How Organizations Should Prepare for AI
Nathan Tymann
Abstract
In today’s workplace, it is critically important that people and organizations be adaptable. Increased market pressures, competition, and the rapid pace of change brought about by technology require a nimbleness that wasn’t needed in the past. Specifically, artificial intelligence and machine learning are likely to influence the future of work in ways that require new ways of thinking about job roles. This chapter begins by discussing how the future of work is being scripted due to the influence of artificial intelligence and machine learning. Next is an overview of research on the importance of continuous learning for individuals, teams, and organizations, followed by an examination of the type of leadership that learning organizations require to foster a culture of innovation, creativity, and stability. To close, a discussion of what these changing times mean for measuring job performance and for hiring the right people has been added.
One exciting part about working today is that technology gives us the opportunity to figure out what makes us uniquely human within the context of work. Technologies such as artificial intelligence and machine learning are making it possible to continue to offload certain critical, but transactional, job functions to free us up to perform job roles that humans do best. According to a recent article by the World Economic Forum (2018), this dynamic driven by technology is accelerating the evolution of the future of work into three types of jobs.
Figure 2.1 New types of jobs
Image credits: Center for generational kinetics
The first type includes jobs that some people are afraid about losing, but really shouldn’t be. These are predictable or highly repetitive jobs that don’t require intuition or judgment. These include jobs that computer programs perform better than humans. We’ve seen this dynamic since the Industrial Revolution and in manufacturing plants for a long time. These include many data entry jobs or other administrative jobs. Self-driving trucks are being experimented to replace human drivers on routes that travel through remotely populated areas. By enabling jobs like those to go to machines, humans are free to aspire to the next two categories of jobs.
The second category includes collaborating with the machine. Think of the evolution of these types of jobs as akin to what the advent of better tools meant to skilled craftsman over the centuries. A civil engineer used to rely on measurements and calculations done by hand to build a bridge, but now she will use computers and software to enable her to perform her task with more elegance and efficiency. Surgeons can now use computer-guided lasers to excise tumors with greater precision than most could accomplish with their hands.
The third category of jobs includes those that cannot be performed by machines. Jobs in the creative arts are in this category, along with other roles that require design, judgment, leadership, entrepreneurship, or innovation.
The benefits that technology provides are here to stay. So how do organizations adjust to maximize the creative and innovative capabilities that only humans offer while sustaining organizational excellence?
Organizational leaders need to foster a culture of continuous learning where new ideas and concepts are discussed and used to create innovation. Research shows that organizations cannot create innovative ideas by themselves, but they can create conditions by which teams can learn. Then teams can distribute new knowledge for the benefit of the entire organization (Stelmaszczyk 2016).
The Need to Embrace Continuous Learning
Team learning and organizational learning are different, but they are closely related. Team learning requires that teachers and learners actively collaborate to leverage the strengths of the teachers and that learning can be absorbed by the learners (Goolsarran, Hamo, Lane, Frawley, and Lu 2018). Within most teams, however, the teacher role and learner roles rotate because individuals on the team are experts in different bodies of knowledge. Team learning is a connector between learning at the individual level and learning at the organizational level. Research shows a linear relationship between individual learning, team learning, and organizational learning. According to Stelmaszczyk (2016), individual learning is manifested within teams. Team learning occurs when individuals communicate, test, and implement knowledge that they bring into the team. The collective knowledge of many teams represents organizational learning. While organizations benefit by the implementation of knowledge by teams, they cannot create knowledge on their own (Stelmaszczyk 2016).
Crossan, Lane, and White (1999) described organizational learning using the 4I model: intuiting, interpreting, integrating, and institutionalizing. The 4I framework posits that there are subconnections within an organization that span across individuals, teams, and organizations (Schilling, and Kluge 2009). Intuiting refers to the way in which individuals gain new ideas based on their own personal experiences. Interpreting happens when an individual explains what he knows to other people, either through words or actions. Integrating occurs at the team level when knowledge is shared more widely until it becomes common understanding within a team and then the team uses the knowledge to act. Finally, institutionalizing is when shared knowledge forms the organizational infrastructure through applied systems, processes, policies, and strategies. Therefore, organizational learning includes all four of these phases, inclusive of and dependent upon, individual learning and team learning (Schilling, and Kluge 2009).
