Factors Influencing Employees’ Perception of Human Resource Practice: A Fuzzy Interpretive Structural Modeling Approach

Abstract

In past two decades, researchers have identified many factors, which influence employee’s perception of human resource (HR) practices. How employees perceive HR practice is a strong determinant of both employee’s and organizational outcome. However, how these factors are structured or their relative importance is not so well understood. Without this vital input, it is difficult to deploy scare resource to impact organizational outcome. This research uses fuzzy interpretive structural modeling (Fuzzy ISM) technique to fill this gap. The result will help deploy resources for changing the perception of vital HR practices so as to enhance organizational performance. Demographic dissimilarity of employee with coworker and manager, and quality of manager’s communication were found as the most relevant drivers of employee’s perception of HR practice. The factors having highest driving power are the one which needs to be addressed by Line and HR managers.

Keywords

Perception of HR practice fuzzy interpretive structural modeling hierarchical relationship qualitative methods HRM MICMAC

Introduction

Last few decades, research in the area of strategic human resource management (SHRM) proved the benefits of human resource (HR) practices and established that the practices which improve employee ability, motivation, and opportunity to grow have a positive impact on employee and organizational outcome (e.g., Chuang & Liao, 2010; Gong, Law, Chang, & Xin, 2009; Huselid, 1995; Wright & McMahan, 1992). The performance impact of HR practice is achieved when these practices direct employee behavior (e.g., Jiang, Lepak, Hu, & Baer, 2012). Properly configured HR strategy significantly contributes to organizational performance (Chang & Huang, 2005). The meta-analysis of 92 studies of practice to performance model revealed that unit increase of high-performance work system (HPWS) was related to 20 percent deviation in firms’ performance (Combs, Liu, Hall, & Ketchen, 2006), reinforcing importance of sound HR practices.

Several studies have shown that it is the employee’s perception of HR practice, rather than the actual implemented practices, which impacts behavior and outcome (e.g., Den Hartog, Boon, Verburg, & Croon, 2013; Kehoe & Wright, 2013; Liao, Toya, Lepak, & Hong, 2009), thus, making employee’s perception and understanding a critical factor in practice-to-performance model. Still most of the previous human resource management (HRM) research ignored how employees actually experience the HR practice. Its only recently researchers started paying attention to HR practices from employee’s perspective (Bowen & Ostroff, 2004; Nishi & Wright, 2008; Paauwe, Wright, & Guest, 2013). Study of Nishi and Wright (2008) showed that employees may not perceive HR practices as reported by supervisors. A wide variation in perception may also exist among member of the same group, who all are subjected to the same HR practices. The variation of almost 83 percent was reported in study of Liao et al. (2009). So, even if organization meticulously define HR policies in alignment with organization strategy, and then those policies and practices are perfectly implemented by line manager, still there is huge chance that these practices are not able to drive employees to expected attitude and behavior. Thus, it is important to understand “What factors influence employee’s perception of HR practice and how those factors are inter-related?”

This study uses fuzzy interpretive structural modeling (Fuzzy ISM), which is a well-defined and established method, to organize and structure the interrelationship of factors related to the given problem domain. First, the factors, which influence employee’s perceptions, are identified from the literature survey. Then using an expert’s opinion, the factors were validated in the context of current study. Thereafter, structural model involving those factors is created using ISM. At the end, the structural model is validated. The article ends with future direction for further research.

Fuzzy ISM is used extensively by scholars in various areas of study; some representative ones are: risk and impact in supply chain (Chaudhuri, Srivastava, Srivastava, & Parveen, 2016), responsible for consumption and production (Wang, Ma, Kuo-Jui Wu, Chiu, & Nathaphan, 2018), knowledge management (Anantatmula & Kanungo, 2006), and cold chain (Joshi, Banwet, & Shankar, 2009). Most applications of Fuzzy ISM are in the area of logistics, supply chain, and manufacturing. There is a dearth of study wherein Fuzzy ISM-based approach is used in the area of HRM. This article addresses this research gap by applying Fuzzy ISM approach in the area of HRM specifically by structuring factors which influence employees’ perception of HR practice into hierarchical relationship. The incremental contribution is first, to understand how Fuzzy ISM can be applied in behavioral science and HRM studies to get better insight and secondly to identify critical influencer of employees’ perception.

