International Journal of Management Science and Engineering Management
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Performance evaluation of employees using Bayesian belief network model Golam Kabir, Razia Sultana Sumi, Rehan Sadiq & Solomon Tesfamariam To cite this article: Golam Kabir, Razia Sultana Sumi, Rehan Sadiq & Solomon Tesfamariam (2017): Performance evaluation of employees using Bayesian belief network model, International Journal of Management Science and Engineering Management, DOI: 10.1080/17509653.2017.1312583 To link to this article: http://dx.doi.org/10.1080/17509653.2017.1312583
Published online: 12 May 2017.
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Date: 12 May 2017, At: 10:46
International Journal of Management Science and Engineering Management, 2017 https://doi.org/10.1080/17509653.2017.1312583
Performance evaluation of employees using Bayesian belief network model Golam Kabira , Razia Sultana Sumib, Rehan Sadiqa and Solomon Tesfamariama a
School of Engineering, University of British Columbia (UBC), Kelowna, Canada; bDepartment of Marketing, Jagannath University, Dhaka, Bangladesh
ABSTRACT
It is a generally acknowledged fact that employee performance evaluation is a critical managerial tool for any organisation. In the global economy, the modern industrial and commercial organisation needs to develop effective methods for assessing the performance of their human resources. In this study, a Bayesian belief network (BBN) model is developed to evaluate the performance of an employee considering the dependencies and correlations between the criteria. The capabilities of the proposed approach are demonstrated on the lumber assembly section of a furniture manufacturing company in Bangladesh. Kendall’s rank correlation coefficient is used to identify the correlation between the criteria. The results indicate that the proposed BBN-based model can explicitly quantify uncertainties and handle the complex relationships between the criteria better when compared with existing performance evaluation methods. The proposed model is also capable of assessing the credibility of multiple experts and ranking employees for different purposes such as reward, improvement, training, promotion, termination, compensation, etc.
1. Introduction
ARTICLE HISTORY
Received 17 August 2016 Accepted 26 March 2017 KEYWORDS
Performance evaluation; Bayesian belief network (BBN); correlation analysis; dependencies; uncertainty; sensitivity JEL CLASSIFICATION
C02; C11; C44; C45; C61
quality function deployment (FQFD) (Manoharan et al., 2012; Manoharan et al., 2011) for performance evaluation. Owing to rapid economic development and global competition, As is widely acknowledged in the literature, these perhuman resource management (HRM) has become an essenformance evaluation criteria are highly interdependent and tial factor for advancing the competitive power of companies a nonlinear relationship exists between them (Ahmed et al., (Avazpour, Ebrahimi, & Fathi, 2013; Islam, Kabir, & Yesmin, 2013; Galinec & Vidovic, 2006; Kamath, 2014; Manoharan 2013). Performance appraisal (PA) is one of the most critical et al., 2011, 2012). Kamath (2014) concluded that there is a postools of HRM to assess employees’ efficiency in the workplace itive relationship between teaching, learning, and evaluation of (Manoharan, Muralidharan, & Deshmukh, 2011). The importhe overall performance of teaching staff. Ahmed et al. (2013) tance of employee performance evaluation and its relationship showed the nonlinear and complex relationships between the to overall organisation performance is well documented in the quantity and quality of work produced, late attendance, rate of literature (Ahmed, Sultana, Paul, & Azeem, 2013). absenteeism, initiative, innovation, dependability and perforBased on the organisational objectives, mission, and vision, mance index. To identify the interrelationships between the the employee performance evaluation criteria vary significantly criteria, Manoharan et al. (2011) used a relationship matrix (Albayrak & Erensal, 2004; Avazpour et al., 2013; Mahdavi, in the house of quality (HOQ). However, the proposed matrix Mahdavi-Amiri, Heidarzade, & Nourifar, 2008). Because of represented the relationship between the main factors with multiple and conflicting criteria, multi-criteria decision- sub-factors of same level only. In another study, Manoharan making (MCDM) is required for evaluating the performance et al. (2012) proposed interpretive structural modelling of employees. Different MCDM methods like the analytical (ISM) for analysing the interrelationships between factors hierarchical process (AHP) (Albayrak & Erensal, 2004), the by dividing them into autonomous, linkage, dependent, and technique for order preference by similarity to an ideal solution driver enablers. Galinec and Vidovic (2006) also