Role of Career Adaptability and Its Resources in Predicting Vocational Attitudes of Indian IT Professionals

Abstract

Indian Information Technology (IT) market is going through a dynamic state of existence with employment uncertainties, but simultaneously creating promising opportunities. Amidst such unpredictability and possibilities, Indian IT professionals have been striving hard to build a career identity, demonstrating their capabilities to withstand unforeseen and abrupt vocational shocks. In career construction, such capability comes from career adaptability, reflected through resources of concern, control, curiosity and confidence. Yet, attention towards career adaptability is sparse in Indian IT vocational literature. The present study attempts to fill that gap by examining career adaptability and its links with vocational attitudes of career satisfaction and turnover intention. For this, data were collected from 434 Indian IT professionals. Overall, career adaptability was positively related to career satisfaction while turnover intention was independent of it. At the dimensional level, independently, all resources of career adaptability were positively related to career satisfaction. Concern and confidence had relatively greater degree of positive association than curiosity and control. Further, concern led to increased turnover intention, other resources being unrelated to it. Findings may facilitate human resource management of Indian IT organizations and independent career practitioners to enhance the vocational success of their employees and clients.

Keywords

Career adaptability career satisfaction turnover intention Indian IT professionals

Indian Information Technology (IT) has been in a dynamic state of existence. On the one hand, careers associated with this industry have remarkably transformed lives of its employees, providing great financial inflows, scope for travelling abroad and maintaining high living standards (Upadhya, 2008). On the other hand, uncertainties and disquiets derived from political shifts, technological transitions and business unpredictability pose threats to the labour market (Verma & Agarwal, 2017). Incidences such as dot-com bust, stricter visa regulations, and digital technologies pose a threat to current jobs in the IT market. Although fluctuations in this industry may jeopardize careers of many professionals, it simultaneously generates possibilities for more promising prospects ahead (MichaelPage, 2017). In response to such market turbulence and employment opportunities, Indian IT people have learned to take accountability in managing ones’ career by withstanding challenges that come along. For example, professionals were reported to proactively engage in upskilling themselves with advancing technologies (Khatri, 2017). They were also reported to choose entrepreneurship paths outside corporate world for sustaining vocational persistency (Victor, 2011). In literature, such elements of adjustment, coping with adversities and navigating through an unstable environment are known as career adaptability (Bimrose & Hearne, 2012).

Nevertheless, though career adaptability can serve as a critical tool to withstand ongoing market turmoil, research regarding such occupational phenomenon in Indian IT workforce is limited. Only a few newspapers and consultancy agencies reported its urgency within Indian IT workforce in terms of renovating competencies, knowledge and learning to confront uncertainty (MichaelPage, 2017; India Today, 2018; The Hindu, 2017). In addition, literature considers career satisfaction as an important vocational attitude in the contemporary world of work, but it remains an unexplored concept in Indian IT occupational domain. Perhaps, extant studies convey that satisfaction in Indian IT careers is mainly linked to the notion of job satisfaction, pay-satisfaction, and work–life balance (Bhal & Gulati, 2007; Lacity, Iyer, & Rudramuniyaiah, 2008; Singh, 2010). Furthermore, researchers annotate employees’ turnover intent, which is a key focus of the Indian IT industry and has to be understood from different perspectives (Lacity et al., 2008). Hence, in the present frame of reference, aspects of career adaptability, career satisfaction, and turnover intention seem relevant and timely for shaping Indian IT workforce’s competitive edge in the global market. Thus, to shed light on such pertinent, yet understudied concepts, we attempt to unearth a link between career adaptability and vocational attitudes of career satisfaction and turnover intention. We also delve into details by capturing the independent association between individual resources of adaptability and mentioned vocational attitudes of Indian IT workforce. The results of this study may render a pragmatic understanding of adaptability in determining vocational attitudes of Indian IT professionals, who are the potential contributors to the country’s growth, beyond facing frequent career instability.

Literature Review

Career Adaptability

Career adaptability is one of the most fundamental career construction components that help individuals combat various vocational issues by gaining synergy between self, identity, and environment (Bimrose & Hearne, 2012; Savickas, 2005). In literature, career adaptability has been illustrated in several ways including planning, exploration, decision-making, information and realism (Super, 1974); career planning, exploring and deciding (Phillips & Blustein, 1994); career decidedness and career confidence (Skorikov, 2007). Nevertheless, conceptualization of career adaptability is extensively elucidated in Savickas’s (1997, 2002, 2005) theory of career construction, providing the most comprehensive description. According to Savickas (2005), career adaptability is a multidimensional and hierarchical construct, constituting four adaptive resources of concern, control, curiosity, and confidence. These resources are psychological together with social proficiencies, derived from specific attitudes, behaviours, and competencies of career construction (Savickas & Porfeli, 2012). Concern shows the extent to which an individual cares about vocational future. Control indicates taking responsibility to prepare for present and future vocational situations. Curiosity reflects personal career inclination to explore selves and external environments. Confidence describes perceived self-efficacy to overcome obstructions and strengthening belief to pursue career aspirations (Savickas, 2005; Savickas & Porfeli, 2012).

Researchers demonstrated the significance of career adaptability aggregately and through its distinct resources in framing and directing ones’ vocational graphs. Studies revealed the role of adaptability and its dimensions in predicting various career consequences such as vocational identity and academic engagement (Merino-Tejedor, Hontangas, & Boada-Grau, 2016; Porfeli & Savickas, 2012), career calling (Douglass & Duffy, 2015), happiness and work stress (Johnston, Luciano, Maggiori, Ruch, & Rossier, 2013), school satisfaction (Urbanaviciute, Pociute, Kairys, & Liniauskaite, 2016) and entrepreneurial outcomes (Uy, Chan, Sam, Ho, & Chernyshenko, 2015). Furthermore, career adaptability as a whole was reported to increase career satisfaction in low-ranking personnel from China (Chan & Mai, 2015). Zacher (2014) showed that overall career adaptability in divergent Australian employees positively predicted subjective career success in terms of career satisfaction and self-rated career performance. In Iranian workers belonging to various jobs and occupations, the overall adaptability was positively linked with career satisfaction and entrepreneurial intentions (McKenna, Zacher, Ardabili, & Mohebbi, 2016). At the dimensional level, Zacher (2015) found that on a daily basis, all resources of career adaptability (i.e., daily concern, daily control, daily curiosity, and daily confidence) positively predicted career satisfaction in heterogeneous professionals from the Netherlands and the USA. Additionally, in Australian employees, concern, followed by confidence, independently related to subjective career success (Zacher, 2014). In full-time service sector employees from China, researchers found that all resources of career adaptability were positively correlated with career satisfaction (Chan, Mai, Kuok, & Kong, 2016). Similarly, career satisfaction was also reported to have positive correlations with all four career adaptability resources in employees working for different organizations in China (Guan, Zhou, Ye, Jiang, & Zhou, 2015). McKenna et al. (2016) found, of the career adaptability dimensions, that only concern was positively associated with career satisfaction of Iranian workers.

With respect to turnover intention, literature showed that career adaptability had both positive and negative association. For example, Chan and Mai (2015) indicated that overall career adaptability in low-ranking Chinese employees negatively estimated their turnover intention. However, Ito and Brotheridge (2005) reported that overall career adaptability, both directly and indirectly, enhanced intention to leave in full-time Canadian federal civil services professionals. Regarding facet-wise links of career adaptability, inverse correlations were obtained between all four dimensions and turnover intention of Malaysian Information and Communication Technology (ICT) workers. In addition, regression analysis results indicated that career adaptability overall, plus its control and confidence resources, decreased turnover intention of these workers (Omar & Noordin, 2013). Negative correlations between resources of adaptability and turnover intention were also reported in Chinese employees (Chan et al., 2016; Guan et al., 2015). In another study, findings suggested that higher levels of career adaptability (particularly curiosity) in Brazilian employees made them more likely to leave jobs and consider other options (Zacher, Ambiel, & Noronha, 2015).

In Indian vocational context, there is a lack of empirical support that can confirm the functionality of career adaptability. Only few propositions were made to determine the antecedents and consequences of one’s career adaptability (Chatterjee, 2013). For Indian IT professionalism, few implicit clues could be deduced from the literature that defined employees’ adaptability and flexibility through abilities to grasp knowledge, learning new skills, adjusting to cross-cultural shocks, innovation and networking activities (Krishna, Ojha, & Barrett, 2000; Radhakrishnan, 2007). Though recent media and consultation reports highlight the urgency of adaptability in Indian IT careers, there exists a gap in literature to examine Indian IT workforce’s career adaptability in a more comprehensive manner. Indeed, in response to the constant unpredictability of Indian IT employment market, it becomes even more relevant to comprehend adaptability and its operationalization on professionals’ vocational attitudes.

