Career Adapt-Abilities Scale-Short Form: Validation for Use in India

Abstract

The Career Adapt-Abilities Scale (CAAS) is popular among researchers and validated in many countries but has not yet been tested for the Indian context. This study examined the CAAS Short Form (CAAS-SF) among a sample of Indian business school students. The CAAS-SF consists of four subscales, each with three items measuring concern, control, curiosity and confidence as psychosocial resources. A sample of 369 Indian business-school students with human resource management as major participated in the scale validation process. The internal consistency estimates for the subscales and total scores were found to be comparable or better than previous CAAS-SF assessments. Based on this study’s findings, the CAAS-SF appears to be appropriate for measuring career adaptability resources in Indian business school students.

Keywords

Career Adapt-Abilities Career Construction Model Psychometric Properties India

Introduction

India is a developing country with a population of 1.3 billion people and many diverse cultures. Career choice and development for India’s young people are mediated not just by individual agency but are also enmeshed in a modality of collective decision making that involves many external influences, such as parents, educators and siblings. Family values and family income also provide a socio-economic impetus in the career development and career choices of Indian students. Indian youth choose their career to maintain a livelihood that is emblematic of their social position and the prestige they get in their chosen career.

Hughes (1937) defined career as ‘the moving perspective in which persons orient themselves links to the social acceptance and the typical way the workplace operates’. Researchers have explored whether cultural values have an impact on the factors that influence the career choices of young people (Tao et al., 2018; Wambu et al., 2017). Asian countries are aligned more closely with collectivism (Hofstede, 1980), which can help to explain the differences in career decision making among youth from different cultures and regions (Amit & Gati, 2013; Sinha, 2014). From ancient times, Indians’ career choices have been caste-based, a practice that is gradually breaking down in urban India (Arulmani & Baksi, 2012), and cultural factors such as parental approval and the attribution of prestige have had a strong influence on the career interests of Indian youths (Arulmani, 2013). In the Indian subcontinent, career development is portrayed ‘not only as achieving mastery over age-specific development tasks but as overlapping movements whereby the individuals’ engagements with work are a continuous elaboration and construction, characterized by adaption discovery and renewal’ (Arulmani, 2011, p. 84).

Currently, India projects the highest population concentration within the age group of 15–29 years (Ministry of Statistics and Programme Implementation, 2017). Therefore, India needs to be more cognizant of this demographic dividend to develop the career productivity of its youth in a more robust and holistic manner. Indian youths’ employability depends on how the country’s people think about their career choice beyond caste, gender and the economic condition of the family. Career choice research by Agarwala (2008) on Indian management students career choice reported skills, competency and abilities was the most important factor and father was the most individual factor.

Career Adaptability as a Concept: A Review of Literature

Five theories dominate international career research: the theory of work adjustment by Dawis (2002, 2005) and Dawis and Lofquist (1984), the Holland theory of vocational personalities in work environments (Holland, 1985, 1997), Super’s (1969, 1980, 1990) self-concept theory of career development, Guttfredson’s theory of circumscription and compromise (Gottfredson, 1981, 1996, 2002, 2003), the social cognitive career theory by Lent et al. (1994) and Lent (2005) and the Indigenisation career theory by Enriquez (1993). Leaning on Super’s notion of self-concept theory, Savickas (2002) took a constructivist perspective to proffer that ‘the process of career construction is essentially that of developing and implementing vocational self-concept at work roles’ (p. 155). The term ‘career adaptability’ was proposed by Savickas (1997) to replace the concept of career maturity.