Figure 2.2 4I’s for organizational learning
One example of an implementation of the relationship between team learning and organizational learning took place at a cancer treatment center in the United States (Valentine 2018). In this study, project leads, managers, and clinical operations staff members from across the cancer treatment center provided individual input through their functional teams to benefit the entire organization. A challenge in the study was the attempt to synchronize inputs from one team to another. Leaders needed to work with their teams to institutionalize their combined knowledge, creating rules and systems for the entire organization to use. However, one of the conclusions of the study was that collaboration took great effort and time on the part of all members of the organization even though their knowledge sharing was conducted with friendliness and positive intentions. There was so much complexity involved in gathering requirements and sharing knowledge from one person to another, that integrating learning within a small team structure was the only way that the organization would be able to eventually institutionalize learning (Valentine 2018). This study demonstrated that team learning was the key connector between individual learning and organizational learning.
Overcoming Obstacles to Becoming
a Learning Organization
There are many obstacles to overcome when transforming an organization to a learning organization. According to McKenna (2017), four obstacles to change include a fear of failure, fear of the loss of competence, lack of trust, and a lack of commitment to change. Fear of failure causes paralysis for people who are afraid to take a risk. This fear can prevent an organization from giving the proposed change a chance to be adopted before giving up. Fear of the loss of competence especially affects experts in a given job role who think that transformational changes may make their special abilities irrelevant. Lack of trust can go in any direction of the organizational hierarchy. Often people do not trust the leaders in their organizations to make the right decisions or to lead them in the right direction. Also, sometimes leaders do not trust that their people are skilled enough or adaptable enough to accomplish a new goal, regardless of the quality of their own leadership. Finally, lack of commitment to change occurs when leadership changes their focus so often that people see the next change initiative as just the flavor of the month. All these obstacles are poisonous when leaders attempt to create a learning organization because they are so self-centered and negative.
Systems thinking increases the number of available options for overcoming fear of failure, fear of the loss of competence, lack of trust, and lack of commitment to change. Two important characteristics of general systems theory are seeing an organization as an open system and emphasizing the interrelationship of smaller wholes bounded within a larger collective whole (von Bertalanffy 1968). Viewing an organization as an open system should drive leaders to hold loosely to narrow organizational identities, because an open system implies that the organization needs new ideas from the teams and individuals within it. The vulnerability that leaders show by embracing the concept of an open system brings down barriers between leaders and followers. According to Vaughn (2016), leaders should admit to their people that they do not always know what the best decision is, and that if they fail, then they will fail together. Similarly, while the competence of people who perform well today may not be the same skills they need to succeed tomorrow, the organization will support people with training to enable them to learn new competencies. By providing people with support and training that they need to succeed and by emphasizing the interrelationship of smaller wholes within a larger whole, people within teams see the importance their contributions make to the overall organization. As a result, leaders build trust between themselves and their teams.
Lack of commitment to change requires another strategy for overcoming obstacles to change into a learning organization. According to McKenna (2017), there are five good options by which team learning can influence the change process. The first option is to learn from outside experts to gain perspective on how one organization compares with another. The second is to invest in a pilot project to implement barrier-breaking actions on a small scale before deciding whether to expand the actions across the entire organization. The third is to appeal to people’s natural sense of rewards for work done well by beginning a routine that celebrates good behaviors. The fourth is to pull in internal people to volunteer to work together on small teams and then present their findings to leadership. The fifth is to issue a survey to the organization asking for honest, anonymous feedback. It is best for leaders to try one or several of these approaches to increase the likelihood that they are going to be successful in creating a learning organization.