represented method (TOPSIS) (Islam et al., 2013; Mahdavi et al., 2008), the relationships by a matrix rule between management and the preference ranking organisation method for enrichment responsibility, efficiency, reliability, and competence/ability. evaluations (PROMETHEE) (Jati, 2014), visekriterijumska For this, a sophisticated process was required to capture the optimizacija i kompromisno resenje, which means multi-crirelationships between the criteria and to determine overall teria optimisation and compromise solution (VIKOR) (Kuo & performance index. Liang, 2012), data envelopment analysis (DEA) (Manoharan, However, very few of these MCDM techniques capture the Muralidharan, & Deshmukh, 2012), and exploratory factor dependencies and correlations between the criteria consistent analysis (EFA) (Mohammadi & Karami, 2013) are presented with real world conditions. Moreover, because of the data intefor employee performance evaluation. In consideratation gration from various sources (e.g. production flow, attendance of the ambiguity and vagueness of decision-making, differlog chart), experts’ participation in data interpretation, and ent researchers have applied a fuzzy inference system (FIS) incomplete and partial information, uncertainties become an (Galinec & Vidovic, 2006; Kamath, 2014), fuzzy rule-based inevitable and vital part of the employee performance evalmodels (FRBMs) (Ahmed et al., 2013), a fuzzy analytical uation model. Furthermore, organisational decision-making hierarchical process (FAHP) (Islam et al., 2013), and fuzzy CONTACT Golam Kabir
[email protected]
© 2017 International Society of Management Science and Engineering Management
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becomes more difficult and uncertain when multiple experts (e.g. managers, supervisors) are involved who have different levels of credibility and experience of the problems under consideration (Tesfamariam, Sadiq, & Najjaran, 2010). The main purpose of this study is to develop a novel and effective model to evaluate the performance of employees considering the dependencies and correlations between the criteria. This paper presents a knowledge-based Bayesian belief network (BBN) model for performance evaluation that can be used to rank the employees of any organisation. Such a performance evaluation tool will aid managers to assess performance and effective decision-making for such purposes as employee recognition, promotion, transfers, termination, salary increase, performance improvement, counseling, training, compensation, etc.
2. Bayesian belief network The Bayesian belief network is a graphical model that is capable of representing the probabilistic relationship or causal dependencies between a set of variables (Laskey, 1995; Pearl, 1988). In the BBN model, the variables of interest are represented by nodes, and causal or probabilistic dependencies between the variables are indicated by links between them (Jensen, 1996; Laskey, 1995). BBN considers uncertainty explicitly based on the Bayes’ theorem by indicating the conditional probability dependencies between variables (Cockburn & Tesfamariam, 2011; Pearl, 1988). In a BBN analysis, for given observed data D and p mutually exclusive variables or parameters Vm (m = 1,2,…,p), the updated probability based on Bayes’ theorem can be computed by the following equation: � � � � � p D�Vm × p Vm � p Vm �D = ∑ � � � �, (1) n p D�Vn p Vn where p(V) indicates the prior occurrence probability of V, p(D) represents the marginal (total) occurrence probability of
Figure 1. Proposed BBN based employee performance evaluation model.
D (constant as the obtained data is in hand), p(D|V) refers to the conditional occurrence probability of D given that V occurs too (likelihood distribution), and p(V|D) refers to the posterior occurrence probability of V given the condition that D occurs (Cockburn & Tesfamariam, 2011; Jensen, 1996; Kabir, Tesfamariam, Francisque, & Sadiq, 2015; Pearl, 1988). The conditional probabilities for the BBN can be obtained through training from data (Cooper & Herskovits, 1992) or expert knowledge elicitation (Cockburn & Tesfamariam, 2011). Where multiple experts are involved in decision-making, the credibility factor of each decision-maker can be considered based on confidence and experience to improve the overall decision (Cockburn & Tesfamariam, 2011; Tesfamariam et al., 2010). The BBN is capable of performing predictive analysis or top-down inference for observing the cause (parent) and deducing the possible effect (child), and diagnostic analysis or bottom-up inference for observing the effect (child) and identifying the possible cause (parent) (Cockburn & Tesfamariam, 2011; Cooper & Herskovits, 1992; Pearl, 1988). Moreover, whenever new information is available, BBN can be used to update probabilities (Cockburn & Tesfamariam, 2011; Jensen, 1996).