Career adaptability is a significant construct according to career construct theory of Savickas (1997, 2002, 2005, 2013) and has inherent links to specific attitudes, beliefs and competencies (Savickas, 2005, 2013). Savickas and Porfeli (2012, p. 662) defined career adaptability as ‘the self-regulation of strengths or capacities that a person may draw upon to solve the unfamiliar, complex and ill-defined problems presented by developmental vocational tasks, occupational transitions, and work traumas’. The Career Adapt-Abilities Scale (CAAS) was developed based on the Savickas and Porfeli’s (2012) multidimensional hierarchical model of career adaptability. The four career adaptability resources suggested includes concern (i.e., becoming concerned about the vocational future), control (i.e., taking control of preparing for one’s vocational future), curiosity (i.e., displaying curiosity by exploring possible selves and future scenarios) and confidence (i.e., strengthening the courage to pursue one’s aspirations) (Savickas, 2005; Savickas & Porfeli, 2012). Later work by Savickas (2013) explained links between career adaptability and workers’ work attitudes and competencies, including how employees aligned themselves with professions that suit them. It was also documented that the process of career construction helps in developing and implementing vocational self-concepts in individuals’ work roles (Savickas, 2002), and in the field of vocational psychology (Rudolph et al., 2017).

Previous studies on career adaptability were carried out in different study populations and compared it with several outcome variables. Hartung et al. (2008) and Germeijs and Verschueren (2007) reported that youth having higher career adaptability are more successful in mastering vocational transitions and professional development, and thereby their decision-making skills and commitment at their respective school’s increase (Ferrari et al., 2010). Career construct theory reported career adaptability as a key psychological resource for coping with developmental tasks, work traumas and career transitions (Savickas & Porfeli, 2012).

Research Gap and Objective

The CAAS international form (CAAS 2.0; Savickas & Porfeli, 2012) comprises 24 total items and is divided into four constructs and has been studied across 13 countries (from English speaking countries, such as the USA to countries where English is a common second language, such as Brazil, China and Italy). The CAAS 2.0 has displayed similar constructs across countries (Johnston et al., 2013; Oncel, 2014), but also several cross-cultural differences and ethnic differences (Olugbade, 2016; Savickas & Porfeli, 2012; Teixeira et al., 2012). The CASS 2.0 has been one of the widely used questionnaires to assess career adaptability. To promote the CASS 2.0 integration into extensive surveys and link it with other study variables, Maggiori et al. (2017) developed a brief 12-item version, the CAAS Short Form (CAAS-SF). Maggiori et al. (2017) found the 12-item CASS-SF (containing four dimensions with three items each) to be strongly associated with the 24-item CASS 2.0.

Haenggli and Hirschi (2020) examined the CAAS-SF in a sample of 574 German employees. They found acceptable scale reliability comparable with other study variables. Similar results were found by Isik et al. (2018) in the Turkish context, by Perera and Mcllveen (2017) in Australian graduate students, by Urbanaviciute et al. (2019) in working population samples from the French and German parts of Switzerland, and by Yu et al. (2019) in three different Chinese samples. Since 2012 more than 76 career adaptability related articles have been published in Journal of Vocational Behaviour, but none of the paper tested the construct on Indian data. This study aims to contribute to the career research literature by validating the CAAS-SF English-language version among Indian B-school students, which could help this scale to be widely used in India.

Method

Participants

Around 537 postgraduate students with human resource management as major in premier management institutions in the Indian subcontinent were enrolled to participate in the study in Spring 2019. We did continuous follow up exercise with students to get the filled in questionnaire. 369 responses were received after a period of 3 months, indicating a response rate of 68.71%. For dealing with missing values, the procedure for multiple imputations prescribed by Newman (2014) was employed. Around 344 (64.05%) participants were found to be fit for carrying out further analysis. The final sample comprised 59% female respondents with a mean age of 23.7 years (SD = 0.89).

Measures

The CAAS-SF as developed by Maggiori et al. (2017) was used. The scale consists of 12 items reflecting the conglomeration of attitude, competencies and behaviours to conquer varied tasks and assignments (Yang et al., 2019) when managing unpredictable challenges.

The CAAS-SF requires respondents to self-assess how well they utilize their strength to form their career path by rating statements in a 5-point Likert-type scale (ranging from 1 as ‘not a strength’ to 5 as ‘greatest strength’). As described above, the CAAS-SF splits into four subscales: concern (e.g., ‘Becoming aware of the educational and vocational choices that I must make’), control (e.g., ‘Taking responsibility for my actions’), curiosity (e.g., ‘Investigating options before making a choice’) and confidence (e.g., ‘Taking care to do things well’). To ameliorate response bias, the approach of non-indication of questionnaire subscales (Podsakoff & Organ, 1986) was followed. Likewise, to minimize response consistency motives, the items were presented in an arbitrary order. Some of the previous studies reported the same relating to the sub scales items in a validated scale (Gaan, 2011; Soni & Behal, 2015).