In summary, a healthy organization is one that delivers value to its stakeholders for a sustained amount of time. However, an organization needs to adjust to changing markets and competitive pressures to be sustainable. This sustainability can only be achieved when an organization becomes a learning organization with an influx of innovative ideas (Cai, and Li 2018). Leaders will face obstacles in becoming a learning organization, so they need to use strategies to foster a culture of continuous learning where new ideas and concepts are discussed and used to create innovation (Vaughn 2016). When leaders are successful in creating a learning organization, then the entire organization will benefit (Stelmaszczyk 2016).
Importance of Collaborative Leadership
Organizations will not be sustainable over time unless they adapt to changing market conditions and evolve to overcome competitive pressures. Therefore, leaders need to infuse a culture of innovation and creativity into their organizations to remain adaptable (Boylan, and Turner 2017). Collaborative leadership is an effective method to enable the growth of innovation and creativity, which results in maintaining a sustainable organization (Steiner 2009).
A collaborative leader plays an important role in creating a culture and attitude that enables an organization to meet the objectives of today and will also prepare the organization to overcome the challenges of tomorrow (Boylan, and Turner 2017). A collaborative leader brings stakeholders together to engage in decision making through sharing of knowledge and experiences (Ansell, and Gash 2008). He recognizes that he needs the expertise of other people to solve complex problems, because he doesn’t have all the best answers (Levine 2011). There are many advantages to using a collaborative leadership approach. For example, a collaborative leader introduces a diversity of perspectives to the organization by connecting key resources together across any functional boundaries that may exist. This diversity of perspectives brings a fresh and unbiased view to the organization, which fosters a culture of continuous learning (Cuellar, Krist, Nichols, and Kuzel 2018). Also, a collaborative leader empowers her team to recommend decisions as a team, after ideas are shared and expected outcomes are understood. This feeling of empowerment encourages members of the organization to identify with the organization and to care about the success that the organization can achieve (Boylan, and Turner 2017).
One potential disadvantage to a collaborative leadership approach includes the threat of collaboration overload (Cross, Rebele, and Grant 2016). Effective collaboration requires participants to sustain energy and effort. If too much demand is placed on certain individuals, then they may experience burnout. Another potential disadvantage is that collaboration can result in decisions being delayed while different perspectives are being considered. An extreme case of delaying decisions is commonly called “analysis paralysis,” where people become so weighed down with considering every possible decision that all forward progress stops (Bensoussan and Fleisher 2013).
Benefits of Collaboration for Innovation and Creativity
A learning organization has a culture that encourages people to collaborate both within their own teams and also with stakeholders outside their teams (Crossan et al. 1999; Steiner 2009). The leaders of learning organizations set the expectation that everyone needs to continually learn and share knowledge with the others in the organization. Jain (2015) supports the same conclusion by writing that collaboration enhances innovative behaviors. Since collaboration is present within a learning organization, a learning organization supports innovative behaviors as well. In addition, when effective collaboration takes place in an organization, people will strive to contribute innovative behaviors even if those behaviors are beyond what is expected of them within their normal job roles (Panaccio, Henderson, Liden, Wayne, and Cao 2015).
Relationship of Innovation and Creativity With Sustainability
An effective leader will test and then implement innovative ideas that improve the organization by making it more efficient, effective, and adaptable to changing needs. The implementation of good, new ideas provides sustainability to an organization. For example, nonprofit organizations face many pressures that threaten their sustainability, including limited financial resources. Nonprofits need to attract financial supporters as well as dedicated people who choose to remain affiliated with the crowded group of organizations that compete for their participation (Wemmer, Emrich, & Koenigstorfer 2016). Wemmer et al. (2016) conducted a quantitative study of 292 nonprofit board members in Germany, examining the effectiveness of collaboration to maintain stable organizations within nonprofit sports clubs. The researchers concluded that sports clubs who collaborated with competing sports clubs had greater instances of innovation. Innovation enabled by sharing operational best practices led to the creation of new services and operational processes that enabled the sport clubs to become more efficient, more competitive, and more sustainable.
