Impact of Compensation on Inclusive Organizations

Abstract

Discrimination in compensation for minority groups and individuals with regard to gender, physical disability, religion, and culture affects inclusion in an organization. This study is a combination of two studies and endeavors to verify our initial inference that compensation gaps are significantly related to inclusion. A mixed method approach has been adopted; in first part of the study, compensation data obtained from 32 organizations (608 observations) have been analyzed quantitatively. The study finds significant correlation between components of compensation gaps and inclusion. Gender as basis of discrimination was found insignificantly correlated to compensation, while pay for performance was found negatively related to inclusion. We have proposed a model to predict feeling of inclusion if components of compensation and discriminatory factors are known. In second part of the study, based on 25 in-depth interviews, cognitive basis of compensation gaps has been divulged, and we conclude that implementation of compensation equity and removal of cognitive bases of discrimination seem mandatory actions for inclusion.

Keywords

compensation inclusion compensation gap inclusive organization gender and disability compensation discrimination cultural and religious compensation discrimination

Introduction

Cultivating inclusive and diverse culture is a central mandate of present-day talent management. Managing diverse workforce for inclusion of all members in the organizational activities requires equitable work environment void of discrimination and unfair practices (Channar, Abbassi, & Ujan 2011). Different perspectives and experiences stemming out from inclusion bring richness of ideas, creativity, and innovation in an organization for long-term sustainability (Thompson, 2017). Diversity improves worker’s performance and creates healthy competition when inclusive policies and practices are implemented (Gomez & Bernet, 2019). Inclusion in organizations is reflected by the degree to which employees are respected, valued, and supported to fully participate (Ramsay, 2018). Traditionally, it is believed that for their wholehearted participation and contribution toward achievement of organizational goals, compensation of employees is a key factor (Akerlof & Yellen, 1986).

Compensation remains pivotal to the work life of an employee from the time of joining an organization to the time one decides to quit or retire (Umar, 2014). Generally, members of minority groups, women, and persons with disability are adversely affected by compensation discrimination which ultimately leads to their exclusion. Compensation inequality on the basis of race, gender, age, ethnicity, religion, or color has been regarded as an unfair and unethical organizational practice which affects employee’s work attitude and behavior (Buttner & Lowe, 2017; Cornwell, Rivera & Schmutte, 2017; Kesinro, Ojo, & Elueze, 2015). Compensation has different components like base salary, pay for performance, bonus, allowances for medical treatment, conveyance allowance, and rent of accommodation etc. Although, compensation constitutes one of the most significant aspects of employer–employee relationship, yet, pay inequality is still evident and affects employees from all backgrounds, ages, and levels of education (Buttner and Lowe, 2017; John, Loewenstein, & Rick, 2014; Rasch & Szypko, 2013). For example, in many advanced countries, women earn an average of 81% of what men earn. By removing discriminatory factors, compensation can be used as a catalyst for inclusion in an organization (Thompson, 2017).

Inclusion in an organization is critical to the employers being intimately related to employee satisfaction, engagement, and performance (Gomez & Bernet, 2019). Any adverse effects on inclusion due to compensation discrimination are likely to reduce organizational performance (profitability, innovation, and reputation). Compensation (viewed as reward to one’s hard work) is keenly watched by employees and even minor variations due to factors other than merit may impact inclusion quite seriously (Cornwell et al., 2017). Therefore, employers/organizations must monitor the level of inclusion of their employees to pick early signs of dissatisfaction due to discrimination in compensation. To the best of authors’ knowledge, no study has empirically linked compensation with inclusion to provide a tool to employers for gauging comparative level of inclusion of an employee from the standpoint of different bases of discrimination. This study fills the gap by proposing a model for determining level of inclusion of each employee taking into account impact of various components of compensation and different basis of discrimination (gender, disability, religion, and culture). Through the proposed model, employers can determine changes in the comparative feeling of inclusion which can lead them to the underlying problem (gap) in a specific component of compensation.

To adequately explore above mentioned multidimensional effects on inclusion, this article combines two studies; the first study (quantitative) aimed at finding a relationship between compensation and inclusion from the perspectives of various discriminatory factors, that is, gender, disability, religion, culture, and employment tenure. The second study (qualitative) has been conducted to explore the cognitive basis of discriminatory compensation gaps to be eliminated/reduced for fostering inclusive organizational culture.

Review of Literature

Employees’ feeling of fairness in the allocation of rewards usually arises from the perception of fairness in the distribution of reward or the process used for distribution of rewards (Rasch & Szypko, 2013). If compensation is not viewed as fair and equitable, it can lay a foundation of mistrust to tarnish even the most futuristic culture. When pay equity is given priority, organizations enjoy 13% higher employee engagement and are 19% more likely to exceed industry averages of productivity (Lombardi, 2017). Compensation discrimination is defined as arbitrarily assigning wages to workers based on factors unrelated to job performance or merit (Coleman, 2003). It can also be explained as an operation of the labor market where similar workers receive different compensation on the basis of color, sexual orientation, ethnicity, age, gender, or other ascribed features.

Compensation Discrimination Based on Gender

Gender-based discrimination is nothing new; women in occupational settings often have to deal with sexist stereotypes that women are less capable and effective leaders than men (Eagly, Makhijani, & Klonsky, 1992; O’Leary, 1974). Women earn considerably less than men, even for the same job, with slight variations in different countries and industries (Blau & Kahn, 2017; Hofer, Titelbach, Winter-Ebmer, & Ahammer, 2017; Mehaila, 2017). In some states of the United States, women working full time typically make only 82 cents for every dollar a man makes and the size of the disparity varies by state (National Women Law Center, 2018). In Indian context, Agrwal (2014) found existence of large wage gaps due to gender and social differences. Similarly, scholars have found large wage differentials between men and women in labor market in Bangladesh (Ahmed & McGillivray 2015; Ahmed & Maitra 2010). Companies in certain countries are required to publish a gender pay gap report like in the United Kingdom; companies with at least 250 employees are legally required to disclose pay gaps based on gender. Similarly, many countries are taking steps to reduce gender pay gap by enforcing salary history inquiry bans (Deyermenjian, 2018).

Compensation Discrimination Based on Disability

Discrimination against persons with a disability is a point of concern, given the emphasis on human rights and combating discrimination (Schirle, 2008). Facilitating employment of persons with disabilities is acrucial aspect of their integration into society and organizations (Gunderson, 2006). Usually the talent of persons with some disability is not fully utilized by firms due to stereotypes which may not be accurate (Lengnick, Guant, & Kulkarni, 2008). According to Colella and Stone (2008), there is a great deal of literature that explains discrimination against persons with disabilities (Jones, Latreille, & Sloane, 2006; Kidd, Sloane, & Ferko, 2000). The Equality and Human Rights Commission (EHRC) (2017) report has revealed that the disability pay gap is about 13.6%. Moreover, disabled people are significantly more likely to be unemployed, lose a job, or be in low-waged work than nondisabled people. The World Health Organization (WHO) has defined disability as a restriction of functional ability and activity caused by an impairment. This highlights that a disability need not result in a handicap or disadvantage at work (Baldwin & Johnson, 2006; WHO, 2001). Common disabilities such as those involving pain or restricted mobility or agility may make work more difficult but may not affect performance. Campolieti (2009) indicated that there is still a lot to be done for stopping discrimination against persons with disabilities and for their inclusion in the society and organizations.

