By Angela J. Cathey, M.A.

(Guest Author post to, original post Sept 8th, 2016)

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 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 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).


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),


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),


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


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,


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-


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),


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.