Findings

AMOS 20.0 was used to carry out structural equation modelling analysis for the pooled measurement/structural model. Descriptive statistics, along with item-wise loadings obtained from the confirmatory factor analysis model are presented in Table 1. The total score of the CAAS-SF displayed a reliability of 0.94, higher than its subscale scores of concern (0.88), control (0.81), curiosity (0.78) and confidence (0.87). Overall construct reliability, as indicated by Cronbach alpha (α), also was greater than the rule-of-thumb acceptability threshold of 0.70 (Nunnally, 1978). Skewness γ(u) and kurtosis (β₂) ranged from −0.49 to −0.34 and from −0.61 to −0.23, respectively, implying that the items conform to the assumptions of correlation-based statistics (presented in Table 2) for the given sample. The four subscales were correlated from 0.26 to 0.47, at the significance level of 0.05 and 0.01.

Table 1.

Career Adapt-Abilities-Scale (CAAS-SF): First Order Indicator Items, Factor Loadings (Standardized and Unstandardized) and Item/Sub-scale Reliabilities

Constructs	First Order Indicator Items	Statements	Mean	SD	Standardized Factor Loadings	Unstandardized Factor Loadings	Item Reliability	Sub-scale Reliability
Concern	CON03	Becoming aware of the educational and vocational choices that I must make.	3.79	0.92	0.88	0.94	0.84	0.88
	CON01	Thinking about what my future will be like.	3.82	0.93	0.91	1.00	0.91
	CON02	Preparing for the future.	4.04	0.89	0.82	0.86	0.87
Control	COL01	Making decision by myself.	3.66	0.81	0.78	0.92	0.91	0.81
	COL03	Counting on myself.	3.94	0.73	0.81	0.84	0.85
	COL02	Taking responsibility for my actions.	3.81	0.82	0.95	1.00	0.83
Curiosity	CUR03	Observing different ways of doing things.	4.21	0.86	0.83	0.81	0.85	0.78
	CUR01	Looking for opportunities to grow as a person.	3.96	0.91	0.96	1.00	0.93
	CUR02	Investigating options before making a choice.	3.83	0.78	0.72	0.79	0.81
Confidence	COF01	Taking care to do things well.	3.77	0.84	0.79	0.84	0.84	0.87
	COF02	Learning new skills.	4.26	0.90	0.88	1.00	0.87
	CON03	Working up to my ability.	3.75	0.81	0.78	0.96	0.88

Source: The authors.

Note: The respective loadings are significant at α = 0.01.

Table 2.

Descriptive Statistics and Correlational Matrix

Sl. No	Constructs	AVE	MSV	ASV	CR	1	2	3	4
1	Concern	0.68	0.52	0.48	0.82	(0.88)
2	Control	0.66	0.57	0.41	0.78	0.42*	(0.81)
3	Curiosity	0.71	0.61	0.53	0.83	0.38*	0.37**	(0.78)
4	Confidence	0.78	0.59	0.44	0.77	0.47**	0.39**	0.26*	(0.87)

Source: The authors.

Notes: ASV, average shared variance; AVE, average variance extracted; CR, composite reliability; MSV, maximum shared variance. Diagonal values (italicized and bracketed) represent the Cronbach’s alpha (α). *p < .05; **p < .01.

Chi-squared (χ²) was used to analyse the possible variations among the demographic profile (age and gender). The variance inflation factor was found to be in the range of 1.00–1.17, while the tolerance scores ranged from 0.77–1.00. Outliers were assessed using Cook’s distance and the resultant findings (0.00–0.29; at M = 0.04, SD = 0.03) demonstrated the required frequencies.

