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\title{Political Environment, Stereotypes and Implementation of Workforce Diversity Policies in Public Universities in Kenya}
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             \author[1]{Catherine Kathure  Kaimenyi}

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\date{\small \em Received: 7 December 2019 Accepted: 5 January 2020 Published: 15 January 2020}

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The increasing diversity of the workplace in Kenya has continued to put pressure on leaders in all sectors and levels to become more sensitive as they create an all-inclusive workplace. A traditional managerial approach to this diversity has been the struggle for compliance with the implementation of diversity management policies, procedures, and regulations. Although limited empirical research has considered links between organizational related factors thatplay in diversity management activity, very little research has examined the individual psychological related factors that influence the implementation of diversity management practices.

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\section[{Political Environment, Stereotypes and Implementation of Workforce Diversity Policies in Public Universities in Kenya}]{Political Environment, Stereotypes and Implementation of Workforce Diversity Policies in Public Universities in Kenya}\par
Catherine Kathure Kaimenyi Abstract-The increasing diversity of the workplace in Kenya has continued to put pressure on leaders in all sectors and levels to become more sensitive as they create an all-inclusive workplace. A traditional managerial approach to this diversity has been the struggle for compliance with the implementation of diversity management policies, procedures, and regulations. Although limited empirical research has considered links between organizational related factors that play in diversity management activity, very little research has examined the individual psychological related factors that influence the implementation of diversity management practices. This study begins with the premise that organizations develop diversity management programs as a means of responding to the requirements of the external environments, yet the stereotypes that insiders have determine whether or not such programs will fully be implemented. Among the key external environmental factors is the political environment in which diversity management is found. Literature in the field of workforce diversity management points to a relationship between political factors and implementation of workforce diversity policies, but is silent on the role played by psychological factors. This study singles out stereotype and tries to establish whether stereotype has a statistically significant influence on the relationship between political environment and implementation of workforce diversity policies. To achieve this objective primary data is gathered from 392 respondents working in public Universities in Kenya. Results indicate a statistically significant relationship between political environment and implementation of WDP as well as significant moderating effect of stereotype on the relationship between the two variables. Specifically, the more diverse groups are stereotyped to be insignificant the less related policies are likely to be implemented. A recommendation is thus made that managers need to recognize the role of stereotype and shun from negative ethnic stereotype that limit policy implementation. 
\section[{I.}]{I.} 
\section[{Introduction a) Background of the Study}]{Introduction a) Background of the Study}\par
orkforce diversity is a global phenomenon and has continued to increase particularly in terms of gender, ethnicity, race and religion which has created a need for organisational leadership to offer special attention to workforce diversity and its related policies. The International Labour Organization (ILO) promotes diversity by outlining the need for equality and justice in employment regardless of the person's background. There exists various Instruments like the Beijing Platform for Action  {\ref (BPFA, 1995)} and the Convention on the Elimination of all Forms of Discrimination Against Women  {\ref (CEDAW, 1979)} which are all United Nations (UN) tools aimed at promoting gender equality at all levels in member states.\par
Besides the United Nations and other international bodies advocating for an inclusive workplace, Regional instrument exists that guide African countries on matters of employment. For example the Protocol to African Charter on Human and People's Rights advocate for enjoyment of women rights through increased representation at all levels where decision making is made. Similarly, the solemn Declaration on gender equality in Africa which was ratified in 2004 endorses the commitment of the African Union to promotion of gender equity and equality. This is in line with the African Charter that restricts all forms of discrimination in its member states.\par
In Kenya, workforce diversity is promoted largely through the Constitution, 2010. Article 81 (b) of the Constitution, for example, outlines that not more than two-thirds of the members of elective public bodies shall be of the same gender. Article 55, 56 and 232 (h) further provides for equality of opportunities regardless of age, gender, physical abilities, and ethnicity. Despite the existence of all these diversity policies, implementation has remained sluggish. The public universities have attracted scrutiny with claims that their workforce does not meet the required diversity threshold.\par
Despite such policies, there exists a gap at the implementation stage. For example, the National Cohesion and Integration Commission (NCIC) report (2012) revealed that more than half of the Kenya's ethnic groups barely get representation in the Civil Service, an indication of inequality in resource distribution in the country. Although NCIC recorded a slight improvement 2016, the situation still remains wanting. Imbalances in the workplace have also taken an age dimension where the youth are reported to be missing in the mainstream. The 2009 Census by the Kenya National Bureau of Statistics indicated that the youth comprised of over 9.4 million, equivalent to 60\% of the total labour force, yet a government report  {\ref (GoK, 2011)} showed majority are still struggling to enter into the labour market. The World Bank Report  {\ref (2010)} shows that Kenya lags behind in women employment in formal governments and private sectors as compared to other sub-Saharan countries like Burundi, Ghana, Siera Leone and Gambia. The United Nations Global Development Report (2013) ranks Kenya in position 145 out of 187 in employment inclusiveness, which can be attributed mainly to low numbers of youth as well as gender inequality in employments. In a government appointment of 26 parastatal heads in 2013, the minorities in terms of disabilities and the youth were not represented whereas only two women were included.\par
Literature in the field of policy implementation has tried to offer explanations as to why workforce diversity policy implementation is a challenge. One such explanation points to factors external to an organization, key among them is political factors. This study argues that the strength with which political environment influences diversity policy implementation can either be enhanced or reduced by the kind of stereotypes held by those responsible for implementation. 