Career Satisfaction

Career satisfaction is considered one of the main determinants of subjective career success (Ng, Eby, Sorensen, & Feldman, 2005), expressed as individuals’ overall affective attitude towards their careers and work responsibilities (Gattiker & Larwood, 1988). Research on global IT workforce revealed various intrinsic and extrinsic parameters of career satisfaction. Intrinsic factors included personality traits such as assertiveness, emotional resilience, extraversion, openness, teamwork disposition, optimism, and work drive (Lounsbury, Moffitt, Gibson, Drost, & Stevens, 2007). Professional enhancement, organizational knowledge and skills, career anchors, external opportunities, and supportive supervisors were some of the extrinsic factors (Chang Boon Lee, 2002; Jiang, Klein, & Balloun, 2001; Mahatanankoon, 2007; Wickramasinghe & Jayaweera, 2010). Among Indian IT employees, career satisfaction can relate to continuous learning, training and development (Manjula & Balachandra, 2017), perceived organization support, self-efficacy, and work passion (Johri, 2015), organizational culture (Mathew, Ogbonna, & Harris, 2012), work–life balance (Rani, Kamalanabhan, & Selvarani, 2011) and so on. However, past findings limit the understanding of career satisfaction manifested through their flexibility and adjustments in dynamic vocational scenarios of Indian IT industry.

Turnover Intention

The intention to leave one’s job or organization is a meaningful indicator of voluntary turnover behaviour (Jha, 2009). In global IT careers, factors such as better job scopes outside the present organization, perceived vocational alternatives, job satisfaction, workplace stress, and burnout were some of the reasons for quitting intention (Joseph, Ng, Koh, & Ang, 2007). Among Indian IT employees, such intention was related to lucrative job offers that matched salary demands, interest, skills and competencies (Ghapanchi & Aurum, 2011), domestic issues (Aboobaker, Edward, & Pramatha, 2017), sociocultural and demographic norms (Lacity et al., 2008), pay dissatisfaction (Bhal & Gulati, 2007), innovative career passions (Victor, 2011) and so on. Findings across literature implied IT professionals’ turnover cogitation as a key concern of human resource management.

Given the fact that India’s global footprint is underscored by its IT workforce, focusing on their career adaptability, satisfaction, and turnover cogitation can furnish gainful vocational information. Thus, with theoretical support, we develop our hypotheses to examine how career adaptability and its resources are related to professionals’ vocational attitudes of career satisfaction and turnover intention. The proposed hypotheses are evaluated using structural equation modelling (SEM) method, results are compared with previous studies and finally, implications and conclusion are presented.

Theoretical Framework and Hypotheses Development

According to Career Construction Theory (CCT), identifying actions necessary to make appropriate choices, and successfully withstanding vocational shifts, is pivotal to sustain one’s career (Savickas, 1997, 2002, 2005). Savickas (2002) suggested that an individual’s career development is driven by adaptation to the environment rather than by maturation over time. Therefore, occupational development could be more in line with a contextualistic view rather than an organic view of unfolding careers. Career adaptability, a central construct of CCT, represents the willingness and ability of people to synchronize with vocational demands and changes (Savickas, 2005).

Sustaining a vocational path in Indian IT occupational context is challenging for its workforce. The Indian IT job market is intertwined with geopolitical, technological, economical, and sociocultural complexities. Amidst these complexities, Indian IT professionals have to strive hard to remain employable and lead a successful career path. As per CCT, such characteristic of endeavour may manifest through career adaptability that makes individuals resilient and capable of accomplishing vocational development tasks. These aspects can be evinced through media reports that reflect Indian IT professions’ positive approach and proactive actions to manage upcoming digitalization, automation, and H1B visa-related problems (Malik & Garg, 2018; Sapam & Pathak, 2017). Such forward-looking and self-regulatory tactics of moving up the value chain may signify adaptability, comprehensively improving performance, thereby increasing satisfaction in careers. Hence, we expect that overall career adaptability in Indian IT workforce possibly increases their career satisfaction. Past studies have also confirmed that overall career adaptability will be positively related to career satisfaction (Chan & Mai, 2015; Zacher, 2014). Therefore, we hypothesize:

H1a: Overall career adaptability will be positively related to career satisfaction.

In addition, career adaptability makes individuals conscious about their vocational decisions (Bimrose & Brown, 2015). Hence, in today’s frequent episodes of layoff and downsizing, Indian IT employees possibly think of continuing the same job as a rational act. Moreover, Indian IT companies have been involved in optimizing employees’ proficiencies to manage the current transforming business needs (Kumar, 2015). It may perhaps motivate professionals to be in the same organization as a means to aspire in their career. Therefore, we assume that an overall adaptable outlook in Indian IT workforce can restrict their turnover motives. Similar findings have also been reported in other Asian work setups (Chan & Mai, 2015; Omar & Noordin, 2013). Hence, we propose:

H1b: Overall career adaptability will be negatively related to turnover intention.

Savickas and Porfeli (2012) characterized career adaptability resources as psychosocial attributes that get accumulated over time, changes due to education, training and work experiences and drive individuals’ self-regulatory strategies. These resources represent as ‘4Cs’, namely dimensions of concern, control, curiosity, and confidence, and each facilitates coping with tasks, transitions, and traumas in careers through different mechanisms. Savickas (2002) suggested that career adaptability dimensions could not be interchangeable. Moreover, researchers have confirmed that four dimensions of career adaptability differ in their relationship to predictors and vocational outcomes (e.g., Guan et al., 2013, Hirschi & Valero, 2015; Hirschi, Herrmann, & Keller, 2015, Rossier, Zecca, Stauffer, Maggiori, & Dauwalder, 2012). Perhaps, researchers have emphasized exploring both the higher-order construct of career adaptability and its dimensions to deduce an appropriate picture of adaptation results (Zacher et al., 2015). Therefore, in this study, besides analysing the impact of overall adaptability on career satisfaction and turnover intention, we also look into its dimensional-level investigation.

In CCT, concern comprises behavioural activities geared towards future perspective, anticipation, planning and preparation for future goal achievement (Savickas, 2005; Savickas & Porfeli, 2012). Previous findings have reported that concern increased occupational self-efficacy (Hirschi et al., 2015), career performance (Zacher, 2014), job satisfaction and contentment with vocational opportunities (Coetzee & Stoltz, 2015). These positive effects elevated professionals’ career satisfaction (Abele & Spurk, 2009; Greenhaus, Parasuraman, & Wormley, 1990). Within Indian IT industry, vocational future of employees is always uncertain, raising concern for upcoming career challenges. The concern factor in Indian IT professionals possibly get expressed through their readiness and planning for future, motivating them to advance in their career, thereby increasing satisfaction. In fact, these aspects of future preparedness can be seen in employees of Indian IT industry, helping them withstand the contemporary technological changes, and improving their vocational states (Khatri, 2017). Such improvement possibly increases satisfaction in their careers. Moreover, literature have revealed a positive link between concern and career satisfaction (Chan et al., 2016; Guan et al., 2015; Zacher, 2015). Hence, we posit the following:

H2a: Concern will be positively related to career satisfaction.

Furthermore, recent sociopolitical fluctuations and technological alterations have imposed a big challenge on the Indian IT industry’s employers to reskill and upgrade the workforce. Accordingly, organizations are providing relevant training to their employees to enhance competencies for a competitive edge (Chandran, 2016). Therefore, we expect that concerned Indian IT professionals may prefer remaining in the present organization to develop their skills for future employability, thereby reducing turnover motives. Past findings have also reflected an inverse association between concern and turnover cogitation (Chan et al., 2016; Guan et al., 2015). Thus, we hypothesize:

H2b: Concern will be negatively related to turnover intention.

The dimension of control consists of making independent decisions and doing what is optimally best for oneself (Savickas & Porfeli, 2012). Control aligns with individuals’ self-determination component (Ryan & Deci, 2000), making them accountable to shape themselves and their environments, hence gaining vocational expertise and mastery (Fiori, Bollman, & Rossier, 2015).

