Striving for Social Good Through Organizational Inclusion: A Latent Profile Approach

Abstract

Purpose:

Promoting social good in the context of diverse organizations inevitably involves creating inclusive work environments wherein all members feel valued and appreciated for who they are. However, limited empirical work has uncovered evidence-based tools that can help organizational leaders strive for more inclusive organizations.

Method:

The current study used latent profile analysis as an empirical tool to provide a more in-depth understanding of workplace inclusion. Data were collected from a diverse nonprofit organization with 213 employees.

Results:

Findings suggested two latent profiles or subgroups of employees (those who felt less valued and those who felt more valued) who shared similar personal characteristics.

Discussion:

This information can be used to develop culturally sensitive and inclusive evidence-based workplace interventions that achieve social good by ensuring everyone in the organization feels valued. Implications are discussed for expanding macrolevel social work practice for professionals interested in promoting social good in diverse organizations.

Keywords

social good social justice diversity inclusion macrolevel social work

Striving for social good is a fundamental value of the social work profession (Mor Barak, 2018; National Association of Social Workers [NASW], 2017) and has recently become a priority for many organizations, businesses, social entrepreneurs, and global leaders (Foley & Chowdhury, 2007; Makwara, 2011; Mor Barak, 2018; Roy & Karna, 2015; Schminke, Arnaud, & Taylor, 2015; Thompson, 2016; Viswanathan, Seth, Gau, & Chaturvedi, 2009). Social good has generally been referred to as the services or products that promote human well-being (Business Dictionary, 2017), and in a work context, the diversity and social inclusion efforts made by the organization (Mor Barak, 2018). As workforce diversity continues to grow (Colby & Ortman, 2014; Roberson, Holmes, & Perry, 2017), organizational leaders are tasked with addressing the challenge of ensuring that all employees feel valued and appreciated for who they are (i.e., feel included; Mor Barak et al., 2016; Nishii, 2013). Helping everyone in the workplace feel included increases employee well-being and promotes social justice (i.e., everyone’s basic human rights, such respect and dignity, are given equitably without prejudice; Downey, Werff, Thomas, & Plaut, 2015; Mor Barak, 2017; Morgaine, 2014; Shore et al., 2011; Thompson, 2016), both of which are critical factors for achieving social good (Mor Barak, 2018).

Many organizations, both for-profit and nonprofit, realize the need to create inclusive workplaces in which everyone feels valued (Mor Barak et al., 2016; Nishii, 2013; Shore, Cleveland, & Sanchez, 2018; Shore et al., 2011), yet limited empirical tools exist that can help organizational leaders understand where and how to intervene to create more socially just and inclusive workplaces. Given that workforce diversity and inclusion is a key domain of achieving social good (Mor Barak, 2018), this article examined and highlighted an empirical tool that may be able to help increase inclusion in diverse organizational settings.

Literature Review

Definitions

Diversity typically refers to the composition of differences among individuals (e.g., race, ethnicity, gender, education) in the group or organization (Gonzalez & DeNisi, 2009; Homan & Greer, 2013; Roberson, 2006). Inclusion generally refers to the extent to which employees feel valued for their unique attributes and have a sense of belonging as an important member of the group (Mor Barak, 2015; Nishii, 2013; Shore et al., 2011). Diversity management refers to the recruitment strategies and mentoring programs that organizations use to create diverse workforces (Roberson, 2006). Inclusion management refers to the extent to which an organization creates policies and practices that recognize employees’ individual talents and encourage the full participation of each employee in both formal and informal organizational activities (Mor Barak, 2015; Nishii, 2013). A workplace climate for inclusion refers to the shared employee perceptions of the extent to which an organization helps each member feel valued and appreciated as important members of the group (Mor Barak et al., 2016).

Social good is a broad term that encompasses charitable and equitable distribution, social justice, social well-being, environmental sustainability, and access to prosocial resources such as health care and education (Makwara, 2011; Mor Barak, 2018; Taute & McQuitty, 2004; Wright, 2013). This umbrella term applies to services, practices, and products that promote the betterment and well-being of individuals, communities, and societies, with special emphasis on large-scale social impact or change (Business Dictionary, 2017; Mor Barak, 2018). A recent review of existing literature on social good defined the concept as

individual, community and society well-being related to (a) domains such as environmental justice and sustainability, diversity and inclusion, and peace, harmony and collaboration; (b) engaging unconventional systems of change such as grass roots and business collaborations, national and international NGOs [nongovernmental organizations], and social entrepreneurs; and (c) utilizing innovative technologies and approaches, such as design thinking, big data driven models, and harnessing social media for social change, all aiming to promote social justice. (Mor Barak, 2018, p. 2)

Social justice has been defined by the NASW as “the view that everyone deserves equal economic, political, and social rights and opportunities” (Morgaine, 2014, p. 4). The Council on Social Work Education referred to social justice as “each person, regardless of position in society, has basic human rights, such as freedom, safety, privacy, an adequate standard of living, health care, and education” (Morgaine, 2014, p. 5). In essence, social justice practices in organizations ensure that everyone’s basic human rights are distributed equitably without prejudice (Morgaine, 2014). In the context of organizations striving for social good, workforce diversity encourages heterogeneous representation, social justice ensures everyone has equal access to resources, and inclusion invites everyone to feel valued and appreciated for who they are.

Diversity

Diversity in the United States is expected to increase such that one in five Americans will be 65 or older by 2030, and more than half of all Americans will be members of a racial and ethnic minority group by 2044 (Colby & Ortman, 2014). The expected increase in diversity will inevitably increase heterogeneity in the workplace (Craig, Rucker, & Richeson, 2017; Köllen, 2015; Roberson et al., 2017; Shore et al., 2018). Diverse workforces are associated with both positive and negative organizational outcomes (Acquavita, Pittman, Gibbons, & Castellanos-Brown, 2009; Gonzalez & DeNisi, 2009; Mor Barak et al., 2016; Nishii, 2013; Shore et al., 2011). Mor Barak and colleagues’ (2016) recent meta-analysis of workforce diversity in human services organizations indicated that some of the beneficial outcomes of workplace heterogeneity are improved employee commitment, satisfaction, and retention, whereas some of the detrimental outcomes of workforce diversity have been increased employee distrust, conflict, and turnover (Acquavita et al., 2009; Gonzalez & DeNisi, 2009; Mor Barak et al., 2016; Nishii, 2013; Shore et al., 2011; Travis & Mor Barak, 2010). Recently, scholars have suggested inclusion is a key factor for ensuring diversity results in beneficial organizational outcomes (Mor Barak et al., 2016; Nishii, 2013; Shore et al., 2018; Shore et al., 2011).

Inclusion

A recent review of the inclusion literature (Shore et al., 2018) suggested that inclusive workplaces, particularly in diverse organizational contexts, increase employee commitment (Cho & Mor Barak, 2008; Findler, Wind, & Mor Barak, 2007; Hwang & Hopkins, 2015; Shore et al., 2018; Shore et al., 2011), job satisfaction (Acquavita et al., 2009; Brimhall, Lizano, & Mor Barak, 2014; Hwang & Hopkins, 2015; Shore et al., 2018; Shore et al., 2011), and organizational citizenship behavior (i.e., a willingness to exceed job descriptions; Cottrill, Lopez, & Hoffman, 2014; Shore et al., 2018), and reduce employee conflict (Nishii, 2013) and turnover (Brimhall et al., 2014; Shore et al., 2018; Shore et al., 2011; Waters & Bortree, 2012).

