Research Proposal for a Quantitative Study

In pairs read the following proposal and the below questions:
1- What is the research problem?
2- What is the purpose of the research?
3- What are the research questions?
4- What is the research methodology include:
a. The population and sample,
b. Data collection,
c. Steps for the analysis of the data, and
d. How the reliability and validity of the study framework measured.
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Research Proposal for a Quantitative Study on the Performance Impacts of Information
Technology
By Fahmeena Odetta Moore
This paper is a proposal for a quantitative study on the performance impacts of information
technology. First, I present the research problem, purpose statement, and research questions. Then, I
discuss options for the study design and methodology using previous studies as guides. I will propose a
design for the study that includes the selected population, samples that will be used, plans for data
collection, and data analysis techniques.
Research Problem
Companies face decisions on the allocation of resources to produce desired output or
performance. One such decision is the amount to invest in people (human resources or HR) vs. the
amount to invest in technology. There are related questions such as the impact of technology investment
on firm-level performance, the rate of return of technology investments, and areas that would benefit the
most from technology investment. Given the results of a 2013 Gallup poll that found an alarming 63% of
employees worldwide are not engaged at work and are not motivated to be the productive, high
performing employees organizations desire, companies will also consider whether technology could be
used in additional ways to improve employee engagement and productivity. There is significant research
on the business value of information technology (IT) such as research on how technology impacts
productivity and how technology impacts performance. A recent paper by Turulja and Bajgoric (2016)
that looked at the contributions of HR and technology to firm-wide performance concluded that HR
management capabilities are more important, and IT capabilities should be combined by HR management
capabilities for superior performance. Additional/new research is needed on the business value of IT in
areas such as healthcare (Gholaim, Higon, & Emrouznejad, 2015). Research such as the study by Devaraj,
Ow, and Kohli (2013) that looked at the direct and indirect impact of technology on firm-wide
performance in healthcare noted that future research could look at services or departments within a
hospital rather than the total hospital level.
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Much could be learned from the infection control area, an area important to the operations of
healthcare institutions that has had much success applying technology to increase performance. Some
healthcare institutions found very impactful combinations of the many options or strategies available for
improving infection control that include compensation, technology, and reducing nurse workload, among
others (Pincock et al., 2012). For instance, technology assisted with the monitoring of nurses and other
healthcare workers, which resulted in increased compliance with hand hygiene policies and procedures
and therefore increased infection control performance. There are no known studies on the (relative)
contributions of strategies/investments in this area. This study will fill this gap and should be useful to
healthcare institutions as well as other organizations.
Purpose of Study
The purpose of this quantitative, correlational, non-experimental survey study is to more fully
understand how IT was used to improve infection control performance in healthcare and to determine its
impact on infection control performance through an indirect effect on labor. IT is commonly viewed as an
enabler – a way to make labor more productive by improving processes, tracking performance etc. Using
data from 200 acute care hospitals across the U.S., the study will determine the contribution of
compensation to infection control performance and then the increment in the contribution of
compensation when IT is added/applied. In the study, the dependent variable will be infection control
performance, defined as the overall hospital-acquired infection rate (rate for all infections acquired at the
healthcare institutions, also referred to as healthcare-associated infections). Independent and/or control
variables include:
(1) Compensation of various categories of staff that directly affect infection prevention and control:
compensation of infection control staff (staff that are directly responsible for or oversee infection control),
compensation of nurses (staff who care for patients and directly affect infection control), compensation of
sterilization staff (staff who sterilize equipment), and compensation of housekeeping/environmental
cleaning staff (Royal Cornwall Hospitals, 2015; Hicks, 2012). Compensation is defined as basic pay,
benefits and other rewards paid to staff.
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(2) Hospital IT investments for infection control performance improvement. This includes expenditures
that resulted in the successful implementation of systems or other technology to improve hand hygiene
compliance (by nurses and doctors) or improve infection control in other ways (improve the performance
of other staff that affect infection control etc.).
(3) Other variables that may have an effect on performance: size of the employer, whether profit driven,
and the compensation of executives (defined as CEO, CFO, and other members of top/executive
management).
