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empirical and theoretical part of the research work

Home Market Research

Empirical Research: Definition, Methods, Types and Examples

What is Empirical Research

Content Index

Empirical research: Definition

Empirical research: origin, quantitative research methods, qualitative research methods, steps for conducting empirical research, empirical research methodology cycle, advantages of empirical research, disadvantages of empirical research, why is there a need for empirical research.

Empirical research is defined as any research where conclusions of the study is strictly drawn from concretely empirical evidence, and therefore “verifiable” evidence.

This empirical evidence can be gathered using quantitative market research and  qualitative market research  methods.

For example: A research is being conducted to find out if listening to happy music in the workplace while working may promote creativity? An experiment is conducted by using a music website survey on a set of audience who are exposed to happy music and another set who are not listening to music at all, and the subjects are then observed. The results derived from such a research will give empirical evidence if it does promote creativity or not.

LEARN ABOUT: Behavioral Research

You must have heard the quote” I will not believe it unless I see it”. This came from the ancient empiricists, a fundamental understanding that powered the emergence of medieval science during the renaissance period and laid the foundation of modern science, as we know it today. The word itself has its roots in greek. It is derived from the greek word empeirikos which means “experienced”.

In today’s world, the word empirical refers to collection of data using evidence that is collected through observation or experience or by using calibrated scientific instruments. All of the above origins have one thing in common which is dependence of observation and experiments to collect data and test them to come up with conclusions.

LEARN ABOUT: Causal Research

Types and methodologies of empirical research

Empirical research can be conducted and analysed using qualitative or quantitative methods.

  • Quantitative research : Quantitative research methods are used to gather information through numerical data. It is used to quantify opinions, behaviors or other defined variables . These are predetermined and are in a more structured format. Some of the commonly used methods are survey, longitudinal studies, polls, etc
  • Qualitative research:   Qualitative research methods are used to gather non numerical data.  It is used to find meanings, opinions, or the underlying reasons from its subjects. These methods are unstructured or semi structured. The sample size for such a research is usually small and it is a conversational type of method to provide more insight or in-depth information about the problem Some of the most popular forms of methods are focus groups, experiments, interviews, etc.

Data collected from these will need to be analysed. Empirical evidence can also be analysed either quantitatively and qualitatively. Using this, the researcher can answer empirical questions which have to be clearly defined and answerable with the findings he has got. The type of research design used will vary depending on the field in which it is going to be used. Many of them might choose to do a collective research involving quantitative and qualitative method to better answer questions which cannot be studied in a laboratory setting.

LEARN ABOUT: Qualitative Research Questions and Questionnaires

Quantitative research methods aid in analyzing the empirical evidence gathered. By using these a researcher can find out if his hypothesis is supported or not.

  • Survey research: Survey research generally involves a large audience to collect a large amount of data. This is a quantitative method having a predetermined set of closed questions which are pretty easy to answer. Because of the simplicity of such a method, high responses are achieved. It is one of the most commonly used methods for all kinds of research in today’s world.

Previously, surveys were taken face to face only with maybe a recorder. However, with advancement in technology and for ease, new mediums such as emails , or social media have emerged.

For example: Depletion of energy resources is a growing concern and hence there is a need for awareness about renewable energy. According to recent studies, fossil fuels still account for around 80% of energy consumption in the United States. Even though there is a rise in the use of green energy every year, there are certain parameters because of which the general population is still not opting for green energy. In order to understand why, a survey can be conducted to gather opinions of the general population about green energy and the factors that influence their choice of switching to renewable energy. Such a survey can help institutions or governing bodies to promote appropriate awareness and incentive schemes to push the use of greener energy.

Learn more: Renewable Energy Survey Template Descriptive Research vs Correlational Research

  • Experimental research: In experimental research , an experiment is set up and a hypothesis is tested by creating a situation in which one of the variable is manipulated. This is also used to check cause and effect. It is tested to see what happens to the independent variable if the other one is removed or altered. The process for such a method is usually proposing a hypothesis, experimenting on it, analyzing the findings and reporting the findings to understand if it supports the theory or not.

For example: A particular product company is trying to find what is the reason for them to not be able to capture the market. So the organisation makes changes in each one of the processes like manufacturing, marketing, sales and operations. Through the experiment they understand that sales training directly impacts the market coverage for their product. If the person is trained well, then the product will have better coverage.

