The populations of North America and Western Europe are significantly aging, due largely to increasing longevity and declining fertility (Earl, Taylor, Roberts, Huynh, & Davis, 2017). These populations will inevitably require greater amounts of health care, but there is a growing concern about the number of health care professionals available to meet demand. The issue itself is multifaceted: The combination of economic recessions and longer life-spans has led many people in industrialized countries to work for more years; as a result, health care organizations avoid hiring new clinicians and gain a higher proportion of older professionals. But those individuals will eventually retire: For instance, some 7.1 million people are expected to exit the health care labor market in the next 15 years across the EU27 (Schultz, Coda Moscarola, Golinowska, Radvanský, & Geyer, 2013). Similar trends can be found in the United States, where “demographic changes, especially the on-going process of population aging, increase demands for health services while simultaneously shrinking the pool of workers available to offer these services” (Rechel, Dubois, & McKee, 2006, p. 11).
When discussing the risks implied by an increasingly older workforce, the literature typically talks in terms of personnel shortage, high replacement costs, and the challenges in retaining older workers (e.g., Brannon, Kemper, & Barry, 2009). There has been less discussion about how massive retirement could engender the potential loss of a substantial amount of human capital (De Long, 2004) or, more broadly, how aging could impact knowledge acquisition and transfer (Burmeister & Deller, 2016). Nonetheless, there are at least two distinct and relevant challenges to the management of knowledge that are especially critical in health care because of the specific features of clinical knowledge.
The first challenge involves the acquisition of knowledge. Health care is a knowledge-intensive sector where medical knowledge is constantly renovating itself. Young clinicians are trained with the most updated knowledge and can be more prone to using newly available technologies (e.g., surgical robots), whereas their older colleagues more likely command the most traditional techniques and approaches (e.g., laparoscopic surgery). An aging workforce could exacerbate this dichotomy, preventing health organizations from updating their stock of knowledge and capabilities.
The second challenge refers to the transfer of knowledge from older clinicians to their younger colleagues. The diminished recruitment of young clinicians over time, coupled with the high rate of older workers' retirement, may reduce opportunities for transferring knowledge across age groups. This trend could trigger deskilling processes and “forgetting” dynamics in organizations (de Holan & Phillips, 2004), which could have detrimental effects on hospital safety and effectiveness.
In short, aging is an increasingly important phenomenon in the health care sector, especially in how it might impact knowledge acquisition and transfer. That said, this is a surprisingly under-researched area. In order to fill this gap, we apply the ability–motivation–opportunity (AMO) theoretical model (Blumberg & Pringle, 1982) to argue that, at the individual level, a person’s engagement in knowledge acquisition and transfer depend on his or her ability, motivation, and opportunity (Argote, McEvily, & Reagans, 2003; Tho & Trang, 2015). Furthermore, we draw on life-span aging approaches (e.g., Baltes & Baltes, 1990; Carstensen, Isaacowitz, & Charles, 1999), which have emerged based on empirical work on age-related changes and how people adapt to those changes. Specifically, we contend that those three factors (ability, motivation, opportunity) are moderated by a professional’s age. Building on these theoretical perspectives, we develop a conceptual framework and advance a number of testable research propositions to better understand how age affects knowledge acquisition and transfer in health care organizations.
Our theoretical framework has two distinctive characteristics. First, it provides an individual-level perspective. We recognize that health care organizations are multilevel systems where several actors (individuals, teams, organizational units, etc.) contribute to knowledge management processes and outcomes (Argote et al., 2003); however, individual clinicians are still the main enablers of knowledge acquisition and transfer (Kothari, Hovanec, Hastie, & Sibbald, 2011). Second, our framework focuses on clinical knowledge. The literature widely acknowledges the heterogeneity of knowledge in health care organizations (e.g., organizational knowledge, relational knowledge; Argote et al., 2003; Kothari et al., 2011) but has devoted less attention to the continuous acquisition and transfer of clinical knowledge. Clinical knowledge poses significant challenges to the management of knowledge (Nicolini, Powell, Conville, & Martinez-Solano, 2008; Tanner, 2006): It not only has a high rate of growth but is also particularly complex, encompassing scientific knowledge (that can be codified, measured, counted) as well as “interpretive action and interaction—factors that involve communication, opinions, and experiences” (Malterud, 2001, p. 397).
