Concepts

Definition

A concept is an abstract idea, notion, or mental representation that represents a specific category of objects, events, behaviors, or phenomena (Podsakoff et al., 2016).

Concepts are fundamental to

human cognition and communication in general, as they enable us to organize and make sense of the complex world around us; and
research in the organizational, behavioral, and social sciences, as they serve as a building block for theories, hypotheses, and empirical investigations (Podsakoff et al., 2016).

Concepts can vary in complexity, ranging from simple and well-defined ideas to more complex and multidimensional constructs. They are typically developed based on experience, that

can be of real phenomena (e.g., weight) as well as of some latent phenomena that we can agree upon (e.g., usefulness), and
can be linked to one another via propositions.

Visualization

Figure 1: Concepts in the scientific research process (based on Recker (2021, p. 26)

Importance

Well-defined concepts are crucial for theoretical development, empirical testing, and communication among researchers. They provide a solid foundation for scientific inquiry by guiding research design, measurement, analysis, and communication. They contribute to the reliability, validity, and credibility of scientific findings, fostering progress and innovation in the respective fields of study. Poor concept definitions can result in measurement problems, misinterpretations, and difficulties in replicating studies (Podsakoff et al., 2016).

Cumulative knowledge: Science is built on the cumulative advancement of knowledge. Clear definitions ensure that the progress made in one study can be built upon by subsequent studies. Inaccurate or vague definitions can lead to confusion and contradictory findings, hindering the cumulative nature of scientific discovery.
Theoretical development: Clear concept definitions are the foundation of theoretical development in any scientific discipline. Well-defined concepts provide a common language and framework for researchers to build and refine theories, leading to a more organized and systematic understanding of phenomena.
Empirical research: In empirical research, clear conceptual definitions are crucial for designing experiments, surveys, and other research methods. If concepts are not well-defined, researchers may struggle to accurately measure and assess them, leading to inconsistent or unreliable results.
Replication and verification: Replicating research is a fundamental aspect of the scientific method. Without clear concept definitions, replication becomes difficult or even impossible, as different researchers might interpret and operationalize concepts differently, undermining the validity of the findings.
Validation and theory testing: Clear concepts are essential for testing and validating theories. Researchers can assess whether a concept behaves as expected based on its definition, allowing for the refinement and improvement of theoretical frameworks.
Communication and collaboration: Clear definitions facilitate effective communication among researchers. When concepts are defined precisely, there is less room for ambiguity or misunderstanding. Researchers can collaborate more efficiently, share insights, and contribute to the accumulation of knowledge.
Academic rigor: Precise and well-defined concepts demonstrate a commitment to academic rigor and intellectual honesty. Researchers who take the time to carefully define their concepts show their dedication to producing high-quality and reliable research.
Theoretical and practical applications: Clear concepts enable the translation of scientific findings into practical applications. Whether in business, policy-making, or other fields, well-defined concepts ensure that research insights can be effectively applied to real-world situations.

Guidelines

Podsakoff et al. (2016) propose a set of recommendations for creating better concept definitions:

Clarity and precision: Concept definitions should be clear, concise, and unambiguous. Authors should avoid jargon and complex language that might obscure the intended meaning.
Differentiation: Concepts should be clearly differentiated from related concepts. Authors should explicitly state the boundaries of the concept and explain how it differs from similar terms.
Explicitness: Authors should explicitly specify the elements, dimensions, or components that constitute the concept. This helps readers understand the concept’s structure and characteristics.
Descriptive and functional information: Definitions should include both descriptive information (what the concept is) and functional information (how it operates or affects other variables).
Theoretical foundation: Concept definitions should be grounded in relevant theories and existing literature. Authors should connect their definitions to established concepts and frameworks.
Measurement implications: Authors should consider the practical implications of their concept definitions for measurement. A well-defined concept should have clear measurement indicators.
Multiple perspectives: Authors should acknowledge that different perspectives may exist for a concept, and they should strive to incorporate these various viewpoints into their definition.
Dynamic nature: Some concepts may evolve over time or vary across contexts. Authors should address this dynamism in their definitions.