Although inclusion has been associated with positive workplace outcomes, it has yet to be examined in depth using a person-centered approach. In essence, little is known about whether certain subgroups of employees exist in a diverse workforce who may feel less valued and therefore do not benefit from inclusion’s positive outcomes. Some evidence suggests that individuals who belong to minority social groups (i.e., race and ethnicity and gender) are often left out of important work-related information networks and consequently feel less valued and included in the workplace (DiTomaso, Post, & Parks-Yancy, 2007; Dumas, Phillips, & Rothbard, 2013; Hurst, Gibbon, & Nurse, 2016). These studies primarily focused on more homogeneous organizations in which the majority of employees are White and male, resulting in members of racial and ethnic minority groups and female employees feeling less valued (Cheng & Chen, 2014; DiTomaso & Parks-Yancy, 2014; Hatzenbuehler, 2009). However, less is known about whether these trends exist in highly diverse work contexts. In the past, organizational practitioners argued that increasing diversity representation would essentially solve the dilemma of certain individuals not feeling valued in the workplace (Herring, 2009; Mannix & Neale, 2005; Robinson & Dechant, 1997). However, as workforce diversity has increased (Craig et al., 2017; Roberson et al., 2017; Shore et al., 2018), organizational researchers have realized that diversity alone cannot guarantee that everyone will feel included in the workplace (Mor Barak et al., 2016; Nishii, 2013; Shore et al., 2018). For example, although an organization may look diverse on the outside (i.e., a high level of diversity representation at the organizational level), there may be more homogeneous subgroups of individuals inside the organization who share similar personal characteristics and do not feel valued and appreciated. Using a person-centered approach, such as latent class or latent profile analysis where participant characteristics drive the analysis can uncover possible homogeneous subgroups in an organization and provide a deeper understanding of the nature of inclusion in the workplace (Jason & Glenwick, 2016; Nylund, Asparouhov, & Muthén, 2007). This information has the potential to promote social justice in the workplace by uncovering whether certain subgroups of employees exist that may feel socially excluded or undervalued, thereby providing leaders with critical information to ensuring that all organizational members feel equally valued and appreciated (i.e., fostering a climate for inclusion).

Theoretical Framework

Several theories can help explain why creating inclusive workplaces promotes social good at an organizational level. Social good has three main domains: environmental justice; peace, harmony, and collaboration; and diversity and inclusion (Mor Barak, 2018). According to optimal distinctiveness theory (Brewer, 1991), individuals want to feel like they belong as important members of the organization and that they are recognized for their unique personal characteristics as individuals. Inclusion strives to create a balance between helping everyone feel appreciated for who they are and that they are important members of the group (Shore et al., 2011). When individuals feel valued and appreciated, improvement occurs in employee job satisfaction, commitment, intention to leave (Brimhall et al., 2014; Shore et al., 2011), and workplace outcomes such as retention and performance (Mor Barak, 2015; Shore et al., 2011).

Institutional theory (DiMaggio & Powell, 1983; Selznick, 1957) suggests that organizations adapt to the values of individuals inside of the organization and the values of society. The ability of an organization to meet the needs of their internal members and those in their external environment is key to the organization’s long-term survival (Hatch & Cunliffe, 2006). In the context of workforce diversity, organizations are tasked with the challenge of meeting the demands of diverse organizational members and being sensitive to and aware of their impact on communities and the global nature of current businesses (Greenwald, 2008; Mor Barak, 2017). Given the current trend for social entrepreneurs, businesses, and global leaders to be key agents in promoting social good, and that a critical domain of social good is workforce diversity and inclusion (Mor Barak, 2018), organizations may be a particularly valuable system for encouraging and acting on values for social good.

To promote social good in the context of diversity, organizations need to use inclusive techniques and approaches (Ferdman & Deane, 2014; Mor Barak, 2017). However, limited empirical work has uncovered evidence-based tools that can help leaders strive for more inclusive organizations. Thus, the current study used a latent class and latent profile analysis to provide a more in-depth understanding of workplace inclusion. In a diverse workforce, it is possible that not everyone in the organization feels valued by their leader (i.e., leader engagement), included in decision-making (i.e., included), or happy with their jobs (i.e., job satisfaction), and therefore it is important to understand whether certain employee subgroups exist whose members feel more or less valued relative to their peers. This information can then be used to develop evidence-based culturally sensitive interventions that ensure all employees are being valued and included in important work-related processes. Ensuring all organizational members are appreciated in the workplace fosters a climate for inclusion and encourages socially just work environments (Mor Barak, 2018).

Method

Participants

Participants were recruited from a very diverse urban human services nonprofit health-care department located in the Western region of the United States in 2015. Employees were engaged in interprofessional collaboration with various departments and members outside of their immediate department focused on procedures and processes related to increasing innovation and the quality of care for the entire organization. Of the 300 employees, 277 agreed to participate in the initial demographic survey (92% response rate) and 213 (71% response rate) agreed to participate in the main survey.

Procedures

To protect confidentiality, the primary investigator of the study e-mailed two surveys to all current department employees. To limit common method bias, demographic questions were asked in an initial survey 4 weeks prior to the main questionnaire (temporal separation; Podsakoff, MacKenzie, Lee, & Podsakoff, 2003) and kept separate from the main questionnaire, which asked about leader engagement, inclusion, and job satisfaction. Participants created unique study ID codes that were used to connect the demographic survey with the main questionnaire. No personally identifying information was obtained on either survey (e.g., names or e-mail addresses). After completing the main questionnaire (121 items), participants received US$15 Amazon.com gift cards. Prior to data collection, the study was reviewed and approved by the institutional review boards of the participating hospital and university.

Measures

Leader engagement

To measure whether employees felt valued by their leader, 3 items developed by Nembhard and Edmondson (2006) represented leader engagement behaviors. All leadership items asked respondents to indicate the extent to which their work group supervisor or manager engages in specific leadership behaviors. After receiving feedback from study participants that the reverse-scored item for leader engagement was difficult to interpret, this item was slightly revised to eliminate reverse-scored wording (original wording: “Does not value the opinion of others equally”; revised wording: “Values the opinion of others equally”). Response options ranged from 1 (not at all) to 5 (to a very great extent) on a Likert-type scale. The overall Cronbach’s α was .93.