  • Correlational research: Correlational research is used to find relation between two set of variables . Regression analysis is generally used to predict outcomes of such a method. It can be positive, negative or neutral correlation.

LEARN ABOUT: Level of Analysis

For example: Higher educated individuals will get higher paying jobs. This means higher education enables the individual to high paying job and less education will lead to lower paying jobs.

  • Longitudinal study: Longitudinal study is used to understand the traits or behavior of a subject under observation after repeatedly testing the subject over a period of time. Data collected from such a method can be qualitative or quantitative in nature.

For example: A research to find out benefits of exercise. The target is asked to exercise everyday for a particular period of time and the results show higher endurance, stamina, and muscle growth. This supports the fact that exercise benefits an individual body.

  • Cross sectional: Cross sectional study is an observational type of method, in which a set of audience is observed at a given point in time. In this type, the set of people are chosen in a fashion which depicts similarity in all the variables except the one which is being researched. This type does not enable the researcher to establish a cause and effect relationship as it is not observed for a continuous time period. It is majorly used by healthcare sector or the retail industry.

For example: A medical study to find the prevalence of under-nutrition disorders in kids of a given population. This will involve looking at a wide range of parameters like age, ethnicity, location, incomes  and social backgrounds. If a significant number of kids coming from poor families show under-nutrition disorders, the researcher can further investigate into it. Usually a cross sectional study is followed by a longitudinal study to find out the exact reason.

  • Causal-Comparative research : This method is based on comparison. It is mainly used to find out cause-effect relationship between two variables or even multiple variables.

For example: A researcher measured the productivity of employees in a company which gave breaks to the employees during work and compared that to the employees of the company which did not give breaks at all.

LEARN ABOUT: Action Research

Some research questions need to be analysed qualitatively, as quantitative methods are not applicable there. In many cases, in-depth information is needed or a researcher may need to observe a target audience behavior, hence the results needed are in a descriptive analysis form. Qualitative research results will be descriptive rather than predictive. It enables the researcher to build or support theories for future potential quantitative research. In such a situation qualitative research methods are used to derive a conclusion to support the theory or hypothesis being studied.

LEARN ABOUT: Qualitative Interview

  • Case study: Case study method is used to find more information through carefully analyzing existing cases. It is very often used for business research or to gather empirical evidence for investigation purpose. It is a method to investigate a problem within its real life context through existing cases. The researcher has to carefully analyse making sure the parameter and variables in the existing case are the same as to the case that is being investigated. Using the findings from the case study, conclusions can be drawn regarding the topic that is being studied.

For example: A report mentioning the solution provided by a company to its client. The challenges they faced during initiation and deployment, the findings of the case and solutions they offered for the problems. Such case studies are used by most companies as it forms an empirical evidence for the company to promote in order to get more business.

  • Observational method:   Observational method is a process to observe and gather data from its target. Since it is a qualitative method it is time consuming and very personal. It can be said that observational research method is a part of ethnographic research which is also used to gather empirical evidence. This is usually a qualitative form of research, however in some cases it can be quantitative as well depending on what is being studied.

For example: setting up a research to observe a particular animal in the rain-forests of amazon. Such a research usually take a lot of time as observation has to be done for a set amount of time to study patterns or behavior of the subject. Another example used widely nowadays is to observe people shopping in a mall to figure out buying behavior of consumers.

  • One-on-one interview: Such a method is purely qualitative and one of the most widely used. The reason being it enables a researcher get precise meaningful data if the right questions are asked. It is a conversational method where in-depth data can be gathered depending on where the conversation leads.

For example: A one-on-one interview with the finance minister to gather data on financial policies of the country and its implications on the public.

  • Focus groups: Focus groups are used when a researcher wants to find answers to why, what and how questions. A small group is generally chosen for such a method and it is not necessary to interact with the group in person. A moderator is generally needed in case the group is being addressed in person. This is widely used by product companies to collect data about their brands and the product.

For example: A mobile phone manufacturer wanting to have a feedback on the dimensions of one of their models which is yet to be launched. Such studies help the company meet the demand of the customer and position their model appropriately in the market.

  • Text analysis: Text analysis method is a little new compared to the other types. Such a method is used to analyse social life by going through images or words used by the individual. In today’s world, with social media playing a major part of everyone’s life, such a method enables the research to follow the pattern that relates to his study.

