Sage Journals: Discover world-class research

Abstract

This article delves into the critical role of learning agility in the Indian context. It specifically explores how an organizational learning culture (OLCu) fosters an employee’s ability to learn and adapt—their learning agility—which ultimately translates into improved firm performance. Employing a cross-sectional research design, the study gathers data through self-administered surveys from over 313 employees working in hardware service firms within the Indian IT sector. Path analysis is then utilized to analyse the collected data and unveil the underlying relationships. The findings unveil a fascinating interplay between OLCu, learning agility and firm performance. The study suggests that OLCu acts as a catalyst, fostering a work environment that encourages continuous learning and development. This, in turn, fosters employee learning agility. However, the research also reveals that learning agility plays a mediating role, meaning it partially explains the effect of OLCu on firm performance. In simpler terms, while a strong OLCu is crucial, it is the employee’s enhanced learning agility that truly unlocks the door to improved firm performance. This research offers valuable insights that can empower organizations in the Indian IT landscape. By nurturing a vibrant OLCu, companies can equip their employees with the tools and support they need to become more learning agile. This agility translates into a workforce that can readily adapt to new technologies, evolving customer demands, and a dynamic business environment. Consequently, firms can expect greater innovation, improved service delivery, and, ultimately, a significant boost in overall performance. This research also contributes significantly to the existing body of knowledge. It adds a fresh perspective to organizational learning theory by highlighting the crucial role of learning agility as a bridge between OLCu and firm performance, particularly in the Indian IT sector.

Keywords

Firm Performance Information Technology Learning Agility Organizational Learning Culture India

In recent years, scholars have paid more attention to managing and learning external information sources for survival and thriving in the rapidly changing dynamic environment (Ghobakhloo & Fathi, 2019; Puthusserry et al., 2020). Previous literature has highlighted that learning new knowledge is associated with the increased overall performance of the firm, and thereby the firm can create a competitive advantage in a chaotic environment (Ghobakhloo & Fathi, 2019; Inkinen, 2016). Learning can be defined as the acquisition of novel information and the ability to execute that information in the organization to enhance performance (Chen, 2017; Pudjiarti & Darmanto, 2020). From the organizational perspective, learning is crucial for businesses to make them more creative, innovative and competitive (Salem et al., 2018). Moreover, learning provides a way for organizations through which they can be able to survive and thrive in the market in the long run (Choi et al., 2016). As a result, organizations today require eager and willing employees to learn new things to adapt and respond to changes in the environment efficiently and effectively (Tripathi et al., 2020). In other words, for an organization, learning agile employees are the primary concern in addressing the dynamic market changes that can help improve an organization’s performance.

From an organizational learning perspective, organizations are believed to have a learning culture because it facilitates knowledge-sharing systems that directly promote learning within an organization. And, it also motivates employees to learn, enhancing organizational performance (Inkinen, 2016; Oh, 2019). Extensive research from a wide range of multidisciplinary perspectives devoted to understanding organizational learning culture (OLCu) has revealed that an organizational culture embedded with a learning system is now developed as a critical management context (Bae & Grant, 2018; Oh, 2019). Although both OLCu and learning agility play a vital role in improving the performance of an organization (Felipe et al., 2020; Salehzadeh et al., 2017). Furthermore, no study has investigated the relationship between learning agility and firm performance in the presence of OLCu, making this study one of a kind.

A resource-based perspective contends that ‘the possession of strategic resources provides an organisation with a golden opportunity to develop competitive advantages over its rivals’(Barney, 2018, p. 12).In the same vein, the current study has used OLCu, which has been identified as a critical source to improve organizational performance. The information technology (IT) industry has been chosen for this quantitative study since India is a preferred source of hotspot hub for global IT services (Gope et al., 2018; Jain et al., 2019). Overall development and frequent introduction of new technology in the market is the foremost challenge of this very industry. Learning has the utmost importance to respond to the novel changes emerging in the business environment, which is still unexplored concerning the association of OLCu and learning agility on firm performance, particularly in the Indian IT industry (Tripathi et al., 2020).

Thus, to report this gap in the literature, the authors propose to study the association between OLCu and learning agility. This study also aims to ascertain the mediating effect of learning agility in the relationship between OLCu and firm performance in an Indian context. The following three research questions have been addressed in this study to accomplish the purpose: How does the OLCu influence learning agility? How does OLCu influence firm performance? And does learning agility mediate between OLCu and firm performance?

THEORETICAL BACKGROUND

Organizational Learning Culture

A complicated procedure that nourishes novel information and can modify the function is known as organizational learning (Antunes & Pinheiro, 2020; Rezaei et al., 2018). It is a traditional approach where employees and organizations both experience a behavioural change (Martínez-Costa et al., 2019). Organizations incorporating a learning culture into their system allow employees to create, acquire and transmit information and skills. Learning culture also aids in changing behaviour in order to disseminate new information and gain a better understanding of an organization (Oh & Han, 2020; Pantouvakis & Bouranta, 2017). Thus, an organization with a learning culture should first acquire knowledge, then elucidate its meaning before implementing it among employees.

