Case Study: Creating a context for more effective team communication

Case Study: Creating a context for more effective team communication

Case Studies, Culture & Performance by Design, Natural Language Processing & Contextual Technologies, Organizational Behavior Management, Verbal Behavior | 0 comments

An 8-member business unit participated in data-driven consulting and alerts. Their goal was to increase the effectiveness of their communication and discuss difficult, often avoided topics, effectively. Natural Language Processing of staff communications and brief text prompted surveys were used to measure staff behavior and needs during the culture building intervention.

Communication was monitored for a total of three months. Consultants were made available and alerts sent to the team when key behaviors signaled improvement or need.

Pictured to the right: We were able to see changes in communication volume. Through monitoring these and other individual and group communication patterns we were able to monitor distress, satisfaction, and engagement in skills training exercises.

 

 

To the Left: Patterns in expressed emotion alerted us to difficulties within customer and employee interactions.

We offered targeted support and skills building, strengthening performance.

Results

Employees reported a >100% increase in the financial value of their daily performance.

Analysis revealed a >25% increase in conversations that addressed target issues identified by management (e.g., operations & budgetary relevance).

Measurement of team alignment in purpose indicate significant increases in “purpose alignment” and a leveling out of work rhythms that had increased employee stress.

Leadership reported increased ROI from regularly scheduled strategic meetings and a change in strategy that improved the financial viability of the unit. Leadership requested continued monitoring and engagement with ENSO Group to address continuing needs and the needs of other business units.

Ready to work with us? Set up a free consultation to learn more.

How to change a culture.

How to change a culture.

Behavioral Science in Review, Culture & Performance by Design, Organizational Behavior Management | 0 comments

Photo by Pratham Gupta on Unsplash

Article by Angela Cathey

We work with companies to improve their cultures. I’ve noticed this term, “culture” inspires a bit of awe and confusion in both business and behavioral analytic communities.

Business leaders have come to a relative state of agreement that “culture is king” (source unknown) and “culture eats strategy for breakfast. (Peter Drucker)” . There’s less agreement about what culture is and if it is indeed changeable at all.

What is “culture”?

From a behavior analytic perspective, culture isn’t so ambiguous. It’s an emergent quality that arises from the interaction of behaviors. This may sound ambiguous but it makes what business often sees as vital and difficult to change, changeable.

Culture isn’t an amorphous cloud.

It’s the psychological effect of a collection of behaviors. It’s the product of people behaving together or over time (see Houmanfar, Rodrigues, & Ward, 2010) for a more thorough analysis. The import point is – it’s changeable and it’s the product of your interpersonal behavior, biases, policy decisions (laws, strucutres, etc.), and verbal behavior. With everything we do, we show others what we see as important, unimportant, desirable, and undesirable.

It sounds like a lot – but changing a culture is a matter of making different choices. It’s creating an environment that is purposeful, well-designed, and makes the choice to appropriately reinforce or reward what truly ‘matters.’ By looking at the collective behaviors of a culture, a business, and the experiences that relate to them (e.g., psychological safety), we can use behavior analysis to move key switch points and change a culture for the better.

If you’re thinking now, so what’s the answer – what do I do?

1. Start with the realization that the answer is dependent on the problem & desired endpoint. There is no single solution. Thankfully, their are methods and practices that have great evidence supporting them, these include behavior analysis and measurement.

2. With the endpoint you seek in mind, measure actual behavior and how it influences this endpoint.

3. Apply behavior analytic principles to create change and support that change.

4. Continue to measure and apply – real behavior change requires teaching skills to fluency and supporting the use of those skills. It’s not as complicated as it may sound – but it requires real thought and application of science to meet your goal.

What to know more about how to change culture? Contact us for a free consultation.

Sources:

Houmanfour, R., Rodrigues, N. J., & Ward, T. A. (2010). Emergence and metacontingency: Points of contact and departure. Behavior and Social Issues, 19, 78-103.

5 ways technology and behavioral science can create a better world.

5 ways technology and behavioral science can create a better world.

Behavioral Science in Review, Culture & Performance by Design, Organizational Behavior Management

I ran across another article today about how society is slowly devolving, in part, due to technology. This is becoming a more common refrane. Tech leaders are frequently quoted saying they don’t allow their children to play with the devices they create.

The ‘tech elite’ are onto something and we are just coming to terms with it in many sciences. We’ve known for many years, in the behavioral sciences, that the environments we exist in influence us – for better or worse.

We like to think of our experiences simply and tend to perceive only those consequences closest to us temporally, spatially, and socially. We “like” and emoticon ourselves into disconnection as we feel the bright shiny connection in front of us, and in the process, we miss the people sitting next to us.

