6AG528 Human Behavior

In the current research, convincing arguments and experimental pieces of evidence explain the relationship between the company's sustainable growth with employee contentment. This article explores this relationship, using the broad reactions of British employees of ABC Company. The strategies used in the data analysis were to describe the relationship between the type of compliance of individual employee and substantial disposition qualities of the employer. The thesis strongly confirms an increasing relationship between age conformity and profession.

The association of appraised satisfaction with the organization level variable was also investigated. The scope for industrial training and improvement are the main motivations in representative professional decisions. This research also analyzed the relationship between the company provided training and overall job contentment.

Hypotheses

Specifically, the following research hypotheses were addressed:

1. It was hypothesized that there was no significant positive relationship between job satisfaction and job benefits.
2. It was hypothesized that employee benefits and the age of the employees did not have any significant association.
3. The effects when the job benefits of an employee were not in line with the hierarchical positions in ABC Company, was statistically significant.
4. Job satisfaction and training programs were not significantly correlated for the employees of ABC.
5. Materials and Method

                For purpose of the research, management of ABC private limited was contacted for an interview with their employees. The modality of the online interview of interested candidates was conveyed to the company. The online survey questionnaire was sent to the management of the company for their 150 employees, and filled up forms were collected manually from ABC Pvt. Ltd. by the scholar. In addition to demographic records; professional data was collected from the interested employees. Threatened members were contacted over phone and interested participants were also included in the inspection work.

The ethical form was signed by the subjects, where the University ethical committee approved the method. After following up with the management of ABC, overall filled survey form of 30 participants was acquired. The average employment years was calculated at 4.5 years (SD = 3.5 years). The mean age of the employees was 35.6 years (SD = 12.41 years). Some missing data from the filled up questionnaire were found, and they were replaced by the average of the responses from the employees. Due to a small number of samples, collected data was not downsized because of the missing values.

Data Analysis and Results 

Reliability

The reliability of the questionnaire was measured for cross-analysis of the impact factor of the answers. The Cronbach’s Alpha was 0.29 for 14 items reliability analysis (Table 3 in Appendix). For obtaining significantly larger reliability, items were discarded from the analysis. Finally, the Cronbach’s Alpha was improvised to 0.53 with 10 items (Table 4 of Appendix). The factor analysis with the reliable 10 items was conducted for the assemblage of the familiar factors.

Descriptive Analysis

The Likert scale questions were rearranged in accordance with their responses in table 1. Accumulation of the responses was observed to be around the strongly agree and mostly agree with response discourses. Only the recommendations of the company to others evenly distributed between agreeing and the disagree options. The pattern of the response sheet was confirmation of the job oriented contentment of most of the employees. However, from the correlation matrix (Table 2) a smaller mass of correlation coefficients was discovered to be significant.

Most of the workers felt that they were important part of the organization Employees were not satisfied with their pay structure in the company, and the trend was evident  Percentage Distribution of opinions on benefits From the graphical representation it was determined that 63.3% employees thought benefits from ABC were sufficient for them.

It was also evident that benefit programs were one of the reasons for the stay of the employee in the company.Almost 67% employees felt that ABC provided sufficient acknowledgment for their achievements for the company.Almost 67% employees felt that their training was sufficient for their job roles in the company.

Regarding respect as a reason for persisting in ABC, there was a division of opinion. Though, 60% agreed about the fact of getting enough respect, there was a group of employees around 36% who disagreed with other employees about getting enough respect.

It was a matter of concern for the company that almost 53% employees expressed their apprehension in recommending ABC to a friend for employment purpose.

Exploratory Factor Analysis 

Before the inferential scrutiny, the exploratory investigation was performed to obtain pieces of evidence of the orientation of the measures. First, the correlation matrix was inferred regarding the item chosen from the questionnaire.

Table 2: Inter-item Pearson's Correlation

From the nature of the correlation between the reliable items, factor analysis was submitted to a principal components analysis with orthogonal rotation. The measure of KMO and Bartlett's test of Sphericity was obtained from the EFA as 0.50 (p = 0.03). Initially, five factors were extracted explaining 78.10% of the total variance of the items, where the Scree plot ascertained the five factors whose Eigen values ??were more prominent than 1. Factors were stacked in the right factor, with essential loads of more than 0.3. Later, the gears of the implementation measures of the research evaluated by the scholar were subjected to a revision of three main factors (Table 5 in Appendix). The factors were designated as Benefits, Development, and Satisfaction of an employee in ABC. Using SPSS software platform, the three consolidated variables were evaluated.