In summary, our framework provides a twofold contribution to the extant health management literature. One, we suggest how the AMO model—a theoretical framework mostly used to study the antecedents of performance—can be extended to the process of knowledge acquisition and transfer in health care organizations. Two, we integrate the AMO framework with theories of age-related changes and knowledge management in health care organizations. We specifically highlight how this model enhances our understanding about the influence of aging on clinicians’ ability, motivation, and opportunity to acquire and transfer clinical knowledge. We describe our theoretical framework in the next section.
A Life-Span Perspective on Knowledge Management in Health Care: The Application of the AMO Theoretical Framework
This study examines the ways in which clinicians' age affects knowledge processes in the health care sector. Specifically, we analyze how their ability, motivation, and opportunity to acquire and transfer knowledge change according to the life-span developing perspective.
Having its foundations in industrial/organizational psychology, the AMO framework is one of the most established and comprehensive theoretical approaches to studying the antecedents of performance at the individual level of analysis. It has been extensively applied in the realm of human resource management (Paauwe, 2009), as well as used to model innovation adoption behaviors in information system research (Agarwal, Mishra, Angst, & Anderson, 2007) and explain consumer behavior in marketing research (Ölander & Thøgersen 1995).
The AMO framework suggests that individual performance antecedents can be organized into three general dimensions (Blumberg & Pringle, 1982). Ability represents individuals’ skills or knowledge base that relate to their performance, whereas motivation captures individuals’ willingness to perform. The third dimension, which Blumberg and Pringle (1982) termed as “opportunity” (O), captures those elements of the external environment (e.g., tools and information, working conditions, leadership style, procedures) that the individual cannot directly control and cannot be adequately explained by the Ability × Motivation framework. According to the AMO theory, ability, motivation, and opportunity must be present to some degree in order for good performance to occur.
There are three main considerations driving our choice to use the AMO model as the overarching theoretical framework for this study. First, we agree with Argote and colleagues’ (2003) statement that “just as successful individual performance depends on an individual's ability, motivation, and opportunities to perform, successful knowledge management also depends on ability, motivation, and opportunity” (p. 575). Knowledge transfer and acquisition are key components of work performance, especially in health care organizations where clinical knowledge develops at an extremely high rate. Second, this framework has been widely used by previous research to explain performance at the individual level of analysis (Paauwe, 2009); therefore, it is especially suitable for our purpose, which is to provide an individual perspective on knowledge processes in health care. Third, this framework allows us to examine the possible joint effects of age and AMO on knowledge outcomes. In doing so, we expand on previous work on knowledge management in health care organizations (Nicolini et al., 2008) by providing detailed theoretical explanations, developing specific research propositions, and suggesting possible moderating mechanisms. In short, we aim to explore this research question: In what ways does clinicians' age interact with ability, motivation, and opportunity to predict clinical knowledge transfer and acquisition in health care organizations?
Our proposed theoretical model is depicted in Figure 1. In the following paragraphs, we will discuss the mechanisms through which age interacts with AMO to affect knowledge acquisition and transfer and then introduce some propositions to be tested in future research.
Age and Ability to Acquire Knowledge
Clinical knowledge is proliferating because of the innovations in medical technologies, drugs, and life science research. This rapid advancement provides a compelling reason to keep pace with new knowledge and unlearn old knowledge.
A widespread workplace belief is that individuals’ ability to acquire new knowledge decreases with age. Indeed, scholars have noticed that existing/old knowledge can act as a barrier to learning new knowledge (de Holan & Phillips, 2004). It has been argued that both individuals and organizations must forget or “unlearn” old habits and dominant logics in order to learn new and more appropriate ways of doing things. Given that older workers often possess more valuable expertise and accumulated experience than younger workers, it is generally assumed that they will encounter more barriers to acquiring new knowledge and will need to put more effort into unlearning/forgetting existing knowledge.
However, theories on cognitive changes suggest a more nuanced view on the relation between individuals’ age and their ability to acquire knowledge (Cattell, 1971). On one hand, aging workers face a loss of fluid intellectual abilities, which may negatively affect their learning outcomes (Kanfer & Ackerman, 2004). For example, a decline in memory may lead to cognitive slowing, reductions in processing resources, and deficits in reflective processes. On the other hand, aging is also associated with increased experience that tends to raise crystalized intelligence, that is, learned or practiced knowledge, accumulated job skills, and wisdom (Kanfer & Ackerman, 2004). Obviously, these cognitive changes also depend on a variety of environmental and genetic factors, including health status, and as such, they vary considerably among individuals.
