Perceived Effect of Performance Appraisal on Employee Productivity in Selected Hotels in Ethiopia’s Capital City Addis Ababa in Bole Sub City

: The aim of the study was to assess the effect of performance appraisal variables on employee productivity at some selected hotels from the capital city of Ethiopia Addis Ababa city, Bole sub-city. The study used a descriptive and explanatory research design and a mixed research approach. The study used both primary and secondary sources of data, questionnaires, and interview tools, and the data were analyzed using descriptive and inferential statistics overall, an employee of a selected hotel and a sub-city were units of observation for the study. The finding of the study reveals the relationship between independent variables (work-life, employee benefit, organization policy, performance appraisal process, workplace interaction, and effective leadership) and dependent variable employee performance. Therefore, with regard to this model, the relationship between the independent and dependent variables is strong with 0.644(R). The R Square number of 0.414 means about a 41.4% increase in employee productivity is accounted for by the variables in the model while the remaining 58.6 % is accounted for by other factors not captured by the model. The study result demonstrated that five out of six independent variables (organization policy, effective leadership, employee benefit, workplace interaction, performance appraisal, and work-life balance) were significantly related to productivity among employees. However, only performance appraisal failed to show a significant relationship with employee productivity. The result indicated that the independent variables for the study such as organization policy, effective leadership, employee benefit, performance appraisal, workplace interaction, and work-life balance have a positive relationship with employee productivity. Thus, hiring competent expertise, designing reward and recognition schemes, sustain appraisal schemes are the forwarded recommendations by the researcher.


INTRODUCTION
The word 'hotel industry' can be defined as the business administration pertaining to lodging, put up to receive payments from travelers or the people who seek a temporary stay, with services on the food, drink, and a systematic control on the particular administration.Modern hotel industry, i.e., the breadth of the range of hotel services, is conditioned by the category of the hotel facility, the size of the hotel facility, hotel location, the weather aspect of the hotel business, the complexity of hotel organizational structure and business policy in the market Employee performance appraisal refers to an opportunity to take an overall view of work content (loads and volume), and to look back at what has been achieved during the reporting period and agreed objectives for the next planning period (URT, 2011).To this end, a performance appraisal system is an evaluation of individuals' work performance and their potential to develop with the objective of improving their future performance.That is to say, the working goals or expectation has to be a joint venture between the supervisor and the employee.That's why performance appraisal is an integrated process that occurs regularly and frequently between supervisors and workers.Rao (2005) opines that performance appraisal is a method of evaluating the behavior of employees in the Workplace and it normally includes both the quantitative and qualitative aspects of the job performance of an individual employee.Performance appraisal can be an important tool for supporting and improving the quality of services provided by Local Government employees to the Public.Unfortunately, employee evaluation has been frequently viewed not as a vehicle for growth and improving employee productivity, but rather as a formality that must be endured (Stronge and Tucker, 2003).As such; Performance Appraisal has been observed as an important step to avoid making the exercise look like a yearly ended system.In other words, Performance

Sampling Techniques
In order to address a research objective, probability and non-probability sampling techniques were applied.Among the probability sampling techniques, the stratified sampling technique was employed to conduct this study because it is not feasible to address respondents through simple random sampling.Therefore, the study designed three strata based on their level of standards (threestar, four-stars, and five-star hotels).Then, an equal number of target populations were selected from each stratum through a simple random sampling technique.For the non-probability sampling technique, the researcher employed a purposive sampling technique to select key informants for interviews based on expert knowledge and hotel management.

Sampling size
To determine the entire sample size of a hotel employees, a representative sample size the researcher convinces with a 95% confidence level and P = 0.5 used the following formula (Soliven, 1989)  = N 1 + N(e)2  = 6586 1 + 6586(0.05)2= 377, Where, n = is the desired sample size, N= Population size , e = level of precision 2.6.Data Type and Source of Data Both primary sources of data were administered to the respondents.Well-designed on structured questionnaire structured interview was utilized.Primary data were obtained from employee through questionnaire.Secondary data were obtained from government annual reports, magazines, and institution records which have information about hotel standards and employees.

