Abstract
Potential entrepreneurs encounter challenges, which influence their entrepreneurial behaviors. In the study done on the MBA students (N = 286), the study hypothesized that self-fulfillment, financial gain, career development, and job security are factors that have positive relationships with moral disengagement. T-test analysis of scores of these factors revealed that gender does not influence the behaviors of potential entrepreneurs. Further analysis using correlation showed that self-fulfillment has positive relationships with financial gain, career development, and job security. Additionally, financial gain has positive relationships with career development and job security while career development has a positive relationship with job security. Nevertheless, the findings indicated that moral disengagement does not have statistically significant relationships with self-fulfillment, financial gain, career development, and job security. In this perspective, the correlation analysis rejects the hypothesis that financial gain, self-fulfillment, job security, and career development have positive relationships with moral disengagement. Therefore, the study recommends the inclusion of more variables, the use of established scales, and the performance of comprehensive statistical analyses.
Introduction
Moral disengagement is a significant social and psychological factor that determines the behaviors of individuals in certain circumstances. In his comprehensive analysis of behavioral traits of humans, Bandura (2015) established that people exhibit selective behaviors, and thus, provides a simple definition that is quite imperative to this study. Bandura (2015) defined moral disengagement as a form of behavioral misconduct in which a sudden shift in the moral standards occurs in people with low moral values, who resort to self-conviction that some ethical standards are inapplicable in some particular situations. In an organizational environment and an entrepreneurial context, psychologists and sociologists have discovered that moral disengagement has some triggers, which either originate from physical, social, or mental circumstances. Nevertheless, such moral disengagements have recently become contentious, and researchers are investigating their relationship with organizational affairs. Therefore, this research paper intends to examine the moral disengagement roots and the associated risks of unethical decision-making.
Literature Review
Moral Disengagement
Moral disengagement determines behaviors or actions that individuals choose to perform. Bandura (2015) postulated that individuals, who decide to disengage morally, tend to adopt inhuman characteristics because of repulsion from the established moral standards. Moral disengagement prompts people to develop various unethical behaviors that do not reflect the moral sanctity of the actions of these individuals (Milner 2012). While studying moral disengagement in jobs of intense frustrations such as the police department and the military profession, Bandura (2013) discovered that moral disengagement leads to self-regulatory mechanisms and self-deceptive tactics that make individuals live in deception believing that they uphold moral principles. Caroli and Sagone (2014) state that even though moral engagement has been studied in the military, police carriers, and youth, little has been studied concerning the connection between moral disengagement and entrepreneurship.
Ethics Status in Entrepreneurship
Askew, Beisler, and Keel (2015) conducted a study to establish the current trends of unethical behaviors in organizations following previous studies on a similar topic and conducted a systematic review of the most recent studies to examine the above subject. In their research, Askew et al. (2015) established that moral disengagement is becoming common although certain triggers of psychological, physical, and social nature have promulgated these changes in the moral conduct of people. Nonetheless, moral standards and principles remain paramount in the protection of the welfare of organizations and their people, even as some individuals consider these moral behaviors to be against their moral reasoning (Miller 2012). Cases of moral disengagement have increased rapidly with some individuals demonstrating selective disengagement of moral self-sanctions even at their workplaces.
Moral Disengagement and Entrepreneurial Drives
The developer of the moral disengagement theory, Albert Bandura once stated that moral standards do not always guarantee good behaviors, as they are not constant internal regulators of the conduct of people. The growth of personal motives in entrepreneurship has proven these assumptions, which predispose businesspersons to make unethical decisions (Sezer, Gino & Bazerman 2015). A perfect case of moral disengagement can be traced in the business activities of Chinese entrepreneurs, namely, Baron, Zhao, and Miao (2012) who sampled and studied Chinese entrepreneurs, found out that moral disengagement intervenes the positive relationship between financial gain and the probability of making unethical decisions among entrepreneurs (Baron et al. 2012). In the study, respondents claimed that moral disengagement sometimes results from the urge to make positive financial achievements, and as a result, entrepreneurs engage in unethical business decisions.