We as a society are overwhelmed, over-worked, and driven, most logically, to distraction in the screen faces we carry with us. In the Behavioral Sciences, B. F. Skinner (1986) wrote “What’s wrong with the Western World” and described the impact of modern work processes on how we as human’s function. The summary, “distance from the impact of our behaviors, is not a good thing.”

 

In a hyper-connected world – we have customized technologies around natural human desires. We buy ‘shiny’ distraction avoidance tools. As tech has evolved, it has also come to offer us other tools shaped around human values and goals. We now have FitBits and Alexa to help us live better lives.

The missing piece; however, is that the solutions themselves are one-sided. We have learned to embrace algorithms to help us choose movies (Netflix, etc.) and quickly tag pictures of our friends (Facebook), but we distrust technology in its ability to help us truly improve ourselves.

We offer a few ideas from the nexus of Behavioral Science and Technology, to help humanity connect to itself again.

1) Build technologies that adapt to intended purpose. 

When we build technologies without awareness to our own essential humanness we inadvertatly reinforce our worst behaviors. People will always be attracted to ‘shiny’ things, they will want quick rewards AND we need to plan technologies for exceptional #humanexperience right along with our #USERexperience.

More on that, below.

2) Build technologies that help us engage other perspectives. 

In a hyper-connected world, we’ve become sucked into news loos and algorithm echo chambers. We see the news that we “like,” not what we need to see about our own worlds. Unawareness, we are silently told, “is bliss.” It is not, we are progressively more lonely, less able to self-regulate, more depressed, more anxious… and of course, given all this we stare at our phones more unable to handle perspectives that differ from our own.

The solutions we typically hear for this are “put down your phone” and, in deed, this is one solution but only short-sighted. The long-term route is to appreciate that we are human, flawed and beautiful all the same.

If we want to pick a realistic way forward, maybe we should begin to use technology to help us see our individual strengths, weaknesses, and perspectives in relation to one another.

3) Build technologies that make us aware of our strengths and weaknesses, in RELATION.

Remember the human tendency to seek out the ‘shiny’? We tend to look for easy answers, for the “best” answer. What we miss is how to balance ourselves in relation to our world, and those in it. We then design FitBits, Alexas, and solutions around the easy answers that inevitably lead us in the wrong direction. Any healthy behavior can go wrong taken to full tilt, and yet we persist… chasing easy answers.

One way that we can address this is to begin to focus on answers in relation to context. Contextual technologies allow us to notice what the best answer is for the current situation, environment, and purpose.

4) Use Intelligence Augmenting technologies to alert us of when we are falling into our patterns of dysfunction, bias, and unawareness.

 

Intelligence Augmenting technologies are one possible way forward here. Our patterns of avoidance, bias, and unawareness are no secret. The Behavioral Sciences have been using studying how to reduce mismatch between person and environment, and labeling it “psychopathology” for many decades.

It is time that we begin to work across lines to find solutions that bring us together.

5) Build technologies through “open” paticipatory design methods.

One key to advancing us must be opening the doors to participatory development. We need to recognize human motivations and flaws in our processes. If “tech” is not working, it is because “we” are not working towards a common perspective in what we create.

In this world, there are few easy answers; however, one I’d stake my work on is:

We as humans need to work on embracing our natural complexity and celebrating our strengths. If technology can help us do that, we should be working towards that purpose together.

Angela Cathey, MA, LPC

Angela Cathey, MA, LPC

Director Enso Group, Trainer, & Consultant

Angela is experienced in leading and coordinating the operations of research and intervention teams. She has a master’s in Clinical Psychology from the University of Houston – Clear Lake. She has trained in Acceptance and Commitment Therapy (ACT), Functional Analytic Psychotherapy (FAP), and Cognitive Behavioral Therapy (CBT) extensively. She has been well-trained in the treatment of anxiety and mood disorders. She has also trained extensively in treatment of trauma, utilizing Prolonged Exposure (PE) and obsessive-compulsive spectrum conditions, utilizing Exposure and Response Prevention (ERP).

A behavioral scientist’s take on providing effective instructions for optimal learning.

A behavioral scientist’s take on providing effective instructions for optimal learning.

Behavioral Science in Review, Organizational Behavior Management

Teaching is a large part of any leader’s position. Whether academic or business, most leader’s are promoted due to others’ perceptions that they have mastered a domain of knowledge. Unfortunately, most leaders are simply assumed to then know how to impart this knowledge on their supervisees/mentees.

Effective teaching is an art and science. Here we’ll discuss guidelines for understanding, breaking, up and imparting your knowledge of complasks for others.