\section[{b) Political Environment and Stereotype}]{b) Political Environment and Stereotype}\par
Many authors claim that politicization of public services has increased over the years resulting to those in leadership assuming responsibilities that traditionally belonged to public servants (Peter,  {\ref Guy \& John 2004} {\ref , Hart, 2006} {\ref , Dunn, 1997)}. In her research about public universities in Africa, \hyperref[b8]{Josephine (2005)} established that there are rampant political influenced appointments occurring in public institutions in Africa. The research established that those heading public universities in most African countries are politically appointed as a reward for their loyalty to their governments. Such appointments commonly known as patronage hiring have a long term challenge where public servants at senior levels lack the skills and competencies required to carry out their functions effectively, which results to poor productivity. \hyperref[b12]{(Matheson, Weber, Manning, \& Arnould, 2007)}.\par
Lippmann  {\ref (1922)} defined stereotype as a typical picture that comes to mind when thinking about a particular social group.  {\ref McLeod (2008)} describes stereotype as a whole range of characteristics and capabilities that people attribute other categories of group to collectively posses. Such groups can take any form including those associating themselves with a particular relation, age, race, gender, class, economic abilities, education, geographical backgrounds and cultural origins. The National Cohesion and Integration Commission's (2010) findings from their study on the use of Coded Language and Stereotypes among Kenyan Ethnic Communities revealed that all Kenyan ethnic communities have preconceived beliefs about others. It was also established that every Kenyan ethnic community has both positive and negative stereotypes about themselves and about other communities  {\ref (NCIC, 2010)}. From a positive dimension, some groups were associated with aggressiveness and seal to achieve, kindness, supportive, learned, and enthusiastic while others were stereotyped to be highly corrupt, use witchcraft, lazy, jealous, and dependent. Where stereotypes are positive, the group is likely to influence decisions positively unlike in cases where negative stereotypes take precedent. This study argues that stereotypes held by key leaders in the public universities can either strengthen or weaken the relationship that political environment has on the implementation of workforce diversity in those institutions. 
\section[{c) Public Universities in Kenya}]{c) Public Universities in Kenya}\par
The The study focuses on public and not private universities because although by implication diversity policies cuts across all institutions, they are more explicitly pronounced in the public sector. For example, Article 81 (b) of the Constitution demands that not more than two-thirds of the members of elective public bodies shall be of the same gender. Article 232 of the Constitution sets out the values and principles of the public service in Kenya as affording adequate and equal opportunities for appointment, training and advancement, at all levels of the public service of men and women. The public sector appointments are also required to reflect the face of Kenya through inclusion of the marginalized communities in mainstream. In the same spirit, this study focuses on workforce diversity in the public sector, in particular the public universities in Kenya. 
\section[{d) Objectives of the Study}]{d) Objectives of the Study} 
\section[{i.}]{i.}\par
To establish the relationship between political environment and implementation of workforce diversity policies in public universities in Kenya ii. To explore the moderating influence of stereotype on the relationship between political environment and implementation of workforce diversity policies in public universities in Kenya.\par
II. 
\section[{Literature Review a) Theoretical Review}]{Literature Review a) Theoretical Review}\par
This study is anchored on Rawls's Theory of Justice (1971) which posits that justice means giving each person what they deserve. The theory fits in this study as it explains that workforce diversity policies should be implemented so that the categories that are 'marginalized' on various perspectives can be brought in the limelight. Rawls argues that the best society would be founded on principles of justice chosen by rational citizens in an original position. He argues that rational citizens would choose principles of justice that would grant the most extensive liberties to its citizens while ensuring fairness of opportunity and those inequalities benefit the least advantaged. Four classifications of justice can be derived from Rawls Theory of Justice.\par
First, Distributive justice is getting fair share of the resources in question. It is the form of justice that is economic in nature and looks at the extent to which outcome distribution reflect an equitable situation. If people do not think that they are getting their fair share of something, they will seek first to gain what they believe they deserve.\par
Second, Procedural Justice refers to the perceived fairness of the processes by which allocation decisions are made. It is about fair play. Literature in the area of procedural justice identifies consistency, accuracy, representativeness, bias suppression and ethical consideration as procedural rules that shape opinions about justice (Leventhal, Karuza \& Fry 1980). When individuals believe that procedures and processes followed in allocation of resources were fair, they are likely to identify with the resultant outcomes regardless of whether such outcomes were favourable or unfavourable. Recent authors like Gray (2011) conceive that there must be equal opportunity and that greater inequality must benefit those who have the least social and economic goods -the disadvantaged categories of the society.\par
Third, Compensatory Justice, also known as Restorative Justice requires that compensation be provided to the specific individuals/groups who were wronged or harmed by a decision to treat people in certain ways. The principle demands that society has an obligation to overcome historical discrimination against specific groups of people and to compensate those who have been intentionally and unjustly wronged. This, the approach believes, would bring the aggrieved category at the level of those advantaged.\par
The last type of justice under Rawls theory classification is Retributive Justice which stands on the premise that all crimes involves social harm and that violating the law has also moral consequences. According to Rawls people would simply design a society in which their personal interests and plans take precedence at the expense of everybody else, which cannot result in a just society.\par
Rawls theory of justice is applicable to this study in that it advances that all people are equal by nature and so differences in recruitment, advancement opportunity and other prospects in organizations should not exist. This can be enhanced if organizations implement diversity policies that are stipulated in various instruments to bring about a diverse workforce. 
