Employee performance motivation and performance management system-exploring the pertinence

This study is based on research data of 416 working professionals from telecommunication companies in India. We propose a new three step model indicating the interrelationships of the two components of the performance management system viz. Design and Execution identified as latent classes responsible for the employee motivation to perform because of performance management system.

International Journal of Management (IJM) Volume 11, Issue 3, March 2020, pp 8–15, Article ID: IJM_11_03_002 Available online at http://www.iaeme.com/ijm/issues.asp?JType=IJM&VType=11&IType=3 Journal Impact Factor (2020): 10.1471 (Calculated by GISI) www.jifactor.com ISSN Print: 0976-6502 and ISSN Online: 0976-6510 © IAEME Publication Scopus Indexed EMPLOYEE PERFORMANCE MOTIVATION AND PERFORMANCE MANAGEMENT SYSTEM-EXPLORING THE PERTINENCE Prof Anant Deogaonkar* Assistant Professor, Shri Ramdeobaba College of Engineering & Management, PhD Scholar, Parul University, Vadodara, India Dr Bijal Zaveri Dean, Faculty of Management Studies, Parul University, Vadodara, India Dr Chandan Vichoray Head, Department of Management Technology, Shri Ramdeobaba College of Engineering and Management, India *Corresponding Author Email: Deogaonkara1@rknec.edu ABSTRACT People are intellectual assets and the success of any organization depends largely on how the people are valued Performance management system is the very manifestation of the effective intellectual asset management in the organization The purpose of this article is to understand the performance management system and its relationship with employee performance motivation This study is based on research data of 416 working professionals from telecommunication companies in India We propose a new three step model indicating the interrelationships of the two components of the performance management system viz Design and Execution identified as latent classes responsible for the employee motivation to perform because of performance management system The relationship is tested with the research hypotheses on these classes using multinomial logistics regression Keywords: Performance management system; employee; motivation Cite this Article: Anant Deogaonkar, Dr Bijal Zaveri and Dr Chandan Vichoray, Employee Performance Motivation and Performance Management System-Exploring the Pertinence, International Journal of Management (IJM), 11 (3), 2020,pp 8–15 http://www.iaeme.com/IJM/issues.asp?JType=IJM&VType=11&IType=3 INTRODUCTION AND BACKGROUND As per the research report of Indian brand equity foundation (Ministry of commerce & Industry, Government of India 2018 December), India is currently the world’s second-largest http://www.iaeme.com/IJM/index.asp editor@iaeme.com Anant Deogaonkar, Dr Bijal Zaveri and Dr Chandan Vichoray telecommunications market with a subscriber base of 1.17 billion and has registered strong growth in the past decade and half The liberal and reformist policies of the Government of India have been instrumental along with strong consumer demand in the rapid growth in the Indian telecom sector The deregulation of Foreign Direct Investment (FDI) norms has made the sector one of the fastest growing and a top five employment opportunity generator in the country This is an imperative to device a robust mechanism of employee performance measurement and management mechanism to ensure motivating reward, award and recognition system in place Periodic reviews by the immediate supervisor are not just enough Informal interactions between the employee and supervisor related to performance excellence indicate more positive outcomes as compared to formal appraisal process (Koen & Hans 2013) The organizational compensation survey by KPMG focused on innovative performance management systems to be the key area being worked by the corporate.(KPMG 2017-18).There is a need to exactly understand how the employee perceives the performance management mechanism and what are the expected inclusions to be incorporated in the performance management system to make it a motivating force for employee performance excellence The institutes for corporate productivity researches also have negative findings about the performance management system A lot many survey have been conducted to understand the effectiveness of performance management system in corporate but there is always a possibility of some indications underlying the responses given by the employees towards the performance management system to be considered as motivating force for performance To identify such latent factors this research data is collected using survey method with the help of a structured undisguised questionnaire RESEARCH DESIGN This study is based on research data of 416 working professionals from telecommunication companies in India We propose a three step model indicating the interrelationships between design of PMS, execution of PMS and employee motivation to perform The responses obtained on five-point Likert scale for design, execution