Data Gathering Technique and Instruments
The data was collected through a questionnaire, interview, and document analysis.First, close-ended questionnaires were prepared in Amharic and it is translated into English later for data analysis.The structured interview was also prepared for the interviewee like the questionnaire.Secondary data such as research, report, journal, and other significant data was also assessed based on their level of significance to the research objective and the title of the research.

Method of Data Analysis
A Descriptive type of data analysis employed a summary of frequency and percentile taken from the frequency table.For quantitative data, standard statistical software called (SPSS version 21) for data entry and analysis using descriptive statistics was used.

Inferential Statistics
Under this section, the effects of work-life balance, employee benefit, organization policy, performance appraisal process, workplace interaction, and effective leadership on employee productivity were elaborated using linear regression Regression analysis is a statistical technique useful for examining and modeling the relationship between variables (Sugiyono, 2016).Linear regressions are often used to address regression analysis issues that result in relationships of two or more free variables.The model of linear regression equation is as follows: Y= β0+ β1X1+ β2X2+ β3X3 + β4 X4 β1X1+ β5X5+ β6X6 + ε Where, Ŷ = Employee productivity, X1 = Organization policy, X2 = Effective leaderships, X3 = Performance appraisal, X4=Employee benefit, X5=Work place interaction, and, X6=Work life balance.

Correlation Test
The correlation test was undertaken to test the independent variables such as organization policy, effective leadership, employee benefit, performance appraisal, workplace interaction, and work-life balance dependent variable employee productivity.

Validity and Reliability
Reliability -reliability refers to the data collected by an independent collector and if the same questionnaire is administered by another person who yields the same results.In this study, the reliability test was used to determine which factor or item to be analyzed, discussed, and used.To increase reliability, each respondent was given a preamble sample describing the objective of the study and its implications.Respondents were asked to be free and anonymity was preserved.In addition to that Cronbach's Alpha test output was used.Validity -validity refers to the correctness and reasonableness of data.Each questionnaire obtained was checked for validity.The verified questionnaire responses were then entered in the window SPSS using codes.This statistical/software package was set with some validation rules for some fields and only reasonable and correct entries are captured.
All errors were corrected before data analysis.

Response Rate
The study was conducted by making an interview with four managers and team leaders at the hotel.A total of 377 questionnaires were distributed to leaders and permanent and part-time employees of sample respondents.Out of these, 341 (90.45%) of the questionnaires have been returned and, 36 (9.55%) respondents couldn't return for different reasons.Accordingly, the analysis of this study is based on the number of questionnaires collected.Thus, the response rate was 88.53%, which is greater than the required acceptable 30% response rate (Sekaran, 2003).

Research Reliability
Prior to analysis, Cronbach's alpha was calculated for the employee satisfaction scales to assess reliability.The alpha coefficient of 0.910 was found and it indicates extensive reliability.According to the guidelines provided by Robinson et al., (1991), where "0.80 or better, for example, 0.70-0.79was rated extensive, 0.60-0.69was rated moderate, <0.60 was rated minimal".Furthermore, all the measurements of alpha coefficients also have shown extensive reliability with a slight indifference to their score (table 4.1).4.1, the reliability of organization policy was 0.904; effective leadership was 0.814; Performance appraisal was 0.0824; employee benefit was 0.822; workplace interaction was 0.845 and work-life balance was 0.798; therefore, the reliability was good consistence and good reliable.

Demographic Characteristics of the Respondents
As Table 4.3 depicts, 191(56%) of respondents were male and the remaining 150 (44%) were females.This implies that males are highly involved in this research response rate.Concerning the age structure of all categories 248 (72.7 %) of respondents were under the age category of 18-24 years which shows as the majority of respondents were found young 91(27.7 %) of age category of respondents were found in the age group 35 -50 and only (0.6 %) of the respondent above age 50.Therefore, most of the respondents in all categories are productive groups of society.Regarding the educational qualification of respondents, 190 (55.7%) respondents were degree and above degree qualification and 119 (34.9%) were TVET or college diploma academic status.Concerning with employment type, 327 (95.9%) were full-time employees and 14 (4.1%) were part-time workers.In relation to the type of employee, 327(95.9%)were full-time workers and 14(4.1%) were part-time workers, thus, the majority of informants included in this study were full-time type of workers at the study area hotels.