Research Questions
The research questions are:
RQ1. To what extent did IT expenditures improve infection control performance through an
effect on labor?
RQ2. Where did IT make the most impact in the improvement of infection control
performance through labor (in terms of the category of staff that improved the most after IT
expenditures)?
RQ3. To what extent did IT expenditures impact infection control performance directly (not
focusing on the indirect effect on labor)?
Some hypotheses include:
For RQ1:
H10: There is no indirect effect of IT expenditures on infection control performance through
labor.
H2A. IT indirectly improves infection control performance by making labor more productive
to the tune of a 10% – 30% increase in labor productivity.
For RQ2:
H30: There is no statistically significant difference in the increase in labor productivity of the
three staff categories (nurses, sterilization staff, and housekeeping/environmental cleaning
staff) brought about by IT expenditures.
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H4A: Investment in IT expenditures resulted in a larger increase in the productivity of nurses
than the other staff categories (because increases in hand hygiene compliance were larger
than performance improvements in other areas of infection control).
For RQ3:
H50: There is no positive, statistically significant relationship between total IT expenditures
for infection control and infection control performance.
H6A. There is a positive, statistically significant relationship between total IT expenditures for
infection control and infection control performance.
Research Design and Methodology
Proposed Research Method
The proposed research method is the survey method, which includes options: descriptive research
and correlational studies (Farrelly, 2013). Related studies, such as studies on the relationship between
compensation and performance, used surveys, specifically questionnaires, to collect data from employees
on how well they are compensated, how satisfied they are etc. and then performed correlation and/or
statistical analysis. A study by Turulja and Bajgoric (2016) that looked at the relative importance of
human resources and information technology to firm performance used a questionnaire to collect data
from a random sample of companies. Companies provided information on whether there are
comprehensive policies and procedures for training and development of employees, whether the company
provided incentives to employees related to their performance, and so on. This study will not survey
employees and companies in that manner. As noted in Turulja and Bajgoric (2016), a limitation of such
surveys is the subjective measures used. Similar to the study by Devaraj, Ow, and Kohli (2013) on the
impact of IT expenditures on healthcare performance, this study will utilize data available from secondary
sources such as organizations that collect data from all hospitals in the United States. State offices may
have data on all hospitals in their state. I identified one state – State of Washington – that provides
infection data for hospitals in the state at a cost. In cases where data is not available from secondary
sources, the data will be collected from the hospitals themselves (expected to be in a spreadsheet-like
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format). Data from several sources may be merged as Devaraj, Ow, and Kohli (2013) did in their study
that collected and merged IT expenditure data from two secondary sources.
Population, Sampling, and Data Collection
Hospitals from all 50 states in the U.S. provide data on infection rates to the Centers for Disease
Control (Centers for Disease Control and Prevention, 2016). At least 8 hospitals (acute care facilities)
from each state report infection rates so there is a large population (of at least 50 X 8 = 400) to choose
from. The data is time series data – different from the data collected in the study by Turulja and Bajgoric
(2016), which used a seven-point Likert scale for responses to questions/indicators.
For this study, one option for the sample is a small sample of hospitals with data for a large
number of years. For example, data from three hospitals referenced in Hill-Rom (2014) that used
technology to significantly improve hand hygiene compliance could be used. Data could be collected for
the period 2000 – 2015. Another option is to use a large number of hospitals with a smaller number of
years, such as 100 hospitals for the period 2010 – 2015. This seemed to be the approach used in the study
by Devaraj, Ow, and Kohli (2013), which used a sample of 567 hospitals across the U.S to analyze how
IT investments impact performance. However, a narrower time period could introduce a bias because the
time period selected could be a period of high levels of employee compliance and performance. A broader
time period would include varying levels of performance, compensation and other expenditures. In recent
years, there have been significant improvements in hand hygiene compliance and employee performance.
To determine the sample, I also need to consider Type I and Type II errors, power, and minimum
sample sizes based on statistical rules/calculations. G*Power is one software available to calculate sample
size, among others such as Minitab and PS (Heinrich Heine Universitat Dusseldorf, n.d; McCrumGardner, 2010; Hsieh, Bloch, & Larsen, 1998). Comparison calculations will be performed at a later time.