Operationalization

Concepts need to be operationalized to something in the real world that can be measured. The concept education, for instance, could be operationalized as “highest degree earned”, which in turn could be measured by ascertaining what type of course (high school, under-graduate, post-graduate, etcetera) a person had completed (Recker, 2021).

Most constructs are composed of a multidimensional set of underlying concepts. Intelligence, for example, can hardly be measured with just one variable, since we understand this concept to include abilities such as abstract thinking, comprehension, communication, reasoning, learning, planning, problem solving, and others, including the emotional intelligence mentioned above. We refer to such constructs as multidimensional constructs because they have multiple underlying dimensions, all of which are relevant to our understanding and use of the construct and, consequently, all of which we need to measure separately through specific variables. The example of intelligence shows why there is the IQ score (intelligence quotient), which is the standardized result of a complex test that includes measurement variables to determine the intelligence level of individuals alongside a number of ability dimensions such as abstract thinking, communication, creativity, learning, memory, problem solving, reasoning, visual processing, and others.

Figure 2: Operationalization of concepts (based on Recker (2021, p. 26)

Theory

Definition

Sutton & Staw (1995) define theory as a comprehensive framework that goes beyond simple descriptions, hypotheses, data, or metaphors. A true theory provides a systematic and explanatory understanding of a set of phenomena, offering insights into the underlying mechanisms and causal relationships.

Theory is about the connections between phenomena, a story about why actions, events, structures, and thoughts occur.
Theory emphasizes the nature of causal relationships by determining what occurs first and the timing of such events.
Theory delves into the underlying processes to understand the systematic reasons for a particular event or non-event.
The theory is usually accompanied by a series of convincing and logically coherent arguments.

What theory is not

Sutton & Staw (1995) argue that theory is often misunderstood and misused in academic and scholarly contexts. They provide a clear distinction between what theory is and what it is not:

Theory is not data: Data alone is not theory. Data provides evidence and information, but theory involves interpreting and making sense of the data by providing underlying principles or causal mechanisms.
Theory is not descriptive: Theory is not merely a description of observed phenomena. It goes beyond summarizing empirical findings and involves explaining why and how certain patterns or relationships occur.
Theory is not hypotheses: While hypotheses are specific statements or predictions derived from theory, theory itself is a broader framework that provides a coherent explanation for a set of related phenomena (the why).
Theory is not trivial insights: Theory should offer more than just common-sense observations or insights. A true theory provides a systematic and organized understanding that extends beyond obvious observations.
Theory is not model building: While models can illustrate the components and relationships within a theory, the authors assert that creating models is not the same as developing a theory. Theory involves explaining the logic and rationale behind the model.

Function

Theories guide and give meaning to what we see.

Help to organize relevant empirical facts (i.e., what can be observed and measured) to provide description, explanation or prediction of phenomena
Allow us to generalize from „the known“ to „the unknown“ (e.g., guide the design of artefacts)

Scientific theories

Theory is something we use all the time in our everyday life to construct explanations about the world in which we live.

What is specific to scientific theories?

According to Bacharach (1989), a scientific theory

helps us to synthesize prior empirical findings and to reconcile contradictory findings by discovering contingent factors;
can be empirically tested using scientific methods;
needs to be able to be falsified, but not necessarily to be proven positive;
offers guidance for future research by helping identify constructs (reflecting concepts) and hypotheses (relationships between the constructs)
contributes to cumulative knowledge by bridging gaps between theories and causing existing theories to be re-evaluated

Levels of theory

High-level theories are usually known by their more-common label of “perspectives”. A perspective is simply a way of looking at and understanding the world or social constructions within it. Different researchers, working within different perspectives, construct different theories about the nature of that world. Mid-range and low-level theories are often based on the principles underpinning these persepctives. Thus, if you understand the basic principles of high-level theories, you will find it easier to understand other types of theory.

Types of theory

Gregor (2006) elaborates five types of theories used in information systems research.

