Today, companies face threats to their competitiveness on the fiscal, global, technical, and labor markets. Economic challenges include confusion about growth and how to place oneself in a global economy that is dominated by services, and the need to satisfy various stakeholders (shareholders, workers, society and environment) (Meister & Willyerd 2010). Technological challenges include determining when and how to use mobile devices and social media, which provide staff, administrators, consumers and suppliers with unparalleled exposure, accessibility and immediacy to communications.
The dynamics of the labor market and the labor force present yet another set of challenges. Organizations are struggling to identify workers with appropriate skillsets for open positions and are unsure as to how best to handle and draw on the skills of an increasingly diverse workforce in terms of age, ethnicity and national origin (Galagan 2010; SHRM Find. 2010, Toossi 2009). The expectations of the employees regarding work are also changing. We appreciate input on their job results, opportunities to improve their talents, and work that is demanding and rewarding in person and contributes to the goals of their organizations. They also want flexibility in deciding when and where to work to effectively balance the demands of work and life (Butts et al., 2013).
By its corporate plan, an essential way a company seeks to turn competitive problems into a competitive advantage. According to resource-based theory, a resource is something that could theoretically offer a competitive advantage to a company (Barney, 1991). Assets include tangible assets, such as financial resources ( e.g. monetary assets and cash), physical capital (equipment, infrastructure, distribution systems), and intangible assets or human capital. A meta-analysis by Crook et al. ( 2011) found that the resources of human capital are substantially related to firm results. While most work has shown that firm-specific human capital has a stronger relationship to firm success than general human capital does, Campbell et al . ( 2010) findings indicate that, under some conditions, general human capital may also be a source of competitive advantage. Human capital resources lead to a competitive advantage when they are important (i.e. exert control on the strategic goals of the company), exceptional (i.e. not widely owned by competitors), inimitable (i.e. difficult to imitate), and non-substitute (i.e., an alternate resource can not be substituted for the same strategy) (Acedo et al . 2006, Barney & Wright 1998)
Human capital resources are unit-level resources resulting from the interaction of expertise, skills, abilities, and other resources (KSAOs) of employees (Ployhart & Moliterno 2011). The most important source of competitive advantage may be the overt and tacit knowledge of the employees (Grant 1991, Kogut & Zander 1992). Explicit knowledge is knowledge well known and expressed with ease. Tacit knowledge, which is arguably more important, is knowledge based on experience that is subconsciously understood (Nonaka & Takeuchi 1995).
For example, policies and procedures can be taught, but learning through experience plays a critical role in determining when and how those practices should be applied, adopted or abandoned. It is important to note that human capital resources are not likely to be simply an aggregate of individual characteristics to the organizational level, but rather evolving, that is, they are affected by interactions between individual characteristics and influences at the team and organizational level (Barney & Felin 2013, Ployhart & Moliterno 2011). This means that the human resources strategies and talent management activities of companies, including training and development programs, and the organizational context ( e.g., structure, culture, and job design) play a significant role in the use and creation of human capital resources.
3 companies that used E learning
Cisco invested in a social media awareness program for employees and contractors in 2014. The curriculum contained more than 46 courses for participants which was daunting. Some weren’t sure where to start, so the organization decided to gamify by adding credential rates. Three key levels — expert, strategist, and master — and four sub-levels were developed (Gatto, 2017). Challenges faced by teams facilitated friendly competition. It gave the participants inspiration and a specific collection of steps to be taken to complete the programme. More than 650 Cisco workers have completed more than 13,000 courses and earned certification since introduction of gamification.
LMS systems have allowed capabilities to be registered, processed, and sorted to ensure workers are used to their full potential. At the 2017 Pluralsight Live End User Meeting, Adobe addressed the advantages of using an LMS to boost its employees. Their eLearning program made it possible for them to retrain 25 per cent of their technical workers to support a new project (Gatto, 2017). Companies need to be able to adapt, especially those in fast-paced industries like technology, when new opportunities come up. An LMS helps companies to stay competitive and adaptable. You can easily review a list of workers with expertise you need or identify missing areas and offer training to fill the void. Such procedures would take much longer and would be much more costly without the assistance of eLearning
A 2013 AARP survey showed that over a fifth of 45 to 74-year-old adults want part-time jobs because they enjoy it. Those that were closer to 70 continued to work but wouldn’t want to be on-site all day. The eLearning situation produced a special one. It allows older people to remain comfortably working without full-time job demands (AARP, 2014). Many organizations, from home, put older staff in training positions they can handle online. This also encourages professional staff to share their expertise and to help sustain a qualified work force. There are other advantages of eLearning for older people as well. Education is known to create self-confidence and offer creativity outlet. For older generations, that can have profound socio-economic benefits. It’s one of the many ways in which companies use eLearning to improve their business and their employees’ lives
How can one think differently when it comes to learning?