Inclusion

To measure whether employees felt valued in the work environment, perceptions of work group inclusion were measured using subscales from the Mor Barak Inclusion–Exclusion Scale (Mor Barak, 2017). This measure consists of several scales that assess inclusion in three areas (decision-making, information networks, and involvement in social activities) and at five levels in the organization (work group, supervisor, social informal, organization, and upper management). To measure work group inclusion, the Work Group (3 items), Supervisor (3 items), and Social Informal (3 items) subscales were used. The phrase “my lab section/work group” was added to each question for clarity regarding the level of analysis. For example, participants were asked to respond to statements such as “I have influence in the decisions taken by my lab section/work group regarding our tasks” (work group), “In my lab section/work group, my supervisor often asks for my opinion before making important decisions” (supervisor), and “In my lab section/work group, I am rarely invited to join my coworkers when they go for lunch or drinks after work” (social informal). Two items were reverse scored (Items 5 and 15). Responses were ranked on a Likert-type scale ranging from 1 (strongly disagree) to 6 (strongly agree). All items were averaged to create a mean score for work group inclusion, which was treated as an observed variable. Higher scores represented higher feelings of inclusion. The Cronbach’s α for work group inclusion was .85.

Job satisfaction

Quinn and Staines’s (1979) 4-item Work Satisfaction Scale was used to measure employee job satisfaction. An example item is “All in all, I am satisfied with my job.” Responses were rated on a Likert-type scale ranging from 1 (strongly disagree) to 6 (strongly agree). A composite job satisfaction score was created, with higher scores indicative of higher job satisfaction. Cronbach’s α was .91.

All demographic and work-related questions (i.e., gender, race and ethnicity, age, education, job position, and job tenure) were asked in an initial survey that was separate from the main questionnaire, which assessed leadership, inclusion, and job satisfaction.

Data Analysis

Prior to data analysis, the evaluation of the distribution of variables and missing data patterns were completed. All skew and kurtosis values fell below the absolute values of 2 (skew) and 7 (kurtosis), indicating acceptable normality (Curran, West, & Finch, 1996; Ryu, 2011). Of the 213 survey respondents, 9 did not answer all questions about leader engagement (4% of the values were missing), 2 did not answer all questions about inclusion (1% of the values were missing), and 14 did not answer all questions about job satisfaction (7% of the values were missing). To use full information maximum likelihood estimation, data must be considered missing at random (Schafer & Graham, 2002; Scheffer, 2002). Data can be reasonably considered missing at random and full information maximum likelihood estimation methods employed if missingness in the outcome variable does not depend on the outcome (Rubin, 1996). Logistic regression analyses (data not shown) were conducted to test whether missing data in the outcome variables (leader engagement, inclusion, and job satisfaction) were associated with any of the variables in the model (leader engagement, inclusion, job satisfaction, gender, race and ethnicity, age, education, job tenure, job position; McArdle, 2013). The logistic regression models yielded null results, suggesting that missingness was not due to any other variable examined among study respondents. Based on these results, full information maximum likelihood was used to handle missing data in the analysis.

Latent class and latent profile analysis was used to examine patterns among employees who felt valued in the workplace (i.e., had higher levels of leader engagement, inclusion, and job satisfaction). Latent class and latent profile analyses are relatively new and underutilized person-centered analytic tools that may be particularly useful in promoting inclusive work environments. Unique to latent class and profile analyses is the emphases placed on participants and their personal characteristics (Jason & Glenwick, 2016). In essence, this is a person-centered approach wherein participant characteristics drive the analysis (Jason & Glenwick, 2016; Neely-Barnes, 2010). Researchers and organizational practitioners then interpret the data based on how many subgroups of employees exist. Substantive interpretation, therefore, is based on the sample and shared personal characteristics among participants (Jason & Glenwick, 2016). This tool can provide organizational practitioners with a more in-depth look at the composition of their employees in particular subgroups based on organizational factors. For example, leaders striving to create inclusive work environments may use this approach to better understand whether everyone in the workplace feels valued and appreciated, that is, whether their leaders engage everyone equitably—and value everyone’s input (leader engagement; Nembhard & Edmonson, 2006), whether they feel included in critical organizational processes and decisions (inclusion; Mor Barak, 2017), and as a result, whether they feel satisfied with their jobs (job satisfaction; Quinn & Staines, 1979).

In the current study, a mixture modeling approach with both categorical demographic variables and continuous work-related variables was used. This combined latent class and latent profile analysis is recommended when researchers have both categorical and continuous variables of interest (Neely-Barnes, 2010). The categorical variables (race and ethnicity, gender, age, education, and job tenure) identified distinct patterns, profiles, or classes of employees, whereas the continuous latent variables (leader engagement, inclusion, and job satisfaction) described the continuum along which the classes exist (Neely-Barnes, 2010). A series of models were estimated to determine the appropriate number of classes or profiles in the sample, beginning with a one-class model and iteratively increasing the number of classes until a model with appropriate model fit was identified. As recommended by other researchers, model fit was evaluated using the Lo–Mendell–Rubin likelihood ratio estimate (LMR LRT), Akaike’s information criterion (AIC), Bayesian information criterion (BIC), entropy estimate, and substantive theoretical interpretation (Muthén & Muthén, 2004; Neely-Barnes, 2010; Nylund et al., 2007). Once the optimal model was identified, it was interpreted for substantive meaning. Each class was named to provide a description of the class composition. Binomial logistic regression analysis was used to determine whether specific personal characteristics were associated with higher levels of leader engagement, feelings of inclusion, and job satisfaction.

Results

Sample

Table 1 presents demographic descriptive statistics of the overall sample. The sample was racially and ethnically diverse, with the largest category of respondents self-reporting as Asian (41%), followed by White (21%), mixed race or other (20%), Latino/a (14%), and African American (4%). Approximately 69% of participants were female and 31% were male. Approximately 27% self-reported being between the ages of 30 and 39, followed by 22% between 50 and 59, 21% between 40 and 49, 15% between 18 and 29, and 15% older than 60. Approximately half of the participants in this sample had a bachelor’s degree (49%). The two largest categories of job tenure among the respondents were those who had worked between 3 and 5 years (22%) and less than 1 year (20%). Means and standard deviations for leader engagement, inclusion, and job satisfaction are presented in Table 1.

Table 1.

Descriptive Statistics of Demographic Variables for the Overall Sample.

Variable	n	%	M	SD	Range
Race and ethnicity	247
African American	10	4
Asian	102	41
White	51	21
Hispanic or Latino	35	14
Mixed race or other	49	20
Gender	206
Female	142	69
Male	64	31
Age	206
18–29 years	31	15
30–39 years	56	27
40–49 years	43	21
50–59 years	45	22
60–69 years	26	13
70+ years	5	2
Education	206
High school diploma	28	14
Associate’s degree	19	9
Bachelor’s degree	100	49
Master’s degree	24	12
MD	17	8
PhD	11	5
Other	7	3
Job tenure	206
Less than 1 year	41	20
1–2 Years	33	16
3–5 Years	45	22
6–10 Years	23	11
11–20 Years	35	17
21–30 Years	16	8
More than 31 years	13	6
Leader engagement	204	96	3.87	.94	1–5
Inclusion	211	99	4.21	.91	1–6
Job satisfaction	199	93	4.62	.96	1–6

Latent Profiles

Table 2 presents the model fit statistics for the models examined. A two-class model provided the best overall fit of the data as determined by the AIC being smaller than the BIC and adjusted BIC, significant LMR LRT (p < .001), and an acceptable entropy value (.80; Muthén & Muthén, 2004; Neely-Barnes, 2010; Nylund et al., 2007). Employees who felt less valued (Class 1; 41% of the sample, n = 87) had lower levels of leader engagement (M = 3.20, SD = .15, t = 21.55, p < .001), feelings of inclusion (M = 3.48, SD = .11, t = 32.15, p < .001), and job satisfaction (M = 3.88, SD = .10, t = 39.56, p < .001), relative to employees who felt more valued (Class 2; 59% of the sample, n = 126) and who had higher levels of leader engagement (M = 4.32, SD = .07, t = 60.81, p < .001), inclusion (M = 4.72, SD = .06, t = 74.17, p < .001), and job satisfaction (M = 5.15, SD = .09, t = 58.37, p < .001).