Compensation Discrimination Based on Culture and Religion

Previous studies (Berthoud, 2000; Heath & McMahon, 1997; Kan and Heath, 2006; Longhi, Nicoletti, & Platt, 2012) have shown that caste- and religion-based wage discrimination exists, but there is no definitive answer on whether the degree of discrimination differs between public and private sectors (Sukhdeo & Negi, 2005). The literature on ethno-religious impact on earnings and income inequality reveals an inconsistent picture. Lindley (2002) found that even after controlling for religion, substantial ethnic labor market disadvantage still existed. In terms of religious differences, the study found an evidence for a substantial “Muslim penalty.” Longhi and Platt (2008) argued that substantial penalties were experienced by women of all ethno-religious groups, with little variation among different groups of women. Longhi et al. (2012) found a substantial effect of religion and ethnicity on compensation. Sengupta and Das (2014) have shown that female workers were discriminated more if they belonged to economically backward caste and religious minority groups.

Age of Employee and Inclusion

Age is a major determinant of inclusion because older employees have enhanced sensitivity to any maltreatment or discrimination. Discrimination based on age is observed in almost all regions of the world, we take the example of advanced nations; research shows that almost 24% of older Europeans suffer due to age discrimination (Abrams, Russell, Vauclair, & Swift, 2011). Despite the ubiquitous nature of age discrimination and the solid evidence for its adverse social consequences, usually policies confine responses for tackling age discrimination in the workplaces. This is done to enhance the productivity and employability of older workers and to deter their dependence on the welfare state (Macnicol, 2005). Various countries have legislated against this discrimination (for instance, European Union Council Directive 2000/78/EC of November 27, 2000), to ensure equal treatment of individuals at the workplace, regardless of their religion or belief, disability, age, or sexual orientation. However, the implementation and effects of the said legislations are difficult to assess (Duncan & Loretto, 2004; Herring, 2011; Neumark, 2009).

Despite negative effects of age discrimination, age diversity might lead to improved inclusion (Herring, 2009; Kearney, Gebert, & Voelpel, 2009). Employees of different ages do not see each other as competitors for promotions and advancement rather older colleagues are viewed by youngsters as experienced role models. Various studies have found that age diversity contributes to innovation and creativity due to the group’s greater access to a varied range of knowledge and experience (Choi, 2007) and leads to lower emotional conflict (Pelled, Eisenhardt, & Xin, 1999). Older employees are not likely to experience compensation discrimination while being in their parent organization due to their experience and loyalty. However, in case an older employee takes job in another organization, discrimination on the basis of age is more likely as older employees cannot be assigned to more rigorous jobs. Employees in similar age groups feel more included, while older employees may feel comparatively more included in younger age groups as compared to younger employees in majority of old workers (Kearney et al., 2009).

Employment Tenure and Inclusion

Several work-related factors such as employment tenure and job position etc. have been found significantly related to turn over and may predict attrition (DePanfilis & Zlotnik, 2008; Mor Barak, Findler, & Wind, 2001). Evidence has suggested a positive correlation between inclusion and job tenure (Pelled, Ledford, & Mohrman, 1999). In other words, feeling of inclusion is likely to increase with passage of time for an employee in the same organization, provided that the organizational environment remains relatively stable (Pelled, Ledford, et al., 1999). Unfortunately, the existing few studies on linkage between employment tenure and inclusion are cross-sectional, and it remains unclear whether inclusion did in fact change with tenure. However, studies do indicate that employment tenure is an important factor in determining an employee’s feeling of inclusion and is positively related to inclusion. It has been found that there is no evidence in the literature of any compensation discrimination on the basis of employment tenure (Stypinska & Turek, 2017).

Inclusive Organization and Compensation

In modern organizations, interaction among people from different origins, backgrounds, beliefs, and cultural diversity has become a rising trend (Mazur & Bialostocka, 2010). Diversity requires inclusion of all members at all levels of hierarchy through an organizational culture in which they can utilize their full capacity without being hurdled by religion, ethnicity, race, gender, or any other factor (Mousa & Alas, 2016). In an inclusive organization, every employee is treated as an insider and experiences a kind of mutual trust with the organization (Nishii, 2013). Organizational inclusion practices consist of organizational rules, plans, and initiatives, the purpose of which is to form an accepting, helpful, and participative atmosphere (Tang, Zheng, & Chen, 2017). If employees feel that they are included in all affairs of an organization, it would result in higher levels of organizational commitment and job satisfaction (Hwang & Hopkins, 2015).

Inclusive organizations provide a level playing ground for everyone and offer similar compensatory benefits based on merit without any discrimination (Mor-Barak, 2000; Roberson, 2006). Inclusion, equal opportunity, and antidiscrimination policies add to the strength of an organization and can provide a sustainable competitive advantage (Agbontaen, 2019). Compensation discrimination on any basis other than performance/merit affects employee work behavior negatively (Ahmed & McGillivray, 2015). Compensation refers to both monetary and nonmonetary benefits and is closely linked with organizational commitment (Adeniji & Osibanjo, 2012). Results of empirical studies indicate that sense of belonging and inclusion is a major factor in the service industry which is significantly related to employee’s perception of fairness of compensation received by them (Ahmed & McGillivray, 2015; Ogunnaike, 2011).

Theories Explaining the Relation Between Compensation Gaps and Inclusion

The baseline theory for this study is the organizational justice theory (Greenberg, 1990) which has been used to link compensation gaps with degree of inclusion in an organization. Greenberg (1990) defines organizational justice as the degree to which an organization is fair, equitable, and ethical (Cropanzano, Bowen, & Gilliland, 2007; Greenberg, 1990; Malik & Singh, 2014). Organizational justice is conceptualized as a multidimensional construct having four components, that is, distributive, procedural, interpersonal, and informational justice. Distributive justice is founded on the premise that human social behavior is conditioned by the distribution of outcomes (Ghosh, Rai, & Sinha, 2014). Perception of organizational decisions as fair or unfair can influence the individual’s subsequent attitudes and behaviors (De Gieter, De Cooman, Hofmans, Pepermans, & Jegers, 2012). Distributive justice seems to be closely related to compensation gaps based on various types of discriminations (Swalhi, Agoulli, & Hofaidhllaoui, 2017). Organizational justice enhances the employees’ feeling that they are involved in decision-making or other organizational procedures (Bies & Shapiro, 1988; Greenberg & Folger, 1983). The opportunity or ability to participate in decision-making improves an individual’s perceptions of procedural justice (Bies & Shapiro, 1988). We consider distributive justice with regard to compensation, an essential ingredient of inclusion where an employee willingly participates in organizational activities and the organization rewards him/her without any discrimination. Other studies (Luo, 2007; Morand & Merriman, 2012), in consonance with our view, have indicated that employee participation (inclusion) is related to distributive, procedural, as well as interpersonal justice perceptions of an employee.

Two other theories seem to explain the relation between compensation gaps and inclusion: Expectancy theory and equity theory. The expectancy theory, formulated by Vroom (1964), proposes that an individual is motivated to the extent that he or she trusts that effort will lead to satisfactory performance outcome (expectancy), performance will be compensated (instrumentality), and the value of compensation will be positive (valence). The expectancy theory predicts that employees will participate in behavior that will eventually lead to desired or valued outcomes (Lunenberg, 2011). If organizations continually meet the expectations of their employees, then it may impact positively on employee retention (Scholl, 1981) and feeling of inclusion (Tang et al., 2017). The equity theory, developed by Adams (1963), underpins the notion that if employees perceive that their pay is in proportion with their efforts and contribution, particularly when compared with that of their counterparts, they tend to experience a sense of equity, fairness, and inclusion. The opposite of this is a sense of inequity, which implies that employees perceive that their reward is not in concord with their effort in the workplace.