Organizations have historically relied on, and scholars have concentrated on, learning that takes place through formal training and growth programmes. In 2012 US organizations spent some $164.2 billion on formal training and development (Miller 2013). Participation in these services is also mandatory. Development may include some forms of training but typically refers to formal education, work experiences, relationships, and assessments of personality and skills that help employees prepare for future jobs or positions. Increasingly, most formal training and development programs are and should be, strategic in the sense that they prioritize obtaining the KSAOs needed to help companies improve their ability to identify change, adjust, and predict trends (Kraiger & Ford 2006).
Nevertheless, pressures on time and workload, budget limitations and a geographically dispersed population make it difficult for companies to provide structured services, and for workers to participate. Also, when workers attend formal classes, it is difficult for them to carry the amount of energy and attention they need to understand, owing to the demands of their jobs. One way that organizations try to overcome the learning difficulties in the workplace today is by providing formal training and development programs using online delivery and instructional methods. In 2012, technology-based learning, which includes e-learning, online learning, and mobile learning, was used on average in the formal learning hours of 39 per cent of organizations (Miller 2013).
Continuous learning, which takes place beyond the context of formal training and development, maybe more essential for human capital resource growth (Sessa & London 2006). Continuous learning includes casual learning (Marsick & Watkins 1990), intentional practice (Ericsson et al . 1993), accidental learning (Marsick et al . 1999), learning in the workplace (Raelin 1997), and self-development (Orvis & Leffler 2011). Informal learning is estimated to account for up to 75 per cent of organizational learning (Bear et al . 2008).
Informal learning includes both cognitive activities and behaviours, including self-reflective learning; learning from others such as peers, supervisors, and mentors; and learning from non-interpersonal sources such as reading print or online material (Doornbos et al. 2008, Lohman 2005). Informal learning encourages individuals to acquire on-the-job expertise and skills, offering the ability for more realistic learning opportunities than structured training and growth provide (Benson 1997; Tannenbaum et al . 2010).
Human capital development also requires consideration of how expertise and knowledge can be transferred from the experts who have it to the novices who need it (Connelly et al . 2012, Hinds et al . 2001). One way to do this is by sharing information. Sharing information can occur directly through face-to-face or technology-aided encounters with experts, or it can occur indirectly through the recording, compilation and collection of information for others (Cummings 2004, Pulakos et al . 2003).
Sharing of information between workers and through departments helps a company to exploit established knowledge-based tools (Cabrera & Cabrera 2002, 2005; Damodaran & Olphert 2000). The sharing of knowledge can contribute to the competitive advantage of an organization in a number of ways, including cost reduction, faster completion of new product development, increased innovation capabilities, and increased sales growth and revenues from new products and services (e.g., Mesmer-Magnus & DeChurch 2009).
Studies in the literature on information systems and organizational behaviour have examined the sharing of knowledge at different levels of analysis (Wang & Noe 2010).
Learning from Self-Regulation and Self-directing
Self-regulated learning is the synthesis during the knowledge of affective, cognitive, and behavioural processes in an attempt to achieve the desired goal (Sitzmann & Ely 2011). During self-regulated learning, regulatory mechanisms used include planning, tracking, metacognition, concentration, commitment, and time management. Aligning learner goals with learning goals, learner perceptions that goals are achievable, and maintaining learner motivation is essential both for goal self-regulation and self-directed learning.
Self-regulation failure in the form of either goal abandonment or goal switching can result if goals are misaligned or deemed unattainable or if learner motivation is poor (Schunk & Zimmerman 2012). Goal abandonment has been studied as a cause of attrition from voluntary online training. Sitzmann (2012 ) found that the mechanism of self-regulation completely mediated the relationship between perception and the attrition from training. Conscientiousness moderated the effects of attrition on commitment and self-efficacy.