Determinants of Employee Performance
For the sake of this study, the main determining variables that determine employee performance includes, organizational policy, Work life balance, employee benefit, performance appraisal, workplace interaction, and effective leadership were described below.

Organization policies' Effect on Employee Performance
The study sought to establish the level at which respondents rated the performances between not at all effective, a little effective, moderately effective, quite effective, and very effective with the below statements in relation to organization policy and its role in employee performance in Bole sub-city selected Hotels.Thus, the summary of descriptive statistics of all variables that are evaluated is based on a five-point Likert scale (from, 1; not at all effective, 2; a little effective, 3; moderately effective, 4; quite effective, and 5; very effective).According to Zaidation and Bagheri (2009), the mean score below 3.39, was considered as low (not at all effective/ a little effective), the mean score from 3.40 to 3.79 was considered moderate (moderately effective) and the mean score above 3.8 was considered as high (quite effective/very effective) by comparison base of mean score of five Likert scale instrument.Table 4.3 below provides descriptive statistics included are the number of subjects (N), the Minimum (lowest) and Maximum (highest) scores, the Mean (or average) for each variable and the standard deviation From organization policy effects for employee productivity; providing customers with a positive hospitality experience has the highest mean value of 3.87 and 1.23 standard deviation, it implies that providing customers with a positive hospitality experience has relatively higher effects on the performance of employees.Followed by the best thing in this job is to follow the procedures that are laid-down which has the mean value of 3.77 and the response variation was (1.02) standard deviation, implies that the best thing in this job is to follow the procedures that are laid-down have moderate effects for productivity of employees.The results in Table 4.3 also show that, obey rules and regulations even when no one is watching as shown by the aggregate mean of 3.66 with a significance variance of 1.10.Moreover, the mean values on recruiting workers with a passion for hospitality ranges The response rate thus, implies, from the five organization policy measures, providing customers with a positive hospitality experience has higher effects for employee performance, while the four such as the best thing in this job is to follow the procedures that are laid-down, obey rules and regulations even when no one is watching, recruiting workers with a passion for hospitality and recruiting workers who work hard are moderately effective for enhancing employee productivity.The numeric response rate was shown at Table 4.3 below.

Effective Leaderships effect on productivity of workers
For examining the effects of effective leadership style about employee performance; three questions are provided for selected hotel employees; and all rated the listed leadership effectiveness at higher range and the response deviation was higher relative to the other, when to see the average mean score of each statement, like having communications channels used by management team to express their targets and ideas has higher effects for employee productivity with average mean score of 4.87 and the higher effects was varied by (1.01) standard deviation; which follows the determining variable such as resolving work problems quickly which was the other variable for employee productivity with (3.87) mean score that is ranged at higher effects and the higher response rate was deviated by (1.06) standard deviation and the remaining statement was relating to sharing work among its staff which holds (3.84) mean score and founds at higher effects for employee productivity and on the statement, there is (1.15) standard deviation response variation between respondents.Thus, leadership variables that have higher effects on employee productivity include having communication channels used by the management team to express their targets and ideas, resolving work problems quickly and sharing work among its staff and their effective performance to maximize employee performance and the reverse becomes the truth at the selected Hotels.The response rate was revealed at Table 4.5 below.

Employee Benefits Effects on employee performance
As shown at Table 4.6 below; on effects of employee benefits for employee productivity; and there is general consensuses about the higher the benefits acquired for employee and the higher their productivity, from the statements that has higher effects for employee performance were setting clear standards for individual workers with mean values (3.85)  and setting clear standards for departmental performance has (3.89) mean scores and (1.13) standard deviation, which implies, the setting of clear standards for individual workers and setting clear standards for departmental performance has higher effects that determines employee performance at selected hotels of Bole sub city.On the other sides; statements like continuing to develop workers so that their skills are regularly updated which holds mean score of (3.66) and standard deviations (1.17); and providing incentives that motivate workers to improve performance takes (3.45) mean values and there is wider response variation between informants with (1.29) standard deviation, hence, continuing to develop workers so that their skills are regularly updated and providing incentives that motivate workers to improve performance were performed moderately and they has moderate effects for employee productivity at the study area.
In general, from the variables of employee benefit the statements such as the setting of clear standards for individual workers and setting clear standards for departmental performance has higher effects that determines employee performance; while statement such as continuing to develop workers so that their skills are regularly updated and providing incentives that motivate workers to improve performance were performed moderately and they has moderate effects for employee productivity at selected Hotels at Bole sub city.