Theoretical Framework

Based on the literature review, we anticipate a relationship between the compensation gaps caused by discrimination and degree of inclusion in an organization. The compensation gap can exist due to many types of discriminations; however, for this study, we have considered five types of discriminations creating compensation gaps. These consist of gender, disability, religion, and culture in addition to two main factors determining level of inclusion, that is, age of the employee and employment tenure (Figure 1). Other types of discriminations creating compensation gaps like linguistic differences, color, and race have been assumed to be included in the cultural discrimination. Few others like financial background and sexual orientation (gay and bisexual etc.) have been ignored intentionally in accordance with the consideration of parsimony; as in the light of extant literature, these have little effects on compensation gaps (Singhari & Madheswaran 2016; Srivastava, 2019). Our assumptions are aligned with recent research which has distinguished surface-level diversity from deep-level diversity. Surface-level diversity is directly and easily noticeable in groups (such as gender, religion, culture, age, and disability), whereas the characteristics of deep-level diversity (attitudes, opinions, information, and values) become known only over time through verbal and nonverbal communication (Harrison, Price, Gavin, & Florey, 2002). Surface-level diversity is easy to measure and is considered to have more pronounced effects than deep-level diversity (Jackson & Ruderman, 1995).

Figure 1.

Factors influencing inclusion. Discriminatory factors (gender, disability, religion, and culture) influence various components of compensation creating gaps in each component for a minority and an ordinary employee. The gaps in compensation along with employment tenure and age of employee determine inclusion.

We have decomposed compensation into five components, that is, base salary, bonus, pay for performance, take home salary, and other perks (Traveling allowance/daily allowance while on special duty, allotment of company house/accommodation, and benefits admissible while being on an executive position). The purpose of decomposing compensation into its components is to find out the components which are strongly related to inclusion and need to be taken care of by organizations aspiring to become inclusive. In addition, we have taken into account the employment tenure and age of the employee as important factors influencing the feeling of inclusion.

Methodology

This is a mixed method exploratory study which combines two studies. In the first study, a quantitative approach has been used to verify our inference that variation in compensation due to various discriminatory factors influences inclusion in an organization. Four organizations from each of the eight sectors were selected for investigation (making a total of 32 organizations), and data on compensation were collected from their finance/accounts departments. The eight sectors were selected on the basis of employment of sizable (more than 20%) diverse workforce, that is, at least 5% employees from each female, disabled persons, religious minorities, and cultural minorities (Pakistan Bureau of Statistics, 2017). Selected included manufacturing, construction, consultancy (real estate), information technology, security services, medical college, departmental store, and hotel. The research was conducted at the local headquarters of the organizations at major cities. Initial access was gained through gatekeepers who were known to the authors. Selection criteria for participants of the study were that employees had been in the organization for more than 5 years and had experienced inclusion as well as were cognizant of compensation gaps existing in the organization. All potential respondents were then invited to participate on a voluntary basis and anonymity and confidentiality was guaranteed.

Data from 32 organizations consisted of 608 observations after deleting incomplete or invalid observations. We assumed that compensation gaps mostly exist at worker level (blue collar, pay grade 1–13). Due to variation of compensation policies in the organizations, instead of actual amount of money, comparative percentages have been used to make the data more useful and comparable. For example, an ordinary worker who is a man, member of majority culture, without any disability and belonging to majority religion, is considered to be receiving 100% in all components of compensation (observed on ground). The amount received by any other worker who is part of minority group (say a woman) has been considered in percentage compared to the ordinary worker. In the second study, 25 semi-structured interviews with employees were arranged to explore cognitive basis of compensation gaps. Responses were coded to see the comparative perception of respondents on the patterns of data observed during the first (quantitative) study. Details of respondent profiles are given in Table 1.

Table 1.

Study 2 (Qualitative): Respondent Profiles.

Respondent identity	Sector	Pay grade/Status	Age	Experience	Gender	Religion	Culture/Ethnicity	Disability
Respondent 01	Mfr	13	40	18	Male	Islam	Majority	No
Respondent 02	Mfr	11	36	15	Male	Islam	Majority	Blind
Respondent 03	Mfr	9	35	12	Male	Islam	Afghani	No
Respondent 04	Mfr	13	38	17	Female	Islam	Majority	No
Respondent 05	Mfr	10	37	15	Male	Christian	Majority	No
Respondent 06	IT	10	37	15	Male	Islam	Majority	No
Respondent 07	IT	13	36	15	Male	Islam	Majority	W/Chair
Respondent 08	IT	9	35	12	Male	Islam	Afghani	No
Respondent 09	IT	11	40	15	Female	Islam	Majority	No
Respondent 10	IT	13	41	18	Male	Sikh	Majority	No
Respondent 11	Med	9	40	18	Male	Islam	Majority	No
Respondent 12	Med	5	37	12	Male	Islam	Majority	Mute
Respondent 13	Med	9	35	12	Male	Islam	Afghani	No
Respondent 14	Med	6	36	14	Female	Islam	Majority	No
Respondent 15	Med	9	37	15	Male	Hindu	Majority	No
Respondent 16	SS	4	40	18	Male	Islam	Majority	No
Respondent 17	SS	11	36	15	Male	Islam	Majority	One arm
Respondent 18	SS	9	35	12	Male	Islam	Mohajir	No
Respondent 19	SS	10	38	16	Female	Islam	Majority	No
Respondent 20	SS	4	36	15	Male	Christian	Majority	No
Respondent 21	Conslt	11	40	18	Male	Islam	Majority	No
Respondent 22	Conslt	8	40	16	Male	Islam	Majority	Deaf
Respondent 23	Constl	9	35	12	Male	Islam	Afghani	No
Respondent 24	Conslt	4	35	11	Female	Islam	Majority	No
Respondent 25	Conslt	10	37	15	Male	Ahmadi	Majority	No

Note. Mfr = Manufacturing; Med = Medical College; SS = Security Services; Conslt = Consultancy.

Research Design and Procedures

We adopted a two-step exploratory mixed method design based on the philosophical standpoint of pragmatism (Creswell 2013; Johnson & Gray, 2010). Two methods were used for data collection; first, content analysis, documentary record inspection, and survey for quantitative data, and second, semi-structured qualitative interviews. Data related to compensation were collected from the payrolls of the organizations, while data about perceived inclusion were obtained through use of an instrument having five-point Likert scale. Quantitative analysis consisted of the multiple regression analysis and principal factor analysis using SPSS software. The qualitative data were analyzed by thematic analysis with NVivo Software. This incorporated both the deductive technique, generating themes from the data which echoed with previous research and prior theories (Crabtree & Miller, 1999), and the inductive technique, generating new themes from the raw data (Creswell 2013). We followed the well-established phases of thematic analysis: familiarization with data, generating initial codes, searching for themes among codes, reviewing themes, and defining and naming themes (Creswell 2013). Numerous past studies have used similar methodology (Oulton et al., 2016; Seghers, Boone, & Van Avermaet, 2019).

Measurement Instrument

For determining feeling of inclusion by an employee, the scale developed by Mor Barak has been adopted (Mor-Barak and Cherin, 1998; Mor Barak et al., 2001). The same has been used in various other studies to measure the construct of inclusion (Khan & Jabeen, 2019). It is a 15-item scale which obtains responses on a Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). All items were averaged to create mean inclusion–exclusion score and treated as an observed variable. Higher scores represented a higher sense of inclusion. Previous studies using this scale reported Cronbach’s alphas of .88, .90, .81, .87 (Booysen et al. 2013), and .84 (Kim, Yu, & Hassan, 2018).

