Role of e-learning technology and culture on learning agility: An empirical evidence

Abstract

BACKGROUND:

The complexity and dynamism involved in the current business environment requires agile workforce. Workforce agility requires learning agility which is the capability to acquire knowledge willingly, quickly and effectively. Promoting learning agility requires organisations to explore and identify facilitator or barriers for higher performance. In this context, organisational culture and E learning technology may play an important role to promote learning agility for better performance.

OBJECTIVE:

The study aims to investigate the role of culture and e-learning technology on learning agility. In detail, the research examines the relationship between learning agility and outcome. Further, the research also seeks to examine the mediating relationship of culture and e-learning between learning agility and outcome.

METHODS:

The survey-based research has been designed following validated survey instruments. Data collected from 776 executives across all levels has been analysed using structural equational modelling using IBM AMOS software.

RESULTS:

The result proved learning agility significantly related with outcome. Secondly, culture and e learning technology mediate between learning agility and outcome. The result suggests organisations to nurture suitable culture and adopt e-learning technology to facilitate effective learning agility.

CONCLUSIONS:

Learning agility is critical for making workforce and business agile. The unpredictable and complex business environment can be managed through agile learners. Organisations need to nurture and adopt suitable culture and technology for better performance.

Keywords

Learning agility culture e-learning technology individual outcome organisational outcome

Susmita Ghosh is a PhD scholar working in the field of talent management. Her career as HR professional spans over 14 years, both in industry and academia. She has been teaching organisational behaviour, human resource management, and economics to students of management both at graduate and postgraduate level. She has presented various papers in national and international conferences. Her areas of interest are organisational psychology, skill and talent management, human resource development, economics and networking. ORCID ID: 0000-0002-9202-4215

Dr. Muduli is an academician, researcher and consultant in the area of Human Resource Management. A Doctorate in HRM, has an exclusive academic concentration in HRM (Graduation (Hon’s) and Post-Graduation in Industrial Relations and Personnel Management); 21 years of experience, associated with corporate and academia in variety of roles including Practitioner, Faculty, Trainer, Researcher, and Consultant in the area of HRM; published several research papers in peer reviewed referred Journals and presented papers in National and International conferences. Research interest includes Strategic Human Resource Management, Empowerment, Achievement Motivation, High Performance Management Practices, Training/Learning Transfer, Workforce Agility, Instruction/Training methods, Recruitment Methods. ORCID ID: 0000-0001-5645-5440

Dr. Pingle has been engaged in industry, teaching and research from seventeen years; Awarded NET in Management by UGC in 2005 and NET in HRM and Industrial Relations in 2012; Won Best Paper award in International Conference; Participated in many workshops and faculty development programmes. Prof. Pingle has conducted sessions in In-house Management Development Programmes as well as for business houses. He was awarded ERASMUS fellowship during 2017 and he was part of International Teaching Week at HoF University of Applied Sciences, HoF, Germany. His areas of interest are Performance Management, Recruitment and Selection, HR Practices in MSMEs, Expatriate Management and Management Education. ORCID ID: 0000-0002-0323-4561

1 Introduction

In the volatile and complex business world, organisations require agile workforce who can respond to a turbulent and unpredictable marketplace [1]. An agile workforce needs to be an agile learner, i.e. affirmative approach towards learning and self-development, creative thinking capacity and ready to accept new responsibilities. Research on workforce agility has recognised learning agility as a critical factor responsible for organisational and individual performance.

Agility is influenced by various factors including culture and e learning technology. Recently, agility research has reported the influence of culture on agility effectiveness [2 –4]. Felipe et al. [2] studied the impact of cultural values and principles on the organisational agility level. Cameron and Quinn [5] commented that the inability to implement cultural change is responsible for the failure of most of the organisation. Gunasekaran and Yusuf [3] suggested companies to focus on culture in combination with strategies, business practice, and technological innovation, to make the workforce agile. Carvalho et al. [4] found that sustainability of operational excellence can be achieved when organisations promote organisational agile capabilities and adaptable culture. Thus, culture is critical for agility including learning agility.

Recently, e-learning technology is of high demand for both developed and developing countries. Organisations prefer e-learning as it eliminates the barriers of employees who have the potentiality but have the fear to talk with other learners [6, 7]. E-learning has been more preferred as it allows self-spacing, decreases stress and increases collaboration, satisfaction of learners [8]. The increasing preference for e learning is attributed to individual’s motivation to interact with others, exchange their views, taking the feedback, sharing the knowledge, improves the communication and eases the relationship that sustain learning agility [9].

