Performance of an organization is often thought to be connected to the kind of workers it employs. To reinforce such as supposition, is the current need of the CEO to determine whether there exists a difference in employee performance across the four locational branches and the two divisional branches, which is part of an effort to improve organizational performance. With the increasing orientation of the labor market toward knowledge and information which has pushed the management of organizations to need of quality workers.
Despite the high productivity of star workers, “…they cannot constitute a sustained competitive advantage if their skills are mobile and transferable across firms.” In his paper on the myth of talent and performance portability notes that the mantra of “the people make the place”, has been prevalent in many an organization hence acting as a drive force for choice of human resources.
Generally, organizational performance refers to the measure extent to which an organization performs in terms of its underlying mission, vision as well as goals. Therefore, organizational performance can be split into performance measures and performance referent where performance measures refer to the metrics employed in gauging organizations while performance referents are used in assessment of how well the organization is doing.
Usage of a range of performance metrics and referents are key due to the value imported from the depth and information offered on the organization performance.
Project objectives
Description of data and key measures
The data used in this project contains quantitative information on 138 employees working in its 2 business divisions (Community outings and Home cares) from the four branches that is: Community outings from Brighton, Denver, Eaton as well as Victoria and Home cares from Brighton, Denver, Eaton, Victoria.
In addition, there are 12 descriptive variables which include: employee code, last name, first name, Location, Division description for each employee, Gender, Employee status code, Employee position, Year that the employee begun working, date of birth, Work experience, Year of education.
Key measures
In an article on “Measuring Your Organization’s Performance”, the author reinstates that, there is importance of performance measurement as a means of keeping track of the organizational performance. He further argues that performance measurement includes gauging of the actual performance outcomes. Profit, productivity, sales and market share, customer services, subjective estimates of financial performance are some of the measures of how an organization is performing. Such factors are then classified to performance measurements that is: efficiency, efficiency and business outcomes.
Therefore, from the organization’s data, there are three key sets of HR process measures that is, Efficiency measures, Effectiveness measures, and Business outcome measures.
Efficiency measures
Efficiency measures “…focus on cost and report the financial efficiency of human resources operations”
In broad terms, efficiency measures are the metrics used to examine the relationship between production inputs and outputs, it is also viewed as the success rate of the conversion of inputs into outputs. It is therefore the ability of the organization to implement its plans with minimal resource expenditure. According to Porter’s Total productivity, an organization should seek to remove the six losses which comprise:
Thus in measuring organizational efficiency, exploration of how well the inputs are optimized is key. In analysis of organizational efficiency, factors such as the staffing process, and focus on time to fill in, hiring cost and salary associated with positions will be analyzed.
Effectiveness measures
Efficiency measures are inclined towards successful input conversion outputs, while effectiveness examines interaction of outputs with economic and social environment. An organization’s effectiveness is therefore an examination of how the organization is performing in both long term and short term targets. As such, analysis of Focus on target groups, beneficiaries, clients that is, sponsor satisfaction score in the organization.
Therefore, effectiveness has an orientation towards output, sales, profits, cost reduction, innovativeness etcetera. As a result, in analysis of the organization’s effectiveness factors such as sponsor satisfaction, the staffing process, focus on speed to competency, and performance rating are analyzed.
Business outcome measures
“…business performance measures are a set of quantifiable metrics taken from various sources.” Consequently, business performance measures enable the executive to keep track of a given business process that is being examined. Hence, in measuring the performance of the business, profitability and worker engagement of the organization are explored.
Analysis of the relationship between key measures
Predictive analysis
The initial objective of the project is to determine if there is a difference in employee performance a factor which is highly correlated with the performance of the organization
In order to explore the relationship between effectiveness, efficiency and business outcome measures, the method of predictive analytics is used. To achieve successful analysis, exploration of the two business divisions is done separately i.e. for their independent organizational performance. Initially, effectiveness measure is measured through which efficiency can then be measured. Now, regression and correlation analysis will be used to determine the relationship between the following factors which are drawn from efficiency, effectiveness and business performance:
Used as preparatory for predictive linear regression models correlation analysis explores the association between quantitative variables. For instance, in determination of the relationship between organizational performance, the executive might be interested in determining whether there is a relationship between education level and engagement score.