Findings – Study-1 (Quantitative)

Data were entered in Excel spread sheets for further export to SPSS statistical software. It is customary to check the data for their suitability before they are statistically analyzed. As the first step, data for each individual variable were tested for normality using the Jarque-Bera normality test. The results indicated that data were normally distributed and was suitable for correlation and regression analysis (p-values less than .05). Then, correlations between inclusion and selected components of compensation were determined. Pearson correlation coefficients between inclusion and base salary (.3712), bonus (.7181), pay for performance (.6337), miscellaneous perks (.7010), and take home salary (.6529) indicated that the degree of inclusion in an organization was highly correlated with bonus, followed by miscellaneous perks, take home salary, and then pay for performance. It was surprising to note that inclusion was weakly correlated with base salary.

The correlation analysis between inclusion and components of compensation provided sufficient basis for further analysis. Thereafter, our focus was to explore compensation gaps caused by various types of discriminations and their linkage with inclusion. To calculate compensation gaps in various components, the comparative percentages used for calculation of coefficients of correlation were subtracted from 100. The idea behind was that an ordinary worker earns 100%, whereas a discrimination victim earns less than 100% (say 80% or so of the income of an ordinary worker). Subtraction from 100 gave compensation gap in each particular component of compensation caused by discriminatory factors (gender, culture, disability, and religion). In other words, the obtained values represented a percentage gap (penalty) in a particular component of compensation (say bonus) for a minority group worker (say a disabled worker). The correlation matrix of gaps in various components of compensation and inclusion was obtained through SPSS software. It was found that there was no difference of numerical values from the above mentioned Pearson correlation coefficients, except a negative sign with each coefficient of correlation. It gave an indication of correctness of our initial inference that gaps in various components of compensation are negatively correlated with inclusion. It further confirmed that in order to increase degree of inclusion in an organization, the gaps in components of compensation due to discrimination have to be reduced/eliminated and the organization will have to move toward pay equity (Gomez & Bernet, 2019).

Regression analysis was used for examining the impact of each basis of discrimination (gender, disability, religion, and culture) along with age and employment tenure, on inclusion. Two variables, age of the employee and employment tenure, being important determinants of inclusion have also been taken into account to make the analysis more comprehensive. In total, it made seven covariate quantitative variables (five components of compensation—base salary, bonus, pay for performance, miscellaneous perks, and take home salary), employment tenure, and age and four dummy variables (basis of discrimination—gender, disability, religion, and culture) used in regression (Table 2).

Table 2.

Description of Variables in the Model.

Variable Name	Type	Symbol (Coefficient)	Description
Base salary	Covariate	X₁ (β₁)	It is the salary which is admissible to every employee according to job description/pay grade
Bonus	Covariate	X₂ (β₂)	It is part of compensation which is granted to selected employee on achievement of an extraordinary objective and is based on evaluation/recommendation of superior. Its frequency may be quite low, that is, once a year, six monthly basis, quarterly, or at special occasions
Pay for performance	Covariate	X₃ (β₃)	It is part of compensation which is granted on each unit of additional work done beyond a given level. It is also based on evaluation and recommendation of superior. This component of compensation is directly related to marginal increase in productivity by an individual
Miscellaneous perks	Covariate	X₄ (β₄)	It includes traveling allowance/daily allowance while on special duty, allotment of company house/accommodation, and executive benefits based on the recommendations of superior in addition to organizational policies
Take home salary	Covariate	X₅ (β₅)	It is the final amount available to the employee for meeting personal and domestic expenditures
Age of employee	Covariate	X₆ (β₆)	Age of the employee in years (rounded off to nearest 1 year)
Employment tenure	Covariate	X₇ (β₇)	Tenure of employee in the organization (rounded off to nearest 1 year)
Culture	Dummy	D₀	It indicates ordinary employees belonging to cultural majority group or others (omitted in the model)
Gender	Dummy	D₁	Indicates gender (1 = woman and 0 = others)
Disability	Dummy	D₂	Indicates disability (1 = disabled person and 0 = others)
Religion	Dummy	D₃	Indicates religion (1 = minority religion and 0 = majority religion)

The dependent variable “inclusion” has been denoted by Y which represents a comparative percentage of feeling of inclusion by an employee (compared to feeling of inclusion by a majority group employee). The constant (intercept) has been denoted by β₀. Using the above mentioned covariate and dummy variables, we propose a linear regression model to predict a comparative percentage of feeling of inclusion as given below

Y = β_{0} + β_{1} X_{1} + β_{2} X_{2} + β_{3} X_{3} + β_{4} X_{4} + β_{5} X_{5} + β_{6} X_{6} + α_{1} D_{1} + α_{2} D_{2} + α_{3} D_{3} + α_{4} D_{4} + µ

The variables used in the model have been described in Table 2. While formulating the model, only additive effects of various basis of discrimination have been considered because only negligible multiplicative effects could be observed during field work (Jones, Finkelstein, & Koehoorn, 2018; Tettey et al., 2014). This can be understood by an example, for instance, a woman was being paid at a lesser rate of pay for performance due to gender discrimination. If she belonged to a minority culture or religion or disabled category, then the effects of discrimination on her pay for performance due to discriminatory factors other than gender were very less. It means, substantial reduction in any component of compensation was observed due to one basis of discrimination (like gender in the given example) with little effect due to other basis. For determining multiplicative effects and their segregation from additive effects (Tettey et al., 2014), a separate study is needed which is beyond the scope of this study. The regression results (SPSS output) are given in Table 3a–c; the model takes the following form with obtained coefficient values from regression results as

Y = 51.2 + 0.09 X_{1} + 0.23 X_{2} - 0.1 X_{3} + 0.05 X_{4} + 0.1 X_{5} + 0.38 X_{6} - 0.26 X_{7} - 5.22 D_{1} - 18.8 D_{2} - 6.24 D_{3} - 7.77 D_{4} + µ

Table 3a.

Model Summary, ANOVA Table, and Coefficients of Initial Model.

Model summary
Model	R	R square	Adjusted R square	Std. error of the estimate
1	.848^a	.719	.713	6.74927

^aPredictors: (Constant), age of employee, base salary, religion, culture, gender, employment tenure, bonus, take home salary, miscellaneous perks (accommodation, travelling/daily allowance, and executive benefits), pay for performance, and disabled persons.

Table 3b.

Model Summary, ANOVA Table, and Coefficients of Initial Model.

ANOVA^a
Model		Sum of squares	df	Mean square	F	Sig
1	Regression	69,310.256	11	6300.932	138.322	.000^b
	Residual	27,149.369	596	45.553
	Total	96,459.625	607

^aDependent variable: Inclusion.

^bPredictors: (Constant), base salary, bonus, pay for performance, miscellaneous perks (accommodation, TA/DA, and executive benefits), take home salary, age of employee, employment tenure, religion, culture, gender, and disability.

Table 3c.

Model Summary, ANOVA Table, and Coefficients of Initial Model.

Coefficient^a
		Unstandardized coefficient		Standardized coefficient
Model		B	Std. error	Beta	t	Sig
1	(Constant)	51.228	13.214		3.877	.000
	Base salary	.092	.110	.023	.838	.403
	Bonus	.234	.025	.408	9.363	.000
	Pay for performance	−.101	.038	−.271	−2.685	.007
	Take home salary	.051	.064	.042	.799	.425
	Miscellaneous perks (accn, TA/DA, and exec benefits)	.101	.051	.213	1.962	.050
	Employment tenure	.038	.082	.012	.462	.044
	Gender	−5.195	3.130	−.166	−1.660	.197
	Disability	−18.858	3.654	−.596	−5.161	.000
	Culture	−6.247	1.153	−.197	−5.418	.000
	Religion	−7.774	1.032	−.243	−7.535	.000
	Age of employee	.268	.089	.067	2.994	.003

^aDependent variable: Inclusion.