Researchers have annotated that control (e.g., possessing a distinct sense of one’s strengths and preferences) helped in choosing self-congruent career aims and goals, attaining which might bring satisfaction in careers (Greenhaus et al., 1990; Hirschi, Niles, & Akos, 2011). In Indian IT individuals, such career controlling facet could evince through their deterministic outlook, autonomy, and self-reliance characteristics that lead them to fulfil vocational aims by establishing a desired career path successfully (Kulkarni, 2016). It can further have resulted in gaining professionals’ career satisfaction. Thus, we assume that control in Indian IT workforce may increase their career satisfaction. Past studies have also appraised that control resource enhanced career satisfaction (Chan et al., 2016; Guan et al., 2015; Zacher, 2015).Therefore, we postulate:

H3a: Control will be positively related to career satisfaction.

The notion of control is also associated with taking responsibility for vocational actions (Zacher et al., 2015). This adaptability resource can be manifested among Indian IT employees through their greater accountability towards the career that perhaps make them engage in networking and competencies development activities. The present organizational culture of Indian IT companies encourages professionals to build networks, promotes innovation, and provides scopes for skill advancement (Kumar, 2015; Nayak, Bhatnagar, & Budhwar, 2018). Consequently, Indian IT individuals, being accountable for own career growth, may perceive staying in the same organization as an advantageous deal (Raman, Bharathi, Allen, & Joseph, 2013). Hence, we expect that control may decline turnover intention in Indian IT professionals. Literature also supports a negative link between control and turnover cogitation (Omar & Noordin, 2013). Hence, we posit the following:

H3b: Control will be negatively related to turnover intention.

Curiosity expressed through individuals’ explorative behaviours for opportunities, knowledge, skills, and aptitudes (Savickas & Porfeli, 2012). It directs one’s information-seeking strategies, analysing the extrinsic versus intrinsic rewards of alternative options, interpreting occupational information, and taking risk (Bimrose & Hearne, 2012). Studies have indicated that curiosity helped acquire a realistic image of oneself and corresponding vocational situations, thereby bringing productive career exploration results (Koen, 2013; Koen, Klehe, & Van Vianen, 2012; Zacher, 2015). Such results might induce a sense of vocational meaningfulness, bringing satisfaction in careers (Kearney, 2012). Within the Indian IT workforce, researchers have observed the characteristics of inquisitiveness and risk-taking outlook (Budhiraja, Pathak, & Kaushik, 2017). Perhaps, such characteristics may also determine their curiosity resource, possibly enhancing productive exploration, provoking interest, and shaping aspirations, thereby increasing the level of satisfaction in careers. An impression of this can be inferred from media report, which supported the career curiosity in Indian IT professionals, exhibited through constructive exploring and calculative risk-taking abilities. Such aspects of exploration and risk-taking, further helped these professionals to grab the best vocational opportunities, thus navigating a successful path even during the global meltdown (Kumar, 2013). These facts suggest that curiosity may increase career satisfaction of Indian IT professionals. Besides, a previous finding has confirmed the role of curiosity in increasing career satisfaction (Zacher, 2015). Therefore, we propose the following:

H4a: Curiosity will be positively related to career satisfaction.

Furthermore, curiosity conveys a realistic image of oneself and career situations (Savickas & Porfeli, 2012). In the contemporary Indian IT labour market, technological alterations and geopolitical shifts have reported to reduce conventional job opportunities and slacken hiring activities (Verma & Agarwal, 2017). Hence, under such circumstances, curious professionals, being realistic about their career, may opt to be on a safer side by retaining jobs in the current organization. Thus, we expect that curiosity in Indian IT professionals could inversely predict their turnover intention. Literature also supported a negative association between curiosity and turnover intent (Chan et al., 2016; Guan et al., 2015). Therefore, we propose:

H4b: Curiosity will be negatively related to turnover intention.

Confidence reflects one’s self-belief in own abilities to achieve career goals (Savickas, 2005). It includes a problem-solving approach, accomplishing tasks efficiently, learning and developing new competencies to excel vocationally (Savickas & Porfeli, 2012). Studies have indicated that confidence in people was associated with higher self-efficacy and belief in succeeding and overcoming career barriers (Dries, Van Esbroeck, van Vianen, De Cooman, & Pepermans, 2012; Zacher, 2015). Such aspects of self-efficacy and self-belief would facilitate individuals to do tasks aptly, enhancing job performance, thereby increasing their career satisfaction (Alessandri, Borgogni, Schaufeli, Caprara, & Consiglio, 2015). In Indian IT work context, researchers have reported elements of self-efficacy, learning, and problem-solving orientation in employees that possibly shape their confidence (Upadhya & Vasavi, 2006; Vohra & Goel, 2009). Therefore, amidst employment uncertainty of Indian IT industry, professionals’ confidence may make them to adopt advanced technologies, securing knowledge, enhancing effort, thereby honing their vocational talents, subsequently increasing the level of career satisfaction. Literature have also shown links between confidence and increased career satisfaction (Chan et al., 2016; Guan et al., 2015; Zacher, 2015). Hence, we posit the following:

H5a: Confidence will be positively related to career satisfaction.

It may be relatable that employees who are more confident, possibly have greater marketability and more chances of getting better vocational prospects. However, the recent slowdown in Indian IT job market may hinder employees’ perception of career advancement through job hopping even though they possess a confident outlook. This fact indicates an inverse link between confidence and turnover intention of Indian IT professionals. Researchers have also revealed that confidence predicted turnover cogitation negatively (Omar & Noordin, 2013).

Therefore, we hypothesize the following:

H5b: Confidence will be negatively related to turnover intention.

The hypothesized models are shown in Figures 1(a) and 1(b).

Method

Participants and Procedures

Total 850 self-reported questionnaires were distributed to Indian IT professionals through web-based survey and paper–pencil test. Data were collected voluntarily. Complete anonymity was assured. Out of 850, 434 usable responses were obtained (51.02% effective response rate). Out of 434 respondents, 71.4 per cent were males and 28.6 per cent were females, with average age of 34.41 years (SD = 4.65). By and large, respondents were married (72.5%), had children (69.4%) and additional familial responsibilities (80.9%) and resided away from their hometowns (77.0%). Respondents were graduates (46.3%) and postgraduates or above in educational qualification (53.7%). They earned average annual salary of ₹13.79 lakhs per annum (SD = 5.84), had average work experience of 9.56 years (SD = 4.64). The data were normally distributed with skewness and kurtosis indices of all variables within range of –2 and +2 (Kline, 2005).

Figure 1(a).

Source: The authors.

Figure 1(b).

Source: The authors.

Measures

Career Adaptability

Career Adaptability was measured using 12-item standard scale, Career Adapt-Abilities Scale -Short Form (CAAS-SF), developed by Maggiori, Rossier, and Savickas (2017). The scale represents a second-order higher construct of career adaptability, consisting four first-order latent dimensions of (a) concern, (b) control, (c) curiosity and (d) confidence. Each dimension consists of three items. A sample item on group characteristics includes (a) ‘I became aware of the educational and vocational choices that I must make’; (b) ‘I take responsibility for my actions’; (c) ‘I investigate options before making a choice’ and (d) ‘I work up on my abilities’. Respondents₹ rated against each item on a 5-point Likert scale—from ‘not strong’ = 1 to ‘strongest’ = 5.

Career Satisfaction

Career satisfaction was assessed by using a five-item standard scale (Greenhaus et al., 1990). The scale is a unidimensional measurement of overall level of satisfaction with career. A sample item includes: ‘I am satisfied with the progress I have made towards meeting my goals for income’. All items were rated on a 5-point Likert scale—from ‘extremely dissatisfied’ = 1 to ‘extremely satisfied’ = 5.

Turnover Intention

Turnover intention was measured using a four-item standard scale (O’Reilly, Chatman, & Caldwell, 1991). This scale is a unidimensional measurement of intention to quit the present job or organization. A sample item includes: ‘I have seriously thought about leaving this company’. Response categories of items were given on a 5-point Likert scale—from ‘strongly disagree’ = 1 to ‘strongly agree’ = 5.

Control Variables

We incorporated age, gender (male = 1, female = 0), marital status (married = 1, not married = 0), having children (yes = 1, no = 0), additional familial responsibilities (yes = 1, no = 0), proximity of work location from hometown (near = 1, far = 0), annual salary, educational qualification (postgraduate or above = 1, graduate or diploma = 0) as control variables. These variables could impact the focal relationships (Lacity et al., 2008; Shanker, 2008).