3. BBN for employee performance evaluation The framework of the proposed BBN-based employee performance evaluation model is shown in Figure 1. The proposed model can be applied to any manufacturing or service organisation. However, to demonstrate the step-by-step methodology of the proposed model, it is implemented on a furniture manufacturing company. This study was unable to mention the real name of the organisation under investigation as a condition of obtaining access to data collection. The company is situated in the eastern part of Bangladesh and has more than two decades of successful operation. The company, which began manufacturing, retail, and export in the early 1990s, is one of the largest furniture manufacturing plants in Bangladesh. The area of the factory is
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Table 1. Derivation of credibility factor (Wk) for different decision-makers.
Table 2. Details of performance measures of parent or independent nodes.
DMs
Criteria
1 2 3 4
Ek 10 8 9 8
Ck 6 8 9 8.5
CkEk 60 64 81 68
Wk 0.74 0.79 1.00 0.84
3.1. Determination of expert credibility At the beginning of the approach, an expert panel or group was constituted consisting of four decision-makers (DMs) (i.e. a Section Manager, a Floor Manager, Floor Supervisor 1, and Floor Supervisor 2) having different experience, job responsibilities, and qualifications. For this, their opinion enjoys different credibility in the decision-making, so an expert weight is assigned to each decision-maker on this basis. To determine an expert’s credibility factor Wk in the assessment, Kiremidjian (1985) proposed a technique based on the DM’s confidence Ck ∈ [0, 10](and experience Ek ∈ [0, 10]. The normalisation factor ) (MaxKk=1 Ck Ek ) ensures that the weight of the most credible DM is 1 and the remaining DMs will be Wk ≤ 1. Therefore, the credibility factor (Wk) for K experts was computed as
Ck Ek ( ). K Maxk=1 Ck Ek
Sub-criteria
Performance measure Bad 0 ≤ KE ≤ 4 Moderate 4 < KE ≤ 7 Good 7 < KE ≤ 10 Educational Level Secondary EL = 2 (EL) Higher secondary EL = 5 College/University EL = 8 Professional Short course PK = 2 Knowledge (PK) Diploma 1 PK = 5 Diploma 2 PK = 8 Work Performance Bad 0 ≤ WE ≤ 4 (WP) Moderate 4 < WE ≤ 7 Good 7 < WE ≤ 10 Quantity of Work Low 0 ≤ QnW ≤ 175 (QnW) Medium 175 < QnW ≤ 225 High 225 < QnW Quality of Work Good 0 ≤ QlW ≤ 5% (QlW) Moderate 5% < QlW ≤ 8% Bad 8% < QlW Time Management Bad 0 ≤ TM ≤ 4 (TM) Moderate 4 < TM ≤ 7 Good 7 < TM ≤ 10 Leadership & Bad 0 ≤ LC ≤ 4 Communication Moderate 4 < LC ≤ 7 (LC) Good 7 < LC ≤ 10 Teamwork & Coop- Bad 0 ≤ TC ≤ 4 eration (TC) Moderate 4 < TC ≤ 7 Good 7 < TC ≤ 10 Leadership (LD) Bad 0 ≤ LD ≤ 4 Moderate 4 < LD ≤ 7 Good 7 < LD ≤ 10 Communication Bad 0 ≤ CS ≤ 4 Skills (CS) Moderate 4 < CS ≤ 7 Good 7 < CS ≤ 10 Interpersonal Bad 0 ≤ IS ≤ 4 Skills (IS) Moderate 4 < IS ≤ 7 Good 7 < IS ≤ 10 Problem Solving Bad 0 ≤ PS ≤ 4 Ability (PS) Moderate 4 < PS ≤ 7 Good 7 < PS ≤ 10 Ethics & Integrity Bad 0 ≤ EI ≤ 4 (EI) Moderate 4 < EI ≤ 7 Good 7 < EI ≤ 10 Confidence (CO) Low 0 ≤ CO ≤ 4 Medium 4 < CO ≤ 7 High 7 < CO ≤ 10 Flexibility & Low 0 ≤ FV ≤ 4 Versatility (FV) Medium 4 < FV ≤ 7 High 7 < FV ≤ 10 Innovation & Bad 0 ≤ IP ≤ 4 Planning (IP) Moderate 4 < IP ≤ 7 Good 7 < IP ≤ 10 Knowledge & Education (KE)
more than 350,000 square feet and the total number of employees is almost 2,500. The company has more than 60 outlets and showrooms for sales. Currently, the company has a joint venture with Bangladesh and Denmark and is exporting to Germany, the Netherlands, Denmark, Sweden, Switzerland, the Ukraine, Japan, India, Singapore, and Thailand.