Literature Review

The research in the field of SHRM focused primarily on two aspects, first, on employee’s perspective to understand the influence of HR practice on employee’s outcome and second on understanding the factors which results in variability in employee’s perception of HR practice in different contextual settings (Jiang, Hu, Liu, & Lepak, 2017).

Perception of HR practice found to effect adaptivity, task performance, organizational commitment, and firm’s performance (Bodla & Ningyu, 2017; Chang, 2005; Chang, Nguyen, Kuo-Tai, Kuo, & Lee, 2016; Wright, Gardner, Moynihan, & Allen, 2005). The organizations use HR practices as communication mechanism to signal what attitude and behaviors are expected and rewarded. Such signal must be unambiguous and consistent to help employees to have a share common understanding. Strength of HR system explains why variability exists in the perception of HR practices among different employees in the same unit (Bowen & Ostroff, 2004). Strong HR system creates a shared perception of what behavior and attitude is expected and rewarded, and this shared perception helps organizations to achieve goals through accumulated employees’ attributes. Organizations with weak HR system let its employees to construct own individual understanding of HR practices, which may or may not be consistent with organizational intentions; thus, organizations are not able to leverage on employee’s collective efforts and behavior. HR system with high distinctiveness, consistency and consensus creates a strong situation to promote shared meaning of situation among employees. This suggests that distinctiveness, consistency, and consensus are strong determinant of employees’ perception of HR practice.

Employees understand and evaluate the reasoning of why an organization adopt a certain kind of HR practice, and this attribution does have impact on their attitude and behavior (Nishi, Lepak, & Schneider, 2008). The idea of attribution gets further support from causal chain model of Nishi and Wright (2008), which suggests that the employees’ perception of HR practices is likely to precede the employee attitude and behavior. Thus, attribution (organization’s motive of HR practice) is one such critical factor which impacts employee’s perception of HR practice.

Multilevel analysis of SHRM suggests HR practices exist at multiple level (Nishi & Wright, 2008). At design level, it is defined by senior management to align with organization’s goal and strategy and is referred as “intended HR practice.” These intended practices are understood and implemented by line manager referred to as “actual HR practice,” which may not be exactly what described in “intended HR practice.” Further, each individual employee perceives the implemented HR practice based on their understanding, contextual inputs, and past experiences. Since “actual HR practices” are employees’ day-to-day experience, so it is one of the potential factors which may impact employees’ perception of HR practices.

Social information processing theory (Salancik & Pfeffer, 1978) suggests that individuals (employees) rely on information gathered from others in their workplace to form their perceptions of the organization’s practices. Both line manager and coworker are such information providers in workplace context, as employee frequently interact with them. Line manager and coworker frequently discuss about certain HR practices and thus influence their perception by making practices more salient and shaping their attentional process (Salancik & Pfeffer, 1978). Thus, how line manager and coworker perceive HR practice has direct bearing on how employees perceive HR practice. On similar lines, Jiang et al. (2017) identified that demographic dissimilarity of employee with manager and coworker also impacts the perception of HR practice.

Table 1 presents identified factors which influence employees’ perception of HR practice.

Table 1.

Factors influencing perception of HR practice

Factor Code	Factor	Reference
F1	HR system strength	Bowen and Ostroff (2004)
F2	Actual HR practice implemented by line manager	Nishi & Wright (2008)
F3	Quality of manager’s communication	Den Hartog et. al. (2013)
F4	Line manager’s perception of HR Practice	Jiang et. al. (2017)
F5	Co-worker’s perception of HR Practice	Jiang et. al. (2017)
F6	Demographic dissimilarity with line manager	Jiang et. al. (2017)
F7	Demographic dissimilarity with co-worker	Jiang et. al. (2017)
F8	Employee attribution	Nishi, Lepak, & Schneider (2008)

Source: The authors.

