Variable: Job Satisfaction “Now”
Model 1 Unstandardized Coefficients Standardized Coefficients
Beta
t Sig.
B Std. Error
(Constant) 3.944 0.252 15.669 0.000
Stress “Now” -0.174 0.074 -0.229 -2.362 0.020
Excluded Variables ‒ Stepwise
Dependent Variable: Job Satisfaction “Now”
a. Predictors in Model: (Constant), Stress “Now”
Model 1 Beta In t Sig. Partial Correlation Collinearity Statistics
Tolerance
Workload “Now” -0.107a
-0.909 0.365 -0.091 0.674
Hours WFH Past Week 0.027a
0.275 0.784 0.027 0.961

14. Results: Effect of
STRESS on
WELL-BEING
Adjusted R2
= 0.18, i.e. 18% of the
variability in well-being can be
explained by regression model.
Model significant for both current
situation [F(3, 99) = 8.462, p < 0.001]
and pre-pandemic situation [F(2, 100)
= 8.596, p < 0.001].
Stress significantly predicted
well-being.
β = -0.466, t(99) = -4.261, p < 0.001
i.e. higher stress is more likely to
reduce well-being.
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Model Summary
Predictors: (Constant), Stress “Now”, Workload “Now”, Hours WFH Past Week
R R2
Adj. R2
Std. Error of Estimate
0.452 0.204 0.180 0.63894
ANOVA
Dependent Variable: Well-being “Now”
Predictors: (Constant), Stress “Now”, Workload “Now”, Hours WFH Past Week
Model 1 Sum of Squares df Mean Square F Sig.
Regression 10.364 3 3.455 8.462 0.000
Residual 40.416 99 0.408
Total 50.780 102
Coefficients
Dependent Variable: Well-being “Now”
Model 1 Unstandardized Coefficients Standardized Coefficients
Beta
t Sig.
B Std. Error
(Constant) 3.901 0.239 16.336 0.000
Stress “Now” -0.323 0.076 -0.466 -4.261 0.000
Workload “Now” 0.007 0.084 0.010 0.088 0.930
Hours WFH Past Week 0.003 0.004 0.079 0.795 0.429

15. Wordcloud:
“Describe the
current
situation in
one word”
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16. Results: Non-significant Findings
● Perceived workload was unchanged before vs after pandemic situation.
t(102) = 0.330, p = 0.742
○ Note that this could be due to a possible cancelling effect from extreme values on both ends.
○ Some people perceived higher workload while others did not. Not everyone was able to fully
work from home.
● Linear regression models showed no significant effect of perceived workload
or number of hours working remotely on job satisfaction.
β = -0.107, t(101) = -0.909, p = 0.365 (workload)
β = 0.027, t(101) = 0.275, p = 0.784 (weekly working-from-home hours)
● Linear regression models showed no significant effect of perceived workload
or number of hours working remotely on well-being.
β = 0.010, t(99) = 0.088, p = 0.930 (workload)
β = 0.079, t(99) = 0.795, p = 0.429 (weekly working-from-home hours)
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17. Acknowledgments
For guidance and helping with reaching out to respondents.
● Prof Dr Jing Zhang
Professor, Lucas College and Graduate School of Business, SJSU
● Prof Tonja Green
Program Director, College of Science, SJSU
● Ms Sun Chou
MBA Programs Coordinator, SJSU
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