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Psychological Capital: A Positive Approach to Enhance Commitment to Change among University Students

Shashika Naotunna

Department of Business Management, Sabaragamuwa University of Sri Lanka, Belihuloya, Sri Lanka

Abstract: Higher Education Institutions need to engage in planned change initiatives in order to enhance the quality of higher education. However, gaining commitment for such change efforts is still problematic. Even though, considerable scholarly work has been devoted to commitment to change over the years it remains as an issue. Following this problem, the study aimed to investigates the impact of Psychological Capital (PsyCap) on dimensions of commitment to change (i.e. Affective Commitment to Change, Continuance Commitment to Change, and Normative Commitment to Change). The respondents of the study were undergraduates of Faculty of Management Studies of Sabaragamuwa University of Sri Lanka. Data were collected through a standard and validated questionnaire survey. Survey resulted in 396 usable questionnaires. SPSS 21 and Amos 16 were used to analyze data. Results suggest that PsyCap positively and significantly related to affective commitment to change, normative commitment to change and negatively affects continuance commitment to change. The results imply the importance of Psychological capital in enhancing affective and normative commitment to change while minimizing negative effects of continuance commitment. This research makes a novel contribution by being among the first to examine the impact of Psychological Capital in explaining commitment to change among university students.

Keywords: affective commitment to change; continuance commitment to change; normative commitment to change, psychological capital

Received May 12, 2015; Revised May 24, 2015; Accepted May 29, 2015

Citation: Shashika Naotunna, Psychological Capital: A Positive Approach to Enhance Commitment to Change among University Students. American Journal of Educational Research, 3 (6). 765-769. January 2015. doi:10.12691/education-3-6-16

1. Introduction

Commitment to change is a unique construct which has more power in explaining behavioural support for a change than commitment to organization [1,2], openness to change [3] and readiness to change [4]. As mentioned in [5] the uniqueness of the commitment to change can be explained as “people want to see the change happen, and will do whatever is necessary to see that it does happen” (p.5). Therefore, commitment to change is undoubtedly, one of the most important factors contribute in successful change effort [4,6].

Although commitment plays a significant role in change there are a small amount of studies which focused on the issue of commitment to change [20]. Further, the explanatory variable of this study is Psychological Capital (PsyCap) which is considered as a new branch in Psychology. However, current researcher could not come across with a single research which attempts to combine Psychological Capital and Commitment to Change in Higher Education Institutions. Hence, this research makes a novel contribution by being among the first to examine the impact Psychological Capital on commitment to change.

1.1. Literature Review and Hypotheses

Commitment refers to “a force [mind set] that binds an individual to a course of action of relevance to one or more targets”. Further they explain this mind set includes three different forms: desire (affective commitment), perceived cost (continuance commitment) and obligation (normative commitment) which is known as commitment profile [7]. Therefore, [1] developed a new construct called commitment to Change with same three dimensions.

Though, some researchers [8,9] tend to conceptualize commitment to change as a unidimensional construct, [1], argue it should be a three dimensional construct. As far as the commitment to change studies are concerned, majority of studies on commitment to change [e.g. [2,10,11,12,13,14]] have borrowed a conceptual definition developed by [1] in their three component commitment to change model.

Commitment to change is defined as “a force (mind-set) that binds an individual to a course of action deemed necessary for the successful implementation of a change initiative” [1]. This force includes three dimensions called affective, continuance and normative commitment to change.

The first dimension of commitment to change is Affective commitment to change (ACC). ACC refers to “a desire to provide support the change based on its inherent benefits [1]. Affective commitment refers to the emotional attachment; identification and involvement with the target [7]. This commitment towards the change is developed since the employee understands its worthiness to generate positive outcomes to the organization [1,2]. Strong affective commitment is highly correlated with the success change implementation [1,10]. Therefore, affective commitment is considered as a crucial form of commitment in a planned change [15].

The second dimension of commitment to change is continuance commitment to change (CCC). CCC can be defined as “recognition that there are costs associated with failure to provide the support for the change” [1]. Continuance commitment takes place due to lack of alternatives and external pressure to go along with the target [2]. According to [1] organizational participants feel continuance commitment to change, when there is no other option than go along with the change (i.e. lack of alternatives). In addition to that, continuance commitment to change is developed due to the fear of losing important rewards [1,2]. As a result of this background continuance commitment involves less commitment than is expected from the employees [2]. Therefore, though there are some views to justify the adequacy of continuance commitment it does not sufficient to ensure higher level of support towards a planned change [1,2]. The possible explanation for this would be continuance commitment is based on external pressure rather than recognizing the benefits of the change or felt deepest obligation about the change [16]. Overall continuance commitment is less desirable in organizational settings.