Pilot Study and Statistical Analysis

A Total of 172 Indian IT professionals were included in the pilot survey. As studies on career adaptability and career satisfaction scales have not reported earlier, we performed exploratory factor analysis (EFA) for establishing dimensionality and initial validity of these constructs along with the turnover intention. The normally distributed pilot data (skewness and kurtosis indices of variables within +2 to –2) were used in this regard (Kline, 2005). It is recommended to conduct EFA and confirmatory analysis on a different set of samples (Hinkin, 1995) so we collected responses for two different sized samples with similar demographic and professional characteristics. Software SPSS 20.0 and AMOS 16.0 were employed for data analysis. SPSS 20.0 was utilized for EFA, performed in a small sized pilot sample (n = 172). AMOS 16.0 was used to examine measurement models of constructs through confirmatory factor analysis (CFA) and investigate proposed links between them, with a larger sample (n = 434). SEM, using Maximum Likelihood (ML) estimation, was deployed to perform CFA and hypotheses testing. Both data sets (n = 172 and n = 434) were normality distributed, following assumptions for EFA and SEM.

Results

Exploratory Factor Analysis

Table 1 reports the results of EFA obtained by following guidelines of Fabrigar, Wegener, Maccallum, and Strahan (1999). As expected, four dimensions of career adaptability (i.e., concern, control, curiosity, and confidence) were obtained along with two unidimensions of career satisfaction and turnover intention. Cronbach α scores above 0.70 for all factors suggested adequate internal consistency (Hair, Black, Babin, Anderson, & Tatham, 2010). Higher item loading in a specific factor as compared to others indicated the presence of convergent validity, whereas the absence of extreme cross loading provided support for discriminant validity between factors (Hair et al., 2010).

Table 1.

Exploratory Factor Analysis Results (n = 172)

KMO Measure of Sampling Adequacy = 0.899; Bartlett’s Test of Sphericity < 0.001
Items (Code)	Factors						Cronbach’s α
	Career Satisfaction	Turnover Intention	Concern	Control	Curiosity	Confidence
Thinking about what my future will be like (CA1)	0.15	0.20	0.84	0.30	0.17	0.11	0.86
Preparing for the future (CA2)	0.20	0.12	0.83	0.49	0.19	0.01
Becoming aware of the educational and vocational choices that I must make (CA3)	0.19	0.25	0.80	0.11	0.31	0.22
Making decisions by myself (CA4)	0.15	0.02	0.34	0.75	0.22	0.16	0.83
Taking responsibility for my actions (CA5)	0.18	0.12	0.25	0.82	0.21	0.23
Counting on myself (CA6)	0.24	0.05	0.23	0.80	0.28	0.29
Looking for opportunities to grow as a person (CA7)	0.13	0.03	0.43	0.33	0.73	0.31	0.79
Investing options before making a choice (CA8)	0.23	0.10	0.40	0.24	0.78	0.03
Observing different ways of doing things (CA9)	0.09	−0.06	0.16	0.37	0.75	0.21
Taking care to do things well (CA10)	0.06	−0.10	0.27	0.20	0.46	0.72	0.81
Learning new skills (CA11)	0.18	−0.09	0.15	0.27	0.18	0.82
Working up to my abilities (CA12)	0.22	−0.03	0.30	0.44	0.11	0.74
Satisfied with overall career goals (CS1)	0.81	0.07	0.15	0.13	−0.02	0.04	0.91
Satisfied with career success (CS2)	0.86	0.05	0.19	0.12	0.01	0.12
Satisfied with income (CS3)	0.80	−0.01	0.14	0.13	0.05	0.08
Satisfied with advancement (CS4)	0.83	0.13	0.13	0.11	0.13	0.08
Satisfied with development of new skills (CS5)	0.76	0.09	0.18	0.10	0.21	0.09
Preferring another job (TOI1)	−0.03	0.85	0.09	−0.05	0.06	0.12	0.90
Thinking to leave company (TOI2)	0.09	0.85	0.15	0.07	−0.08	−0.09
Having serious thoughts on leaving company (TOI3)	0.09	0.87	0.08	0.03	−0.03	−0.14
Leaving the job/organization in the next 12 months (TOI4)	0.13	0.77	0.07	0.12	0.05	−0.01

Source: The authors.

Notes: The bold character specify highest factor loading on each item; Total variance extracted is 76.8 per cent; Factors are extracted using the ML method with Promax rotation based on eigenvalues greater than one and commonalities of items greater than 6 (Fabrigar et al., 1999).

Confirmatory Factor Analysis

Figures 2(a), 2(b), and 2(c) represent the CFA measurement model of career adaptability, career satisfaction, and turnover intention, respectively. The career adaptability was shown as a second-order model (Figure 2[a]), which had an adequate model fit with relative Chi-Square value (χ2/df) of 2.63, comparative fit index (CFI) = 0.97, Tucker Lewis index (TLI) = 0.96, and root mean square error of approximation (RMSEA) = 0.06. This confirmed four-factor model of career adaptability measure (CAAS-SF) with 12 items (Hair et al., 2010). The gamma loadings for ‘concern’, ‘control’, ‘curiosity’, and ‘confidence’ were 0.77, 0.88, 0.86 and 0.88, respectively. Within each first-order dimension of career adaptability, item loadings above 0.70 were observed. Figures 2(b) and 2(c) indicate first-order measurement models of career satisfaction and turnover intention constructs. Appropriate model fit was obtained for career satisfaction after re-specification (χ2/df = 1.16, CFI = 0.99, TLI = 0.99, and RMSEA = 0.02). Model fit was sufficient for turnover intention (χ2/df = 2.35, CFI = 0.99, TLI = 0.99 and RMSEA = 0.05). In each model, item had above 0.70 loadings (Hair et al., 2010).

Figure 2(a).

Source: The authors.

Figure 2(b).

Source: The authors.

Figure 2(c).

Source: The authors.

Table 2 shows the mean, standard deviation, reliability estimates and correlation between variables. The Cronbach’s α scores of reliability for all four dimensions of career adaptability indicated adequate internal consistency (Hair et al., 2010). The α estimates were 0.82, 0.75, 0.83 and 0.79 for concern, control, curiosity, and confidence, respectively. Similarly, overall career adaptability had sufficient reliability with α scores of 0.91. The subscales reliability scores for Indian IT professionals’ sample were similar to the reliability scores in the CAAS-International sample (concern (α = 0.83), control (α = 0.74), curiosity (α = 0.79) and confidence (α = 0.85) (Savickas & Porfeli, 2012). The Pearson correlations between career adaptability dimensions are significant and range from 0.52 to 0.62. Their correlation with overall adaptability was significant and ranged from 0.81 to 0.86. Altogether, the total CAAS-SF along with its four-dimensional subscales demonstrated sufficient estimates of internal consistency and a multidimensional, higher-order structure, fitting the theoretical model of career adaptability. These findings suggested career adaptability as a formative, aggregated construct, with each dimension contributing to the overall level of adaptability separately and in combination (Savickas, 2013).

Table 2.

Means (M), Standard Deviations (SD), Reliability Estimates and Correlations of Variables (n = 434)

Variable	M (SD)	Correlations
		1	2	3	4	5	6	7
1. Overall career adaptability (CA)	3.85(0.76)	(0.91)
2. Concern (Conc)	3.62(0.80)	0.85***	(0.82)
3. Control (Cont)	3.95(0.76)	0.81***	0.59***	(0.75)
4. Curiosity (Cur)	3.91(0.73)	0.82***	0.53***	0.59***	(0.83)
5. Confidence (Conf)	3.87(0.75)	0.83***	0.52***	0.62***	0.61***	(0.79)
6. Career satisfaction (CS)	3.67(0.83)	0.48***	0.44***	0.38***	0.33***	0.41***	(0.90)
7. Turnover intention (TOI)	3.17(1.03)	0.07	0.19**	0.03	0.06	−0.06	0.04	(0.89)

Source: The authors.

Notes: Correlation coefficient significant at ***p < 0.001; **p < 0.01.

Reliability estimates (Cronbach’s α) are shown in parentheses across the diagonals.

Reliability scores of career satisfaction and turnover intention as estimated by Cronbach’s α were 0.90 and 0.89, respectively, demonstrating adequate internal consistency. The overall career adaptability showed a positive and significant correlation with career satisfaction. However, it had a nonsignificant correlation with turnover intention. All subscales of career adaptability were positively and significantly correlated with career satisfaction. Only concern showed a positive and significant correlation with turnover intention.

Common method bias (CMB) was addressed by using Harman’s single factor test, performed in SPSS 20.0. Results indicated that the study was free from biases as a single factor accounted for by 24.27 per cent of the variance, below the threshold for exhibiting CMB (i.e., 0.50) (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003). In addition, multicollinearity test was conducted in SPSS 20.0 to determine whether high intercorrelations existed among predictor variables within hypothesized models. Results showed variance inflation factor (VIF) values ranged from 1.07 to 2.01, below the threshold limit of VIF = 5.0, suggesting the absence of multicollinearity effects (Hair et al., 2010).