Fuzzy ISM is an extension of ISM introduced by Warfield (1974). ISM is a well-defined iterative technique for structuring the elements involved in system, by application of graph theory. Work of Harary, Norman, and Cartwright (1965) highlights its mathematical foundation, but the conceptual and analytical details are provided by Warfield (1976, 1999). ISM aimed at supporting human to understand what they believe and recognize what they do not know, and thus transform the poorly articulated mental models which are mostly unclear into a system of visible models. The factors of the system when taken together describe situation more holistically and accurately than when they are studied in isolation. Thus, ISM helps in understanding of these relationships. Many scholars discussed the application of ISM in different contexts like higher education planning (Hawthrone & Sage, 1975), energy conservation policy (Saxena, Sushil, & Vrat, 2006), cross-cultural studies (Jedlicka & Meyer, 1980), and manufacturing practice (Haleem, Sushil, Quadri, & Kumar, 2012).

Fuzzy ISM captures the uncertainties of real world by applying fuzzy theory of Zadeh (1965) in ISM methodology. Fuzzy theory is significant in dealing vagueness and uncertainty in human language and thought. The uncertainty in decision-making or judgment is due to vagueness and impreciseness of information. The ability to capture the opinion or judgment in terms of linguistic terms allows experts to present their view on real minimum value or maximum value rather than forcing them to select the extreme minimum and maximum value. For example, in ISM, the relation between two factors is captured as either exist or does not exist, but it fails to give option to an expert to specify that relation weakly, moderately, or strongly exists. Thus, Fuzzy ISM helps in capturing the real response of experts to certain extent. The fuzziness in fuzzy set theory is defined by membership function which helps in determining the degree of truth about membership of an element. In the current study, triangular membership function is used which is shown in Figure 1, and can be represented as a triplet (l, m, u) where l ≤ m ≤ u.

Figure 1.

Source: The authors.

Methodology

In this study, Fuzzy ISM is used for identifying the interrelation among the seven factors which influence perception of HR practice. This methodology helps in delineating the hierarchical relationship among the factors. MICMAC analysis is also used for classification of factor into independent, dependent, and linkage variables.

Fuzzy Interpretive Structural Modeling

Fuzzy ISM begins with identification of variables or factors which are relevant for the study. Then an expert’s opinion is sought for pair-wise comparison of factors and thus creating contextual relationship. The contextual relationship is captured in structural self-interaction matrix (SSIM). Contextual relationship between any two factors (i and j) results in two independent directional relationship, that is, i influence j and j influence i. ISM uses following four symbols to denote direction of relationship between two factors:

V representing relation from factor i to factor j and nonexistence of relation in reverse direction.

A representing relation from factor j to factor i and nonexistence of relation in reverse direction.

X representing relation in both directions.

O representing nonexistence of relation in any direction.

Along with the contextual relation, the information about perceived strength of the relation is also solicited from an expert using linguistic terms as shown in Table 2.

Table 2.

Linguistic scale of influence

Linguistic Terms	Triangular Fuzzy Value	Notation
Strongly influence	(0.66, 1.00, 1.00)	S
Moderately influence	(0.33, 0.66, 1.00)	M
Weakly influence	(0, 0.33, 0.66)	W
No influence	(0, 0, 0.33)	N

Source: The authors.

So, V will be represented by either V(S), V(M), V(W) depending on strength of influence of factor i on factor j. Similarly, A will be represented by A(S), A(M), A(W). In case of X, where bi-directional relation exists, the situation is represented by strength of relation in each direction. So, the following nine combinations are possible X(S,S), X(S,M), X(S,W), X(M,S), X(M,M), X(M,W), X(W,S), X(W,M), and S(W,W). When no relation exists as in case of O, only representation is O(N).

From SSIM, fuzzy initial reachability matrix is developed. For further steps, the reachability matrix is first de-fuzzified to crisp value. Transitivity check is performed on de-fuzzified matrix to find out hidden indirect relationships. After transitivity check, level partitioning is performed to form the structural model.

MICMAC Analysis

MICMAC is the abbreviated form of Matrices d’Impacts croises-multiplication applique an classment (cross-impact matrix multiplication applied to classification). The purpose of this analysis is to classify the variables/factors into one of the four different categories based on the driving and dependence power of the factor. The four different categories are as follows:

Autonomous factors: The factors having weak driving as well as dependence power are relatively isolated from other factors of the system. Such factors can be discarded in the study. In current study, there are no autonomous factors.