The first step is to understand the task:

Task analysis (TA) is a method for identifying and documenting the process of task completion. Many different methods exist for documenting and analyzing task completion (Tofel-Grehl & Feldon, 2012).

Minimalist Instruction
In its most fundamental form, TA instruction is merely a list of steps.

    1. Read task analysis (TA) article
    2. Do your own task analysis (TA) of a complex task
    3. Present simple step-by-step instructions for task to learnerSimple is important because new learners to a complex task will tend to become frustrated and give up.In addition to helping you impart skills to learners, task analysis (TA) can be a way of discovering how to optimize the efficiency, productivity, or safety of a task

Interested in learning more about Behavioral Science?

Yes, please!

Task Analysis for Instruction Optimization

Instruction optimization involves a data-based assessment of instructional effectiveness. Any material or stimulus intended to prompt, guide, or teach a response can potentially be improved if you take the time to identify what components promote the effective behavior.

 

 

From an operations perspective, optimally effective instructions have many benefits such as:

  • Saving time during onboarding and training
  • Reducing the frequency of errors
  • Maintaining knowledge retention over time

Best Practices in Task Analysis Instruction
Learning outcomes and performance can generally be enhanced for most learners by:

  • Presenting steps in smaller pieces (Crist, Walls, & Haught, 1984)
  • Limiting jargon, presenting images, and providing examples (Graff & Karsten, 2012)
  • Presenting images of and describing stimuli that should trigger the response and the expected outcome(s) of a correct response (Tyner & Fienup, 2015)
  • Updating instructions frequently to match task changes (Dixon et al., 2007)

Though these general guidelines should improve the effectiveness of your instructions, optimizing ultimately requires measuring and adapting to what creates the best outcome for your learners.

Build a stronger business with Behavioral Science?

Yes, how can I start?

Remember, though examining the effectiveness of your instruction formally may initially require some time investment this cost should be offset by improvements in efficiency and reduction of errors.

Other considerations may also inform whether the expected ROI justifies the cost, including the risk or danger of the task, the criticality of accuracy, the acceptable threshold for errors, the cost and time required to correct errors, and the qualitative experience of those who are performing the task.

Summary

Task analysis is a method for investigating and documenting the process of completing a task that is prevalent across diverse industries. TA instruction is robust for training and guiding task performance, and many best practices have been published to improve its effectiveness. Robust methods from behavioral science are also available to further enhance instructional effectiveness and efficiency.

Bryan Tyner, PhD

Bryan Tyner, PhD

Optimized Behavior Technology

Bryan Tyner is a behavioral scientist and research-strategy consultant. He has a PhD in behavioral psychology from The Graduate Center at the City University of New York (CUNY). His research on instructional design, assessment, and optimization has been published in the Journal of Applied Behavior Analysis and the Journal of Behavioral Education. Bryan is the founder of Optimized Behavior Technology, an independent research agency that consults on the use of research methodology and data analytics to inform business operations, strategy, and product development. More information about his services is available at www.optimizedbehavior.technology 

References

Crist, K., Walls, R. T., & Haught, P. A. (1984). Degrees of specificity in task analysis. American Journal of Mental Deficiencies, 89, 67-74.

Dixon, M. R., Jackson, J. W., Small, S. L., Honer-King, M. J., Mui Ker Lik, N., Garcia, Y., & Rosales, R. (2009). Creating single-subject design graphs in Microsoft Excel 2007. Journal of Applied Behavior Analysis, 42, 277-293.

Gero, J. S. & Mc Neill, T. (1998). An approach to the analysis of design protocols. Design Studies, 19, 21-61.

Graff, R. B. & Karsten, A. M. (2012). Evaluation of a self-instruction package for conducting stimulus preference assessments. Journal of Applied Behavior Analysis, 45, 69-82.

IBM SPSS. (2016). Multivariate linear regression in SPSS. IBM Support. Accessed April 19, 2017 from http://www-01.ibm.com/support/docview.wss?uid=swg21476743

May, J. & Barnard, P. J. (2004). Cognitive task analysis in interacting cognitive subsystems. In Diaper, D. & Stanton, N. A. (eds.) The Handbook of Task Analysis for Human-Computer Interaction (pp. 295-325). New Jersey: Lawrence Erlbaum Associates, Publishers.

Tofel-Grehl, C. & Feldon, D. F. (2012). Cognitive task analysis-based training: A meta-analysis of studies. Journal of Cognitive Engineering and Decision Making, 7, 293-304.

