Recent Research on #EmployeeEngagement (Part 1)

The understanding of employee engagement, which has been studied extensively in recent years, has taken on new urgency in the current labor market environment. Organizations that do not understand the fundamentals of employee engagement may find themselves dealing with unmotivated, underappreciated, and dissatisfied employees who may be spending their free time searching for better opportunities. Two recent studies seek to bring order to the vast research on engagement, and to explore the role of employee engagement in the formation of the employee-organization relationship. For purposes of this overview, the definition of the “Utrecht Group” (Wimar Schaufeli and associates) will be used:

A positive, fulfilling, work-related state of mind characterized by a sense of vigor towards, dedication to, and absorption in work activities.

The first study, The Meaning, Antecedents and Outcomes of Employee Engagement: A Narrative Synthesis (Bailey et al., 2017), a literature review is performed to answer three questions:

  • How has engagement been defined and theorized?
  • What antecedents are associated with engagement?
  • What evidence is there that engagement is associated with employee morale and performance?

The study begins with a master class in structuring and executing a literature review. Anyone familiar with research in the social sciences will have come across many literature reviews–but this article is unique in the level of description and analysis of the research and review process that is provided. Key to the process is the narrative evidence synthesis method of planning, structured search, evaluation of material against agreed eligibility criteria, analysis and thematic coding, and finally reporting. An initial search resulting in 712,550 records was reduced through this process to 38 items with theoretical/conceptual models; 172 empirical papers; and 4 meta-analyses. The following is a brief recap of discoveries of this analysis.

How has engagement been defined and theorized?

The authors found six headings into which study definitions could be grouped:

  • Personal role engagement: the individual’s cognitive, emotional, and physical expression of the authentic self at work;
  • Work task or job engagement: the Utrecht Group’s definition of engagement quoted above;
  • Multidimensional engagement: a distinct and unique construct consisting of cognitive, emotional and behavioral components that are associated with individual role performance;
  • Engagement as a composite attitudinal and behavioral construct: research featuring measures of cognitive, emotional, and behavioral engagement;
  • Engagement as management practice: an emerging field of inquiry with no firmly established definition or conceptualization; and
  • Self-engagement with performance: defined as the individual’s sense of responsibility for and commitment towards performance.

The predominant definition of engagement is that of the Utrecht Group (noted above) which was adopted in 86% of the studies reviewed. After the definitions, five principal theoretical frameworks utilized in the studies were discussed:

  • The Job Demands-Resources (JD-R) framework was utilized in 38% of the studies reviewed. The JD-R framework distinguishes between resources (job-related or personal) and job demands. Resources energize employees and lead to engagement, while demands which require additional effort can lead to disengagement, and thus, negative outcomes.
  • In Social Exchange Theory (SET) relationships between employees and employers are based on reciprocity: when employees feel they are being treated well, they respond with higher levels of engagement.
  • Conservation of Resources theory suggests that employee engagement can be driven by the provision of resources;
  • Broaden-and build theory argues that engagement can occur when individuals experience positive emotions, which create the space for a broader range of thought-action repertoires; and finally,
  • Kahn’s engagement theory, which is based on the premise that engagement is influenced by meaningfulness of work, psychological safety, and experienced availability.

Antecedents of Engagement

The authors reviewed 155 empirical studies of antecedents to engagement, and found five main headings:

  • Individual Psychological States: the most studied attributes included self-efficacy, resilience, and personal resources were found to be positively associated with engagement.
  • Experienced job-design-related factors: multiple studies showed positive associations between job resources (supervisory and colleague support, feedback, and autonomy) and engagement.
  • Perceived Leadership and Management: multiple studies found a positive association between transformational leadership and engagement.
  • Individual perceptions of organizational and team factors: perceived organizational support, organizational identification, and team-level climate and communication were shown to be positively associated with engagement.
  • Organizational interventions or activities: some studies noted associations between training and development programs and engagement.

Outcomes of Engagement

The outcomes of engagement were explored under two headings, Performance and Morale. Not surprisingly, ample evidence exists in multiple studies demonstrating the positive relationship between engagement, better performance, and morale. The most critical conclusion of this meta-study is that evidence suggests that engagement is associated most strongly with job satisfaction and organizational commitment.  Specific recommendations supported by the author’s analysis include:

  • Job designs that allow for autonomy and feedback on performance;
  • Ensuring that workers have sufficient and appropriate resources;
  • Leadership that is positive and authentic; and
  • Enhancing individual resilience and personal resources.

Furthermore, there is some limited evidence that interventions can positively impact engagement levels, and that there may be ways for employers to develop and enhance engagement. Evidence on this last point is limited, however, and it is hoped that interventions to improve engagement will be the focus of future studies.

In a future blog post, I’ll be recapping the findings from the Eldor and Vigoda-Gadot article noted below.


Bailey, C., Madden, A., Alfes, K., & Fletcher, L. (2017). The Meaning, Antecedents and Outcomes of Employee Engagement: A Narrative Synthesis. International Journal of Management Reviews, 19(1), 31–53.

Eldor, L., & Vigoda-Gadot, E. (2017). The nature of employee engagement: rethinking the employee–organization relationship. International Journal of Human Resource Management, 28(3), 526–552.

Fun with #Prototyping!

As part of an ongoing project, I have been prototyping pieces of a new recognition program I’m preparing to roll out. I have been meeting with people one on one to test one of the most critical components of my plan, involving motivation and sustainment. I knew going into these interviews that I would learn a lot, but I wasn’t initially prepared to deal with the amount of content generated that was not directly related to the piece of my plan that I was testing.