Wk =
3
(2)
The experience (Ek) and confidence (Ck) levels of the four DMs are shown in Table 1. Thus, using Equation (2), the corresponding credibility factors of the DMs were computed as 0.74, 0.79, 1.0, and 0.84 respectively (Table 1). 3.2. Identification of evaluation criteria At the beginning, the employee evaluation criteria for the manufacturing organisations were identified from Ahmed et al. (2013), Avazpour et al. (2013), Manoharan et al. (2011, 2012), Dursun and Karsak (2010), Taormina and Gao (2009), and Albayrak and Erensal (2004) and presented to the DMs. Based upon the experts’ opinions, suitable and appropriate employee performance evaluation criteria and sub-criteria for the furniture manufacturing company were identified (Table 2). The thirteen sub-criteria selected to be incorporated in the performance evaluation model were clustered into four main categories: Knowledge & Education (KE), Work Performance (WP), Leadership & Communication (LC), and Interpersonal Skills (IS) by the DMs. The performance measure details of the inputs are provided in Table 2. Professional Knowledge (PK) was grouped into Short Course (for one to three months), Diploma 1 (equal to or less than one year), and Diploma 2 (more than one year) (Ahmed et al., 2013; Albayrak & Erensal, 2004; Avazpour et al., 2013; Manoharan et al., 2011, 2012). Educational Level (EL) was classified into Secondary, Higher Secondary and College/University levels (Albayrak & Erensal, 2004; Avazpour et al., 2013). Quantity of Work (QnW) indicated the total number of units produced per month by the employee and is grouped into Low, Medium,
and High, respectively, corresponding to [0, 175], [176, 225], and greater than 225 (Ahmed et al., 2013; Albayrak & Erensal, 2004; Avazpour et al., 2013; Manoharan et al., 2011, 2012). On the other hand, Quality of Work (QlW), indicating the percentage of rework or repair needed for the produced units and their extents, are quantified as Good, Moderate, and Bad, respectively, corresponding to [0, 5%], [5%, 8%], and greater than 8% (Ahmed et al., 2013; Albayrak & Erensal, 2004; Avazpour et al., 2013; Manoharan et al., 2011, 2012). Time Management, Teamwork & Cooperation, Leadership, Communication Skills, Problem Solving Ability, Ethics & Integrity, Innovation & Planning, Knowledge & Education, Work Performance, Leadership & Communication, and Interpersonal Skills were defined as Bad (B), Moderate (M), and Good (G) states (Ahmed et al., 2013; Avazpour et al., 2013; Manoharan et al., 2012; Manoharan et al., 2011; Albayrak & Erensal, 2004). Similarly, Confidence and Flexibility & Versatility were also classified into Low (L), Medium (M), and High (H) states. (Ahmed et al., 2013; Albayrak & Erensal, 2004; Avazpour et al., 2013; Dursun & Karsak, 2010).
