The Chief Learning Officer’s Survival Guide: Part One

 Step One: Learning Trends and Obstacles to Change


Learning officers face a number of challenges in the current market. In this series of posts we are going to identify the top obstacles for innovation in digital learning and identify solutions based on the research literature. Over this period of time we will familiarize you with the research on learning innovation in the real world. It is one thing to want to innovate, it is another to succeed. We begin this series with a discussion of the idea of capacity starvation, the idea that learning officers and trainers are too busy to produce innovative solutions and that change, while desirable, will have to wait for another day. A real buzz kill. We begin with the top 3 “let’s put this on the back burner” messages.

The 3 Top Back Burner Messages:

1. We do not have the capacity.
2. We do not have the budget.
3. We do not have support from the team.

Let us address each of these in succession over the first three blog posts. We begin with the oft-repeated adage, “not now, we are too busy”. Otherwise known as “bandwidth limits”, “at capacity” or just good old “another day”.

Capacity Starvation

Many learning managers find themselves pushed to capacity in terms of workload and continuing the operation of existing systems. They may find themselves unable to focus on digital innovation due to the pressure of time and institutional momentum. As early as 1969 Paul Lawrence wrote in the Harvard Business review that the nature of change needs to be described and identified. He quotes the study of Coch and French who studied resistance to change in four groups of factory workers. The first was a non-participation method, in which they were simply told about operational changes in the staff room. The second group was able to participate, but only by selecting a representative. The third and fourth groups were consulted about how to reduce cost and a strategy was implemented that involved the workers.

The output of the first group dropped by 30% over the next month, marked aggression toward managers was noted, and 17% of staff quit within 40 days. Hostility and reduction of production were also evidenced when compared to the 3rd and 4th groups. There were no quits in these latter groups, change was adopted rapidly and there were no signs of hostility toward the management group. Lawrence went on to identify sources of resistance in terms of technological vs. social change. Technological change was not difficult, social change was challenging. The figure below, reprinted from Lawrence’s summary of Coch and French illustrates the form that these might take. Clearly, top-down implementation creates resistance in workers when they have a non-participative role.

Carol Packard, writing for ASTD comments on the work of Antonia Mercedes García-Cabrera and Fernando García-Barba (2014), who showed that resistant thought, feeling and behaviour were all central concerns in the research literature. They surveyed 143 employees across 7 large companies in Spain and showed, similar to Coch and French, that preservation of the social network in the organization was central to dealing with thought resistance. Employee participation reduced all forms of resistance. Organization-based self-esteem (OBSE) was positively associated with change; that is, when they felt valued as members of the change process toward improvement of the organizational outcomes, resistance was minimal.

Yilmaz and Kilocuglu showed in the 2013 volume of the International Association of Social Science Research that blind, political and ideological resistance variables could be identified in the literature reviews. Blind resistance is simple obstruction of change without knowledge of the process, political resistance involves alignments of actors in a social context in the organization and ideological resistance lay in the beliefs and values about the work they performed. These were broken down into several sub sections, such as interference with need fulfillment and selective perception. These were mixed with habit dedication, changes in security which is established on the basis of the past and inconvenience. They conclude their study by looking at the work of Kotter and Schlesinger, 1979, on the varieties of overcoming resistance to change, which include education, communication, participation and involvement and both implicit and explicit coercion.

There are numerous studies which outline these variables within specific industries, but the initial argument, that management capacity is a major factor in blocking innovation seems unfounded. The research shows that the way change is implemented profoundly influences success. The US Agency for Healthcare Research and Quality reports that in studies of emergency department innovation that capacity and budget interact. They cite a case study, reprinted below:

Example 10. St. Francis Hospital: Securing a Champion

St. Francis Hospital in Indianapolis, IN, focused on front-end improvement strategies. One of the strategies employed by St. Francis was registration zoning, which assigns a staff member to fully register patients in a specific “zone” of rooms using workstations on wheels (WOWs). Initially, hospital leadership refused to approve the purchase of WOWs because funds were limited, and the entire health system was moving toward standardized mobile units. With the support of the director of business transformation, the chief operating officer became a champion for the project. These two leaders eventually succeeded in lobbying for the purchase of two WOWs. Since the WOWs were not in place until February 2010, progress was held back during the early phases of the collaborative.

This shows the power of identifying a champion for change, a role which the Chief Learning Officer needs to fulfill unless they can select a designate. The problem seems familiar to any training officer, funds are limited, the system is moving in a given direction and introduction of change to an already difficult scenario is blocked. However, the chief officer here, working with the business transformation leader, were able to lobby for adoption. So technological change in digital learning is always dependent upon a champion to isolate and reduce limits to innovation.