Research on culture and e learning has been attracting attention by management scholars owing to its capability to affect organizational effectiveness [10 –12]. While most of the culture related management research are on training, compensation, performance appraisal [13], very few has been conducted in the context of learning agility. Although research on agility and culture has been reported [2 –4], no research is available on the role of culture on learning agility and outcome.

Most of the learning agility researches are either explored the construct of learning agility [14 –16] or examined its impact on various outcome variables [17 –21]. Further, responding to the call for future research by Kalinina [22] that contextual factors like culture shouldn’t be ignored in future learning agility studies, the role of culture has been studied. In the context of e learning technology, although few researches are reported but most of them are either related to information technology or agility. Mesfin [23] suggested that future research will also address more about the relationship between agility and e-learning, the status variations among institutions, and the progression in e-learning adoption in the upcoming years. Further, as few researches have been conducted on learning agility involving a mediating variable [24], we propose to consider ‘culture’ as well as ‘e-learning technology’ as mediating the relationship between learning agility and outcome.

This paper consists of conceptualisation and hypotheses, methodology, results, discussion, implications, future research and limitations. The research model indicating the relationship between learning agility, culture, e-learning and outcome has been conceptualized considering learning agility as independent variable, outcome as dependent variable and culture and e-learning as mediating variable.

2 Conceptualisation

Conceptually, agility refers to the ability of thinking and moving quickly, easily and drawing conclusions quickly [25]. Stretching out agility to a learning context, learning agility is the speed of learning (i.e., a capacity to get things rapidly) and simplicity of development across thoughts (i.e., moving among different thoughts or perspectives and across different circumstances) [17]. Iinfluenced by ‘cultural agility’ approach [26] which refers to the capacity to move rapidly starting with one culture then onto the next, we propose to examine the role of culture on learning agility. Similarly, the advancement of e-learning technology and its increasing acceptance has encouraged us to investigate the role of e learning technology on learning agility.

Hereunder, an attempt is made to illustrate the concept of learning agility, culture, e learning and outcome.

2.1 Learning agility

Learning agility is an ability of learners to gain knowledge from their past experiences and implement that learning in different circumstances [15]. From the behavioural and theoretical perspective, Mitchinson et al. [27] described learning agility as ‘flexibility and speed’ for acquiring a skill set through self-awareness, feedback seeking, collaborating and reflecting. More distinctively, Burke et al. [16] commented that ‘learning’ refers seven behaviour-based dimensions; feedback seeking, information seeking, collaborating, interpersonal risk taking, performance risk taking, reflecting, experimenting; and ‘agility’ refers speed and flexibility.

2.2 Organisational culture

Organisational culture is the basic assumptions, hold by specified groups, coped with the internal and external problems, worked well enough to accept as true and applicable [28, 29]. Aswathappa [30] stated that culture consists of knowledge, faiths, ethics, habits, traditions, practices and capacities within the environment. Hofstede [31] defined culture as: “the collective programming of the mind distinguishing the members of one group or category of people from others”. Abu-Jarad et al. [32] defined organisational culture as a holistic, rituals and symbols formed and conserved by group of individuals and tough to change. Parmelli et al. [33] defined culture as shared values, beliefs, norms among employees within the organisation are the organisational culture. Pareek [34] suggested eight ethos of organisational culture such as openness, confrontation, trust, authenticity, pro-action, autonomy, collaboration and experimentation.

2.3 E-learning technology

E-learning technology is the utilization of the web technology to deliver learning [35]. E-learning as an idea covers a scope of uses, learning techniques and procedures [36]. E-learning allows the learners to watch different activities and listen to lessons repeatedly as required [37].

2.4 Outcome

Researcher commented that instead of ‘performance’, it is advisable to use ‘outcome’ as outcome replicates a broad scope of factors. Outcome in the present framework refers to both individual and organisational outcome. Rigby et al. [38] considered increasing market share, competitive edge, profits, long term survivals etc. as organisational outcome [39 –41]. Individual outcome is measured following Buckingham and Goodall [42]. Sample items are: Fulfils responsibilities specified in job description; Performs tasks that are expected of him or her. Items are calculated through a 5-point Likert-type scale.

