Organizational commitment is described as the state in which an individual identifies with the environment and atmosphere in which he works in and desires to continue his membership with.

Based on Allen & Meyer’s framework, we will look at three measures of organizational commitment. Affective commitment comes from an emotional attachment an employee develops towards the company through relating to its values. Normative commitment is a result of feeling obligated to stay with the company due to ethical and moral reasons. If the individual has financial reasons to stay, it is categorized as continuance commitment.

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We will look at a small tech startup operating in British Columbia, Canada. There hasn’t been much research on organizational commitment in startups and we believe this to be a subject of interest considering the high growth rate of such organizations.

There are a few fundamental differences between a small business and a startup. Small businesses are more structured and stable while startups focus on top end revenue and rapid growth. They aim to disrupt a specific area in the market in a short period of time and for this they require agility. These unique characteristics call for special studies investigating organizational behaviors within these startups and the implications, low or high commitment might have for them.

Startups demand a high degree of productivity and creativity. They also have fast paced environments and require long working hours. These are all basic reasons they need to focus on organizational commitment and increase the affective commitment among their employees.

The company we will be looking at demonstrates a high attention to values and positive family and work relations on their website and social media and their core operations and purpose is built around these characteristics. Looking at their promotion of community, safety, mental and emotional health, it seems that these values are embedded in the company’s culture.

Literature Review

There are several studies introducing models to measure organizational commitment. One of the most famous and dominant papers on this subject is written by Allen and Meyer (1990). This paper not only proposes the construct of commitment into affective, normative and continuance, but also contributes to the development of reliable measures and demonstrates commitment as a negative indicator of turnover. Job challenge, role clarity, goal clarity, goal difficulty, management receptiveness, peer cohesion, organizational dependability, equity, personal importance, feedback and participation are among the proposed antecedents of affective commitment. Skills, education, relocation, self-investment, pension, community and alternatives are among the proposed antecedents of continuance commitment. There are no unique antecedents of normative commitment proposed. (Allen, N., & Meyer, J. 1990).

In another study, the relation between affective, continuance and normative commitment was measured through meta-analyses and correlations of their antecedents were examined. The results showed that although the three forms of commitment have a relation to one another, they are still distinguishable. As expected, all three had a negative relation to turnover. Affective turnover showed the strongest relation to organizational outcomes such as attendance, performance and organizational citizenship behavior, and the strongest relation to employee related outcomes such as stress and family-work conflicts. Normative commitment showed weak relation to these outcomes and continuance commitment a weak or negative one (Meyer et al. 2002).

There is a classification of the three commitments into two categories. “value-based commitment includes affective commitment and that aspect of normative commitment that reflects obligation to achieve valued outcomes. The second, which is called exchange-based commitment, includes continuance commitment and that aspect of normative commitment that reflects an obligation to meet other expectations” (Meyer et al. 2006).

There has been a lot of research on the antecedents of organizational commitment. In one study the effects of employees’ hierarchical distance (the number of reporting levels between top management and the employee) and transformational leadership of their direct manager on organizational commitment was examined. The results showed that hierarchical distance was negatively related to Affective and Normative Commitment (Hill et al. 2012).

The managerial perceptions of employee commitment was surveyed in a 1995 paper where 231 managers and 339 subordinates were studied. The results indicated that manager-rated affective commitment was predicted by organizational citizenship behavior, whereas age, tenure and education predicted continuance commitment (Shore et al. 1995).

A recent study compared job satisfaction, turnover intention and organizational commitment in private and public sector organizations. Organizational commitment was measured by Meyer and Allen scale. Job satisfaction and all three types of organizational commitment were high in the public sector. Turnover intention was higher in the private sector. Job satisfaction predicted normative and continuance commitment in the public sector better than the private sector. Overall, organizational commitment was a strong predictor of turnover intention and there was a negative correlation between the two (Agarwal & Sajid 2017).

The relationship between affective commitment to the organization and affective commitment to the supervisor was studied and their effect on turnover was examined. The results showed that affective commitment to the organization precedes affective commitment to the supervisor. Also they found that affective commitment to the supervisor mediated a negative relationship between turnover and affective commitment to the organization (Vandenberghe et al. 2017).