That is, a high degree of conscience seemed to serve as a buffer against dropping-out learners, especially for those who were less committed or less confident. Interventions in which learners are recalled from self-regulation tend to help them control their time efficiently during training and mitigate attrition, a common drawback to online volitional learning (Sitzmann et al. 2009, Sitzmann & Ely 2010). Self-directed learning may occur formally or informally, electronically, or within the social learning context. Self-directed learning has been discussed in the literature for decades, but research has been anecdotal or based on case studies on its antecedents and consequences. Gureckis & Markant (2012 ) propose that learners will benefit from self-directed learning by improving the encoding and processing of information as they are involved in the learning process.
Community Practice and Social Learning
Social learning is well developed by observation, imitation, and reinforcement (Bandura, 1962). Social learning remains essential but the social learning background has changed drastically with advances such as social media offering access to more examples or other others to learn from. It means we will rethink our conventional paradigm of social learning. One emerging area of research focuses on the effectiveness of an evolutionary perspective of learning from others.
Using a computer-simulated game, Rendell et al. ( 2010) found that social learning was the most successful method of learning as opposed to asocial forms of learning (trial and error learning, a mixture of social and antisocial learning) because it is an adaptive mechanism. Adaptation during social learning may take place through the implementation of either specific behavioural changes or new actions arising from mistakes. Errors that remain in the population to the degree that those errors are imitated and passed on. Although this research focused primarily on individual behavioural adoption and adaptation, the evolutionary perspective can help understand how human capital evolves from the personal level to the level of team, company, and industry.
Recently also, social learning has been examined in the sense of functional cultures (Lave & Wenger 1991, Wenger 1998). Organizations have used groups of practice in an attempt to promote informal learning which is directly applicable to a defined field of expertise (Li et al. 2009). Work on practising groups has been criticized for lack of theoretical grounding and the use of the construct to explain several types of social learning (Storberg-Walker 2008, Li et al. 2009).
Kirkman et al.’s latest research (2011) is an example of the theory-driven approach needed to consider functional societies. The authors illustrated how organizational practising communities (OCoP) are important for the creation of human capital resources through the sharing of information at the organizational level. Our model included leadership, accountability, mission structure and organizational effectiveness relevance to the OCoP. They found that external community leaders play a significant role in enhancing the empowerment of OCoP, particularly to the extent that interdependence of tasks was strong. Empowerment, in addition, responded positively to the efficacy of the OCoP. The organization’s classification of OCoP as core, that is, focusing on essential issues, was more successful than noncore ones.
Use technology to deliver and promote learning is becoming increasingly popular, as conventional face-to-face methods of learning are costly and difficult to introduce to geographically dispersed workers. However though e-learning has high implementation costs, companies can theoretically minimize their total learning costs relative to face-to-face training by minimizing travel and lodging costs, ongoing teaching costs, and learners’ missed wages. These cost savings come from the ability of learners to access e-learning from a personal computer, tablet, or smartphone anywhere and anytime, thus eliminating an instructor’s need (Bedwell & Salas 2010).
However, e-learning is necessarily no more efficient than other forms of instruction. Emerging research indicates that companies need to ensure that they can build human capital through e-learning through the use of the practice, feedback, relevant content, multi-sense interaction, action planning, follow-up, and manager and organizational support, to promote learning and transition of training.
Learning can be performed on- or off-line, with single or multiple players through gaming and simulation (Castranova 2005, Malaby 2006). It affects the learner profoundly while offering a fun and engaging way to learn from realistic scenarios that bring reality closer together (Kriz 2009). Recent game attribute taxonomies that help researchers investigate the properties of successful gaming and simulations. Wilson et al. ( 2008) proposed different relationships between game attributes (adaptation; assessment; challenge; conflict; control; fantasy; equipment, interpersonal or social interaction; language/communication; location; mystery; pieces or actors; progress and surprise; representation; rules/goals; safety; sensory stimuli) and learning outcomes (cognitive, skill-based, or affective learning). Bedwell et al. ( 2012) developed a more parsimonious taxonomy of gaming characteristics based on the assessments of subject matter experts who were serious gamers, including the language of action, evaluation, conflict/challenge, power, setting, game writing, human interaction, immersion, and rules/goals.