They provide a second case study we will reprint here to comment on its generalization to learning and development.

Example 11. Overcoming Staff Resistance and Culture Change at Hahnemann University Hospital

As part of its participation in UMLN II, Hahnemann implemented an open-bed policy, where patients are directed to an open bed as soon as it becomes available for triage and registration. The traditional protocol at Hahnemann had been to triage and register patients when they arrived in the ED and have them sit in the waiting room until a nurse was ready to see them. Patients waited hours, even if a bed was empty, because nurses thought that they had too many patients to care for and were overwhelmed at taking on more patients. The open-bed policy was designed to reduce the bottleneck of patients in the waiting room, getting them into a bed sooner. Additionally, it reduced the likelihood of patients leaving the ED if they were already in a bed.

The implementation of the open-bed policy occurred gradually. The ED director stressed the importance of the open-bed policy at all staff meetings, but there was resistance by staff. Nurses focused on the number of patients that they were responsible for, regardless of the intensity of time that patients required. The nurses were overwhelmed when they had responsibility for more than four or five patients, even if some of the patients were simply waiting for laboratory results.

In addition, many staff members were skeptical about the implementation of the open-bed policy because of failures by previous department leaders to sustain change. This situation resulted in staff being skeptical that the ED leaders were serious about making it a permanent part of operations. It was initially treated as a “flavour of the month,” where operations would be modified for a while but would slowly revert back to the old method.

One factor that helped foster acceptance of the open-bed policy among staff nurses was that the triage or charge nurses would often begin patient work-ups when they brought a new patient to an open bed, relieving the staff nurse from the responsibility. Further, in 2008 the department experienced considerable turnover, resulting in a need to hire 30 new nurses. Department leaders and nurses reported that it was easier for the new nurses to adapt to the process changes because they were not as familiar with previous processes. The open-bed policy gradually gained acceptance during the day shift. It is the hope of ED leadership that the night shift will soon follow in acceptance.

In addition, to sustain the changes, there were constant reminders by the department leaders about the importance of the changes. The presence of outside technical advisors and evaluators under the UM collaborative also conveyed a message to staff that these changes were different and would be sustained.


These discussions of hospital policy may seem foreign to non-health care trainers, but the variables are identical to those seen in corporate environments. Note that newer hires are eager to embrace changes, while those with seniority viewed them as a flavour of the month. Staff meetings were not helpful, and trainers are certainly aware of the limited power of presentations in encouraging behavioural change. Again, leadership through transition was more important than the technology itself.

As learning officers move toward creative solutions such as gamification and game-like elements, these research data show that adoption of the technology is the easy part. The preservation of social harmony and organizational support is the difficult aspect of implementation. Therefore, it is essential that change planning focus on the human element more than the technology. In fact, without this preparation, of the cultivation of program champions and early involvement of end users, innovation will likely fail.

According to Daron Acemoglu from the Massachusetts Institute of Technology (MIT), investments in skills are suboptimally low. Innovation and training lead to an amplification of inefficiency…we end up further from our goal by creating new training! Acemoglu comments that workers are more “willing to invest in their skills by accepting lower wages today if they expect more firms to innovate and pay them higher wages in the future”. Firms are willing to innovate when there is expectation that future workforce skills will be higher. So the main drivers for change appear to be the consideration of future skill demands and the prospect of higher wages as a result of training.

Therefore, digital innovation in training has little to do with technology and manager workload, and everything to do with perceived benefits on a micro level among workers. Every day we see dozens of opportunities lost as managers report high bandwidth and leverage that as their reason for resisting change. Until the nature of change itself aligns with a clear view of expected outcomes, its adoption rate will be low.

How does this impact a chief learning officer? It is clear that most digital change is ill-fated by design and that without the social elements being addressed first, there is no value proposition in training innovation. This is why we see stale learning management systems, board room talk training and independent study still dominant in both industry and higher education to this day, despite the volumes of research on the effectiveness of these methodologies. Preparing for change is more important than change itself. The top down management style which characterizes many training initiatives reinforces the tried and true, since there is no dialogue on innovation supporting the view that change will signal new opportunities.

In our next post we will examine the role of budget in adoption of digital learning innovations. It is not enough to say we have limited funds and often the solutions do not lie in spending more. Rather, they lie in spending smart. Good gamification systems encourage social adoption of change and learning and this is where they can do their best work.

Begin to think of gamification as a strategy for change rather than as a learning system and you will begin to see the problem in a whole new way. Gamification reduces resistance to change, and that is something that training in conventional systems cannot do purposively.

Until then, keep on looking up!