Overall, theories on cognitive changes suggest that an increase in crystallized intelligence can offset the loss of cognitive skills, especially for older people who take on roles such as a higher-level supervisor or manager. For instance, empirical evidence has shown that older adults are better able to cope with emotionally salient and interpersonal problems than younger people (Blanchard-Fields, 2007), due to increased crystallized intelligence and emotion regulation skills.
These considerations suggest that age will have a differential effect on the ability to acquire new competences/knowledge, depending on the extent to which they require fluid (e.g., cognitive processing and reaction speed) versus crystallized intelligence (e.g., coping with emotions, activation of social network, integration of different expertise). For clinicians, competences that require crystallized intelligence are certainly no less important than those demanding high fluid intelligence. Clinicians work in emotionally charged settings, and research shows that emotions can affect clinical decision-making and responses to clinical situations (e.g., Bate, Hutchinson, Underhill, & Maskrey, 2012; Croskerry, Abbass, & Wu, 2010). Clinical practice also requires managing relationships in order to ensure effective teamwork, leadership, and patient experience—all critical issues in health care (Heyhoe et al., 2016).
On the basis of these considerations, we propose the following:
Proposition 1: The positive relation between the ability to acquire clinical knowledge and knowledge acquisition will be stronger for older clinicians when knowledge requires crystallized intelligence.
Proposition 2: The positive relation between the ability to acquire clinical knowledge and knowledge acquisition will be stronger for younger clinicians when knowledge requires fluid intelligence.
Age and Motivation to Acquire and Transfer Knowledge
Several studies have pointed out the importance of individuals’ learning motivation, showing that higher levels of motivation to learn result in improved learning performance (Quinones, 1995). However, it is less clear just how age affects motivation.
Socioemotional selectivity (SES) life-span theory (Carstensen, Pasupathi, Mayr, & Nesselroade, 2000) provides a useful theoretical lens for examining how age affects individuals’ motivation to learn. SES proposes that workers’ perception of time is essential in the selection and pursuit of social goals. This theory identifies two types of social goals: those related to knowledge acquisition and those related to emotion regulation. The fundamental proposition of SES is that knowledge-related goals are prioritized when time is perceived as being more open-ended. Thus, SES would predict that older workers are less motivated by growth or knowledge-related goals (acquiring new information or social interactions) that can be useful in the more distant future, as they generally perceive themselves as having limited time and fewer remaining opportunities at work than younger employees. In the realm of clinical health care decisions, several studies have documented that older workers tend to use their existing clinical knowledge rather than access newly available guidelines and protocols, which implies a lower motivation to acquire new clinical knowledge (e.g., Bridges, Bierema, & Valentine, 2007).
In light of these theoretical arguments, we propose the following:
Proposition 3: The positive relation between the motivation to acquire clinical knowledge and knowledge acquisition will be stronger for younger clinicians.
According to life-span development approaches, generativity motives—which refer to the tendency to care for others, act as a parent, and help society and future generations (Kanfer & Ackerman, 2004)—are a related motivational change that characterize midlife development. Generativity motives seem to arise in midlife and continue into later adulthood, suggesting that older workers may be more motivated to share and transfer valuable knowledge to others than their younger colleagues. The massive wave of retirements among older workers means that this motive is especially important for retaining valuable organizational knowledge (Burmeister & Deller, 2016). In one study of Canadian nurses, for instance, Leiter, Jackson, and Shaughnessy (2009) found that older nurses were more likely than younger nurses to be involved in activities related to knowledge sharing.
Therefore, we propose that the rise of generativity motives will lead to increased motivation among older employees to share and transfer knowledge to other colleagues:
Proposition 4: The positive relation between the motivation to transfer clinical knowledge and knowledge transfer will be stronger for older clinicians.