Inferential Analysis

The correlation between job satisfaction and job benefits was statistically insignificant (r = 0.16, p = 0.39). Hence the null hypothesis 1 was accepted based on the pieces of evidence of Pearson's correlation.  The satisfaction from job and age of the employees was found to be statistically uncorrelated (r = -0.30, p = 0.87).  The null hypothesis 2 failed to get rejected, based on the responses from the employees.  One way ANOVA was executed for verification of the null hypothesis 3. The result (F = 0.11, P =0.96) was quite not motivating, as no statically significant relation was observed between the position of employees in the company and benefits earned from ABC. The shreds of evidence were not enough to reject hypothesis 3. The benefit of the employees and training program was also statistically uncorrelated (r= -0.08, p =0.66). Hence, the null hypothesis 4 was also failed to get rejected.       

Conclusion

                The consequences of this examination demonstrated an insignificant association between job-related contentment and dedication towards the company. This may signify a few things to the workers, and to ABC, for the association of the employees with the company. This investigation affirms that workers’ esteem was not the key component in satisfaction with the workplace.  Impact of training and development were insufficient in increasing association with the organization to enhance their capacity to build themselves. Organizational monetary benefits for the workers and the management offers were not significant evidence for motivation in work execution.

Employees were not engaged in company affairs and no association with their work execution was found within the company (Khan, R.A., 2014). Probably, workers were reluctant in their occupational duties and more inclined to hierarchical objectives and qualities. Additional eagerness to apply effort in the working environment was not observed, and probable inclination to remain in an association with the company has failed to get noticed as an expected outcome (Lautizi, Laschinger, &Ravazzolo, 2009).

A few restrictions of this examination were recognized to help control future research. This examination was based on employees in a single specific occupation. Results were restricted to that kind of occupation only. Moreover, this investigation used the comfort of interviewing, and the sample was not exhaustive in nature. Hence, the sample can't be regarded as representative of the populace. Nonetheless, Wallen and Fraenkel (2001) take note of that in comfort inspecting; the scholar finds a way to depict the sample as plausible for factors relevant to the investigation. Statistical questions were utilized to portray the example as plainly as could be expected under the circumstances.

Since respondents had the choice of finishing this review on the web, multiple reactions might be an issue also. To avoid numerous reactions from the same individual, every member needed to provide an individual email address before getting to the overview. Every email account could be utilized to respond to the study just a single time.

Semi-structured interviews are face-to-face interactions where the interviewer and respondent are engaged in a formal manner (Rabionet, 2011). The interview procedure progresses with an interview guide along with an interview guide. A list of questions for the interview usually progresses in a chronological manner. The interviewer can, however, deviate away from the conversation in case there is a need. Bernard (1988) stated that semi-structured interviews are used when there is a single chance to interview a person (Harrell & Bradley, 2009).

In a semi-structured interview, a clear set of instructions is encompassed to provide comparable, reliable set of qualitative data. As semi-structured interviews are preceded by observation and open-ended questions, it acts as the most appropriate means for collecting as well as analyzing data (Van Teijlingen, 2014). This type of interview provides in-depth data collection. Standardization of some questions will enhance date reliability; however, there might be the possibility of replication. It provides a large amount of rich detailed subjective data.

Creswell (2005:206) in his research stated that a method needs to be devised which does not generalize the population rather is effective in the in-depth exploration of the central phenomenon. Multiple data collection methods present in qualitative research and interviews for developing an appropriate sample. The interview allows a flexible method for developing questions with freedom for the central issue to be analyzed (Whiting, 2008). Semi-structured interviews with open-ended questions provide participants with sample scope and time for expressing their opinion.

It provides the researcher with innovative ideas as participants express their views regarding general themes. Semi-structured interviews allow participants to elicit opinions, convictions, and statements. Often they are able to capture personal experiences hence minimizing the effects of researcher's attitudes with earlier findings. Through such anonymity, participants are able to freely express their views hence gather a broader range of the sample.