AARP. Older Works in an Easy Job Market. Washington DC: AARP Research, 2014. PDF.
Acedo FJ, Barroso C, Galan JL. 2006. The resource based theory: dissemination and main trends. Strateg. Manag. J. 27:621–36
Barney JB. 1991. Firm resources and sustained competitive advantage. J. Manag. 17:99–120
Barney JB, Felin T. 2013. What are microfoundations? Acad. Manag. Perspect. 27:138–55
Bandura A. 1962. Social learning through imitation. In Nebraska Symposium on Motivation, ed. MR Jones, pp. 211–74. Lincoln: Univ. Neb. Press
Bear DJ, Tompson HB, Morrison CL, Vickers M, Paradise A, et al. 2008. Tapping the Potential of Informal Learning: An ASTD Research Study. Alexandria, VA: Am. Soc. Train. Dev.
Bedwell WL, Salas E. 2010. Computer-based training: capitalizing on lessons learned. Int. J. Train. Dev. 14:239–49
Bedwell WL, Pavlas D, Heyne K, Lazzara EH, Salas E. 2012. Toward a taxonomy linking game attributes to learning: an empirical study. Simul. Gaming 43:729–60
Benson G. 1997. Informal training takes off. Train. Dev. 51:93–94
Butts MM, Casper WJ, Yang TS. 2013. How important are work–family support policies? A meta-analytic investigation of their effects on employee outcomes. J. Appl. Psychol. 98:1–25
Cabrera A, Cabrera EF. 2002. Knowledge-sharing dilemmas. Organ. Stud. 23:687–710
Campbell BA, Coff R, Kryscynski D. 2010. Rethinking sustained competitive advantage from human capital. Acad. Manag. Rev. 37:376–95
Castranova E. 2005. Synthetic Worlds: The Business and Culture of Online Games. Chicago: Univ. Chicago Press
Connelly CE, Zweig D, Webster J, Trougakos JP. 2012. Knowledge hiding in organizations. J. Organ. Behav. 33:64–88
Crook TR, Todd SY, Combs JG, Woehr DJ, Ketchen DJ Jr. 2011. Does human capital matter? A meta-analysis of the relationship between human capital and firm performance. J. Appl. Psychol. 96:443–56 Demonstrates that human capital is related positively to firm performance.
Cummings JN. 2004. Work groups, structural diversity, and knowledge sharing in a global organization. Manag. Sci. 50:352–64
Damodaran L, Olphert W. 2000. Barriers and facilitators to the use of knowledge management systems. Behav. Inf. Technol. 19:405–13
Doornbos AJ, Simons RJ, Denessen E. 2008. Relations between characteristics of workplace practices and types of informal work-related learning: a survey study among Dutch police. Hum. Resour. Dev. Q. 19:129–51
Galagan P 2010. Bridging the skills gap: new factors compound the growing skills shortage. T+D 64:44–49
Gatto, S. “5 Innovative Ways Companies Are Using ELearning.” N.p., 11 Dec. 2017. Web. 23 May 2020.