3 Hypotheses

3.1 Learning agility and outcome

Learning agility is the capability to gain knowledge, and willingness to learn quickly and effectively [18]. Learning agility endorses learning within and across various situations which assures positive performance and outcome [17]. Learning agility research has studied the relationship between learning agility and individual outcome [43] such as being uphold [44], leadership efficiency [19], job performance [20], student engagement [21] etc. DeMeuse [45] found that individuals who have a significant level of learning agility engage and help themselves to learn in unfamiliar contexts and tend to perform in uncertain situations. Dries et al. [20] found that high learning agility improved worker’s probability of categorised as high performing employees. In a learning context, Kim et al. [21] found that learning agility predicts students’ engagement in academic life. The research found that student’s intention to use digital technologies is mediated by learning agility for continuous engagement in academic life. DeRue et al. [17] found that learning agility attributes facilitate people to develop their knowledge, awareness and skills for meeting the changing demands within and outside of the organisation and improve their performance over time. Smith [46] studied learning agility and performance of leaders and executives in the financial services industry, specifically in consulting organization and found a strong significant correlation between learning agile leaders and their performance outcome. DeMeuse [47] referred to Smith [46] and commented that leader’s performance and leader’s potential (learning agility) are the indicators of leader success. DeMeuse [45] did a meta-analysis on learning agility and different outcomes and observed high correlation between learning agility and leader’s performance. Ahmadi et al. [48] shows that there is a strong relation between managers’ skills, knowledge sharing and high-performance work. Rigby et al. [38] found that agile employees are best suited to innovation, supports organizational creativity, reduces the cost of learning, and increases profit. Their contribution is noteworthy in terms of client reliability and employee commitment which enhances overall outcomes.

On the basis of the above discussion, we hypothesize the following

Hypothesis 1. Learning Agility is positively related with Outcome

3.2 Learning agility, organisational culture and outcome

Research on agility and performance has acknowledged the role of several contextual factors. Sumukadas and Sawhney [49] found that a supportive environment foster workforce agility and further performance. Alavi et al. [50] discovered that authoritative learning, decentralization of structure is emphatically related with workforce agility. Muduli [13] found that organisational environment that encourages teamwork, reward methods, employee participation, and organizational learning and training, and information systems promotes employee agility. The author also found that psychological empowerment influences workforce agility too. Going by the above literature, we have considered culture as a critical contextual factor for learning agility performance. However, researcher has been reiterated that mere culture alone may not generate superior performance [11 , 52]. Strategic human resource researchers proved that organisational culture can influence performance by motivating employees to enhance their abilities, skills, knowledge [53 –56]. Muduli [57] found that high performance work practices influence organizational performance through encouraging enlargement surroundings (HRD climate) based on openness, confrontation, trust, authenticity, pro-action, autonomy, collaboration and experimentation.

Hung et al. [51] showed that despite the fact that organizational learning culture fundamentally influences performance, its impact was intervened by dynamic capacity. Ogbonna and Harris [58] found that connection between leadership style and performance is intervened by organisational culture. With regards to insecure and unstable condition of public sector, Parry and Proctor-Thomson [59] discovered transformational/value-based authoritative culture mediates between transformational leadership and organisational outcomes. Yoon et al. [60] found that strong learning society had indirect impact on the group performance through group innovativeness and information creation approaches. Dries et al. [20] examined the interceding impact of occupation content learning and behavioural skill learning on learning agility and being distinguished as a high potential. The researcher proved that content learning and behavioural skill learning was positively and significantly mediating between learning agility and recognized as a high potential. Ghosh and Muduli [61] through an empirical study proved that culture acts as a mediating factor between learning agility and individual and organisational performances and overall outcomes.

This discussion leads to present the next hypothesis:

Hypothesis 2. Organisation culture would positively mediate the relationship between learning agility and outcome

3.3 E-learning and outcome

E-learning environments provide interactive lessons which support individuals to outperform in their respective fields [37]. Baloch et al. [62] suggested for using the support of information technology to increase organisational agility. Menon et al. [63] suggested the policy makers to take the support of information technology for enhancing the performance and productivity [64]. Cheng and Chen [65] found that e-learning system helps organisation to enhance employees’ skills and competencies. It provides cost-time-saving tool to assist employees. Cheng [66] commented that e-learning tool can facilitate long lasting cognitive changes within employees. Mihăilă et al. [67] study suggests that e-learning system has positive impact on individual performance [68]. Chen and Wang [69] concluded that wherever there are individual differences, then also learning styles and information technology or e-learning technology significantly affect the e-learning performance of individuals, i.e. e-learning technology mediates the different individuals and their performances. Cheng and Chen [65] found that the use of the e-learning technology support employees to achieve desired outcomes. Aparicio et al. [70] observed that use of e-learning system positively influences the learners’ which further positively influence individual performance and organisational performance. This discussion helps in formulation of the next hypothesis:

Hypothesis 3. E-learning positively related with the outcome

3.4 Learning agility, e-learning technology and outcome

E-learning increase learner’s learning ability, critical thinking and problem-solving skill that improves learning outcome [37, 71]. E-learning system facilitates assimilation of information required for employees, who assist to transmit their learning to their workplaces [29]. E-learning system can explore the potentiality of human resources and moderate the effect of outcome [72]. It has also been found that individual’s potentiality can be enhanced by quality information as it positively impacts on e-learning usage, satisfaction as well as individual performance [68 , 74].