Through data reduction, simple and efficient findings can be arrived at. Qualitative research study's strengths and weaknesses encompass developing dependence on the analyst abilities. The semi-structured interview allows collection of a wide variety of data, which subsequently needs to be analyzed. Qualitative data collected can be analyzed through thematic analysis, Grounded theory, Discourse analysis and other methods. Qualitative data analysis has to progress through identification, coding, and categorization of themes or patterns within such data. However, application and clarity of findings are dependent on the analytical capabilities of the research scholar.

Documenting analytical procedures and processes would allow credibility of the researcher for validating his findings. There are a number of Qualitative data management software program which facilitates analysis of such data. Computer Assisted Qualitative Data Analysis (CAQDAS) can provide data management, coding, and analysis. There are two types of qualitative data management software programs, one includes coding and retrieval program and the other is a theory-generating program.

Sampling error arises from the un-representativeness from the selected sample for observation. Non-sampling error arises from a human error such as error from methods, problem identification or procedures (Flowerdew & Martin, 2008). Sampling error arises from using a sample from the population defined. It is considered when the difference between estimate for a population and true value for the population is undertaken.

In case of any study, the aim is to control any types of error as it might impact final findings from a research. Impact of sampling error on the interpretation of research findings can be tracked by comparing the mean values of population parameter than mean value obtained in the study. Sampling error arises in the case of a certain sample, which has the unrepresentative population of interest, is taken into consideration (Wang et al., 2012). Therefore this error is when the selected sample does not have true characteristics reflecting the entire population. In case of individual variances between sampling units, this type of error might arise.

In case defective designs are made, the wrong choice of statistics is undertaken or faulty demarcation of units is done for sake of convenience such errors might appear. The greater the error lesser is the reliability of the data. The focus of every study is to select a credible source of data collection such that errors can be minimized. Transparency regarding the size of the expected sample also needs to be present to reflect the fitness of the sample.

Non-sampling error arises due to other sources of deficiency and is not connected with sample size (Gideon, 2012). Due to non-selection of the sample, the presence of any factor random or systematic this type of error might creep in. In non-sampling error might arise from a number of factors, whereby two types of non-sampling error might arise. Response error is a non-sampling error which might affect significantly results of the study by altering expected outcomes. Sampling error can easily be calculated hence controlled however, it becomes often difficult to determine and measure non-sampling error.

In case of non-response error of non-sampling again the absence of some sample response might alter final outcomes and have tremendous implications on the study (Scheaffer et al., 2011). Sampling or non-sampling errors need to be mitigated such that the findings of the study are reliable and accurate. This will determine the applicability of the findings from the study. In various studies, the size of the overall sample is increased to ensure that error levels are reduced significantly.

Reference

Flowerdew, R. and Martin, D.M., 2008. Questionnaire design and sampling.Methods in human geography (pp. 102-133).Routledge.

Gideon, L., 2012. Handbook of survey methodology for the social sciences. New York, NY: Springer.

Harrell, M.C., and Bradley, M.A., 2009. Data collection methods.Semi-structured interviews and focus groups. Rand National Defense Research Instsantamonica ca.

Khan, R.A., 2014. The Impact of Career Planning and Development on Job Satisfaction And Retention in Pakistani Banking Sector. Global Management Journal for Academic & Corporate Studies, 4(2), p.109.

Lautizi, M., Laschinger, H.K. and Ravazzolo, S., 2009. Workplace empowerment, job satisfaction and job stress among Italian mental health nurses: an exploratory study. Journal of Nursing Management, 17(4), pp.446-452.

Norman, E.W. and Fraenkel, R.J., 2001. Educational research: a guide to the process. New Jwrsey: Lawrence Erlbaum.

Rabionet, S.E., 2011. How I Learned to Design and Conduct Semi-Structured Interviews: An Ongoing and Continuous Journey. Qualitative Report, 16(2), pp.563-566.

Scheaffer, R.L., Mendenhall III, W., Ott, R.L. and Gerow, K.G., 2011. Elementary survey sampling.Cengage Learning.

Van Teijlingen, E., 2014. Semi-structured interviews. Retrieved from.

Wang, J.F., Stein, A., Gao, B.B. and Ge, Y., 2012. A review of spatial sampling. Spatial Statistics, 2, pp.1-14.

Whiting, L.S., 2008. Semi-structured interviews: guidance for novice researchers. Nursing Standard, 22(23).