Another example of innovation and creativity encouraging sustainability was a qualitative case study that examined how women in Mexico used social innovation to achieve greater empowerment in a traditionally male-dominated society (Maguirre, Ruelas, and De La Torre 2016). After analyzing the data gathered in 70 interviews, Maguirre et al. (2016) concluded that the creation of gender-equality policies gave the women the opportunity to earn a living along with men. More importantly, these innovative policies empowered women to create their own businesses and to shift the focus on equality within the Mexican culture. As a result, this paved the way for both Mexican businesses and Mexican women in general to experience a more optimistic future outlook.
Balancing Innovation, Creativity, and Stability
It is a difficult, but critical, responsibility of organizational leaders that they balance the pursuits of innovation, creativity, and stability. According to Boylan and Turner (2017), there are four ways that leaders can achieve a proper balance of these factors. First is by communicating that the organization needs to take prudent risks. Taking prudent risks implies that leaders have a strategy that they believe will lead to stability and success, but they acknowledge that mistakes will likely occur along the journey. People don’t need to fear that every mistake will result in catastrophic consequences. When leaders set expectations in this way, they establish an environment of psychological safety with their people.
Second, feeling psychologically safe will encourage people to generate and share new ideas. New ideas are essential ingredients for innovation and creativity. However, leaders need to discern whether an idea is likely to be useful in the short term or the long term, since not all new ideas will be beneficial for organizational success. Given this inherent uncertainty, the best approach that a leader can take is to encourage new ideas to continually flow in from his stakeholders as matter of normal practice (Boylan, and Turner 2017).
Third, a leader needs to facilitate collaboration inside and outside of her organization (Boylan, and Turner 2017; Steiner 2009). An important condition for a healthy collaboration is to include an allowance of dissenting opinions. A collaborative leader will ensure that people with diverse viewpoints are interacting, because the tension that is created by different ideas is necessary for adaptability, creativity, and learning that lead to organizational stability (Uhl-Bien, Marion, & McKelvey, 2007). Collaboration brings the opportunity to identify risks and then to strategize how to adapt to mitigate those risks.
Fourth, a leader needs to balance innovation, creativity, and stability by recognizing and rewarding good ideas that emerge from his organization (Boylan and Turner 2017). Rewards can take many forms including financial prizes, public recognition, and commitment of resources to further develop the ideas into actions. The most important lesson that leaders can take about giving rewards is that it creates a cycle of continuous innovation within the organization when people are motivated to share their ideas and personally identify with the success of the organization.
In summary, organizations need to adapt to changing market conditions and evolve to overcome competitive pressures to be sustainable. Therefore, leaders need to infuse a culture of innovation and creativity into their organizations. An environment of psychological safety is an important prerequisite that leaders need to foster for collaboration to flourish (Boylan, and Turner 2017). Collaborative leadership is an effective way to enable innovation and creativity to grow, which results in a sustainable organization (Steiner 2009).
Measurement of Job Performance
It is important that employers use fair and effective hiring practices to build effective and sustainable organizations. Since employers rarely have complete knowledge of the traits that a job candidate possesses, they need to find ways to assess whether the candidate will be a good fit for any open job role using incomplete knowledge. Research on job performance supports that assumptions can be made between certain individual characteristics and future job performance (Van Iddekinge, and Ployhart 2008). Hiring managers can make better decisions by being aware of the conclusions that can be drawn from prior research.