At this time, I expect to use data from a sample of 200 acute care hospitals over a large number of
years such as 2005 – 2015. The data would include:
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i. Monthly infection rates for each hospital – data from the Centers for Disease Control and
Prevention, State offices, and hospitals (if needed).1A stratified sample is an option,
which would ensure hospitals with varying infection rates are included in the sample
(hospitals could be divided into hospitals with very low infection rates, low to medium
levels, and medium to high levels). However, a random sample would work for this study
similar to the sample used in Turulja and Bajgoric (2016) to select companies without
ensuring companies of a specific size are included (micro, small, medium or large).
ii. For the hospitals in (i), aggregate monthly compensation data (basic pay, benefits, and
other rewards) for: (a) all hospital staff, (b) all hospital executives, (c) all infection
control staff, (d) nurses, (e) sterilization staff, and (f) housekeeping/environmental
cleaning staff will be collected. Data source: hospitals.
iii. Number of employees that correspond to the compensation data in (ii). Data source:
hospitals.
iv. Expenditures that resulted in the successful implementation of systems/technology for the
improvement of infection control performance by each category of staff in (ii) with
information on when a new system or technology was put into use/production (especially
important for multi-year projects). Data source: hospitals.
v. Other IT expenditures (expenditures on IT projects not included in (iv)) for the
improvement of infection control performance. Data source: hospitals.
vi. Total patient revenue, a measure of financial performance and also size of a hospital.
Data source: secondary source such as ahd.com.
vii. Net Income After Taxes, a measure of financial performance of a hospital. Data source:
secondary source such as ahd.com.
viii. Whether the hospital is for-profit or non-profit. Data source: secondary source such as
ahd.com.
1The infection rate is calculated as: (Total number of hospital infections for the period X 100) / (Total number of
discharges).
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Analysis
The study will use statistical analysis to reach conclusions about the relationship between
information technology expenditures, compensation, and performance from available data. Conclusions
will not be based on the opinions of executives, staff or other stakeholders so there should be no personal
bias or other similar ethical issues. However, there could be issues with the data. Some institutions may
have data quality issues, i.e. there are errors in their data collection and/or analysis. Part of the statistical
analysis in this study will include a check for outliers. Options for outliers include: (1) leave the outlier
and sample data “as is,” which may distort results, (2) “treat” the outlier by winsorizing it or normalizing
the data, and (3) eliminate the outlier (Ghosh & Vogt, 2012). Treating the outlier would be the best option
for ensuring valid results.
Turulja and Bajgoric (2016) used confirmatory factor analysis to check for reliability and validity
of measurement models along with chi-square tests to check for a relationship between variables. This
study will not use chi-square tests to test the hypotheses. The study will use similar tests for reliability
such as the Cronbach alpha test to determine whether relationships are different during different time
periods.
Devaraj, Ow, and Kohli (2013) performed ordinary least squares regression analysis using
mediating, independent, and control variables to determine the impact of IT on performance. The model
tested whether IT expenditures affected performance through an effect on Length of Stay. Control
variables included variables such as for-profit status and age of the hospital that might influence
performance. This study will perform similar regression analysis.
The regression models shown in Table 1 below will provide the needed information on the impact
of IT expenditures on infection control performance. Additional models (combinations) may be added.
The independent variables may be lagged.
Table 1
Hierarchical Regression Analysis
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(Infection Rate as the Dependent Variable, Variables Marked with an X included as Independent
Variables)

Conclusion
The proposed quantitative research will add knowledge on the contribution of compensation and
information technology to infection control performance in hospitals. It will supplement the qualitative
study on factors that caused or contributed to the successful increase in hand hygiene compliance in the
healthcare industry. The paper considered various options for the research, concluding that the collection
of objective data rather than subjective data is best, and the sample from the population of healthcare
institutions should be a large, random sample of hospitals (200 hospitals for the period 2005 – 2015). The
study will use regression analysis to test the hypotheses and provide answers to the research questions.
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