Chau [75] found a basic association between learners’ motivation, self-controlled learning, organisational contextual factors, and outcomes in the context of self-directed e-learning. Anuradha [76] found that successful e-learning framework depends on management of working environment issues of certain manufacturing organizations. Wang [77] found that learners’ perceptions and attitudes toward workplace e-learning applications influence learning outcome. Researchers [66, 78] suggested that knowledge or potentiality provides competitive advantage to any organisation and e-learning system can enhance them appropriately. So, organisation should have to identify the importance of knowledge management and through which important skills and information can be looked after. Van der Stappen, and Zitter [79] found that ‘ease of use and surprise effect’ of e learning technology enhance work-place learning. Cheng and Chen [65] found that in the context of on the job training, e-learning system use and user satisfaction are positively related with outcome.

This discussion leads to the next hypothesis:

Hypothesis 4. E learning would positively mediate the relationship between learning agility and outcomes

4 Methodology and methods

4.1 Population and sample

Multi-Stage Random Sampling Method was used in the study to select the sample. The sample has been selected from a pool of managers representing lower, middle and top management level across public and private sector companies of India. Loehlin, [80] suggested that a sample size of 200 to 400 range is sufficient for studies with 10 to 15 indicators, especially if the research used structural equation modelling. In this study a total of 1000 questionnaires were administered using On-line mode (400 respondents) and Off-line model (600 respondents) with 776 returned, for a response rate of 77.6 per cent. And, out of 776 responses, the response of lower, middle and top management level was 308 (39.69%), 272 (35.05%), and 196 (25.25%) respectively. The sample data collected from both male (79.64%) and female (20.36%) employees from different age groups with different experienced. Age is divided into five categories i.e. less than 25 years, 25–35 years, 36–45 years, 46–55 years and more than 55 years; the responses from these age groups are 11.86%, 32.60%; 31.44%; 18.81% and 5.28% respectively. Obviously, responses are also from different experienced employees, such as less than 2 years (13.27%), 2–5 years (12.63%), 6–10 years (15.59%), 11–15 years (17.27%), 16–20 years (13.92%), 21–25 years (16.49%), 25–30 years (7.09%) and more than 30 years (3.74%).

4.2 Procedure

For the purpose of the research, the instruments for data collection are designed using information from past research [81, 82]. Learning agility instrument was designed influenced by Burke et al. [16], Organisational culture following the OCTAPACE profile instrument [34], Outcome was measured following the organisational performace and individual performance instrument suggested by Chand and Katou [83] and Buckingham and Goodall [42] and E learning technology measured following Venkatesh et al., [84]. All the measurement instruments were further refined with the help of several senior practitioners from organizations in public and private sector. Further, a pilot study using a sample of 42 (20 from the public sector and 12 from the private sector) was conducted to test for any construct weaknesses, and for weaknesses in the research design [85]. The pilot study result has been analyzed and all the variables passed the threshold with Cronbach alpha reliability score (0.7). So, they all are considered without any modification.

4.3 Measures

The survey instrument consisted of learning agility (38 items), organisational culture (24 items), E learning technology (23 items) and outcome (11 items), for a total of 96 items. Each component is described next. A Likert-type scale of 1 to 5 was used to measure the items.

4.3.1 Learning agility

Burke Learning Agility Inventory (BLAI) instrument suggested by Burke et al. [16] has been considered. BLAI has been considered as it is more research-based than any other measure. All the 38 items are grouped into nine different dimensions: feedback seeking, information seeking, interpersonal risk-taking, collaborating, performance risk-taking, reflecting, experimenting, flexibility and speed. Sample item includes ‘Seek feedback from my manager about my performance’, ‘Take on new roles or assignments that are challenging’ etc. The alpha reliability of learning agility is 0.94.

4.3.2 Organizational culture

OCTAPACE by Pareek [34] has been used for studying culture. OCTAPACE contains 24 items which are grouped into eight dimensions. Sample items are: helping attitude among employees, encouragement for innovation etc. All the items are measured through a scale ranging from 1 = highly dissatisfied to 5 = highly satisfied. Sample item includes ‘Policies Facilitate Employee Learning Development’; ‘Encouragement for Innovation’; ‘Opportunity for Applying Knowledge after Training’ etc. The alpha reliability of learning agility is 0.96.