\section[{b) Empirical Review}]{b) Empirical Review}\par
In our everyday life, politics has been regarded as a dirty game. The public service being a political creation is thus political and has resulted to politicization of public goods and services. This in turn has resulted to substitution of political criteria for merit-based criteria in the selection, retention, promotion, rewards, and disciplining of members of the public service \hyperref[b3]{(Clifford \& Wright, 1997)}. Mulgan (1998) described politicization as a situation where appointments are subjective depending on the party affiliation and loyalty of the individual concerned other than any objective measure. The authors further argue that politicization of public service has globally increased over the years resulting to political leadership finding its way in institutions and manipulating organization to perform roles that initially belonged to public servants.\par
A study by Gotsis and Kortezi (2010) on ethical considerations in organizational politics revealed that opportunities for career advancement and growth are availed to those politically aligned to people in power without any regard to any ethical imperative. Although the research by Obong'o (2013) did not take an ethical dimension, it did establish that distribution of public offices through patronage relationships was a rampant practice in Africa. This was found to be in form of nepotism, corruption and other forms of particularistic exchanges which was tantamount to making public service a political environment.\par
The civil service has been viewed as a bureaucratic oligarchy marked with strong connections to the political establishments where elites in various positions, both within and outside governments are keen on protecting their positions and status, thus reinforcing patronage relationships. These elites are able to manipulate, subvert or simply ignore the state and its institutions' reforms and policies in their interests. This persuasion power and loyalty is a major criteria for appointment to senior offices (Oketch, 2009). Consistent with this view is the findings of Cameron (2010) who conducted a survey in South African public service. The survey reported that patronage appointments were a common practice to those who showed loyalty to the government of the day. The study further established that lack of institutional integrity and a weakened trust among the actors in the external Similarly, Hollyer (2010) views political patronage as a mechanism for public employment in exchange for victorious partisan labour and bidding for public offices by offering public services in exchange for public posts.\par
Studies have shown that political patronage relationships have influenced consideration for employment in the public sector (Obong'o, 2013, Owino, 2013). Besides, Olalunji and Ugoji (2013) from their study on political environment and public corporations in Nigeria add the dimensions of Host Community and Government Regulations as key factors influencing public sector appointments.\par
The host community factor is a situation where the community where the organization is located tries to impose certain employees on such organizations for immediate employment without going through the due process. Public education systems have not been spared from political patronage hiring as indicated by research findings. For instance, Josephine's (2005) study in public universities in Africa established that public universities' top management is appointed as a reward to their loyalty to political leadership.\par
According to  {\ref McLeod (2008)} stereotyping results to a belief that every individual in a certain group possesses characteristics that others in the same group have. When people hold stereotypes about others, they develop prejudice attitudes that view outsiders as aliens, creating an in-group and out-group mentality. Generally stereotypes produce prejudice and discrimination to the out-group members by systematically influencing perceptions, interpretations and judgments \hyperref[b6]{(Eagly \& Diekman, 2005)}. For instance, a study by  {\ref Smith (1990)} indicated that whites believe that blacks are unintelligent, sluggish, lazy and incompetent. On the other hand, the National Cohesion and Integration Commission (2010) findings from their study on the use of coded language and stereotypes among Kenyan ethnic communities revealed that all Kenyan ethnic communities have entrenched beliefs among the people about the behaviour, attributes, attitudes, abilities and weaknesses of members of other ethnic communities.\par
Studies by  {\ref Catalyst (1990)} established that among the key barriers for women progression in leadership and management position was stereotype. Along the same line, the Executive Leadership Council surveyed fifty senior African-American chief executives heading huge American companies to establish the opportunities and challenges that surround women leaders (Baskeville \& Tucker, 1991). The survey established discrimination, caused by negative stereotypes about African-Americans hindered advancements of women managers. Similarly, Morrison (1992) surveyed 196 managers from 16 organizations and found that the biggest barrier for women managers is stereotype.\par
Stereotypes about disability are not uncommon at the workplace. For example a study done by  {\ref (Amoako, 1977)} in East Africa revealed that the physically handicapped were perceived as individuals possessed with evil spirits and as such should be isolated to prevent possible calamities befalling others and the society. Such people were regarded as outcastes in Ghana and excluded from participation in the mainstream. In communities like Zimbabwe and Kenya, the handicapped were seen as a symbol of curse and thus rejected by the society (Franzen, 1990), a view that was supported by the study by  {\ref Henderson (1994)}. Prejudiced people on whichever basis may miss opportunities even where they are qualified and even when workforce diversity policies require their inclusion.\par
Discrimination results from prejudice where unequal treatment of individuals or groups of individuals based on personal characteristics, such as race, age, gender, ethnicity, or sexual orientation is observed (Williams \& Patten, 2012). Cognitive social psychological research has demonstrated that discrimination can persist despite earnest efforts like workforce diversity policies to minimize it. For instance, the United States has witnessed discrimination in form of slavery, segregation and denial of voting rights, all of which have been outlawed  {\ref (Thompson \& Benjamin, 2005)}. In South Africa, apartheid was an enormous form of discrimination where blacks used separate and inferior facilities  {\ref (Thompson \& Benjamin, 2005)}. In Kenya, discrimination extends to issues of sex, pregnancy, marital status, health status, ethnic or social origin, colour, age, disability, religion, conscience, belief, culture, dress, language which results to the current socio-economic and political imbalances (Ministry of Devolution, May 2015). 