I Analysis: The theory says what is. It does not extend beyond analysis and description. No causal relationships among phenomena are specified and no predictions are made. Example: Gregor’s Types of Theory.
II Explanation: The theory says what is, how, why, when, and where. It provides explanations but does not aim to predict with any precision. There are no testable propositions. Example: Adaptive Structuration Theory.
III Prediction: The theory says what is and what will be. It provides predictions and has testable propositions but does not have well developed justificatory causal explanations. Example: Moore’ Law.
IV Explanation and prediction: The theory says what is, how, why, when, where, and what will be. It provides predictions and has both testable propositions and causal explanations. Example: Technology Acceptance Model (TAM).
V Design and action: The theory says how to do something. It gives explicit prescriptions (e.g., methods, techniques, principles of form and function) for constructing an artifact. Example: Progressive Learning Theory — “learning by doing”.

Building blocks

Concepts and constructs

The “what” of theories.

Concepts and constructs form the “what” of theories (Whetten, 1989).

A construct is the operationalization of a concept that is chosen to explain the phenomenon (Podsakoff et al., 2016).
Constructs must have clear and unambiguous operational definition that should specify exactly how the construct will be measured and at what level of analysis (individual, group, organizational, etc.).
The constructs included in a theory must be equally comprehensive and parsimonious—all relevant concepts are included but but only those that enhance understanding (Whetten, 1989).

Propositions and hypotheses

The “how” of theories.

Propositions and hypotheses form the “how” of theories.

Propositions are associations postulated at the theoretical level (they relate concepts), hypotheses are tested at the empirical level (they relate constructs) (Whetten, 1989).

Propositions are stated in declarative form and should ideally indicate a cause-effect relationship (e.g., if X occurs, then Y will follow).
Propositions may be speculative but must be testable, and should be rejected if they are not supported by empirical observations.

Nomological nets

Figure 4: The roles and relationships of nets of constructs in a theoretical model

Logic

The “why” of theories.

The logic provides the basis for justifying the propositions as postulated (Whetten, 1989).

Acts like a “glue” that connects the theoretical constructs and provides meaning and relevance to the relationships between these constructs.
Represents the “explanation” that lies at the core of a theory.

Without logic, propositions will be ad hoc, random, and meaningless

Boundary conditions

The constraints of theories.

Assumptions about values, time, and space that govern where the theory can be applied and where it cannot be applied—the boundaries of generalizability (Whetten, 1989).
If a theory is to be properly used or tested, all its implicit assumptions must be properly understood (e.g., cultural assumptions, temporal assumptions, or spatial assumptions)
Although it is important to consider potential constraints through theory development, these boundaries are usually discovered through subsequent tests.

Conclusion

Just as a collection of words does not make a sentence, a collection of constructs and variables does not make a theory. Bacharach (1989, p. 496)

Example

Problem statement

Phenomenon

Firms heavily invest in IT applications to improve organizational performance
There are often low financial returns on IT investments
IT applications need to be used to improve performance
Understanding user acceptance of new IT is of high importance

Research aim

Providing an explanation of IT acceptance

Contribution

The Technology Acceptance Model (TAM) (Davis, 1989)

Theoretical basis

Theory of Reasoned Action (TRA)—an established theory in psychology to understand an individual’s behavior (Ajzen, 1985).

Figure 5: The nomological net of the Theory of Reasoned Action (Ajzen, 1985)

Technology Acceptance Model

The Technology Acceptance Model (TAM) (Davis, 1989) translates the key tenets of TRA into the IT acceptance domain (except for subjective norms).

Operationalization of concepts

Construct	Operational Definition	Variables (Measurement Items)
Behavioral intention	Participants intentions to use a particular system in the future	I intend to use the system in the next months
		I predict I would use the system in the next months
		I plan to use the system in the next months.
Attitude towards Behavior	An individual’s positive or negative feelings about performing the target behavior.	Using the system is a bad/good idea.
		Using the system is a foolish/wise idea.
		I dislike/like the idea of using the system.
		Using the system is unpleasant/ pleasant.
Perceived Usefulness	The degree to which a person believes that using a particular system would enhance his or her job performance.	Using the system in my job would enable me to accomplish tasks more quickly.
		Using the system would improve my job performance.
		Using the system in my job would increase my productivity.
		Using the system would enhance my effectiveness on the job.
		Using the system would make it easier to do my job.
		I would find the system useful in my job.
Perceived Ease of Use	The degree to which a person believes that using a system would be free of effort.	Learning to operate the system would be easy for me.
		I would find it easy to get the system to do what I want it to do.
		My interaction with the system would be clear and understandable.
		I would find the system to be flexible to interact with.
		It would be easy for me to become skillful at using the system.
		I would find the system easy to use