Table 2.

Fit Statistics for the Latent Profile Models Examined.

Classes/Profiles	AIC	BIC	Adj. BIC	LMR LRT p	Entropy
1	4,648.617	4,752.817	4,654.587	—	—
2	4,516.920	4,718.597	4,528.475	.0006	.80
3	4,482.286	4,781.441	4,499.426	.9423	.84

Note. N = 213. Bold-faced values indicate the best fitting model. Adj. = adjusted; AIC = Akaike information criterion; BIC = Bayesian information criterion; LMR LRT = Lo–Mendell–Rubin likelihood ratio test.

Figure 1 presents the conditional probabilities for each demographic variable by latent class or profile (i.e., employees who felt less valued relative to employees who felt more valued), and Table 3 presents the conditional probabilities in percentage scores with 95% confidence intervals (CIs). Overall, noticeable differences emerged between employees who felt less valued compared to those who felt more valued. For example, employees who felt less valued had higher probabilities of being Asian (56%, SE = 0.07, p < .001, 95% CI [0.45, 0.66]), being between 50 and 59 years old (32%, SE = 0.07, p < .001, 95% CI [0.22, 0.42]), having their highest level of education be a high school diploma (23%, SE = 0.05, p < .001, 95% CI [0.14, 0.32]), and having job tenure between 6 and 10 years (17%, SE = 0.05, p < .001, 95% CI [0.10, 0.25]) or 11–20 years (27%, SE = 0.06, p < .001, 95% CI [0.18, 0.36]). On the other hand, employees who felt more valued in the workplace had higher probabilities of being White (29%, SE = 0.05, p < .001, 95% CI [0.22, 0.37]), being between 18 and 29 years old (23%, SE = 0.04, p < .001, 95% CI [0.17, 0.29]), having a medical degree (12%, SE = 0.03, p < .001, 95% CI [0.07, 0.17]), and having job tenure of less than 1 year (24%, SE = 0.04, p < .001, 95% CI [0.17, 0.31]) or more than 31 years (9%, SE = 0.03, p < .001, 95% CI [0.04, 0.13]).

Figure 1.

Conditional probabilities of demographic characteristics by latent profile.

Table 3.

Percentages of Demographic Characteristics by Latent Profile.

Demographic Characteristics	Less Valued		More Valued
	(n = 87)		(n = 126)
	%	95% CI	%	95% CI
Gender
Male	30	[20, 40]	32	[25, 39]
Female	70	[60, 80]	68	[61, 76]
Race/ethnicity
African American	3	[0, 7]	5	[1, 8]
Asian	56	[45, 66]	45	[37, 53]
Latino/a	21	[12, 30]	18	[12, 24]
Mixed race or other	5	[1, 9]	3	[1, 6]
White	16	[8, 25]	29	[22, 37]
Age
18–29 Years old	0	—	23	[17, 29]
30–39 Years old	30	[21, 40]	26	[19, 33]
40–49 Years old	23	[13, 33]	20	[13, 26]
50–59 Years old	32	[22, 42]	16	[10, 22]
60+ Years old	15	[7, 22]	16	[10, 22]
Education
High school diploma	23	[14, 32]	10	[5, 15]
Associate’s degree	9	[2, 17]	7	[2, 11]
Bachelor’s degree	48	[37, 59]	51	[44, 59]
Master’s degree	12	[5, 19]	12	[6, 17]
Medical degree	2	[0, 5]	12	[7, 17]
PhD or doctorate	6	[1, 11]	9	[5, 13]
Job tenure
Less than 1 year	7	[2, 12]	24	[17, 31]
1–2 Years	16	[8, 23]	20	[14, 26]
3–5 Years	20	[12, 29]	22	[16, 29]
6–10 Years	17	[10, 25]	5	[1, 9]
11–20 Years	27	[18, 36]	14	[8, 20]
21–30 Years	11	[4, 17]	6	[2, 10]
31+ Years	3	[0, 6]	9	[4, 13]

Note. CI = confidence interval.

Table 4 presents the binomial logistic regression results. Employees who felt less valued had an 8% (odds ratio [OR] = 1.08, SE = 0.40, t = 2.68, p < .01, 95% CI [0.59, 1.99]) increase in the odds of being female, relative to being male and feeling more valued. They also had a 30% decrease in odds of being Asian (OR = 0.70, SE = 0.27, t = 2.61, p < .01, 95% CI [0.37, 1.31]), a 46% decrease in odds of being Latino/a (OR = 0.54, SE = 0.24, t = 2.29, p < .02, 95% CI [0.26, 1.11]), and a 54% decrease in odds of being of mixed race and ethnicity (OR = 0.46, SE = 0.22, t = 2.08, p < .04, 95% CI [0.21, 1.02]), relative to being White and feeling more valued (i.e., being in Class 2). In terms of age, there was a 19% decrease in odds of being between 30 and 39 years old (OR = 0.81, SE = 0.31, t = 2.58, p < .01, 95% CI [0.43, 1.53]), a 27% decrease in odds of being between 40 and 49 years old (OR = 0.73, SE = 0.30, t = 2.43, p < .02, 95% CI [0.37, 1.42]), and a 73% decrease in odds of being between 50 and 59 years old (OR = 0.27, SE = 0.12, t = 2.19, p < .03, 95% CI [0.13, 0.58]), relative to being between the ages of 18 and 29 years old and feeling more valued. Regarding educational attainment, there was a 3% increase in the odds of having a bachelor’s degree (OR = 1.03, SE = 0.38, t = 2.70, p < .01, 95% CI [0.56, 1.90]), a 1% increase in the odds of having a master’s degree (OR = 1.01, SE = 0.38, t = 2.66, p < .01, 95% CI [0.55, 1.88]), a 77% increase in the odds of having a medical degree (OR = 1.77, SE = 0.72, t = 2.47, p < .01, 95% CI [0.91, 3.45]), and almost double the odds of having a PhD or doctoral degree (OR = 2.71, SE = 1.32, t = 2.05, p < .04, 95% CI [1.22, 6.04]) compared to having a high school diploma and being in Class 2 (feeling more valued). Last, in terms of job tenure, employees who felt less valued had a 36% increase in the odds of having between 1 and 2 years of job tenure (OR = 1.36, SE = 0.61, t = 2.23, p < .03, 95% CI [0.65, 2.85]), a 31% increase in the odds of having 3–5 years of job tenure (OR = 1.31, SE = 0.47, t = 2.78, p < .01, 95% CI [0.72, 2.36]), a 21% decrease in the odds of having 6–10 years of job tenure (OR = 0.79, SE = 0.27, t = 2.88, p < .01, 95% CI [0.45, 1.40]), a 60% decrease in the odds of having between 11 and 20 years of job tenure (OR = 0.40, SE = 0.16, t = 2.44, p < .02, 95% CI [0.20, 0.78]), and a 78% decrease in odds of having 21–30 years of job tenure (OR = 0.22, SE = 0.11, t = 1.95, p < .05, 95% CI [0.09, 0.51]), relative to employees who had less than 1 year of job tenure and who felt more valued.