4.3.3 E-learning technology

E Learning technology refers to the use of the internet and other important technologies to produce materials for learning, teach learners, and also regulate courses in an organization. Influenced by the unified theory of acceptance and use of technology [84], E learning technology has been measured through a 5-point Likert scale consisting of 23 items. The scale is found reliable with a Cronbach’s alpha score of 0.955 for all the 23 items. Sample item includes ‘Using the e-learning system improves the productivity’; ‘It is easy for me to become skilful at using the e-learning system’; ‘Organisation supported the use of e-learning system’s etc.

4.3.4 Outcome

Outcome has been measured through two measures: Organisational outcome and Individual outcome. Chand and Katau [83] used multiple organisational performance variables such as sales growth, productivity, profitability, goal achievement, and good services to measure organizational outcome. Organisational outcome considered for this study are and are measured using 7-item scale. Individual performance is measured using 4-item scale developed by Buckingham and Goodall [42]. All items are measured through a 5-point Likert-type scale. Sample items are: ‘My organization has been innovating products and processes’, ‘Adequately completes assigned duties’.

5 Statistical techniques

The hypothesized model (Fig. 1) has been tested using Structural Equational Modelling (Amos 16). Confirmatory Factor Analysis result suggests that all factor loadings and path coefficients were statistically significant. The t-values were above the required value of 1.97. As mentioned in Table 4, each construct was higher than the corresponding inter-construct correlation estimates, suggesting good discriminate validity. The convergent validity was measured through correlation matrix and factor analysis [86, 87]. Further result of correlation matrix found that the correlation coefficients among the constructs do not exceed 0.85, indicating that Multicollinearity is not a problem [88] and suggests a good fit for construct validity. Factor analysis was performed on all items of learning agility, culture, e learning technology and outcome (Table 6). The result shows that a total of 38 learning agility variables (except five variables), 24 culture variables (except two variables), 28 E learning variables (except one variable) and 11 outcome related variables (except one variable) have significantly high loading scores (higher than 0.6). Based on CFA, reliability has been estimated and the value is found acceptable (LAG-.947; CUL:.963; ELR:.955; OUT:.921) (Table 1). CFA also helped us in assessing common method biases [89]. CFA helped in extracting the factors with eigenvalues ›1.0. The aggregated variance was 53.4%. The first factor contributes to 27.27% of the variance, where as second and third accounts for 25.38 % and 24.87% respectively. Hence, common method biasness may not be an issue as more than one factor is identified, and none of them contributes to majority of the explained variance [90]. The overall fit statistics of the CFA areCMIN/df = 1.256; p = 0.084; CFI = 0.713; GFI = 0.827; RMSEA = 0.067; RMR = 0.013; and NFI = 0.938 (see Table 4). The model fits the data very well, and all the indices were within the recommended ranges.

Fig. 1

Research model.

Table 1

Cronbach’s Alphas

Constructs	Cronbach’s Alpha
Learning agility	0.947
Culture	0.963
E learning technology	0.955
Outcome	0.921

5.1 Structural model and findings

The assessment of path coefficients helped us to find the fitness of the structural model. The goodness-of-fit statistics also indicated a good fit to the data (Table 5). The c² statistics falls within the acceptable fit level (χ2 = 103.347; d.f. = 75; p = 0.08) The CFI, GFI, and NFI exceeds the acceptable fit level of 0.90 (Table 3). The RMSEA is within 0.08 and RMR exceed 0.05 and hence acceptable. The ‘p’ value of the model is a close fit and acceptable at 0.950.

Table 2
Correlations among Variables

Variables 1 2 3

1.Learning Agility 1

2.Culture 0.615^∗∗ 1

3.Outcome 0.528^∗∗ 0.634^∗∗ 1

4. E Learning Technology 0.526^∗∗ 0.723^∗∗ 0.530^∗∗

Variables	1	2	3
1.Learning Agility	1
2.Culture	0.615^∗∗	1
3.Outcome	0.528^∗∗	0.634^∗∗	1
4. E Learning Technology	0.526^∗∗	0.723^∗∗	0.530^∗∗

^∗p < 0.05, ^∗∗p < 0.01.