Dependent factors: These factors have weak driving power but considerable dependent power. In the current study, coworker’s perception of HR practice and employee’s attribution are identified as dependent factors.

Linkage factors: These factors have strong driving as well as dependence power, thus any cation on these factors will have an effect on other factors. HR system strength, HR practice implemented by line manager, and line manager’s perception of HR practice are the linkage factors.

Independent factors: These factors have high driving power but low dependence power. Demographic dissimilarity with coworker and manager, and quality of manager’s communication are found as strongest driving factors which influence employee’s perception of HR practice.

Research Design

The complete study was conducted in the following three distinct phases:

factor identification and verification

structural modeling using Fuzzy ISM

assessment of the structural model

Factor Identification and Verification

The factors have been identified theoretically from literature. The experts panel consisted two senior development managers and a senior HR manager, all having more than 15 years of experience in Indian information technology (IT) industry. For factor validation, very simple questionnaire is used in format “<Factor> influences employees’ perception of HR practice,” for example, “Quality of manager’s communication influence employees’ perception of HR practice” on a 5-point Likert scale ranging from strongly agree to strongly disagree.

Structural Modeling Using Fuzzy Interpretive Structural Modeling

The verified factors are further used for Fuzzy ISM. Instead of taking input in SSIM format, the input is sought in form of 8 × 8 matrix, wherein each cell represents influence of factor in row on the factor in column. For example, cell (3,5) represents influence of factor 3 (i.e., Quality of manager’s communication) on factor 5 (i.e., Coworker’s perception of HR practice). This is done to ease respondents from learning how to use SSIM notation (V, A, X, O). The input was requested in the form of linguistic terms (Strongly influence, Moderately influence, Weakly influence, and No influence). Thus, each expert provided fuzzy reachability matrix. To construct aggregated fuzzy reachability matrix, mode of each cell from individual fuzzy reachability matrix is considered. Table 3 presents the aggregated fuzzy reachability matrix.

The final fuzzy reachability matrix with triangular fuzzy numbers of aggregated fuzzy reachability matrix is presented in Table 4. For calculating the crisp value, centroid method is used.

Table 3.

Aggregated fuzzy reachability matrix

Factors	F1	F2	F3	F4	F5	F6	F7	F8
F1		W	N	M	M	N	N	M
F2	M		N	N	S	N	N	W
F3	M	N		N	S	N	N	M
F4	N	S	W		W	N	N	W
F5	N	N	N	N		N	N	W
F6	N	M	M	N	N		N	0
F7	N	M	N	W	N	N		0
F8	N	N	N	N	N	N	N

Source: The authors.

Table 4.

Final fuzzy reachability matrix with driving and dependence power

Factors	F1	F2	F3	F4	F5	F6	F7	F8	*	#
F1	(1,1,1)	(0, 0.33, 0.66)	(0,0,0.33)	(0.33,0.66,1)	(0.33,0.66,1)	(0,0,0.33)	(0,0,0.33)	(0.33,0.66,1)	(1.99, 3.31, 5.32)	3.54
F2	(0.33,0.66,1)	(1,1,1)	(0,0,0.33)	(0,0,0.33)	(0.66, 1, 1)	(0,0,0.33)	(0,0,0.33)	(0, 0.33,0.66)	(1.99, 2.99, 4.65)	3.21
F3	(0.33,0.66,1)	(0,0,0.33)	(1,1,1)	(0,0,0.33)	(0.66, 1, 1)	(0,0,0.33)	(0,0,0.33)	(0.33,0.66,1)	(1.32, 2.32, 4.32)	2.65
F4	(0,0,0.33)	(0.66, 1, 1)	(0, 0.33, 0.66)	(1,1,1)	(0, 0.33, 0.66)	(0,0,0.33)	(0,0,0.33)	(0, 0.33,0.66)	(1.66, 2.66, 4.31)	2.88
F5	(0,0,0.33)	(0,0,0.33)	(0,0,0.33)	(0,0,0.33)	(1,1,1)	(0,0,0.33)	(0,0,0.33)	(0, 0.33,0.66)	(1, 1.33, 3.31)	1.88
F6	(0,0,0.33)	(0.33,0.66,1)	(0.33,0.66,1)	(0,0,0.33)	(0,0,0.33)	(1,1,1)	(0,0,0.33)	(0,0,0.33)	(1.33, 1.66, 3.65)	2.21
F7	(0,0,0.33)	(0.33,0.66,1)	(0,0,0.33)	(0, 0.33, 0.66)	(0,0,0.33)	(0,0,0.33)	(1,1,1)	(0,0,0.33)	(1.33, 1.99, 3.98)	2.43
F8	(0,0,0.33)	(0,0,0.33)	(0,0,0.33)	(0,0,0.33)	(0,0,0.33)	(0,0,0.33)	(0,0,0.33)	(1,1,1)	(1, 1, 2.98)	1.66
**	(1.66,2.33,4.65)	(2.32,3.65,5.65)	(1.33,1.99,4.31)	(1.33,1.99,4.31)	(2.65, 3.99, 5.65)	(1,1,3.31)	(1,1,3.31)	(1.66,3.31, 5.64)
#	2.88	3.87	2.54	2.54	4.10	1.77	1.77	3.40