The third dimension of commitment to change is, Normative Commitment to Change (NCC) which is defined as “a sense of obligation to provide support for the change” [1]. Normative processes are highly related to organizational effectiveness [17]. Normative commitment to change involves strong emotional obligation towards the change [1]. Therefore, people are committed to the change because of they want to, have to and ought to support it [1,2]. Higher level of normative commitment fosters the person to strongly engage in the change hence it involves higher support than is required from them [2]. Therefore, as a conclusion, out of the three (03) dimensions of commitment to change affective and normative commitment to change are highly desirable whereas continuance commitment to change is less desirable during a planned change implementation.

Having defined the dependent variable, this section discusses the predictor variable (i.e. Psychological Capital - PsyCap) of the study. According to [18], PsyCap can be defined as “ an individual’s positive psychological state of development that is characterized by the following: (a) having confidence (self – efficacy) to take on and put in the necessary effort succeed at challenging tasks; (b) making a positive attribution (optimism) about succeeding now and in the future; (c) persevering toward goals and, when necessary, redirecting paths to goals (hope) in order to succeed; and (d) when beset by problems and adversity, sustaining and bouncing back even beyond (resiliency) to attain success” [18]

Contrary to traditional economic capital PsyCap open avenues to gain competitive advantage [19]. Therefore, self-efficacy, hope, optimism, and resiliency are considered as very positive approach in developing organizational performance [18]. Those four traits are found to be positive, unique, measurable, developable and performance related [18]. First, self efficacy rooted the scholarly work of Albert Bandura [18]. People with self efficacy seek challenging tasks, extend motivation and effort to see end result [18]. More than 100 scholarly work conducted by Stajkovic & Lutnans concluded that self efficacy has a positive correlation with performance. Moreover, they found that self efficacy positively correlate with organizational commitment [18]. As a whole, “Self efficacy has substantial research backup as to its positive impact in organizational settings” (18, p. 153). The second dimension of psychological capital is hope. Positive psychologist Risk Snyder initially developed hope as a positive psychological state based on goals and pathways [18]. People those who are high in hope generate alternative ways of accomplishing a goal [18]. Though the hope and work related outcomes are emerging there are researches combining hope and educational performance [18].Third dimension in the positive psychological movement is optimism [18]. Optimism entails positive explanatory style of people in their life events [18]. Seligman found that such positive explanatory style leads to positive organizational outcomes [18]. Last and fourth dimension of Psychological capital is resiliency. Resiliency is the capacity of bounce back from adverse situations and keep continue in hard times [18]. Hence, high resiliency people constantly thrive for high performance.

Confident, hopeful, optimistic, and resilient people actively engage in achieving organizational goals [18]. Therefore, arguably organization participants with high psychological capital should support change initiative proposed by an organization since it is considered as an important goal that should be attained by the organization. Further, such positive psychological capacities should positively impact on affective commitment and normative commitment. Positive psychological capacities should be able to reduce the continuance commitment. When people have such positive psychological capacities they tend to develop a commitment with inner sense. Thus it should reduce the continuance commitment which is formed without any desire or heartfelt obligation [20].

In the light of the above literature the following hypotheses are deduced.

H1A: Overall Psychological capital (self efficacy, hope, optimism, and resiliency) positively impact on Affective Commitment to E-learning system of undergraduates.

H2A:Overall Psychological capital (self efficacy, hope, optimism, and resiliency) negatively impact on Continuance Commitment to E-learning system of undergraduates.

H3A: Overall Psychological capital (self efficacy, hope, optimism, and resiliency) positively impact on Normative Commitment to E-learning system of undergraduates.

2. Methods

The target respondents of the current study were undergraduates of Faculty of Management Studies of Sabaragamuwa University of Sri Lanka. Data were collected when the Faculty implemented E-learning system as a new change intervention. The population of this study was all the students of Faculty of management Studies of Sabaragamuwa University of Sri Lanka. Based on the probability sampling technique 420 questionnaires were distributed among the students. The survey resulted in 396 usable questionnaires, which resulted in a response rate of 94%.

2.1. Measures

Commitment to change was measured by using [1] eighteen item commitment to change scale. Out of 18 items six items assessed the affective commitment to change, six assessed the continuance commitment to change and six assessed the normative commitment to change. The authors of three component model cited that Cronbach’s alphas ranging from 0.94 for affective commitment to change (six items) 0.94 for continuance commitment to change to change (six items) and 0.86 for normative commitment to change (six items).

