The Impact of Employees’ Gender and Age on Organizational Citizenship Behavior Using a Fuzzy Approach

Abstract

Organizational citizenship behavior (OCB) is vital for continuous organizational growth and development. OCB also plays an evident role in advancing the quality of health care services and practicing OCB in hospitals can be useful. Although OCBs’ contributing factors have been studied in Jordan in earlier researches, the utilization of OCB in health sector is still a new topic. Therefore, this article aimed to investigate the impact of employees’ gender and age on OCB at Jordanian governmental hospitals using a fuzzy approach. A convenient sample of 126 employees working in three main governmental hospitals in the north of Jordan has been taken. The collected data include linguistic terms that suffer from uncertainty which, in turn, cannot be dealt with traditional numerical values. This study indicates that gender impact on OCB has statistically significant differences at α = .05 in favor of males with the total score of 0.011% as far as altruism, courtesy, and civic virtue are concerned. Similarly, the effect of age factor on OCB is statistically significant differences at α = .05 in relation to courtesy, sportsmanship, and civic virtue with the total score of 0.27%. Finally, the results provide baseline data for further studies that may pay more attention toward employees’ demographics in OCB. This study can support mangers and leaders holding dominant positions in public sector health care organizations.

Keywords

gender age organizational citizenship behavior fuzzy approach employees

Recently, behavioral science has emerged as a scientific study, and researchers have started giving more attention to organizational citizenship behavior (OCB) due to its working performance effects (N. P. Podsakoff et al., 2009). At the beginning of the past decade, researchers simply described OCB as “willingness to cooperate” (Organ et al., 2006). OCB refers to discretionary employee behavior and contributes toward making organizations’ functioning more effective (Klotz et al., 2018). The discretionary behaviors are not a formal requirement of employees’ role or job description. This behavior is rather a matter of personal choice and its oversight is not generally considered punishable (Organ et al., 2006). OCB was initially presented by Organ (1997) with its five dimensions as follows: Altruism is a helping behavior, courtesy is a communicating and commitment behavior, sportsmanship is forgiveness behavior, civic virtue is a shared governance behavior, and finally, conscientiousness is an attendance and compliance behavior (O’Grady, 2018).

OCB is deemed as a notion produced by the modern administrative ideas and its positive effects are believed by many researchers. Many government and private employees adopt this approach to gain better competence and efficiency (Kamel et al., 2015). However, an organization must play a vital role in satisfying and encouraging its employees because, in return, employees have an important role in advancing their countries’ economics (Uzonwanne, 2014).

OCB has an eminent role in advancing the quality of health care facilities (Ng et al., 2014; Sutharjana et al., 2013). At the same time, good employee attitudes and behaviors significantly improve patient care (Parasuraman et al., 1988).

Fuzzy logic provides extremely valuable flexibility in thinking by making it possible to take into account inaccuracies and uncertainties (Kóczy et al., 2020). Fuzzy logic has rules developed in natural language to study human thinking (Dernoncourt, 2013; Kóczy et al., 2020). Fuzzy numbers or fuzzy set can appropriately represent inaccurate parameters and can be processed through various operations (Kahraman et al., 2006). Various fields apply fuzzy logic when handling partial truth (Lowen, 1990). Fuzzy set theory is used for problems in medical, business, engineering health sciences, and natural sciences (Kahraman et al., 2006; Kóczy et al., 2020).

Jordan facing many challenges in health care; therefore, it was trying to apply OCB in hospitals for more organizational success. Few studies were found to investigate factors affecting OCB in Jordanian hospital. However, the impact of these two employees’ demographics (gender and age) on OCB is not studied in details. Therefore, this article aims to investigate them at Jordanian governmental hospitals using a fuzzy approach. Finally, the results of this study can help mangers in adopting strategies to enhance OCB in Jordanian health care employees, and this study can serve as the starting point for more future studies in Jordan.

Literature Review

This section summarizes some of the key earlier studies related to the impact of age and gender on (OCB) and fuzzy set.

Employees’ Gender and Age With OCB

Many factors contribute in enhancing the practice of OCB among employees. The most important variables explored were employees’ characteristics in addition to job attitude, task characteristics, and leadership behaviors (P. M. Podsakoff et al., 2000). Generally, shared governance behaviors were significantly affected by employees’ gender (Shah et al., 2014). Moreover, significant relations of managers’ genders and their education level with OCB have been found (Farzianpour et al., 2011). Similar significant relations of OCB have been observed with employees’ demographics such as age, work position, institutional experience, and professional characteristics (Altuntas & Baykal, 2014). Saleem et al. (2017) reported that OCB is significantly affected by all demographics including gender and age at public- and private-sector universities of Punjab, which was evident in their study for female and employee of age 40 and above. Study by Mahnaz et al. (2013) about relations between employees’ demographic characteristics and OCB in Tehran’s hospitals indicated that practice of OCB was affected by all demographics except ethnicity. Moreover, male employees exhibit more OCB than females. Beyond employees’ demographics, working environment also influences OCB, and psychological empowerment also has a positive impact on it (Abazeed, 2010).