Source: The authors.

Note: * Driving Power ** Dependence Power # Crisp Value.

Based on the crisp value of driving and dependence power of each factor, factor classification is performed to identify if there are any autonomous factors. If such factor exists, then those factors are dropped. There is no autonomous factor as shown in MICMAC chart prepared using driving and dependency power of final fuzzy reachability matrix (see Figure 2).

Figure 2.

Source: The authors.

Before further steps of Fuzzy ISM are applied, the aggregated fuzzy reachability matrix needs to be de-fuzzified to crisp values. For de-fuzzification, very simple approach is adopted, only strongly and moderately influenced relations were set as 1 and the rest of the cells were all set as 0. Diagonal cells are always marked as 1. The similar approach is used in other researchers (Mohanty & Shankar, 2017). The de-fuzzified matrix is presented in Table 5.

Table 5.

De-fuzzified initial reachability matrix

Factors	F1	F2	F3	F4	F5	F6	F7	F8
F1	1	0	0	1	1	0	0	1
F2	1	1	0	0	1	0	0	0
F3	1	0	1	0	1	0	0	1
F4	0	1	0	1	0	0	0	0
F5	0	0	0	0	1	0	0	0
F6	0	1	1	0	0	1	0	0
F7	0	1	0	0	0	0	1	0
F8	0	0	0	0	0	0	0	1

Source: The authors.

The de-fuzzified matrix is run through transitivity check. Transitivity identifies the hidden indirect relationships. Two factors are said to have transitive relation if they have indirect relationship through a third factor. For example, factor F1 influences factor F2, and factor F2 influences factor F3, then a transitive relationship exists between factor F1 and factor F3.

Table 6 presents the final reachability matrix, after full transitivity check, which will be used for level partitioning. The transitive links are revalidated with an expert to see whether such relations are significant or not. In the current study, none of the transitive relations were marked as significant by any of the expert.

Table 6.

Final reachability matrix after transitivity checks

Factors	F1	F2	F3	F4	F5	F6	F7	F8
F1	1	1*	0	1	1	0	0	1
F2	1	1	0	1*	1	0	0	1*
F3	1	1*	1	1*	1	0	0	1
F4	1*	1	0	1	1*	0	0	1*
F5	0	0	0	0	1	0	0	0
F6	1*	1	1	1*	1*	1	0	1*
F7	1*	1	0	1*	1*	0	1	1*
F8	0	0	0	0	0	0	0	1

Source: The authors.

Note: * represent transitive entries.

MICMAC analysis is again performed, using driving and dependence power of factors from the final reachability matrix, so to classify the factors into four categories. The factors having strong driving power like independent factors and linkage factors are the “key factors.” Figure 3 presents the factor classification based on the final reachability matrix. Factors F3, F6, and F7 were classified as independent factors, whereas factors F5 and F8 were classified as dependent factors. Rest of the factors are classified as linkage factors. The similar approach was also taken earlier by Jain, Sharma, and Ilavarasan (2018).

Figure 3.

Source: The authors.