Organizational learning has also emerged as an essential and obscure notion of organizational culture because of the inclusion of several approaches. There are several ways to define organizational learning along with its several characteristics. Previous studies explained organizational learning as ‘continuous testing of experience and its transformation into knowledge available to the whole organisation and relevant to their mission’ (Senge, 1990, p. 7). On the other hand, Martínez-Costa et al. (2019) defined organizational learning as a fundamental concept that aids in acquiring and interpreting information, as well as changes in behavioural and cognitive aspects that impact organizational performance.

Learning Agility

DeMeuse et al. (2010) defined learning agility as an essential construct for improving an organization’s long-term leadership. Learning agility can predict future individual performance because it has proven to be the most accurate predictor (DeMeuse et al., 2010). Lombardo and Eichinger (2000) explained learning agility as the eagerness and aptness of an individual towards learning novel attributes when exposed to different and new situations in the environment.

Yadav and Dixit (2017) demonstrated that employees could produce novel competencies through learning agility, particularly when exposed to different situations. The construct of learning agility comprises four factors: people agility, change agility, mental agility and results agility (Gravett & Caldwell, 2016).

Firm Performance

Firm performance is the extent to which an organization conveys its objectives and goals (Tajvidi & Karami, 2017). Pratono (2018) defined firm performance as a task-attainment procedure by employees within a firm. A firm performance can be measured either by a subjective scale or by an objective scale. Shafiq et al. (2019) demonstrated that an accumulation of subjective and objective procedures could be utilized to prevent defects. ‘Market share, sales, customer satisfaction, employee satisfaction, and profitability’ are usually used as measures of subjective performance; on the other hand, objective performance indicators are ‘return on earnings and return on assets’ (Chowdhury et al., 2019, p. 14). Till the date, in order to measure business performance, no single universally accepted procedure has been found, in spite of several frameworks’ evolution proposed by numerous researchers.

Linking OLCu and Learning Agility

Due to the frequent changes in the market, an organization must sharpen its skills and learn to respond to the changes quickly and effectively (Lin & Huang, 2020; Tripathi et al., 2020). To respond to those changes, an organization requires employees equipped with learning agility. Previous studies have demonstrated that organizations with learning agile employees were getting 25% more profits compared to their market competitors. Therefore, learning agile employees is a primary concern for an organization nowadays (Tripathi & Kalia, 2024; Tripathi et al., 2020). Previous research has demonstrated that an organizational culture embedded with a learning system can integrate individual learning with organizational learning (Islam et al., 2014). Tripathi et al. (2020, p. 5) defined culture as ‘a metaphor about the organisations, as it is how reality is being shaped and is deeply rooted in day-to-day routines of a particular organisation’. Prior studies have also manifested that there is a significant influence of OLCu on readiness to take risks (Chowdhury et al., 2019), organizational learning activities (Oh & Han, 2020) and organizational agility (Felipe et al., 2016; Govuzela & Mafini, 2019). These studies indicate that there is a relationship between organizational learning the agility. Only a limited number of studies have explored the relationship between OLCu and agility, particularly learning agility in the Indian IT sector. Hence, this study assumes that:

H₁: OLCu is positively related to learning agility.

Linking Learning Agility and Firm Performance

The essential characteristics of agility tend to recognize changes pertaining to the market. A positive and significant association has been found between agility and organizational performance (Carvalho et al., 2017; Chan et al., 2017; Pantouvakis & Karakasnaki, 2018). A study carried out by Ashrafi et al. (2019) on Korean small and medium enterprises explored the positive impact of agility on the retention of customers as well as on the performance of an organization. Research has also shown that agility can gain a competitive advantage while improving organizational performance. Similarly, Ahammad et al. (2020) suggested that an organization can create a competitive advantage through agility.

Further, Kale et al. (2019) also suggested that an organization can enhance performance by gaining a competitive edge with the help of making organizations agile. Holbeche (2018) and Pereira et al. (2019) also argued that agility positively and significantly affects organizational performance. Previous studies have discovered a link between agility and firm performance, but only from an organizational standpoint. However, no study has yet been conducted that examines the relationship between employees’ learning agility and organizational performance, particularly in the Indian IT sector. Therefore, the present study explores the association between employees’ learning agility and firm performance. Thus, this study hypothesizes:

H₂: Learning agility is positively related to firm performance.

Mediating Effect of Learning Agility

In addition to the direct impacts of OLCu on learning agility and learning agility on firm performance, learning agility can also mediate between OLCu and firm performance (Figure 1). Few studies have attempted to investigate the mediating role of learning agility in the prior literature (Tripathi et al., 2020). Furthermore, a study carried out by Kale et al. (2019) on 112 large Spanish companies demonstrated the role of organizational agility as a mediator in the association of the application of knowledge and performance of an organization. This study also investigated the mediating effect of firm agility in the association between strategic IT alignment and the firm’s performance (Kale et al., 2019).

Figure 1.

Hypothesized Model.