Contrary to above, the study of Francis (2014) regarding OCB and demographic factors among oil workers in Nigeria revealed some demographics such as gender, educational level, and marital status did not affect practice of OCB. A similar study was conducted by El-Badawy et al., (2017) to assess and compare the effect of demographics on OCB in Egypt and Mexico. Two samples taken from Egypt and Mexico, respectively, demonstrated that OCB level was higher in Mexico than Egypt. The results highlighted that there was no significant relationship among gender, age, years of experience, level of education, and OCB of Egyptian and Mexican employees.

In Jordan, OCB has significantly improved companies’ performance (Nooh, 2013). However, most of the reviewed literature investigated OCB in different companies and reported that Jordanian employees expressed acceptable level of OCB (Abazeed, 2010; Ajlouni, 2010; Khasawneh, 2011). Employees of Jordanian telecom companies expressed high level of OCB with statistical differences according to their gender, age, job title, and years of experience (Shury, 2010), while OCB among social security corporation employees in Jordan was not affected by their demographics (Abazeed, 2010). Finally, there were no statistically significant differences among OCB level of managers and subordinates related to age and qualification (Taamneh, 2015). Furthermore, enhancing daily practice of OCB increases organizational productivity. However, the practice of OCB in Jordanian hospitals can be encouraged by developing new strategies of promoting OCB culture. Thus, the review of existing literature shows no published investigations related to the impact of employees’ demographics on OCB in Jordanian hospitals. Moreover, inconsistency is found in the results of studies pertaining to the impact of employees’ gender and age on OCB, and this can be attributed to cultural diversity (Waris, 2005). El-Badawy et al. (2017) showed indirect positive and significant relationship between culture and OCB. This study hopes to explore the impact of employees’ gender and age on OCB in Jordanian governmental hospitals.

Fuzzy Set

In humanities and social sciences, the evaluation of data obtained from responses given to questionnaires is a complex task, and dealing with the inherent subjectivity and vagueness in such data was also essential (Kóczy et al., 2020). A method based on fuzzy signatures suitable for analyzing questionnaires with hierarchically connected (partially) vague responses was proposed, and its applicability was demonstrated by a real-life problem by Kóczy et al. (2020). Li et al. (2020) used the fuzzy-set qualitative comparative analysis approach to examine the contextual embeddedness of women’s entrepreneurship through a focus on gendered institutions. This study confirmed the complex interactions occurring among institutional components and highlighted the key role that gender equality in entrepreneurial cognitions plays in achieving high female entrepreneurship.

According to Arefi and Taheri (2011) and Bodjanova (2005), fuzzy hypothesis testing was statistical technique that empowered them to test the hypothesis. Due to the quality and flexibility of the technique, it has a wide range of applications especially in social science research and other areas (Buckley, 2005; Manton et al., 1994; Wu, 2009). The literature leads to the development of following conceptual framework of employee’s gender and age with OCB as shown in Figure 1.

Figure 1.

The conceptual model of employee’s gender and age with organizational citizenship behavior.

Hypotheses

Following hypothesis was established to describe association among studied variables based on previous literature review:

Hypothesis 1: Does any significant relationship exist between employees’ gender, age, and OCB at Jordanian governmental hospitals?

Hypothesis 1(a): Does employees’ gender affect their perception about OCB at Jordanian governmental hospitals?

Hypothesis 1(b): Does employees’ age affect their perception about OCB at Jordanian governmental hospitals?

Proposed Works of Fuzzy Set

First, this section provides a brief review of fuzzy set theory, fuzzy hypothesis testing, fuzzy t test, and fuzzy one-way analysis of variance (ANOVA). Then, the proposed approach and fuzzy hypothesis testing were given in detail.

Fuzzy Hypothesis Testing

The data were collected from employees or a group of participants through a questionnaire for testing the hypothesis (de Sáa et al., 2014; Devore, 2008; Guajardo et al., 2015; Wong et al., 2007). Their responses were obtained on a Likert-type scale range from strongly agree to strongly disagree with the numerical values in the range of 4–1 since Likert-type scale is psychometric scale (it proposes as four fuzzy values to express the individual response from agree to strongly disagree). However, Zadeh (1965) pointed that these numerical values do not handle the impreciseness/vagueness in these linguistic variables. Moreover, Zadeh (1965) introduced the concept of fuzzy set theory to handle such type of situations. Another way followed by several researchers like Buckley (2005), Manton et al. (1994), and Wu (2009) to handle impreciseness/vagueness in these linguistic variables was to use fuzzy numbers instead of positive integers for representing the linguistic terms.

Preliminaries

The definitions of a fuzzy set are given as hereunder:

Definition 1: Let U be a universal set and a set $\tilde{A} = {x, μ_{\tilde{A}} (x) | x \in U}$ is called a fuzzy set, where $μ_{\tilde{A}} : U \to [0, 1]$ indicates the degree of membership of $x$ in $\tilde{A}$ .