For example: A lot of companies ask for feedback from the customer in detail mentioning how satisfied are they with their customer support team. Such data enables the researcher to take appropriate decisions to make their support team better.

Sometimes a combination of the methods is also needed for some questions that cannot be answered using only one type of method especially when a researcher needs to gain a complete understanding of complex subject matter.

We recently published a blog that talks about examples of qualitative data in education ; why don’t you check it out for more ideas?

Since empirical research is based on observation and capturing experiences, it is important to plan the steps to conduct the experiment and how to analyse it. This will enable the researcher to resolve problems or obstacles which can occur during the experiment.

Step #1: Define the purpose of the research

This is the step where the researcher has to answer questions like what exactly do I want to find out? What is the problem statement? Are there any issues in terms of the availability of knowledge, data, time or resources. Will this research be more beneficial than what it will cost.

Before going ahead, a researcher has to clearly define his purpose for the research and set up a plan to carry out further tasks.

Step #2 : Supporting theories and relevant literature

The researcher needs to find out if there are theories which can be linked to his research problem . He has to figure out if any theory can help him support his findings. All kind of relevant literature will help the researcher to find if there are others who have researched this before, or what are the problems faced during this research. The researcher will also have to set up assumptions and also find out if there is any history regarding his research problem

Step #3: Creation of Hypothesis and measurement

Before beginning the actual research he needs to provide himself a working hypothesis or guess what will be the probable result. Researcher has to set up variables, decide the environment for the research and find out how can he relate between the variables.

Researcher will also need to define the units of measurements, tolerable degree for errors, and find out if the measurement chosen will be acceptable by others.

Step #4: Methodology, research design and data collection

In this step, the researcher has to define a strategy for conducting his research. He has to set up experiments to collect data which will enable him to propose the hypothesis. The researcher will decide whether he will need experimental or non experimental method for conducting the research. The type of research design will vary depending on the field in which the research is being conducted. Last but not the least, the researcher will have to find out parameters that will affect the validity of the research design. Data collection will need to be done by choosing appropriate samples depending on the research question. To carry out the research, he can use one of the many sampling techniques. Once data collection is complete, researcher will have empirical data which needs to be analysed.

LEARN ABOUT: Best Data Collection Tools

Step #5: Data Analysis and result

Data analysis can be done in two ways, qualitatively and quantitatively. Researcher will need to find out what qualitative method or quantitative method will be needed or will he need a combination of both. Depending on the unit of analysis of his data, he will know if his hypothesis is supported or rejected. Analyzing this data is the most important part to support his hypothesis.

Step #6: Conclusion

A report will need to be made with the findings of the research. The researcher can give the theories and literature that support his research. He can make suggestions or recommendations for further research on his topic.

Empirical research methodology cycle

A.D. de Groot, a famous dutch psychologist and a chess expert conducted some of the most notable experiments using chess in the 1940’s. During his study, he came up with a cycle which is consistent and now widely used to conduct empirical research. It consists of 5 phases with each phase being as important as the next one. The empirical cycle captures the process of coming up with hypothesis about how certain subjects work or behave and then testing these hypothesis against empirical data in a systematic and rigorous approach. It can be said that it characterizes the deductive approach to science. Following is the empirical cycle.

  • Observation: At this phase an idea is sparked for proposing a hypothesis. During this phase empirical data is gathered using observation. For example: a particular species of flower bloom in a different color only during a specific season.
  • Induction: Inductive reasoning is then carried out to form a general conclusion from the data gathered through observation. For example: As stated above it is observed that the species of flower blooms in a different color during a specific season. A researcher may ask a question “does the temperature in the season cause the color change in the flower?” He can assume that is the case, however it is a mere conjecture and hence an experiment needs to be set up to support this hypothesis. So he tags a few set of flowers kept at a different temperature and observes if they still change the color?
  • Deduction: This phase helps the researcher to deduce a conclusion out of his experiment. This has to be based on logic and rationality to come up with specific unbiased results.For example: In the experiment, if the tagged flowers in a different temperature environment do not change the color then it can be concluded that temperature plays a role in changing the color of the bloom.
  • Testing: This phase involves the researcher to return to empirical methods to put his hypothesis to the test. The researcher now needs to make sense of his data and hence needs to use statistical analysis plans to determine the temperature and bloom color relationship. If the researcher finds out that most flowers bloom a different color when exposed to the certain temperature and the others do not when the temperature is different, he has found support to his hypothesis. Please note this not proof but just a support to his hypothesis.
  • Evaluation: This phase is generally forgotten by most but is an important one to keep gaining knowledge. During this phase the researcher puts forth the data he has collected, the support argument and his conclusion. The researcher also states the limitations for the experiment and his hypothesis and suggests tips for others to pick it up and continue a more in-depth research for others in the future. LEARN MORE: Population vs Sample

LEARN MORE: Population vs Sample

There is a reason why empirical research is one of the most widely used method. There are a few advantages associated with it. Following are a few of them.