Age and Opportunity to Acquire and Transfer Knowledge
There are numerous factors in an individual's work environment that directly and indirectly affect knowledge processes. Even if individuals have the ability and are motivated to acquire and transfer knowledge, they can only facilitate effective knowledge management if they have the appropriate opportunities to learn and/or share knowledge. To that end, the work environment should be supportive of employees' involvement, communication, leadership processes, and engagement in problem solving (Boxall & Purcell, 2003). Failing to provide these supports can be detrimental to knowledge transfer: For instance, in a review of nursing preceptorship and mentorship studies, Omansky (2010) found that contextual factors (e.g., a lack of role definition or recognition for the extra work, as well as work overload and a lack of time) may hinder the opportunities for knowledge transfer between senior nurses and student nurses.
These organizational relationships are especially important for enhancing knowledge processes because they provide members an opportunity to learn from each other. Indeed, knowledge acquisition and transfer are improved in contexts where there is less distance, either physically or psychologically, between employees (Argote et al., 2003). Although there are various environmental factors that can increase perceived distance (e.g., culture, rewards, norms, and beliefs), research widely recognizes that age stereotypes are particularly potent in driving a sense of distance between employees, especially in aging work settings. In a recent study of the European Union, 58% of respondents indicated that age discrimination based on age stereotypes was widespread in the labor market (European Commission, 2009). Age stereotypes can be defined as beliefs and expectations about workers based on their age, such as negatively characterizing older workers as lacking competence and being unable to learn (Posthuma & Campion, 2009). Their diffusion has relevant implications for how human resource management decisions are made, for example, who is offered a training opportunity or a challenging job assignment. According to common belief, older workers provide a lower return on training and development practices because they have less time left in their career paths to compensate organizations for these investments. As a consequence, organizations devote most training investments to young “high potentials” (Rhebergen & Wognum, 1997) and discriminate against older employees with respect to learning and development (Tougas, Lagacé, Sablonniere, & Kocum, 2004), which means the latter have less opportunity to acquire knowledge. In a study of Canadian health care settings, Lagacé, Tougas, Laplante, and Neveu (2010) found evidence of age stereotypes against older nurses: In particular, these workers were excluded from training programs and decision-making processes, which negatively affected their work engagement and self-esteem.
Age stereotypes may also inhibit effective knowledge transfer. For example, supervisors holding negative age stereotypes will be less likely to provide older subordinates with support or stimulating feedback, to include them in teams that have to deal with innovative tasks, or to assign them mentoring or training responsibilities, thus hindering their contribution to knowledge transfer. Negative age stereotypes can be particularly detrimental to knowledge sharing among age-diverse employees because they lead people to see out-group members as less valid sources of information and underestimate their skills and knowledge (Sammarra, Profili, Maimone, & Gabrielli, 2017). On the basis of these considerations, we propose the following:
Proposition 5: The positive relation between the opportunity to acquire clinical knowledge and knowledge acquisition will be weaker for older clinicians in work contexts affected by negative age stereotypes.
Proposition 6: The positive relation between the opportunity to transfer clinical knowledge and knowledge transfer will be weaker for older clinicians in work contexts affected by negative age stereotypes.
Additional moderators besides age may also affect the relationships between AMO and knowledge acquisition and transfer: namely, individual factors (e.g., attitudes toward evidence-based medicine [EBM], personality traits, and subjective age) and organizational factors (e.g., task/job characteristics and knowledge management governance).
Individual differences. Matzler and colleagues (2008) discovered that individuals’ stable characteristics (i.e., agreeableness, conscientiousness, and openness) influence their knowledge sharing. People with low openness, even if motivated to acquire new knowledge, may be less inclined to seek it out. People with low agreeableness, meanwhile, may be less likely to transfer knowledge, even when given the opportunity.
Furthermore, people’s attitudes toward EBM (Aarons, 2004) may play a moderating role in the model: The lower the attitude to adopt EBM, the weaker the relationship between motivation and knowledge acquisition of EBM.
Another individual factor is subjective age, which numerous studies have shown to possess greater explanatory power than chronological age. Subjective age refers to how old or young individuals perceive themselves to be. Looking at the health care setting, Rioux and Mokounkolo (2013) found a positive correlation between subjective age in general and satisfaction with professional life.
Organizational factors. Job characteristics may have a moderating role as well. According to Karasek and Theorell’s (1990) demand–control–support model, people working in jobs characterized by high demands and high control will develop high intrinsic motivation for learning and personal growth.