Examining Hypothesized Framework

Figures 3(a) and 3(b) represent the structural equation model for hypotheses testing (Byrne, 2001). In the first model (Figure 3[a]), overall career adaptability (independent variable) was represented in higher-order formative form, whereas career satisfaction (dependent variable) and turnover intention (dependent variable) were considered in their latent forms. In remaining models, as shown in Figure 3(b), four dimensions (latent forms) were taken as the independent variable(s) to analyse unique impact on career satisfaction and turnover intention. A specific effect of a dimension was obtained by controlling influence of other dimensions (i.e., setting their regression paths to zero). Fit indices of all structural equation models were tested to examine the model adequacy. Residual error terms were imposed on career satisfaction and turnover intention. In a structural model, standardized path coefficients were similar to standardized beta values in multiple regression analysis (Byrne, 2001). Table 3 presents the results of hypotheses testing.

Figure 3(a).

Source: The authors.

Figure 3(b).

Source: The authors.

Relationship Between Overall Career Adaptability and Career Satisfaction and Turnover Intention

The structural model 1 showed an appropriate fit to the data (χ2/df = 1.64, CFI = 0.97, TLI = 0.95, RMSEA = 0.04) (Table 3). Results indicated overall career adaptability positively and significantly predicted career satisfaction (β = 0.49, p = 0.001), providing support to H1a. However, career adaptability could not significantly predict turnover intention (β = 0.05, p = 0.377), rejecting H1b.

Table 3.

Hypotheses Testing (n = 434)

Model	Hypothesis	β	SE	CR	Model Fit				Decision
					χ²/df	CFI	TLI	RMSEA
Model 1: Overall career adaptability’s impact on vocational attitudes	H_1a: CA→CS (+)	0.49	0.08	7.75***	1.16	0.97	0.96	0.04	Support H_1a Reject H_1b
	H_1b: CA→TOI (−)	0.05	0.10	0.09
Significant influence of control variables in Model 1^#	Children→CS Marital status→TOI	0.15 −0.14	0.09 0.07	2.30* −2.03*
Model 2: Concern’s unique impact on vocational attitudes	H_2a: Conc→CS (+)	0.46	0.06	7.64***	3.55	0.91	0.90	0.08	Support H_2a Reject H_2b
	H_2b: Conc→TOI (−)	0.31	0.08	7.73**
Significant influence of control variables in Model 2^#	Children→CS Marital status→TOI	0.14 −0.12	0.09 0.10	2.23* 2.95*
Model 3: Control’s unique impact on vocational attitudes	H_3a: Cont→CS (+)	0.39	0.06	7.46***	3.59	0.91	0.89	0.08	Support H_3a Reject H_3b
	H_3b: Cont→TOI (−)	0.02	0.08	0.354
Significant influence of control variables in Model 3^#	Children→CS Marital status→TOI	0.16 −0.15	0.10 0.10	2.46* −2.23*
Model 4: Curiosity’s unique impact on vocational attitudes	H_4a: Cur→CS (+)	0.34	0.07	5.68***	3.67	0.91	0.90	0.08	Support H_4a Reject H_4b
	H_4b: Cur→TOI (−)	0.01	0.10	0.24
Significant influence of control variables in Model 4^#	Children→CS	0.17	0.10	2.31*
Model 5: Confidence’s unique impact on vocational attitudes	H_5a: Conf→CS (+)	0.44	0.07	7.31***	3.58	0.90	0.88	0.08	Support H_5a Reject H_5b
	H_5b: Conf→TOI (−)	−0.02	0.10	−.17
Significant influence of control variables in Model 5^#	Children→CS Marital status→TOI	0.16 −0.15	0.09 0.10	2.51* 2.21*

Source: The authors.

Notes: β = standard beta coefficient; SE = standard error; CR = critical ratio; # = showing only significant control variables in the model; regression path significant at ***p < 0.001; **p < 0.01; *p < 0.005.

Relationships Among the Dimensions of Career Adaptability and Career Satisfaction and Turnover Intention

All the four models (2 – 5) showed almost adequate model fit with χ2/df below 5.0, CFI and TLI approximately 0.90 or above, and RMSEA below 0.08 (Hair et al., 2010). When controlling other career adaptability dimensions, concern positively predicted career satisfaction significantly (β = 0.46, p = 0.001), providing support for H2a. Inconsistent with our assumption, concern also predicted turnover intention positively and significantly (β = 0.31, p = 0.004), refuting H2b. Similarly, consistent with framed hypothesis, control had a significant positive impact on career satisfaction (β = 0.39, p = 0.001), supporting H3a. However, control did not have any significant association with turnover intention (β = 0.02, p = 0.723), making us reject H3b. Further, curiosity and confidence both significantly and positively predicted career satisfaction at (β = 0.34, p = 0.002) and (β = 0.44, p = 0.001), respectively. These results provided grounds to support H4a and H5a. The effects of these career adaptability dimensions on turnover intention were not significant (β = 0.01, p = 0.813 and β = 0.02, p = 0.865, respectively), thereby refuting H4b and H5b. In sum, findings illustrated that overall career adaptability, and its dimensions individually, contributed to increase career satisfaction. Overall, career adaptability seemed to have no effect on turnover intention, but on considering at its dimension level, the resource of concern was found to increase turnover intention.

Among control variables, across all the models (Table 3), professionals with children were more satisfied in careers, implying towards a positive spillover effect of personal domains of life in supplementing vocational contentment (Rao & Indla, 2010). Further, married professionals had reduced turnover intention because of increased commitment towards familial demands (Ghapanchi & Aurum, 2011).

Discussion

Though research on career adaptability is rapidly expanding, studies on such important vocational construct are limited in Indian occupational contexts. Specifically, in Indian IT employment market where business volatility and technological transformation are inevitable, career adaptability can be a saviour. Therefore, adaptability quotient among Indian IT employees needs to be understood, identified and nurtured. In this regard, we believe that this study offers a first known empirical evidence regarding operationalization of career adaptability within Indian IT workforce’s vocational attitudes. We examined how overall adaptability and its resources could predict Indian IT professionals’ vocational attitudes of career satisfaction and turnover intention. In line with previous findings, the present study reflected that adaptability overall, as well as its dimensions, was required for installing satisfaction in careers, manifesting professionals’ vocational success (Chan & Mai, 2015; Zacher, 2014, 2015). Besides, turnover intention, which apparently seemed unrelated to overall career adaptability, was predicted by the resource of concern. This means employees who are thoughtful about their future and made plans accordingly will be more likely to quit if they get right opportunities.

The findings, overall career adaptability with dimensions of concern, control, curiosity and confidence increasing career satisfaction, could be aligned with CCT. It indicates that professionals with higher career adaptability quotient may possess greater psychosocial attributes and competencies to successfully adjust and manage the changing vocational demands of Indian IT market. Such adjustments and conquering abilities possibly imbibe feelings of accomplishment and intrinsic fulfilment, thereby accelerating satisfaction in careers. However, on examining closely, dimensional level interpretation of adaptability provided a differential relationship with career satisfaction. It is observed that resources of adaptability did not equally contribute in determining employees’ satisfaction in careers. Aspects of concern and confidence contributed more to increase career satisfaction than control and curiosity, indicating consistency with Zacher’s (2014) findings. Furthermore, this study contradicts previous findings which supported overall career adaptability’s significant impact on turnover intention (Chan & Mai, 2015; Ito & Brotheridge, 2005; Omar & Noordin, 2013). At the dimensional level, concern was found to accelerate turnover intention of Indian IT workforce while other resources remained unrelated, showing inconsistency with past studies (Chan et al., 2016; Guan et al., 2015; Omar & Noordin, 2013). Altogether, though resources of adaptability can prepare employees to cope with future challenges and transformations leading to satisfaction in careers, they mostly fail to determine turnover cognition.