  • It is used to authenticate traditional research through various experiments and observations.
  • This research methodology makes the research being conducted more competent and authentic.
  • It enables a researcher understand the dynamic changes that can happen and change his strategy accordingly.
  • The level of control in such a research is high so the researcher can control multiple variables.
  • It plays a vital role in increasing internal validity .

Even though empirical research makes the research more competent and authentic, it does have a few disadvantages. Following are a few of them.

  • Such a research needs patience as it can be very time consuming. The researcher has to collect data from multiple sources and the parameters involved are quite a few, which will lead to a time consuming research.
  • Most of the time, a researcher will need to conduct research at different locations or in different environments, this can lead to an expensive affair.
  • There are a few rules in which experiments can be performed and hence permissions are needed. Many a times, it is very difficult to get certain permissions to carry out different methods of this research.
  • Collection of data can be a problem sometimes, as it has to be collected from a variety of sources through different methods.

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Empirical research is important in today’s world because most people believe in something only that they can see, hear or experience. It is used to validate multiple hypothesis and increase human knowledge and continue doing it to keep advancing in various fields.

For example: Pharmaceutical companies use empirical research to try out a specific drug on controlled groups or random groups to study the effect and cause. This way, they prove certain theories they had proposed for the specific drug. Such research is very important as sometimes it can lead to finding a cure for a disease that has existed for many years. It is useful in science and many other fields like history, social sciences, business, etc.

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With the advancement in today’s world, empirical research has become critical and a norm in many fields to support their hypothesis and gain more knowledge. The methods mentioned above are very useful for carrying out such research. However, a number of new methods will keep coming up as the nature of new investigative questions keeps getting unique or changing.

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empirical and theoretical part of the research work

Difference between Theoretical and Empirical Research

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The difference between theoretical and empirical research is fundamental to scientific, scholarly research, as it separates the development of ideas and models from their testing and validation.

These two approaches are used in many different fields of inquiry, including the natural sciences, social sciences, and humanities, and they serve different purposes and employ different methods.

Table of Contents

What is theoretical research.

Theoretical research involves the development of models, frameworks, and theories based on existing knowledge, logic, and intuition.

It aims to explain and predict phenomena, generate new ideas and insights, and provide a foundation for further research.

Theoretical research often takes place at the conceptual level and is typically based on existing knowledge, data, and assumptions.

What is Empirical Research?

In contrast, empirical research involves collecting and analysing data to test theories and models.

Empirical research is often conducted at the observational or experimental level and is based on direct or indirect observation of the world.

Empirical research involves testing theories and models, establishing cause-and-effect relationships, and refining or rejecting existing knowledge.

Theoretical vs Empirical Research

Theoretical research is often seen as the starting point for empirical research, providing the ideas and models that must be tested and validated.

Theoretical research can be qualitative or quantitative and involve mathematical models, simulations, and other computational methods.

Theoretical research is often conducted in isolation, without reference to primary data or observations.

On the other hand, empirical research is often seen as the final stage in the scientific process, as it provides evidence that supports or refutes theoretical models.

Empirical research can be qualitative or quantitative, involving surveys, experiments, observational studies, and other data collection methods.

Empirical research is often conducted in collaboration with others and is based on systematic data collection, analysis, and interpretation.

It is important to note that theoretical and empirical research are not mutually exclusive and can often complement each other.

For example, empirical data can inform the development of theories and models, and theoretical models can guide the design of empirical studies.

The most valuable research combines theoretical and empirical approaches in many fields, allowing for a comprehensive understanding of the studied phenomena.

It is important to note that this table is not meant to be exhaustive or prescriptive but rather to provide a general overview of the main difference between theoretical and empirical research.

The boundaries between these two approaches are not always clear, and in many cases, research may involve a combination of theoretical and empirical methods.

What are the Limitations of Theoretical Research?