Martínez-Costa et al. (2019) demonstrated supply chain agility as a mediator in an association between absorptive agility and firm performance. In another study, Salehzadeh et al. (2017) argued an essential mediating role of strategic agility in the relationship between corporate risk management and organizational performance. However, no study has yet investigated the role of learning agility in mediating the relationship between OLCu and firm performance. Based on previous research, this study hypothesizes that learning agility may act as a mediator in the relationship between OLCu and firm performance. As a result, the following hypothesis is proposed:

H₃: Learning agility mediates the association between OLCu and firm performance.

METHODOLOGY

Context and Sample

The present research study has utilized data collection between August 2019 to December 2019 from IT sectors, particularly hardware firms in Southern India. This study used a deductive approach because, in this approach, a researcher can study prior investigations and then develop hypotheses for their research based on previous investigations (Reyes, 2004; Tripathi et al., 2021). The targeted organizations were selected randomly, and an online-mode data collection approach was adopted to conduct the research study. The online mode was chosen because it allows the researcher to collect data from various sources quickly. First and foremost, the respondents for this study were recognized with the aid of social platforms such as Facebook and LinkedIn. The e-mail-invitation method was also utilized to recruit participants for an online survey. Prior studies have demonstrated that ‘working with an e-mail list of potential respondents, sending a follow-up invitation can help increase the response rate’ (Ritter & Sue, 2007, p. 10). The present study utilized some criteria to choose the potential respondents, such as (a) the respondents must be from IT organizations; (b) the respondents must be from hardware firms of IT organizations; (c) the respondents must be from Southern India and (d) the respondents must be middle-level and lower level executives.

Before preparing and sending a Google doc research questionnaire to the respondents, the present study identified the target respondents based on the selection criteria mentioned. To eliminate ambiguity, a brief introduction about OLCu, learning agility and firm performance was also given in the questionnaire before each set of the questionnaire. The English language was chosen as a medium of communication with the respondents. For data collection, the convenience sampling method was utilized. A total of 633 questionnaires were sent to the targeted respondents; with a response rate of 53.08%, 313 questionnaires were returned in a completed form (Table 1). Among 313 valid responses, 254 (81.21%) were male, and 59 (18.79 %) were female. The data had different age ranges such as 16 (5.19%) were 51–60 years of age; 42 (13.41%) were 41–50 years of age; 124 (39.61%) were 31–40 years of age and 131 (41.85%) were 20–30 years of age.

Table 1:

Respondents’ Demographic Profile.

No. of Respondents (313)	Frequency	%
Gender
Male respondents	254	81.21
Female respondents	59	18.79
Marital Status
Single	115	36.74
Married	198	63.25
Age
20–30 years	131	41.85
31–40 years	124	39.61
41–50 years	42	13.41
51–60 years	16	5.19
Education
Graduate	110	35.14
Postgraduate	182	58.14
Others	21	6.70
Work Experience
0–5 years	202	64.53
6–10 years	45	14.37
11–15 years	31	9.93
16–20 years	23	7.34
>20 years	12	3.83

The participants were at different career stages: 202 (64.53%) had worked for 0–5 years; 45 (14.37%) had worked for 6–10 years, 31 (9.93%) had worked for 11–15 years, 23 (7.34%) had worked for 16–20 years and 12 (3.83%) had worked for more than 20 years. Keeping in view educational qualifications, 110 (35.14%) employees hold a graduate degree, 182 (58.14%) employees have a postgraduate degree and 21 (6.70%) employees hold other qualifications.

Measures

This study has used the self-reported questionnaire method (Paulhus & Vazire, 2007), in order to measure the variables such as OLCu, learning agility and firm performance as mentioned below.

Organizational Learning Culture

OLCu was assessed with the help of a scale consisting of seven items (Watkins & Kim, 2018). This measure emphasizes a few aspects such as continuous learning opportunities, dialogue and inquiry, team learning, systems to capture and share learning, empowerment, connectivity to the environment and strategic leadership for learning—all of which are required capacities for organizations to be capable of continuous learning and transformation (Watkins & Marsick, 1997). The sample item includes ‘My organization makes its lessons learned available to all employees’ and ‘My organization works together with the outside community to meet mutual needs’. The scale showed good reliability, and Cronbach’s alpha was 0.97.

Learning Agility

Learning agility was ascertained with the help of a scale consisting of eight items (Gravett & Caldwell, 2016). The sample item includes ‘I can deliver results amid changing circumstances’, ‘I seek out people to learn about subjects outside my work field’ and ‘I look for ways to use new knowledge’. The scale showed good reliability, and Cronbach’s alpha was 0.98.

Firm Performance

Firm performance was investigated with the help of a scale consisting of five items (Lee & Choi, 2003). The sample item includes ‘My company is more successful’ and ‘My company is more innovative’. The scale showed good reliability, and Cronbach’s alpha was 0.93.

Five-point Likert scale (1 = strongly disagree and 5 = strongly agree) was utilized to record the responses for the variables of this study. Cronbach’s alpha value for OLCu, learning agility and firm performance was higher than the threshold limit of 0.07 (Hair et al., 2019), further showing high reliability.