In a 2017 study the impact of normative, affective and continuance commitment on intrinsic, extrinsic and overall work motivation was investigated. The study was conducted on 150 state university teachers and 130 private university teachers. Among the state university teachers, intrinsic motivation was positively affected by overall organizational commitment. For private university teachers this was seen on both extrinsic and overall work motivation (Ahluwalia & Preet 2017).

The effect of employee empowerment and engagement on organizational commitment was examined in public and private sector banks in India. The results showed a significant correlation between employee empowerment and organizational commitment as well as employee engagement and organizational commitment (Prathiba 2016).

Method

Participants

Participants were a sample of the company which included 5 full-time employees at a technology start-up in British Columbia. This sample represents 27% of the employees employed by the company at the time. Females comprised of 20.0% of this sample. Participants worked in each of the three departments within the organization: marketing team (N=2) one of which is also in a leadership role, art team (N=2), and development team (N=1).

Organizational Commitment

Organizational commitment was measured using an adjusted version of the Allen and Meyer’s (1990) three scales of commitment. Measuring Affective, Continuance, and Normative Commitment. Allen and Meyer’s 7-point scale was adjusted to a 5-point scale ranging from 1 (strongly disagree) to 5 (strongly agree) in order to simplify the data analysis.

Predictive Factors of Commitment

Along with the measures for Affective, Continuance, and Normative Commitment, 13 single-item measures were included to assess the magnitude of employees attitudes towards a variety of organization-relevant factors that might be predictive of organizational commitment. The questions measured: job challenge, management receptiveness, motivation, personal importance, relocation, alternatives, goal clarity, organizational dependability, self investment, department, and age. The scale used to measure the magnitude of employee attitudes was a 5-point scale ranging from 1 (strongly disagree) to 5 (strongly agree). This scale was not used for department, age and gender, where participants were asked to indicate which option was the best fit.

Demographics

Certain demographics of each participant was measured using a self-report approach. Each participant indicated their gender, their age, and their department so that it matched alongside of their results and could be analyzed in conjunction with them.

Age was measured with a self-report, where participants indicated which of the 7 age groups applied to them. This was less personally intrusive compared to requesting exact ages. The age groups and numbers were: under 21, 21-28, 29-37, 37-44, 45-52, 53-60 and over 60.

Procedure and Design

The study was a cross-sectional design, with commitment data collected at the same time as the predictive factors. The survey was administered electronically using a custom made Google Form. Data was then collected and analyzed. The survey questions asked can be seen in Appendix A. The survey was 27 questions long, in order to be short enough to encourage more participation by employees.

All members of staff were contacted personally and given a letter of consent by the authors in order to assure them that participation in the survey was voluntary and that information which was collected was confidential and not tied to any personally identifiable information.

Data Analysis

For the data analysis the data was analyzed looking at each prediction factor in isolation of each other and in isolation of the demographics. Making an assumption that each factor was independent of the others and that there were no interdependent relationships between any of the predictive factors or demographics. This was done to Simplify the analysis and make the conclusion easier to understand.

These factors and demographics were then compared to the results of overall commitment of each employee and correlations were made accordingly.

Results

Descriptive Statistics

With the exception of the demographic markers (age, department and gender) the descriptive statistics (average, standard deviation) and correlations of the variables in the study are shown in Table 1.

The overall averages of affective, continuance and normative commitment for the entire sample was calculated by first tabulating the organizational commitment for each result and then taking the average of the results to determine the overall organizational commitment in the three areas. It can be seen in Table 1. that overall affective, continuance and normative commitment was quite low.

The correlations between affective, continuance and normative commitment was calculated using all 5 samples and comparing each type of commitment with each of the predictive factors measured.

Affective commitment was strongly correlated with job challenge, motivation, organizational dependability and then negatively correlated with alternatives. Affective commitment seemed to be weakly correlated with organizational dependability, goal clarity and self investment. Personal investment, managerial receptiveness and relocation appeared to be mildly correlated with affective commitment.