Assumptions and simplifications may be made that do not accurately reflect the complexity of real-world phenomena, which is one of its limitations. Theoretical research relies heavily on logic and deductive reasoning, which can sometimes be biased or limited by the researcher’s assumptions and perspectives.

Furthermore, theoretical research may not be directly applicable to real-world situations without empirical validation. Applying theoretical ideas to practical situations is difficult if no empirical evidence supports or refutes them.

Furthermore, theoretical research may be limited by the availability of data and the researcher’s ability to access and interpret it, which can further limit the validity and applicability of theories.

What are the Limitations of Empirical Research?

There are many limitations to empirical research, including the limitations of the data available and the quality of the data that can be collected. Data collection can be limited by the resources available to collect the data, accessibility to populations or individuals of interest, or ethical constraints.

The researchers or participants may also introduce biases into empirical research, resulting in inaccurate or unreliable findings.

Lastly, due to confounding variables or other methodological limitations, empirical research may be limited by the inability to establish causal relationships between variables, even when statistical associations are identified.

What Methods Are Used In Theoretical Research?

In theoretical research, deductive reasoning, logical analysis, and conceptual frameworks generate new ideas and hypotheses. To identify gaps and inconsistencies in the present understanding of a phenomenon, theoretical research may involve analyzing existing literature and theories.

To test hypotheses and generate predictions, mathematical or computational models may also be developed.

Researchers may also use thought experiments or simulations to explore the implications of their ideas and hypotheses without collecting empirical data as part of theoretical research.

Theoretical research seeks to develop a conceptual framework for empirically testing and validating phenomena.

What Methods Are Used In Empirical Research?

Methods used in empirical research depend on the research questions, type of data collected, and study design. Surveys, experiments, observations, case studies, and interviews are common methods used in empirical research.

An empirical study tests hypotheses and generates new knowledge about phenomena by systematically collecting and analyzing data.

These methods may utilize standardized instruments or protocols for data collection consistency and reliability. Statistical analysis, content analysis, or qualitative analysis may be used for the data collection type.

As a result of empirical research, the findings can inform theories, models, and practical applications.

Conclusion: Theoretical vs Empirical Research

In conclusion, theoretical and empirical research are two distinct but interrelated approaches to scientific inquiry, and they serve different purposes and employ different methods.

Theoretical research involves the development of ideas and models, while empirical research involves testing and validating these ideas.

Both approaches are essential to research and can be combined to provide a more complete understanding of the world.

  • Dictionary.com. “ Empirical vs Theoretical “.
  • PennState University Libraries. “ Empirical Research in the Social Sciences and Education “.
  • William M. Landes and Richard A. Posner. “ Legal Precedent: A Theoretical and Empirical Analysis “, The Journal of Law and Economics, 1976.

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1.2: Theory and Empirical Research

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  • Page ID 7203

  • Jenkins-Smith et al.
  • University of Oklahoma via University of Oklahoma Libraries

This book is concerned with the connection between theoretical claims and empirical data. It is about using statistical modeling; in particular, the tool of regression analysis, which is used to develop and refine theories. We define theory broadly as a set of interrelated propositions that seek to explain and, in some cases, predict an observed phenomenon.

Theory: A set of interrelated propositions that seek to explain and predict an observed phenomenon.

Theories contain three important characteristics that we discuss in detail below.

Characteristics of Good Theories Coherent and internally consistent Causal in nature Generate testable hypotheses

1.2.1 Coherent and Internally Consistent

The set of interrelated propositions that constitute a well-structured theory are based on concepts . In well-developed theories, the expected relationships among these concepts are both coherent and internally consistent. Coherence means the identification of concepts and the specified relationships among them are logical, ordered, and integrated. An internally consistent theory will explain relationships with respect to a set of common underlying causes and conditions, providing for consistency in expected relationships (and avoidance of contradictions). For systematic quantitative research, the relevant theoretical concepts are defined such that they can be measured and quantified. Some concepts are relatively easy to quantify, such as the number of votes cast for the winning Presidential candidate in a specified year or the frequency of arrests for gang-related crimes in a particular region and time period. Others are more difficult, such as the concepts of democratization, political ideology or presidential approval. Concepts that are more difficult to measure must be carefully operationalized , which is a process of relating a concept to an observation that can be measured using a defined procedure. For example, political ideology is often operationalized through public opinion surveys that ask respondents to place themselves on a Likert-type scale of ideological categories.