One of the challenges I’ve experienced in prototyping sessions is keeping people on topic. Even with a very simple A/B design to test a response to a change in conditions, there are a lot of questions and comments, many not related to the exact component I am testing. What I have learned is the Power of the Parking Lot. Now, when I start my Prototyping test, I make sure that I have a parking lot prominently posted on either a whiteboard or easel, so that I can capture all the off-topic ideas that are generated in the course of the test.

Now that I am nearly done with my tests, I plan to bring together small groups of test participants to review all the ideas surfaced in my prototype tests. These ideas can then drive another round of tests, or perhaps be integrated directly into my overall plan.

So, when you start meeting with people to prototype parts of a larger plan:

  • Be prepared with a parking lot to capture ideas that are not directly related to your test;
  • Keep your subject focused on the content you are testing by letting them know that you can capture all of their off-topic ideas for later discussion; and
  • Bring test participants back together to review all the ideas generated in your tests.

My Approach: Creating solutions that incorporate the unique context of the organization

In an earlier post, I wrote about the challenge of staying in the problem space and not jumping to solutions too quickly. One should keep in mind that there are no easy or obvious solutions out there—if there were, they would have already been successfully implemented. Along with this, one should also be cautioned against generic, off-the-shelf solutions, particularly those sold by the innumerable management gurus whose books litter the (few remaining) bookstores. Generic solutions aren’t going to solve your problems—every organization has different processes and a different culture, both of which must be considered when designing solutions.

The most effective solutions will have two characteristics: they will be specific to the exact challenges faced by the organization, and the solutions will be a joint creation of the designer and the organization members. In my recent work, I’ve created an opportunity tree that’s helped me focus on the specific challenges faced in my organization. Armed with that detailed knowledge, I have reviewed ideas with organization members, to generate solutions that are desirable and workable in the context of the organization. Prototype testing of those ideas has allowed me to further refine and rework my solution, and with a little more testing, I hope to be able to offer a sustainable solution that is tailor-made to the context of my target population.

My Approach: Surfacing Assumptions

The third stage of the Design Thinking Process calls for Ideation, that is, identifying new solutions to the problems surfaced in the previous stage in the process. Before proceeding to the next stage of Prototyping, however, the designer needs to examine the assumptions underlying each of the proposed solutions. One solution can be founded on many assumptions, and any one of those assumptions, if proven false, could invalidate a proposed solution, or at the very least, point the way to rethinking a solution. One potential challenge, however, is that there can be many different assumptions built into a proposed solution—so many that it’s impossible to test them all. The key is to identify the assumptions that are most critical to the success of the solution—the assumptions that have the potential to render a proposed solution unworkable.

To take a single example from my own work, I had discovered that, quite reasonably, people wanted more public recognition of their successes. As part of one of my solutions, I included public recognition via Yammer, but recognized that I was merely assuming that recognition via Yammer would be valued. In prototype testing, though, I received mixed results on that proposition, and therefore had to rethink that part of my solution.

I had prototyped that part of my solution as a single item, to determine if it would work—and the great thing about prototyping is that in this case, the feedback opened the door to other ideas that would achieve the desired result.

My Approach: Developing Empathy

In the course of their work, OD practitioners and consultants will be engaged by business leaders to solve problems. These leaders may come to the table with facts, their perceptions, data from internal sources, and, inevitably, their own prejudices about how a business, and the people driving the business, should work. In what, then, will the practitioner’s solutions be grounded?

Solutions to the organizational opportunities that arise from people must be found within the people themselves. No solution can be successful or sustainable if it is not grounded in the needs, behaviors, and circumstances that form the foundation of the work environment. At all costs, the practitioner must avoid using stereotypes or depersonalized constructions of the users for whom the solution is being designed. Shortcuts like mere numbers or market segmentation data won’t allow the practitioner to get to the heart of the problem to be solved.

My experience in problem solving has led me to talking to people, in interviews and round tables, to help me understand exactly what they are thinking, and most importantly, doing. By talking to people, and observing how they work and what they themselves experience, I have been able to experience their challenges through their eyes. Developing empathy for end users leads to a deeper understanding of how and why they work the way they do. Experiencing work as others do opens the door to genuinely insightful problem-solving.

This doesn’t, however, need to mean that there is no room for hard data in the problem-solving process. I have used “big data” in the form of function-wide learning reports, to help understand where learning gaps exist that I can explore through interviews and observations. Bringing together insights based on empathy and data analytics lays the foundation for sustainable solutions that meet the needs of all stakeholders.

My Approach: Drawing and Mapping the Way to Clarity

When I was first asked to use a visual tool as part of a Design Thinking project, my first response was, “I can’t do this—I just don’t have the skills to draw out anything that would be useful.” I can’t draw, I don’t have any particular ability to create anything visual at all—I just don’t have that capability. My skill is writing—I can research and write about anything, and I’m always tempted to think I should try to leverage whatever skills I think I have.

The root of my challenge here is that I was thinking too literally—since I can’t draw, I can’t use visual tools. It took someone more perceptive than I to help me see beyond this supposed limitation. Recently, in a workshop, I was using the online tool to create a Story Map, a kind of flow chart, of a process that I was putting together. One of the instructors came over and said, “You know, you’re drawing right now.”