Control Variables

‘Control variables are factors that researchers include in their work to rule out alternative explanations for their findings (Schmitt & Klimoski, 1991, p. 31) or to reduce error terms and increase statistical power’ (Becker, 2005, p. 12). Therefore, as per the suggestions mentioned above by the authors, the present study utilized demographic variables such as gender, age, work experience and education level as control variables.

RESULTS

Descriptive Statistics

The descriptive statistics, namely mean, standard deviation (SD) and the inter-item correlation among variables, are shown in Table 2. To assess the data, SPSS 20 and AMOS 20 have been utilized. Also, to assess the path analysis, the present research has utilized PROCESS macro-model number 4, an SPSS computational tool (Hayes, 2013; Tripathi & Kumar, 2023). Confirmatory factor analysis (CFA) was used to confirm the factor structure. To utilize the CFA analysis, the values of model fitness were investigated to check the appropriateness of the data. It suggested a good model-fit as proposed by Anderson and Gerbing (1988), Hair et al. (2019), Hu and Bentler (1999), MacCallum et al. (1996), Tripathi et al. (2020) and Tripathi and Sankaran (2021). The values are as follows: χ²/df = 2.37, goodness-of-fit index = 0.91, root mean square error of approximation = 0.06, adjusted goodness-of-fit index = 0.90, comparative fit index = 0.98, normed fit index = 0.97, Tucker–Lewis index = 0.97, standardized root mean square residual (SRMR) = 0.06.

Table 2:

Descriptive Statistics.

N = 313	Mean (SD)	1	2	3
1. OLCu	3.78 (0.78)	1.00
2. Learning agility	3.94 (0.80)	0.23*	1.00
3. Firm performance	3.89 (0.93)	0.68*	0.33*	1.00

Notes: OLCu: Organizational learning culture.

*p< .01.

The values for OLCu were in the range of 0.79 and 0.99, the values for learning agility were in the range of 0.85 and 0.99, and finally, for firm performance, the values were in the range of 0.72 and 0.99. The correlation values suggested that high convergent validity (Table 3) was found among all variables (Cable & DeRue, 2002; Hair et al., 2019; Tripathi & Dhir, 2023; Tripathi & Sankaran, 2021; Tripathi et al., 2020). Hence, the findings exhibit that the model possesses good adaptability.

Table 3:

The Values of Reliability and Factor Loadings.

Construct Name	Indicators	AVE	MSV	ASV	Cronbach’s α/CR	Factor Loadings	t Value
OLCu	OLCu1	0.88	0.37	0.17	0.97/0.97	0.98	24.33*
	OLCu2					0.95	22.68*
	OLCu3					0.97	23.61*
	OLCu4					0.79	16.92*
	OLCu5					0.99	24.73*
Learning agility	LA1	0.87	0.10	0.07	0.98/0.98	0.99	24.50*
	LA2					0.97	23.69*
	LA3					0.95	22.80*
	LA4					0.97	23.50*
	LA5					0.94	22.49*
	LA6					0.89	20.33*
	LA7					0.89	20.25*
	LA8					0.85	18.93*
Firm performance	FP1	0.71	0.37	0.21	0.93/0.92	0.96	23.37*
	FP2					0.99	24.66*
	FP3					0.76	15.89*
	FP4					0.74	15.51*
	FP5					0.72	14.52*

Notes: OLCu: Organizational learning culture, AVE: Average variance extracted, MSV: Maximum shared variance, ASV: Average shared variance, CR: Composite or construct reliability.

*p < .001.

The square root of each construct’s average variance extracted (AVE) was determined to investigate the discriminant validity. Table 4 shows that the constructs have high discriminant validity because these values were higher than the correlation values proposed by Hair et al. (2019) and Tripathi et al. (2020). In addition, the high value of AVE, as compared to the values of average shared variance and maximum shared variance, validates the notion of discriminant validity (Hair et al., 2019). Further, the present study investigated the common method bias (CMB). When data collection is based on self-report estimation, and the same respondents provide responses for independent and dependent variables, the issue of CMB may be detected. The current study used the unmeasured latent method factor, also known as the common latent factor (CLF) method, to address this issue. Compared to other measures like the Harman single factor, the CLF method gives more precise results as proposed by previous researchers (Podsakoff et al., 2003; Tripathi et al., 2020).

Table 4:

The Values of Discriminant Validity.

N = 313	1	2	3
1. OLCu	0.94
2. Learning agility	0.22*	0.93
3. Firm performance	0.61*	0.32*	0.84

Notes: The diagonal values show square root values of AVE.

® Organizational learning culture.

*p < .01.

As Podsakoff et al. (2003) suggested, comparing standardized regression weights was made using CFA by using CLF at one point and removing it at another. Since the reported values were lower than 0.2, further claiming that the present study is free from CMB issues (Gefen & Straub, 2005).