Psychological Capital (PsyCap) was measured by using twenty four (24) item Psychological Capital Questionnaire (PCQ) developed by Luthans, Youssef, & Avolio, 2007. PCQ was obtained from [21]. Out of 24 items six measured self efficacy, six measured hope, six measured optimism, and six measured resiliency. As cited in [21] the internal reliability (Cronbach’s α) of the self efficacy, hope, optimism, resiliency sub scales as .87, .84, .80, .86 respectively.

3. Results

3.1. Results of the Preliminary Analysis

Sixty five (65%) percent of the sample were represented by female students. Within this sample 24% students were studying in year 1, 33% were in year II, 21% were in year III and the remaining 22% were in year IV. Table I presents the means, standard deviations of the variables which were used in this study. As shown in Table 1, students believe the value and importance (i.e. affective commitment) of e-learning for their education. Normative commitment to change is at a moderate level which means they have an average level of strong obligation towards the e-learning system. Students continuance commitment to e-learning system is at a low level which means students are committed to the e- leaning system not because of they are compelled to do so. Rather, they have understood the inner value of it. Further, it is clear that students’ level of psychological capital is at a higher level.

Table 1. Mean and Standard Deviation of Variables

Table 2. Reliabilities and Correlations among Variables

Table 2 reports internal reliabilities of the measures (Cronbach’s alpha) and correlations between variables. Internal reliabilities are shown in diagonals. All the variables reported good internal consistency which exceeds the minimum threshold of .70 [13].

As well as, psychological capital was significantly correlate with all the variables (i.e. affective commitment to change r = .63; continuance commitment to change r = -.68; normative commitment to change r = .54).

3.2. Model Fit Indices

Confirmatory Factor Analysis (CFA) is used to verify the already developed factor structure of a set of observed variables [22]. Current study used already developed and validated set of observed variables to measure the latent variables (i.e. affective commitment to change, continuance commitment, normative commitment to change, Psychological Capital). Therefore, CFA is the most appropriate statistical analysis in assessing the model fit since it involves in affirming the model with sample data [23].

CFA was performed by using AMOS 16.0 according to the instructions of [24]. CFA results yield numerous indices, which can be used to evaluate the goodness of fit of the model [25]. Some of the frequently used indices are chi-square to degrees of freedom (χ2/df), Root Mean Square Error of Approximation (RMSEA), Normed Fit Index (NFI), Comparative Fit Index (CFI), and Tucker - Lewis Index (TLI). Results of the models fit indices are shown in Table 3.

Table 3. Summary of Model Fit Indices for CFA Model

According to [26], the ratio of chi-square to degrees of freedom (χ2/df) as a good measure of model fit. He recommends it should be 2 (two) or less than 2 (two) for the acceptable fit. Therefore chi – square to degrees of freedom exemplifies good fit (i.e. 1.5). TLI, CFI and NFI exceed or very close to 0.9. Hence those indices acquaint with fairly acceptable fit [27]. After testing for the satisfactory overall fit, the specified model has been further evaluated for the convergent and discriminant validity by following the instructions of [25, 28]. Average Variance Extracted (AVE) is recommended to measure the convergent validity [28]. High AVE value signifies the indicators are truly converging around the latent construct [23]. The value of AVE should exceed 0.5 for all constructs [25,28]. Table V indicates that AVE values ranged from 0.71 to 0.81 those which exceed the 0.5 minimum threshold values. Therefore, AVE values (see Table 4) prove the convergent validity of the model.

Table 4. Average Variance Extracted Values of the Measurement Model

Disciminant validity is used to ensure the construct validity [30]. Discriminant validity refers to the extent to which one construct is differed from other construct [25]. According to [28], Average Variance Extracted (AVE) is a good measure of discriminant validity. Moreover, they explain theoretically different constructs should have low correlations with each other. Therefore, low cross construct correlations provide evidence for discriminant validity. Further, if the average variance extracted is higher than the Squared Inter Correlations (SIC) between the construct and all other constructs that indicates the discriminant validity [28]. Table 5 exhibits high discriminant validity from all the constructs. For an instance continuance commitment to change (CCC) had the highest discriminant validity from all the other constructs (i.e. .81). All constructs exhibited AVE values which are greater than the squared inter correlations. Hence, it provides evidence for discriminant validity.

Table 5. Discriminant Validity Matrix

3.3. Hypotheses Testing

This section describes the results of the hypotheses testing.