Continuance commitment was most strongly correlated with job challenge, motivation, and then negatively correlated with alternatives and goal clarity. Continuance commitment was weakly correlated with managerial receptiveness, personal importance, relocation, organizational dependability and self importance.

Normative commitment was most strongly correlated with job challenge, personal importance, and then negatively correlated with alternatives and goal clarity. For management receptiveness, organizational dependability and self importance the correlation was quite low. And there was a mild correlation between normative commitment and motivation.

Demographics

The demographics of the participants including age, department and gender of the group were correlated to affective, continuance and normative commitment. This can be seen in Figure 1, Figure 2, and Figure 3 respectively.

Age commitment values were calculated by sorting the data by each of the categories, and taking the average of commitment value of each. Age had no real effect on affective or normative commitment, but was shown to have an effect for continuance commitment. It can be seen that the younger age group (21-28) showed a much higher continuance commitment than the other age groups.

Department commitment values were calculated by sorting the data by each of the department categories, and taking the average of affective, continuance and normative commitment value of each. The department the employee worked in did now show any strong correlations to normative commitment but some correlations can be seen for affective and continuance commitment. For affective commitment, the marketing team showed a lower commitment than the other departments and for continuance commitment the art team showed a higher commitment in this area.

Gender commitment values were calculated by sorting the data into either male or female or other, and taking the average of commitment value of each. Gender did not show any real effect on commitment. For men it can be seen that they showed a slightly higher continuance commitment as to women.

Discussion

It could be seen that there were several factors that appeared to have fairly significant correlations with job satisfaction in the tech startup. There are several factors that were significantly stronger correlated than others as well as a few demographic differences that could be measured.

Based on the overall data, organizational commitment in all of forms is low. It would be best if the startup took action to increase organizational commitment as this has been shown to decrease turnover rates.

Factors Affecting Affective Commitment

With respect to affective commitment we got some interesting results. Affective commitment was most strongly correlated with job challenge indicating that affective commitment would increase as impression of job challenge increases. In regards to the start-up, most employees held very low affective commitment, and did not feel that their job was overly challenging. The low affective commitment overall is concerning. For a start-up company with so few employees, where long work hours are expected for company success, commitment from its employees is very important (Wang & Wu, 2012). In an ideal world all employees would feel strongly attached to the new company and its belief.

The other interesting one to note was that affective commitment was strongly correlated to motivation, this finding indicates that the motivation tactics used by management impact what employees affective commitment levels are. If the tactics are successfully motivating, affective commitment increases. If management can motivate employees in a way that empowers the employee you would see an increase in affective commitment.

The other finding that makes sense logically is that affective commitment was seen to be negatively correlated to alternatives, or in other words the more job alternatives there are the less affective commitment shown by an employee. This makes sense logically as when there are a higher number of job alternatives available to employees to be able to switch jobs, affective commitment decreases.

Factors Affecting Continuance Commitment

Continuance commitment had very similar results to affective commitments results with the exception that it also was quite negatively correlated with goal clarity. Once again it is seen that continuance commitment was strongly correlated to job challenge, and motivation. This repeat in predictive factors would point to emphasis that these two factors are important to job commitment.

The negative correlation with goal clarity was an interesting result. This was an interesting result that shows as goals become more clear, continuance commitment seems to decrease. This result does not make a lot of sense logically, and could potentially be explained by the small sample size or that this factor is in fact not independent to one of the other factors measured. It has been shown that as goal clarity decreases this can lead to increased stress and decreased job satisfaction which in turn leads to lower commitment (Langton et al., 2016) which is the opposite of what our study found.

There was once again a negative correlation with alternatives which once again makes sense logically as discussed above, but once again goal clarity is a bit of a strange result.

It can be presumed that for the company to improve continuance commitment they should focus on improving job challenge and motivation by management.

Factors Affecting Normative Commitment

Normative commitment followed a slightly different pattern to the rest of the results. Although it also is positively correlated with job challenge, it was positively correlated with personal importance. This shows that as employees feel that their job specifically is important to the overall company, than their normative commitment would increase. This makes sense for normative commitment, as normative commitment shows that employees feel an obligation to the company such as being a lead on a new project (Langton et al., 2016) it is logical that when employees feel that the projects they are leading are important, their normative commitment would increase. What this shows is that as a company to make sure not only that employees jobs are challenging but to also give each employee a job that allows them to feel that their work is meaningful and important to the company as a whole.