Table 3

Measurement Fit Model

Overall model measure	Model score	Acceptable model fit	Acceptable range
CFI	0.713	Not Passed	>0.90
GFI	0.817	Passed	>0.90
RMSEA	0.067	Passed	<0.06 to 0.08
CMIN/DF	1.256	Passed	<3
RMR	0.013	Passed	Smaller the better; 0 indicates perfect fit
NFI	0.938	Passed	>0.95

Table 4

Measurement Structural Model

Overall model measure	Model score	Acceptable model fit	Acceptable range
CFI	0.917	Passed	>0.90
GFI	0.957	Passed	>0.90
RMSEA	0.082	Passed	<0.06 to 0.08
CMIN/DF	2.568	Passed	<3
RMR	0.025	Passed	Smaller the better; 0 indicates perfect fit
NFI	0.819	Passed	>0.95

Table 5

Summary of Effects

Variables	Culture	E-learning technology	Outcome
	Direct	Direct	Direct	Indirect/Mediating effect through culture	Indirect/Mediating effect through E-learning
Learning agility	β= 0.615(^∗∗)	β= 0.528(^∗∗)	β= 0.526(^∗∗)	IE = 0.708(^∗∗)	IE = 0.604(^∗∗)
	R² = 0.378	R² = 0.278	R² = 0.277
	F = 471.190(^∗∗)	F = 298.660(^∗∗)	F = 296.757(^∗∗)
Culture	–	–	β= 0.723(^∗∗)	_	_
			R² = 0.523
			F = 849.060(^∗∗)
E-learning technology	–	–	β= 0.530(^∗∗)	_	_
			R² = 0.281
			F = 302.466(^∗∗)

^∗p < 0.05, ^∗∗p< 0.01; IE = Indirect Effect.

Table 6

Factor Analysis for all variables item-wise

Factor Analysis of Learning Agility			Factor Analysis of Culture			Factor Analysis of E-learning Technology		Factor Analysis of Outcome
	Extraction		Extraction		Extraction		Extraction
LAG1	0.763	CUL1	0.629	ELR1	0.713	OUT1	0.658
LAG2	0.722	CUL2	0.643	ELR2	0.788	OUT 2	0.494
LAG3	0.691	CUL3	0.659	ELR3	0.805	OUT 3	0.659
LAG4	0.663	CUL4	0.656	ELR4	0.802	OUT 4	0.711
LAG5	0.616	CUL5	0.606	ELR5	0.781	OUT 5	0.697
LAG6	0.545	CUL6	0.661	ELR6	0.702	OUT 6	0.741
LAG7	0.639	CUL7	0.636	ELR7	0.708	OUT 7	0.581
LAG8	0.636	CUL8	0.698	ELR8	0.661	OUT 8	0.728
LAG9	0.652	CUL9	0.577	ELR9	0.761	OUT 9	0.798
LAG10	0.622	CUL10	0.641	ELR10	0.734	OUT 10	0.798
LAG11	0.674	CUL11	0.662	ELR11	0.751	OUT 11	0.788
LAG12	0.661	CUL12	0.678	ELR12	0.706
LAG13	0.519	CUL13	0.658	ELR13	0.775
LAG14	0.657	CUL14	0.666	ELR14	0.506
LAG15	0.605	CUL15	0.569	ELR15	0.764
LAG16	0.564	CUL16	0.658	ELR16	0.668
LAG17	0.621	CUL17	0.701	ELR17	0.763
LAG18	0.661	CUL18	0.716	ELR18	0.651
LAG19	0.626	CUL19	0.686	ELR19	0.659
LAG20	0.636	CUL20	0.665	ELR20	0.678
LAG21	0.638	CUL21	0.663	ELR21	0.668
LAG22	0.683	CUL22	0.682	ELR22	0.712
LAG23	0.604	CUL23	0.623	ELR23	0.697
LAG24	0.612	CUL24	0.681
LAG25	0.639
LAG26	0.631
LAG27	0.603
LAG28	0.513
LAG29	0.654
LAG30	0.698
LAG31	0.611
LAG32	0.576
LAG33	0.677
LAG34	0.665
LAG35	0.678
LAG36	0.635
LAG37	0.656
LAG38	0.634

Additionally, all path estimates were significant and in the expected direction. In other words, the hypothesis 1 that is learning agility is significantly related to outcome, is accepted (r = 0.526^**, R² = 0.277, p < 0.01).

To examine the mediating effect of Culture and E learning on Learning agility, all the three conditions suggested by Little et al [91] were complied. Firstly, the independent variable (Learning agility) must be related to the mediators (Culture, E learning). The result proved significant positive direct relation of Learning agility and Culture (β= 0.615, p = 0.000), and E learning (β= 0.528, p = 0.000) (Table 5) and hence satisfied the first condition. Second condition is that the independent and dependent variables must be related. The result proved that Learning agility and Outcome are positively and significantly related (β= 0.526, p < 0.001). The third and final condition was the mediating and dependent variable must be related was satisfied as Culture and E learning are significantly related with Outcome (β= 0.723, p = 0.000) and (β= 0.530, p = 0.000) respectively.