Indeed, the dualism of Indian IT industry, comprising both scopes of advancement and threats to employment, may render a fundamental explanation for such findings. With rising technologies such as automation, cloud computing, big data and artificial intelligence, new and more promising job avenues are emerging amidst ongoing disturbances. Despite visa irregularities, countries such as Canada and Mexico are welcoming Indian IT workforce, marking stronger employment platforms for future (Bhattacharya, 2017). Apart from corporate jobs, start-ups are supposed to play a crucial role in employment generation in time to come. Such facts greatly enhance professionals’ concern resource by making them look ahead, plan and prepare for forthcoming vocational prospects, directing their energy towards achieving long-term occupational gains. Perhaps, aiming for long-term gains redefines professionals’ anticipation to optimize their talent functions, synchronizing with rapidly emerging digital needs and business demands. This may, in turn, increase participation in talent development programmes, which increases self-efficacy and self-belief, thereby fostering confidence to withstand abrupt vocational challenges. Therefore, engaging in worthwhile activities related to vocational success may amplify career satisfaction among adaptable Indian IT professionals with greater resources of concern and confidence.

It may be a chaotic external environment, producing misleading information, confusing employees, hampering exploration and decision-making processes that dilute functions of curiosity and control resources. Nevertheless, these psychosocial attributes strengthen their responsibility and determination towards careers, enable Indian IT professionals to attain realistic self-congruent vocational goals and invest in acquiring relevant proficiencies. All these factors possibly improve vocational performance, hence bringing satisfaction in careers. Besides, misleading information from the chaotic external environment could bring apprehensive thoughts of vocational crisis, enhancing professionals’ anticipation regarding better future positions, making them concern, thereby stimulating their turnover cogitation. Thus, contradicting our expectation that concerned employees might find staying in the same organization more appropriate, an inverse relation was observed. It may suggest that adaptable employees with higher concern, despite being satisfied in careers, still harbour quitting intention as they possibly look for better chances of vocational growth outside (Tams & Arthur, 2010). In fact, the availability of various technical and management courses offered by premium educational institutes as well as increased entrepreneurial avenues can support concern employees’ future career plans, hence stimulating turnover cogitation. However, in industrial turmoil and labour market chaos, other resources of adaptability could not get expressed to have a substantial impact on turnover intention. Moreover, occupational benefits (e.g., decent salary, urban living, etc.) associated with Indian IT careers possibly sideline professionals’ thoughts of quitting, thus making turnover intention independent of other adaptability resources.

Implications

Findings of the present study can add to the extant literature by specifying how resources of career adaptability shapes vocational attitudes of working adults. Identifying a particular impact of career adaptability dimensions on Indian IT workforce’s career satisfaction and turnover intention manifests the uniqueness of each resource as suggested by CCT. The study can guide researchers to better comprehend notions of career construction and deduce novel insights into professionals’ vocational development. Also, initial validation of CAAS-SF scale to assess career adaptability provides a ground for its implementation within Indian occupational contexts. The results may help in understanding and developing capabilities of human capital of Indian IT industry. It is evinced that adaptability resources bring forth a sense of contentment and satisfaction in the career, with different but moderate degrees.

In this regard, human resource management could step forward to foster different resources of career adaptability in IT workforce. Besides addressing employees’ concerns and boosting confidence, emphasis can be given to improving curiosity and control resources, integrating together to accelerate professionals’ career satisfaction. Further, Indian IT people may encourage to think innovatively, engage in creative behaviours, proactively participate in talent advancement programmes and learn to accept career failures. Such inputs would strengthen their career adaptability resourcefulness, increase vocational satisfaction, stimulate productivity and result in business success. But at the same time, management and leaders do need to handle employees’ concerns more aptly. As concerned employees always look for opportunities and formulate strategies to opt them, such concerns and disquiets can be dealt with beforehand to reduce turnover motives. For this, informal mentorship sessions with senior leaders may facilitate professionals in getting an impression about self-capability, accurate knowledge regarding future and contemporary business requirements (Singh, Bains, & Vinnicombe, 2002).

As Indian IT workforce represents a worldwide identity, this research could be informative to human resource management globally. It can benefit Western leaders to understand and manage cross-cultural teams comprising Indian professionals in better ways. Setting higher standards of goals, providing constructive feedback, expressing confidence in their competencies, supporting independent thinking and decision-making may imbibe resources of adaptability in Indian employees (Gupta, 2013). Such aspects possibly improve their performance, increasing productivity, amplifying satisfaction and retention parameters. In addition, findings may be useful to independent career coaches and counsellors who deal with clients facing missteps and dissatisfaction in careers. Career guidance practitioners can give attention to carefully evaluating attitudes, behaviours, and competencies of their clients that are associated with distinct career adaptability dimensions. Based on such evaluations, the focus needs to be placed on addressing insufficient resources along with promoting other resources through adequate training, feedbacks, and consultations. This may help clients to smartly handle both career possibilities and adversities simultaneously, thereby sustaining a career even under extreme vocational situations.

Limitations and Future Scopes

This study has some limitations. First, it is cross-sectional in nature, thus cannot account for causal relationships between career adaptability and vocational attitudes of career satisfaction and turnover intention. Longitudinal studies in future may extend support for establishing causality. Furthermore, though the current study offers preliminary support for CAAS-SF in Indian IT setting, further validation research is required to fully establish its psychometric properties and factor structure through invariance tests.

Second, this research is restricted to assessing links between career adaptability resources and career satisfaction as well as turnover intention among Indian IT professionals without factoring in age and gender. Results of the study may not be generalized in other occupational backgrounds. Thus, we encourage researchers to perform similar studies in different professional groups, including moderating effects of age and gender. Also, other relevant job, career and organization-related variables (e.g., job performance, career commitment, and actual turnover) can be studied to capture an in-depth dimensional level interpretation of career adaptability.

Third, this research reveals only a direct relationship between elements of career adaptability and vocational attitudes of career satisfaction and turnover intention. It doesn’t explain the mechanism through which concern, control, curiosity and confidence resources are linked, or not linked, to these vocational attitudes. Hence, it would be interesting whether researchers show the indirect effects of mediating variables (e.g., work–life balance, organizational engagement, etc.) to demonstrate relationships between career adaptability resources and these vocational attitudes. Additionally, researchers may consider exploring resources of adaptability in Indian IT professionals qualitatively to provide useful information for shaping the competitive edge.

Conclusion

This study advances literature on Indian IT professionalism by capturing how career adaptability overall along with its resources predicts vocational attitudes of career satisfaction and turnover intention. Findings have revealed that overall adaptability increases professionals’ career satisfaction but unrelated to turnover intention. Independently, its resources of concern and confidence increase career satisfaction more as compared to curiosity and control. Of all the resources, the concern seems to increase turnover intention. Findings of the present study can be useful to human resource management and vocational practitioners to understand the importance of career adaptability in ever-changing Indian IT industry.

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.

References

Abele

A. E.

, & Spurk

(2009). The longitudinal impact of self-efficacy and career goals on objective and subjective career success. Journal of Vocational Behavior, 74(1), 53–62.

Aboobaker

, Edward

, & Pramatha

K. P.

(2017). Work–family conflict, family–work conflict and intention to leave the organization: Evidences across five industry sectors in India. Global Business Review, 18(2), 524–536.

Alessandri

, Borgogni

, Schaufeli

W. B.

, Caprara

G. V.

, & Consiglio

(2015). From positive orientation to job performance: The role of work engagement and self-efficacy beliefs. Journal of Happiness Studies, 16(3), 767–788.

Bhal

K. T.

, & Gulati

(2007). Pay satisfaction of software professionals in India. Vikalpa, 32(3), 9–22.

Bhattacharya

(2017, February 23). Indian techies don’t need the US: Their talent is welcome elsewhere. Quartz India. Retrieved from https://qz.com/917824/indian-techies-dont-need-the-us-their-talent-is-welcome-elsewhere/

Bimrose

, & Brown

(2015). Career decision making and career adaptability. In Maree

& Fabio

A. D.

(Eds.), Exploring new horizons in career counselling (pp. 249–261). Rotterdam: Sense.

Bimrose

, & Hearne

(2012). Resilience and career adaptability: Qualitative studies of adult career counseling. Journal of Vocational Behavior, 81(3), 338–344.

Budhiraja

, Pathak

U. K.

, & Kaushik

(2017). A framework for untapped creativity: Leveraging components of individual creativity for organizational innovation. Development and Learning in Organizations: An International Journal, 31(6), 7–9.

Byrne

B. M.

(2001). Structural equation modeling with AMOS: Basic concepts, applications, and programming. Mahwah, NJ: Lawrence Erlbaum.

10.

Chan

S. H. J.

, & Mai

(2015). The relation of career adaptability to satisfaction and turnover intentions. Journal of Vocational Behavior, 89, 130–139.

11.