That was all it took to unlock my thinking—I suddenly realized that I drew things out all the time to help me understand them. I’ve sketched out an Input/Output model to help understand the components of a proposed dashboard, and how those elements fit together:


I’ve learned to use Mind Mapping (and a nifty tool called The Brain) to lay out diagrams to pull together and organize ideas:


And I draw out academic models all the time, just to help me understand them better, and to help me gain insights. I recently wrote about the Job Demands/Resource Model, in an earlier blog post—it wasn’t until I drew the model out that I really started to make sense of the connections in the model:

JDR Model

In all of these examples, I’ve intuitively used some sort of a visualization to get me closer to understanding and gaining insight from whatever I was working with. But I would never have said that that was what I was doing. Sometimes it just takes a little nudge, like the one I got in my workshop, to open your mind–and remind you of what you’ve been doing all along.

My Approach: Using theory as inspiration

Since I was first exposed to Design Thinking, I have always gone back to Tim Brown’s Change by Design for ideas and inspiration. Brown (President and CEO of IDEO, and the leading exponent of Design Thinking) has written in his book the definitive guide to the application of Design Thinking to both product and process problems. Now that I am starting to think about prototyping solutions for my DOEC project, I’ve gone back to Brown to get some inspiration from the basic prototyping principles he lays out.

When I think about how I might start prototyping solutions, I’m going to keep in mind Brown’s admonition that early prototypes should be “fast, rough, and cheap.” Why? Because the greater the investment in an idea, the more committed one becomes to it. The whole point of prototyping is to learn quickly from iterative experiments: the prototyping process creates the opportunity to discover new ideas that can be further built out in successive iterations. Well taken, also, is the point that prototypes should only involve as much time and effort as necessary to generate good feedback that can drive ideas forward.

I’ve also taken prototyping inspiration from Lim and Stolterman’s article on the anatomy of prototypes. One of the ideas that they explore is that of the prototype as filter: a prototype can be used to explore certain aspects of a design idea by filtering to specific qualities. Rather than prototyping a complete solution, it is possible to filter the solution down to specific components that can be tested individually. Therefore, the best prototype is the one that filters to the specific qualities the designer wants to explore.

As I approach my design prototypes, I will therefore keep an eye out to experiments that are quick and easy to put together, and that are designed to explore specific components of my overall solution.


Brown, T. (2009). Change by Design. New York: HarperCollins.
Lim, Y.-K., Stolterman, E., & Tenenberg, J. (2008). The anatomy of prototypes: Prototypes as filter, prototypes as manifestations of design ideas. ACM Transactions on Computer-Human Interaction, 15(2), 1–27.

Working Out Loud to Reframe the Question

I think I am getting to caught up going around and around in my problem space. I am caught in a cycle that’s leading me nowhere, as I consider how change plays out in my work group. I get stuck on the challenges that arise with change management where incomplete information is transmitted at the beginning of the change process. Change information communicated from senior leadership will differ in quality, depending on the circumstances: sometimes more or less information will be available at the outset, because the change is a work in progress. Perfect information about organizational changes will probably never be possible, because organizational change is a dynamic process. 

How did I get here? By asking, “Why is change management so hard here?” I think what I need to do is completely reframe the problem. In Tom and David Kelley’s book Creative Confidence, five techniques for reframing problems are laid out:

  1. Step back from obvious solutions: if the solution is obvious or simple, maybe you are solving the wrong problem.
  2. Alter your focus or point of view: shifting your focus to the viewpoint of another stakeholder can open up new insights on the problem.
  3. Uncover the real issue: how much time have I spent thinking the problem was one thing, when it’s entirely possible that it’s something else?
  4. Look for ways to bypass resistance or mental blocks: framing a problem in an entirely new way can help people accept new solutions to old, seemingly intractable problems.
  5. Think about the opposite: Flipping the question around can help think about problems in new ways.

The problem I have been trying to think through, “Why is change management so difficult?” needs to be reframed–how about “What can we do to make change management easier?” That’s a very different question, that could potentially lead to an easier path to solutions. Maybe that’s still not the best reframing of the problem, but I think that’s the direction I need to go.

My Approach: Staying in the Problem Space

The Design Thinking methodology, based in the deep understanding of the end-user experience, can illuminate complex and difficult problems. As with any problem-solving discipline, however, there are challenges inherent in the process that must be anticipated and resolved. Of the many potential challenges, there are three related to staying in the problem space that designers must be cognizant of and address:

  • As the discovery process begins to revel the contours of the problem, there can be an overwhelming temptation to jump in with solutions. The problem is there to be solved, right? So why wait? The solution is to take a step back, and be ready to acknowledge that if the answers were easy, they would already have been implemented. Dive deep, and seek a holistic understanding of the problem.
  • Groupthink on a problem-solving team can be deadly, as it can shut down ideas that haven’t been expressed. Team leaders need to create an open environment for exploration that enables team members to freely express their thoughts and ideas.
  • Finally, misdiagnosing the problem can result in failure. Designers and teams must dig deeper into their challenge, and expose and account for all the factors that can impact the problem.

Leaders of Design Thinking projects must enter the problem space fully aware of the potential challenges noted above, and be ready to focus effort on building a deep and holistic understanding of the opportunity being explored. This deep and holistic understanding will enable the designer to develop the empathy needed to move into the next problem-solving steps.

What is Organizational Effectiveness? (Pt.2)

Much of the research on organizational effectiveness in over the last two decades has revolved around one major concern: bringing products to market. Specifically, the ability to innovate, adapt, and stay ahead of changing market conditions form the foundation of the studies that I have recapped below. In these studies, organizational effectiveness can be seen as successfully meeting the goals of the organization, the efficient use of resources, performing well in the marketplace, and, ultimately, the ability to deliver more value to customers than rival organizations. Below is a brief recap of articles that lay out the current state of thinking as of 2018 on organizational effectiveness.