\section[{III.}]{III.} 
\section[{Methodology}]{Methodology}\par
This study adopted a positivist paradigm which Creswell (2009), describes as quantitative in nature and based on rigid rules of logic measurements, truth, absolute principles and prediction. This paradigm is appropriate for this study because of its objectivity and use of quantitative research methodology to measure perceptions, attitudes and opinions about workforce diversity. Although the study recognizes the strength of pragmatist approach to research, adopting it would reduce the objectivity of the study due to its subjective nature. The study utilizes a descriptive cross sectional survey research design. Zikmund (2003) points out that a survey gives fast and precise methods of accessing information promptly. A descriptive cross-sectional survey collects data to make inferences about a population of interest (universe) and have been described as snapshots of the populations from which researchers gather data. A descriptive cross-sectional manipulate them making it inappropriate to use other research designs such as experimental research design  {\ref (Kothari, 2003)}. Moreover, Zikmund (2003), points out that using this survey method is inexpensive and enables researchers to collect large amount of primary data from respondents in a short period of time.\par
The target population for this study comprises of all academic and non-academic staff of public universities in Kenya. The public universities are those that have been chartered and are usually funded partially or wholly by the Government of Kenya. According to the Commission for University Education, there are twenty two \hyperref[b21]{(22)} chartered public universities in Kenya (CUE, 2014). The public universities were preferred since their structures are well defined and are likely to exhibit elaborate relationships among the study variables. Moreover, a NCIC (2012) study had revealed massive imbalances in workforce diversity which were of interest to the researcher. Respondents were selected from both the academic and non-academic staff from the universities that form the unit analysis in this study. The total population for the public universities workforce is about 25,000.\par
Purposive sampling technique was used to select seven public universities Several factors were considered to arrive at the choice of the participating universities. First, the NCIC (2012) survey that reported massive discrimination in workforce diversity focused on these universities. These universities would therefore provide most appropriate answers to the study questions. Second, nearly all the other public universities were constituent colleges of the seven and so are well represented by their 'mother' universities. Third, the seven universities have been in existence longer than the others and likely to have a workforce that has a relatively longer service thereby being in a position to respond in a more informed manner to the research questions. Finally, the seven universities command a total of 17,721 which translated to more than 70\% of the total workforce of the public universities in Kenya.\par
The second stage of sampling was to identify respondents from the selected seven universities. The sample size was calculated using sample formula by \hyperref[b31]{Yamane (1967)} as below:N n = \textunderscore \textunderscore \textunderscore \textunderscore \textunderscore \textunderscore \textunderscore \textunderscore \textunderscore \textunderscore \textunderscore \textunderscore \textunderscore \textunderscore  1+N(e) 2\par
Where "n" is the sample size, "N" is the population size and "e" is the level of precision at 95\% confidence level with an error 5\%. A sample of 393 was selected which includes 125 and 268 individuals from teaching and non-teaching staff respectively.\par
Primary data was collected using a questionnaire which was pre-tested using a sample of 40 respondents from two public Universities which were not participants in the study. This study uses face validity and content validity to ensure that it can effectively measure the study variables. Cronbach Alpha was used to measure reliability of the study instrument which was distributed using a drop and pick method. Data was analyzed using both descriptive and inferential statistics. Pearsons product moment correlation (r) was derived to show the nature and strength of the relationship between variables while Coefficient of determination (r 2 ) measured the amount of variation between the independent and dependent variables.\par
IV. 
\section[{Findings a) Response Rate}]{Findings a) Response Rate}\par
The sample response rate was 94\%, which was a high response rate in comparison with others in similar field. For example, Shah et al (2012) in a Pakistani University recorded a response rate of 56\% while Omari (2012) in the public sector of Kenya had a response rate of 48\%. 
\section[{b) Descriptive Statistics of Political Environment}]{b) Descriptive Statistics of Political Environment}\par
The study selected two components of political environment; political patronage and favoritism which focused on external political environment of universities.\par
Respondents were asked to indicate the extent to which they perceived their organizations workforce composition as being influenced by political factors. A five point Likert-type scale was used. The responses were analyzed using mean scores and standard deviation of mean (SD). The public universities overall score of political environment was computed as the average of the mean score of political patronage and political favoritism. 
\section[{c) Political Patronage}]{c) Political Patronage}\par
Political patronage is the dispensation of rewards such as public offices and jobs by a patron (who controls their dispensation) to a client (World Bank, 2011). It is based on the premise that those who are loyal to power offices are rewarded for their loyalty. In an environment where political patronage is strong, regulatory requirements may be overlooked with opportunities dispensed through partisan treatment. The study used six items to measure political patronage in public universities. Results are presented in Table \hyperref[tab_5]{4} The results in Table \hyperref[tab_5]{4}.1 reveal that the political environment of the universities is characterized by a high degree of political patronage (mean=4.21, SD=0.071). The political connection of senior university management was ranked highest (mean =4.35, SD=.068) which was consistent with previous studies by Josephine  {\ref (2005)} and Owino (2013) in a similar sector. This may suggest that implementing workforce diversity policies when making hiring decisions may result in political patronage hiring thereby limiting the chances of fully balancing the workforce as stipulated particularly as regards to ethnic balance requirements.\par
The results further indicated that hiring to a large extent was influenced by powerful officials in organizations (mean=4.25), with a high degree of suspicion that there were hidden agendas in the management hiring decision with a mean of 4.23. 
\section[{d) Political Favoritism}]{d) Political Favoritism}\par
Political Favoritism is viewed as the distributing resources inequitably, the practice of giving unfair preferential treatment to one person or group, particularly through nepotism at the expense of another. Eight items are used to measure this and results are presented in Table \hyperref[tab_5]{4} The results in Table \hyperref[tab_5]{4}.2 indicate that the average mean score for political favoritism was 4.31, SD=.072, portraying that public universities were perceived as institutions where political favoritism was high, with a mean=4.31. Consistent with this finding is the study of Oladipo (2011) on Hiring Decisions of institutions of higher education in state universities in Nigeria that hiring decisions were pegged on nepotism and favouritism. The statement indicating existence of people who gets things done their way and cannot be challenged in organizations received the highest rating (mean=4.38, SD=.068). This implies that decisions can be manipulated depending on the personality and political affiliation thereby creating a situation where some may perceive a sense of bias. 