Definition 2: Let $\tilde{A}$ be a fuzzy set in $U$ and $α \in [0, 1]$ . Then, the crisp set $A_{α} = {x \in X : μ_{\tilde{A}} (x) \geq α}$ as shown in Figure 2.

Figure 2.

α cut of a triangular fuzzy number.

Definition 3: Let $\tilde{A}$ be a fuzzy set in $U$ . Then, the crisp set $S (\tilde{A}) = {x \in X : μ_{\tilde{A}} (x) > 0}$ is called the support of the fuzzy set $\tilde{A}$ .

Definition 4: Let $\tilde{A}$ be a fuzzy set in $U$ . Then, $h (\tilde{A}) = \underset{x \in X}{Sup} {μ_{\tilde{A}} (x)}$ is called the height of the fuzzy set $\tilde{A}$ . If $h (\tilde{A}) = 1$ , then the fuzzy set $\tilde{A}$ is called a normal fuzzy set.

Definition 5: A fuzzy set $\tilde{A}$ , defined on the universal set U, is said to be convex, if $μ_{\tilde{A}} (λ x_{1} + (1 - λ) x_{2}) \geq minimum$ $(μ_{\tilde{A}} (x_{1}), μ_{\tilde{A}} (x_{2}))$ , for all $x_{1}, x_{2} \in X$ and $λ \in [0, 1]$ .

Definition 6: A fuzzy set $\tilde{A}$ , defined on the universal set of real numbers, is called a fuzzy number if it satisfies the following conditions:

(i) (i) $\tilde{A}$ is a normal fuzzy set.

(ii) (ii) $A_{α}$ is a closed interval for every $α \in [0, 1]$ .

(iii) (iii) $S (\tilde{A})$ is a bounded set.

Definition 7: A fuzzy set $\tilde{A}$ is said to be a triangular fuzzy number if its membership function $μ_{\tilde{A}} (x)$ is defined as:

μ_{\tilde{A}} (x) = {\begin{matrix} \frac{x - a}{b - a}, a < x \leq b \\ \frac{c - x}{c - b}, b \leq x < c \\ 0, otherwise . \end{matrix}

A triangular fuzzy number $\tilde{A}$ is generally denoted by $\tilde{A} = (a, b, c)$ and represented as shown in Figure 3.

Figure 3.

Triangular fuzzy numbers.

Arithmetic Operations on Interval Fuzzy Numbers

Let $\tilde{A} = [L_{1}, U_{1}]$ and $\tilde{B} = [L_{2}, U_{2}]$ be two interval fuzzy numbers. Then,

\tilde{A} + \tilde{B} = [L_{1} + L_{2}, U_{1} + U_{2}],

$\tilde{A} \times \tilde{B} = [L, U]$ where, $L = minimum {L_{1} L_{2}, L_{1} U_{2}, U_{1} L_{2}, U_{1} U_{2}}$ and $U = maximum {L_{1} L_{2}, L_{1} U_{2}, U_{1} L_{2}, U_{1} U_{2}}$

\tilde{A} - \tilde{B} = [L_{1} - U_{2}, U_{1} - L_{2}],

{\tilde{A}}^{- 1} = [\frac{1}{U}, \frac{1}{L}] .

Fuzzy ANOVA Method

In this section, the proposed method is illustrated as follows:

1. Collected the data from employees or a group of participants through a questionnaire in terms of linguistic variables like were strongly agree, agree, disagree, strongly disagree, and so on. Transformed the linguistic data into triangular fuzzy numbers, using the fuzzy triangular scale as shown in Table 1.

Table 1.

The Fuzzy Triangular Scale of Fuzzy Analysis of Variance.

Linguistic Variables	Code	Triangular Fuzzy Number
Strongly disagree	SDA	$\tilde{1} = (0, 1, 2)$
Disagree	DA	$\tilde{2} = (1, 2, 3)$
Agree	A	$\tilde{3} = (2, 3, 4)$
Strongly agree	SA	$\tilde{4} = (3, 4, 5)$

The triangular fuzzy number $\tilde{A} = (a, b, c)$ and represents the information of employees or a group of participants as shown in Figure 4.

Figure 4.

Response of participants.

Let $\tilde{A}$ be a fuzzy random variable, using the relation $[a + (b - a) α, c - (c - b) α],$ $α \in [0, 1]$ to replace the triangular fuzzy number $\tilde{A} = (a, b, c)$ with the interval fuzzy number $[A_{α}^{L}, A_{α}^{U}]$ .

