6 hours, JWI599 week 7:Discussion:Funding Your Plan and ROI
StevenChristie
prevpapers.zipprevpapers/1618306718 growth in care giving.docx
Running Head: GROWTH IN CAREGIVING BY NURSE NEXT DOOR 1
GROWTH IN CAREGIVING BY NURSE NEXT DOOR 2
Title: GROWTH IN CAREGIVING BY NURSE NEXT DOOR
(Name)
(Institutions affiliation)
Growth in caregiving by Nurse Next Door
This research paper's primary focus area will be the health sector, whereby I chose to highlight the growth that we as a human race have acknowledged in the caregiving sector in the last decade. Going by data researched by the Bloomberg team("Bloomberg - Are you a robot?", 2020), I lay my focus on "Nurse Next Door," a caregiving company mainly operating in the United States with branches across neighboring countries such as Canada. Since its acquisition date, it is reported that the company registered a fixed growth rate of 2% per annum ("Bloomberg - Are you a robot?", 2020).
The data type gathered, which the paper intends to use in evaluating the solution's applicability to the identified gap, includes research reports and financial analysis that support the growth. Through this data, I will forecast and project results (Fredriksson, 2018) which have undoubtedly played a significant role in identifying the opportunity inset. This data is derived from different sources, including a cross-reference across the internet from other sites ("Caregiving in the US 2020 | The National Alliance for Caregiving", 2021).
References Bloomberg - Are you a robot?. (2020). Retrieved 13 April 2021, from https://www.bloomberg.com/press-releases/2020-01-30/diversified-royalty-corp-announces-preliminary-q4-2019-results-for-mr-lube-air-miles-sutton-mr-mikes-and-nurse-next-door Caregiving in the US 2020 | The National Alliance for Caregiving. (2021). Retrieved 13 April 2021, from https://www.caregiving.org/caregiving-in-the-us-2020/ Duarte, N. (2019). Data story (1st ed.). Ideapress Publishing. Fredriksson, C. (2018). Big data creating new knowledge as support in decision-making: practical examples of big data use and consequences of using big data as decision support. Journal Of Decision Systems, 27(1), 1-18. doi: 10.1080/12460125.2018.1459068
prevpapers/1619172227 Board Overview (1).docx
Running Head: BOARD BRIEF– FRAMING THE PROBLEM/OPPORTUNITY 1
BOARD BRIEF– FRAMING THE PROBLEM/OPPORTUNITY
Title: Board Brief– Framing the Problem/Opportunity
Student’s name:
Professor’s name:
Course title:
Date:
Board Brief – Framing the Problem/Opportunity
General overview
Over the last fourteen months, we have faced a challenging growth pattern in our operations owing to issues pertinent to fatigue and burnout among our employees. This has proven to be challenging and has affected our output levels and ability to maximize profits as an organization. It has also affected the quality of services and our ability to deliver as per our vision. This has left us on the wrong foot and demands attention as it has left us vulnerable to competition from other similar service providers in the same field. Our organizational ignorance of the above challenges has opened doors to the possibility of a threat of new entrants into the market. They hope to capitalize on our mistakes to make a fortune. Despite our challenges, we still boast of a significant market share as our loyal customers still deem us fit and regard us as their first choice.
However, simply because something is working, it does not mean it is not subject to improvement (Honey& Mumford, 2000). Put into context a SWOT analysis into our organization and performance has offered some eye-opening insights from which we were able to identify the problem with the hope of fixing it to improve our services and guarantee longevity. Burnout and fatigue among employees are common, and research shows it is more common in nursing (Azmoon et al., 2018), which serves as our leading service and trade tool in caregiving at Nurse Next Door.
Two of our competitors, namely American Care Partners and InnovAge, have gained traction over the last year. They boast of a higher employee number than us and will in due time topple us in the number of outlets as well foreseeably over the next two years. Their significant growth in market share comes as a result of their aggressive way of doing business. Their growth is rampant, and their number of employee to outlets ratio is high, meaning they have many employees working shifts. This has presented us with a challenge. Many of our clients have inquired how we intend to match our competitor’s techniques and better our services to maintain our lead. Therefore, we are expected to address the issues raised and make necessary, timely changes in our organization within the shortest time possible to fight competition. Rather than panicking and treating this as a rescue mission, we should steadfastly consider it an opportunity and fully utilize it to bring a new face to our company.