From this initial form of the model, we can predict feeling of inclusion by an employee in terms of a comparative percentage. The unknown values to be put in the model for this purpose include comparative percentages of base salary (X₁), bonus (X₂), pay for performance (X₃), miscellaneous perks (X₄), take home salary (X₅), age of employee (X₆), and employment tenure (X₇). Similarly, value of relevant categorical variable to be put in the model will be either one or zero (being discrete variables) depending upon obtained data. The model with values of coefficients determined from the regression analysis indicates that all types of discriminations are negatively related to inclusion. Largest negative impact on inclusion is by disability (−18.8D₂), then culture (−7.77D₄), followed by religion (−6.24D₃) and gender (−5.22D₁). Age (X₆) and employment tenure (X₇) were found to be positively related to inclusion as shown by their respective coefficient values (.268 and .038). The findings were somewhat different from our expectations which have been explained in the Discussion section.

With regard to the overall performance of the initial model, coefficient of determination (R²) was .719 which meant that variables in the model explained 71% of variation in the dependent variable (inclusion). Adjusted R² was .714, standard error of the regression was 6.740, and value of F statistics with 10 variables and 598 degrees of freedom was 152.568 with zero p-value indicating that the model was overall significant. Looking at the p-values of covariate variables, it seemed that two covariate variables, base salary and take home salary, and one categorical variable, gender, were not significantly related to inclusion (p-values higher than .05). Therefore, the model was revised by deleting insignificant variables and regression was run again. Results of regression are given in Table 4a–c showing that the value of coefficient of determination (R²) was .717 and adjusted R² value was .713, even after deletion of three variables; it meant that still variables in the model explained 71% of the variation in the dependent variable and the deleted variables were not contributing much to the explanation of variation in the dependent variable. The value of F statistics with 599° of freedom for the revised model was 189.496 with zero p-value indicating that the revised model was more significant than the initial model.

Table 4a.

Revised Model After Deleting Insignificant Variables.

Model summary
Model	R	R square	Adjusted R square	Std. error of the estimate
1	.847^a	.717	.713	6.75453

^aPredictors: (Constant), minority religion, duration of Svc, minority culture, bonus, disabled persons, miscellaneous perks (accommodation, TA/DA, and executive benefits), pay for performance, and age of employee.

Table 4b.

Revised Model After Deleting Insignificant Variables.

ANOVA^a
Model		Sum of squares	df	Mean square	F	Sig
1	Regression	69,131.037	8	8641.380	189.406	.000^b
	Residual	27,328.588	599	45.624
	Total	96,459.625	607

^aDependent variable: Inclusion.

^bPredictors: (Constant), minority religion, duration of Svc, minority culture, bonus, disabled persons, miscellaneous perks (accommodation, TA/DA, and executive benefits), pay for performance, and age of employee.

Table 4c.

Revised Model After Deleting Insignificant Variables.

Coefficient^a
		Unstandardized coefficient		Standardized coefficient
Model		B	Std. error	Beta	t	Sig
1	(Constant)	65.102	11.125		5.852	.000
	Bonus	.238	.025	.414	9.605	.000
	Pay for performance	−.065	.031	−.175	−2.088	.037
	Misc perks (accn, TA/DA, and exec benefits)	.162	.038	.342	4.232	.000
	Age of employee	−.179	.533	−.045	−.336	.031
	Employment tenure	.442	.534	.111	.827	.048
	Disability	−13.631	1.002	−.431	−13.601	.000
	Culture	−5.187	.845	−.164	−6.137	.000
	Religion	−7.163	.857	−.224	−8.357	.000

^aDependent variable: Inclusion.

After removal of insignificant variables, the model took the following form with values of coefficients taken from rerun regression analysis

Y = 65.102 + 0.238 X_{2} - 0.065 X_{3} + 0.162 X_{5} + 0.179 X_{6} + 0.44 X_{7} - 13.6 D_{2} - 5.18 D_{3} - 7.16 D_{4} + µ

In the above model, the issue of multicollinearity still existed because it used various components of compensation which were closely related to each other. Although, the issue had reduced by deletion of insignificant variables (base salary, take home salary, and gender), yet multicollinearity was required to be addressed. For this purpose, we carried out principal component analysis of the remaining variables for merging the ones which had higher communality (described in SPSS Communality Table). The results of principal component analysis are given in Table 5a–c.

Table 5a.

Results of Principal Component Analysis.

KMO and Bartlett’s Test
Kaiser–Meyer–Olkin measure of sampling adequacy		.711
Bartlett’s test of sphericity	Approx. chi-square	3.038 × 103
	df	28
	Sig	.000

Table 5b.

Results of Principal Component Analysis.

Total variance explained
Component	Initial Eigenvalue			Extraction sums of squared loadings			Rotation sums of squared loadings
Component	Total	% of Variance	Cumulative %	Total	% of Variance	Cumulative %	Total	% of Variance	Cumulative %
1	3.605	45.057	45.057	3.605	45.057	45.057	3.604	45.056	45.056
2	1.268	15.854	60.911	1.268	15.854	60.911	1.268	15.855	60.911
3	.976	12.205	73.115
4	.915	11.441	84.556
5	.567	7.088	91.644
6	.472	5.899	97.543
7	.159	1.989	99.532
8	.037	.468	100.000

Note. Extraction method: Principal component analysis.

Table 5c.

Results of Principal Component Analysis.

Component matrix^a
	Component
	1	2
Bonus	.869	−.078
Pay for performance	.947	−.033
Misc perks (accn, TA/DA, and exec benefits)	.962	.000
Age of employee	.246	.073
Employment tenure	.339	.269
Disability	−.775	−.078
Culture	.302	.772
Religion	.398	−.763

^a2 components extracted.

In the SPSS output, KMO and Bartlett’s test statistics (.711) indicated that principal component analysis was suitable for the data. The scree plot (Figure 2) and component matrix indicated that the variables could be loaded on two extracted components. However, the merging of variables which loaded well on one component was done considering theoretical support for such merging. Therefore, bonus, pay for performance, and miscellaneous perks were merged by taking their average being parts of compensation under new variable name “discretionary payments” (name is given because for all of them, recommendations from the superior is needed—it can be said that these are at the discretion of the superior). Conversely, age of employee and employment tenure did not load well on any of the two components and were kept as such. Minority religion and minority culture loaded well on component-2 but merging them was likely to lead to wrong conclusions because both were dummy variables and taking their average or ratio was not possible. Disability, although loaded well on component-1, yet could not be merged with other variables which loaded well on component-1 (bonus, pay for performance, and miscellaneous perks) being of different type. If merged together, it would have been spurious as no theory explained their combined effects on inclusion. The refined model after merging components of compensation will take the following form

Y = 61.923 + 0.26 {6 Z}_{2} + 0.271 X_{6} + 0.056 X_{7} - 14.571 D_{2} - 7.399 D_{3} - 9.095 D_{4} + µ

Figure 2.

Scree plot showing components extracted from principal component analysis plotted against Eigenvalues.

In this model, Y is the comparative percentage of inclusion felt by an employee, Z₂ is the average of bonus, pay for performance, and miscellaneous perks, X₆ is the age of the employee, and X₇ is the employment tenure. D₂ is the dummy variable representing disability, D₃, culture, and D₄, religion. Regression results and collinearity diagnostic statistics (tolerance and variance inflation factors (VIF)) are shown in Table 6a–c. Looking at the model statistics, R² is .696 and adjusted R² is .693 which revealed that 69% of variance in inclusion was explained by the model. Similarly, F statistics of 229.195 and zero p-value indicated significance of the model, while tolerance and VIF indicated that there was no multicollinearity problem in the model. We concluded that the proposed model was workable and could be used to determine comparative feeling of inclusion of an employee. We assumed that symptoms of the problem (discrimination) would be indicated by fluctuations in the level of inclusion of an employee. Such symptoms will be observed in employees belonging to various minority groups that are usually more susceptible to discrimination earlier than ordinary employees.