Relationship between efficiency, effectiveness and business outcome
From the previous section, organizational efficiency is assumed to be measured by time to fill in, hiring cost and salary whereas effectiveness is assumed to be measured by sponsor satisfaction, focus on speed to competency, and performance rating. Business outcome performance is measured by profitability and worker engagement.
Interpretation
From table 2 and figure 1 above, there is a positive correlation between performance and time taken to fill the position a worker is holding i.e. with correlation coefficient of 0.7851. Other factors that indicate a strong positive correlation are:
However, speed to competency has got a strong negative correlation with performance rating, Sponsor satisfaction, productivity and engagement with a Pearson correlation of -0.8013, -0.8448, -0.8653, and -9405 respectively.
Linear Regression
When exploring how a response variable related with predictor variables, linear regression models are used as one of the methods of predictive analysis. In regression analysis, examination of which combination of factors lead to optimum productivity among workers is examined.
Predictive analytics using linear regression
Linear regression is used to examine the factors that influence a worker’s productivity.
Yi= β0 +β1X1+β2X2 +…+ βnXn + £I Where: Yi is the response variable, βi are the coefficients of the explanatory variables Xi and £i is the error term
Worker’s productivity model:
Productivity= β0 + β1 (Age)+ β2 (years of service) + β3 (Work experience) + β4 (Years of education) + β4 (Salary) eqn 1
Hypotheses
At a confidence level of 95% the following two hypotheses are formulated:
There is sufficient statistical evidence to indicate a relationship between productivity and age, years of service, work experience, years of education and salary.
Alternative
There is no sufficient statistical evidence to indicate a relationship between productivity and age, years of service, work experience, years of education and salary.
Regression Results
The r-squared statistic which is used to measure how good the model is has a value of 0.906047 when the salary variable is not included (table 3)
Table 3
While it is 0.92268 when the salary variable is included hence increases which indicates that salary is relevant in predicting productivity.
Table 4
Table 5
Table 6
When taking the explanatory variables together, the P-value for the Fisher’s statistic is 0.182236 which is greater than 0.05 indicating that the model cannot be used in predicting productivity. However, when using years of experience and education as explanatory variables, the following regression output is obtained:
The p-value of the F-statistic is 0.00401 indicating that the two explanatory variables are suitable for use in the model. The p-value of Work experience is 0.026487< 0.05 at 95% confidence level indicating that work experience is significant when measuring a worker’s productivity. Elsewhere, the p-value of Years of education is 0.023799< 0.05 at 95% confidence level, implying that years of education is significant in predicting productivity.
From table 7, the regression coefficient of the regression model is 11.57801 while that of work experience and years of education is 0.305069 and 1.174513 respectively which gives the final regression model as:
Productivity= β0 + β1 (Work experience) + β2 (Years of education)
Since the other variables are not significant in predicting productivity hence:
Productivity= 11.57801+ 0.305069(Work experience) + 1.174513(Years of education)
That is, for every unit increase in years of education there is an increase of 12.7525 in worker productivity. While for every unit increase in work experience there is an increase of 11.8830 worker productivity.
Lastly, it can be inferred that work experience and years of education are the only variables that are statistically significant in predicting productivity. Hence, after modification of the null and alternative hypothesis, we fail to reject the null hypothesis and conclude that there is sufficient statistical evidence to conclude that there is a relationship between productivity, work experience and years of education. Recommendations on using the significant variable in staffing policy are given in the succeeding section.
Recommendations on staffing policy
From the predictive analytics on the factors that influence worker productivity in the previous section, the following recommendations on staffing policy regarding employee recruiting, promotion, salary increment considerations are made:
Employment
From the preceding section of analysis, years of education are directly correlated with employee productivity that is, the higher the level of education the higher the productivity of the worker in their respective fields. For instance, if the executive employs a person with an educational level of 14 years, their productivity is projected to reach up to 28.02. In contrast, a person with educational level of 17 years will have a productivity rate of 31.544731.