2. Assumed the following fuzzy testing hypothesis:

{\tilde{H}}_{0} : {\tilde{μ}}_{1} = {\tilde{μ}}_{2} = \dots = {\tilde{μ}}_{n},

{\tilde{H}}_{1} : {\tilde{μ}}_{1} \neq {\tilde{μ}}_{2} \neq \dots \neq {\tilde{μ}}_{n},

where ${\tilde{H}}_{0}$ is the null hypothesis, and ${\tilde{H}}_{1}$ is the alternative hypothesis. Let

{\tilde{X}}_{i j} = {\tilde{μ}}_{i} \oplus {\tilde{1}}_{{ε_{i j}}},

where, ${\tilde{1}}_{{ε_{i j}}}$ is the crisp random variable, and $(N, σ^{2})$ is the normal distribution. Then, using the relation $[a + (b - a) α, c - (c - b) α], α \in [0, 1]$ and Equation 1 to obtain the following two crisp ANOVA models ${({\tilde{X}}_{i j})}_{α}^{L} = {({\tilde{μ}}_{i})}_{α}^{L} \oplus ε_{i j}$ and ${({\tilde{X}}_{i j})}_{α}^{U} = {({\tilde{μ}}_{i})}_{α}^{U} \oplus ε_{i j}$ embrace the following notation:

{\tilde{X}}_{i} = \oplus_{j = 1}^{n_{i}} {\tilde{X}}_{i j} and {\tilde{X}}_{\dots} = \oplus_{i = 1}^{r} \oplus_{j = 1}^{n_{i}} {\tilde{X}}_{i j} .

3. Using Equation 2 as mentioned above and subsection 3.1, to transform the fuzzy ANOVA model into two crisp ANOVA models, this can be written as follows:

{({\tilde{X}}_{i})}_{α}^{L} = \sum_{j = 1}^{n_{i}} {({\tilde{X}}_{i j})}_{α}^{L} and {({\tilde{X}}_{i})}_{α}^{U} = \sum_{j = 1}^{n_{i}} {({\tilde{X}}_{i j})}_{α}^{U},

{({\tilde{X}}_{\dots})}_{α}^{L} = \sum_{i = 1}^{r} \sum_{j = 1}^{n_{i}} {({\tilde{X}}_{i j})}_{α}^{L} and {({\tilde{X}}_{\dots})}_{α}^{U} = \sum_{i = 1}^{r} \sum_{j = 1}^{n_{i}} {({\tilde{X}}_{i j})}_{α}^{U} .

4. Applied crisp ANOVA method to obtain sums of squares based on the interval valued observations ${({\tilde{X}}_{i j})}_{α}^{L}$ and ${({\tilde{X}}_{i j})}_{α}^{U}$ as follows:

{SSTO}_{α}^{L} = \sum_{i = 1}^{r} \sum_{j = 1}^{n_{i}} {[{({\tilde{X}}_{i j})}_{α}^{L}]}^{2} - \frac{{[{({\tilde{X}}_{\dots})}_{α}^{L}]}^{2}}{n_{T}}, \begin{array}{l} {SSTO}_{α}^{U} = \sum_{i = 1}^{r} \sum_{j = 1}^{n_{i}} {[{({\tilde{X}}_{i j})}_{α}^{U}]}^{2} - \frac{{[{({\tilde{X}}_{\dots})}_{α}^{U}]}^{2}}{n_{T}} \end{array},

{SSTR}_{α}^{L} = \sum_{i = 1}^{r} \frac{{[{({\tilde{X}}_{i})}_{α}^{L}]}^{2}}{n_{i}} - \frac{{[{({\tilde{X}}_{\dots})}_{α}^{L}]}^{2}}{n_{T}}, {SSTR}_{α}^{U} = \sum_{i = 1}^{r} \frac{{[{({\tilde{X}}_{i})}_{α}^{U}]}^{2}}{n_{i}} - \frac{{[{({\tilde{X}}_{\dots})}_{α}^{U}]}^{2}}{n_{T}}

{SSE}_{α}^{L} = \sum_{i = 1}^{r} \sum_{j = 1}^{n_{i}} {[{({\tilde{X}}_{i j})}_{α}^{L}]}^{2} - \sum_{i = 1}^{r} \frac{{[{({\tilde{X}}_{i})}_{α}^{L}]}^{2}}{n_{i}}, {SSE}_{α}^{U} = \sum_{i = 1}^{r} \sum_{j = 1}^{n_{i}} {[{({\tilde{X}}_{i j})}_{α}^{U}]}^{2} - \sum_{i = 1}^{r} \frac{{[{({\tilde{X}}_{i})}_{α}^{U}]}^{2}}{n_{i}}, and also relations {SSTO}_{α}^{L} = {SSTR}_{α}^{L} + {SSE}_{α}^{L} and {SSTO}_{α}^{U} = {SSTR}_{α}^{U} + {SSE}_{α}^{U} .

5. Computed the mean squares using Step 5: ${MSTR}_{α}^{L} = \frac{{SSTR}_{α}^{L}}{r - 1}$ and ${MSTR}_{α}^{U} = \frac{{SSTR}_{α}^{U}}{r - 1}$ , ${MSE}_{α}^{L} = \frac{{SSE}_{α}^{L}}{n_{T} - 1}$ and ${MSE}_{α}^{U} = \frac{{SSE}_{α}^{U}}{n_{T} - 1}$ and also constructed the two crisp ANOVA in Tables 2 and 3, respectively.

Table 2.

Lower Crisp Analysis of Variance.