Moral Disengagement and Employee Life Cycle
Engaged employees are important in running productive and profitable organizations. However, a morally disengaged employee can make irrational career decisions. According to a study conducted by Askew et al. (2015) who reviewed numerous studies related to moral disengagement in workplaces, employees with low levels of moral engagement are likely to possess wrong workplace motives that either result in termination from work or personal resignations. Hence, an employee with disengaged morals is likely to have a shorter employment life cycle than a morally engaged employee.
Moral Disengagement and Job Security
Moral engagement among employees causes some negative workplace behaviors including changes in work attitude, low motivation, and poor job satisfaction. Huang et al. (2016) conducted a study to examine the relationship between the possibilities of developing deviant behaviors and exit intentions at the cost of job security and moral disengagement. In their first hypothesis, Huang et al. (2016) found out that job insecurity has positive relationships with the three variables, namely, turnover intentions, interpersonal deviance, and organizational deviance. With a sample of 150 to 1,000 employees from different employees, Huang et al. (2016) discovered that low moral engagement has positive relationships with high levels of turnover intentions in organizations, organizational deviance, and even interpersonal deviance.
Moral Disengagement and Unethical Decision-Making
From his early works on selective moral behaviors, Bandura (2015) postulated that even though moral standards exist in organizations, these moral standards do not act as the internal controllers of an individual’s conduct. A morally disengaged employee or even businessperson is normally at risk of making unethical decisions. Moore et al. (2012) conducted a study to examine why employees do dreadful things. In this study, the researchers wanted to investigate further the existing relationship between moral disengagement and unethical organizational behaviors. Using a web-based survey, Moore et al. (2012) approached one hundred and ninety-four adults to examine this assumption. Based on the collected results, people with high moral disengagement have a high propensity to engage in unethical decisions.
Methodology
Method
To identify the main reasons that make MBA students start their businesses, the study employed a survey as a research design. According to Fowler (2013), survey design is advantageous in research because questionnaires are cheap to formulate, data collection is simple, surveys provide valid responses, and sampling procedures enhance the representation of the population. The questionnaires were designed and structured to collect valid and reliable information from MBA students. The variables that the questionnaire measured are self-fulfillment (SF), financial gain (FG), career development (CD), job security (JS), and moral disengagement (MD). The study measured these variables on a five-point Likert scale to quantify and allow quantitative analysis of data.
Hypotheses
In line with the research design and methodology, the study formulated four hypotheses. Financial gain, self-fulfillment, unethical decisions, job security, and career development are the independent variables while moral disengagement is the dependent variable that the study used in formulating the hypotheses. The following are the hypotheses that the study formulated and tested using the t-test and correlation analysis.
- Financial gain positively related to moral disengagement
- Self-fulfillment positively related to moral disengagement
- The relationship between entrepreneurs’ moral disengagement and unethical decisions is moderated (mediated) by employee job security such that the relationship is stronger in the highly secured job than those vulnerable to be laid-off due to automation or downsizing.
- The relationship between entrepreneurs’ moral disengagement and unethical decisions is moderated (mediated) by employee lifecycle such that the relationship is stronger during the early stages of employee career development than during later stages.
Data Collection
As the secondary data about the entrepreneurial motivation of potential entrepreneurs among MBA students lack, the study conducted a survey to collect primary data. The structured questionnaires were used to collect data from MBA students in Sydney CBD Region. The researcher informed the MBA students about the essence of the study, sought informed consent to administer the questionnaires, and allowed them to make an independent decision regarding voluntary participation in the study. Moreover, the researcher assured the MBA students that their information would be confidential and anonymous. Once the ethical procedure was complete, the researcher administered questionnaires for about 15 minutes to the MBA students in their classrooms. Moreover, the postal survey was conducted by mailing the questionnaires to all contacts. A follow-up survey was mailed to non-respondents to prompt them to answer the questionnaires and provide the required information.