\section[{e) Descriptive Statistics on Stereotypes}]{e) Descriptive Statistics on Stereotypes}\par
Communities possess both positive and negative stereotypes about themselves and other groups. Although stereotypes on their own may not influence decisions on the composition of an organization's workforce, they can solidify or reduce the strength of other factors that have potential to play in this regard. Where stereotypes are positive, they are likely to influence decisions positively unlike in cases where negative stereotypes take precedent. Stereotypes held by key leaders in public universities can either strengthen or weaken the relationship that contextual factors have on the implementation of workforce diversity policies in those institutions. Prejudice and discrimination are identified as key measures for stereotype. 
\section[{f) Prejudicial Judgment}]{f) Prejudicial Judgment}\par
When a group is negatively prejudiced, it is likely to be depressingly judged which in work setting can curtail inclusion. Table \hyperref[tab_5]{4}.3 summarizes results of prejudicial judgment found in public universities. The results in Table \hyperref[tab_5]{4}.3 reveals that the average mean score for prejudicial judgment was 4.30., SD=.072. The perception about disabled people being unproductive had the highest mean score (mean=4.40, SD=0.06). It was also revealed that some tasks were better performed by employees from certain ethnic communities, with a mean of 4.40, SD=0.067. This implies that to a large extent the public universities hold prejudices that may influence decision making regarding workforce diversity in these institutions. 
\section[{g) Discriminative Behaviour}]{g) Discriminative Behaviour}\par
characteristics, such as race, age, gender, ethnicity, or sexual orientation (Williams \& Patten, 2012). Where discrimination is rampant, the level of workforce diversity is likely to be low. Respondents were asked to indicate how strongly they viewed their organizations as having unequal regard to individuals in terms of gender, ethnicity and physical abilities. To measure the level of discriminative practices, a set of five items was used. Results are presented in Table \hyperref[tab_5]{4}  
\section[{A}]{A}\par
Discrimination is indicated by unequal treatment of individuals or groups based on personal when various categories get to the workplace, the treatment they receive is viewed as fair. This is shown in the statement that persons with disability are stigmatized in the university which recorded the lowest mean (mean=1.79, SD=0.75). 
\section[{h) Regression statistics of Political environment and Workforce Diversity Policies}]{h) Regression statistics of Political environment and Workforce Diversity Policies}\par
Results for the test of the hypothesis that there is no significant relationship between political environement and implementation of workforce diversity policies in public universities in Kenya are presented in Table \hyperref[tab_5]{4}.5 The results in Table \hyperref[tab_5]{4}.5 indicates that political environment had a statistically significant influence on implementation of workforce diversity policies explaining a 82.4\% of its variation (R 2 =0.824). The hypothesis is therefore rejected. The Regression coefficient value of the computed scores of political environment was -.992 with a t-test of -41.276 and a significant level of P-value<0.001. This implies that an increase in political environment decreases the overall implementation of workforce diversity policies. This suggests that organizations would disregard the policies that require inclusivity in favour of political gains.\par
These findings concur with Oketch (2009) and Cameron (2010) that patronage appointments in the public sector are linked to loyalty to the government of the day. Further, a study by Hollyer, (2010) found out that bidding for public offices by offering public services in exchange for public posts was common in African public service. Similarly studies by \hyperref[b8]{Josephine (2005)} in public universities in Africa established that the heads of public universities in many countries are political appointees and usually men who are rewarded for supporting the political goals of their governments. Consistence to those findings is Owino (2013) whose study found that nepotism hindered equal employment opportunities in universities in Kenya. 
\section[{i) Moderation of Stereotype on Political Environment and Implementation of Workforce Diversity Policies}]{i) Moderation of Stereotype on Political Environment and Implementation of Workforce Diversity Policies}\par
To test hypothesis 5 d that there is no significant moderation of stereotype on the relationship between political environment and implementation of workforce diversity policies in public universities in Kenya, a moderated multiple regression model was used: Y = ? 0 + ? 1 X 4 + ? 2 Z + ?izX 4 Z + ?, where Y is the implementation of workforce diversity policies, ? 0 is the constant, ? 1 , ? 2 , ? 3 are the slope coefficients representing influence between independent variable and the dependent variable, X 4 represents political environment, Z is stereotype used as a moderating variable while X 4 Z is the interaction term which is the product of stereotype and political environment (stereotype* political environment). The results are presented in Tables 4.6, 4.7 and 4.8. Results in Table \hyperref[tab_5]{4}.6 shows that the F statistics in model one, F ( 1,363 ) = 1703.746, P <0.001 was valid and there is a significant influence between political environment and the implementation of workforce diversity policies in public universities. When stereotype was introduced as a moderating variable in model two, the F statistics, F ( 2, 362 ) = 910.137, P < .001 remained valid and indicated that there is a significant influence by political environment, stereotype on the implementation of workforce diversity policies. When the interaction term (stereotype*political environment) was added in model two, the new model three was found valid F ( 3,361 ) = 652.927, P = .001) indicating that there is a significant influence by political environment, stereotype, the interaction term (stereotype*political environment) on the implementation of workforce diversity policies in public universities in Kenya. The R 2 in model one of Table \hyperref[tab_5]{4}.7 indicates that 82.4\% of the total variation in implementation of workforce diversity policies in public universities in Kenya can be explained by the political environment. The adjusted R 2 shows that when the constant is excluded from the study there is no change in variation in implementation of the policies. The second model introduced stereotype into the relationship between political environment and implementation of workforce diversity policies in public universities. The change in R 2 from 82.4\% to 83.4\% implied that stereotype improves the relationship between political environment and implementation of workforce diversity policies by 1\% which is statistically significant (P<0.001). The third model show the influence of political environment, stereotype and the interaction term (stereotype*political environment) and implementation of workforce diversity polices. The results indicated that the interacting term improves the R 2 by 1.8\% from 83.4\% to 84.4\%, an improvement which is statistically significant (P <0.001). This implies that stereotype significantly influences the relationship between political environment and implementation of WDP in public universities in Kenya. As indicated by the results in Table \hyperref[tab_5]{4}.8, the political environment in which public universities operate remained significant in model one and two. When stereotype was introduced in the second model, it became significant (P < .001). When the interaction term (stereotype*political environment) was introduced in model three the model remained statistically significant at P<.001. This study therefore concluded that stereotype is a significant moderator of the influence of political environment on the implementation of workforce diversity policies in public universities in Kenya V. 