Source of Variation	SS	df	MS
Between treatments	${SSTR}_{α}^{L}$	$r - 1$	${MSTR}_{α}^{L}$
Error (within treatments)	${SSE}_{α}^{L}$	$n_{T} - r$	${MSE}_{α}^{L}$
Total	${SSTO}_{α}^{L}$	$n_{T} - 1$

Table 3.

Upper Crisp Analysis of Variance.

Source of Variation	SS	df	MS
Between treatments	${SSTR}_{α}^{U}$	$r - 1$	${MSTR}_{α}^{U}$
Error (within treatments)	${SSE}_{α}^{U}$	$n_{T} - r$	${MSE}_{α}^{U}$
Total	${SSTO}_{α}^{U}$	$n_{T} - 1$

6. Finally, in order to test whether the hypothesis is accepted or not, the following test statistic was used:

{\tilde{F}}^{*} = [F_{α}^{L}, F_{α}^{U}] = [\frac{{MSTR}_{α}^{L}}{{MSE}_{α}^{L}}, \frac{{MSTR}_{α}^{U}}{{MSE}_{α}^{U}}] .

Using the defuzzification function $m_{α} = ω (\frac{1}{a_{i j} + (b_{i j} - a_{i j}) α}) + (1 - ω) (\frac{1}{c_{i j} - (c_{i j} - b_{i j}) α}),$ $ω \in [0, 1]$ to obtain crisp $F^{*} = \frac{MSTR}{MSE}$ , the following cases arose:

Case I: If $F^{*} \leq F_{1 - α; r - 1, n_{T} - r}$ , then we accept the null hypothesis ${\tilde{H}}_{0}$ .

Case II: If $F^{*} > F_{1 - α; r - 1, n_{T} - r}$ , then we accept the alternative hypothesis ${\tilde{H}}_{1}$ (Wu, 2007).

Fuzzy t Test

Let $\tilde{X} = (a_{i}, b_{i}, c_{i}); i = 1, 2, \dots, m$ be a random sample of triangular fuzzy numbers with size m and $\tilde{Y} = (a_{j}, b_{j}, c_{j}); j = 1, 2, \dots, n$ be a random sample of triangular fuzzy numbers with size $n$ . Using the relation $[a + (b - a) α, c - (c - b) α], α \in [0, 1]$ to replace the triangular fuzzy number $\tilde{A} = (a, b, c)$ with the interval fuzzy number $[A_{α}^{L}, A_{α}^{U}]$ and suppose that $[η_{1}^{L}, μ_{1}^{U}]$ and $[η_{2}^{L}, μ_{2}^{U}]$ be mean of normal population of $\tilde{X}$ and $\tilde{Y}$ , respectively.

Now for the null hypothesis, ${\tilde{H}}_{0} : [η_{1}^{L}, μ_{1}^{U}] = [η_{2}^{L}, μ_{2}^{U}]$ and alternative hypotheses are given. ${\tilde{H}}_{1} : [η_{1}^{L}, μ_{1}^{U}] > [η_{2}^{L}, μ_{2}^{U}]$ or ${\tilde{H}}_{1} : [η_{1}^{L}, μ_{1}^{U}] < [η_{2}^{L}, μ_{2}^{U}]$ or ${\tilde{H}}_{1} : [η_{1}^{L}, μ_{1}^{U}] \neq [η_{2}^{L}, μ_{2}^{U}]$ . The sample means and standard deviations of $\tilde{X}$ and $\tilde{Y}$ have been collected as follows:

Case I: If the standard deviations of the population are assumed to be equal, then the null hypothesis ${\tilde{H}}_{0} : [η_{1}^{L}, μ_{1}^{U}] = [η_{2}^{L}, μ_{2}^{U}]$ is expressed as follows:

\tilde{t} = [t_{l}, t_{u}] = [\frac{{\bar{X}}_{L} - {\bar{Y}}_{L}}{s_{L} \sqrt{\frac{1}{m} + \frac{1}{n}}}, \frac{{\bar{X}}_{U} - {\bar{Y}}_{U}}{s_{U} \sqrt{\frac{1}{m} + \frac{1}{n}}}],

where,

\tilde{S} = [s_{L}, s_{L}] = [\sqrt{\frac{(m - 1) s_{X_{L}}^{2} + (n - 1) s_{Y_{L}}^{2}}{m + n - 2}}, \sqrt{\frac{(m - 1) s_{X_{U}}^{2} + (n - 1) s_{Y_{U}}^{2}}{m + n - 2}}] .

Case II: If the standard deviations of population are assumed to be unequal, then the null hypothesis ${\tilde{H}}_{0} : [η_{1}^{L}, μ_{1}^{U}] = [η_{2}^{L}, μ_{2}^{U}]$ is expressed as follows:

\tilde{t} = [t_{l}, t_{u}] = [\frac{{\bar{X}}_{L} - {\bar{Y}}_{L}}{\sqrt{\frac{s_{X_{L}}^{2}}{m} + \frac{s_{Y_{L}}^{2}}{n}}}, \frac{{\bar{X}}_{U} - {\bar{Y}}_{U}}{\sqrt{\frac{s_{X_{U}}^{2}}{m} + \frac{s_{Y_{U}}^{2}}{n}}}] .