Hypotheses Testing

As Hayes et al. (2017) suggested, PROCESS is a more suitable and modern way of testing hypotheses than structural equation modeling (SEM) because of its ease of management and fewer complications. A complex procedure like mediation and moderation analysis can also be simplified using PROCESS. However, it does not provide any graphical diagram; it gives similar results to SEM. ‘PROCESS estimates all the statistics (path coefficients, t value, p value, standard errors, etc.) by implementing bootstrapping that facilitates inference using those statistics; this is not possible in all SEM programs’ (Hayes et al., 2017, p. 51). The Baron and Kenny (1986) approach is followed by the abovementioned method. The findings of each hypothesis are demonstrated in Table 5. The positive impact of OLCu was found on employees’ learning agility (β = 0.18, t= 4.20, at p < .001), which indicates H₁ is confirmed. While controlling OLCu, a positive and significant relationship (β = 0.21, t = 4.40, at p < .001) was found between learning agility and firm performance, confirming H₂. Further, as presumed in H₃, the positive (0.03) and indirect effect between OLCu and firm performance was found via learning agility. Two-tail significant test values assume the normal distribution among variables; further, it indicates the value of indirect effect (Sobel = 3.41, p < .001), which is significant. In addition, since no zero was found between the lower confidence interval 0.01 (95% CI) and the upper confidence interval 0.07 (95% CI), the bootstrap analysis confirmed the findings of the SOBEL test. Furthermore, the findings also demonstrated that there is a direct effect of OLCu on firm performance (β = 0.58, t = 15.57, at p < .001) while controlling the construct learning agility. Therefore, the overall findings exhibit a partial mediating effect of learning agility in the relationship between OLCu and firm performance, confirming H₁, H₂ and H₃.

Table 5:

The Results of Hypotheses.

Variables	B	SE	t	p
Direct and total effect model
Firm performance on OLCu	0.58	0.03	15.57	.000
Learning agility on OLCu	0.18	0.04	4.20	.000
Firm performance regressed on learning agility, controlling for OLCu	0.21	0.04	4.40	.000
Firm performance regressed on OLCu, controlling for learning agility	0.58	0.03	15.57	.000
Variable	Value	SE	z	p
Indirect effect and significance using a normal distribution
SOBEL	0.03	0.01	3.41	0.000
Variable	M	SE	LL 95% CI	UL 95% CI
Bootstrap results for the indirect effect
Effect	0.03	0.01	0.01	0.07

Notes: N = 313.

OLCu: Organizational learning culture, bootstrap sample size: 5,000, CI: Confidence interval, LL: Lower limit, UL: Upper limit.

DISCUSSION

The outcomes exhibited a significantly positive influence of OLCu on the learning agility of employees, particularly in the Indian IT sector. It can be said that organizations embedded with a learning culture motivate employees towards learning agility. This finding indicates that with the help of an OLCu, employees will be more eager to learn for themselves and the organization. OLCu also helps develop capabilities within employees, such as dealing with diverse situations, various views, determination and logic towards change, willingness to enhance their skills and eagerness to disseminate knowledge, among others. Previous studies have supported that OLCu stimulates learning agility within employees, making them more agile towards learning something new (Tripathi et al., 2020). Organizational learning theory supports this view that a learning culture within the organization encourages employees to strengthen their skills, attributes, and knowledge and allows employees to respond to the challenges introduced in the market.

Second, as there will be an increment in employee learning agility, an organization’s performance will automatically be enhanced. When employees have learning agility, there will be no resistance to any novel changes introduced in the market. As a result, the new changes will not affect an organization’s performance because employees will be able to respond to the changes effectively and efficiently. Resource-based view also supports that OLCu is a critical resource to enhance a firm’s performance. The findings also revealed that learning agility predicts firm performance, implying that more learning agile employees will improve an organization’s performance by adapting and responding to new changes (refer to Figure 2). In addition, the findings exhibited that learning agility is a partial mediator in the association between OLCu and firm performance. It indicates that there is a direct and indirect impact of OLCu on firm performance via employee learning agility.

Figure 2.

Structural Model Results.

The outcome of this study validated the study of Cegarra-Navarro et al. (2016) and Tripathi et al. (2020), who proposed that OLCu provides a way towards enhancing firm performance via learning agility that behaves as a partial mediator.

IMPLICATIONS FOR THEORISTS

This research intends to contribute two ways to the field of organizational behaviour and human psychology from a theoretical perspective.

As mentioned in the previous literature (Ashrafi et al., 2019; Chan et al., 2017; Inkinen, 2016), agility is a crucial element in improving a firm’s performance. In the study, Tripathi et al. (2020) stated that frequent changes in new technology are widespread in Indian IT firms, necessitating employees with high learning agility to respond to the changes efficiently and effectively. However, to my knowledge, no prior research has empirically examined the impact of learning agility on firm performance. Thus, the present study fills the void in this area. It adds value to the existing knowledge about the cognitive state of employees through which an organization can experience high performance in this competitive era.