\section[{Summary, Conclusions and Recommendations}]{Summary, Conclusions and Recommendations}\par
The findings from this study revealed that there is a positive and significance influence of political environment on implementation of workforce diversity policies. This means that when political patronage and favoritism deeply embed in the environment, there is lower implementation of the regulatory requirements for diversity. This implies that when the university top management is highly politically influenced, there are high chances of hiring individuals regardless of whether or not the principles of diversity are met. A conclusion is made that public universities are to some extent being influenced by political factors particularly in relation to ethnicity which to a great deal compromises the composition of the workforce.\par
However, contrary to study expectations, stereotype did not seem to significantly influence the relationship between political environment and implementation of workforce diversity policies. The result findings revealed deep ingrained gender stereotypes against women which would limit their inclusion in the workplace. Article 81 (b) indicates that the workforce in the public sector should not have more than two-thirds of the same gender while Article 54 provides that persons with disability should account for at least five per cent of the organization's workforce. Further, Article. 232(g),(h),(i) of the Constitution of Kenya provides that fair competition and merit shall be the basis of appointments and promotions in the Public Service. On the same note, Article 73(2) (a) provides that selection should be on the basis of personal integrity, competence and suitability. For the recruitment function to balance the policy requirement and the needs of the universities knowledge and skill gaps, a clear guideline should exist to enhance value based recruitment. A system should be in place which is easy to manage, economical to operate and sufficiently flexible to meet changing operational needs of the stakeholder. The bottom line of the composition of the workforce should be one where merit and representativeness, merit and equity and equality, and the net effect on efficiency and effectiveness of the workforce is not compromised.\par
The study recommends that the National Cohesion and Integration Commission and other institutions responsible for peace and unity promotion in the country, as well as human resource managers and other personnel charged with the staffing function should make deliberate efforts to shirk negative stereotypes. When a group is negatively stereotyped it is likely to receive depressed treatment. As Kenya strives to ensure effective implementation of the Constitution (2010), an understanding of possible predicaments that may bring constraints is critical. Introducing legal penalties to those who do not comply would enhance implementation.\par
The general notion in Kenya that gender means women should be erased through education as it leaves the society to perceive the policies as discriminatory against men. The universities, or better still, the Commission for University Education and other institutions charged with the responsibility of developing human resource policies for university staff should ensure that not only are policies gender sensitive at recruitment level but also for training and development, promotions and reward management. Women and minorities should be empowered to understand that they need to strive to get into top management positions to be able to influence policy in order to bring about a gender inclusive society.\begin{figure}[htbp]
\noindent\textbf{41} \par 
\begin{longtable}{P{0.85\textwidth}}
Year 2020\\
Volume XX Issue XV Version I\\
( ) A\\
Global Journal of Management and Business Research\end{longtable} \par
 
\caption{\label{tab_1}Table 4 . 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{42} \par 
\begin{longtable}{P{0.7480769230769231\textwidth}P{0.02692307692307692\textwidth}P{0.03846153846153846\textwidth}P{0.03653846153846154\textwidth}}
Descriptive\tabcellsep N\tabcellsep Mean\tabcellsep SD\\
a) Decisions in the University are often taken outside formal meetings or behind closed doors.\tabcellsep 351\tabcellsep 4.32\tabcellsep .070\\
b) I would first find out my supervisor's political preferences before discussing politics with him/her.\tabcellsep 351\tabcellsep 4.31\tabcellsep .069\\
c) Favoritism rather than merit determines who gets ahead around in this organization.\tabcellsep 356\tabcellsep 4.31\tabcellsep .070\\
d) There is a group of people in my organization who always get things their ways because no one wants to challenge them.\tabcellsep 351\tabcellsep 4.38\tabcellsep .068\\
e) I have seen rules bent here in favour of well politically connected individuals.\tabcellsep 350\tabcellsep 4.32\tabcellsep .072\\