Sampling
The study employed convenience sampling in selecting MBA students to participate in the study. In tandem with the research design, convenience sampling is advantageous because it is cheap, expedient, and enhances the representation of the population (Takhar-Lail & Ghorbani 2015). The convenience sampling was employed as the researcher administered questionnaires to all MBA students in the classroom and mailed them to all respondents in the contact list. Out of 317 questionnaires administered, the MBA students answered 286 questionnaires correctly, and they contained all the required data. The response rate shows significant representation as the sampling error was 7.8% and the confidence level was 95%. Table 1 below shows the sampling parameters of the potential entrepreneurs.
Table 1: Sample Parameters of the Potential Entrepreneurs
Source: Researcher
Data Analysis
The study undertook analysis to determine the relationship between moral disengagement as the dependent variable and financial gain, self-fulfillment, unethical decisions, job security, and employee lifecycle as independent variables. The stud reduced the number of variables using principal components analysis to facilitate the analysis and the interpretation of the findings (Giles 2013). The identification of the main motivational factors among the MBA students paved the way for the analysis of how these factors influence moral disengagement among potential entrepreneurs. The study employed cluster analysis of previous studies (Barba-Sanchez & Atienza-Sahuguillo, 2012; Sloka et al. 2014; Christiansen 2015) in classifying motivational factors and elucidating topology of potential entrepreneurs. The cluster analysis formed the basis of data analysis because they indicated the degree and extent to which each dependent variable influences moral disengagement among the MBA students.
As inferential statistics, the study utilized t-test and correlation analysis. In the ANOVA test, the study used moral disengagement as the dependent variable and SF, FG, CD, and JS as the independent variables. The gendered difference in moral disengagement was determined using the ANOVA test and the significance of the difference in the means was tabulated. Fundamentally, ANOVA is one of the inferential statistics that determine if a difference between means among groups is statistically significant or not (Roberts & Russo 2014). Comparison of the male and female scores of moral disengagement in each of the independent variable provides important information regarding gendered variation in moral disengagement among the MBA students. According to Andy (2013), correlation analysis measures the nature and the degree of the relationships between two variables. In this case, the study used correlation in measuring the nature and the degree of relationship between moral disengagement and each of the dependent variables.
Results
Table 2 below shows outcomes of t-test comparing male and female scores of moral disengagement and each of the independent variables, namely, MJ, EL, AC, DISR, DIFR, DC, AB, DEH, SF,FG, CD, and JS.
Table 2: T-Test Outcomes of Gendered Moral Disengagement
Note: ns = not significant, * = significant at.05 level, ** = significant at.01 level, MJ = Moral Justification, EL = Euphemistic Labelling, AC = Advantageous comparison, DISR = Displacement of Responsibility, DIFR = Diffusion of Responsibility, DC = Distortion of Consequences, AB = Attribution of Blame, DEH = Dehumanisation, SF = Self-Fulfilment, FG = Financial Gain, CD = Career Development , JS = Job Security, MD = total scores of Moral Disengagement.
Table 3 below is a correlation matrix shows correlation coefficients and their respective significance levels at 0.05, 0.01, and 0.001.
Table 3: Correlation Matrix of All Variables
Note: *=significance at.05, **=significance at.01 and ***=significance at.001.
Discussion
Descriptive statistics of self-fulfillment, financial gain, career development, job security, and moral disengagement exhibit gendered variation. Female participants have a higher mean score in self-fulfillment (M = 4.20, SD = 0.75) than male participants (M = 3.97, SD = 0.90). In financial gain, male participants have a higher mean score (M = 4.01, SD = 0.95) than female participants (M = 3.86, SD = 0.94). Comparison of career development indicates that female participants have a higher mean score (M = 3.48, SD = 0.91) than male participants (M = 3.18, SD = 0.82). In the aspect of job security, female participants are more secure (M = 3.32, SD = 0.89) than male participants (M = 2.19, SD = 0.74). Regarding moral disengagement, male participants are have a higher score (M = 2.11, SD = 0.52) than female participants (M = 1.96, SD = 0.38). T-test analysis disapproves of the apparent depiction of gendered differences that they are not statistically significant at both 0.05 and 0.01 levels. Overall, these findings contradict that of previous studies for they indicate that there is a gendered difference in moral disengagement among individuals (Cory & Hernandez 2014; Lv & Huang 2012; Robson & Witenberg 2013). The apparent difference in the means shows that males have higher scores in moral disengagement and financial gain than females. In contrast, females have higher scores in self-fulfillment, career development, and job security.