For each factor from the final reachability matrix, reachability and antecedent sets are derived. The reachability set consists of the factor itself, and all other factors it influences. Whereas, the antecedent set consists of the factor itself, and all other factors which influence this factor. Then intersection of reachability and antecedent sets is derived. The factor where reachability set and intersection set is same capturing the highest level in structure model. The top-level factors are those which are influenced by most of the other factors but does not influence any other factor. Once a level is frozen, the subsequent iteration is performed to identify the subsequence levels. In any iteration, factors frozen in the previous iterations are ignored. The steps of level partitioning are presented in Table 7.

Table 7.

Level partitioning of final reachability matrix

Factors	Reachability	Antecedent	Intersection	Level
F1	1,2,4,5,8	1,2,3,4,6,7	1,2,4
F2	1,2,4,5,8	1,2,3,4,6,7	1,2,4
F3	1,2,3,4,5,8	3,6	3
F4	1,2,4,5,8	1,2,3,4,6,7	1,2,4
F5	5	1,2,3,4,5,6,7	5	I
F6	1,2,3,4,5,6,8	6	6
F7	1,2,4,5,7,8	7	7
F8	8	1,2,3,4,6,7,8	8	I
Second Iteration
F1	1,2,4,	1,2,3,4,6,7	1,2,4	II
F2	1,2,4,	1,2,3,4,6,7	1,2,4	II
F3	1,2,3,4,	3,6	3
F4	1,2,4,	1,2,3,4,6,7	1,2,4	II
F6	1,2,3,4,6	6	6
F7	1,2,4,7	7	7
Third Iteration
F3	3	3,6	3	III
F6	3,6	6	6
F7	7	7	7	III
Fourth Iteration
F6	6	6	6	IV

Source: The authors.

Once level partitioning is completed, the factors are pictorially presented as per their level, starting with highest level at the top. This pictorial representation is call interpretative structural mode. Figure 4 shows the interpretive structural model of the current study.

Figure 4.

Source: The authors.

Assessment of Structural Model

With eight factors, 56 total relations are possible. After applying Fuzzy ISM in the final reachability matrix, only 12 relations were found significant, as all the transitive relations were assessed as not significant by experts. Once the model is built, it was assessed by second panel of five senior development managers. The acceptability of model was established using a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree) to assess the degree of agreement for each relation. The model is said to be accepted if the average score of relation exceed the mean value of 3. The expert’s feedback of model assessment is present in Table 8; the model average score is 3.6 which shows that the model is accepted by second panel of experts. The average score of relations range from 3.2 to 4.0. This method of validation was earlier performed by Betaraya, Saboohi, and Mukhopadhyay (2018).

Table 8.

Model Assessment

S. No	Relation	E1	E2	E3	E4	E5	Average Score Relation	Average Score Model
1	HR system strength influence line manager’s perception of HR practice	4	4	3	4	4	3.8	3.6
2	HR system strength influence co-worker’s perception of HR practice	4	3	4	3	4	3.6
3	HR system strength influence employee’s attribution	4	4	4	4	4	4.0
4	HR practice as implemented by line manager influence HR system strength	3	4	3	4	4	3.6
5	HR practice as implemented by line manager influence co-worker’s perception of HR practice	4	4	4	3	4	3.8
6	Quality of manager’s communication influence HR system strength	4	4	3	3	3	3.4
7	Quality of manager’s communication influence co-worker’s perception of HR practice	3	4	4	4	3	3.6
8	Quality of manager’s communication influence employee’s attribution	3	3	3	4	4	3.4
9	Line manager’s perception of HR practice influence the actual HR practice implemented by line managers	3	4	4	4	4	3.8
10	Demographic dissimilarity of employee with line managers influence actual HR practice implemented by line manager	3	4	3	4	4	3.6
11	Demographic dissimilarity of employee with line manager influence quality of line manager’s communication	3	3	4	3	4	3.4
12	Demographic dissimilarity of employee and co-worker influence actual HR practice implemented by line manager	3	3	3	3	4	3.2

Source: The authors.