The knowledge management governance models that organizations adopt may also moderate AMO and knowledge processes. On this topic, Touati, Denis, Roberge, and Brabant (2015) distinguished between two approaches for managing knowledge in health care: the “laissez faire” model and the “mechanic” model. In the first model, the top managers of health care organizations do not define specific policies for supporting or fostering knowledge acquisition and transfer. In the “mechanic” or “vertical” model, a central authority imposes the adoption and use of EBM knowledge. In this model, the main emphasis is on circulating scientific evidence rather than experiential knowledge. The best governance model would be one that merges the two approaches (Touati et al., 2015): EBM results are not superimposed, but rather translated into practice by communities of expert knowledge users and then made readily available to practitioners.
In summary, these individual and organizational factors could act as boundary conditions for our proposed model and should be examined in future work.
This article has identified several risks that clinicians' aging implies for effective knowledge management in health care organizations. Although these risks may negatively impact health care organizations’ performance in terms of efficiency, clinical effectiveness, and patient safety, there has been virtually no research into why they occur and how they may be managed.
This study offers a first step in this direction by developing a conceptual model that sheds light on the effect of aging on two key, individual-level behaviors: knowledge acquisition and transfer. In order to do so, we applied the AMO framework to the context of life-span theoretical approaches, which allowed us to provide detailed theoretical explanations, develop specific research propositions, and identify possible moderating mechanisms and boundary conditions.
Through this undertaking, this study expands prior theoretical and empirical literature in several research areas. Our propositions are new in the context of the knowledge management literature insofar as they shed more light on the nature of knowledge acquisition and transfer in professional contexts affected by workforce aging. To our knowledge, this is one of the first studies to provide such a contribution. Our propositions also offer the possibility of expanding knowledge in the area of human resource management in health care. Namely, we elucidated some important mechanisms through which the process of aging affects clinicians' ability, motivation, and opportunity to acquire and transfer knowledge in these complex and fast-changing organizations.
Our conceptual model has several practical implications that may equip organizations with a better understanding of how they can improve clinicians’ ability, motivation, and opportunity to acquire and transfer knowledge.
First, administrators and human resource managers should consider that, aside from simply pushing for training among the oldest age groups, organizations should also customize the content and methodologies of their training programs to older clinicians’ needs. For instance, previous studies (Knowles, 1978) have described the unique characteristics of adult education, showing that older adults learn best in social situations, giving information to and absorbing information from other participants as well as from teachers. According to Carter and Beier (2010), older workers may benefit from the adoption of error management training, which encourages trainees to accept making errors and view mistakes as learning experiences.
Moreover, organizations can sustain older workers’ motivation to acquire new knowledge by expanding subjective perceptions of future time at work and ensuring that opportunities for career and professional development are available to clinicians at any age (Innocenti, Profili, & Sammarra, 2013). These interventions are likely to foster older health care professionals’ learning motivation, while discouraging their turnover and retirement intentions. These interventions are also likely to increase their motivation to transfer knowledge. Indeed, when older employees receive positive treatment and inducements from the organization, such as training and promotion opportunities, they will repay the organization by exerting positive work attitudes and behaviors toward the organization. Knowledge transfer is one such extra-role behavior that reflects an individual’s positive emotions toward the organization.
Of course, enabling knowledge transfer requires that organizations work to decrease the physical and psychological distance between older and younger clinicians. This could be facilitated through mentoring, reverse-mentoring, and coaching programs, which may be useful to younger workers and attractive to older workers. These interventions create opportunities for high-quality interaction between clinicians of different ages, which can facilitate trust and thereby enhance knowledge acquisition and transfer (Sammarra et al., 2017).
However, these interventions can be hampered by age biases, which can then inhibit clinicians’ ability, motivation, and opportunity to engage in learning and knowledge transfer. Therefore, it is important that line managers and human resource practitioners become aware of the existence and relevance of age stereotypes at work. Even more importantly, they need to know that age-related beliefs, biases, and stereotypes can be altered through appropriate interventions. For example, age-related beliefs can be minimized by diffusing information about the evolution of individual ability (e.g., increased crystallized intelligence) and motivations (e.g., increased motivation to collaborate and behave altruistically) over time.