Correlation analysis of self-fulfillment, financial gain, career development, job security, and moral disengagement provided diverse relationships. Self-fulfillment has a moderate positive relationship with financial gain (r = 0.40), which is statistically significant p<0.001). These findings are consistent with the findings of Baron et al. (2012) who demonstrated that financial gain has a positive relationship with unethical decision-making under the mediation of moral disengagement. Barba-Sanchez and Atienza-Sahuguillo (2012) add that financial gain motivates individuals to explore entrepreneurial opportunities and establish new ventures. Moreover, self-fulfillment has a weak positive relationship with career development (r = 0.29) and job security (r = 0.26), which is statistically significant (p<0.01). Financial gain has a moderate positive relationship, which is statistically significant (p<0.001), with career development (r = 0.38) and job security (r = 0.31). Career development has a moderate positive relationship with job security (r = 0.31, p < 0.001). Barba-Sanchez and Atienza-Sahuguillo (2012) hold that self-fulfillment, career development, job security, and financial gain are factors that motivate individuals to explore entrepreneurial opportunities. However, the correlation analysis shows that moral disengagement has no statistically significant relationship with self-fulfillment (r= -0.05, p>0.05), financial gain (r = 0.01, p>0.05), career development (r = 0.01, p >0.05), and job security (r = -0.03, p>0.05). Therefore, the correlation analysis does not support hypotheses that financial gain, self-fulfillment, job security, and career development correlate positively with moral disengagement. The absence of statistically significant correlations between moral disengagement and self-fulfillment, financial gain, career development, and job security contradicts previous studies (Barba-Sanchez & Atienza-Sahuguillo 2012; Askew et al. 2015; Sloka et al. 2014). The design of the questionnaire and the scales employed in the measurement of the independent variables contributed to low internal validity, resulting in the occurrence of insignificant relationships. Moreover, the target population of the MBA students might have reduced the external validity of the findings due to the homogeneity and underrepresentation.
Recommendation
- To enhance the internal consistency of data, the study recommends the use of established scales because they are not only valid but also reliable in the measurements of self-fulfillment, financial gain, career development, job security, and moral disengagement.
- To improve the external validity of the findings, future research should increase the sample size and include other potential entrepreneurs who are not students. Moreover, the study should incorporate other variables such as moral justification, euphemistic labeling, advantageous comparison, displacement of responsibility, diffusion of responsibility, distortion of consequences, attribution of blame, and dehumanization for they have statistically significant relationships with moral disengagement.
- The study also recommends the inclusion of comprehensive statistical analyses such as descriptive statistics, analysis of variance, and regression analysis to measure how each variable influences morals disengagement and consequently entrepreneurial behaviors.
Conclusion
Potential entrepreneurs experience numerous factors that motivate them to start their businesses. Comparative analysis of self-fulfillment, financial gain, career development, job security, and moral disengagement among the MBA students indicated that gender does not influence the predisposition of potential entrepreneurs. Correlation analysis showed that self-fulfillment has positive relationships with financial gain, career development, and job security. Financial gain has positive relationships with career development and job security while career development has a positive relationship with job security. However, moral disengagement has no statistically significant relationship with self-fulfillment, financial gain, career development, and job security. Hence, the correlation analysis rejects the hypothesis that financial gain, self-fulfillment, job security, and career development correlate positively with moral disengagement. Therefore, the study recommends the inclusion of other variables that strongly predict moral disengagement, the use of established scales with high validity and reliability, and the performance of comprehensive statistical analyses.
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