Hunt and Duhan, 2002

  • Hunt and Duhan see organizational effectiveness as the ability to deliver more value to customers.
  • This paper is not a work of empirical research, but rather an examination of competition, efficiency seeking, and effectiveness seeking.
  • Competition is now about becoming an effectiveness seeking enterprise.
  • Businesses will become more dependent on innovations that help firms deliver more value to customers.
  • An effective outcome is superior profits flowing to firms that innovate to deliver superior value while controlling costs.
  • This article looks at effectiveness through the lens of Resource Advantage theory, which views firms and resources as ‘heritable, durable units of evolutionary selection, with competition for comparative advantages in resources constituting the selection process.”
  • Firms seek positions of competitive advantage because those positions result in superior financial performance. Desire ultimately is to produce goods more efficiently with more value (more effective).
  • Organizational effectiveness is therefore seen as the result of producing most efficiently the goods with the highest value to consumers. 

Buganza and Verganti, 2006

  • B & V see organizational effectiveness as an outcome of processes and procedures that enhance an organization’s ability to tailor offerings to the needs of customers.
  • Italian online brokerages were chosen by the authors to form the basis of their research, because of the high level of turbulence in the industry. Environmental turbulence was identified as critical because developing products in a turbulent environment poses particular challenges to the organization: the market or technology may shift rapidly and unpredictably during the product development window, thus requiring sound developmental processes.
  • The purpose of the study was to examine Life-Cycle Flexability: the ability to adapt and redesign products or services according to changes in the market after the product is first released. The main components of the model are the frequency of adaptation, the rapidity of adaptation, and the quality of adaptation.
  •  Case study research was performed to identify the main research hypotheses, and then two surveys were designed to measure both practices and performance in the organizations examined.
  • Result of the research was validation of the LCF model and the identification of a methodology for investigating the model.

Gregory et al 2009

  • Uses the competing values framework (CVF) to understand the impact of employee attitudes on effectiveness.
  • CVF model consists of multiple domains (Group, Developmental, Rational, Hierarchical, and Balanced),each of which has some impact on organizational effectiveness. The most important of these here are Group, which is characterized by high flexibility and internal focus, and Balanced, in which the values of each of the domains are strongly held.
  • Surveys were sent to 677 hospital managers to measure these different domains.
  • Findings indicated that organizations that value teamwork, cohesion, and employee involvement will outperform organizations that do not. Findings also suggest that organizations with a balanced culture will possess a mix of values required to manage the mix of conditions that may be encountered.

Zheng et al 2010

  • In this article, the goal is to examine the relationship between knowledge management and organizational effectiveness, which is here understood to be the degree to which an organization realizes its goals. Zheng posits that knowledge management relates positively to organizational effectiveness, and that there is a relationship between organizational culture, knowledge management, and effectiveness.
  • Mail and web-based surveys were administered to HR professionals in 301 organizations in the service, manufacturing, and agricultural sectors.
  • Statistical analysis of the survey results supported multiple findings:
    • In addition evidence connecting knowledge management and organizational effectiveness, evidence places knowledge management as an intervening mechanism between organizational context and organizational effectiveness.
    • Organizational strategy has an impact on knowledge management.
    • Most importantly, knowledge management was found to mediate the influence of organizational culture on effectiveness. In other words, how well knowledge is managed is largely associated with how well cultural values are translated into value in the organization.
  • The implication for managers is that knowledge management can impact organizational effectiveness when it is in alignment with organizational culture, structure, and strategy. Focus on knowledge management practices can help transfer the impact of resources to the bottom line.

Zheng model

Figure 1: Zheng’s Model of Knowledge Management Effectiveness Mediation

Jimenez-Jimenez and Sanz-Valle 2011

  • The goal of this study was to understand the relationship between organizational learning, innovation, and performance.
  • Past research demonstrates that companies with the capacity to innovate will be able to respond to market challenges faster than less-innovative companies. Organizational learning, the process by which an organization develops the new knowledge and insights of associates, has also been demonstrated in studies to be a key variable in the enhancement of organizational performance. When associates share their learnings, the development, transformation, and exploitation of new knowledge can enhance and drive innovation.
  • Research was performed by conducting personal interviews of 451 employees chosen from 1600 Spanish firms from the manufacturing and service sectors.
  • Among the findings of this research:
    • Innovation has a positive and significant effect on performance, which supports the widely held understanding that innovation is a key driver of company success.
    • Organizational learning has a positive effect on both performance and innovation.
    • Because the impact of organizational learning is higher on innovation than on performance, the implication is that organizational learning influences performance by facilitating innovation.
  • The research therefore suggests that organizations hoping to drive performance through innovation should improve organizational learning processes.

Arnett et al 2018

  • This paper represents an extension of Buganza and Verganti’s research on life-cycle flexability, and multiple studies of the new product development process.  The authors posit that new product development (NPD) capability enables the improvement of organizational effectiveness by improving both product advantage and life-cycle flexability.
  • Telephone survey test data was gathered from 180 Norwegian hotels.
  • Study findings confirmed that NPD capability can improve both product advantage and life-cycle flexability. Therefore, to improve organizational effectiveness, managers should work to develop higher levels of NPD capability.