Up to now, sparse studies have been done on empirical examination of learning agility, particularly in the Indian context (Tripathi et al., 2020). The current research study has also investigated the role of the organizational learning theory and resource-based perspective regarding the relationship among OLCu, learning agility and firm performance. Previous studies (Antunes & Pinheiro, 2020; Cegarra-Navarro et al., 2016; Kale et al., 2019) have also suggested that firms must foster OLCu to increase market performance to survive and thrive in this cutthroat competitive era.

Thus, the findings of this study can contribute to the existing body of knowledge by arguing that OLCu can help employees become more learning agile in the face of change, thereby increasing an organization’s competitive edge.

IMPLICATIONS FOR PRACTITIONERS

There can be an inclusion of OLCu as a crucial factor to be investigated by HR practitioners to improve the level of concentration among employees towards learning and disseminating new skills within the organization. If employees are nurtured in an organizational culture embedded with the learning system, they will be more focused on learning new skills, behaviours and knowledge. The novel knowledge will then be shared from one employee to another, building vital learning agility among employees. A firm’s performance is directly proportional to the employees; thus, the more learning agile employees, the higher the firm’s performance. Since the present study also found that OLCu positively impacts firm performance, the practitioners should bring a harmonious learning culture system that needs an hour in this current scenario to meet the novel changes. Organizations must also involve their employees in specific training and development sessions to boost their confidence and willingness to learn. A learning agility test can be included when recruiting employees; this procedure can provide a pathway to HR managers of large IT firms to recognize individuals scoring high on the level of learning agility and then put them suitably in an organization. The outcomes of this study also affirm that learning agility, together with OLCu, is crucial to enhance a firm’s performance in this competitive edge. This study can be a valuable insight for organizations, particularly IT firms, where learning a new skill is a prime concern to survive in the market.

LIMITATIONS AND FUTURE SCOPE OF THE STUDY

Like other studies, the present research also has a few limitations that are mentioned as follows:

This study is based on a cross-sectional design that is merely aimed towards Indian IT firms. As mentioned before, the particular attribute of the IT sector is its service-aligned culture and its flexible work behaviour. Because responses were gathered from only those employees working in the IT sector, generalization of the findings may be limited. Therefore, future studies with a larger sample size may be able to generalize the current study’s findings.

Furthermore, in this study, a few demographic variables have been taken as controlled variables, and hence investigation of these variables’ impact on study variables has not been analyzed. In the future, researchers may investigate these variables’ impact on other variables, providing several new aspects regarding OLCu, learning agility and firm performance.

The present research has been restricted to a few demographic variables. Therefore, future studies can take other pertinent demographic groups to explore the varied responses regarding OLCu, firm performance and employees’ learning agility.

In this research, the predominance of male respondents (81.21%) from the IT sector can be noticed. Future studies can take other sectors like hospitality sectors where the female proportion can be seen as higher than its counterpart.

The present research has been done with self-report measures that can raise issues of CMBs. However, the present study has utilized a post-hoc statistical technique, the CLF method, to eliminate the problem of CMBs. Future studies can use some a priori statistical approach to address the CMBs.

In this study, top-level executives have not been included due to unavailability. Therefore, the future study can be accomplished by taking responses from top-level executives such as C-suite executives that will provide an insightful and interesting result regarding exploring OLCu, learning agility and firm performance.

Finally, the quantitative method was used to investigate the relationships between OLCu, learning agility, and firm performance. As a result, future studies can utilize either qualitative or mixed methods to assess the rigour and robustness of the findings.

Annexure A

Measurement Items	Descriptor
	Organizational Learning Culture
OLC1	In my organization, people are rewarded for learning.
OLC2	In my organization, people spend time building trust with each other.
OLC3	In my organization, teams/groups revise their thinking as a result of group decisions or information collected.
OLC4	My organization makes its lessons learned available to all employees.
OLC5	My organization recognizes people for taking initiatives.
OLC6	My organization works together with the outside community to meet mutual needs.
OLC7	In my organization, leaders continually look for opportunities to learn.
	Learning Agility
LA1	I am comfortable when circumstances that affect my work change.
LA2	I can deliver results amid changing circumstances.
LA3	I seek out feedback on my skills and abilities.
LA4	I seek out people to learn about subjects outside my work field.
LA5	I easily retain new information.
LA6	I am optimistic that I can learn new information.
LA7	I find satisfaction in digging deeply into ways to solve problems.
LA8	I look for ways to use new knowledge.
	Firm Performance
FP1	My company is more successful.
FP2	My company has a greater market share.
FP3	My company is growing faster.
FP4	My company is more profitable.
FP5	My company is more innovative.