f) Information here is jealously guarded and not shared openly between groups and departments.\tabcellsep 351\tabcellsep 4.35\tabcellsep .071\\
g) Those who take credit are not always those who made the biggest contribution.\tabcellsep 348\tabcellsep 4.28\tabcellsep .076\\
h) Employees assumed to have loyalty to management get more opportunities in the university.\tabcellsep 365\tabcellsep 4.24\tabcellsep .075\\
Average Score\tabcellsep 353\tabcellsep 4.31\tabcellsep .072\end{longtable} \par
 
\caption{\label{tab_2}Table 4 . 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{43} \par 
\begin{longtable}{P{0.7138279932546374\textwidth}P{0.03583473861720068\textwidth}P{0.05160202360876897\textwidth}P{0.048735244519392915\textwidth}}
Prejudicial Judgment\tabcellsep N\tabcellsep Mean\tabcellsep SD\\
a) There is a difference in performance between males and females in management positions\tabcellsep 369\tabcellsep 4.24\tabcellsep .078\\
b) One gender is more aggressive than the other in this organization\tabcellsep 373\tabcellsep 4.14\tabcellsep .078\\
c) Being independent and objective is associated to employees of one gender\tabcellsep 363\tabcellsep 4.27\tabcellsep .073\\
d) Employees from some tribes are more loyal than others\tabcellsep 352\tabcellsep 4.32\tabcellsep .072\\
e) Some tasks are better performed by employees from certain ethnic communities\tabcellsep 356\tabcellsep 4.40\tabcellsep .067\\
f) Employees from certain communities are generally lazy\tabcellsep 356\tabcellsep 4.35\tabcellsep .070\\
g) People with disability are unproductive\tabcellsep 354\tabcellsep 4.40\tabcellsep .069\\
Average Score\tabcellsep 360\tabcellsep 4.30\tabcellsep .072\end{longtable} \par
 
\caption{\label{tab_3}Table 4 . 3 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{444} \par 
\begin{longtable}{P{0.7292801556420233\textwidth}P{0.03142023346303502\textwidth}P{0.046303501945525294\textwidth}P{0.04299610894941634\textwidth}}
Discriminative Behaviour\tabcellsep N\tabcellsep Mean\tabcellsep SD\\
a) Potential employees have missed opportunities because their communities are discriminated against\tabcellsep 351\tabcellsep 4.44\tabcellsep .065\\
b) Potential employees have missed opportunities because of gender discrimination\tabcellsep 353\tabcellsep 4.28\tabcellsep .067\\
c) Persons with disability are stigmatized in my university\tabcellsep 357\tabcellsep 1.79\tabcellsep .075\\
d) At my organization, people from certain communities are preferred for certain types of jobs\tabcellsep 356\tabcellsep 4.35\tabcellsep .072\\
e) Some jobs in my institutions are suitable for persons of one gender\tabcellsep 360\tabcellsep 3.48\tabcellsep .076\\
Average Score\tabcellsep 355\tabcellsep 3.67\tabcellsep .071\end{longtable} \par
 
\caption{\label{tab_4}. 4 Table 4 . 4 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{4} \par 
\begin{longtable}{P{0.85\textwidth}}
Year 2020\\
Volume XX Issue XV Version I\\
( )\\
Global Journal of Management and Business Research\end{longtable} \par
 
\caption{\label{tab_5}Table 4 .}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{4} \par 
\begin{longtable}{P{0.32504780114722753\textwidth}P{0.23078393881453152\textwidth}P{0.05688336520076482\textwidth}P{0.014627151051625239\textwidth}P{0.09751434034416825\textwidth}P{0.08938814531548757\textwidth}P{0.03575525812619503\textwidth}}
\tabcellsep \multicolumn{5}{l}{5: Political Environment and Workforce Diversity Policies}\\
(a) Goodness-of-Fit\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
R\tabcellsep R Square\tabcellsep \multicolumn{3}{l}{Adjusted R Square}\tabcellsep \multicolumn{2}{l}{Std. Error of the Estimate}\\
.908 a\tabcellsep .824\tabcellsep \tabcellsep \tabcellsep .824\tabcellsep .54124\\
(b) Overall Significance\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep \multicolumn{2}{l}{Sum of Squares}\tabcellsep Df\tabcellsep Mean Square\tabcellsep F\tabcellsep Sig.\\
Regression\tabcellsep 499.092\tabcellsep \tabcellsep 1\tabcellsep 499.092\tabcellsep 1703.746\tabcellsep .000 a\\
Residual\tabcellsep 106.336\tabcellsep \tabcellsep 363\tabcellsep .293\tabcellsep \\
Total\tabcellsep 605.428\tabcellsep \tabcellsep 364\tabcellsep \tabcellsep \\
(c) Individual Significance\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep \multicolumn{3}{l}{Unstandardized Coefficients}\tabcellsep Standardized Coefficients\tabcellsep t\tabcellsep Sig.\\
\tabcellsep B\tabcellsep \multicolumn{2}{l}{Std. Error}\tabcellsep Beta\tabcellsep \\
(Constant)\tabcellsep 6.150\tabcellsep .105\tabcellsep \tabcellsep \tabcellsep 58.572\tabcellsep .000\\
Political environment\tabcellsep -.992\tabcellsep .024\tabcellsep \tabcellsep -.908\tabcellsep -41.276\tabcellsep .000\\
\multicolumn{5}{l}{a. Dependent Variable: Implementation of workforce diversity policies}\tabcellsep \end{longtable} \par
 
\caption{\label{tab_6}Table 4 .}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{46} \par 