Thus, it is proved that Learning agility is directly and indirectly relates with Outcome. However, the strength of the relationship between Learning agility as predictor variables and Outcome as response variables has streignthened when taking into account the mediator(Culture) (β increased from 0.526 to 0.708; R² increased from 0.277 (F = 296.775) to 0.502 (F = 778.728). In case of E learning, the streignth has increased when taking into account the mediator (E-learning technology) (β increased from 0.526 to 0.604; R² increased from 0.277 (F = 296.757) to 0.365 (F = 222.50). This proves support for culture and E learning as a mediators between learning agility and outcome (Table 5). Thus, the hypotheses that Culture and E learning acts as a mediator between Learning agility and Outcome are accepted. Details of the path coefficients explained in Fig. 2.

Fig. 2

Path model.

6 Discussion and findings

The constant changing business environment requires managers to acquire learning agility, and unique individual attribute for successful performance. The purpose of this study is to explore the relationship between learning agility and outcome. Further, the study also examined the mediating relationship of culture and E learning between learning agility and outcome.

The study result proved that learning agility significantly predicts outcome (r = 0.526^**, R² = 0.277, p < 0.01). In other words, managers having the quality of agile learner can enable organisational performance by helping organisations to meet their objectives through limited use of resources, innovating products and processes, producing quality products and services. Similarly, the result also proved that managers having strong learning agility are better in terms of meeting formal performance requirement of the job; fulfil responsibilities, tasks and duties assigned to them. The result agrees with most of the studies conducted in the recent past [18, 45 , 92]. For example, DeMeuse [45] found that learning agility is highly correlated with leader’s performance. The meta-analysis result suggests that learning agility predicts different outcomes. Similarly, Smith [46] found a positive relationship between learning agility and performance, thus lending support to the hypothesis that learning agility would be related to performance. De Meuse et al. [18] conducted a longitudinal study at AT and T and observed that managers who had been assessed low for potential frequently were more successful than expected when they had developmental opportunities. Lombardo et al. [92] compared successful versus derailed executives. The authors found that derailed executives were unable or unwilling to change or adapt. Adaptability and quick learning are essential to navigate the situations soldiers face on the ground winning both skirmishes and hearts and minds in theatres’ like Iraq and Afghanistan. The study also reveals that there is no significant difference between the mean scores of male and female at 95% level for both the cases learning agility and outcome. But the study result shows that high learning employees provide higher outcome than lower learning agile employees.

Secondly, the study result proved that culture mediates between learning agility and outcome. The result proved the importance of OCTAPACE culture in organisations. In other words, the result proved and hence suggests organisations to nurture and develop a culture based on openness, confrontation, trust, authenticity, pro-action, autonomy, collaboration, and experimentation characteristics. The result agrees with distant literature related to individual attribute, culture as well as outcome. For example, Bipath [93] found evidence of culture as a mediator between an individual attribute called emotional intelligence and organisational performance. However, the result also didn’t agree with Catenacci-Francois [81] as the researcher found that in the context of safety climate, high learning agile individuals perform better when the organizational climate is perceived as low in psychological safety. The research justified the result by stating that focusing too much on learning can negatively influence performance simply because the time and effort devoted to learning can prevent groups from reaching a solution. However, as the context of our study is a broader organisational culture, the result is not relevant for us.

Finally, the study result shows that e-learning technology mediates between learning agility and outcome. The result proved that e-learning can facilitate better learning agility effectiveness by allowing learners to gain new knowledge and skills as per their learning requirements. Agreeing with the past research [94 –96], the result proved that learning agility as an individual attribute, e-learning technology as well as outcome are closely related. The findings also proved that highly learning agile individuals were found to perform better when employees get adequate support for increasing their knowledge by adopting e-learning technology. The result may encourage organisations to adopt e learning technology to upgrade employee’s aptitudes and update information to make them more agile and hence better performance. The result also proved e-learning’s importance at workplace as it may drive employees’ continuous learning and help them reaching their expected outcomes.

7 Managerial implications

The result that learning agility is significantly related to individual and organisational outcome suggests manager to leverage learning agility of employees’ for augmenting their individual productivity and organisational performance. Managers are to explore and adopt relevant managerial policies and practices that facilitate better learning agility. Managerial practices proved as significant enabler of agile workforce such as team work environment, reward system; empowerment can be relied upon [56]. Managers to ensure and promote suitable internal team working environment, external team working environment, intra group team working environment and cross-functional team working environment [97]. Similarly, a performance-based reward system can be useful for motivating employees for better learning agility and higher performance. Unlike a traditional pay system, a performance-based pay can encourage workers by linking their initiatives, adaptability and flexibility and ultimately final performance. The manager may experiment with all varieties, including individual-based, group-based and company-wide incentive programs. Further, managers can also rely on empowerment employees as power sharing practices offer the greatest potential to support the employee agility architecture by improving efficiencies of training, switching, multi-tasking, and collaboration [1, 98].