Arnett Model

Figure 2: Dual Effects of NPD capability on organizational effectiveness


Arnett, D. B., Sandvik, I. L., & Sandvik, K. (2018). Two paths to organizational effectiveness – Product advantage and life-cycle flexibility. Journal of Business Research, 84, 285–292.

Buganza, T., & Verganti, R. (2006). Life-Cycle Flexibility: How to Measure and Improve the Innovative Capability in Turbulent Environments. Journal of Product Innovation Management, 23(5), 393–407.

Gregory, B. T., Harris, S. G., Armenakis, A. A., & Shook, C. L. (2009). Organizational culture and effectiveness: A study of values, attitudes, and organizational outcomes. Journal of Business Research, 62(7), 673–679.

Hunt, S. D., & Duhan, D. F. (2002). Competition in the third millennium: efficiency or effectiveness? Journal of Business Research, 55(2), 97–102.

Jiménez-Jiménez, D., & Sanz-Valle, R. (2011). Innovation, organizational learning, and performance. Journal of Business Research, 64(4), 408–417.

Zheng, W., Yang, B., & McLean, G. N. (2010). Linking organizational culture, structure, strategy, and organizational effectiveness: Mediating role of knowledge management. Journal of Business Research, 63(7), 763–771.

What is Organizational Effectiveness? (Pt.1)

As with any construct, like “Leadership” or “Management”, the answer to the question “What is Organizational Effectiveness?” is necessarily complex. So complex, in fact, that over the last three decades, tens of thousands of words have been devoted to describing multiple different models, or to saying that it is impossible to have any kind of model for organizational effectiveness at all. The problem stems from a variety of sources: the functions of different types of organizations (for-profit vs. non-profit), the requirements of various constituents, questions of measurement, and so on. Since effectiveness is a product of the values and preferences of different types of institutions and the individuals that direct them, the best criteria for evaluating effectiveness are difficult to identify. For my purposes, however, I can focus on organizational effectiveness as it can be construed in the context of a for-profit company.

In this first part of my overview of research on Organizational Effectiveness, I want to briefly review the different models discussed in Cameron and Whetten’s 1983 collection, Organizational Effectiveness: A Comparison of Multiple Models (Cameron & Whetten, 1983). Rising anxiety about the competitiveness of American corporations in the late seventies and early eighties gave rise to a lot of thinking about the topic of Organizational Effectiveness (OE), and this volume sums up the state of research through its publication. As such, this is a good place to start trying to understand OE.

Schneider’s Interactionist Model

The first model examined is based in the behaviors follow from the naturally occurring interactions between people and things. In this “interactionist” perspective, organizations are characterized by the people in them, and the people who join organizations that are like themselves. That is, people with similar abilities and needs are attracted to particular settings, and people with positive experiences in those settings tend to stay there (Schneider, 1983). One of the attractors for organizations are their goals, and organizations will naturally select people who appear to be able to help achieve those goals. People who achieve their own goals within the organization, and help the organization achieve its goals, will tend to remain. Those people that do not achieve their goals, or do not fit in with the organization with the organization will leave, and the resulting cycle, illustrated below, shows this cyclical relationship between organizational goals, attraction, selection, and attrition.

Interaction model

This cycle can, however result in organizational decay, as inertia may set in from like-minded people interacting in the stable environment supported by the attraction-selection-attrition cycle. To survive, the necessity for change must be legitimized, and people with diverse views and capabilities, as opposed to those like people already employed, must be sought out. Ultimately, one measure of effectiveness in this model would be the resources devoted to attracting, hiring, and retaining people whose primary contribution to the organization is the push to change driven by constantly evaluating the organization’s long-term viability.

Seashore’s Integrated Model of Organizational Effectiveness

The second perspective examined (Seashore, 1983) is a framework incorporating multiple models:

  • The Natural System Model sees the organization as a self-maintaining system in equilibrium with the environment. Effectiveness here is described and evaluated with reference to all aspects of the organization that have some function in its adaptation, maintenance, and transformation processes.
  • The Goal Model assumes that the organization has clearly definable goals, and effectiveness is a measure of the organization’s ability to achieve its stated goals.
  • The Decision-Process Model states that organizations develop distinctive ways of managing information resources for the attainment of goals. Here, an effective organization is one that optimizes knowledge management.

Integration is suggested by the need of organizations to balance all three of these models, and integration take place in the process of attending to each of them. Evaluation of effectiveness is a function of the perspective of the different possible evaluating parties or stakeholders, a relational construct fit to the needs and interests of constituents.


Weick and Daft’s Effectiveness of Interpretation Systems

The third model explored hinges on the ability of organizations to interpret, or make sense of, their surroundings in a way that enables them to take productive actions. Interpretation is the process of making sense of the events that occur both within and outside of an organization. All the activities that can impact an organization must be captured and mapped conceptually to bring out meaning. Interpretations, furthermore, are reasonable rather than right. Reasonable explanations accommodate more data than they exclude, are sufficient in the eyes of more than one person, and can be used to explain new occurrences that were not used to generate the original interpretation (Weick & Daft, 1983).

Effectiveness in this model is the ability of an organization to interpret the environment in such a way as to suggest actions. Interpretation of the environment can either take place from an objective assessment (which sees events and processes as hard, measurable, and determinant) or a subjective assessment (where the interpretation shapes the environment more than the environment shapes the interpretation.) Organizations that can push the interpretive boundaries of their interpreted environment are called “test makers”, and will develop interpretations very different from those of organizations that do not push those boundaries (the test avoiders). For example, experimenting with a new product that pushes boundaries by violating expectations will yield valuable insight, if the product is successful.