Job performance can be measured according to several different dimensions. Four common measurements include task performance, citizenship performance, counterproductive work behavior, and adaptive performance (Van Iddekinge, and Ployhart 2008). First, task performance is the degree to which the results of an individual’s work meets the required expectations for that job role (Tams, Thatcher, and Grover 2018). The specific measurements of task performance will vary from one job role to another, but they can often be categorized according to metrics that apply to financial, time-bound, customer service, or organizational development goals (Dhamayantie 2018). Next, citizenship performance, or organizational citizenship behavior, refers to interpersonal actions that create a positive work environment while supporting the overall business objectives of the organization (Becton, Carr, Mossholder, and Walker 2017). Citizenship performance is associated with the attitude that an individual displays at work. For example, someone who volunteers to help colleagues or who takes on extra work that is not within the scope of her own job responsibilities displays high citizenship performance (Van Iddekinge, and Ployhart 2008). Yet someone with high citizenship performance might also engage in counterproductive work behavior. According to Van Iddekinge and Ployhart (2008), counterproductive work behavior refers to actions that an individual takes that violates social norms or otherwise does not support a positive work environment. For example, Rehman and Shahnawaz (2018) researched the negative organizational impacts of theft, rumor spreading, and intentional absenteeism across several types of firms in India. Finally, adaptive performance is the ability for an individual to adjust his work behaviors to meet changing job or organizational expectations (Van Iddekinge, and Ployhart 2008). A good example of adaptive performance is the ability of successful salespeople to change the approaches that they use to sell products or services to customers with a wide range of business needs, budgets, and personalities (Wang, Wang, and Hou 2016).
Measurement of Predictors
While there are several lenses that can be used to measure job performance, there are two main categories that researchers use to describe predictors of individual job performance: cognitive ability and noncognitive ability. According to Woodley of Menie, Piffer, Peñaherrera, and Rindermann (2016), cognitive ability refers to a person’s intelligence, with the term “g” referring to the general baseline of intelligence that most people inherently possess. The noncognitive predictor often studied is personality, which can be defined as the traits that a person most consistently displays in response to certain situations. One common approach to understanding personality is to examine the following five factors: conscientiousness, or the level of responsibility one feels to meet expectations; neuroticism, or emotional stability; extroversion, or the propensity that one has to work with others; agreeableness, or the desire to prioritize social relationships; and openness, or willingness to share with others (Judge, and Zapata, 2015). Researchers continue to study different combinations of these predictors to understand how they relate to building sustainable organizations (Wallace et al. 2016).
Predicting Job Performance
According to Wallace et al. (2016), it is understood that studying cognitive ability and personality traits in isolation cannot give complete insight into predicting organizational performance. Instead, studying the relationships between cognitive ability and personality traits within certain situations will yield the most valuable insights. One quantitative correlational study by Diedrich, Neubauer, and Ortner (2018) examined the role of both cognitive and noncognitive abilities to predict the job performance of 648 apprentices within diverse vocational areas in Austria. The authors concluded that there was a linear relationship between cognitive ability and job performance. However, the strongest predictor of job performance resulted from using an interactional approach where an intelligence measurement was combined with one of the five personality traits, namely conscientiousness. An important third variable used in this study was job satisfaction. In summary, the best job performance in this study was exhibited by participants with a combination of intelligence, satisfaction with their vocational area, and an inner drive to meet expectations.
Conclusions by Judge and Zapata (2015) supported the importance of the interactional approach as well. The authors conducted a quantitative correlational study to explore the degree to which each of the five personality traits was related to job performance in the literature. While they did not include measures of cognitive ability in their study, the authors did determine that there was a generally linear relationship between positive manifestations of each personality trait and job performance. However, Judge and Zapata (2015) concluded that the presence of certain personality traits within specific contexts were stronger indicators of job performance. For example, extroversion was a strong predictor of high performance in job roles that required social interaction and openness was a strong predictor of high performance in job roles that required creativity and innovation.