Performance Appraisal Effects on Employee Performance
Based on the views of full-time and part-time employees that are included in this study rated the effectiveness of performance appraisal issues at higher and medium ranges based on the measurement of Zaidation and Bagheri (2009), thus, the higher performances promoting workers from within the company, continuously tracking individual performance and training workers in customer services skills were some of the variables and assured by informants withholding(3.95), (3.80) and (3.80) mean scores with (1.16) and (1.17)standard deviation respectively.While performance appraisal statements which comprise allocating performance-related rewards to individuals were moderately effective with a (3.66) mean score and there is a wider response deviation between respondents a (1.21) standard deviation.Thus, the performance appraisal variables that are performed at a higher rate were promoting workers from within the company, continuously tracking individual performance, and training workers in customer services skills, whereas, allocating performancerelated rewards to individuals was moderately effective at Bole sub-city selected Hotels.The numeric data was shown in table 4.7 below.The other determining issue for employee productivity especially for the hotel sector was workplace interaction and the views of hotel employees on the effective performance were rated at a higher range at which the statements listed in table 4.8; were performed an effective manner and each can be set as holding regular reviews and meetings to solve departmental problems which scores In general, the availability of a suitable workplace and employee interaction has a prominent role in employee productivity and this was too good for Hotels to create suitable environments to insure their productivity.The result shows that the tolerance value for each independent variable (work life balance, employee benefit, organization policy, performance appraisal process, work place interaction and effective leaderships) is (0. 519; 0.500, 0.462; 0.518; 0.462; and 0.404) respectively.Therefore, multi Collinearity assumption is not violated.This is also supported by the VIF value, which is 1.928, 2.000, 2.165; 1.930; 2.165 and 2.476 which is well above the cut-off 0.1).

Normality Test
The researcher requested some graphs from the Plots sub-menu when running the regression analysis so that the researcher could test our assumptions.The histogram (Figure 4.3) shows that, there may have problems with the residuals as they are not quite normally distributed though they roughly match the overlaid normal curve, the residuals are clearly clustering around and just above the mean more than is desirable.Thus, the residuals are approximately normally distributed There is also a P-P plot to check that the residuals are normally distributed (Figure 4.3 below).This plot is used to compare the observed residuals with what is expected if they were normally distributed (represented by the diagonal line).It can see that, aside from a minor departure at the observed cumulative probability of far apart from each other among respondents, the data is normally distributed.There are some issues with the distribution of the residuals and may need to make a judgment call about whether to remove outliers or alter our model to fix this problem.In this case, the researcher decided that the benefits of keeping all cases (employee productivity) in the sample outweighed the issues regarding the ambiguous interpretation of whether or not our residuals were normally distributed.