and motivation related questions were summarized in terms of numbers and percentage In order to classify respondents based on their responses to questions in each category, Latent class analysis was performed This analysis was carried out independently for design, execution and motivation related questions to group respondents based on their responses Latent Class Analysis i s a technique for the analysis of clustering of observations in multiway tables of categorical variables The central idea it to fit the model in which any confounding between the manifest variable can be explained by a single unobserved “latent” categorical variable The analysis was performed using R-library poLCA, which uses the assumption of local independence to estimate a mixed model of latent multi-way tables and the number of nclass A sequence of models with group to groups were generated and the parameters like BIC, AIC and likelihood were obtained The group with minimum BIC value was considered to provide the best classification of respondents After deciding the number of clusters, the membership for each respondent was obtained, thereby classifying each respondent to one of the clusters Such analysis was performed for all the three categories viz.,design, execution and motivation The main objective of study was to develop a model specifying the relationship of motivation of employees and the design and execution of performance management system In other words, how the design of Performance Management System and its execution by supervisors influence the motivation level of employees By using Latent Class Analysis, from design perspective, the respondents were grouped into three classes viz., Rational, Somewhat rational and Weak design Similarly, based on execution related http://www.iaeme.com/IJM/index.asp editor@iaeme.com Employee Performance Motivation and Performance Management System-Exploring the Pertinence responses, they were grouped into Good execution, Fair execution and Poor execution Also, the motivation related questions were used to classify them into highly motivated, moderately motivated and non-motivated The motivation variable was treated as dependent, while design and execution were referred as independents Since, the motivation is an ordered variable, to determine its relatedness with the independents, ordinal logistic regression was used However, due to violation of assumption of proportional odds, the dependent variable was treated as nominal and accordingly, multinomial logistic regression analysis was performed The independent variables were coded with dummy variables and thus the coefficients were obtained with reference to their respective lowest level The analysis was performed using SPSS ver 20.0 (IBM Corp Armonk, USA) The statistical significance was evaluated at 5% level DATA ANALYSIS AND INTERPRETATION The questionnaire quality, reliability is supported by the statistical measure –Cronbach’s alpha value of more than 0.8 Table provides the reliability statistics of the questionnaire administered during pilot study on 40 respondents Table 02 provides the latent class analysis for question related to design of PMS Baysian Information Content (BIC) criterion was referred, which indicated a minimum value of 5997.57 corresponding to model Thus, the respondents were classified into three clusters Table 03 provides the latent class analysis for question related to execution of PMS Baysian Information Content (BIC) criterion was referred, which indicated a minimum value of 3966.45 corresponding to model Thus, the respondents were classified into three clusters Table 04 provides the latent class analysis for question related to motivation Again referring to BIC criterion, the minimum was observed for model (3.447.62) suggesting three clusters of respondents Accordingly, they were partitioned into three groups based on motivation related questions In order to determine the effect of design and execution of PMS on the motivation levels of the employees, multivariate modeling approach was followed Each employee was assigned a cluster membership with reference to design, execution and motivation Thus, the analysis data set consisted of design variable at three levels (clusters), execution at three levels and motivation also at three levels The first two variables were treated as independent and motivation was regarded as dependent Further, years of experience were included in the analysis as independent covariate and designation as dichotomous variable Since, the dependent variable (motivation) has three levels in the ordered manner, ordinal logistic regression was the right choice to determine the relationship between motivation and independent predictors However, the assumption of proportional odds was violated using the data set, hence, the dependent was treated as multinomial and accordingly the multinomial logistic regression was performed The result obtained following this analysis is shown in Table 05 During analysis, low motivation was regarded as reference level and the effect of changing the