Table 4.

Binomial Logistic Regression Results of Employees Who Felt Less Valued (n = 87) Versus Employees Who Felt More Valued (n = 126).

Demographic Characteristics	OR	SE	p	95% CI
Gender
Female	1.08	0.40	.01	[0.59, 1.99]
Race and ethnicity
African American	1.69	1.84	.36	[0.28, 10.07]
Asian	0.70	0.27	.01	[0.37, 1.31]
Latino/a	0.54	0.24	.02	[0.26, 1.11]
Mixed race or other	0.46	0.22	.04	[0.21, 1.02]
Age
30–39 Years old	0.81	0.32	.01	[0.43, 1.53]
40–49 Years old	0.73	0.30	.02	[0.37, 1.42]
50–59 Years old	0.27	0.12	.03	[0.13, 0.58]
60+ Years old	0.00	0.00	—	[0.00, 0.00]
Education
Associate’s degree	0.69	0.55	.21	[0.19, 2.56]
Bachelor’s degree	1.03	0.38	.01	[0.56, 1.90]
Master’s degree	1.01	0.38	.01	[0.55, 1.88]
Medical degree	1.77	0.72	.01	[0.91, 3.45]
PhD or doctorate	2.71	1.32	.04	[1.22, 6.04]
Job tenure
1–2 Years	1.36	0.61	.03	[0.65, 2.85]
3–5 Years	1.31	0.47	.01	[0.72, 2.36]
6–10 Years	0.79	0.27	.01	[0.45, 1.40]
11–20 Years	0.40	0.16	.02	[0.20, 0.78]
21–30 Years	0.22	0.11	.05	[0.09, 0.51]
31+ Years	0.24	0.14	.08	[0.09, 0.61]

Note. Reference categories were male for gender, White for race and ethnicity, 18–29 years old for age, high school diploma for education, and less than 1 year for job tenure. CI = confidence interval.

Discussion

Latent class and latent profile analysis demonstrated an empirical tool that can be used to determine whether employee subgroups exist who feel less valued relative to their peers. Findings suggest that a diverse workforce may feature subgroups of individuals who share similar personal characteristics and may feel less valued and included in the workplace. The latent profile analysis indicated that two employee subgroups existed: those who felt less valued (i.e., lower levels of perceived leader engagement, inclusion, and job satisfaction) and those who felt more valued (i.e., higher levels of perceived leader engagement, inclusion, and job satisfaction). Among those who felt less valued, higher percentages of employees self-reported as being Asian or Latino/a, being between the ages of 50 and 59 years old, having their highest educational attainment be a high school diploma, and having worked for the organization between 11 and 20 years. On the other hand, among employees who felt more valued, there were higher percentages of employees who self-reported as being White, being between the ages of 18 and 29 years old, having worked for the organization less than 1 year, and generally having higher educational attainment. More specifically, results from the binomial logistic regression analysis indicated that employees who self-reported as being Asian, Latino/a, or of a mixed race and ethnicity were more likely to feel less valued relative to their White peers. Likewise, employees between 18 and 29 years old appeared to feel more valued relative to older employees, particularly employees between 50 and 59 years old. More educated employees appeared to feel more valued relative to less educated employees, and individuals with less job tenure felt more valued than employees with increased job tenure.

Findings corroborate past research that indicated employees from racial and ethnic minority groups tend to be excluded from important work-related processes and as a result feel less satisfied with their jobs (Hwang & Hopkins, 2015; Ibarra, 1995; Zanoni, Janssens, Benschop, & Nkomo, 2010). In fact, recent research suggested that organizations, both large and small, continue to discriminate against and exclude individuals from racial and ethnic minority groups (Banerjee, Reitz, & Oreopoulos, 2018; Stoermer, Hitotsuyanagi-Hansel, & Froese, 2017). In addition, there is growing concern that workplace bias exists toward older employees, often connected to the stereotype that older workers are unwilling to learn new technology and are no longer competent in their tasks and responsibilities (Newsom & Vogt, 2016). Younger employees may exclude older colleagues based on the negative stereotypes of older workers (Roscigno, 2010), and as a result, many older employees experience feelings of distress and decreased overall well-being (Rippon, Kneale, de Oliveira, Demakakos, & Steptoe, 2014). In essence, older employees may experience age discrimination wherein they are left out of work group activities and decision-making that may lead them to feel less included in their work groups (Brimhall & Mor Barak, 2018). Likewise, research has corroborated the current study’s findings in that less educated employees tend to feel less valued in the workplace (Brimhall & Mor Barak, 2018; Pelled, Ledford, & Mohrman, 1999), and employees with less job tenure tend feel more satisfied with their jobs (Laird, Harvey, & Lancaster, 2015).

Last, results suggest that although an organization may look diverse from the outside (i.e., high-diversity representation at the organizational level), there may be more homogeneous subgroups of individuals in the organization who do not feel included or valued in the workplace. Organizations are a reflection of the values of the communities in which they reside (institutional theory; DiMaggio & Powell, 1983; Selznick, 1957). By striving to create inclusive organizations, this may help engender value for diversity and inclusion at the community level (i.e., organizations may be an avenue for macrolevel social work professionals to help foster social good at community, national, and global levels). Therefore, it is critical that leaders and macrolevel social work practitioners strive for more inclusive workplaces that represent values of diversity and inclusion. Understanding which subgroups of employees may feel less valued can help social workers and organizational leaders develop focused and culturally sensitive interventions aimed at helping these particular individuals feel more appreciated in the workplace. Understanding in greater depth whether all organizational members feel equally valued and appreciated can help organizational leaders know where to invest resources in creating more inclusive work environments. Helping every organizational member feel appreciated for their unique talents and perspectives and that they belong as an important member of the group (optimal distinctiveness theory; Brewer, 1991; Shore et al., 2011) is part of achieving social good and improves everyone’s social and emotional well-being (Mor Barak, 2018). Not only is helping everyone feel valued the right thing to do (i.e., social good), it also makes good business sense by decreasing employee stress and improving employee well-being, job satisfaction, commitment, and retention (Mor Barak et al., 2016; Shore et al., 2011).