In addition to pursuing the above interventions, health care administrators should also ensure that middle managers work alongside human resource practitioners to identify those older and retiring clinicians who possess valuable knowledge that the organization needs to retain (Burmeister & Deller, 2016). In order to preserve those workers’ valuable knowledge, it is important that organizations introduce effective transfer mechanisms. This could be done by implementing knowledge codification mechanisms and repository systems or by developing programs that favor the socialization of knowledge among clinicians (e.g., mentoring programs, job shadowing, joint projects). As pointed out by Kothari et al. (2011), knowledge management initiatives in health care tend to focus on one solution (e.g., information and communication technologies, evidence-based practice) rather than adopting a comprehensive strategy. We suggest that organizations adopt an integrative strategy that values both explicit and tacit knowledge, as well as accounts for the differentiated attitudes among employees of different age groups.
Aarons G. A. (2004). Mental health provider attitudes toward adoption of evidence-based practice: The Evidence-Based Practice Attitude Scale (EBPAS). Mental Health Services Research
, 6(2), 61–74.
Agarwal R., Mishra A., Angst C., & Anderson C. (2007). Digitizing healthcase: The ability and motivation of physician practices and their adoption of electronic health record systems. ICIS 2007 Proceedings, 115.
Argote L., McEvily B., & Reagans R. (2003). Managing knowledge in organizations: An integrative framework and review of emerging themes. Management Science
, 49(4), 571–582.
Baltes P. B., & Baltes M. M. (1990). Psychological perspectives on successful aging: The model of selective optimization with compensation. Successful Aging: Perspectives From the Behavioral Sciences
, 1(1), 1–34.
Bate L., Hutchinson A., Underhill J., & Maskrey N. (2012). How clinical decisions are made. British Journal of Clinical Pharmacology
, 74(4), 614–620.
Blanchard-Fields F. (2007). Everyday problem solving and emotion. Current Directions in Psychological Science
, 16, 26–31.
Blumberg M., & Pringle C. D. (1982). The missing opportunity in organizational research: Some implications for a theory of work performance. Academy of Management Review
, 7(4), 560–569.
Boxall P., & Purcell J. (2003). Strategy and human resource management
. Oxford, UK: Blackwell.
Brannon S. D., Kemper P., & Barry T. (2009). North Carolina’s direct care workforce development journey: The case of the North Carolina New Organizational Vision Award Partner Team. Health Care Management Review
, 34(3), 284–293.
Bridges P. H., Bierema L. L., & Valentine T. (2007). The propensity to adopt evidence-based practice among physical therapists. BMC Health Services Research
, 7, 103.
Burmeister A., & Deller J. (2016). Knowledge retention from older and retiring workers: What do we know, and where do we go from here? Work, Aging and Retirement
, 2(2), 87–104.
Carstensen L. L., Isaacowitz D. M., & Charles S. T. (1999). Taking time seriously: A theory of socioemotional selectivity. American Psychologist
, 54(3), 165–181.
Carstensen L. L., Pasupathi M., Mayr U., & Nesselroade J. R. (2000). Emotional experience in everyday life across the adult life span. Journal of Personality and Social Psychology
, 79(4), 644.
Carter M., & Beier M. E. (2010). The effectiveness of error management training with working aged adults. Personnel Psychology
, 63(3), 641–675.
Cattell R. B. (1971). Abilities: Their structure, growth, and action
. Oxford, UK: Houghton Mifflin.
Croskerry P., Abbass A., & Wu A. W. (2010). Emotional influences in patient safety. Journal of Patient Safety
, 6(4), 199–205.
de Holan P. M., & Philips N. (2004). Remembrance of things past: The dynamics of organizational forgetting. Management Science
, 50(1), 1603–1613.
De Long D. W. (2004). Lost knowledge: Confronting the threat of an aging workforce
. Oxford, UK: Oxford University Press.
Earl C., Taylor P., Roberts C., Huynh P., & Davis S. (2017). The workforce demographic shift and the changing nature of work: Implications for policy, productivity and participation. In Profili S., Sammarra A., Innocenti L. (Eds.), Age diversity in the workplace: An organizational perspective
(Advanced Series in Management, pp. 3–34). WA, UK: Emerald Publishing Limited.
Heyhoe J., Birks Y., Harrison R., O’Hara J. K., Cracknell A., & Lawton R. (2016). The role of emotion in patient safety: Are we brave enough to scratch beneath the surface? Journal of the Royal Society of Medicine
, 109(2), 52–58.