From the (vastly simplified) forgoing, Weick and Daft’s Model of Organizational Interpretation Styles (below) describes the continuum (Objective-Subjective, Test-Making and Test-Avoiding) onto which organizations’ interpretive styles can be mapped.

Interp model

The effectiveness of each style will be evaluated differently according to the expectations of different organizations, but the authors lay out criteria that can define the effectiveness of interpretations: the extent to which they are grounded in more detailed knowledge of the data, the extent to which the data are tied together with strong causal linkages, and the ability to reconstruct with more accuracy the initial data that the interpretation was built to explain. Ultimately, the final indicator of effectiveness in this model is the correspondence between interpretation and reality.

Nord’s Political-Economic Perspective on Organizational Effectiveness

Nord’s discussion gets to the heart of the challenges of defining organizational effectiveness. As Nord puts it, the definition of effectiveness must address what organizations should be doing for whom (Nord, 1983). Recognizing the organizations have economic and political effects, Nord argues that the consequences of these effects must be incorporated into evaluations of their effectiveness. If one believes that organizations have a responsibility for promoting the well-being of members of society, then the set of criteria used to define organization effectiveness needs to be expanded. However, Nord has no clear model to define: rather, he calls on students of organizational effectiveness to develop, administer, and publicize indices that would lead to greater acceptance of the political-economic dimension of organizational performance.

If you’ve made it this far, thanks for reading through to the end! In future posts, I will be bringing the discussion of organizational effectiveness up to date by looking at more recent models that reflect the latest thinking on the subject.


Cameron, K. S., & Whetten, D. A. (1983). Organizational Effectiveness: A Comparison of Multiple Models. New York: Academic Press.

Nord, W. R. (1983). A Political-Economic Perspective on Organizational Effectiveness. In K. S. Cameron & D. A. Whetten (Eds.), Organizational Effectiveness: A Comparison of Multiple Models. New York: Academic Press.

Schneider, B. (1983). An Interactionist Perspective on Organizational Effectiveness. In K. S. Cameron & D. A. Whetten (Eds.), Organizational Effectiveness: A Comparison of Multiple Models. New York: Academic Press.

Seashore, S. (1983). A Framework for an Integrated Model of Organizational Effectiveness. In K. S. Cameron & D. A. Whetten (Eds.), Organizational Effectiveness: A Comparison of Multiple Models. New York: Academic Press.

Weick, K. E., & Daft, R. L. (1983). The Effectiveness of Interpretation Systems. In K. S. Cameron & D. A. Whetten (Eds.), Organizational Effectiveness: A Comparison of Multiple Models. New York: Academic Press.


How Hard is it to Recognize People? MB#5

Honestly, how hard is it to recognize associates in the workplace? Why is there resistance to something so simple? As I have talked to more people, exploring barriers to sustaining change, one of the things people talk about is recognition. To sustain change, you have to recognize and reward those individuals and teams that are exemplifying the desired behaviors. But you can’t wait until the end—you have to continuously recognize people along the way. I wonder how hard it would be to break a change down into milestones, and recognize people and teams at each milestone—who’s got it, and is demonstrating for others how to make it work. Dead simple, right, so why not do it?

Knowledge Management, Organizational Effectiveness, and Microsoft #OneNote

Working in a high-turnover function, I have learned first-hand the importance of knowledge management. When associates can be assumed to rotate out into new roles every 18 months, capturing and organizing knowledge becomes a critical exercise for managers and team members. I am defining knowledge management here as efforts to facilitate the acquiring, creating, storing, sharing, diffusing, developing, and deploying knowledge by individuals and groups. The knowledge-based view of the firm holds that the firm’s capability to create and utilize knowledge is the most important source of a firm’s sustainable competitive advantage (Zheng, Yang, & McLean, 2010). Zheng and his research partners surveyed 301 organizations to shed more light on the relationship between knowledge management and organizational effectiveness. Their findings indicated that knowledge management can influence organizational effectiveness when it is in alignment with organizational culture, structure, and strategy. Focus on knowledge management practices, such as providing knowledge management tools, and supporting knowledge management initiatives, helps transfer the impact of organizational resources to the bottom line (Zheng et al., 2010).

I have had considerable success using Microsoft OneNote as a knowledge management tool. OneNote is ideal for capturing knowledge: the notebook and page structure allows for easy and straightforward organization, and the ability to hyperlink throughout the text facilitates movement between different parts of the overall notebook. My team members and I work together to capture not just every day processes, but new learnings that result from the resolution of the challenges we face every day. As situations arise that we have not yet encountered, we document the processes that we develop and make note of any open loops or anticipated future challenges. These notebooks become a frequently consulted reference, a living document of our evolving learning, and a tool to teach new associates.

The adoption of SharePoint within our organization has given us an ideal platform to share our collected OneNote notebooks with other teams. Publicizing our tools and methods via SharePoint has helped us to build a culture of knowledge management, which in turn drives organizational effectiveness.


Zheng, W., Yang, B., & McLean, G. N. (2010). Linking organizational culture, structure, strategy, and organizational effectiveness: Mediating role of knowledge management. Journal of Business Research, 63(7), 763–771.

Social Network Disruption (MB#4)

Last fall, in conjunction with MSLOC 455, I built a social network graph of the people in my function that captured patterns of advice and knowledge seeking among the network members. My intent was to understand how information flowed between individuals and teams, where there were strong connections, and where there were holes between teams. My thinking was that understanding this network could help effectively disseminate communications about change.