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Table 3. Details of performance measures of output or overall performance. Performance measure Output Overall Performance (OP)
Very low Low Medium High Very high 0 ≤ OP < 2 2 ≤ OP < 4 4 ≤ OP < 6 6 ≤ OP < 8 8 ≤ OP < 10
3.3. Identification of performance index The Overall Performance index was defined using five states, OPVH, OPH, OPM, OPL, and OPVL, which were related to Very High (VH), High (LH), Medium (M), Low (L), and Very Low (VL) performance (Table 3) (Ahmed et al., 2013; Dursun & Karsak, 2010; Manoharan et al., 2011, 2012). To motivate the employees to work in the best interests of the organisation, according to the HR policy of the company, each employee receives a certain amount of financial incentive (reward) at the end of the year based on his/her overall performance. An employee will receive 15, 9, 5, 0, or 0% of his/her basic salary as an incentive if his/her overall performance is Very High, High, Medium, Low, or Very Low, respectively. 3.4. Determination of employee performance under different criteria In order to validate the effectiveness of the proposed approach, 11 employees, called EM1, EM2, EM3,…, EM11, of the Lumber Assembly Section were selected randomly. Moreover, this approach can determine the performance of more than a thousand employees within a very short time. These employees were mid-level workers who worked in a manufacturing and production line environment and their usual working hours were 7 h/day or 35 h/week. The second author was responsible for data/information collection. The data for Professional Knowledge and Educational Level were collected from the human resources (HR) department. The total number of units produced by each of the employees for the previous six months was collected from the production department. The Quantity of Work (QnW) indicated the average number of units produced per month by the employee. Similarly, the total number of units needing rework or repair by each of the employees for the last six months were collected from the production department. The Quality of Work (QlW) indicated the average percentage of rework or repair needed by the employee. The total number of working days absent by each of the employees for the last six months were collected from the service department. Time
Table 5. Overall synthesised weighted scores for employee performance evaluation using FAHP. Criteria
Sub-criteria
KE
Local weights 0.285
WE
0.34
QlW QnW TM
0.334 0.369 0.297
0.114 0.125 0.101
LC
0.141
TC LD CS
0.418 0.212 0.37
0.059 0.030 0.052
0.234
PS EI CO FV IP
0.158 0.287 0.245 0.168 0.142
0.037 0.067 0.057 0.039 0.033
Criteria
IS
Sub-criteria PK EL
Local weights 0.645 0.355
Global weights 0.184 0.101
Management (TM) indicated the average number of working days absent by the employee. The data for rest of the criteria were determined based on a performance score (0–10) provided by the four DMs. The DMs considered different factors such as supervision of a new employee, working in different shifts, involvement in new designs or assignments, relation with co-workers, working on different floors, and reports from co-workers to provide their judgement. After determining the performance score of each DM, they were multiplied by the corresponding experts’ credibility. The weighted average performances of four DMs for eleven employees are presented in Table 4. Right now, the organisation is using the fuzzy analytic hierarchy process (FAHP) presented by Avazpour et al. (2013), Islam et al. (2013), and Manoharan et al. (2011) to determine the overall performance index. The modified Chang extent analysis method is used in this study to determine the importance weights of criteria and sub-criteria (Kabir & Sumi, 2014). The detailed methodology of the FAHP is not presented in this paper. For more detailed information about the FAHP method, interested readers are referred to Saaty (2000), Chang (1996), Tesfamariam and Sadiq (2006), and Kabir and Sumi (2014). The local and global weights of the criteria and sub-criteria are presented in Table 5. Based on the performance of employees presented in Table 5, the overall performance indices of the employees using FAHP are shown in Table 6.
Table 4. Performance of 11 employees. Criteria EL PK QnW QlW TM TC LD CS PS EI CO FV IP
EM1 CU Dip 1 182 7.85 6.5 5.04 6.5 8.4 5.15 7.85 6.72 5.65 7.56
EM2 SD SC 212 5.15 5.5 5.15 7.56 4.2 6.85 5.04 6.72 5.45 6.88
EM3 CU Dip 2 234 4.6 7.25 6.72 8.4 6.25 7.85 6.85 7.25 8.4 7.9
EM4 HS Dip 1 168 7.91 5.25 7.85 4.2 5.04 6.4 4.2 5.45 6.72 5.95
EM5 SD SC 172 7.55 4.25 4.35 6.72 5.45 4.2 5.15 5.04 3.85 5.35
Note: SD = Secondary; HS = Higher secondary; CU = College/University; SC = Short course; Dip 1 = Diploma 1; Dip 2 = Diploma 2.