Footnotes

DECLARATION OF CONFLICT OF INTERESTS

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

FUNDING

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

Aastha Tripathi is an Assistant Professor at the Centre for Social and Organisational Leadership, School of Management and Labour Studies, Tata Institute of Social Sciences (TISS), Mumbai, in the Human Resource Management/Organizational Behaviour area. Before this, she worked as a Post-doctoral Researcher at the Indian Institute of Management, Ahmedabad (IIMA). She was a Senior Research Scientist with the Indian Institute of Technology, Delhi (IITD). Her areas of interest are organizational learning culture, leadership, employee behaviours, AI-augmented HR practices, HPWS, innovation and learning agility. She serves as a reviewer in various reputed journals such as Academy of Management, International Journal of Information Management (A*), International Journal of Manpower (A), South Asian Journal of Human Resources Management (B), International Journal of Organizational Analysis (B), to name a few. She is also an ‘Editorial Advisory Board Member’ of the International Journal of Organisational Analysis (IJOA; ABDC-B/SCOPUS), the International Journal of Knowledge Management (IJKM; ABDC-B/SCOPUS) and the International Journal of Technology & Human Interaction (IJTHI; ABDC/SCOPUS). She has published in top-notch journals (such as the American Business Review (ABDC-A), the International Journal of Organisational Analysis (ABDC-B), Innovation: Organisation & Management (ABDC-B), and International Journal of Knowledge & Learning (ABDC-C), to name a few.

e-mail: aasthatripathi315@gmail.com

References

Ahammad

M. F.

, Glaister

K. W.

, & Gomes

(2020). Strategic agility and human resource management. Human Resource Management Review , 30(1), 100700.

Anderson

, & Gerbing

(1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin , 103(3), 411.

Antunes

H. D. J. G.

, & Pinheiro

P. G.

(2020). Linking knowledge management, organizational learning and memory. Journal of Innovation & Knowledge , 5(2), 140–149.

Ashrafi

, Ravasan

Ahad Zare

, Trkman

, & Afshari

(2019). The role of business analytics capabilities in bolstering firms’ agility and performance. International Journal of Information Management , 47, 1–15.

Bae

H. S.

, & Grant

D. B.

(2018). Investigating effects of organisational culture and learning on environmental collaboration and performance of Korean exporting firms. International Journal of Logistics Research and Applications , 21(6), 614–630.

Barney

J. B.

(2018). Why resource-based theory’s model of profit appropriation must incorporate a stakeholder erspective. Strategic Management Journal , 39(13), 3305–3325.

Baron

R. M.

, & Kenny

D. A.

(1986). The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology , 51(6), 1173.

Becker

T. E.

(2005). Potential problems in the statistical control of variables in organizational research: A ualitative analysis with recommendations. Organizational Research Methods , 8, 274–289.

Cable

D. M.

, & DeRue

D. S.

(2002). The convergent and discriminant validity of subjective fit perceptions. Journal of Applied Psychology , 87(5), 875–884. https://doi.org/10.1037/0021-9010.87.5.875

10.

Carvalho

A. M.

, Sampaio

, Rebentisch

, Carvalho

J. Á.

, & Saraiva

(2017). Operational excellence, rganisational culture and agility: The missing link? Total Quality Management & Business Excellence , 0(13-14), 1495–1514.

11.

Cegarra-Navarro

J. G.

, Soto-Acosta

, & Wensley

A. K. P.

(2016). Structured knowledge processes and firm erformance: The role of organizational agility. Journal of Business Research , 69(5), 1544–1549.

12.

Chan

, Ngai

, & Moon

(2017). The effects of strategic and manufacturing flexibilities and supply chain gility on firm performance in the fashion industry. European Journal of Operational Research , 259(2), 486–499. https://doi.org/10.1016/j.ejor.2016.11.006

13.

Chen

I. S.

(2017). Computer self-efficacy, learning performance, and the mediating role of learning engagement. omputers in Human Behavior , 72, 362–370.

14.

Choi

, Kim

J. Y.

, & Yoo

(2016). A study on the effect of learning organisation readiness on employees’ uality commitment: The moderating effect of leader–member exchange. Total Quality Management & Business Excellence , 27(3-4), 325–338.

15.

Chowdhury

, Rana

, & Azim

M. I.

(2019). Intellectual capital efficiency and organisational performance. Journal of Intellectual Capital , 20(6), 784–806.

16.

DeMeuse

K. P.

, Dai

, & Hallenbeck

G. S.

(2010). Learning agility: A construct whose time has come. Consulting Psychology Journal: Practice and Research , 62(2), 119–130. https://doi.org/10.1037/a001998

17.

Felipe

C. M.

, Leidner

D. E.

, Roldan

J. L.

, & Leal-Rodrguez

A. L.

(2020). Impact of IS capabilities on firm performance: The roles of organizational agility and industry technology intensity. Decision Sciences , 51(3), 575–619.

18.

Felipe

C. M.

, Roldán

J. L.

, & Leal-Rodríguez

A. L.

(2016). An explanatory and predictive model for rganizational agility. Journal of Business Research , 69(10), 4624–4631. https://doi.org/10.1016/j.jbusres.2016.04.014

19.

Gefen

, & Straub

(2005). A practical guide to factorial validity using PLS-graph: Tutorial and annotated example. Communications of the Association for Information Systems , 16, 91–109. https://doi.org/10.17705/1cais.01605

20.

Ghobakhloo

, & Fathi

(2019). Corporate survival in Industry 4.0 era: The enabling role of lean-digitized manufacturing. Journal of Manufacturing Technology Management , 31(1), 1–30.

21.