Strengths and Limitations

Given the recent attention on defining social good and its application in macrolevel social work (Mor Barak, 2018), the current study sought to provide an avenue for social work practitioners interested in social good organizational practice. More specifically, latent profile analysis was used in a diverse nonprofit human services organization to help uncover employee dynamics regarding social justice issues of feeling equally valued and appreciated in the workplace. Latent profile analysis is a relatively new and underutilized empirical tool that provides a person-centered approach (Jason & Glenwick, 2016) to understanding more in depth which employees feel valued by their leaders, are more included in work processes, and have higher job satisfaction. This information can help macro social workers develop targeted workplace interventions that promote inclusion and help everyone in the workplace feel valued. In addition, latent profile analysis accounts for multiple individual attributes, clustering individuals based on the shared combination or intersection of personal characteristics. In essence, this provides an empirical tool that helps account for the intersectional nature of workforce diversity.

Some limitations involved the slight revision made to the reversed scored item in leader engagement. Based on participant feedback to increase clarity, 1 item of leader engagement was revised to eliminate reverse scored wording. Challenges to consider when using latent profile analysis include generalizability. Latent profile and latent class analyses are person-centered approaches and therefore may be more context specific. Although the sample used in this study came from a highly diverse nonprofit human services organization, it may not be representative of all diverse human services organizations. Likewise, although nonprofit health-care providers share similar characteristics with other human services agencies (Gillingham, 2015; Hopkins, Meyer, Shera, & Peters, 2014; Mor Barak et al., 2016; Smith, 2015), they may not be representative of all nonprofit health-care or human services organizations. The study found two latent profiles of employees, yet it is possible that in different organizational contexts, more than two employee subgroups exist. Latent profile analysis can be an empirical tool for social work practitioners and organizational leaders striving to improve social justice in the workplace through gaining a more in-depth understanding of employee dynamics. This suggests several avenues for future research and development.

Implications and Future Research

Many organizations recognize the value of social good (Foley & Chowdhury, 2007; Makwara, 2011; Mor Barak, 2018; Roy & Karna, 2015; Schminke et al., 2015; Thompson, 2016; Viswanathan et al., 2009), yet lack the specialization, training, and skill to ensure their organizations are socially just and inclusive (i.e., striving for social good). Although social workers are specifically trained to implement culturally sensitive interventions that promote social justice and inclusion (Cournoyer, 2017; NASW, 2015), many social work programs place little emphases on macrolevel social work that would prepare graduates for organizational-level practice (de Saxe Zerden, Sheely, & Despard, 2016; Mor Barak, 2018; Rothman & Mizrahi, 2014). Investing in macrolevel social work education, by training students in the practice and science of promoting social good in organizations, can help create new opportunities for the social work profession. More specifically, developing value for social workers in organizational settings may foster new internship opportunities in human resources and talent management departments. Social workers trained in macrolevel organizational practice may be ideal candidates for developing workplace interventions that create more socially just and inclusive work environments.

For example, findings from the current study suggest that two employee subgroups exist: those who feel less valued and those who feel more valued. Those who feel less valued share similar personal characteristics (i.e., Asian or Latino/a ethnicity, between 50 and 59 years old, highest education level of a high school diploma, and 11–20 years of job tenure). Macrolevel organizational social workers can use this information to develop outreach efforts that increase the inclusion of employees who may feel less valued. This may involve ensuring that employees who share these characteristics are invited to important organizational meetings and that their ideas and input are actively sought when making important decisions (critical factors for increasing inclusion; Mor Barak, 2017; Nishii, 2013; Shore et al., 2011). In addition, leaders who seek the input of others and express equal appreciation for all ideas given help foster feelings of inclusion and a sense of being valued (Nembhard & Edmondson, 2006). Future research can replicate the methods used in this study to include other organizational contexts. In essence, research is needed to further develop the science and practice of social good in both organizational and community settings.

Conclusions

Promoting social good in the context of diverse human services organizations inevitably involves finding ways for all organizational members to feel valued and appreciated for who they are (i.e., inclusive work environments). Creating an inclusive workplace, wherein everyone feels valued and appreciated, is an important factor for achieving social good and may be an ideal avenue for macrolevel social work practitioners (Mor Barak, 2018). Although more research is needed on the science and practice of social good, the current study demonstrates how latent profile analysis can be used as a person-centered analytic approach to understand in greater depth the nature of employee dynamics and feelings of inclusion in the workplace. This information can be used to develop culturally sensitive interventions that promote increased inclusion by ensuring that everyone in the organization feels valued.

Footnotes

Acknowledgments

Thanks are due to participants for sharing their perspectives and all of their involvement in this study.

Declaration of Conflicting Interests

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

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by the U.S. Department of Health and Human Services Agency for Healthcare Research and Quality (grant no. 1R36HS024650-01), the University of Southern California Suzanne Dworak-Peck School of Social Work, and the University of Southern California Management, Organizations, and Policy Transformation Research Cluster.

ORCID iD

Kim C. Brimhall

References

Acquavita

S. P.

Pittman

Gibbons

Castellanos-Brown

(2009). Personal and organizational diversity factors’ impact on social workers’ job satisfaction: Results from a national Internet-based survey. Administration in Social Work, 33, 151–166. doi:10.1080/03643100902768824

Banerjee

Reitz

J. G.

Oreopoulos

(2018). Do large employers treat racial minorities more fairly? An analysis of Canadian field experiment data. Canadian Public Policy, 33, 1–12. doi:10.3138/cpp.2017-033

Brewer

M. B.

(1991). The social self: On being the same and different at the same time. Personality and Social Psychology Bulletin, 17, 475–482. doi:10.1177/0146167291175001

Brimhall

K. C.

Lizano

E. L.

Mor Barak

M. E.

(2014). The mediating role of inclusion: A longitudinal study of the effects of leader–member exchange and diversity climate on job satisfaction and intention to leave among child welfare workers. Children and Youth Services Review, 40, 79–88. doi:10.1016/j.childyouth.2014.03.003

Brimhall

K. C.

Mor Barak

M. E.

(2018). The critical role of workplace inclusion in fostering innovation, job satisfaction, and quality of care in a diverse human service organization. Human Service Organizations: Management, Leadership & Governance, 42, 474–492. doi:10.1080/23303131.2018.1526151

Business Dictionary. (2017). Social good. Retrieved from http://www.businessdictionary.com/definition/social-good.html

Cheng

Chen

C. J.

(2014). Modifying effects of gender, age and enterprise size on the associations between workplace justice and health. International Archives of Occupational and Environmental Health, 87, 29–39. doi:10.1007/s00420-012-0831-z

Cho

Mor Barak

M. E.

(2008). Understanding of diversity and inclusion in a perceived homogeneous culture: A study of organizational commitment and job performance among Korean employees. Administration in Social Work, 32, 100–126. doi:10.1080/03643100802293865

Colby

S. L.

Ortman

J. M.

(2014). Projections of the size and composition of the U.S. population: 2014 to 2060. Retrieved from U.S. Census Bureau website, https://www.census.gov/content/dam/Census/library/publications/2015/demo/p25-1143.pdf

10.

Cottrill

Lopez

P. D.

Hoffman

C. C.

(2014). How authentic leadership and inclusion benefit organizations. Equality, Diversity and Inclusion, 33, 275–292. doi:10.1108/EDI-05-2012-0041

11.

Cournoyer

B. R.

(2017). The social work skills workbook (8th ed.). Boston, MA: Cengage Learning.

12.