EM6 SD SC 197 5.75 5.5 6.5 8.4 8.25 7.56 6.72 8.4 6.65 7.75
EM7 CU Dip 2 243 5.65 6.5 6.35 7.85 6.45 8.4 7.35 8.25 6.15 6.5
EM8 HS Dip 1 156 7.35 7.5 7.35 8.25 5.88 5.45 5.45 5.15 8.25 8.4
EM9 CU Dip 2 208 7.12 8.5 5.88 5.15 5.65 6.72 5.88 8.25 6.65 6.55
EM10 HS SC 171 8.35 4.5 3.85 3.85 3.4 3.65 5.65 4.5 3.65 3.4
EM11 CU Dip 1 179 6.65 5.75 4.35 5.88 5.45 5.65 5.04 6.5 5.15 5.88
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Table 6. Performance evaluation of employees using FAHP. FAHP Overall performance Rank Incentive amount (%)
EM1 6.72 H 4 9
EM2 5.36 M 9 5
EM3 8.09 VH 1 15
EM4 5.60 M 8 5
EM5 4.42 M 11 5
EM6 5.84 M 7 5
EM7 7.77 H 2 9
EM8 6.18 H 6 9
EM9 7.30 H 3 9
EM10 4.64 M 10 5
EM11 6.31 H 5 9
Table 7. Correlation coefficient between the criteria. PK EL QlW QnW TM TC LD CS PS EI CO FV IP
PK 1.00
EL 0.59 1.00
QlW 0.86 0.41 1.00
QnW 0.69 0.31 0.56 1.00
TM 0.02 0.80 0.11 0.73 1.00
TC 0.68 0.43 0.26 0.29 0.46 1.00
LD 0.78 0.10 0.00 0.26 0.28 0.45 1.00
CS 0.38 0.68 0.39 0.42 0.59 0.32 0.61 1.00
PS 0.80 0.49 0.35 0.31 0.36 0.12 0.77 0.39 1.00
EI 0.47 0.65 0.55 0.20 0.84 0.01 0.39 0.14 0.28 1.00
CO 0.66 0.63 0.41 0.21 0.49 0.35 0.49 0.29 0.44 0.54 1.00
FV 0.23 0.75 0.84 0.34 0.08 0.29 0.50 0.45 0.56 0.19 0.43 1.00
IP 0.65 0.94 0.23 0.49 0.59 0.77 0.36 0.48 0.46 0.40 0.49 0.02 1.00
3.5. Correlation analysis
3.6. Bayesian belief network model development
To determine the interrelationships between the criteria, a correlation analysis was performed. The correlation coefficients were determined to find out the dependencies between the criteria. Pearson’s correlation coefficient indicates a linear relationship between two variables and assumes that these variables are normally distributed (Pearson, 1896) whereas Spearman’s and Kendall’s rank correlation coefficient is widely used to measure the nonlinear relationship between the variables (Kendall & Gibbons, 1990; Spearman, 1904). Kendall’s correlation coefficient (τ) is preferable to Spearman’s coefficient in the case of a small data-set with multiple scores having the same rank or a large number of tied ranks (Kendall & Gibbons, 1990). Moreover, according to Nelsen (2001), Kendall’s correlation coefficient provides better estimation of the population correlation compared to Spearman’s coefficient. Therefore, Kendall’s rank correlation coefficients were used to determine the interrelationships between the criteria in this study. Let (m1, n1), (m2, n2), …, (mx, nx) be a set of observations of the joint random variables or criteria M and N, respectively, such that all the values of (mi) and (ni) are unique. Any pair of observations (mi, ni) and (mj, nj) are said to be discordant, if mi > mj and ni mj and ni > nj or if both mi