The study result proves a positive role of culture to facilitate agile learners to achieve higher performance. The results suggest managers to evolve and grow from their experiences. Managers need to let go of old habits and ways of performing their jobs and willingly latch on to new techniques and supervisory practices for success [99, 100]. The study result should encourage managers to establish a positive organisational culture based on OCTAPACE culture. However, creating and maintaining a true learning agility culture requires continuous measurement, disciplined use of processes and overcoming resistance while integrating the concept of learning into company’s operation. Further, establishing a learning agile culture may require the managers to promote learning behaviours. Management should have clarity about the ‘behaviours you want’ and the ‘behaviours you don’t want’. Steps to be taken nurture and develop a learning culture aiming at producing the predetermined behaviour [101].

The study result proves a positive role of E learning technology to facilitate agile learners to achieve higher performance. The result organisations to focus on their strategies based on e-learning technology. Employees’ should be facilitated by e-learning supported technologies and equipments and also should provide adequate training according to the need of the employees. So, human resource managers should have to know how well-embed e-learning system is good for developing an excellent human capital management. As e-learning meets the learners’ requirements, it also motivates to learn, facilitates to access the information in multiple formats, improves confidence, and helps in creative thinking, logical analysis, and ease of access of new and repetitive information; so managers should have to encourage learners to acquire new skills to deal with new technology [8]. As e-learning technology has solution for all problems, and then managers must provide opportunities to the employees to learn and experiment with their newly learned ideas. If managers are implementing e-learning technology, then most important beneficiaries are agile learners or employees and organisations.

8 Limitations and points for future research

Learning agility is a strategic imperative in a world characterized by change and complexity. The study result proved that learning agility positively relate with both individual and organisational outcomes. The research also proved the importance of establishing a positive favourable organisational culture for better learning agility effectiveness. Further, the result also proved the significant mediating role of e learning technology. However, the research not free from limitations. Firstly, the research framework has used culture and e learning technology as a mediating variable between learning agility and outcome. Researcher may explore several external and internal factors and test their role as mediating variable. Secondly, the research has been conducted in the context of India. Hence the result may not generalise to other country context. Future research may be conducted in other country context. Finally, the research has largely used survey result. It is advisable to conduct Culture research by adopting qualitative research methodology. Future research can be conducted using both quantitative and qualitative research methodologies.

Author contributions

CONCEPTION: Susmita Ghosh

METHODOLOGY: Susmita Ghosh

DATA COLLECTION: Susmita Ghosh

INTERPRETATION OR ANALYSIS OF DATA: Susmita Ghosh, Ashutosh Muduli and Sameer Pingle

PREPARATION OF THE MANUSCRIPT: Susmita Ghosh and Ashutosh Muduli

REVISION FOR IMPORTANT INTELLECTUAL CONTENT: Susmita Ghosh and Ashutosh Muduli

SUPERVISION: Ashutosh Muduli

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Role of e-learning technology and culture on learning agility: An empirical evidence

Abstract

BACKGROUND:

OBJECTIVE:

METHODS:

RESULTS:

CONCLUSIONS:

Keywords

1 Introduction

2 Conceptualisation

2.1 Learning agility

2.2 Organisational culture

2.3 E-learning technology

2.4 Outcome

3 Hypotheses

3.1 Learning agility and outcome

3.2 Learning agility, organisational culture and outcome

3.3 E-learning and outcome

3.4 Learning agility, e-learning technology and outcome

4 Methodology and methods

4.1 Population and sample

4.2 Procedure

4.3 Measures

4.3.1 Learning agility

4.3.2 Organizational culture

4.3.3 E-learning technology

4.3.4 Outcome

5 Statistical techniques

Table 2 Correlations among Variables Variables 1 2 3 1.Learning Agility 1 2.Culture 0.615∗∗ 1 3.Outcome 0.528∗∗ 0.634∗∗ 1 4. E Learning Technology 0.526∗∗ 0.723∗∗ 0.530∗∗

7 Managerial implications

8 Limitations and points for future research

Author contributions

References

Table 2
Correlations among Variables

Variables 1 2 3

1.Learning Agility 1

2.Culture 0.615^∗∗ 1

3.Outcome 0.528^∗∗ 0.634^∗∗ 1

4. E Learning Technology 0.526^∗∗ 0.723^∗∗ 0.530^∗∗