Like continuance commitment, it was also shown that normative commitment correlated negatively with goal clarity and alternatives. Like for continuance and affective commitment, it is logical that job alternatives are negatively correlated to all types of commitment. Goal clarity being negatively correlated with normative commitment is also a bit of an oddity, but in the sense of normative commitment it may be due to there being complicated goals associated with specific projects that the employee feels committed too. Therefore as the goals become more complicated and less clear this then increases the job complexity and importance and thereby increasing normative commitment. Another explanation can be the dual nature of normative commitment as explained in Meyer et al. (2006). Part of this commitment reflects the sense of duty to meet others presumptions while another part of it is the desire to obtain value driven goals.

Demographic Differences

Differences between different demographics was a bit interesting and can help the company pay more attention to areas that are maybe lacking attention. Affective commitment was seen to lowest in the marketing team. This was an interesting result as the marketing team is responsible for the promotion of the company, and plays a key role in the start-ups success. This is something that should be addressed immediately, with either hiring a few new marketers who are dedicated to the company’s mission and goals or by addressing some of the factors discussed to increase affective commitment for this demographic.

For continuance commitment the demographic results were rather interesting. There was a significantly higher continuance commitment from those who were in the 21-28 age range. This was interesting as continuance commitment has been associated with “lower intention to quit but an increased tendency to be absent and lower job performance” (Langton et al., 2016). This result could be due to the younger age group having spent less time in the workforce and being more financially dependent on their paycheques. This result could be utilized by the company, since this group is less likely to quit immediately, the company has more time to improve other types of commitment (affective and normative) by adjusting the factors of commitment found in the study.

The other demographic that had a notable variance in continuance commitment was the art department. The art department had a significantly higher continuance commitment than other departments. This could be due to lack of other job options as artist positions are much harder to find than marketing and development.

Demographic factors had very little effect on normative commitment, which remained fairly consistent across ages, departments and genders. This could be due to normative commitment being more closely related to employees feeling that they are obligated to the company which would not see significant differences across age, department or gender and would be more connected to individual differences.

Changes To Be Made

With the low commitment in affective, continuance, and normative commitment across the sample, the technology start-up should make a several changes in order to increase organizational commitment of their employees. For all types of commitment, it can be seen that potentially adding to job challenge of all employees would have a positive effect on organizational commitment. This could be done by adding more constrained deadlines to projects, creating more defined project expectations and deliverables. All of these would increase job challenge by adding time constraints, and clearly defined deliverables that must be met. By making employees feel like their jobs are challenging and utilizing their skills, overall organizational commitment will be increased.

The second thing is that the number of alternatives had a negative correlation to organizational commitment, now it is not feasible for the start-up to eliminate competitors, but they can emphasize the company’s uniqueness and highlight what makes them different from the alternatives. By making the company seem more unique and different to their competitors this would narrow down the number of alternatives available to employees. This would decrease alternatives available and therefore increase all types of commitment.

The last thing to note is that the startup should look at how managers motivate their employees. With a fairly strong correlation to affective and continuance commitment it would be worthwhile to evaluate what tactics management is using to motivate employees. It would be a reasonable step to implement more one-one performance evaluations to create open communication in order for management to create personalized motivation tactics for each employee. The other thing management should evaluate what sort of tactics are being used across departments and regulate the tactics used to be consistent. Continuance commitment is slightly higher in the art department so, a good place to start would be evaluating what motivational tactics are used differently than compared to the other departments. Since we can see that affective and continuance commitment is quite low in the organization, the tech start-up should look at changing what tactics they are using to motivate their employees.

Overall, the cost that a startup would incur by losing an employee might be greater than an established firm and thus making the subject of organizational commitment and employee retention a priority is even more critical.