Tyner, B. C. & Fienup, D. M. (2015). The effects of describing antecedent stimuli and performance criteria in task analysis instruction for graphing. Journal of Behavioral Education, 25, 379-392.

Yu, R., Gero, J., & Gu, N. (2015). Achitects’ cognitive behavior in parametric design. International Journal of Architectural Computing, 1, 83-101.

Opportunities In OBM: Addressing Conflict, Creativity, And Motivation With RFT

Opportunities In OBM: Addressing Conflict, Creativity, And Motivation With RFT

Geek Out: Theory, Methodology, & Philosophy of Science, Natural Language Processing & Contextual Technologies, Organizational Behavior Management, The Workroom, Verbal Behavior | 0 comments

By Angela J. Cathey, M.A.

(Guest Author post to bSci21.org, original post Sept 8th, 2016) http://www.bsci21.org/opportunities-in-obm-addressing-conflict-creativity-and-motivation-with-rft/

Organizational Behavior Management (OBM) is booming and poised to grow exponentially. There are several fairly recent advancements in the behavior analysis of symbolic thought (RFT; Hayes, Barnes-Holmes, Roche, 2001) and technology (e.g., Natural Language Processing, NLP and Machine Learning, ML; Nadkarni, Ohno-Machado, & Chapman, 2011) that can help improve the reach of behavior analysts in OBM. 

Relational Frame Theory

Relational Frame Theory (RFT; Hayes, Barnes-Holmes, & Roche, 2001) describes, among other things, how language consists of stimuli hooked in relation to ‘external’, and ‘internal’ stimuli. These relations demonstrate known properties and characteristics based on the way stimuli are related and number of times they are related. Humans, under many circumstances, demonstrate a tendency to become more heavily influenced by contingencies hooked to their verbal/symbolic context (Hayes, 1989) than their external environment. For example, given “rules” about what to expect in novel situations humans will often become insensitive to detecting solutions that do not fit the rule provided.

Understanding the influence of verbal relating allows us to understand and influence a variety of more complex behaviors within OBM contexts (Hayes, Bunting, Herbst, Bond, & Barnes-Holmes, 2006; Roche, Barnes-Holmes, Stewart, & O’Hora, 2002; Stewart, Barnes-Holmes, Bond, & Hayes, 2006). These patterns of relating to inner experience are evident in external behaviors like verbal behavior (e.g., ‘languaging’).

The Role of Natural Language Processing (NLP)

Recent research from functional contextual behavior analysts has recognized the utility of tracking relations in verbal behavior (Atkins & Styles, 2016; Collins, Chawla, Hsu, Grow, Otto, & Marlatt, 2009). However, most of this has not utilized Natural Language Processing (NLP; not to be confused with Neurolinguistic Programming). NLP refers to entire bodies of well-developed research and technologies developed in the fields of business over the last thirty years. NLP has long been used to gain knowledge in business settings as a method of examining customer relations (CRM) and tracking of other key performance indicators. This and other areas of research have long since demonstrated the utility of tracking verbal relating in prediction of behavior. These technologies have been under-recognized and utilized within the field of psychology as they were expensive and required special technological skill to apply. This is changing as companies like my own, Enso Contextual Behavioral Innovations, take on the task of shaping natural language processing to the needs of behavior analysts in a variety of settings.

What can RFT and NLP provide to behavior analysts in OBM?

Verbal relating is ever present in our world and research on RFT and therapies that have been built from it provide guidance for behavioral interventions that may be used to influence behavior. Detection of verbal relations (e.g., in writing, email, verbal conversational content, Facebook posts, etc.) using NLP can provide insight to behavior analysts about the contingencies controlling behavior that they may not otherwise be able to observe directly. This knowledge can then be used to intervene or to track the effect of other OBM interventions in verbal relations. As many businesses are accustomed to the use of NLP to understand customer relations they are generally accepting of such technologies. Here I will discuss the application of these two technologies to the OBM environment in regard to conflict, creativity, and motivation. 

Verbal relating and conflict

Industrial/organizational research has consistently supported the importance of psychological safety (Edmondson, 1999) in supporting communication patterns that promote productivity, creativity, and even employee mental health. Psychological safety is thought to arise from establishing patterns of communication and behavior in the organizational environment that promote open and safe engagement with difficult issues. The examination of verbal relating through a RFT perspective can assist the behavioral OBM specialist in assessing the psychological safety of an OBM environment and providing interventions that promote adaptive communication.