Improving the Hiring Process
Research on the relationships between cognitive ability and personality traits as predictors of job performance support three conclusions that can enable employers to make better hiring decisions. First, employers should understand that an interactional approach that combines measurements of cognitive ability and personality traits is effective because people cannot be characterized solely by their intelligence quotient (IQ) nor by a single personality trait. An interactional approach recognizes that there is an inherent complexity within individuals and that situational contexts will produce a wide range of possible performance outcomes (Dalal et al. 2015; Wallace et al. 2016). Second, while an employer should measure cognitive ability and personality traits, he should also determine the level of vocational interest that a job candidate has for the open position (Diedrich et al. 2018). This determination can be made through using open-ended questions in the interview process that ask the candidate to share their reasons for being interested in the job role for which they are applying (Weller et al. 2018). Third, an employer should understand the types of personality strengths that typically succeed in each job role, given the daily tasks and types of interactions that the jobs require (Judge, and Zapata 2015). This level of understanding can be gained by including subject matter experts in the interview panel and by providing opportunities for the most skilled employees to share the approaches that make them successful (Henry, McCarthy, Nannicelli, Seivert, and Vozenilek 2016).
It is important that employers hire the best people to build effective and sustainable organizations. Employers need to determine whether a candidate will be a good fit for any open job role using incomplete knowledge. Research that relates cognitive ability and personality traits with job performance can provide useful insights to predict future job performance to achieve organizational effectiveness (Dalal et al. 2015; Diedrich et al. 2018; Judge, and Zapata 2015; Van Iddekinge, and Ployhart 2008).
Conclusion
In some ways, the challenges that organizations and people face today are the same as those challenges that were faced in previous generations. However, artificial intelligence and machine learning are more than just the latest cycle of the working revolution. Global access to these technologies offer opportunities that are unprecedented. People and organizations who embrace these opportunities and build an infrastructure of continuous learning will lead their markets and help to define what the future of work will become.
Future jobs do require continuous learning with employees as both teachers and learners. Teacher and learner roles should be rotated because individuals on the team are experts in different bodies of knowledge and managers must create an adaptable culture of innovation and collaboration where employees are encouraged to work together and take risks.
References
Ansell, C., and A. Gash. 2008. “Collaborative Governance in Theory and Practice.” Journal of Public Administration Research & Theory 18, no. 4, 543–571. Retrieved from https://lopes.idm.oclc.org/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=34725965&site=ehost-live&scope=site
Becton, J.B., J.C. Carr, K.W. Mossholder, and H.J. Walker. 2017. “Differential Effects of Task Performance, Organizational Citizenship Behavior, and Job Complexity on Voluntary Turnover.” Journal of Business and Psychology 32, no. 4, 495. doi: 10.1007/s10869-016-9461-x
Bensoussan, B.E., and C.S. Fleisher. 2013. Analysis Without Paralysis : 12 Tools to Make Better Strategic Decisions. Upper Saddle River, NJ: Pearson FT Press. Retrieved from https://lopes.idm.oclc.org/login?url=http:
//search.ebscohost.com/login.aspx?direct=true&db=edsebk&AN=1598049&site=eds-live&scope=site
Boylan, S.A., and K.A. Turner. 2017. “Developing Organizational Adaptability for Complex Environment.” Journal of Leadership Education 16, no. 2, 183–198. Retrieved from https://lopes.idm.oclc.org/login?url=http:
//search.ebscohost.com/login.aspx?direct=true&db=eric&AN=EJ1138760&site=eds-live&scope=sitehttp://www.journalofleadershiped.org/attachments/article/503/0631_boylan.pdf
Cross, R., R. Rebele, and A. Grant. 2016. “Collaborative Overload: Too Much Teamwork Exhausts Employees and Saps Productivity. Here’s How to Avoid it.” Harvard Business Review 94, nos. (1–2), 74. Retrieved from https:
//lopes.idm.oclc.org/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=edsggo&AN=edsgcl.438632758&site=eds-live&scope=site
Crossan, M.M., H.W. Lane, and R.E. White. 1999. “An Organizational Learning Framework: From Intuition to Institution.” The Academy of Management Review 24, no. 3, 522. Retrieved from https://lopes.idm.oclc.org/login?url=http:
//search.ebscohost.com/login.aspx?direct=true&db=edsjsr&AN=edsjsr.259140&site=eds-live&scope=site
Cuellar, A., A.H. Krist, L.M. Nichols, and A.J. Kuzel. 2018. “Effect of Practice Ownership on Work Environment, Learning Culture, Psychological Safety, and Burnout.” The Annals of Family Medicine 16, no. (Suppl 1), pp. S44–S51.