Finally, by using the defuzzification function, $m_{α} = ω (\frac{1}{a_{i j} + (b_{i j} - a_{i j}) α}) + (1 - ω) (\frac{1}{c_{i j} - (c_{i j} - b_{i j}) α}), ω \in [0, 1]$ to transform the fuzzy $\tilde{t} = [t_{l}, t_{u}]$ value into the crisp t value and use the crisp t test (Devore, 2008) to check that the null hypothesis ${\tilde{H}}_{0} : [η_{1}^{L}, μ_{1}^{U}] = [η_{2}^{L}, μ_{2}^{U}]$ whether stands accepted or rejected.

Population and Sampling

As this study aims to investigate the impact of employees’ gender and age on OCB in Jordanian governmental hospitals, the general population of this study is all employees from governmental hospitals in Jordan number to 2,355 (Ministry of Health Hashemite Kingdom of Jordan, 2017). The target population includes employees who work in the selected governmental hospitals located in the north of Jordan (Irbid city). For sampling, the probability sampling method was used. After briefing about purpose of the study and ensuring that these data were treated confidentially, more realistic and not presumptive data were collected. Sample size of the study was based on Cohen power primer; the sample size for medium sample effect size (ES), at power (P) of .08 with significant criterion α of .05, for ANOVA tests was 52 for each group that resulted in at least 126 employees (Cohen, 1992). The inclusion creations included all health employees working in the selected hospitals, providing direct and indirect patient care, that is, physicians, nurses, radiologists, pharmacists, laboratory technicians, and managers.

Research Design and Data Collection

A descriptive and cross-sectional research design was used to collect data related to current research. Questionnaire prepared for the target group was well structured and was distributed directly to employees in the selected hospitals who were on duty at the time of data collection. A brief clarification about the study was given to employees before distributing the questionnaires.

The participants were assured that the data collected would be used solely for the purpose of this research with confidentiality. Of 210 questionnaires distributed, 126 employees participated in the sample with a response rate of 60% because some of them expressed inability to participate due to excess duties.

Study Instrument

A well-structured and pretested questionnaire was used for data collection in line with earlier studies (Ajlouni, 2010; Kóczy et al., 2020; Niehoff & Moorman, 1993; P. M. Podsakoff et al., 1997). The responses were rated on a 4-point Likert-type scale (4 for strongly agree and 1 was strongly disagree). Thus, there were 20 items reflecting OCBs’ five dimensions, viz: altruism, conscientiousness, sportsmanship, courtesy, and civic virtues were considered for the purpose of this study. Content validity was checked on the judgment of 10 experts including physicians and faculty members of different universities. They reported that the instrument-related items sufficiently measured OCB and its dimensions.

Pilot Study

A pilot study with 40 employees of the selected hospitals was conducted. A suitable pilot study sample size was prepared for medium sample ES at a significance criterion α of .05 with 40 participants (Whitehead et al., 2016). The pilot study aimed to assess the feasibility of study (reliability, validity, applicability, readability, and the precise amount of time needed to fill in the questionnaire), to find out any obstacle that could hinder the data collection process, and to make necessary modifications if any. The pilot study showed that all items of instrument were clear, readable, and easily understandable. Time needed to fill the questionnaire was 10–15 min for each participant; therefore, it was considered sufficient in view of the limited information required.

Reliability of this study instrument was assessed by computing Cronbach’s α with a score of .89 for the entire questionnaire, which showed that the instrument had acceptable internal consistency (Polit & Beck, 2010). This pretest process involved subsequent analysis of collected data, confirming that questionnaire items matched research objectives. It also ensured content validity based on expert judgment and ensured that instrument-related items effectively measured OCB and its dimensions.

Setting

Current study was conducted in the northern part of Jordan in three main governmental hospitals. All these three hospitals have specializations in medical–surgical, pediatric, and gynecological ailments that provide a comprehensive health care to about 1.77 million individuals living in this part of Jordan (Census of Population and Housing, 2015). Furthermore, these hospitals are providing teaching facilities to medical and nursing students.

Results

The experimental analysis and results pertaining to the proposed work identify the impact of employees’ gender and age on OCB as per employees’ perception in Jordanian governmental hospitals. Standard deviation, t test, and one-way ANOVA were applied to get the study aims. Tables represent the final transformed and crisp results as follows:

Hypothesis 1(a): Does employees’ gender significantly affect their perception about OCB at Jordanian governmental hospitals?

Table 4 shows the final transformed results of means and standard deviation. Total mean score of 3.07 due to gender variable stands in favor of males, while females lag in all dimensions with a mean score of 2.86. There were statistically significant differences at α = .05 in altruism of .006, civic virtue of .012, and courtesy of .045, respectively. While sportsmanship and conscientiousness had no statistically significant differences at α = .05. The total statistically significant difference is .011, as reported in Table 5.