Table 6a.

Regression Results and Collinearity Statistics of Model After Merging Variables in the Light of Principal Component Analysis.

Model summary
Model	R	R square	Adjusted R square	Std. error of the estimate
1	.834^a	.696	.693	6.98652

^aPredictors: (Constant), religion, employment tenure, age of employee, culture, disability, and discretionary payments.

Table 6b.

Regression Results and Collinearity Statistics of Model After Merging Variables in the Light of Principal Component Analysis.

ANOVA^a
Model		Sum of squares	Df	Mean square	F	Sig
1	Regression	67,123.937	6	11,187.323	229.195	.000^b
	Residual	29,335.688	601	48.811
	Total	96,459.625	607

^aDependent variable: Inclusion.

^bPredictors: (Constant), religion, employment tenure, age of employee, culture, disability, and discretionary payments.

Table 6c.

Regression Results and Collinearity Statistics of Model After Merging Variables in the Light of Principal Component Analysis.

Coefficient^a
		Unstandardized coefficient		Standardized coefficient			Collinearity Statistics
Model		B	Std. error	Beta	T	Sig	Tolerance	VIF
1	(Constant)	61.923	3.846		16.100	.000
	Discretionary payments	.266	.015	.555	17.397	.000	.497	2.013
	Age of employee	.271	.091	.068	2.958	.003	.950	1.052
	Employment tenure	.056	.075	.018	.745	.456	.912	1.097
	Disability	−14.571	.954	−.460	−15.279	.000	.557	1.795
	Culture	−7.399	.800	−.234	−9.245	.000	.791	1.264
	Religion	−9.095	.837	−.285	−10.872	.000	.738	1.355

^aDependent variable: Inclusion.

Findings – Study 2 (Qualitative)

As can be noted, study 1 (quantitative) found that components of compensation were significantly correlated with inclusion and significant covariance existed between inclusion and components of compensation which varied according to different basis of discrimination. However, contrary to our expectation, the relation of base salary and take home salary was found weak with inclusion. Similarly, among the basis of discrimination, gender had the weakest relation with inclusion. These unexpected findings prompted us to explore the matter qualitatively. In addition, the reasons for prevalence of gaps in components of compensation due to each type of discrimination also merited a qualitative analysis. Qualitative examination of the quantitative findings is in consonance with methodology suggested by scholars for explanatory sequential design (Creswell & Plano, 2018).

To uncover the deep rooted causes (mostly cognitive biases) of existence of compensation gaps on the basis of gender, disability, religion, and culture, 25 interviews were conducted. Out of 25 respondents, five respondents belonged to the majority group (may be considered the control group) while five each from women, disabled persons, minority religions (irrespective of a particular religion), and minority cultures (irrespective of any specific culture). An interview guide was prepared to get the opinions of respondents on how they perceived compensation gaps, reasons of their prevalence, and relationship between their feeling of inclusion and compensation gaps. The respondents were asked main questions as per interview guide (Table 7) in addition to numerous probing questions. Responses of the interviewees were transcribed and coded. Initial coding was reviewed and recoding was done to see patterns in responses. Major themes which emerged in response to our questions to respondents are given in the ensuing paragraphs.

Table 7.

Interview Guide–Questions.

Serial	Item ID	Question
1	CB1	What is the cognitive basis of discrimination in compensation against female employees?
2	CB2	What is the cognitive basis of discrimination in compensation against disabled persons?
3	CB3	What is the cognitive basis of discrimination in compensation against employees belonging to minority culture?
4	CB4	What is the cognitive basis of discrimination in compensation against employees belonging to minority religions?
5	CR1	Do you feel the cognitive basis of compensation discrimination against women is real and not a perception only?
6	CR2	Do you feel the cognitive basis of compensation discrimination against disabled are real and not a perception only?
7	CR3	Do you feel the cognitive basis of compensation discrimination against minority religion is real and not a perception only?
8	CR4	Do you feel the cognitive basis of compensation discrimination against minority culture is real and not a perception only?
9	CBO	What are the reasons for existence of compensation gaps other than discrimination due to various cognitive bases?
10	CC	What are the components of compensation most affected by discrimination and why?
11	CI	How compensation discrimination affects inclusion in the organization?

Cognitive Basis of Discrimination Against Female Employees

Respondent numbers 2, 4, 7, and 17 indicated that usually employers considered female and disabled persons as comparatively less useful for the organization. To them, both categories cannot be employed on all types of jobs for which an ordinary employee can be easily tasked. Similarly, respondent numbers 14 and 19 said that at the time of change from one appointment, job, or department to the other, suitability of the next slot used to become a problem for women. Respondent numbers 3, 22, and 25 argued that women were considered less suitable for jobs demanding physical strength, ability to supervise and control male employees, and extended working hours in the office or field. Although this perception was supported, yet respondent numbers 1, 5, and 20 explained that female employees usually resisted any change to their job-related matters; to them, continuity of a particular situation was perceived more important than job variety for better learning. Respondent numbers 9 and 13 pointed out that usually the results produced by women were of lower quality than male employees. In the same way, disabled persons posed constraints in assignment of tasks to them due to various physical and job related restrictions, usually not liked by employers. The supervisors avoided pushing women and disabled persons for putting in more hard work because the same could be easily labeled as harassment, discrimination, or exploitation. Respondent numbers 8 and 11 said that both women and disabled employees were perceived less effective as team leaders. Respondent numbers 6, 11, and 22 explained that any limitation in working with teams would lower the graph of productivity of female and disabled employees. These misperceptions and stereotypes played a pivotal role in creation of compensation gaps on the basis of gender and disability.

Cognitive Dissonance of Female Employee

Female respondents (numbers. 4, 9, 14, 19, and 24) argued that they were at par with their male counterparts with regard to sense of responsibility, and the results produced by them were seldom inferior to male employees. They were assigned jobs and tasks without any consideration of gender or leniency; however, female specific facilitations like maternity leave, separate lavatories, privacy, and protection against harassment were considered as a burden. Respondent numbers 9 and 19 indicated that there were no limitations with regard to women leading teams having male members in majority. Female team leaders cared more for every member of the team, proved to be more focused and produced compatible results. Respondent numbers 9 and 24 explained that male members of teams usually took longer to develop an understanding with female team leaders and team members. Instructions and time lines from female team leaders were usually taken with less seriousness by male members. Another grave misperception of male employees was pointed out by respondent number 14 that male colleagues considered that female team members and leaders had less exposure to various job-related situations, and it was quite easy to befool them. Contrary to reality, female employees had emotional delicacy which made them lesser suited to administrative appointments. Respondent numbers 4, 14, and 19 agreed that they were not as good as men physically, and most of them could not afford to work for prolonged hours due to domestic responsibilities.

Cognitive Basis of Discrimination Against Disabled Employees

Persons with disability almost always posed limitations in assignment of appropriate jobs and then in actual performance of the assigned job. Respondent numbers 2, 7, and 12, from the disabled employee category, indicated that most of the employers considered it a favor to give job to a person with disability which was a major cause of compensation gap for them. Respondent numbers 17 and 22 indicated that although not explicitly, yet their efforts and produced results were down played by their peers and superiors. The components of compensation requiring a positive evaluation and recommendation by superiors carried a bias against them. Respondent numbers 12 and 17 argued that despite their disability, they produced results equal to an ordinary employee and tried to fill the void of their disability through extraordinary hard work and other abilities possessed by them. Many studies have indicated this aspect. Workers are often quite capable of adapting to any limitations that they have at the workplace (Campolieti, 2007; Daly & Bound, 1996). Respondent numbers 2 and 12 argued that workers with a disability usually compensated in other dimensions such as care, diligence, loyalty, and effort.