Chan

S. H. J.

, Mai

, Kuok

O. M. K.

, & Kong

S. H.

(2016). The influence of satisfaction and promotability on the relation between career adaptability and turnover intentions. Journal of Vocational Behavior, 92, 167–175.

12.

Chandran

(2016, September 7). IT firms upskill staff in new technology age. The Hindu. Retrieved from https://www.thehindu.com/business/Industry/it-firms-upskill-staff-in-new-technology-age/article8206324.ece

13.

Chang Boon Lee

(2002). Career goals and career management strategy among information technology professionals. Career Development International, 7(1), 6–13.

14.

Chatterjee

(2013). A conceptual framework examining the antecedents of career decisiveness using motivation systems theory. Turkish Online Journal of Distance Education, 14(4), 31–41.

15.

Coetzee

, & Stoltz

(2015). Employees’ satisfaction with retention factors: Exploring the role of career adaptability. Journal of Vocational Behavior, 89, 83–91.

16.

Douglass

R. P.

, & Duffy

R. D.

(2015). Calling and career adaptability among undergraduate students. Journal of Vocational Behavior, 86, 58–65.

17.

Dries

, Van Esbroeck

, van Vianen

A. E. M.

, De Cooman

, & Pepermans

(2012). Career adapt-abilities scale-Belgium form: Psychometric characteristics and construct validity. Journal of Vocational Behavior, 80(3), 674–679.

18.

Fabrigar

L. R.

, Wegener

D. T.

, Maccallum

R. C.

, & Strahan

E. J.

(1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4(3), 272–299.

19.

Fiori

, Bollman

, & Rossier

(2015). Exploring the path through which career adaptability increases job satisfaction and lowers job stress: The role of affect. Journal of Vocational Behavior, 91, 113–121.

20.

Gattiker

U. E.

, & Larwood

(1988). Predictors for managers’ career mobility, success, and satisfaction. Human Relations, 41(8), 569–591.

21.

Ghapanchi

A. H.

, & Aurum

(2011). Antecedents to IT personnel’s intentions to leave: A systematic literature review. Journal of Systems and Software, 84(2), 238–249.

22.

Greenhaus

J. H.

, Parasuraman

, & Wormley

W. M.

(1990). Effects of race on organizational experiences, job performance evaluations, and career outcomes. Academy of Management Journal, 33(1), 64–86.

23.

Guan

, Deng

, Sun

, Wang

, Cai

, Ye

, … & Li

(2013). Career adaptability, job search self-efficacy and outcomes: A three-wave investigation among Chinese university graduates. Journal of Vocational Behavior, 83(3), 561–570.

24.

Guan

, Zhou

, Ye

, Jiang

, & Zhou

(2015). Perceived organizational career management and career adaptability as predictors of success and turnover intention among Chinese employees. Journal of Vocational Behavior, 88, 230–237.

25.

Gupta

(2013). Leadership and creativity in the Indian R&D laboratories: Examining the role of autonomous motivation, psychological capital and justice perceptions (No. WP2013-11-09). Ahmedabad: Indian Institute of Management, Research and Publication Department.

26.

Hair

, Black

, Babin

, Anderson

, & Tatham

(2010). Multivariate data analysis (7th ed.). Upper Saddle River, NJ: Pearson Prentice Hall.

27.

Hinkin

T. R.

(1995). A review of scale development practices in the study of organizations. Journal of Management, 21(5), 967–988.

28.

Hirschi

, Herrmann

, & Keller

A. C.

(2015). Career adaptivity, adaptability, and adapting: A conceptual and empirical investigation. Journal of Vocational Behavior, 87, 1–10.

29.

Hirschi

, Niles

S. G.

, & Akos

(2011). Engagement in adolescent career preparation: Social support, personality and the development of choice decidedness and congruence. Journal of Adolescence, 34(1), 173–182.

30.

Hirschi

, & Valero

(2015). Career adaptability profiles and their relationship to adaptivity and adapting. Journal of Vocational Behavior, 88, 220–229.

31.

India Today (2018, January 18). Re-skill or don’t complain if you are fired, IT leaders concur as they forecast job boom in India. Retrieved from https://www.indiatoday.in/india-today-conclave-south-2018/story/india-today-conclave-south-2018-it-edge-job-cognizant-satyam-jabong-nestaway-1148825-2018-01-18

32.

Ito

J. K.

, & Brotheridge

C. M.

(2005). Does supporting employees’ career adaptability lead to commitment, turnover, or both? Human Resource Management, 44(1), 5–19.

33.

Jha

(2009). Determinants of employee turnover intention: A review. Management Today, 9(2), 26–33.

34.

Jiang

J. J.

, Klein

, & Balloun

J. L.

(2001). The joint impact of internal and external career anchors on entry-level IS career satisfaction. Information & Management, 39(1), 31–39.

35.

Johnston

C. S.

, Luciano

E. C.

, Maggiori

, Ruch

, & Rossier

(2013). Validation of the German version of the career adapt-abilities scale and its relation to orientations to happiness. Journal of Vocational Behavior, 83(3), 295–304.

36.

Johri

(2015). Effect of self-efficacy and perceived organizational support on employee work passion and career satisfaction (Doctoral dissertation). Noida: Jaypee Institute of Information Technology.

37.

Joseph

, Ng

K. Y.

, Koh

, & Ang

(2007). Turnover of information technology professionals: A narrative review, meta-analytic structural equation modeling, and model development. MIS Quarterly, 31(3), 547–577.

38.

Kearney

N. R.

(2012). Advancing your career: Concepts of professional nursing (5th ed). Philadelphia, CA: F. A. Davis Company.

39.

Khatri

(2017, May 25). Upskilling could help techies survive layoffs: 5 case studies tell you how. The Economic Times. Retrieved from http://economictimes.indiatimes.com/wealth/earn/upskilling-could-help-techies-survive-layoffs-5-cases-studies-tell-you-how/articleshow/58820553.cms

40.

Kline

R. B.

(2005). Principles and practices of structural equation modeling (2nd ed.). New York, NY: Guilford Press.

41.

Koen

(2013). Prepare and pursue: Routes to suitable (re-) employment. Amsterdam: University of Amsterdam

42.

Koen

, Klehe

U. C.

, & Van Vianen

A. E.

(2012). Training career adaptability to facilitate a successful school-to-work transition. Journal of Vocational Behavior, 81(3), 395–408.

43.

Krishna

, Ojha

A. K.

, & Barrett

(2000). Competitive advantage in the software industry: An analysis of the Indian experience. In Avgerou

& Walsham

(Eds.), Information technology in context: Studies from the perspective of developing countries (pp. 182–197). Aldershot: Ashgate.

44.

Kulkarni

(2016, December 23). Why I quit my job at Microsoft to start a startup. Retrieved from https://hackernoon.com/why-i-quit-my-job-at-microsoft-to-start-a-startup-9b4d82545726

45.

Kumar

(2013, November 16). The reverse brain drain: Why NRIs are returning to India. NDTV Profit. Retrieved from http://profit.ndtv.com/news/your-money/article-the-reversebrain-drain-why-nris-are-returning-to-india-372361

46.

Kumar

(2015, September 14). Why IT professionals constantly need to upskill. The Financial Express. Retrieved from https://www.financialexpress.com/jobs/why-it-professionals-constantly-need-to-upskill/135157/

47.

Lacity

M. C.

, Iyer

V. V.

, & Rudramuniyaiah

P. S.

(2008). Turnover intention of Indian IS professionals. Information Systems Frontiers, 10(2), 225.

48.

Lounsbury

J. W.

, Moffitt

, Gibson

L. W.

, Drost

A. W.

, & Stevens

(2007). An investigation of personality traits in relation to job and career satisfaction of information technology professionals. Journal of Information Technology, 22(2), 174–183.

49.

Maggiori

, Rossier

, & Savickas

M. L.

(2017). Career adapt-abilities scale–short form (CAAS-SF) construction and validation. Journal of Career Assessment, 25(2), 312–325.

50.

Mahatanankoon

(2007). The effects of post-educational professional development activities on promotion and career satisfaction of IT professionals. In Lending

& Vician

(Eds.), The global information technology workforce: Proceedings of the 2007 ACM SIGMIS CPR conference on Computer personnel research (pp. 9–14). St. Louis, Missouri: ACM.

51.

Malik

, & Garg

(2018). Psychometric testing of the resilience at work scale using Indian sample. Vikalpa, 43(2), 77–91.

52.

Manjula

, & Balachandra

(2017). A study on various training programmes and their effects offered by the IT firms. In Satapathy

S. C.

, Bhatega

, Udgata

S. K.

, & Pattnaik

P. K.

(Eds.), Proceedings of the 5th International Conference on Frontiers in intelligent computing: Theory and applications (pp. 455–463). Singapore: Springer.