Table 4.

Means and Standard Deviations Sample Responses Related to Participants’ Gender.

	Male			Female
	Crisp Mean	Fuzzy Mean	Crisp SD	Crisp Mean	Fuzzy Mean	Crisp SD
Altruism	3.34	[1.00, 4.68]	.476	3.06	[2.03, 4.09]	.588
Courtesy	3.29	[2.09, 4.49]	.414	3.12	[1.60, 5.18]	.465
Sportsmanship	3.30	[1.33, 5.27]	.467	3.11	[3.00, 3.22]	.539
Civic virtue	2.93	[1.86, 4.00]	.697	2.62	[1.2, 4.04]	.619
Conscientiousness	2.57	[1.5, 3.64]	.748	2.49	[1.49, 3.49]	.748
Total score	3.07	[2.05, 4.09]	.414	2.86	[1.72, 4]	.457

Table 5.

The t-Test Results of Sample Responses Related to Participants’ Gender.

	t Value	Fuzzy t Value	Significance
Altruism	2.801	[1.500, 4.102]	.006
Courtesy	2.030	[1.015, 3.045]	.045
Sportsmanship	1.945	[1.940, 1.950]	.054
Civic virtue	2.551	[1.051, 4.051]	.012
Conscientiousness	0.586	[0, 1.172]	.559
Total score	2.583	[1.050, 4.116]	.011

Hypothesis 1(b): Does employees’ age significantly affect their perception about OCB at Jordanian governmental hospitals?

Table 6 displays the final transformed results of means and standard deviation. Respondents of age 41 and above have the highest mean score in altruism of 3.22, courtesy of 3.29, sportsmanship of 3.31, and conscientiousness of 2.61. The total mean score is 3.03.

Table 6.

Means and Standard Deviation Results of Sample Responses Related to Age Variable.

	Less Than 30			31–40			41 and Above
	Crisp Mean	Fuzzy Mean	SD	Crisp Mean	Fuzzy Mean	SD	Crisp Mean	Fuzzy Mean	SD
Altruism	3.17	[1.97, 4.37]	.674	3.05	[1.85, 4.25]	.503	3.22	[2.02, 4.42]	.548
Courtesy	3.01	1.81, 4.21]	.502	3.10	[1.90, 4.30]	.453	3.29	[2.09, 4.49]	.406
Sportsmanship	3.05	[1.85, 4.25]	.579	3.04	[1.84, 4.24]	.573	3.31	[2.11, 4.51]	.435
Civic virtue	2.90	[1.70, 4.10]	.698	2.44	[1.244, 3.644]	.622	2.82	1.62, 4.02]	.630
Conscientiousness	2.52	[1.32, 3.72]	.800	2.37	[1.17, 3.57]	.762	2.61	[1.41, 3.81]	.714
Total score	2.92		.504	2.77		.421	3.03		.426

Table 7 shows statistically significant differences at ( $α$ = .05) in relation to courtesy of .012, sportsmanship of .016, and civic virtue of .008, while there were no statistically significant differences at ( $α$ = .05) in altruism of .387 and conscientiousness of .387 due to age. The total significant score is .027.

Table 7.

One-Way Analysis of Variance Result of Sample Responses Related to Their Age Variable.

	t Value	Fuzzy t Value	Significance
Altruism	0.958	[0.838, 1.078]	.387
Courtesy	4.602	[3.402, 5.802]	.012
Sportsmanship	4.268	[3.068, 5.468]	.016
Civic virtue	5.050	[3.85, 6.25]	.008
Conscientiousness	1.150	[0.13, 2.17]	.320
Total score	3.732		.027

Pairwise multiple comparisons post hoc test using the Scheffe method were conducted as shown in Table 8. This table shows that there are statistically significant differences at ( $α$ = .05) between respondents of age less than 30 and 41 and above. This result is in favor of respondents of age 41 and above in courtesy. Moreover, there are statically significant differences at ( $α$ = .05) between respondents of age categories 31–40 and 41 and above. This result is in favor of respondents of age 41 and above in sportsmanship. In addition, there are statistically significant differences at ( $α$ = .05) between respondents of age categories 31–40 and both of 30 or less and above. This result is in favor of both respondents of age 30 or less and above in civic virtue. These results also show that employees of age 31–40 are more concerned about OCB.

Table 8.

Pairwise Multiple Comparisons Post Hoc Tests Using the Scheffe Method Due to Age Variable.