\begin{longtable}{P{0.4427756653992395\textwidth}P{0.10342205323193916\textwidth}P{0.12281368821292774\textwidth}P{0.037167300380228134\textwidth}P{0.07110266159695816\textwidth}P{0.037167300380228134\textwidth}P{0.03555133079847908\textwidth}}
\tabcellsep Model\tabcellsep Sum of Squares\tabcellsep df\tabcellsep Mean Square\tabcellsep F\tabcellsep Sig.\\
\tabcellsep Regression\tabcellsep 499.092\tabcellsep 1\tabcellsep 499.092\tabcellsep 1703.746\tabcellsep .000 b\\
1\tabcellsep Residual\tabcellsep 106.336\tabcellsep 363\tabcellsep .293\tabcellsep \\
\tabcellsep Total\tabcellsep 605.428\tabcellsep 364\tabcellsep \tabcellsep \\
\tabcellsep Regression\tabcellsep 504.998\tabcellsep 2\tabcellsep 252.499\tabcellsep 910.137\tabcellsep .000 c\\
2\tabcellsep Residual\tabcellsep 100.430\tabcellsep 362\tabcellsep .277\tabcellsep \\
\tabcellsep Total\tabcellsep 605.428\tabcellsep 364\tabcellsep \tabcellsep \\
\tabcellsep \tabcellsep 511.213\tabcellsep 3\tabcellsep 170.404\tabcellsep 652.927\tabcellsep .000 d\\
3\tabcellsep Residual\tabcellsep 94.216\tabcellsep 361\tabcellsep .261\tabcellsep \\
\tabcellsep Total\tabcellsep 605.428\tabcellsep 364\tabcellsep \tabcellsep \\
\multicolumn{4}{l}{a. Dependent Variable: Implementation of workforce diversity policies}\tabcellsep \tabcellsep \\
\multicolumn{3}{l}{b. Predictors: (Constant), Political environment}\tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{4}{l}{c. Predictors: (Constant), Political environment, stereotype}\tabcellsep \tabcellsep \\
\multicolumn{5}{l}{d. Predictors: (Constant), Political environment, stereotype, stereotype*political environment}\tabcellsep \end{longtable} \par
 
\caption{\label{tab_7}Table 4 . 6 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{47} \par 
\begin{longtable}{P{0.40660592255125283\textwidth}P{0.036788154897494305\textwidth}P{0.0387243735763098\textwidth}P{0.056150341685649204\textwidth}P{0.08519362186788154\textwidth}P{0.052277904328018224\textwidth}P{0.10261958997722095\textwidth}P{0.005808656036446469\textwidth}P{0.01742596810933941\textwidth}P{0.048405466970387244\textwidth}}
Model\tabcellsep R\tabcellsep R Square\tabcellsep Adjusted R Square\tabcellsep Std. Error of the Estimate\tabcellsep R Square Change\tabcellsep \multicolumn{3}{l}{Change Statistics F Change df1 df2}\tabcellsep Sig. F Change\\
1\tabcellsep .908 a\tabcellsep .824\tabcellsep .824\tabcellsep .54124\tabcellsep .939\tabcellsep 350.113\tabcellsep 2\tabcellsep 368\tabcellsep .000\\
2\tabcellsep .913 b\tabcellsep .834\tabcellsep .833\tabcellsep .52672\tabcellsep .086\tabcellsep 11.251\tabcellsep 1\tabcellsep 369\tabcellsep .000\\
3\tabcellsep .919 c\tabcellsep .844\tabcellsep .843\tabcellsep .51087\tabcellsep .041\tabcellsep 10.012\tabcellsep 3\tabcellsep 371\tabcellsep .000\\
\multicolumn{4}{l}{a. Predictors: (Constant), Political environment}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{5}{l}{b. Predictors: (Constant), Political environment, stereotype}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{7}{l}{c. Predictors: (Constant), Political environment, stereotype, stereotype*political environment}\tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_8}Table 4 . 7 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{48} \par 
\begin{longtable}{P{0.15854922279792746\textwidth}P{0.10790155440414508\textwidth}P{0.15854922279792746\textwidth}P{0.10129533678756476\textwidth}P{0.12772020725388603\textwidth}P{0.10790155440414508\textwidth}P{0.08808290155440414\textwidth}}
\tabcellsep Model\tabcellsep \multicolumn{2}{l}{Unstandardized Coefficients}\tabcellsep Standardized Coefficients\tabcellsep t\tabcellsep Sig.\\
\tabcellsep \tabcellsep B\tabcellsep Std. Error\tabcellsep Beta\tabcellsep \tabcellsep \\
1\tabcellsep (Constant)\tabcellsep 6.150\tabcellsep .105\tabcellsep \tabcellsep 58.572\tabcellsep .000\\
\tabcellsep PC\tabcellsep -.992\tabcellsep .024\tabcellsep -.908\tabcellsep -41.276\tabcellsep .000\\
\tabcellsep (Constant)\tabcellsep 6.409\tabcellsep .117\tabcellsep \tabcellsep 54.987\tabcellsep .000\\
2\tabcellsep PC\tabcellsep -.716\tabcellsep .064\tabcellsep -.655\tabcellsep -11.143\tabcellsep .000\\
\tabcellsep ST\tabcellsep -.355\tabcellsep .077\tabcellsep -.271\tabcellsep -4.614\tabcellsep .000\\
\tabcellsep (Constant)\tabcellsep 5.157\tabcellsep .280\tabcellsep \tabcellsep \tabcellsep .000\\
3\tabcellsep PC\tabcellsep -.192\tabcellsep .124\tabcellsep -.176\tabcellsep -1.545\tabcellsep .123\\
\tabcellsep ST\tabcellsep .062\tabcellsep .114\tabcellsep .048\tabcellsep .550\tabcellsep .582\\
\tabcellsep STPC\tabcellsep -.146\tabcellsep .030\tabcellsep -.792\tabcellsep -4.880\tabcellsep .000\\
\multicolumn{5}{l}{a. Dependent Variable: Implementation of workforce diversity policies}\tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_9}Table 4 . 8 :}\end{figure}
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\end{document}