design and execution levels on high and moderate motivation was determined Further, for design variable, weak design was treated as reference, while for execution; poor execution was treated as reference The three classes obtained in case of the PMS design and the PMS execution forms the basis for understanding how the employee motivation to perform varies as per the design and execution of the PMS The overall performance management system though has an impact on employee motivation, it is imperative to study the independent impact of the design and http://www.iaeme.com/IJM/index.asp 10 editor@iaeme.com Anant Deogaonkar, Dr Bijal Zaveri and Dr Chandan Vichoray execution respectively With this, we will be able to derive action plan for enhancement in employee performance motivation related to performance management system Hence the hypotheses to explore this relationship based on the latent classes obtained is as below: 3.1 Hypotheses H0:- PMS Design level and PMS execution level has no impact on employee motivation level HA:- PMS Design level and PMS execution level has impact on employee motivation level Table 05 shows that for highly motivated level, the coefficient for design-1 (Rational design) was 3.756 (SE: 1.111) and corresponding p-value of 0.001, while for design-2 (Somewhat rational design), the coefficient was 1.631 (SE: 1.179) and p-value was 0.166 In other words, this indicates that the odds of getting highly motivated if the design changes from weak structure to a rational structure are 42.77 [95% CI: 4.846 – 377.58] as compared to low level of motivation This effect was statistically significant (p=0.001) Further, the odds of getting highly motivated if the design changes from weak structure to somewhat rational structure are 5.11 [95% CI: 0.507 – 51.507], as compared to low level of motivation, although the effect was statistically insignificant (p=0.166) The effects were obtained after adjusting with experience, treated as covariate in the model The coefficient for execution-1 (Good execution) was 5.145 (SE: 1.387) and p-value < 0.0001, while for execution-2 (moderate execution), the coefficient was 2.723 (SE: 1.132) and p-value of 0.016 This revealed that the odds of employee getting highly motivated when the execution changes from poor to good are 171.63 [95% CI: 11.332 – 2599.612] times higher as compared to low motivation level Further, the odds of employee getting highly motivated when the execution changes from poor to moderate are 15.229 [95% CI: 1.655 – 140.101] times higher with reference to low motivation level Both these effects were statistically significant (p < 0.05) On similar lines, for moderately motivated level, the coefficient for design and execution levels were obtained as shown in Table For design-1 (Rational design), the coefficient obtained was 4.661 (SE: 1.221) and p-value < 0.0001, while for design-2 (Somewhat rational design), the coefficient was 3.344 (SE: 1.26) and p-value of 0.008 In terms of odds, the likelihood of employee getting moderately motivated when the design changes from weak level to rational level is 105.71 [95% CI: 9.652 – 1157.87] times as compared to low motivation level The odds of getting moderately motivated when the design changes from weak level to somewhat rational level are 28.34 [95% CI: 2.398 – 335.089] times as compared to low motivation level Similarly, the effect of execution was studied on the change in motivation level The change from poor execution to good execution of PMS increases the likelihood of moderate motivation by 283.68 [95% CI: 11.981 – 6717.07] times as compared low motivation This effect was statistically significant with p-value < 0.0001 While, change from poor execution to fair execution of PMS increases the likelihood of moderate motivation by 46.6 [95% CI: 3.351 – 648.061] times as compared to low motivation level This effect was also statistically significant with p-value of 0.004.It is evident from the table that likelihood of achieving moderate motivation as compared to high motivation is more when the design changes from weak level to either rational or somewhat rational level This is indicated by the magnitude of ORs corresponding to high and moderate m o t i v a t i o n l e v e l s f o r d e s i g n Similarly, the likelihood of achieving moderate motivation as compared to high motivation is more when the execution changes from poor level to either good to fair level Again this is indicated by the magnitude of ORs corresponding to high and moderate motivation levels for execution http://www.iaeme.com/IJM/index.asp 11 editor@iaeme.com Employee Performance Motivation and Performance Management System-Exploring the Pertinence DISCUSSION The data analysis revealed that the performance management system has important role in contributing towards employee motivation to perform The study specifically indicates that if the companies target to enhance motivation of employees with the performance management system then it is the execution of the PMS that catches immediate attention The execution of the PMS if improved