Table 6. The Impact of Psychological Capital on Students Affective, Continuance, and Normative Commitment To Change

As presents in Table 6, PsyCap explains 60% (R2 = .60, p < 0.01) variation in affective commitment to change. The Adjusted R² reveals that how well the model generalizes to the population. According to table VII the difference between R2 and adjusted R2 is .01 (.60-.59) which means in the population it would account for 1% less variance in the outcome than in the sample.

Beta (β) values explain “what degree each predictor affects the outcome if the effects of all other predictors held constant” [31]. Moreover, if the t - test associated beta values are significant those predictors significantly contribute to the dependent variable. The beta value of PsyCap was significant. Therefore, results revealed that PsyCap significantly associate with affective commitment to change (β = .72, p < 0.05). Therefore, hypothesis 01 is supported.

As presents in Table 6 PsyCap can account for 79% of the variation in continuance commitment to change (R² = .79, p < 0.01). Adjusted R² is .78 which means PsyCap accounts for 78% variance in continuance commitment to change for the population though it is 79% variance in the sample. PsyCap was significantly associated with continuance commitment to change (β = -.82, p < 0.05). Hence, it proves PsyCap negatively impact on continuance commitment to change. The above evidence support Hypothesis 2.

According to Table 6 PsyCap explains 51% (R2 = .51, p < .01) variation in normative commitment to change. Adjusted R2 is .50 (p < .01). Therefore, PsyCap can account for 50% variance in normative commitment to change for the population though it is 51% variance in the sample. Further Beta values reveal that, PsyCap was significantly associated with normative commitment to change (β = .52, p < 0.01). This evident that PsyCap positively impact on normative commitment to change. Therefore, hypothesis 3 is supported.

4. Discussion

4.1. Conclusion

Using the Psychological Capital, researcher hypothesized that Psychological Capital would be positively related affective commitment to change, normative commitment to change whereas negatively related with continuance commitment to change. Even though, there are no other research in relation to psychological capital and commitment to change there are evidence to argue the linkage of those two constructs. For an instance PsyCap is positively correlated with many positive organizational outcomes [18,19]. Affective and Normative commitment are considered to be two positive outcomes displayed by organizational participants. On the other hand PsyCap should reduce the detrimental effects of continuance commitment to change which is treated as a negative mindset of organizational participants. Hence, the findings of this research are aligning with the core ideas of [18,19]. The study concluded that having psychological capital leads to more positive types of affective and normative commitment whereas it reduces detrimental continuance commitment.

4.2. Theoretical and Practical Contributions

First, current research is based on Psychological Capital which is considered as a new branch in Psychology. Thus, it brings much novelty to the existing literature. Still many practitioners try to encourage the commitment to change through superficial newsletters or speeches rather than finding a concrete solution [32]. Current study emphasizes the importance of psychological capital, which will be a long-lasting solution to improve the commitment to change. Second, in the higher education institutions there is no investigation to measure the impact of PsyCap on commitment to change. Hence, it contributes novel knowledge to higher educational institutions in successful implementation of planned change initiatives

4.3. Limitations

Some limitations exist in this study. Firstly, data for this study were collected from Faculty of Management Studies of Sabaragamuwa University of Sri Lanka. Hence generalizability for other institutions and other countries may be an issue. The second limitation of this study is the use of cross sectional research design and this might hinders the assumption of causality. The third limitation of the study is the use of self – report on measuring both the predictor and outcome variables thereby raising concerns about common method variance [33].

