Sunday, December 14, 2014

Theory Worth Considering

I try to emphasize, to my students in Theory Development, the importance theory plays in research and how fields of study (disciplines) are defined by the theories they produce.  One example can be found in England's new theory of life from Wolchover's (2014) article titled A New Physics Theory of Life in Quanta Magazine.

According to England's theory (Wolchover, 2014), Darwin's theory of natural selection may be more than one organism's ability to adapt better than another organism. England's theory expands the second law of thermodynamics stating that one organism may be more capable of dissipating energy than other organisms, thus leading to Darwin's natural selection. 

In Wolchover's (2014) article, the following was mentioned regarding England's theory:

"England's theoretical results are generally considered valid. it is his interpretation - that his formula represents the driving force behind a class of phenomena in nature that includes life - that remains unproven. But already, there are ideas about how to test that interpretation in the lab" (para. 10).

Theories must be relevant and rigorous (Van de Ven, 2007). Relevance determines how well the theory addresses real-world problems or issues (Van de Ven, 2007), whereas rigorous theories meet the requirements of being empirically validated and challenged. In the example provided above, England's theory has been accepted by those in the fields of physics, biology, chemistry, and others. By being accepted other researchers do not necessarily have to agree with the theory, however, they do agree that England's theory holds merit and should be subjected to further testing. This is evident from the last sentence in the above quote stating that 'there are ideas about how to test' this theory. 

This theory has meet two thresholds that every new theory needs to meet in order to be considered relevant: it has been deemed worthy to consider by other researchers and it's validity is being subjected to further empirical testing. This begins the theory validation / refinement stage which begins to place this theory as a formal theory for the field of study that stands behind this theory.  

Formal theories are constantly being tested and challenged through research. Sometimes formal theories are replaced with new theories that better explain current phenomena, other times formal theories withstand the continuous empirical scrutiny. In Wolchover's (2014) article there are two examples of this continuous refinement process. The first is the beginning phases of a new theory that is being exposed to empirical tests. If the empirical tests provide support for England's theory then this theory will begin to become a formal theory. Secondly, formal theories are constantly being tested and refined, ultimately providing the best description of a phenomenon. One example of this can be found by the use of the second law of thermodynamics that was being utilized in the development of England's theory. The second law of thermodynamics is being tested as well as being validated from this line of testing.

All-in-all, when presenting a new theory one needs to consider is it worth considering and by whom?


Van de Ven (2007). Engaged scholarship: A guide for organizational and social research. New York, NY: Oxford University Press.

Wolchover, N. (Jan. 22, 2014). A new Physics theory of life. Quanta Magazine. Retrieved from

Friday, November 28, 2014

Hierarchical Linear Modeling: Testing Multilevel Theories

In the previous post I provided information relating to a recent book chapter. This book chapter describes different techniques in developing and disseminating multilevel theories. In this article I present a new publication that identifies, briefly, how to test multilevel theories. This main statistical analysis methodology is commonly referred to Hierarchical Linear Modeling (HLM; Raudenbush & Bryk, 2002), but is also referred to multilevel regression analysis or random coefficient regression modeling (Cohen, Cohen, West, & Aiken, 2003), multilevel models (Hox, 2010), mixed models and random effects models (McCoach, 2010).

The online reference for the new article is provided below. This article will be available in print at the beginning of 2015.

Turner, J. R. (2014). Hierarchical linear modeling: Testing multilevel theories. Advances in Developing Human Resources [Published Online]. doi:10.1177/1523422314559808

Part of the structured abstract is provided below:

The Problem: While nested structures occur naturally in organizational and educational settings, past research has failed to recognize these nested structures. Ordinary least squares (OLS) methods assume independence of observation, fixing the intercepts and slopes across all groups. By not accounting for nested structures, errors of inference can occur with the risk of compromising the validity of the results.
The Solution: As new theories become more complex multilevel representations of phenomena, testing these complex theories require hierarchical linear modeling (HLM). HLM provides human resource development (HRD) practitioners with a better method to test multilevel theories while taking into account nested structures, providing a more accurate representation across the different levels.


Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.). New York, NY: Routledge.
Hox, J. (2010). Multilevel analysis: Techniques and applications (2nd ed.). New York, NY: Routledge.
McCoach, B. D. (2010). Hierarchical linear modeling. In G. R. Hancock & R. O, Mueller (Eds.), The reviewer's guide to quantitative methods in the social sciences: revise, accept, reject (pp. 123-140). New York, NY: Routledge.
Raudenbush, S. W., & Byrk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods (2nd ed.). Thousand Oaks, CA: Sage.

Building and Disseminating Multilevel Theories

New Book Chapter just published relating to building and disseminating multilevel theories.

Turner J., Firmery-Pretrunin, K., & Allen, J. (2014). Developing multilevel models for research. In V. C. X. Wang (Ed.), Handbook of research on scholarly publishing and research methods (p-p. 467-493). Hershey, PA: IGI Global. doi:10.4018/978-1-4666-7409-1


In the past, a large number of research efforts concentrated on single-level analysis; however, researchers who only conduct this level of analysis are finding it harder to justify due to the advancements in statistical software and research techniques. The validation of research findings comes partially from other’s replicating existing studies as well as building onto theories. Through replication and validation, the research process becomes cyclical in nature, and each iteration builds upon the next. Each succession of tests sets new boundaries, further verification, or falsification. For a model to be correctly specified, the level of analysis needs to be in congruence with the level of measurement.  This chapter provides an overview of multilevel modeling for researcher and provide guides for the development and investigation of these models.

Tuesday, November 25, 2014

Managing The Innovative Process

In talking about a firm’s habitual activities, similar to the concept of functional fixedness, Leonard (1998) identified that: “The problems on which people focus are the ones most relevant to current markets and current operations” (p. 35). This results in problematic processes incapable of reacting to competitive changes and creating new innovative products. Although Leonard did not identify this as a process problem, she did however identify this as an organizational problem by stating: “organizational routines solidify” (p. 35). What solidifies are the policies and processes set by the organization and its management, preventing innovative processes from occurring.

Anthony, Duncan, and Siren (2014) utilized the minimum viable product (MVP) as a means to fostering new innovative products. MVP “denotes a stripped-down functional prototype used as a starting point for developing a new offering” (Anthony et al., 2014, P. 62). By beginning with a basic prototype, creativity comes from the process. This process is defined by those involved in advancing the prototype into a new product. The knowledge, experience, and collaborative efforts among those who are involved in developing this new prototype, drive the new process that ultimately, returns the new innovative product. 

This innovative formula has been identified for some time, however managers and organizations who get caught up in accountability and meeting quarterly budgets often loose sight of this innovation process. Nonaka and Takeuchi (1995) highlighted this process: “Business organizations should foster their employees’ commitment by formulating an organizational intention and proposing it to them” (p. 75). Proposing this organizational intention, whether in the form of a basic prototype or by other means, leaving the innovative process to those involved in the work, will foster the innovative process. 

To be successful the right people need to be involved, providing the required skills and knowledge to complete such a task. In addition, resources need to be made available to those involved so that they can complete the task. These two criterions have been identified as common obstacles to the creative process: “Two obstacles, in our experience, may daunt companies…: a lack of resources and a lack of people with pertinent experience” (Anthony et al., 2014, p. 65). 

Briefly, to create innovative processes, the following minimum requirements are required:

  • Break the firm’s habitual activities
  • Incorporate a team with the required skills and experiences to create the proposed innovative product
  • Assure that all resources are available to the innovation team
  • Introduce the organization’s intention with no restrictions / boundaries
  • Let the innovative process develop by stepping back so that the innovation team is allowed to work
  • Continue to provide resources to the innovation team as needed
  • Accept failures as: (1) a learning experiences and (2) one step closer to the end product


Anthony, S. D., Duncan, D. S., & Siren, P. M. A. (2014). Build an innovation engine in 90 days. Harvard Business Review, 92(12), 60-68. Retrieved from http://www.HBR,org

Christensen, C. M., & Raynor, M. E. (2003). The innovator’s solution: Creating and sustaining successful growth. Boston, MA: Harvard Business Review Press.

Leonard, D. (1998).Wellsprings of knowledge: Building and sustaining the sources of innovation. Boston, MA: Harvard Business School Press. 

Nonaka, I., & Takeuchi, H. (1995). The knowledge creating company: How Japanese companies create the dynamics of innovation. New York, NY: Oxford University Press.
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