from poor to good or even from fair to good the employee motivation drastically improves from moderate to high The way of execution of PMS points towards the interactions between the employee and the supervisors Employee motivation is adversely impacted by pragmatic communication of numbers and measured performance on forced scale The study implies reinventing the performance management system to be a real time feedback system to support employee for growth and development The design of performance management system is mostly referred as the means to capture the quantified performances which is routine activity and hence has less impact on employee motivation But the improvement in execution in turn may demand the design also to change, questioning that “do the companies continue with the existing PMS system? “The research here answers as “NO” The companies will need to consider contemporary issues related to employee satisfaction, stress levels, employee psychological factors as well to come up with totally new model of performance management system CONCLUSION The study indicates that the proposed model based on design, execution and employee motivation is validated by the statistical analysis The three step model here assists the companies to extract the exact pain area to be addressed with respect to PMS to enhance employee motivation to perform The PMS design and the way it is executed need to be reframed with the inputs from the employees Employee wants a simple, easy to understand and execute and real-time feedback mechanism of PMS People are intellectual assets and are more concerned with the execution of performance management system i.e the design of the performance management seems to be fair This is because the design of performance management system aims at providing a tool for measuring and documenting the performance against the targets which is inevitable Employees are aware about the design of PMS being done at strategic level and their immediate supervisors have hardly any role to play in designing the system The employee performance is impacted by the way of execution of the performance management system This is because the execution process involves manual interaction with the supervisors, discussion with supervisor about the performance demonstrated by the employee It also involves inclusion of the facts and figures and ground reality to be captured in the system Table Reliability Statistics Cronbach's Alpha 823 http://www.iaeme.com/IJM/index.asp Cronbach's Alpha Based on Standardized Items 812 12 N of Items 40 editor@iaeme.com Anant Deogaonkar, Dr Bijal Zaveri and Dr Chandan Vichoray Table Latent Class Analysis for questions related to design of PMS Model Model Model Model Model Model Model Log-likelihood -3374.417181 -2632.050992 -2413.812944 -2329.213185 -2280.525306 -2230.693666 Residual DF 352 287 222 157 92 27 BIC 7134.798219 6042.060382 5997.578829 6220.373851 6514.992636 6807.323898 aBIC 6931.709729 5632.710145 5381.966844 5398.500118 5486.857155 5572.926669 cAIC 7198.798219 6171.060382 6191.578829 6479.373851 6838.992636 7196.323898 Likelihood-ratio 3623.585656 2157.289512 1719.814234 1566.075744 1463.30843 1371.229835 Table Latent Class Analysis for questions related to execution of PMS Model Modell Modell Modell Modell Modell Modell Log-likelihood -2036.86148 -1806.816192 -1687.721966 -1623.705928 -1599.753309 -1578.600034 Residual DF 384 351 318 285 252 219 BIC 4266.704887 4005.626927 3966.451088 4037.431626 4188.539002 4345.245064 aBIC 4165.160642 3799.365179 3655.471837 3621.734873 3668.124746 3720.113306 cAIC 4298.704887 4070.626927 4064.451088 4168.431626 4352.539002 4542.245064 Likelihood-Ratio 1277.686194 818.4896499 581.3381218 453.3814771 405.5104565 362.1717127 Table Latent Class Analysis for questions related to motivation due to PMS Model Model Model Model Model Model Model Log-likelihood -1771.809539 -1607.954408 -1536.86118 -1512.469186 -1496.578569 -1485.804169 Residual DF 396 375 354 333 312 291 BIC 3664.232784 3463.166913 3447.624846 3525.485249 3620.348405 3725.443995 aBIC 3600.767631 3333.063349 3250.882871 3262.104863 3290.329608 3328.786788 cAIC 3684.232784 3504.166913 3509.624846 3608.485249 3724.348405 3850.443995 Likelihood-ratio 702.3885624 374.6783003 232.4918434 183.7078556 151.9266211 130.3778209 Table Effect of PMS design and execution on motivation of employee using multinomial logistic regression Parameter Estimates Motivated Highly Motivated Moderately Motivated Intercept Experience [Design=1] [Design=2] [Design=3] [Execution=1] [Execution=2] [Execution=3] [Designation=1] [Designation=2] Intercept Experience [Design=1] [Design=2] [Design=3] [Execution=1] [Execution=2] [Execution=3] [Designation=1] [Designation=2] 95% CI OR Lower Upper Bound Bound B SE Wald DF P-value OR -1.521 -0.152 3.756 1.631 Ref 5.145 2.723 Ref 0.117 Ref -4.112 -0.052 4.661 3.344 Ref 5.648 3.842 Ref -0.717 Ref 1.455 0.091 1.111 1.179 1.092 2.799 11.426 1.916 1 1 0.296 0.094 0.001 0.166 859 42.774 5.111 0.719 4.846 0.507 1.026 377.580 51.507 1.387 1.132 13.769 5.785 1