Dependent Variable	(I) Age in Years	(J) Age in Years	Mean Deviation (I − J)	Standard Error	Significance
Courtesy	30 or less	31–40	−.09	.115	.718
	30 or less	41 and above	−.28*	.102	.024
	31–40	30 or less	.09	.115	.718
	31–40	41 and above	−.19	.093	.131
	41 and above	30 or less	.28*	.102	.024
	41 and above	31–40	.19	.093	.131
Sportsmanship	30 or less	31–40	.01	.132	.996
	30 or less	41 and above	−.26	.117	.095
	31–40	30 or less	−.01	.132	.996
	31–40	41 and above	−.27*	.107	.046
	41 and above	30 or less	.26	.117	.095
	41 and above	31–40	.27*	.107	.046
Civic virtue	30 or less	31–41	.46*	.167	.026
	30 or less	41 and above	.08	.149	.878
	31–40	30 or less	−.46*	.167	.026
	31–40	41 and above	−.38*	.136	.021
	41 and above	30 or less	−.08	.149	.878
	41 and above	31–40	.38*	.136	.021
Total score	30 or less	31–40	.15	.115	.418
	30 or less	41 and above	−.10	.102	.610
	31–40	30 or less	−.15	.115	.418
	31–40	41 and above	−.25*	.093	.027
	41 and above	30 or less	.10	.102	.610
	41 and above	31–40	.25*	.093	.027

* The mean difference is significant at .05 level.

Discussion and Conclusion

This study was descriptive in nature. The aim was to explore effects of demographic variables including gender and age on OCB among employees of Jordanian governmental hospitals. OCB is vital for enhancing the quality of health care services (Ng et al., 2014; Sutharjana et al., 2013). Whereas, there are many factors that contribute to enhancing OCB practice by employees, also including their characteristics (P. M. Podsakoff et al., 2000).

Results of earlier studies were inconsistent regarding the effects of employees’ demographics like gender and age on OCB. However, the results of this study supported the Hypothesis 1(a) that OCB is significantly related to gender at α = .05 in favor of male employees regarding three dimensions of OCB, altruism, civic virtue, and courtesy. This can be attributed to differences in attitudes and interests of males and females, for example; males and females had different attitudes toward shared governance (Shah et al., 2014). This significant impact of gender on OCB was proved earlier by some studies (Farzianpour et al., 2011; Mahnaz et al., 2013; Shury, 2010), while contradictory results were depicted by Saleem et al. (2017) that female employees practiced OCB more than male employees. Similarly, the results of this study were contrary to those of Abazeed (2010), Francis (2014), and El-Badawy et al. (2017), who reported that employees’ gender had no impact on OCB. Finally, more future investigations can provide better results.

Furthermore, the results of this study show strong support for Hypothesis 1(b). A significant impact of employees’ age on OCB at α = .05 in courtesy and sportsmanship was found in favor of respondents of age 41 and above. This results could be interpreted as respondents having more experience and professionalism revealed higher level of OCB (Saleem et al., 2017; Shury, 2010) as well as professional characteristics (Altuntas & Baykal, 2014). Age also had significant impact on civic virtue, and results were in favor of respondents having age of 30 or less and above both. It was also proved that employees of age 31–40 were more concerned about OCB. Similarly, Saleem et al. (2017) showed that employees aged 46 or above were more committed to OCB than younger ones. Many researchers like Shury (2010) found a high level of OCB in Jordanian telecom companies affected by employees’ age, gender, job title, and years of experience. In addition, Altuntas and Baykal (2014) found that some employees’ demographics such as age, work position, and institutional experience in addition to professional characteristics had positive effects on OCB in Turkey. This was also supported by Mahnaz et al. (2013) that OCB was influenced by all employees’ demographics except ethnicity. However, these results were inconsistent with some other studies (El-Badawy et al., 2017; Farzianpour et al., 2011; Taamneh, 2015), which detected no significant impacts of employees’ age on OCB.

Finally, the results of this study showed that OCB at Jordanian governmental hospitals was affected by both employees’ gender and age. Findings of this study will be valuably contribute to the literature, and this study is an initiative toward studies regarding OCB in health sector at one of the developing countries. This was also be useful in further understanding the impacts of employees’ other demographics such as level of education, marital status, and years of experience on OCB in future.

Limitations

This study had some limitations that need to be taken into consideration when examining the findings and can be addressed in future studies. Some of the main limitations of this study are as follows:

Using a convenience sampling method restricts the generalizability of findings. Thus, a randomized sample will be more effective in further studies.

This study takes into consideration only three government hospitals of Jordan. Comprehensive studies involving more Jordanian hospitals in future may enhance the reliability of results.

Further Scope

Future studies can investigate how OCB norms according to gender and age could be developed in hospitals to increase employees’ efficiency and quality of services. Future studies having larger representative samples, more locations, and considering more employees’ demographics such as organizational justice, job satisfaction, loyalty, effectiveness, human resource management, and others can contribute more in this field of research.

Supplemental Material

Supplemental Material, Responses_to_Editor's_Comments - The Impact of Employees’ Gender and Age on Organizational Citizenship Behavior Using a Fuzzy Approach

Supplemental Material, Responses_to_Editor's_Comments for The Impact of Employees’ Gender and Age on Organizational Citizenship Behavior Using a Fuzzy Approach by Wajdee Mohammadkair Ebrheem Ajlouni, Gurvinder Kaur and Saleh Ali Alomari in Social Science Computer Review

Footnotes

Declaration of Conflicting Interests

The author(s) declare no potential conflicts of interests with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Supplemental Material

The supplemental material for this article is available online.

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