Conclusion

Understanding the relationship among the factors that influence employee’s perception of HR practice is critical for line manager and HR to derive expected behaviors from employee. Since demographic dissimilarity of employee with line manager and coworkers was identified as critical factor, manager’s communication quality is also identified as one of the important driving factors. Thus, demographic dissimilarity and communication determine coworker’s perception of HR practice and employee’s attribution. This effect is mediated through overall strength of HR system, and how line manager perceives and then implements the HR practices. The most proximal factor of employee’s perception is coworker’s perception and employee’s own attribution.

Limitations and Future Research

There are numerous limitations of the current study, which researcher was not able to address due to time and resource constraints. The first panel of experts only consisted professionals from industry. Academician and researcher from HRM areas were not present in the panel. They with their vast knowledge base could have provided better understanding and derivation of model. The model assessment is also done by senior managers of IT industry. Cross-sector validation by including respondents from IT service and other services industry like finance were not involved in the study. The representation from manufacturing sector is also missing. Although it was a qualitative study, still sample size of five is too small sample for model assessment.

Future researchers can work on alleviating the limitations of the current study. The field validation of the model is the next logical step. Since demographic dissimilarity is found to be one such critical driver, future researcher could investigate how and why dissimilarity influence perception of HR practice.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

References

Anantatmula

, & Kanungo

(2006). Structuring the underlying relations among the knowledge management outcomes. Journal of Knowledge Management, 10(4), 25–42.

Betaraya

D. M.

, Saboohi

, & Mukhopadhyay

(2018). Modelling subsidiary innovation factors for semiconductor design industry in India. In: Connell

, Agarwal

& Dhir

(Eds.), Global value chain, flexibility and sustainability. Flexible systems management (pp. 89–115). Singapore: Springer.

Bodla

A. A.

, & Ningyu

(2017). Transformative HR practice and employee task performance in high-tech firms: The role of employee adaptivity. Journal of Organizational Change Management, 30(5), 710–724.

Bowen

D. E.

, & Ostroff

(2004). Understanding HRM-Firm performance linkages: The role of the “Strength” of the HRM system. The Academy of Management Review, 29(2), 203–221.

Chang

(2005). Employees’ overall perception of HRM effectiveness. Human Relations, 58(4), 523–544.

Chang

, Nguyen

, Cheng

K.-T.

, Kuo

C.-C.

, & Lee

(2016). HR practice, organisational commitment & citizenship behavior. Employee Relations, 38(6), 907–926.

Chang

W.-J. A.

, & Huang

T. C.

(2005). Relationship between strategic human resource management and firm performance: A contingency perspective. International Journal of Manpower, 26(5), 434–449.

Chaudhuri

, Srivastava

S. K.

, Srivastava

R. K.

, & Parveen

(2016). Risk propagation and its impact on performance in food processing supply chain: A fuzzy interpretive structural modeling based approach. Journal of Modelling in Management, 11(2), 660–693.

Chuang

C.-H.

, & Liao

(2010). Strategic human resource management in service context: Taking care of business by talking care of employees and customers. Personnel Psychology, 63, 153–196.

10.

Combs

, Liu

, Hall

, & Ketchen

(2006). How much do high-performance work practice matter? A meta analysis of their effect on organizational perforrmance. Personnel Psychology, 63, 501–528.

11.

Den Hartog

D. N.

, Boon

, Verburg

R. M.

, & Croon

M. A.

(2013). HRM, communication, satisfaction, and perceived performance. Journal of Management, 39, 1637–1665.

12.

Gong

, Law

, Chang

, & Xin

(2009). Human resources management and firms performance: The differential role of managerial affective and continuance commitment. Journal of Applied Psychology, 94(1), 263–275.

13.

Haleem

, Sushil

M. A. Q.

, & Kumar

(2012). Analysis of critical successfactor of worldclass manufacturing practices: An application of interpretive structrual modelling and interpretive ranking process. Production, Planning and Control, 23(10–11), 722–734.

14.

Harary

, Norman

R. Z.

, & Cartwright

(1965). Structured models—An introduction to the theory of directed graph. New York: Wiley.

15.

Hawthrone

R. W.

, & Sage

A. P.

(1975). On application of interpretive structural modeling to higher education program, planning. Socio-Economic Planning, 9(1), 31–43.