Craig

M. A.

Rucker

J. M.

Richeson

J. A.

(2017). The pitfalls and promise of increasing racial diversity: Threat, contact, and race relations in the 21st century. Current Directions in Psychological Science, 27, 188–193. doi:10.1177/0963721417727860

13.

Curran

P. J.

West

S. G.

Finch

J. F.

(1996). The robustness of test statistics to nonnormality and specification error in confirmatory factor analysis. Psychological Methods, 1, 16–29. doi:10.1037/1082-989X.1.1.16

14.

de Saxe Zerden

Sheely

Despard

M. R.

(2016). Debunking macro myths: Findings from recent graduates about jobs, salaries, and skills. Social Work Education, 35, 752–766. doi:10.1080/02615479.2016.1188915

15.

DiMaggio

P. J.

Powell

W. W.

(1983). The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields. American Sociological Review, 48, 147–160. doi:10.2307/2095101

16.

DiTomaso

Parks-Yancy

(2014). The social psychology of inequality at work: Individual, group, and organizational dimensions. In McLeod

Lawler

Schwalbe

(Eds.), Handbook of the social psychology of inequality (pp. 437–457). Dordrecht, the Netherlands: Springer.

17.

DiTomaso

Post

Parks-Yancy

(2007). Workforce diversity and inequality: Power, status, and numbers. Annual Review of Sociology, 33, 473–501. doi:10.1146/annurev.soc.33.040406.131805

18.

Downey

S. N.

Werff

Thomas

K. M.

Plaut

V. C.

(2015). The role of diversity practices and inclusion in promoting trust and employee engagement. Journal of Applied Social Psychology, 45, 35–44. doi:10.1111/jasp.12273

19.

Dumas

T. L.

Phillips

K. W.

Rothbard

N. P.

(2013). Getting closer at the company party: Integration experiences, racial dissimilarity, and workplace relationships. Organization Science, 24, 1377–1401. doi:10.1287/orsc.1120.0808

20.

Ferdman

B. M.

Deane

B. R.

(2014). Diversity at work: The practice of inclusion. San Francisco, CA: John Wiley.

21.

Findler

Wind

L. H.

Mor Barak

M. E.

(2007). The challenge of workforce management in a global society: Modeling the relationship between diversity, inclusion, organizational culture, and employee well-being, job satisfaction and organizational commitment. Administration in Social Work, 31, 63–94. doi:10.1300/J147v31n03_05

22.

Foley

Chowdhury

(2007). Poverty, social exclusion and the politics of disability: Care as a social good and the expenditure of social capital in Chuadanga, Bangladesh. Social Policy & Administration, 41, 372–385. doi:10.1111/j.1467-9515.2007.00559.x

23.

Gillingham

(2015). Electronic information systems and human service organizations: The unanticipated consequences of organizational change. Human Service Organizations: Management, Leadership & Governance, 39, 89–100. doi:10.1080/23303131.2014.987412

24.

Gonzalez

J. A.

DeNisi

A. S.

(2009). Cross-level effects of demography and diversity climate on organizational attachment and firm effectiveness. Journal of Organizational Behavior, 30, 21–40. doi:10.1002/job.498

25.

Greenwald

H. P.

(2008). Organizations management without control. Thousand Oaks, CA: Sage.

26.

Hatch

M. J.

Cunliffe

A. L.

(2006). Organization theory (2nd ed.). New York, NY: Oxford University Press.

27.

Hatzenbuehler

M. L.

(2009). How does sexual minority stigma “get under the skin”? A psychological mediation framework. Psychological Bulletin, 135, 707–730. doi:10.1037/a0016441

28.

Herring

(2009). Does diversity pay? Race, gender, and the business case for diversity. American Sociological Review, 74, 208–224. doi:10.1177/000312240907400203

29.

Homan

A. C.

Greer

L. L.

(2013). Considering diversity: The positive effects of considerate leadership in diverse teams. Group Processes & Intergroup Relations, 16, 105–125. doi:10.1177/1368430212437798

30.

Hopkins

Meyer

Shera

Peters

(2014). Leadership challenges facing nonprofit human service organizations in a post-recession era. Human Service Organizations: Management, Leadership & Governance, 38, 419–422. doi:10.1080/23303131.2014.977208

31.

Hurst

C. E.

Gibbon

H. M. F.

Nurse

A. M.

(2016). Social inequality: Forms, causes, and consequences (9th ed.). New York, NY: Routledge.

32.

Hwang

Hopkins

K. M.

(2015). A structural equation model on the effects of diversity characteristics and inclusion on organizational outcomes in the child welfare workforce. Children and Youth Services Review, 50, 44–52. doi:10.1016/j.childyouth.2015.01.012

33.

Ibarra

(1995). Race, opportunity, and diversity of social circles in managerial networks. Academy of Management Journal, 48, 673–703. doi:10.5465/256742

34.

Jason

L. A.

Glenwick

D. S.

(2016). Handbook of methodological approaches to community-based research qualitative, quantitative and mixed methods. New York, NY: Oxford University Press.

35.

Köllen

(2015). Diversity management in the European health care sector: Trends, challenges, and opportunities. In Gurtner

Soyez

(Eds.), Challenges and opportunities in health care management (pp. 27–44). Cham, Switzerland: Springer.

36.

Laird

M. D.

Harvey

, & Lancaster

(2015). Accountability, entitlement, tenure, and satisfaction in Generation Y. Journal of Managerial Psychology, 30, 87–100. doi:10.1108/JMP-08-2014-0227

37.

Makwara

E. C.

(2011). Water: An economic or social good? Journal of Social Development in Africa, 26, 141–163.

38.

Mannix

Neale

M. A.

(2005). What differences make a difference? The promise and reality of diverse teams in organizations. Psychological Science in the Public Interest, 6, 31–55. doi:10.1111/j.1529-1006.2005.00022.x

39.

McArdle

J. J.

(2013). Dealing with longitudinal attrition using logistic regression and decision tree analyses. In McArdle

J. J.

Ritschard

(Eds.), Contemporary issues in exploratory data mining in the behavioral sciences (pp. 282–311). New York, NY: Taylor & Francis.

40.

Mor Barak

M. E.

(2015). Inclusion is the key to diversity management, but what is inclusion? Human Service Organizations: Management, Leadership & Governance, 39, 83–88. doi:10.1080/23303131.2015.1035599

41.

Mor Barak

M. E.

(2017). Managing diversity towards a globally inclusive workplace (4th ed.). Thousand Oaks, CA: Sage.

42.

Mor Barak

M. E.

(2018). The practice and science of social good: Emerging paths to positive social impact. Research on Social Work Practice. Advance online publication. doi:10.1177/1049731517745600

43.

Mor Barak

M. E.

Lizano

E. L.

Kim

Duan

Rhee

M. K.

Hsiao

H. Y.

Brimhall

K. C.

(2016). The promise of diversity management for climate of inclusion: A state-of-the-art review and meta-analysis. Human Service Organizations: Management, Leadership and Governance, 4, 305–333. doi:10.1080/23303131.2016.1138915

44.