Homoscedasticity Test
The scatterplot (Figure 4.5 below) shows, how large the standardized residual was for each case at each value of the predicted outcome.The researcher is hoping that this will look pretty random as this would fulfill our assumption of homoscedasticity.The residuals (errors) should not vary systematically across values of the explanatory variable.This can be checked by creating a scatterplot of the residuals against the explanatory variable.The distribution of residuals should not vary appreciably between different parts of the x-axis scale; in this case, there is no problem of homoscedasticity.Likely, there is a pattern in the scatter.The width of the scatter as predicted values increase is roughly the same so the assumption has been met.Under this section, the effects of work-life balance, employee benefit, organization policy, performance appraisal process, workplace interaction, and effective leadership on employee productivity were elaborated using linear regression.Regression analysis is a statistical technique useful for examining and modeling the relationship between variables (Sugiyono, 2016).Linear regressions are often used to address regression analysis issues that result in relationships of two or more free variables.
The model of linear regression equation is as follows: Y= β0+ β1X1+ β2X2+ β3X3 + β4 X4 β1X1+ β5X5+ β6X6 + ε Where, Ŷ = Employee productivity, X1 = Organization policy, X2 = Effective leadership , X3 = Performance appraisal, X4=Employee benefit, X5=Work place interaction, X6=Work life balance, β: Regression coefficient of the independent variables, and, ε =error term Table 4.10 indicates that 'R' reveals the relationship between independent variables (work-life balance, employee benefit, organization policy, performance appraisal process, workplace interaction, and effective leadership) and dependent variables (employee productivity).The rule states that the closer the figure is to 1 the stronger the relationship and vice versa.Therefore, with regard to this model, the relationship between the independent and dependent variables is strong at 0.644.This implies that the model has a strong relationship and goodness fit; meaning that work-life balance, employee benefit, organization policy, performance appraisal process, workplace interaction, and effective leadership has a positive effect on employee productivity.Equally, the model reveals an R 2 of 0.414, meaning that about a 41.4% increase in employee productivity is accounted for by the variables in the model while the remaining 58.6% (100-41.4%) is accounted for by other factors not captured by the model.The robustness and goodness of fit of the model are further confirmed by an adjusted R 2 of 0.404, which implies that 40.4% of the variation in the dependent variable is accounted for by the regresses.The standard error (0.725) of a model fit is a measure of the precision of the model.It is the standard deviation of the residuals.It shows how wrong one could be if s/he used the regression model to make predictions or to estimate the dependent variable or variable of interest.As R² increases the standard error will decrease.On average, our estimates of employee productivity with this model will be wrong by 0.725 which is not an ignorable amount for determining the employee productivity, the standard error is wished to be as small as possible.The SPSS output table labeled ANOVA (table 4.11 below) provides the results of the test of significance for R and R 2 using Fstatistics.In this analysis, the P-value is well below 0.05(P= 0.000).Hence, from the result, we can conclude that the R and R 2 between the independent variables and the dependent variables are statistically significant.The F-test, the linear regression's F-test F = 39.025and337 degrees of freedom the test is highly significant, thus we can assume that there is a linear relationship between the variables in this model.The table in the SPSS output labeled coefficient (table 4.12) provides information that is useful for understanding the regression equation.Under column unstandardized coefficient and sub-column B, the numerical values on the first row labeled (constant) are the value for the intercept (a) in the regression equation.The numerical values on the second row, labeled as Work-life balance, employee benefit, organization policy, performance appraisal, workplace Interaction, and effective leadership, in this case, representing the independent variable) is the value for the slope (b) in the regression equation.Based on table 4.13, linear regressions are obtained as follows: Y = 0.750 + 0.022 X1 -0.060 X2 + 0. 