Grant RM. 1991. The resource-based theory of competitive advantage: implications for strategy formulation. Calif. Manag. Rev. 33:115–35
Gureckis TM, Markant DB. 2012. Self-directed learning: a cognitive and computational perspective. Perspect. Psychol. Sci. 7:464–81
Hinds PJ, Patterson M, Pfeffer J. 2001. Bothered by abstraction: the effect of expertise on knowledge transfer and subsequent novice performance. J. Appl. Psychol. 86:1232–43
Kirkman BL, Mathieu JE, Cordery JL, Rosen B, Kukenberger M. 2011. Managing a new collaborative entity in business organizations: understanding organizational communities of practice effectiveness. J. Appl. Psychol. 96:1234–45
Kogut B, Zander U. 1992. Knowledge of the firm, combinative capabilities, and the replication of technology. Organ. Sci. 3:383–97
Kraiger K, Ford JK. 2006. The expanding role of workplace training: themes and trends influencing training research and practice. In Historical Perspectives in Industrial and Organizational Psychology, ed. LL Koppes, pp. 281–309. Mahwah, NJ: Erlbaum
Kriz WC. 2009. Bridging the gap transforming knowledge into action through gaming and simulation. Simul. Gaming 40:28–29
Lave J, Wenger E. 1991. Situated Learning: Legitimate Peripheral Participation. New York: Cambridge Univ. Press
Li LC, Grimshaw JM, Nielsen C, Judd M, Coyte PC, Graham ID. 2009. Use of communities of practice in business and health care sectors: a systematic review. Implement. Sci. 4:16
Lohman MC. 2005. A survey of factors influencing the engagement of two professional groups in informal workplace learning activities. Hum. Resour. Dev. Q. 16:501–27
Malaby T. 2006. Parlaying value: capital in and beyond virtual worlds. Games Cult. 1:141–62
Marsick VJ, Watkins KE. 1990. Informal and Incidental Learning in the Workplace. New York: Routledge/Kegan Paul
Meister JC, Willyerd K. 2010. The 2020 Workplace: How Innovative Companies Attract, Develop, and Keep Tomorrow’s Employees Today. New York: HarperCollins
Mesmer-Magnus JR, DeChurch LA. 2009. Information sharing and team performance: a meta-analysis. J. Appl. Psychol. 94:535–46
Miller L. 2013. State of the industry. Rep., Am. Soc. Train. Dev., Alexandria, VA
Nonaka I, Takeuchi H. 1995. The Knowledge Creating Company. New York: Oxford Univ. Press
Orvis KA, Leffler GP. 2011. Individual and contextual factors: an interactionist approach to understanding employee self-development. Personal. Individ. Differ. 51:172–7
Ployhart RE, Moliterno TP. 2011. Emergence of the human capital resource: a multilevel model. Acad. Manag. Rev. 36:127–50 Provides a multilevel model of human capital resources.
Pulakos ED, Dorsey DW, Borman WC. 2003. Hiring for knowledge-based competition. In Managing Knowledge for Sustained Competitive Advantage: Designing Strategies for Effective Human Resource Management, ed. SE Jackson, A DeNisi, MA Hitt, pp. 155–61. San Francisco: Wiley
Raelin JA. 1997. A model of work-based learning. Organ. Sci. 8:563–78
Rendell L, Boyd R, Cownden D, Enquist M. 2010. Why copy others? Insights from the social learning strategies tournament. Science 328:208–13
Schunk DH, Zimmerman BJ. 2012. Motivation and Self-Regulated Learning: Theory, Research, and Applications. New York: Routledge
Sessa V, London M. 2006. Continuous Learning in Organizations. Mahwah, NJ: Erlbaum
SHRM Found. 2010. SHRM Foundation Leadership Roundtable: What’s Next for HR? Alexandria, VA: SHRM Found.
Sitzmann T, Bell BS, Kraiger K, Kanar AM. 2009. A multilevel analysis of the effect of prompting self-regulation in technology-delivered instruction. Pers. Psychol. 62:697–734
Sitzmann T, Ely K. 2010. Sometimes you need a reminder: the effects of prompting self-regulation on regulatory processes, learning, and attrition. J. Appl. Psychol. 95:132–44
Sitzmann T, Ely K. 2011. A meta-analysis of self-regulated learning in work-related training and educational attainment: what we know and where we need to go. Psychol. Bull. 137:421–42
Sitzmann T. 2012. A theoretical model and analysis of the effect of self-regulation on attrition from voluntary online training. Learn. Individ. Differ. 22:46–54
Tannenbaum SI, Beard R, McNall LA, Salas E. 2010. Informal learning and development in organizations. See Kozlowski & Salas 2010, pp. 303–32
Toossi M. 2009. Employment outlook: 2008–18: labor force projections to 2018: older workers staying more active. Mon. Labor Rev. 132:30–51
Wang S, Noe RA. 2010. Knowledge sharing: a review and directions for future research. Hum. Resour. Manag. Rev. 20:115–31
Wenger E. 1998. Communities of Practice: Learning, Meaning, and Identity. New York: Cambridge Univ. Press
Wilson KA, Bedwell WL, Lazzara EH, Salas E, Burke CS, et al. 2008. Relationships between game attributes and learning outcomes: review and research proposals. Simul. Gaming 40:217–66