Alignment in Verbal Relating

Verbal behavior between two or more individuals that becomes more similar linguistically (e.g., in tone or meaning) or realigns quickly after misalignment has been known to predict better relational outcomes (Dewulf, Gray, Putnam, Lewicki, Aarts, Bouwen, & Woerkum, 2009; Drake & Donohue, 1996; Richardson, Taylor, Snook, & Bennell, 2014). RFT conceptualizations of social behavior and group identification (e.g., deictic, hierarchical, and coordination/distinction framing) fit behavioral interventions and speak to creating collaborative environments (Quinones, Hayes, & Hayes, 2000). NLP research also indicates that these patterns are detectable and relate to meaningful outcomes (Wasson, 2016). OBM specialists can utilize this knowledge and intervene based on the specific relations noted to promote productive communication patterns.

Awareness in Self and Other Relating

Awareness of contingencies that drive self-or-other behavior supports more effective communication behaviors. This kind of awareness can be detected in verbal relating as well (Atkins & Styles, 2016; Collins, et al. 2009). Natural language processing research has also addressed measuring these processes (Pennebaker, Mehl, & Neiderhoffer, 2003). This knowledge together with basic RFT can be used to shape interventions that promote adaptive self and other awareness in the OBM environment.

Flexibility in Self and Other Relating

Flexibility in goal approach and group identification has been indicated as important to social and self-related outcomes (English & Chen, 2011; Lei, Waller, Hagen, & Kaplan, 2016; Luan, Rico, Xie, Zhang, 2016; Kashdan, 2010; Moran, 2015). This can also be detected in verbal behavior using NLP (Atkins & Styles 2016; Rentscher, Rohrbaugh, Shoham, & Mehl, 2013). The RFT informed OBM specialist utilize this information to shape interventions.

Creativity

Creativity in problem solving and in general in OBM contexts can be a significant asset to businesses. This area too can be a difficult to measure and influence without the consideration of verbal relating. Creativity can be viewed as recombining ideas in new ways and noticing new ways of seeing the old. Awareness, as described above, is a component of creativity as well as flexibility in verbal behavior (e.g., metaphorical; Hayes, Barnes-Holmes, Roche, 2001) and even comedic strategy. NLP can also detect these types of verbal relations (Shutova, 2010).

Motivation

Motivation can also be a challenge that costs significant resources or results in significant gains in the modern work environment. Behavior analysts (Skinner, 1986) have long since recognized issues that lead to poor motivation. RFT speaks to the importance of values and social identification in motivation (Foody, Barnes-Holmes, Barnes-Holmes, & Luciano, 2013), and NLP can again track this identification with sources of motivation and adaptive flexibility in these relations (Atkins & Styles, 2016).

Thus, OBM stands to gain significant knowledge and reach through the integration of RFT to practice and NLP as a method of tracking interventions. Though these technologies may initially appear intimidating, the use of an RFT consultant and use of a behaviorally informed NLP specialist can help take your OBM interventions to a new levels of effectiveness.  For more information on the integration of RFT with other behavioral theory see Barnes-Holmes, Barnes-Holmes, & Cullinan (2000).

References

Atkins, P. W. B., & Styles, R. G. (2016). Measuring the self and rules in what people say:

Exploring whether self-discrimination predicts long-term well-being. Journal of

Contextual Behavioral Science, 5(2), 71-126.

Barnes-Holmes, D., Barnes-Holmes, Y., & Cullinan, V. (2000). Relational frame theory

and Skinner’s verbal behavior: A possible synthesis. Behavior Analysis, 23(1),

69-84.

Collins, S. E., Chawla, N., Hsu, S. H., Grow, J., Otto, J., M., & Marlatt, G. A. (2009).

Language-based measures of mindfulness: Initial validity and clinical utility.

Psychology of Addictive Behavior, 23(4), 743-749.

Dymond, S. & Rehfeldt, R. A. (2000). Understanding complex behavior: The

transformation of stimulus functions. The Behavior Analyst, 23(2), 239-254.

Drake, L. E., & Donohue, W. A. (1996). Communicative frame theory in conflict and

resolution. Communicative Research, 23(3), 297-322.

Dewulf, A., Gray, B., Putnam, L., Lewicki, R., Aarts, N., Bouwen, R., & Woerkum, C.

(2009). Disentangling approaches to framing in conflict and negotiation

research: A meta-paradigmatic perspective. Human Relations, 62 (2), 155- 193.

Edmondson, A. (1999). Psychological safety and learning behavior in work teams.

Administrative Science Quarterly, 44, 350-383.

English, T. & Chen, S. (2011). Self-concept consistency and culture: Differential impact

of two forms of consistency. Personality and Social Psychology Bulletin, 1-12.