References

[1]   | HERSCOVITCH, L., & MEYER, J. P. (2002). Commitment to organizational change: Extension of a three-component model. Journal of Applied Psychology, 87, 474-87.
[2]   | MEYER, J. P., SRINIVAS, E. S., JAYDEEP, B. L., & TOPOLNYTSKY, L. (2007). Employee commitment and support for an organizational change: Test of the three component model in two cultures, Journal of occupational and Organizational Psychology, 80, 185-211.
[3]   | WANBERG, C. R., & BANAS, J. T. (2000). Predictors and outcomes of openness to changes in a reorganizing workplace. Journal of Applied Psychology, 85, 132-142.
[4]   | ARMENAKIS, A. A., HARRIS, S. G., & FIELD, H.S. (1999). Making Change Permanent: A Model for instituitionalizing change interventions. Research in Organizational Change and Development, 12, .97- 128.
[5]   | Skilling, D. (1996). Beyond the quick fix: how to manage more effectively in the heart of Change. Industrial and Commercial Trainning, 28, 3-7.
[6]   | CONNER, D. R., & PATERSON, R.W. (1982). Building commitment to organizational change, Training and Development Journal, 36, 18-30.
[7]   | Meyer, J. P., & Allen, N. J. (1991). A three-component conceptualization of organizational commitment. Human Resource Management Review, 1, 61-89.
[8]   | HARTLINE, M. & FERRELL, O. (1996). The management of customer contact service employees: An empirical investigation. Journal of Marketing, 60, 52-70.
[9]   | LAU, C., & WOODMAN, R. (1995). Understanding organizational change: A schematic perspective. Academy of Management Journal, 38, 537-554.
[10]   | PARISH, J.T., CADWALLDER, S., & BUSCH, P. (2008). Want to, need to, and ought to: employee commitment to change, Journal of organizational change management, 21, 32-52.
[11]   | SHUM, P., BOVE, L., & AUH, S. (2008). Employees’ affective commitment to change: The key to successful CRM implementation, European Journal of Marketing, 42, 1346-1371
[12]   | CUNNINGHAM, G. (2006). The relationship among commitment to change, coping with change and turnover intentions. European Journal of Work and Organizational Psychology, 15, 29-45.
[13]   | KALYAL, H. J., & SAHA, S. K. (2008). Factors Affecting Commitment to organizational change in a public sector organization. NUST journal of Business & Economics, 1, 1-10.
[14]   | MACHIN, M.A. & BANNON, S.F. (2005), Predicting employees’ commitment to and support for organisational change. The Australian and New Zealand journal of organizational Psychology, 2, 10-18.
[15]   | SWAILES, S. (2004). Commitment to change: profiles of commitment and in role performance. Personnel Review, 33, 187-204.
[16]   | MEYER, J. P., STANLEY, D. J., HERSCOVITCH, L., & TOPOLNYTSKY, L. (2002). Affective, continuance, and normative commitment to the organization. Journal of Vocational Behavior, 61, 20-52.
[17]   | WIENER, Y. (1982). Commitment in organizations: A normative view. The Academy of Management Review, 7, 418-428.
[18]   | Luthans, F., & Youssef, C.M. (2004). Human, Social, and Now Positive Psychological Capital Management: Investing in People for Competitive Advantage. Organizational Dynamics, 33, 143-160.
[19]   | Luthans, F., Vogelgesang, G, R., & Lester, P.B. (2006). Developing the Psychological Capital of Resiliency. Human Resource Development Review, 5, 25-44.
[20]   | AUBE, C., ROUSSEAU, V., & MORIN, E.M. (2007). Perceived organizational support and organizational commitment: The moderating effect of locus of control and work autonomy. Journal of Managerial Psychology, 22, 479-495.
[21]   | Sapyaprapa, S., Tuicomepee, A., & Watakakosol, R. (2013). Validation of Psychological Capital Questionnaire in Thai Employees, The Asian Conference on Psychology & the Behavioral Sciences Official Conference Proceedings, 394-399.
[22]   | CHILD, D. (1990). The essentials of factor analysis, (2nd ed.). London: Cassel Educational Limited.
[23]   | NUSAIR, K., & HUA, N. (2010). Comparative assessment of structural equation modeling and multiple regression research methodologies: E – commerce context. Tourism Management, 31, 314-324.
[24]   | ARBUCKLE, J. L. (2007b). Amos 16.0 User's Guide. Chicago: SPSS.
[25]   | HAIR, J., ANDERSON, R., TATHAM, R., & BLACK, W. (1998). Multivariate data analysis (5th ed.). Upper Saddle River, NJ: Prentice-Hall.
[26]   | KLINE, R. B. (2005). Principles and practice of structural equation modeling (2nd ed.). New York: The Guilford Press.
[27]   | JÖRESKOG, K. G., & SÖRBOM, D. (1993). Lisrel 8: Structural equations modeling with the SIMPLIS command language. Chicago: Scientific Software International.
[28]   | FORNELL, C., & LARCKER, D. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18, 39-50.
[29]   | NUSAIR, K., & HUA, N. (2010). Comparative assessment of structural equation modeling and multiple regression research methodologies: E – commerce context. Tourism Management, 31, 314-324.
[30]   | PRABHU, V. (2007). Understanding the effect of proactive personality on job related outcomes in an organizational change setting. (Unpublished phd thesis).
[31]   | FIELD, A. (2009). Discovering Statistics Using SPSS (3rd ed.) Sage Publications Inc. Thousand Oaks, California.
[32]   | SCHNEIDER, B., BRIEF, A., & GUZZO, R. (1996). Creating a climate and culture for organizational change. Organizational Dynamics, 24, 6-9.
[33]   | PODSAKOFF, P., MACKENZIE, S., LEE, J., & PODSAKOFF, N. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88, 879-903.