Cognitive Basis of Discrimination Against Minority Religion and Culture

With regard to religious and cultural diversity, Respondent numbers 5 and 20 explained that separation between employees on the basis of religion and culture would continue to exist until special measures are taken by an organization. Respondent number 10 pointed out although religious practices and cultural rites are a personal domain, yet, it can lead to creation of excluded groups having low level of commitment with the organization. Respondent number 23 opined that employees belonging to minority culture and religion could damage inclusive culture by intentionally or unintentionally supporting employees from their own culture and religion. On the other hand, majority group members were likely to down play the performance and achievements of employees from minority culture and religion. Respondents unanimously agreed that discrimination on the basis of religion and culture, due to a natural bias or tendency to favor, was considered one of the major reasons of compensation gaps.

Perception about Compensation Components Most Affected by Discrimination

Compensation gaps, in the opinion of respondent numbers 3, 9, and 17, were mostly found in the flexible components of compensation like pay for performance, bonus, and miscellaneous perks, which were granted on positive evaluation and recommendations by superiors. The gaps in compensation in flexible components were an outcome of biased evaluation of performance which at times came from negative lobbying and in-group pressures. Such compensation gaps adversely affected inclusion in an organization and further accentuated perceived insecurity of all minority groups. Respondent numbers 6 and 17 explicitly mentioned that all minority groups were vulnerable to exploitation by the majority group; women and disabled persons were particularly more vulnerable being comparatively weaker. With regard to flexible components of compensation, the pay for performance has particularly been described by respondents as main playground of discrimination. Respondent numbers 5, 10, and 15 showed their resentment with regard to policies adopted by organizations. They mentioned that the targets fixed as criteria of eligibility for a particular level of pay for performance were kept too high and exploitative. The rewards given on meeting such targets in the pay for performance were too meager compared to the efforts required for them.

Reasons Other than Discrimination Creating Compensation Gaps

Bulk of the respondents from all categories agreed that compensation gaps were also an outcome of lack of attention by organizations toward inclusion for enabling all members of the organization to fully participate. Respondent numbers 3, 7, 14, and 19 pointed out that discrimination on the basis of gender, disability, culture, and religion was discouraged in every organization, yet degree of inclusion in organizations depended on the top management’s commitment for it. Respondent numbers 11 and 24 argued that compensation gaps created divide between various groups of employees and affected their level of commitment and inclusion. The relationship between inclusion and various components of compensation was something which was experienced by almost every employee. Employees who were given due share in the profitability of their organization in the form of bonus, pay for performance, or miscellaneous perks feel a greater degree of inclusion. Compensation gaps based on any factor other than performance and merit were likely to create excluded groups which were least committed with the organization.

Discussion

All components of compensation were found correlated with inclusion in an organization and the findings were as per our initial inference. This expectation was on the basis of literature review as well as expectancy and equity theories. If employees consistently observed that compensation was being given on the basis of discriminatory factors other than merit or performance, their attitude toward the job would be affected (Neugebauer, 2014). Recent research is emphasizing inclusion for reduction of conflicts nurtured by diversity in the work environment (Tang et al., 2015). Inclusion encompasses a series of strategies to smoothen and enhance involvement of diverse workforce in organizational affairs. Individuals and groups want to be actively involved and desire their voices to be heard (Dehaze, 2018). One of the key factors of satisfaction with compensation is how employees feel their pay package relative to others (Kifle, 2014). Both the expectancy and equity theories indicate that people give importance to compensation as a reward for their contributions while their level of contributions can vary proportionate with the reward. Logically, if people are compensated fairly and equitably without any discrimination, then they feel more included and participate in organizational activities fully. In the corporate world, focus is now moving to pay equality both in terms of ensuring that workers who perform similar jobs receive the same pay and that different people have an equal level of input/efforts while being at different jobs (Filmer, King, & Walle, 2005). Organizations that foster diversity and inclusion in the workplace have a significant edge compared to companies that have not made strides in this arena (Deyermenjian, 2018).

Compensation is one of the major determinants of inclusion and is closely observed by each employee for any discriminatory treatment by an employer. Any changes to compensation due to discrimination are detrimental to inclusion directly affecting organizational performance. A continuous monitoring of employee perception of inclusion is mandatory for sensing any variations as an indication of problem in compensation policy of the organization. First signs are likely to occur in the employees of minority groups through a decline in their level of inclusion. An effective model has been proposed by this study that may be used by organizations as an essential ingredient to their system of feedback on policy matters and practices. Organizational leaders have to closely monitor perception of inclusion of each employee to sense any signs of discriminatory policy or its implementation in part or whole for amelioration at its earliest stages.

In this study, we have found that base salary is weakly related with inclusion; there may be multiple reasons for that. Actually, base salary has the least variance because most of the organizations follow pay systems which lay down each level of base salary in accordance with rules, regulations, and competitive conditions in the job market (Agarwal, 2014). Commonly, a typical salary administration system (SAS) should be in accordance with internal alignment, external competitiveness, and employee performance (Newman, Donohue, & Eva, 2017). Without considering these three factors, overpaying or underpaying can become a problem for the organization (Bussin & Christos, 2017). Organizations have to remain at a certain level of base salary because any pronounced variation in base salary can be readily noticed by regulatory institutions (Umar, 2014). Similarly, base salary differences, especially with regard to discrimination, can be easily challenged in courts of law. Due to low variability in base salary from organization to organization, gaps in base salary are minimal (Cornwell et al., 2017). This may be one of the major reasons that base salary has been found weakly related with inclusion, and we have deleted it from our proposed model linking components of compensation with inclusion.

Contrary to weak correlation of base salary, take home salary, although strongly correlated with inclusion, has not been found significant in explaining variation in inclusion. There are few reasons for this disparity which make changes in take home salary in an irregular pattern with little impact on inclusion. These include disciplinary penalties like deduction from pay for absence or late arrivals, variable rates of allowance for conveyance, medical treatment, and housing accommodation (negotiated at the time of induction), and voluntary participation in various retirement schemes. For example, for the employees who participate in voluntary schemes (say postretirement benefits or insurance etc.), their take home salary reduces with the same proportion due to participatory deductions. Therefore, it seems that variation in take home salary does not impact degree of inclusion of an employee and the same has been indicated by higher p-values of take home salary when it was regressed on inclusion.

The regression results revealed another interesting finding that gender was not a significant variable in explaining variation in inclusion. Probably, compensation gaps due to gender discrimination have reduced a lot, and at present, propagation of gender discrimination is much more than reality (Blau & Kahn, 2008). There are certain reasons for this claim. It has been observed that women have enhanced their relative value in labor market qualifications and commitment to work with passage of time. On the two basic measures of human capital value, schooling and actual labor market experience, women have made tremendous progress during 1981–2011 period, reversing the education gap and greatly reducing the experience differentials (Nyhus & Pons, 2012).We almost obtained similar results from qualitative part of our study and not only women respondents but also respondents belonging to other categories indicated that compensation gaps due to gender discrimination have reduced over time for the same reasons. Similarly, due to the role played by print, electronic, and social media, coupled with accessibility and advancement in information technology, cases of gender discrimination attract attention of every observer, and it has become difficult to keep such discriminatory actions hidden (Rizwan, Arshad, Munir, Iqbal, & Hussain, 2014). Women are much more aware of their rights and relevant legislations for protection of their rights. In the labor market today, women are highly qualified, and at times, they dominate men in qualifications, skills, and experience (Blau & Kahn, 2017).