Foody, M., Barnes-Holmes, Y., Barnes-Holmes, D., & Luciano, C. (2013). An empirical

investigation of hierarchical versus distinction relations in self-based ACT

exercise. International Journal of Psychology & Psychological Therapy, 13(3),

373-388.

Hayes, S. C. (1989). Rule governed behavior: Cognition, contingencies, and instructional

control. New York: Plenum Press.

Hayes, S. C., Barnes-Holmes, D., & Roche, B. (2001). Relational frame theory: A post-

Skinnerian account of human language and cognition. New York: Kluwer

Academic.

Hayes, S. C., Bunting, K., Herbst, S., Bond, F. W., & Barnes-Holmes, D. (2006).

Expanding the scope of organizational behavioral management: Relational frame theory and the experimental analysis of complex behavior. Journal of Organizational Management , 26(1/2), 1-23.

Kashdan, T. (2010). Psychological flexibility as a fundamental aspect of health. A Clinical Psychology Review, 20(7), 865-878.

Lei, Z., Waller, M. J., Hagen, J., & Kaplan, S. (2016). Team adaptiveness in dynamic

contexts: Contextualizing the roles of interaction patterns and in-process planning.

Group & Organizational Management, 4(4), 491-525.

Luan, K., Rico, R., Xie, X.-Y., & Zhang, G. (2016). Collective identification and external

learning. Small Groups Research, 47(4), 384-405.

Moran, D. J. (2015). Acceptance and commitment training in the workplace. Current

Opinion in Psychology, 2, 26-31.

Pennebaker, J. W., Mehl, M. & Nederhoffer, K. G. (2003). Psychological aspects of

natural language use: Our words, ourselves. Annual Review of Psychology, 54,

547-577.

Rentscher, K. E., Rohrbaugh, M. J., Shoham, V., & Mehl, M. R. (2013). Asymmetric

partner pronoun use and demand-withdraw interaction in couples with health

problems. Journal of Family Psychology, 27(5), 691-701.

Richardson, B. H., Taylor, P. J., Snook, B. & Bennell, C. (2014). Language style

matching and police interrogation outcomes. Law and Human Behavior, 38(4), 1-

10.

Roche, B., Barnes-Holmes, Y., Barnes-Holmes, D., Stewart, I., & O’Hora (2002).

Relational frame theory: A new paradigm for the analysis of social behavior.

Behavioral Analysis, 25(1), 75-91.

Skinner, B. F. (1986). What’s wrong with the Western world? American Psychologist,

41(5), 568-574.

Quinones, R., Hayes, L., & Hayes, S. C., (2000). On the benefits of collaboration:

Consumer psychology, behavioral economics and relational frame theory.

Managerial and Decision Economics, 21(3-4), 159-165.

Wasson, C. (2016). Integrating conversation analysis and issue framing to illuminate

collaborative decision-making activities. Discourse & Communication, 10(4),

378-411.

Shutova, E. (2010). Models of metaphor in NLP. Proceedings of the 48th Annual Meeting

of the Association of Computational Linguistics, 688-697.

Stewart, I., Barnes-Holmes, D., Barnes-Holmes, Y., Bond, F., & Hayes, S. C. (2006).

Relational frame theory and industrial/organizational psychology. Journal of Organizational Management, 26 (1-2), 55-90.

Angela Cathey, MA

Angela Cathey, MA

Founder, Partner, Consultant, Data Scientist

Angela is experienced in leading and coordinating the operations of research and intervention teams. She has a master’s in Clinical Psychology from the University of Houston – Clear Lake. She has specialty training in measurement, intervention, People Analytics, natural language processing, and data science. Angela was the entrepreneurial lead in the National Science Foundation i-Corps customer validation program for Enso’s key products. She has a background in innovative technology problem solving, technology development, and resulting market-ready product development.

The paradox of power and the nature of human bias.

The paradox of power and the nature of human bias.

Organizational Behavior Management, Verbal Behavior | 0 comments

A behavior analyst’s notes on Uber, Tesla, Wells Fargo, and multi-systems change processes.

Orignal post to bSci21.org March, 2017

As a behavior analyst my attention is often drawn to cycles of behavior. Where there is a pattern, there can be prediction and change. In recent news, I have seen repeated criticisms of Uber, Tesla, and the “Bro-culture” of Silicone valley’s tech industry. These complaints of misogynistic culture are further situated in the wider news environment depicting large-scale divisions across racial, sexual preference, political, and socieoeconomic divides.