The challenge is therefore on how to attract skilled workers given that the assumption of high education reflects skill is made. To ensure the company gets quality talent workers, it should embrace measures such as:
Attraction and retention of high skilled workers will ultimately ensure improved long run organizational performance.
Worker promotion
Years of work experience are also significant in measuring productivity. Years of experience often are accumulative of how long the employee has been on the given work post. Therefore, in case the executive assumes that years of experience is the only parameter set to determine whether a worker is promoted or not, it should first consider persons with relatively higher level of experience. For instance, an employee with work experience of 15 years has an estimated productivity of 16.15 while an employee with 25 years of experience has a 19.20 productivity rate.
Consequently, persons with more years of experience in any given field should be given a priority when the human resource department is recruiting new employees as well as when considering giving the current employees a promotion.
Worker motivation
Assuming that worker engagement is linearly related to the worker’s productivity, incentives such as health benefits, insurance, etcetera, should be adopted by the executive in a move to ensure worker motivation. Since there is a strong positive correlation between salary and worker engagement, where a salary increment positively affects the engagement level of a worker, the executive should consider revising employee salaries. Such a move, will ensure an increase in worker motivation which will in turn increase engagement score and hence productivity which has a positive correlation with engagement. That is an increase in worker engagement positively affects productivity which ultimately improves the overall organizational performance.
In order to ensure that there is reflection of salary increment in the productivity of workers, an algorithm to determining the rate of salary increment should be implemented for instance, hours put into work, level of education, level of experience, etc.
Equality and production uniformity
Often, workers who feel excluded offer little input in the production process. Measures such as equal involvement in the production chain and decision-making process are prone to reinforce the feeling of belonging and equality among workers and hence foster loyalty, and worker engagement. Hence, the executive should consider setting up structure to ensure total worker involvement such as the aforementioned involvement in decision-making.
Moreover, in order to ensure uniformity in performance across the two business divisions of the organization, the executive should lay emphasis on uniform distribution of human resource. For instance, it should employ equally skilled employees whose pay is relatively the same given similar job fields so as to promote equality in worker productivity. Moreover, the executive should come up with worker motivation programs such as overtime payments, a move that will promote engagement hence productivity.
Conclusion
Organizational performance is a complex concept. From effectiveness through efficiency to business performance, an organization which is keen to improve its sustainability index should be able to put in place measures with which to increase its effectiveness, effiency as well as business performance. However, when examining the organization’s performance there are a number of factors which need to be considered given that various metrics can fall into either groups that is either in efficiency key measure or effectiveness key measure since there is a “thin line” between the two. For instance, it is crucial to measure effectiveness of an organization’s performance before its efficiency since an ineffective organization cannot be efficient whereas an effective organization can be inefficient that is it uses lots of inputs with little output.
Therefore, from the project results, it can be concluded that different factors are determinant of employee performance and hence confirm the CEO’s concerns that there is a difference in the worker performance across the two divisional branches in the 4 locational branches regions.
Furthermore, it can be inferred that there exists a relationship between the various key measures of organizational performance. Hence, the overall organizational performance can only be improved through simultaneous improvement of the three key measures i.e. effectiveness, efficiency and business outcome performance.
References
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Edwards, Janice. Organizational Performance: A Complex Concept. Canada: Press books, 2018.
Fitz-enz. and Mattox John. Predictive Analytics for Human Resources. Wiley, 2014.
Groysberg, Boris, Lee, Linda-Eling and Nanda Ashish. “Can They Take It with Them? The
Portability of Star Knowledge Workers’ Performance.” Management science 54. No. 7 (2007): 213-219. Accessed December 01, 2018. December 01, 2018. DOI: 10.1287/mnsc.1070.0809
Hookana, Heli. “Measurement of Effectiveness, Efficiency and Quality
in Public Sector Services – Interventionist Empirical Investigations.” Managing sustainability 4, no.7. (2011): 491. Accessed December 1st 2018. www.pdfs.semanticscholar.org/05f9/4755aa45b71dffcd686fe6a48173b2ba28ce.pdf
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