Problem/Opportunity Statement
Going by the research conducted, burnout and fatigue among our employees poised a tough challenge which we are keen to spin into an opportunity. Suggestive measures to curb the burnout have been proposed with the most suitable one is the expansion of our outlets to warrant more recruitment that will add to our number of employees. This is regarded as the best way to tackle this problem turned opportunity while reaping double benefits. It will warrant the organization’s growth at the same time solve our problem, making us highly competitive and reliable. From the increased number of employees, we should look into employee work shifts and eventually adjust them accordingly to bring about the desired outcome.
Our source of data will be highly reliant on feedback from our clients and their legal guardians. An interview with the caregivers will also help us understand what promotes and causes burnout and fatigue in their case. This will then be complemented with a cross-reference of other reputable cases that have happened in the past along the same line of duty, raising similar problems. We will analyze their responses and record any observable patterns. Any disparity will also be recorded to understand the data further. The survey conducted will help fetch data from both sides. A comparison of the gathered data will be made to performance appraisals of the caregivers by the HR department to ascertain compatibility. The individuals that will record high levels of burnout will be used as the sample lot from which their results and feedback collected will play a pivotal role in seeking solutions.
This stage will be critical towards determining the subsequent sense of direction the organization will take. The organization’s management will expect much from the HR department as all the appraisal reports will be subject to their work and documentation. The marketing and research team will also be required to apply its expertise in getting the right and necessary data. They will be expected to design the questionnaire with tailor-made questions that will help give variant answers to help improve accuracy in decoding the problem and the opportunity. It may take time and sacrifice to effect it, as the interviews need to be done off the clock to prevent interference with service delivery among the caregivers.
We will do a final assessment to ascertain the validity of the data by the research team. This will involve counterchecking correct entries and analysis of the information given. We will also verify the competency of the team handling the tasks. Upon completing these steps, we will provide the management with a final proposed copy of the agreeable ways to effect the decisions subject to this research, from which they would then have the final say.
References
Honey, P., & Mumford, A. (2000). The learning styles helper’s guide. Maidenhead: Peter Honey Publications.
Azmoon, H., Salmani Nodooshan, H., Jalilian, H., Choobineh, A., & Kargar Shouroki, F. (2018). The relationship between fatigue and job burnout dimensions in hospital nurses. Health Scope, 7(2).
Kelly, L. (2020). Burnout, Compassion fatigue, and secondary trauma in nurses: Recognizing the occupational phenomenon and personal consequences of caregiving. Critical Care Nursing Quarterly, 43(1), 73-80.
Duarte, N. (2019). Data story (1st ed.). Ideapress Publishing.
prevpapers/1619555783 jwi599 (1).docx
Running Head: TURNING DATA INTO INFORMATION 1
TURNING DATA INTO INFORMATION 5
Title: Turning Data into Information
Name
Institutional Affiliation
Apply one or more of the following analytical tools to your dataset:
Correlation
Variance
Correlation and variance analytical tools are selected to analyze the collected data. Quantitative data on company growth and employee exhaustion was collected from research reports and financial reports. Variance helps in explaining the change in company growth over time (Miles et al., 2014). On the other hand, correlation analysis showed the relationship between company growth and the exhaustion of employees (Miles et al., 2014). This analytical tool helps to show the rate at which the company was growing, and therefore enabled forecasting what should and will happen. The problem identified was that Nurse Next Door is yet to set its wings on a global platform. Expanding to other countries and continents would be ideal, with franchising as the best methodology to facilitate the growth. Thus, posing the question of whether expansion Nurse Next Door through franchise will solve the problem of Nurses Burnout and Exhaustion. Therefore, the data collected focused on determining whether franchising helps solve the problem (Azmoon et al., 2018).
Based on data collected in financial reports, Nurse Next Door has registered a fixed growth rate of approximately 2% per annum (Figure 1) (InnovAge Holding Corp (INNV) Quote - XNAS | Morningstar, n.d.). The growth is attributed to franchising which, as shown in Figure 2, has been growing steadily since the company introduced franchising back in 2017. In 2018, Australia became a global partner of Nurse Next door. Since then, it has penetrated the market and is recording an upward growth trajectory. Through our document and article reviews, approximately 2,000 people turn 65 every day while 1000 turn 85. Also, 400,000 people rely on the help of the National Disability Insurance Agency (NDIA). With the increasing population of aging people and expected growth in NDIA, the home care industry in Australia has exploded. Also, 90% of the aged population prefer aging in homes, thus creating a growth opportunity for Nurse Next Door. In the next 20 years, individuals aged 70 years and above will be over 1 million. On whether there is burnout and exhaustion among nurses showed there were shortages in healthcare professionals estimated to be 14,000 and expected to increase to 121,000 by 2030 (InnovAge Holding Corp (INNV) Quote - XNAS | Morningstar, n.d.). From a survey of 50 273 nurses in the US, 43% cited burnout as the reason they are considering leaving their jobs, another 43% indicated inadequate staffing, and 42% cited stressful work environment (Figure 3). With the increasing population of individuals needing healthcare attention, home care provided by Next Door nurse is the best solution to reduce congestions and burnouts among nurses.