But what happens when that network is disrupted? Since I built the graph last fall, many people have either left the function or left the company entirely, disrupting the structure that I had mapped out. Unfortunately, many of those who have left have not been replaced, so I’m afraid there may be gaps now that did not exist previously.

What happens when employee networks are disrupted? What are the consequences? Are there any ways to mitigate network disruptions caused by exiting employees? That’s the focus of my reading right now, and my comments on some of the research will be the subject of a future blog post.

Balancing the Job Demands-Resources Model Equation

I had noted in an earlier post that the Schaufeli’s Job Demand-Resources model was one that I wanted to come back to, to understand better. The model is straightforward: it states that work engagement results from the motivating influence of resources. Resources are bucketed two ways: job resources, meaning those resources that help achieve work goals, reduce demands, or stimulate growth and development; and personal resources, which include personal characteristics related to resiliency, such as optimism, self-efficacy, and emotional stability (W.B. Schaufeli, 2014). According to the model, these resources foster work engagement. On the other side of the model (or the equation, as I am thinking of it) stands job demands, that is, those aspects of the job that require sustained effort, such as work overload, time pressure, role conflict, or the demands of bureaucracy. These demands exhaust the employee, and can lead to burnout. On the positive side, however, demands that have the potential to promote mastery, personal growth, or future gains have the potential to increase work engagement. To help me understand the model, I drew it out:

JDR Model

As demands in my own work environment have been increasing, and as my team and I have struggled with increasing demands (both workload and bureaucracy, that is, red tape), I have had new insight into the model: as I noted above, I now see the model as an equation that must be kept in balance to maintain a steady state of engagement. In other words,  every new demand must be balanced by a new resource.

What are resources? More team members are sometimes the solution, but not always, depending on the nature of the demands, and, of course, the budget. Other resources described in academic literature include  job control, feedback, social support, and opportunities for learning (Schaufeli, Bakker, & Van Rhenen, 2009) The presence of these resources are motivational, and studies have shown the absence of these resources to lead to stress. In addition to these, though, another study found evidence supervisor support, innovativeness, appreciation, and organizational climate were important job resources (Bakker, Hakanen, Demerouti, & Xanthopoulou, 2007).

One takeaway here is the importance of supervisor support. Research has shown that supervisor support may alleviate the influence of job demands on strain because supervisors’ appreciation and support puts demands in another perspective (Bakker et al., 2007). Interestingly, Bakker’s research also indicates that job resources are particularly relevant under highly stressful conditions. My personal takeaway is to hone my coaching skills—I have  coached associates for years, but with a better understanding of how high quality supervisor support can help balance the JDR equation, I know I need to be sure my skills are in peak form.



Bakker, A. B., Hakanen, J. J., Demerouti, E., & Xanthopoulou, D. (2007). Job resources boost work engagement, particularly when job demands are high. Journal of Educational Psychology, 99(2), 274–284.

Schaufeli, Wilmar B. (2014). What is engagement? In Employee engagement in theory and practice (pp. 15–35). New York: Routledge.

Schaufeli, Wilmar B., Bakker, A. B., & Van Rhenen, W. (2009). How changes in job demands and resources predict burnout, work engagement, and sickness absenteeism. Journal of Organizational Behavior, 30(7), 893–917.

Knowledge Management (MB#3)

One critical aspect of organizational effectiveness is knowledge management. I’m struggling right now with a failure in knowledge management resulting from a long-time employee having retired, and not captured and documented his vast and intricate knowledge. It’s hard to believe that knowledge capture as an organizational process is not well established, but it really isn’t. Studies have shown that the stronger a sharing and retaining knowledge culture an organization has, the more capable the organization will be in performing effectively. Of course, mere knowledge capture isn’t enough: the organizational culture has to embrace sharing, learning, and knowing to improve job performance. What are the best practices for building a knowledge management culture? I need to find out!

Toxic Culture (MB#2)

A recent article (Wall Street Analyst Shreds Kraft Heinz’s Culture in Harsh Stock Downgrade) got me thinking about how organizational cultures can become toxic. Having an org culture that is recognized as bad in some way clearly costs, both in terms of turnover and negative reaction in the marketplace. One analyst identified a risk to Kraft-Heinz’s ability to innovate, due to a culture that results in high turnover. The analyst went on to downgrade the company’s stock, which immediately impacted the company’s stock valuation. The trigger to turnover here is cited as the excessive workload, which gets back to the Job Demands-Resources model that I mentioned in a previous post. Kraft-Heinz selects high performers, and promotes them quickly, but then loses them because of the staggering work load.

I wonder to what extent the JDR model can be used to understand other poor-performing or toxic cultures. Does a bad organizational culture always ultimately come back to the JDR model? If there is something wrong with the balance of job and personal resources relative to the job demands, it seems like the result must be the degradation of culture. But is it enough to restore the right balance of resources and demands? Is righting the balance enough to improve the organizational culture?

Quick note on the JDR Model (MB#1)

One of the challenges in the Northwestern MSLOC program is digesting all of the models that are thrown at us in quick succession. Some (very few!) models make immediate sense, and I strive to incorporate them into my work immediately. Some don’t make sense at all, or seem unconvincing, and those I set aside for the future. But others, like Schaufeli’s Job Demands-Resources model, make sense, but I know I need more time to fully digest the model and make sense of it. I’ve had plenty of time to think about it, and recently, wildly escalating job demands, driven by a recent restructure, have really made this model come home to me. My challenging work environment has driven me to a little insight—seeing the model as an equation—that I’ll write about in another post.