076 X3 + 0.016 X4 -0.068 X5 + 0.151 X6 + ε = 0.887 with the help of Work-life balance, employee benefits, organization policy, performance appraisal, workplace Interaction, and effective leadership employee productivity was increased by 0.887 In the study, the interpretations of linear regression equations are: a.If everything on free variables is considered zero then employee productivity value (Y) is 0.750 b.In the event of organization policy increases of 1, employee productivity (Y) will increase by 0.022.c.In the event of an increase in effective leadership by 1, employee productivity (Y) will reduce by-0.060.d.In the event of an employee benefits increase of 1, employee productivity (Y) will increase by 0.076.e.In the event of an increase performance appraisal by 1, employee productivity (Y) will increase by 0.016.f.In the event of workplace interaction increases of 1, employee productivity (Y) will decreased by -0.068.g.In the event of an increase in Work-life Balance style by 1, employee productivity (Y) will increase by 0.151.This study result demonstrated that five out of six independent variables (organization policy, effective leadership, employee benefit, workplace interaction, performance appraisal, and work-life balance) were significantly related to productivity among employees.However, only performance appraisal failed to show a significant relationship with employee productivity.Additionally, among all the independent variables, Work-life Balance (β =0.665, t = 11.377,p < .000)had the highest, strong and positive standardized beta value coefficient.This indicates that work-life balance was the most important variable in predicting high productivity among employees.The other predictors that are important in predicting high productivity are workplace interaction (β = -0.318,t= -5.145, p < .000);employee benefit (β = 0.302, t = 5.078, p< .000);effective leadership (β = -0.176,t= -2.661, p < .008)and organization policy (β = 0.111, t= 1.793, p < .044)were statistically significant and strong predictors for employee productivity.However, among the tested predictors it was only performance appraisal that demonstrated lower and non-significant effects on productivity among employees.From the analysis, the co-efficient value for work-life balance was 0.665.This means that all things being equal, when the other independent variables (organization policy, effective leadership, employee benefit, performance appraisal, and workplace interaction) are held constant, employee productivity increases by 66.5% if there is a 100% improvement in the availability of work- life balance.This was statistically significant (0.00<0.05) i.e., the variable (work-life balance) is making any significant unique contribution to the prediction of the dependent variable (employee productivity).
From the analysis, the co-efficient value for workplace interaction was 0.318.This means that all things being equal, when the other independent variables (organization policy, effective leadership, employee benefit, performance appraisal and work life balance) held constant, employee productivity increases by 31.8% if there is 100% improvement in work place interaction.This was statistically significant (0.00<0.05) i.e., the variable (work place interaction) is making any significant unique contribution to the prediction of the dependent variable (employee productivity).
From the analysis, the co-efficient value for employee benefit was 0.302.This means that all things being equal, when the other independent variables (organization policy, effective leadership, work place interaction, performance appraisal and work life balance) are held constant, employee productivity would increase by 30.2% if there is 100% improvement employee benefit.This was statistically significant (0.00<0.05) i.e. the variable (employee benefit) is making a significant unique contribution to the prediction of the dependent variable (employee productivity).
From the analysis, the co-efficient value for effective leadership was 0.176.This means that all things being equal, when the other independent variables (organization policy, work place interaction, performance appraisal and work life balance) are held constant, employee productivity would increase by 17.6% if there is 100% improvement effective leadership.This was statistically significant (0.008<0.05) i.e. the variable (effective leadership) is making a significant unique contribution to the prediction of the dependent variable (employee productivity).From the analysis, the co-efficient value for organization policy was 0. 111.This means that all things being equal, when the other independent variables (effective leadership, work place interaction, performance appraisal and work life balance) are held constant, employee productivity would increase by 11.1% if there is 100% improvement organization policy.This was statistically significant (0.044<0.05) i.e. the variable (organization policy) is making a significant unique contribution to the prediction of the dependent variable (employee productivity).The co-efficient value for performance appraisal was 0.063.This means that all things being equal, when the other independent variables (organization policy, effective leadership, employee benefit, workplace interaction, and work-life balance) are held constant, employee performance would increase by 6.3% if there is a 100% improvement in performance appraisal.This was not statistically significant (0.282>0.05) i.e., the variable (performance appraisal) is making a non-significant unique contribution to the prediction of the dependent variable (employee productivity).