Figure 1: Rate of Growth of Nurse Next Door Figure 2: Growth in Franchises
Figure 3: Nurses Reason for Leaving Job
Explain whether your analysis of the data confirmed or refuted your testable hypothesis.
There is overwhelming empirical evidence that franchising has had a positive impact on the growth of Next Door Nurse. Also, qualitative data has shown that Nurse Next Door help reduce congestions by providing healthcare services at home. Therefore reducing the congestions and workload of nurses; as a result, reducing their level of burnout and exhaustion. Nurse Next Door should seize the existing opportunity for expansion in the global platform through franchising. Meaning we have to conduct market research on areas and regions that our services are most needed.
References Azmoon, H., Salmani Nodooshan, H., Jalilian, H., Choobineh, A., & Kargar Shouroki, F. (2018, May 31). The Relationship Between Fatigue and Job Burnout Dimensions in Hospital Nurses. Health Scope. https://sites.kowsarpub.com/healthscope/articles/80335.html Diversified Royalty Corp. Announces Preliminary Q4 2019 Results for Mr. Lube, AIR MILES, Sutton, Mr. Mikes and Nurse Next Door. (2020, January 30). Bloomberg.com. https://www.bloomberg.com/press-releases/2020-01-30/diversified-royalty-corp-announces-preliminary-q4-2019-results-for-mr-lube-air-miles-sutton-mr-mikes-and-nurse-next-door InnovAge Holding Corp (INNV) Quote - XNAS | Morningstar. (n.d.). Www.morningstar.com. Retrieved April 25, 2021, from https://www.morningstar.com/stocks/xnas/innv/quote Miles, M. B., Huberman, A. M., & Saldaña, J. (2014). Qualitative data analysis : a methods sourcebook. Sage.
prevpapers/1620219891 Analysis and Data.docx
Running Head: JWI599 WEEK 5 BUSINESS ANALYTICS AND CAPSTONE 1
JWI599 WEEK 5 BUSINESS ANALYTICS AND CAPSTONE 7
TITLE: JWI599 WEEK 5 BUSINESS ANALYTICS AND CAPSTONE
(Name)
(Institutions affiliation)
While evaluating the “Nurse Next Door” company to identify how to tackle caregivers’ burnout and exhaustion, the research employed various data sources. The data collected for analysis included primary data sources and secondary data sources. In the primary data sources, the research involved surveys in identifying if there was burnout among employees. By collecting data from caregivers in the company, the study explained the intensity of the situation to evaluate it needed addressing. Further, the secondary data sources, financial reports and research reports enabled the research to determine possible ways to address the problem. The data showed that increasing the franchise of “Next Door Nurse” would help reduce burnout and exhaustion from the high number of patients who sought care through projections and predicting the trend. Hence, primary and secondary data comprised the most critical data used.
Sources used to gather data
Primary data through surveys was collected through phone call interviews and questionnaires with the employees. This aimed to understand the causes of employees leaving their workplaces at “Nurse Next Door”. From the survey, comprising of 198 respondents, the following were the results from the research:
|
|
Reason for leaving the job |
Respondents |
|
1 |
Better pay/benefits |
50 |
|
2 |
Burnout |
43 |
|
3 |
Inadequate Staffing |
43 |
|
4 |
Stressful work environment |
42 |
After performing the correlation test, the following graphical representation of the data was developed:
Figure 3
The main advantage of using the primary data sources is the confidence of having raw data from the participant (Miles et al., 2020). This data can be described as unadulterated hence more reliable.
Additionally, statistical data from financial reports were used to analyze patterns, modelling the possible outcomes of increasing the “Next Door Nurse” franchise. With an approximated growth rate of 2% per annum (“Diversified Royalty Corp. Announces Preliminary Q4 2019 Results for Mr Lube, AIR MILES, Sutton, Mr Mikes and Nurse Next Door,” 2020), as indicated in figure 1, the growth was attributed to franchising, which reduced the burden on the employees.
Figure 1: Rate of growth at 2% per year
Figure 2, Growth in Franchises
The financial and research reports from secondary sources provided easy ways to acquire data. This was found to be cost-effective, saving on time and money, which would have been required to collect the data. Also, the ability to analyze past data enabled the researchers to easily relate the franchise to growth, which in return resolved the problem of burnout and exhaustion among caregivers.