The convergent and discriminant validity of the measurement model were found to be in line with the recommendations of Anderson and Gerbing (1988): the composite reliability of the variables (more than 0.7) and the average variance extracted (AVE) were over and above the required ceiling (more than 0.5). The maximum shared variance and average shared variance were less than the AVE and thus confirming construct validity (both convergent and discriminant validities) of the measurement model. Overall, the data fit well with the theoretical model.

There were no significant differences in CAAS-SF scores with respect to age (χ² = 1.72, p = 0.11) or gender (χ² = 1.19, p = 0.19). Finally, the structural model fit value (with χ² /df = 1.49, p < .001, CFI = 0.95, TLI = 0.98 and RMSEA = 0.03) supported use of the instrument. The standardized loadings (see Table 3) suggested that all subscales were strong indicators of the second-order construct.

Table 3.

Factor Loadings (Standardized and Unstandardized) for Second Order Construct

Constructs	Mean	SD	Standardized Factor Loadings	Unstandardized Factor Loadings
Concern	3.84	0.91	0.83	0.94
Control	3.77	0.75	0.78	0.83
Curiosity	3.93	0.79	0.72	0.77
Confidence	3.80	0.84	0.79	0.82

Source: The authors.

Note: The respective loadings are significant at α = 0.01.

Indian Factor Model and the International Factor Model Comparison

The factor loading of the first-order items (concern, control, curiosity and confidence) in this Indian sample was similar to those seen with the original CAAS-SF (Maggiori et al., 2017). Out of the 12 items, the Indian sample showed higher or similar loading in nine items compared to the original CAAS-SF. There was lower loading for the curiosity scale item number 3 (‘observing the different way of doing things’) (0.85), the confidence scale item number 1 (‘taking care to do things well’) (0.84) and the confidence scale item number 3 (‘working up to my ability’) (0.87), respectively.

Loading of the second-order adaptability constructs in the Indian data showed slightly different values when compared to that of the original CAAS-SF. The loadings of concern (0.94), control (0.83) and confidence (0.82) in the Indian sample were higher than in the original CAAS-SF, while the loading of curiosity in original CAAS-SF was slightly higher than in the Indian data (0.77).

Discussion, Limitations, and Future Implications

Based on the statistical analyses of this Indian sample, we concluded that the CAAS-SF performs quite similarly in Indian data when compared to other international samples, in terms of psychometric characteristics and factor structures. The total scale and its four subscales demonstrate accepted internal consistency. The factor structure of CAAS-SF Indian data had a higher fit when compared with the international samples, possibly related to differences in cultural background, demographic features or understanding of the notion of career. It appears from the confirmatory factor analysis of the Indian business school students that they are more concerned about career adaptability than other factors.

High concern scores among the Indian business school students might be due to the uncertainty they face getting a proper job or pay appropriate to their qualifications. In the original sample studied by Maggiori et al. (2017), participants manifested higher curiosity than the Indian sample. This difference may reflect differences in perceptions of competition, unemployment and acute pressure in getting suitable jobs. Such cultural differences present compelling topics for future exploration. Douglass and Duffy (2005), reporting on studies of Nigerian undergraduate students, described that curiosity only becomes significant when the use of strengths is taken into account. They found the opposite result from this study, as employees in Nigeria were seen to be more curious about their career rather than being concerned or taking control of their career-related outcomes.

This study needs to be considered in light of limitations that give direction for future research. First, this study is cross-sectional, and thus future research should focus the relationship among the study variables in longitudinal data. Caruana et al. (2015) describe how longitudinal data may provide a more comprehensive approach to research by allowing an understanding of the degree and direction of change over time. Second, the present sample consisted solely of postgraduate business school students from India. Future Indian studies may focus on undergraduate and postgraduate students of other disciplines. Career counselling is a booming profession in Asian countries; future research should consider whether career counselling itself has any linkages with the career adaptability of Indian students.

Conclusion

We conclude that the CAAS-SF is of use in the Indian context. This study provides support for using the CAAS-SF among different age-groups in India. Tools that can rigorously assess and support career development are essential around the world, especially for the enormous population of the Indian subcontinent, where a failure to choose appropriate professional paths may ultimately contribute to a demographic disaster.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

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