Morgaine

(2014). Conceptualizing social justice in social work: Are social workers “too bogged down in the trees?” Journal of Social Justice, 4, 1–24.

45.

Muthén

L. K.

Muthén

B. O.

(2004). Mplus: Statistical analysis with latent variables (4th ed.). Los Angeles, CA: Author.

46.

National Association of Social Workers. (2015). Standards and indicators of cultural competence in social work practice. Retrieved from https://www.socialworkers.org/LinkClick.aspx?fileticket=PonPTDEBrn4%3D&portalid=0

47.

National Association of Social Workers. (2017). Code of ethics. Retrieved from https://www.socialworkers.org/about/ethics/code-of-ethics/code-of-ethics-english

48.

Neely-Barnes

(2010). Latent class models in social work. Social Work Research, 34, 114–121. doi:10.1093/swr/34.2.114

49.

Nembhard

I. M.

Edmondson

A. C.

(2006). Making it safe: The effects of leader inclusiveness and professional status on psychological safety and improvement efforts in health care teams. Journal of Organizational Behavior, 27, 941–966. doi:10.1002/job.413

50.

Newsom

K. L.

Vogt

E. M.

(2016). A conceptual discussion of ageism as a mechanism of discrimination in the workplace. International Journal of Management & Organizational Studies, 5, 60–65.

51.

Nishii

L. H.

(2013). The benefits of climate for inclusion for gender diverse groups. Academy of Management Journal, 56, 1754–1774. doi:10.5465/amj.2009.0823

52.

Nylund

K. L.

Asparouhov

Muthén

B. O.

(2007). Deciding on the number of classes in latent class analysis and growth mixture modeling: A Monte Carlo simulation study. Structural Equation Modeling, 14, 535–569. doi:10.1080/10705510701575396

53.

Pelled

L. H.

Ledford

G. E.

Mohrman

S. A.

(1999). Demographic dissimilarity and workplace inclusion. Journal of Management Studies, 36, 1013–1031. doi:10.1111/1467-6486.00168

54.

Podsakoff

P. M.

MacKenzie

S. B.

Lee

J. Y.

Podsakoff

N. P.

(2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88, 879–903. doi:10.1037/0021-9010.88.5.879

55.

Quinn

R. P.

Staines

G. L.

(1979). The 1977 quality of employment survey. Ann Arbor: University of Michigan, Institute for Social Research, Survey Research Center.

56.

Rippon

Kneale

de Oliveira

Demakakos

Steptoe

(2014). Perceived age discrimination in older adults. Age and Ageing, 43, 379–386. doi:10.1093/ageing/aft146

57.

Roberson

Q. M.

(2006). Disentangling the meanings of diversity and inclusion in organizations. Group & Organization Management, 31, 212–236. doi:10.1177/1059601104273064

58.

Roberson

Q. M.

Holmes

Perry

J. L.

(2017). Transforming research on diversity and firm performance: A dynamic capabilities perspective. Academy of Management Annals, 11, 189–216. doi:10.5465/annals.2014.0019

59.

Robinson

Dechant

(1997). Building a business case for diversity. Academy of Management Executive, 11, 21–31. doi:10.5465/ame.1997.9709231661

60.

Roscigno

V. J.

(2010). Ageism in the American workplace. Contexts, 9, 16–21. doi:10.1525/ctx.2010.9.1.16

61.

Rothman

Mizrahi

(2014). Balancing micro and macro practice: A challenge for social work. Social Work, 59, 1–3. doi:10.1093/sw/swt067

62.

Roy

Karna

(2015). Doing social good on a sustainable basis: Competitive advantage of social businesses. Management Decision, 53, 1355–1374. doi:10.1108/MD-09-2014-0561

63.

Rubin

D. B.

(1996). Multiple imputation after 18+ years. Journal of American Statistical Association, 91, 473–489. doi:10.1080/01621459.1996.10476908

64.

Ryu

(2011). Effects of skewness and kurtosis on normal-theory based maximum likelihood test statistic in multilevel structural equation modeling. Behavior Research Methods, 4, 1066–1074. doi:10.3758/s13428-011-0115-7

65.

Schafer

J. L.

Graham

J. W.

(2002). Missing data: Our view of the state of the art. Psychological Methods, 7, 147–177. doi:10.1037/1082-989X.7.2.147

66.

Scheffer

(2002). Dealing with missing data. Research Letters in the Information and Mathematical Sciences, 3, 153–160.

67.

Schminke

Arnaud

Taylor

(2015). Ethics, values, and organizational justice: Individuals, organizations, and beyond. Journal of Business Ethics, 130, 727–736. doi:10.1007/s10551-014-2251-3

68.

Selznick

(1957). Leadership in administration. New York, NY: Harper & Row.

69.

Shore

L. M.

Cleveland

J. N.

Sanchez

(2018). Inclusive workplaces: A review and model. Human Resource Management Review, 28, 176–189. doi:10.1016/j.hrmr.2017.07.003

70.

Shore

L. M.

Randel

A. E.

Chung

B. G.

Dean

M. A.

Holcombe Ehrhart

Singh

(2011). Inclusion and diversity in work groups: A review and model for future research. Journal of Management, 37, 1262–1289. doi:10.1177/0149206310385943

71.

Smith

R. A.

(2015). Managing human service organizations in the 21st century. Human Service Organizations: Management, Leadership & Governance, 39, 407–411. doi:10.1080/23303131.2015.1087783

72.

Stoermer

Hitotsuyanagi-Hansel

Froese

F. J.

(2017). Racial harassment and job satisfaction in South Africa: The moderating effects of career orientations and managerial risk. International Journal of Human Resource Management. Advance online publication. doi:10.1080/09585192.2016.1278254

73.

Taute

McQuitty

(2004). Feeling good! Doing good! An exploratory look at the impulsive purchase of the social good. Journal of Marketing Theory and Practice, 12, 16–27. doi:10.1080/10696679.2004.11658516

74.

Thompson

(2016). Anti-discrimination practice: Equality, diversity and social justice. New York, NY: Palgrave.

75.

Travis

D. J.

Mor Barak

M. E.

(2010). Fight or flight? Factors influencing child welfare workers’ propensity to seek positive change or disengage from their jobs. Journal of Social Service Research, 36, 188–205. doi:10.1080/01488371003697905

76.

Viswanathan

Seth

Gau

Chaturvedi

(2009). Ingraining product-relevant social good into business processes in subsistence marketplaces: The sustainable market orientation. Journal of Macromarketing, 29, 406–425. doi:10.1177/0276146709345620

77.

Waters

R. D.

Bortree

D. S.

(2012). Improving volunteer retention efforts in public library systems: How communication and inclusion impact female and male volunteers differently. International Journal of Nonprofit and Voluntary Sector Marketing, 17, 92–107. doi:10.1002/nvsm.438

78.

Wright

H. R.

(2013). From individual choice to social good. Higher Education, Skills and Work-Based Learning, 3, 141–148. doi:10.1108/20423891311313171

79.

Zanoni

Janssens

Benschop

Nkomo

(2010). Unpacking diversity, grasping inequality: Rethinking difference thought critical perspectives. Organization, 17, 9–29. doi:10.1177/1350508409350344