53.

Mathew

, Ogbonna

, & Harris

L. C.

(2012). Culture, employee work outcomes and performance: An empirical analysis of Indian software firms. Journal of World Business, 47(2), 194–203.

54.

McKenna

, Zacher

, Ardabili

F. S.

, & Mohebbi

(2016). Career adapt-abilities scale: Iran form: Psychometric properties and relationships with career satisfaction and entrepreneurial intentions. Journal of Vocational Behavior, 93, 81–91.

55.

Merino-Tejedor

, Hontangas

P. M.

, & Boada-Grau

(2016). Career adaptability and its relation to self-regulation, career construction, and academic engagement among Spanish university students. Journal of Vocational Behavior, 93, 92–102.

56.

MichaelPage (2017). Indian automation trends. India: Michael Page India. Retrieved from https://www.michaelpage.co.in/sites/michaelpage.co.in/files/Michael%20Page%20India%20Automation%20Trends%202017_online.pdf?utm_campaign=in_automation&utm_source=linkedIn&utm_medium=socia

57.

Nayak

, Bhatnagar

, & Budhwar

(2018). Leveraging social networking for talent management: An exploratory study of Indian firms. Thunderbird International Business Review, 60(1), 21–37.

58.

T. W.

, Eby

L. T.

, Sorensen

K. L.

, & Feldman

D. C.

(2005). Predictors of objective and subjective career success: A meta-analysis. Personnel Psychology, 58(2), 367–408.

59.

Omar

, & Noordin

(2013). Career adaptability and intention to leave among ICT professionals: An exploratory study. Turkish Online Journal of Educational Technology-TOJET, 12(4), 11–18.

60.

O’Reilly

C. A.

, Chatman

, & Caldwell

D. F.

(1991). People and organizational culture: A profile comparison approach to assessing person-organization fit. Academy of Management Journal, 34(3), 487–516.

61.

Phillips

S. D.

, & Blustein

D. L.

(1994). Readiness for career choices: Planning, exploring, and deciding. The Career Development Quarterly, 43(1), 63–73.

62.

Podsakoff

P. M.

, MacKenzie

S. B.

, Lee

J. Y.

, & Podsakoff

N. P.

(2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903.

63.

Porfeli

E. J.

, & Savickas

M. L.

(2012). The Career Adapt-Abilities Scale: Construction, reliability, and initial validity of the USA form. Journal of Vocational Behavior, 80, 748–753.

64.

Radhakrishnan

(2007). Rethinking knowledge for development: Transnational knowledge professionals and the ‘new’ India. Theory and Society, 36(2), 141–159.

65.

Raman

, Bharathi

, Allen

, & Joseph

(2013). Use of structural equation modeling to empirically study the turnover intentions of information technology professionals in Pune city. Indian Journal of Science and Technology, 6(12), 5612–5624.

66.

Rani

, Kamalanabhan

T. J.

, & Selvarani

(2011). Work/life balance reflections on employee satisfaction. Serbian Journal of Management, 6(1), 85–96.

67.

Rao

T. S.

, & Indla

(2010). Work, family or personal life: Why not all three? Indian Journal of Psychiatry, 52(4), 295–297.

68.

Rossier

, Zecca

, Stauffer

S. D.

, Maggiori

, & Dauwalder

J. P.

(2012). Career adapt-abilities scale in a French-speaking Swiss sample: Psychometric properties and relationships to personality and work engagement. Journal of Vocational Behavior, 80(3), 734–743.

69.

Ryan

R. M.

, & Deci

E. L.

(2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55(1), 68–78.

70.

Sapam

, & Pathak

(2017, February 7). H1B visa norm changes: Indian IT professionals look at returning home. Retrieved from https://www.livemint.com/Industry/vM3kdc1cp5jqjPS3RJxQLN/H1B-visa-norm-changes-Indian-IT-professionals-look-at-retur.html

71.

Savickas

M. L.

(1997). Career adaptability: An integrative construct for life-span, life-space theory. Career Development Quarterly, 45(3), 247–259.

72.

Savickas

M. L.

(2002). Career construction: A developmental theory of vocational behavior. In Brown

(Ed.), Career choice and development (4th ed., pp. 149–205). San Francisco, CA: Jossey Bass.

73.

Savickas

M. L.

(2005). The theory and practice of career construction. In Brown

S. D.

& Lent

R. W.

(Eds.), Career development and counseling: Putting theory and research to work (pp. 40–70). Hoboken, NJ: Wiley.

74.

Savickas

M. L.

(2013). The 2012 Leona Tyler Award Address: Constructing Careers—Actors, Agents, and Authors Ψ. The Counseling Psychologist, 41(4), 648–662.

75.

Savickas

M. L.

, & Porfeli

E. J.

(2012). Career adapt-abilities scale: Construction, reliability, and measurement equivalence across 13 countries. Journal of Vocational Behavior, 80(3), 661–673.

76.

Shanker

(2008). Gender relations in IT companies: An Indian experience. Gender, Technology and Development, 12(2), 185–207.

77.

Singh

(2010). A study on the perception of work-life balance policies among software professionals. IUP Journal of Management Research, 9(2), 51–79.

78.

Singh

, Bains

, & Vinnicombe

(2002). Informal mentoring as an organisational resource. Long Range Planning, 35(4), 389–405.

79.

Skorikov

(2007). Continuity in adolescent career preparation and its effects on adjustment. Journal of Vocational Behavior, 70(1), 8–24.

80.

Super

D. E.

(1974). Measuring vocational maturity for counseling and evaluation. Washington, DC: National Vocational Guidance Association.

81.

Tams

, & Arthur

M. B.

(2010). New directions for boundaryless careers: Agency and interdependence in a changing world. Journal of Organizational Behavior, 31(5), 629–646.

82.

The Hindu (2017, July 8). Adaptability key to survival of IT entities, say experts. Retrieved from http://www.thehindu.com/news/cities/Thiruvananthapuram/adaptability-key-to-survival-of-it-entities-say-experts/article19236222.ece

83.

Upadhya

(2008). Rewriting the code: Software professionals and the reconstitution of Indian middle class identity. In Jaffrelot

& van der Veer

(Eds.), Patterns of middle class consumption in India and China (pp. 55–87). New Delhi: SAGE Publications.

84.

Upadhya

, & Vasavi

A. R.

(2006). Work, culture, and sociality in the Indian IT industry: A sociological study. Bangalore: National Institute of Advanced Studies at Indian Institute of Science.

85.

Urbanaviciute

, Pociute

, Kairys

, & Liniauskaite

(2016). Perceived career barriers and vocational outcomes among university undergraduates: Exploring mediation and moderation effects. Journal of Vocational Behavior, 92, 12–21.

86.

M. A.

, Chan

K. Y.

, Sam

Y. L.

, Ho

M. H. R.

, & Chernyshenko

O. S.

(2015). Proactivity, adaptability and boundaryless career attitudes: The mediating role of entrepreneurial alertness. Journal of Vocational Behavior, 86, 115–123.

87.

Verma

, & Agarwal

(2017, May 16). Bleak future ahead for sacked IT employees: Experts. The Economic Times. Retrieved from http://economictimes.indiatimes.com/tech/ites/bleak-future-ahead-for-sacked-it-employees-experts/articleshow/58688660.cms

88.

Victor

(2011). My life my choice: Mid-life career choices. Chennai: Macmillan.

89.

Vohra

, & Goel

(2009). Influence of positive characteristics on organizational commitment and job satisfaction of Indian middle managers (Working Paper Series No. 63). Calcutta: IIM. Retrieved from https://www.iimcal.ac.in/sites/all/files/pdfs/wps-635_1.pdf

90.

Wickramasinghe

, & Jayaweera

(2010). Impact of career plateau and supervisory support on career satisfaction: A study in offshore outsourced IT firms in Sri Lanka. Career Development International, 15(6), 544–561.

91.

Zacher

(2014). Career adaptability predicts subjective career success above and beyond personality traits and core self-evaluations. Journal of Vocational Behavior, 84(1), 21–30.

92.

Zacher

(2015). Daily manifestations of career adaptability: Relationships with job and career outcomes. Journal of Vocational Behavior, 91, 76–86.

93.

Zacher

, Ambiel

R. A.

, & Noronha

A. P. P.

(2015). Career adaptability and career entrenchment. Journal of Vocational Behavior, 88, 164–173.