In most cases, we learn of these issues only when an incident arises that is of sufficient magnitude to serve as a catalyst. These incidents are generally quite costly for all involved; whether a lawsuit, a death, or a war – everyone pays. In this situation it may be easy to assign blame but I argue that for the most part businesses like Uber, Tesla, Wells Fargo, and the like, are simply experiencing the natural unaddressed impact of their contexts on their employees and organizations. These companies are not exceptions or ‘bad apples’, instead they are the natural result of unaddressed conflicting contingencies. For the Uber’s executives, board, and its employees these issues unaddressed will likely contribute to further financial losses, public relations nightmares, and continuing branding problems.

Let’s look in further detail at the example of Uber to further examine: 1) the nature of in-born human bias as it plays out on the interpersonal, organizational, and multi-systemic levels, and 2) what we can do to prevent harm and promote wellness on the interpersonal, organizational, and multi-systemic levels.

1) The nature of human bias:

            The nature of human thought, language, and behavior naturally pulls for bias (RFT; Hayes, Barnes-Homes, & Roche, 2001; Banaji & Greenwald, 2013). This can be observed on every level of human experience from our ‘internal’ worlds, to our interpersonal behavior, to our effect on our environments. Additionally, none of these exists in exclusion from the others. We are essentially bound environment-to-behavior – on multiple levels – in any moment, as well as to our past experiences through the quirks of human language/thought. This leads to loops of interacting behaviors so large that no human mind, alone, can track them.

This accounts for the vast majority of complex human behavior, and yet, our biases interact and cause literal reductions of awareness (Banaji & Greenwald, 2013). We are, essentially, unaware of our own blind spots as we meet them in daily life.

For clarity, here’s a list of a small number discussed in non-behavioral for the lay reader:

  1.  ‘Reward’, ‘punishment’, and ‘pairing’ – Though sacrificing a great deal of technical detail here; much of our response to our environment is a response to how certain behaviors have been rewarded, punished, or paired with other experiences that were experienced as rewarding or punishing.
  2. Coherence – This refers to our tendency to see, perceive, remember, and seek out what ‘makes sense’ based on our own experience.
  3. ‘Self-as-idea’ and ‘other-as-idea’ – This refers to our tendency to form internal ‘stories’ about who we, others, and even organizations ‘are’.
  4. ‘Rules’ – Humans are sense making, predictability seeking creatures, and must learn from each other. One primary way that we do this is through the description of if-then relationships and concepts. This is a huge advantage to human-kind, who can learn through instruction to reach ‘reward’ and avoid ‘punishment’ socially.
  5. ‘Believing’ the idea or rule – In our efforts for predictability and sense-making we tend to ‘believe’ thoughts, perceptions, and ideas that made impressions on us. This is necessary to organize our experience of the world and allow adaptive interaction; however, it often also creates emotional attachment, and paradoxically, blindness – to what is not described by the rule.
  6. ‘Difference bias’ – This is a collection of biases around how we perceive ‘similar’ and ‘different’. Humans are prone to categorizing, simplifying, and, unfortunately, fearing what is different.

The interaction of these biases play out in many ways interpersonally, organizationally, and multi-systemically:

Let’s return to the example of Uber’s most recent public relations conundrum. On Feb. 19, 2017, former Uber engineer, Susan Fowler, posted an account of sexual harassment she reportedly experienced by her direct supervisor on her blog. This material, still searchable by her name at this point, is of special interest as the writer focused on her experience with the organization and several layers of organizational process over time.

For Uber and any organization undergoing change in a competitive environment, what she describes should serve as a preventative guide and an action plan towards change. As Susan reports and Uber’s stock and business profiles confirm, Uber has been in a period of rapid development. Uber, founded only in 2009, is now worth approximately $68 billion dollars. This exceptional rate of growth, paired with the competitiveness of their business sector [tech, services, etc.] as well as their existence in technology development ‘hot-spots’ nearly assures both internal and external competition for ‘rewards’. As Susan describes, companies experiencing this rate of growth will generally experience competition between employees, shuffling of work assignments as market demands fluctuate, and political shifts. Organizationally, the natural tendency will be to respond to the chaotic competitive market and competitive interpersonal dynamics by rewarding behavior without attention to process and placement of rules meant to minimize risk. In organizations, this usually takes the form of actual organizational policies [rules], or stratification of power and access [organizational hierarchy; also functioning as a rule]. Organizational hierarchy thus amplifies comparison dynamics  [including power dynamics] that amplify further amplify any culturally-bound biases (e.g., socialized gender biases).

The paradox of power then – is that the more access and control we have, the less we can clearly see the problems relevant to our success.