Dalal, R.S., R.D. Meyer, R.P. Bradshaw, J,P. Green, E.D. Kelly, and M. Zhu. 2015. “Personality Strength and Situational Influences on Behavior: A Conceptual Review and Research Agenda.” Journal of Management 41, no. 1, 261–287. doi: 10.1177/0149206314557524
Dhamayantie, E. 2018. “Designing a Balanced Scorecard for Cooperatives.” International Journal of Organizational Innovation 11, no. 2, 220–227. Retrieved from https://lopes.idm.oclc.org/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=132233508&site=eds-live&scope=site
Diedrich, J., A.C. Neubauer, and A. Ortner. 2018. “The Prediction of Professional Success in Apprenticeship: The Role of Cognitive and Non-cognitive Abilities, of Interests and Personality.” International Journal for Research in Vocational Education and Training 5, no. 2, 82–111. doi: 10.13152/IJRVET.5.2.1
Goolsarran, N., C.E. Hamo, S. Lane, S. Frawley, and W.H. Lu. 2018. “Effectiveness of an Interprofessional Patient Safety Team-Based Learning Simulation Experience on Healthcare Professional Trainees.” BMC Medical Education 18, no. 1, 1–8. doi: 10.1186/s12909-018-1301-4
Henry, B.W., D.M. McCarthy, A.P. Nannicelli, N.P. Seivert, and J.A. Vozenilek. 2016. “Patients’ Views of Teamwork in the Emergency Department Offer Insights About Team Performance.” Health Expectations 19, no. 3, 702–715. doi: 10.1111/hex.12148
Jain, R. 2015. “Employee Innovative Behavior: A Conceptual Framework.” Indian Journal of Industrial Relations 51, no. 1, 1–16. Retrieved from https:
//lopes.idm.oclc.org/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=114168248&site=ehost-live&scope=site
Judge, T.A., and C.P. Zapata. 2015. “The Person--Situation Debate Revisited: Effect of Situation Strength and Trait Activation on the Validity of the Big Five Personality Traits in Predicting Job Performance.” Academy of Management Journal 58, no. 4, 1149–1179. doi: 10.5465/amj.2010.0837
Levine, S.R. 2011. “The Collaborative and Innovative Strategic Leader.” Credit Union Times 22, no. 19, 12–18. Retrieved from https://lopes.idm.oclc.org/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=60765042&site=ehost-live&scope=site
Maguirre, M.V., G.C. Ruelas, and C.G.D.L. Torre. 2016. “Women Empowerment through Social Innovation in Indigenous Social Enterprises.” RAM.Revista De Administracao Mackenzie 17, no. 6, 164. doi: 10.1590/1678-69712016/administracao.v17n6p164-190
McKenna, P.J. 2017. “Strategies for Overcoming Obstacles to Change.” Of Counsel 36, no. 7, 6–12. Retrieved from https://lopes.idm.oclc.org/login?url=http:
//search.ebscohost.com/login.aspx?direct=true&db=bth&AN=123824734&site=ehost-live&scope=site
Panaccio, A., D. Henderson, R. Liden, S. Wayne, and X. Cao. 2015. “Toward an Understanding of When and Why Servant Leadership Accounts for Employee Extra-Role Behaviors.” Journal of Business & Psychology 30, no. 4, 657–675. doi: 10.1007/s10869-014-9388-z
Rehman, U., and M.G. Shahnawaz. 2018. “Machiavellianism, Job Autonomy, and Counterproductive Work Behaviour Among Indian Managers.” Revista De Psicologia Del Trabajo Y De Las Organizaciones 34, no. 2, 83–88. doi: 10.5093/jwop2018a10