DOEC Project Progress Report

I detailed my engagement with the work of Rob Cross and my learning about how he has used social network analysis to drive change management in an earlier blog post (Social Network Analysis and Change Management) . In this post I want to take a step back, and outline the intention of the organizational effectiveness project I am working on, and comment on where I am and my next steps.

Back in 2015, my organization administered an organizational health survey, the results of which were shared with functional leadership teams. I was asked in 2016 to lead a team to explore the results on the Change Management portion of the survey. My discovery process utilized round table discussions with a number of groups that represented a broad cross-section of the function, in terms of both level and roles. My team then assembled and analyzed the data collected in these sessions, and distilled the data into a number of themes around change management.  The broad overarching theme was that changes impacting the function were not effectively communicated: round table participants pointed to gaps in timeliness of communication, the communication of intent and benefits, and reporting around progress and achievement of goals.

In response to this, my HR partner and I spent several months in 2017 delivering training intended to build associates’ skills as change agents and leaders. Time well spent, and of great value, but my concern has been that we have not fully addressed some of the key concerns surfaced around change communication. Consequently, as I entered into the Design for Organizational Effectiveness program, I felt I had a ready-made project to explore. My theory, derived from my reading of Rob Cross (noted above) is that social network analysis can be used to facilitate change communication within my organization. I believe that I can use the results of a social network analysis to pull together a team of individuals ideally placed to facilitate the communication of change within my function.

I have spent some time in my fall quarter 2017 class exploring the basic concepts and tools of social network analysis, and I have also invested time in learning an SNA tool, NodeXL. Armed with the results of my discovery round tables and my new tool, I created a survey that I sent out to associates in my function late in 2017, and have used NodeXL to build a social network graph. I have begun but not completed my analysis of my network graph.

I have identified several next steps to flesh out my prototype and  push my project forward:

  • I need to complete my social graph analysis, and assemble my change management team.
  • I have to plan exactly how I am going to use the team.
  • I need to determine the best way to measure effectiveness of the team:
    • Mini-surveys
    • Round tables
    • Another global organizational health survey will take place over the summer, but I hope to have at least some results before then.
  • Finally, I would like to plan out a  couple prototype cycles, if possible, to test my theory and make adjustments.

Fortuitously,  my function just rolled out a substantial change, so when I run a prototype test with my change management team, I will have a change event to which I can draw comparisons. More to come!

Seeking Articles on Social Network Analysis

I’ve spent the last ten days or so furiously “seeking” background research on social network analysis. I have had a basic introduction to the concepts and applications of SNA, and I have a rough idea of how I want to experiment with SNA to drive change management in my organization. Realizing, though, that I have just enough knowledge to be dangerous, I am digging through the research, ransacking libraries and databases,  to build out my understanding of the principles of SNA and how I can use it.

I will readily confess to being overwhelmed: so far, I’ve looked at 57 resources, some broad, some narrowly focused, and I’ve realized how very much I need to learn. I’ve used The Brain application to start building out a simple outline (sense making) of what I have looked at so far:

SNA Outline

I’ve divided the material I’ve reviewed so far into two large areas: basic components of social network analysis (centrality, structural holes, ties, and so on) and applications (Influence, Knowledge Management, and Social Capital being of most interest to me now.)

My critical interest right now lies along the subject of influence, and there is plenty of research covering two different aspects that I want to further explore. The subject of leader influence in networks is covered in articles by Balkundi, Flodgren, and Valente (see references below). Balkundi looks at the impact of team structure and leaders’ networks to understand the impact to team effectiveness. Flodgren looks at local opinion leaders and how they influence medical outcomes. In a similar vein, Valente looks at how opinion leaders can act as gatekeepers for interventions, help change social norms, and accelerate behavior change. I’m also looking at methods for identifying key players, algorithms for which are explored by Borgatti and Kempe.

Review of the Borgatti and Kempe articles is pointing me in the direction I need to go next: before I proceed any further with my project, I need to have a better understanding of centrality, one of the basic concepts of social network analysis. The whole concept of Key Players is based in measures of centrality, so in order to fully understand the former I need to completely ground myself in the latter. Marching orders for the next several days are therefore to take a look at what I have collected so far on the subject of centrality, and get a grip on it. I’ll take it from there!


Balkundi, P., & Harrison, D. A. (2006). Ties, Leaders, and Time in Teams: Strong Inference About Network Structure’s Effects on Team Viability and Performance. Academy of Management Journal, 49(1), 49–68.

Balkundi, P., Kilduff, M., & Harrison, D. A. (2011). Centrality and charisma: Comparing how leader networks and attributions affect team performance. Journal of Applied Psychology, 96(6), 1209–1222.

Borgatti, S. P. (2006). Identifying sets of key players in a social network. Computational and Mathematical Organization Theory; Dordrecht, 12(1), 21–34.

Flodgren, G. (2011). Local opinion leaders: Effects on professional practice and health care outcomes.

Kempe, D., Kleinberg, J., & Tardos, É. (2003). Maximizing the Spread of Influence Through a Social Network. In Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 137–146). New York, NY, USA: ACM.

Valente, T. W., & Davis, R. L. (1999). Accelerating the Diffusion of Innovations Using Opinion Leaders. The ANNALS of the American Academy of Political and Social Science, 566(1), 55–67.

Valente, T. W., & Pumpuang, P. (2007). Identifying Opinion Leaders to Promote Behavior Change. Health Education & Behavior, 34(6), 881–896.

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