Gope

, Elia

, & Passiante

(2018). The effect of HRM practices on knowledge management capacity: A comparative study in Indian IT industry. Journal of Knowledge Management , 22(3), 649–677.

22.

Govuzela

, & Mafini

(2019). Organisational agility, business best practices and the performance of small to medium enterprises in South Africa. South African Journal of Business Management , 50(1), 1–13.

23.

Gravett

L. S.

, & Caldwell

S. A.

(2016). Learning agility: The impact on recruitment and retention . Springer.

24.

Hair

J. F.

, Black

W. C.

, Babin

B. J.

, & Anderson

R. E.

(2019). Multivariate data analysis (8th ed.). Cengage.

25.

Hayes

(Ed.). (2013). Integrating mediation and moderation analysis: Fundamentals using PROCESS. In Introduction to mediation, moderation and conditional process analysis . The Guilford Press. https://doi.org/978-1-60918-230-4

26.

Hayes

, Montoya

, & Rockwood

(2017). The analysis of mechanisms and their contingencies: PROCESS versus structural equation modeling. Australasian Marketing Journal , 25(1), 76–81.

27.

Holbeche

L. S.

(2018). Organisational effectiveness and agility. Journal of Organizational Effectiveness: People and Performance , 5(4), 302–313.

28.

L. T.

, & Bentler

P. M.

(1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling , 6(1), 1–55. https://doi.org/10.1080/10705519909540118

29.

Inkinen

(2016). Review of empirical research on knowledge management practices and firm performance. Journal of Knowledge Management , 20(2), 230–257.

30.

Islam

, Khan

, Ahmad

, Ali

, & Ahmed

(2014). Organizational learning culture and psychological empowerment as antecedents of employees’ job related attitudes: A mediation model. Journal of Asia Business Studies , 8(3), 249–263.

31.

Jain

N. K.

, Celo

, & Kumar

(2019). Internationalization speed, resources and performance: Evidence from Indian software industry. Journal of Business Research , 95, 26–37.

32.

Kale

, Aknar

, & Başar

Ö.

(2019). Absorptive capacity and firm performance: The mediating role of strategic agility. International Journal of Hospitality Management , 78(January 2018), 276–283. https://doi.org/10.1016/j.ijhm.2018.09.010

33.

Lee

, & Choi

(2003). Knowledge management enablers, processes, and organizational performance: An integrative view and empirical examination. Journal of Management Information Systems , 20(1), 179–228.

34.

Lin

C. Y.

, & Huang

C. K.

(2020). Employee turnover intentions and job performance from a planned change: The effects of an organizational learning culture and job satisfaction. International Journal of Manpower , 42(3), 409–423. https://doi.org/10.1108/IJM-08-2018-0281

35.

Lombardo

M. M.

, & Eichinger

R. W.

(2000). High potentials as high learners. Human Resource Management , 39(4), 321–329. https://doi.org/10.1002/1099-050x(200024)39:4<321::aidhrm4>3.0.co;2-1

36.

MacCallum

R. C.

, Browne

M. W.

, & Sugawara

H. M.

(1996). Power analysis and determination of sample size for covariance structure modeling. Psychological Methods , 1(2), 130. https://doi.org/10.1037/1082-989X.1.2.130

37.

Martínez-Costa

, Jiménez-Jiménez

, & DineRabeh

(2019). The effect of organisational learning on interorganisational collaborations in innovation: An empirical study in SMEs. Knowledge Management Research & Practice , 17(2), 137–150.

38.

(2019). Effects of organizational learning on performance: The moderating roles of trust in leaders and organizational justice. Journal of Knowledge Management , 23(2), 313–331.

39.

, & Han

(2020). Facilitating organisational learning activities: Types of organisational culture and their influence on organisational learning and performance. Knowledge Management Research & Practice , 18(1), 1–15. https://doi.org/10.1080/14778238.2018.1538668

40.

Pantouvakis

, & Bouranta

(2017). Agility, organisational learning culture and relationship quality in the port sector. Total Quality Management & Business Excellence , 28(3-4), 366–378.

Organizational Learning Culture and Firm Performance: The Mediating Role of Learning Agility

Abstract

Keywords

THEORETICAL BACKGROUND

Organizational Learning Culture

Learning Agility

Firm Performance

Linking OLCu and Learning Agility

Linking Learning Agility and Firm Performance

Mediating Effect of Learning Agility

Hypothesized Model.

METHODOLOGY

Context and Sample

Respondents’ Demographic Profile.

Measures

Organizational Learning Culture

Learning Agility

Firm Performance

Control Variables

RESULTS

Descriptive Statistics

Descriptive Statistics.

The Values of Reliability and Factor Loadings.

The Values of Discriminant Validity.

Hypotheses Testing

The Results of Hypotheses.

DISCUSSION

Structural Model Results.

IMPLICATIONS FOR THEORISTS

IMPLICATIONS FOR PRACTITIONERS

LIMITATIONS AND FUTURE SCOPE OF THE STUDY

Annexure A

Footnotes

DECLARATION OF CONFLICT OF INTERESTS

FUNDING

References