Schilling, J., and A. Kluge. 2009. “Barriers to Organizational Learning: An Integration of Theory and Research.” International Journal of Management Reviews 11, no. 3, 337–360. doi: 10.1111/j.1468-2370.2008.00242.x
Steiner, G. 2009. “The Concept of Open Creativity: Collaborative Creative Problem Solving for Innovation Generation - A Systems Approach.” Journal of Business and Management, 15, no. 1, 5–33. Retrieved from https:
//lopes.idm.oclc.org/login?url=https://search-proquest-com.lopes.idm.oclc.org/docview/211508893?accountid=7374
Stelmaszczyk, M. 2016. “Relationship Between Individual and Organizational Learning: Mediating Role of Team Learning.” Journal of Economics & Management 26, no. 4, 107–127. doi: 10.22367/jem.2016.26.06
Tams, S., J.B. Thatcher, and V. Grover. 2018. “Concentration, Competence, Confidence, and Capture: An Experimental Study of Age, Interruption-Based Technostress, and Task Performance.” Journal of the Association for Information Systems 19, no. 9, 857–908. doi: 10.17705/1jais.00511
Uhl-Bien, M., R. Marion, and McKelvey. 2007. Complexity Leadership Theory: Shifting Leadership from the Industrial Age to the Knowledge era doi:
//doi-org.lopes.idm.oclc.org/10.1016/j.leaqua.2007.04.002
Valentine, M.A. 2018. “Renegotiating Spheres of Obligation: The Role of Hierarchy in Organizational Learning.” Administrative Science Quarterly 63, no. 3, 570–606. doi: 10.1177/0001839217718547
Van Iddekinge, C. H., and R.E. Ployhart. 2008. “Developments in the Criterion‐Related Validation of Selection Procedures: A Critical Review and Recommendations for Practice.” Personnel Psychology 61, no. 4, pp. 871–925.
Vaughn, K.R. 2016. “Leadership: Three Key Questions.” Tennessee Law Review 83, no. 3, 803–812. Retrieved from https://lopes.idm.oclc.org/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=ofs&AN=117471310&site=eds-live&scope=site
Von Bertalanffy, L. 1968. General System Theory, 40. New York, NY.
Wallace, J.C., B.D. Edwards, J. Paul, M. Burke, M. Christian, and G. Eissa. 2016. “Change the Referent? A Meta-Analytic Investigation of Direct and Referent-Shift Consensus Models for Organizational Climate.” Journal of Management 42, no. 4, pp. 838–861.
Wang, X., G. Wang, and W.C. Hou. 2016. “Effects of Emotional Labor and Adaptive Selling Behavior on Job Performance.” Social Behavior and Personality: An International Journal 44, no. 5, 801. doi: 10.2224/sbp.2016.44.5.801
Weller, S.C., B. Vickers, H.R. Bernard, A.M. Blackburn, S. Borgatti,
C.C. Gravlee, and J.C. Johnson. 2018. “Open-Ended Interview Questions and Saturation.” Plos One 13, no. 6 doi: 10.1371/journal.pone.0198606
Wemmer, F., E. Emrich, and J. Koenigstorfer. 2016. “The Impact of Coopetition-Based Open Innovation on Performance in Nonprofit Sports Clubs.” European Sport Management Quarterly 16, no. 3, 341–363. Retrieved from https://lopes.idm.oclc.org/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=s3h&AN=114819238&site=eds-live&scope=site
Woodley of Menie, M.A., D. Piffer, M.A. Peñaherrera, and H. Rindermann. 2016. Evidence of Contemporary Polygenic Selection on the Big G of National Cognitive Ability: A Cross-Cultural Sociogenetic Analysis 102, 90–97. doi: //doi-org.lopes.idm.oclc.org/10.1016/j.paid.2016.06.054