Limitations

Demographic Limitations

This analysis had several limitations built into the study. The first and most notable is the small sample size. Although the percentage of the company who participated in the survey was a good percentage, due to the company employing so few people the sample size itself was quite small. This can lead to less reliable data when taking the opinions of a few people and extrapolating them out to generalize the population as a whole. To account for this the same survey could be given to a larger company and the results compared as a whole to either confirm or disprove the findings.

The second limitation found in the survey was that the sample could have been more representative of the company. It can be seen that there were two responses from both the marketing department and the art department but only one from the development team. It can also be noted that only one female took the survey which does not represent the actual percentage of females in the company. This could skew the data to not be fully representative of the company. A solution to this would be given more time, more responses could have been collected to create a more representative sample of the company and therefore more accurate results.

The third limitation to the study is that exact ages were not asked, but instead an age range. By using an age range and not exact ages that data loses some of its discriminatory detail compared to using continuous data collection.

Data Analysis Limitations

A fourth limitation to the analysis is that the factors were looked at independently from each other. By doing this the assumption is made that none of the factors are dependent or related to each other. This assumption is a limiting factor because it is highly likely that the factors are interdependent of each other and do not occur in isolation.

References

Agarwal, P., & Sajid, S. M. (2017). A Study of Job Satisfaction, Organizational Commitment and Turnover Intention among Public and Private Sector Employees. Journal of Management Research (09725814), 17(3), 123–136.
Ahluwalia, A. K., & Preet, K. (2017). The Influence of Organizational Commitment on Work Motivation: A Comparative Study of State and Private University Teachers. IUP Journal of Organizational Behavior, 16(2), 55–69
Allen, N., & Meyer, J. P. (1990). The measurement and antecedents of affective, continuance and normative commitment to the organization. Journal of Occupational Psychology, 63, 1.
Hill, N. S., Gu Seo, M., Kang, J.H., Taylor, M.S., (2012). The Influence of Hierarchical Distance and Direct Managers’ Transformational Leadership. Organization Science, Vol. 23. No. 3.758-777.
Langton, Robbins, Judge, Breward, Robbins, Stephen P., Judge, Tim, & Breward, Katherine. (2016). Organizational behaviour : Concepts, controversies, applications(Seventh Canadian ed.).
Meyer J. P., Becker T. and Van Dick R. (2006). “Social Identities and Commitments at Work: Toward an Integrative Model”, Journal of Organizational Behavior, Vol. 27, pp. 665-683.
Meyer J. P., Stanley D. J., Herscovitch L. and Topolnytsky L. (2002). “Affective, Continuance and Normative Commitment to the Organization: A Meta-analysis of Antecedents, Correlates, and Consequences”, Journal of Vocational Behavior, Vol. 61, pp. 20-52.
Prathiba, S. (2016). A Study on Impact of Employee Empowerment and Employee Engagement on Organisational Commitment. SIES Journal of Management, 12(2), 45–54.
SHORE, L. M., BARKSDALE, K., & SHORE, T. H. (1995). Managerial Perceptions of Employee Commitment to the Organization. Academy of Management Journal, 38(6), 1593–1615.
Vandenberghe, C., Bentein, K., & Panaccio, A. (2017). Affective Commitment to Organizations and Supervisors and Turnover: A Role Theory Perspective. Journal of Management, 43(7), 2090–2117.
Wang, & Wu. (2012). Team member commitments and start-up competitiveness. Journal of Business Research, 65(5), 708-715.

Tables & Figures

Table 1.

Summary of survey data

	Avg	St. Dev	JC	MR	M	PI	R	A	GC	OD	SI
AC	1.96	0.410	0.980	0.467	0.802	0.579	-0.262	-0.692	-0.048	0.082	0.366
CC	2.92	0.996	0.623	0.144	0.660	0.293	0.384	-0.768	-0.636	-0.045	-0.100
NC	2.4	0.600	0.609	0.100	0.456	0.609	0.598	-0.549	-0.659	-0.280	-0.250

Note: AC- Affective Commitment, CC – Continuance Commitment, NC- Normative Commitment, JC.- Job Challenge, MR- Management Receptiveness, M- Motivation, PI – Personal Importance, R- Relocate, A- Alternatives, GC- Goal Clarity, OD-Organizational Dependability, SI- Self Investment