Innocenti L., Profili S., & Sammarra A. (2013). Age as moderator in the relationship between HR development practices and employees’ positive attitudes. Personnel Review
, 42(6), 724–744.
Kanfer R., & Ackerman P. L. (2004). Ageing, adult development, and work motivation. Academy of Management Review
, 29(3), 440–458.
Karasek R. A., & Theorell T. (1990). Healthy work: Stress, productivity and the reconstruction of working life
. New York, NY: Basic Books.
Knowles M. S. (1978). Andragogy: Adult learning theory in perspective. Community College Review
, 5(3), 9–20.
Kothari A., Hovanec N., Hastie R., & Sibbald S. (2011). Lessons from the business sector for successful knowledge management
in health care: A systematic review. BMC Health Services Research
, 11(1), 173.
Lagacé M., Tougas F., Laplante J., & Neveu J. F. (2010). Communication âgiste au travail: Une voie vers le désengagement psychologique et la retraite des infirmières d’expérience? Revue Internationale de Psychologie Sociale/International Review of Social Psychology
, 23(4), 91–121.
Leiter M. P., Jackson N. J., Shaughnessy K. (2009). Contrasting burnout, turnover intention, control, value congruence and knowledge sharing between Baby Boomers and Generation X. Journal of Nursing Management
, 17(1), 100–109.
Malterud K. (2001). The art and science of clinical knowledge
: Evidence beyond measures and numbers. The Lancet
, 358(9279), 397–400.
Matzler K., Renzl B., Müller J., Herting S., & Mooradian T. A. (2008). Personality traits and knowledge sharing. Journal of Economic Psychology
, 29(3), 301–313.
Nicolini D., Powell J., Conville P., & Martinez-Solano L. (2008). Managing knowledge in the healthcare sector. A review. International Journal of Management Reviews
, 10(3), 245–263.
Ölander F., & Thøgersen J. (1995). Understanding of consumer behaviour as a prerequisite for environmental protection. Journal of Consumer Policy
, 18(4), 345–385.
Omansky G. L. (2010). Staff nurses' experiences as preceptors and mentors: An integrative review. Journal of Nursing Management
, 18(6), 697–703.
Paauwe J. (2009). HRM and performance: Achievements, methodological issues and prospects. Journal of Management Studies
, 46, 129–142.
Posthuma R. A., & Campion M. A. (2009). Age stereotypes in the workplace: Common stereotypes, moderators, and future research directions. Journal of Management
, 35(1), 158–188.
Quinones M. A. (1995). Pretraining context effects: Training assignment as feedback. Journal of Applied Psychology
, 80(2), 226–238.
Rhebergen B., & Wognum I. (1997). Supporting the career development of older employees: An HRD study in a Dutch company. International Journal of Training & Development
, 1(3), 191–198.
Rioux L., & Mokounkolo R. (2013). Investigation of subjective age in the work context: Study of a sample of French workers. Personnel Review
, 42(4), 372–395.
Sammarra A., Profili S., Maimone F., & Gabrielli G. (2017). Enhancing knowledge sharing in age-diverse organizations: The role of HRM practices. In Profili S., Sammarra A., Innocenti L. (Eds.), Age diversity in the workplace: An organizational perspective
(Advanced Series in Management, pp. 161–187). WA, UK: Emerald Publishing Limited.
Schultz E., Coda Moscarola F., Golinowska S., Radvanský M., & Geyer J. (2013). Impact of ageing on curative health care workforce in selected EU countries. (Neujobs Working Paper No. d 12.1).
Tanner C. A. (2006). Thinking like a nurse: A research-based model of clinical judgment in nursing. Journal of Nursing Education
, 45(6), 204–211.
Tho N. D., & Trang N. T. M. (2015). Can knowledge be transferred from business schools to business organizations through in-service training students? SEM and fsQCA findings. Journal of Business Research
, 68(6), 1332–1340.
Touati N., Denis J. L., Roberge D., & Brabant B. (2015). Learning in health care organizations and systems: An alternative approach to knowledge management
. Administration and Society
, 47(7), 767–801.
Tougas F., Lagacé M., de la Sablonnière R., & Kocum L. (2004). A new approach to the link between identity and relative deprivation in the perspective of ageism and retirement. International Journal of Aging and Human Development
, 59(1), 1–23.