Theorizing

Definition

Theorizing is the application or development of theoretical arguments to make sense of a real account (e.g. an observed phenomenon) (Recker, 2021).

Theorizing can be inductive, deductiveor abductive.
Theorizing can be dependent on data analysis, creative thinking, inspiration, or good luck.

Principles

Theory is always an simplification

It should not be more complex than the phenomenon to be investigated.

A strong theory is an idealization

For instance the idea of a rational human decision-making.

General approaches

From theory to data

Typically called the ‘traditional’ scientific process

You start with a theory.
You develop a modification or an extension of it
Then you collect data specifically to test/falsify the predictions

From data to theory

Often equated with inductive and interpretive research

You start with data, with a study of something that is ‘really happening’
You examine whether there is no theory to explain what you observe
You start developing a novel theoretical account, “grounded” in the data

Theorizing approaches

Theory building

Building theory involves the development of new theoretical frameworks or explanatory models to understand a phenomenon. This process often starts with empirical observations or data collection. Researchers use inductive or abductive approaches to identify patterns, relationships, and underlying mechanisms. They formulate conceptual frameworks that capture these patterns and explain the observed phenomenon. Building theory is particularly useful when there is a lack of established theoretical foundations for a specific area of study (Mueller & Urbach, 2017).

Theory testing

Testing theory involves subjecting existing theoretical frameworks or models to empirical scrutiny to assess their validity and predictive power. Researchers use deductive testing to generate hypotheses based on established theories and then gather data to confirm or refute these hypotheses. Inductive testing, on the other hand, uses empirical observations to challenge or refine existing theories. The goal of testing theory is to assess whether a theory accurately represents the real-world phenomenon and whether its predictions hold up under empirical examination.

Theory extending

Extending theory refers to the process of enhancing or expanding existing theoretical frameworks to refine and broaden their applicability, accommodating new empirical findings and addressing limitations.

Researchers can extend theory in several ways:

Conceptual extension: This involves introducing new concepts, dimensions, or variables to an existing theory, enriching its explanatory scope and relevance.
Theoretical extension: Researchers extend theory by adding new relationships, mechanisms, or components to an existing framework. This expands the theory’s ability to explain complex phenomena.
Interdisciplinary extension: Extending theory by integrating insights and concepts from other disciplines, bridging gaps and enriching the theoretical foundations.

These approaches are not mutually exclusive, and researchers often combine elements from multiple approaches depending on the research context.

The choice depends on the research question, available data, and the goals of the study.

Quality checks

Recker (2021), Mueller & Urbach (2017) and others propose that you have a good theory when you have answer to the following questions:

Is your account insightful, challenging, perhaps surprising, and–importantly–does it seem to make sense?
Is your account (your arguments) testable (falsifiable)?
Do you have convincing evidence to support your account?
Is your account parsimonious?
Are the arguments logical?
What can you say about the boundary conditions of the theory?
What are implications of your theory?

How to theorize — example

Start with an observation

Think about being in college. You’re in class, and the guy next to you -who is obviously a football player -says an unbelievably dumb thing in class. Why?¹
Initial theory: Football players are dumb.

Theories should be about classes of things, not the thing (more general)².

New theory: Athletes are dumb.

Theories should be explanatory. Moreover, they should be free of circular arguments as circularity prevents theories from being falsifiable.

Dumbness cannot be directly observed or measured. The only way we can know if people are dumb is by what they say and do. So we say that they say dumb things because they say dumb things.
New theory: To be a good athlete requires lots of practice time; being smart in class also requires study time. Amount of time is limited, so practicing a sport means less studying which means being less smart in class.