Rules, and unfortunately, punishment are our natural response to not being able to track all important outcomes. These well-intentioned rules, including Uber CEO Travis Kalanick’s recent announcement that any sexual harassment is considered to be a fire-able offense – often malfunction. Clearly sexual harassment should not be overlooked; however, the way in which we speak ‘rules’, in-context, change their overall function. The likely function of CEO Travis Kalanick’s statements on Uber employees, as well as the upcoming ‘investigations’ into Uber’s ‘Bro-Culture’, will almost certainly set the stage for further volatility, turn-over, and costly complaints for the company.

If the company culture was one of fear, competitiveness, change, and division both on performance status and gender – than we can only expect that punishment-based change programs will amplify these behaviors as employees try to preserve their employment. This will cause less transparency in operations as it further amplifies the ‘everyone for himself’ context. This is a real shame, as based on what Susan Fowler discloses in her blog, there is a significant but dwindling group of employees who feel great loyalty to the organization and vitality in their work.

2) What we can do to prevent harm and promote wellness:

What is called for now, based on the context and contingencies Uber finds itself in, is an intervention that embraces paradox. It is one that examines the context and contingencies that result behaviors at the personal, interpersonal, and multi-systemic levels that damage the overall organization. It calls for a focus on ‘rewarding’ adaptive behavior and creating systems where competition and change can exist in a psychologically safe context. Recent developments in assessment and intervention in organizational behavior management (OBM) better support the measurement of contingencies system and contexts system wide. With the use of Natural Language Process (NLP) to measure verbally described contingencies on mass and the ease of system wide feedback systems (e.g., normalized 360 degree feedback) to promote multi-level transparency – organizations such as Uber stand to benefit greatly from evidence-based ‘reward’ driven systems that increase stability, performance, and employee satisfaction. These interventions allow behavior analysts to functionally view the contingencies of an organization over-time to cater and refine interventions. Additionally, for the employees – these interventions guided by external parties can serve as a safe way to anonymous way to provide needed feedback to CEOs, boards, and consultants who can redirect contexts towards growth and stability.

 “The sailor cannot see the North but knows the Needle can.” – Emily Dickenson

For organizations like Uber, for our culture, and our own psychological health – a needle that points north is exceedingly rare. With the ability and willingness of organizations, such as Uber, to respond to the contingencies of employees as described organizations have the opportunity to follow the “North”  determined by an awareness of the contexts and behaviors driving each department and levels of their organization. This ability to listen to the collective wisdom of their own employees may mean the difference between disaster and growth in today’s market.

References and recommended reading:

CNN Money on investigation:

http://money.cnn.com/2017/02/20/technology/uber-eric-holder-sexism-investigation/index.html?iid=EL

Allen, T. D., & French, K. A. (2016). Women and career advancement: Issues and opportunities. Organizational Dynamics, 45, 206-216.

Bear, J. B., Cushenbery, L., London, M., & Sherman, G. D. (2017). Performance feedback, power retention, and the gender gap in leadership, The Leadership Quarterly

Banaji, M. R., & Greenwald, A. G. (2013). Blindspot: Hidden biases of good people. New York, NY: Delecorte Press.

Biglan, A. (1995). Changing cultural practices: A contextualist framework for intervention research. Reno, NV: Context Press.

Guerin, B. (1994). Analyzing social behavior: Behavior analysis and the social sciences. Reno, NV: Context Press.

Hayes, S. C., Barnes-Holmes, D., Roche, B. (2001). Relational Frame Theory: A Post-Skinnerian account of human language and cognition. New York, NY: Kluwer/Academic/Plenum Publishing.

Hayes, S. C., Bunting, K., Herbst, S., Bond, F. W., & Barnes-Holmes, D. (2006).

Expanding the scope of organizational behavior management: Relational Frame Theory and the experimental analysis of complex human behavior. Hasworth Press.

Maraccini, A. M., Houmanfar, R. A., and Szarko, A. J. (2016). Motivation and complex verbal phenomenal: Implications for organizational research and practice. Journal of Organizational Behavior Management, 36 (4), 282-300.

Pela’ez, M. & Moreno, R. (1999). Four dimensions of rules and their correspondence to rule-governed behavior: A taxonomy. Behavioral Development Bulletin, 8 (1), 21-27.

 

 

Angela Cathey, MA

Angela Cathey, MA

Founder, Partner, Consultant, Data Scientist

Angela is experienced in leading and coordinating the operations of research and intervention teams. She has a master’s in Clinical Psychology from the University of Houston – Clear Lake. She has specialty training in measurement, intervention, People Analytics, natural language processing, and data science. Angela was the entrepreneurial lead in the National Science Foundation i-Corps customer validation program for Enso’s key products. She has a background in innovative technology problem solving, technology development, and resulting market-ready product development.