16.

Huselid

M. A.

(1995). The impact of human resource management practice on turnover, productivity and corporate financial performance. Academy of Management Journal, 38(3), 635–672.

17.

Jain

, Sharma

, & Ilavarasan

(2018). Total interpretive structural modeling of innovation measurement for Indian Universities and higher academic technical institutions. In: Singh

S. T.

& Kulkarni

(Eds.), Flexibility in resource management (pp. 29–53). Singapore: Springer.

18.

Jedlicka

, & Meyer

(1980). Interpretive structural modeling: Cross cultural uses. IEEE TransactionsL Systems, Man and Cybernetics, SMC-10(1), 49–51.

19.

Jiang

, Hu

, Liu

, & Lepak

D. P.

(2017). Understanding employees’ perception of human resource practice: Effects of demographic dissimilarity to manager and coworker. Human Resource Management, 56(1), 69–91.

20.

Jiang

, Lepak

D. P.

, Hu

, & Baer

J. C.

(2012). How does human resource management influence organizational outcomes? A meta-analytics investigation of mediating mechanism. Academy of Management Journal, 55(6), 1264–1294.

21.

Joshi

, Banwet

D. K.

, & Shankar

(2009). Indian cold chain: Modeling the inhibitors. British Food Journal, 111(11), 1260–1283.

22.

Kehoe

R. R.

, & Wright

P. M.

(2013). The impact of high performance human resource practice on employees’ attitude and behavior. Journal of Management, 39, 366–391.

23.

Liao

, Toya

, Lepak

D. P.

, & Hong

(2009). Do they see eye to eye? Management and employee perspectives of high-performance work system and influence processes on service quality. Journal of Applied Psychology, 94, 371–391.

24.

Mohanty

, & Shankar

(2017). Modelling uncertainty in sustainable integrated logistics using Fuzzy-TISM. Transportation Research Part D: Transport and Environment, 53, 471–491.

25.

Nishi

L. H.

, Lepak

D. P.

, & Schneider

(2008). Employee attribution of the “WHY” of HR practice: Their effect on employee attitude and behaviors, and customer satisfactions. Personnel Psychology, 61(1), 503–545.

26.

Nishi

, & Wright

(2008). Variability within organization: Implication of strategic human resource management. In: Smith

D. B.

(Ed.), The people make the place: Dynamic linkages between individuals and organizations (pp. 225–248). New York: Lawrence Erlbaum.

27.

Paauwe

, Wright

, & Guest

(2013). HRM and performance: What do we know and where we should go?. In: Paauwe

, Guest

, & Wright

(Eds), HRM and performance: Achievements and challenges (pp. 1–13). Chichester: Wiley.

28.

Salancik

G. R.

, & Pfeffer

(1978). A social information processing approach to job attitudes and task design. Administrative Science Quarterly, 23(2), 224–253.

29.

Saxena

J. P.

, Sushil

& Vrat

(2006). Policy and strategy formulation: An application of flexible systems methodology. New Delhi: GIFT Publishing.

30.

Wang

, Ma

, Wu

K.-J.

, & Chiu

A. S. F.

(2018). Applying fuzzy interpretive structural modeling to evaluate responsible consumption and production under uncertainity. Industrial Management & Data Systems, 118(2), 432–462.

31.

Warfield

J. N.

(1974). Toward interpretation of complex structrual model. IEEE Transactions: System, Man and Cybernatics, 5, 405–417.

32.

Warfield

J. N.

(1976). Societal systems: Planning, policy and complexity. New York: Wiley.

33.

Warfield

J. N.

(1999). Twenty laws of complexity: Science applicable in organization. Systems Research and Behavioral Science, 16(1), 3–40.

34.

Wright

P. M.

, Gardner

T. M.

, Moynihan

L. M.

, & Allen

M. R.

(2005). The relationship between HR practice and firms performance: Examining causal order. Personnel Psychology, 58, 409–446.

35.

Wright

P. M.

, & McMahan

G. C.

(1992). Theoritical perspective of strategic human resource management. Journal of Management, 18(2), 295.

36.

Zadeh

L. A.

(1965). Fuzzy set. Information and Control, 8(3), 338–353.