Data reliability and validity (Completeness)
For the analysis results to be valid, research needs to check the reliability of the tools used to collect the data. Reliability refers to the consistency of achieving the same results under the same circumstances and environment (Miles et al., 2020). If a tool used in data collection yields the same results repeatedly, it is considered reliable. Completeness of the data sought to check its validity. The researcher applied the internal consistency test to assess the reliability of the tool instrument used. In this case, by checking the coefficient alpha, also known as Cronbach’s alpha, it was higher than the threshold set at 0.7. The study used two methods to test for the completeness of the data used; criterion and construct validity. In the criterion validity, the scores from the instrument used were checked if they could predict outcomes using statistical correlations. A correlation of 0.6 or greater shows that criterion validity exists (Miles et al., 2020). Construct validity on the other hand was checking for the calculated means and if generalization can be done.
Although the research took into consideration competitor’s weakness using data available online, it is difficult to determine their next of action. Next Door Nurse may increase their franchise with the aim of reducing caregiver’s burden, and the competitors can do the same (Fredriksson, 2018). This may lead to a strained balance sheet, which in return may result in downsizing. Consequently, the remaining caregivers will have a huge patient clientele to cater for. Since the research cannot measure the probability of increased revenue from the franchise, it is hence difficult to conclusively infer that franchising will reduce burnout and exhaustion.
The study used qualitative data for testing the hypothesis. This involved data collected in the surveys in numerical form and the financial and research reports, which were also numerically prepared. The element influenced this that qualitative data is best used when measuring opinions, attitudes, feelings and behaviours, in this case with reference to how caregivers perceive burnout and exhaustion at their places of work. Correlation and variance analytical tools were used to analyze the data. While variance helps in explaining the change in growth in the company over the years, correlation showed the relationship between the company growth and employee exhaustion and burnout. The tools and techniques applied to establish the relationship between variables make it possible to predict or make inferences (Miles et al., 2020).
After analysis, the data showed a steady growth of the company at 2% per annum, as indicated in figure 1 above. The data analyzed also showed a predictable pattern in the increase of people aging, hence requiring more caregivers, resulting in severe strain for the nurses at Next Door Nurse. Through the projections of the anticipated growth, franchising as a model to reduce exhaustion is hence sustained (“Diversified Royalty Corp. Announces Preliminary Q4 2019 Results for Mr Lube, AIR MILES, Sutton, Mr Mikes and Nurse Next Door,” 2020). The study did not identify anomalies in the data, hence need for further research to be conducted in different regions where Next Door Nurse operates.
The data pattern shows increased growth and growth in the balance sheet from the additional franchise. However, there is an increase in the workload with the increased franchise, hence the need to balance by increasing the workers, thereby reducing exhaustion and burnout. Since leaving the job for better wages was one identified factor, the company can invest in better welfare for its employees to increase retention. Inadequate staffing, which was a variable identified as a contributing factor to employee burnout, can be addressed through modeling to identify optimum human resource utilization (Azmoon et al., 2018).
In conclusion, the empirical evidence from the data indicates that franchising will positively impact Next Door Nurse, consequently reducing the caregiver’s burnout and exhaustion. As the franchise grows, the expansion calls for the recruitment of more staff; hence work can be shared and shifts allocated equitably, reducing overworking and straining the current employees. Data-driven decision making is prone to fewer errors since projections and models can be developed. Further, simulations can be done to identify bottlenecks and areas which need fixing. In the same measure, Next Door Nurse can employ the data in this analysis to evaluate its applicability in alleviating exhaustion and burnout among caregivers.
References Azmoon, H., Salmani Nodooshan, H., Jalilian, H., Choobineh, A., & Kargar Shouroki, F. (2018). The Relationship Between Fatigue and Job Burnout Dimensions in Hospital Nurses. Health Scope, 7(2). https://doi.org/10.5812/jhealthscope.80335 Diversified Royalty Corp. Announces Preliminary Q4 2019 Results for Mr. Lube, AIR MILES, Sutton, Mr. Mikes and Nurse Next Door. (2020, January 30). Bloomberg.com. https://www.bloomberg.com/press-releases/2020-01-30/diversified-royalty-corp-announces-preliminary-q4-2019-results-for-mr-lube-air-miles-sutton-mr-mikes-and-nurse-next-door Fredriksson, C. (2018). Big data creating new knowledge as support in decision-making: practical examples of big data use and consequences of using big data as decision support. Journal of Decision Systems, 27(1), 1–18. https://doi.org/10.1080/12460125.2018.1459068 Miles, M. B., Huberman, A. M., & SaldañaJ. (2020). Qualitative data analysis : a methods sourcebook. Sage.