A good theory is general enough to generate implications for other groups of people and other contexts, all of which serve as potential tests of the theory (i.e., the theory is fertile).

We can shift the focus from an enduring property of a class of people (athletes) to a mechanism. This means that we can apply the same reasoning to other people and other situations.
New theory: There is limited time in a day, so when a person engages in a very time-consuming activity, such as athletics, it takes away from other very time-consuming activities, such as studying (Limited Time Theory).

Often, we find that there are also other explanations:

Everyone has a need to excel in one area. Achieving excellence in any one area is enough to satisfy this need. Football players satisfy their need for accomplishment through football, so they are not motivated to be smart in class (Excellence Theory).
We are jealous of others’ success. When we are jealous, we subconsciously lower our evaluation of that person’s performance in other areas. So we think football players ask dumb questions (Jealousy Theory).

We need to study different contexts to see how our theories explain observations.

Depending on the context, each theory leads to other expectations, e.g.,
- Football players ask dumb questions out of season?
- Will athletes who do not look like athletes ask dumb questions?
The expectations of our theory lead to hypotheses that can be tested by empirical research.

Homework

Read Hund et al. (2021) and make notes on following questions:

How do the authors theorize?
What is their theoretical contribution to the existing body of knowledge?
What are the building blocks of their theory?

References

Ajzen, I. (1985). From intentions to actions: A theory of planned behavior. In Action control: From cognition to behavior (pp. 11–39). Springer.

Bacharach, S. B. (1989). Organizational theories: Some criteria for evaluation. Academy of Management Review, 14(4), 496–515.

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 319–340.

Gregor, S. (2006). The nature of theory in information systems. MIS Quarterly, 611–642.

Hund, A., Wagner, H.-T., Beimborn, D., & Weitzel, T. (2021). Digital innovation: Review and novel perspective. The Journal of Strategic Information Systems, 30(4), 101695.

Lave, C. A., & March, J. G. (1993). An introduction to models in the social sciences. University Press of America.

Mueller, B., & Urbach, N. (2017). Understanding the why, what, and how of theories in IS research. Communications of the Association for Information Systems, 41, 349–388.

Podsakoff, P. M., MacKenzie, S. B., & Podsakoff, N. P. (2016). Recommendations for creating better concept definitions in the organizational, behavioral, and social sciences. Organizational Research Methods, 19(2), 159–203.

Recker, J. (2021). The research process. Scientific Research in Information Systems: A Beginner’s Guide.

Sutton, R. I., & Staw, B. M. (1995). What theory is not. Administrative Science Quarterly, 371–384.

Van de Ven, A. H. (1989). Nothing is quite so practical as a good theory. Academy of Management Review, 14(4), 486–489.

Weber, R. (2003). Theoretically speaking1. MIS Quarterly, 27(3), III.

Weber, R. (2012). Evaluating and developing theories in the information systems discipline. Journal of the Association for Information Systems, 13(1), 2.

Weick, K. E. (1989). Theory construction as disciplined imagination. Academy of Management Review, 14(4), 516–531.

Weick, K. E. (1995). What theory is not, theorizing is. Administrative Science Quarterly, 40(3), 385–390.

Whetten, D. A. (1989). What constitutes a theoretical contribution? Academy of Management Review, 14(4), 490–495.

Footnotes

The example draws from Lave & March (1993).↩︎
Theories that are too narrow and specific (i.e., low-level theories) are not very interesting, even if they are correct.↩︎

Concepts

Definition

Visualization

Importance

Guidelines

Operationalization

Theory

Definition

What theory is not

Function

Scientific theories

Levels of theory

Types of theory

Building blocks

Concepts and constructs

Propositions and hypotheses

Nomological nets

Logic

Boundary conditions

Conclusion

Example

Problem statement

Phenomenon

Research aim

Contribution

Theoretical basis

Technology Acceptance Model

Operationalization of concepts

Theorizing

Definition

Principles

General approaches

From theory to data

From data to theory

Theorizing approaches

Theory building

Theory testing

Theory extending

Quality checks

How to theorize — example

Homework

Recommended readings

References

Footnotes