Gender difference in compensation is mostly due to nondiscriminatory factors and reasons. Poddar and Mukhopadhyay (2018) found in his study that the maximum explained wage gap is due to skill and experience, whereas gender wage gap (occupational segregation) is not significant. Indeed, there is considerable evidence that job experience accounts for a significant portion of the gender wage gap (Blau & Kahn, 2008; Gayle & Golan, 2012; O’Neill & Polachek, 1993). Consistent with women placing a greater priority on family responsibilities to the detriment of their labor market outcomes, evidence indicates that women are more likely to quit their jobs for family-related reasons or to exit to nonemployment, adversely affecting women’s wages relative to men’s (Keith & McWilliams, 1995). Empirical evidence also indicates that motherhood may reduce women’s productivity in a variety of ways not readily captured in wage analysis (Albanesi & Olivetti, 2009). A final possibility is that mothers may face discrimination due to their own compulsions (motherhood wage penalty) which may not be an outcome of gender discrimination (Poddar & Mukhopadhyay, 2018). During qualitative part of our study, we found similar results that gender discrimination has considerably reduced and women suffer mostly due to their own self-imposed limitations and constraints which may not be due to gender discrimination in classical sense. Literature indicates that some female occupations pay less than male occupations for workers with similarly measured characteristics (Levanon, England, & Allison 2009). In 2010, occupation and industry differences accounted for about 51% of the gender compensation gaps (Cornwell et al., 2017).

One surprising finding of the study was negative relationship between pay for performance with inclusion. At the first sight, it appeared like a data collection or measurement error. To us, it was a simple expectation that anyone putting in more efforts was to be given benefit of extra efforts and that particular employee should feel more included. It was surprising to note that more pay for performance reduced feeling of inclusion and less pay for performance increased inclusion. The qualitative part of the study gave us an insight into negative relation between pay for performance and inclusion. The first reason was that employees who showed better performance (earned higher pay for performance) were victimized and socially isolated by average performing employees who were always in majority. The fact became more pronounced when a high performer belonged to a minority group. This victimization, at times, caused harassment to force the high performer to give average performance so that others were not exposed for their incompetence, lethargy, or lack of interest. High performers were stigmatized (as selfish, noncooperative, doing apple polishing, and backbiters) and were marginalized by their colleagues. During the course of our study, the high performer respondents were observed sitting alone or busy with their work not having time for socializing with others. Similarly, respondent numbers 8 and 11 reported negative attitude of others toward them and said that whenever they tried to give any suggestion or participated in any decision-making process, numerous colleagues opposed them without any apparent reason, probably, due to their impression of being high performers. This marginalization reduced their perception of inclusion. Similar findings were reported by other studies (Kim & Glomb, 2014).

With regard to existence of compensation gaps due to religious and cultural discriminations, our findings have been in line with other studies. Longhi and Platt (2008) found that compared to Christian men, only Jewish men and women earned higher hourly pay on average. Metcalf (2009) highlighted beneficial pay gap for Jews and the adverse pay gap for Muslim men suggesting that there might be a religious discrimination or at least a religious penalty (against Muslims) that needed to be uncovered. In an earlier study, Longhi and Platt (2008) had ruled out the possibility of religious penalties arguing that pay gaps are a result of the overconcentration of minorities in particular occupations or types of occupations. This argument had been further confirmed by Brynin and Güveli (2012). They found that most of the ethnic pay gap was explained by occupational segregation, whereas the pay gap within occupations was less substantial. The gap, especially at the bottom of the distribution, could be accounted for by differences in personal and job characteristics, for example, the concentration of ethnic-religious groups in part-time (or full-time) work, which explains part of the mean pay differences due to the impact of occupational segregation (Longhi & Platt, 2008).

Conclusion

Inclusion being the focus of contemporary management is highly desirable and vital for the triangle formed by employers, organizations, and employees. Through inclusion, employers try to get the best from employees and enable them to fully participate by creating a feeling of belongingness and commitment with the organization. In fact, organizational outcomes are adversely affected if employees give performance below their full capacity due to discriminatory barriers. In the light of this study, compensation can be effectively used by employers as a catalyst for fostering inclusive culture in organizations. This aim can be achieved by creating and reinforcing employees’ perception about their compensation as a fair and well-matched reward for their inputs (time, knowledge, hard work, and sincerity etc.). In this study, we have used a mixed method approach to explore the linkage between compensation and feeling of inclusion in an organization. We decomposed compensation into its five main components and investigated the impact of the four basis of discrimination (gender, disability, religion, and culture) on each component to gauge influence of both on inclusion. Similarly, to make our findings more comprehensive in terms of temporal dynamics on inclusion, we also considered two important determinants of inclusion, that is, age of employee and employment tenure. We have proposed a model for the organizations to perpetually monitor feeling of inclusion of their employees for initiating appropriate actions.

This study finds that there is a strong correlation between inclusion and bonus, pay for performance, miscellaneous perks, and take home salary, while base salary is weakly correlated. Its implication for the employers is that, while framing compensation packages, it would be sufficient to keep base salary at the competitive level determined through market survey. Going beyond the level suggested by market survey may be a superfluous expenditure, not yielding any positive impact on inclusion. Few organizations are observed to pay base salary above the prevalent market levels for (probably) employer branding, but we do not advocate this. In the same vein, bonus being the component closest to inclusion in relation may be innovatively managed at appropriate occasions (cultural festivals and religious occasions etc.) to form an image of inclusive and non-discriminating organization. This will be a manifestation of organization’s endeavor to be with their employees on major cultural and religious events. Similarly, pay for performance has to be coupled with recognition and praising high performers publically. Valorizing high performers is essential to eliminate their social isolation and victimization by average performer colleagues (as found in this study). Miscellaneous perks, found significantly related to inclusion, are usually pivots of analysis by employees while comparing themselves with their equals within and outside the organization. Any disparity due to discrimination is likely to reduce feeling of inclusion; therefore, miscellaneous perks must be granted strictly in accordance with organizational policy without any favoritism. The significant relation between take home salary and inclusion is actually the value of the job in the minds of employees’ families, relatives, and acquaintances, who formulate image of the job seeing the money at hand with an individual to spend. The employers should curb the tendency of employees to excessively participate in postretirement schemes reducing their take home salaries considerably. Paucity of money to spend (take home salary) gives rise to frustration and stress ultimately impacting behaviors at workplace. Stressed employees, not finding anyone else to blame, are likely to blame the organization for financial crunch thereby losing interest in their jobs.

We finally conclude that inclusion is the corner stone of intergroup harmony in an organization essential for getting the best from employees. Organizational leaders can use compensation as a catalyst to foster inclusion for ultimately improving organizational performance. Discriminatory compensation policies or practices are harmful for inclusion and may adversely affect organizational performance via reduced inclusion. Gauging level of inclusion on regular basis has to be a mandatory part of organizational feedback. The model proposed by this study provides a workable mean to determine marginal impact of each component of compensation as well as various basis of discrimination, on inclusion. Effectively monitoring employees’ perception of inclusion can indicate weak areas related to discrimination and compensation for timely intervention of organizational leaders.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest 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.

ORCID iD

Muhammad Irfan

Author Biographies

Muhammad Irfan is affiliated with Iqra University Islamabad, Pakistan and is a researcher at department of business administration. He has 28 years of experience and a regular contributor to various journals on the topic of leadership, inclusive organizations, discrimination and organizational change.

Omar Khalid Bhatti is affiliated with Iqra University Islamabad as well as Medipol University Istanbul. He is on the faculty of department of management sciences and has 10 years experience. He is a regular contributor to various journals on workplace spirituality, change management and organizational psychology.

Rashida K. Malik is affiliated with Iqra University Islamabad, Pakistan and is on the faculty of department of management sciences with an experience of 30 years. She is expert in organizational behavior, leadership and social development.

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