Correlation Test
The results in table 4.13 below show that the independent variables such as organization policy, effective leadership, employee benefit, performance appraisal, workplace interaction, and work-life balance have a positive relationship with employee productivity, specifically work-life balance, employee benefit, performance appraisal, organization policy, effective leadership and work place interaction have positive effect on employee productivity with prison correlation such as (r =0.535; 0.388; 0.223; 0.217 and 0.211: df = 339; P<.001) respectively, which indicate that work life balance, employee benefit, performance appraisal, organization policy, effective leadership and work place interaction induce employees to become productive at selected Hotels of Bole sub city.In a summary, the results of correlation analysis indicated organization policy had strong and positive correlations with employee's productivity.Effective leadership had significant positive correlations with employee productivity.Employee benefit exhibited positive and significant correlation across employee productivity; performance appraisal had significant positive correlations with employee productivity; work place interaction had significant positive correlations with employee productivity and work life balance exhibited significant positive correlation with employee productivity at the study area.

CONCLUSIONS
The main objective of this study was to investigate the effect of performance appraisal on employee productivity in some selected hotel in bole sub city.For undertaking the study, descriptive and explanatory research design and mixed research, approach is employed.A structured questionnaire was used to collect primary data from a sample of 377 employees and only 341 questionnaires were returned.The leadership styles are measured through the Questionnaire developed by Avolio and Bass (1995).Descriptive statistical tools were used for data analysis.In inferential statistics, Pearson's correlation and linear regression analysis were used to assess effects of performance appraisal on employee productivity.The effects of work-life balance, employee benefit, organization policy, performance appraisal process, work place interaction and effective leaderships on employee productivity was elaborated using linear regression.'R' reveals the relationship between independent variables (work life balance, employee benefit, organization policy, performance appraisal process, work place interaction and effective leaderships) and dependent variable (employee productivity).The rule states that, the closer the figure to 1 the stronger the relationship and vice versa.Therefore, with regard to this model, the relationship between the independent and dependent variable is strong with 0.644.The model reveals an R 2 of 0.414, meaning that about a 41.4% increase in the employee productivity is accounted for by the variables in the model while the remaining 58.6%(100-41.4%) is accounted for by other factors not captured by the model.The robustness and goodness of fit of the model are further confirmed by an adjusted R 2 of 0.404, which implies that 40.4% of the variation in the dependent variable is accounted for by the regresses.The standard error (0.725) of a model fit is a measure of the precision of the model.It is the standard deviation of the residuals.It shows how wrong one could be if s/he used the regression model to make predictions or to estimate the dependent variable or variable of interest.As R² increases the standard error will decrease.The study result demonstrated that five out of six independent variables (organization policy, effective leadership, employee benefit, workplace interaction, performance appraisal, and work-life balance) were significantly related to productivity among employees.However, only performance appraisal that failed to show significantly relationship with employee productivity.The result shows that the independent variables for the study such as organization policy, effective leadership, employee benefit, performance appraisal, workplace interaction, and work-life balance have positive relationship with employee productivity at selected hotels from Bole sub city.
(4.01) mean values, and (1.12) standard deviation; working as a team has (3.98) mean score and (1.20) standard deviation I have considerable opportunity for independence and freedom in how I do my job has a mean value of (3.95) and response variation was by (1.07) standard deviation; I am always looking for different ways of doing my job taking mean score of (3.83) and deviates with (1.15) standard deviation and the remaining workplace interaction was relating to I can decide on my own how to go about doing my work which has (3.80)mean values as well as, (1.08) standard deviation as per the data get from a questionnaire survey.
Under this section, the basic assumptions should be satisfied in order to maintain the validity and robustness of the regressed result of the research under the linear regression models.Hence, this study has conducted the assumption tests such as multi-Co linearity, linearity, and normality 3.5.1.Multi-Collinearity Collinearity diagnostics on the variables as part of the linear regression procedure is done using tolerance and variance inflation factor (VIF).Tolerance is an indicator of how much of the variability of the specified independent is explained by the other independent variables in the model.If this value is very small (less than 0.10), it indicates that the multiple correlations with other variables is high, suggesting the possibility of multi-Collinearity (Pallant, 2010) furthermore, the other value given is the VIF, which is just the inverse of the tolerance value (1 divided by tolerance).The information in (table 4.9) above also allows us to check for multi Collinearity.A common rule of thumb: for any predictor VIF > 1 should be examined for possible multi Collinearity problem (Dhakal, 2016), in our linear regression model.VIF should be < 10 (or Tolerance > 0.1) for all variables, which they are

Figure 4 . 3 ISSN
Figure 4. 3 Histogram of residuals for the simple linear regression Source: Field Survey, 2022

Table 4 . 7
Work place interaction effectiveness

.6. Work Life balance effects on employee performances
The other effects relating to work life balance with lower effective performance were I am extremely satisfied with my job with (2.97) mean values as well as (0.99) standard deviation and the feels sense of personal satisfaction when I do my job well has (2.90) mean range and (1.07) standard deviation, this response rate indicates, employees does not has sense of personal satisfaction and does not satisfied with their current job which adversely affects their performance at their respective hotels.
For examining work life balance effects on employee performance; six questions were prepared and employees from the sampled Hotel were requested rate the effectiveness of each variable, thus, from the work life balance statement that takes higher mean range were part of my job is to make the customer feel good mean values (3.98) and (1.01) standard deviation; and I try to think of ways of doing my job effectively (3.96) mean values and (0.94) standard deviation While on statements such as employees really enjoy my current job has mean values of (3.73) and (0.91) standard deviation; I have significant autonomy in determining how I do my job with mean score of (3.60) and (0.92) standard deviation; which implies the employees' enjoyment and availability of autonomy are moderately effect and has medium effects for employee performance at selected hotels in Bole sub city.

Table 4 .13 Correlation Test
Correlation is significant at the 0.01 level (2-tailed).