Concepts and Variables

A concept is a commonality across observed individual events or cases. It is a regularity that we find in a complex world. Concepts are our building blocks to understanding the world and to developing theory that explains the world. Once we have identified concepts we seek to explain them by developing theories based on them. Once we have explained a concept we need to define it. We do so in two steps. First, we give it a dictionary-like definition, called a nominal definition. Then, we develop an operational definition that identifies how we can measure and quantify it.

Once a concept has been quantified, it is employed in modeling as a variable . In statistical modeling, variables are thought of as either dependent or independent variables. A dependent variable , Y, is the outcome variable; this is the concept we are trying to explain and/or predict. The independent variable(s) , X, is the variable(s) that is used to predict or explain the dependent variable. The expected relationships between (and among) the variables are specified by the theory.

Measurement

When measuring concepts, the indicators that are used in building and testing theories should be both valid and reliable . Validity refers to how well the measurement captures the concept. Face validity, for example, refers to the plausibility and general acceptance of the measure, while the domain validity of the measure concerns the degree to which it captures all relevant aspects of the concept. Reliability, by contrast, refers to how consistent the measure is with repeated applications. A measure is reliable if, when applied to the repeated observations in similar settings, the outcomes are consistent.

Assessing the Quality of a Measure

Measurement is the process of assigning numbers to the phenomenon or concept that you are interested in. Measurement is straight-forward when we can directly observe the phenomenon. One agrees on a metric, such as inches or pounds, and then figures out how many of those units are present for the case in question. Measurement becomes more challenging when you cannot directly observe the concept of interest. In political science and public policy, some of the things we want to measure are directly observable: how many dollars were spent on a project or how many votes the incumbent receives, but many of our concepts are not observable: is issue X on the public’s agenda, how successful is a program, or how much do citizens trust the president. When the concept is not directly observable the operational definition is especially important. The operational definition explains exactly what the researcher will do to assign a number for each subject/case.

In reality, there is always some possibility that the number assigned does not reflect the true value for that case, i.e., there may be some error involved. Error can come about for any number of reasons, including mistakes in coding, the need for subjective judgments, or a measuring instrument that lacks precision. These kinds of error will generally produce inconsistent results; that is, they reduce reliability. We can assess the reliability of an indicator using one of two general approaches. One approach is a test-retest method where the same subjects are measured at two different points in time. If the measure is reliable the correlation between the two observations should be high. We can also assess reliability by using multiple indicators of the same concept and determining if there is a strong inter-correlation among them using statistical formulas such as Cronbach’s alpha or Kuder-Richardson Formula 20 (KR-20).

We can also have error when our measure is not valid. Valid indicators measure the concept we think they are measuring. The indicator should both converge with the concept and discriminate between the concept and similar yet different concepts. Unfortunately, there is no failsafe way to determine whether an indicator is valid. There are, however, a few things you can do to gain confidence in the validity of the indicator. First, you can simply look at it from a logical perspective and ask if it seems like it is valid. Does it have face validity? Second, you can see if it correlates well with other indicators that are considered valid, and in ways that are consistent with theory. This is called construct validity. Third, you can determine if it works in the way expected, which is referred to as predictive validity. Finally, we have more confidence if other researchers using the same concept agree that the indicator is considered valid. This consensual validity at least ensures that different researchers are talking about the same thing.

Measurement of Different Kinds of Concepts

Measurement can be applied to different kinds of concepts, which causes measures of different concepts to vary. There are three primary levels of measurement ; ordinal, interval, and nominal. Ordinal level measures indicate relative differences, such as more or less, but do not provide equal distances between intervals on the measurement scale. Therefore, ordinal measures cannot tell us how much more or less one observation is than another. Imagine a survey question asking respondents to identify their annual income. Respondents are given a choice of five different income levels: $0-20,000, $20,000-50,000, $50,000-$100,000, and $100,000+. This measure gives us an idea of the rank order of respondents’ income, but it is impossible for us to identify consistent differences between these responses. With an interval level measure, the variable is ordered and the differences between values are consistent. Sticking with the example of income, survey respondents are now asked to provide their annual income to the nearest ten thousand dollar mark (e.g., $10,000, $20,000, $30,000, etc.). This measurement technique produces an interval level variable because we have both a rank ordering and equal spacing between values. Ratio scales are interval measures with the special characteristic that the value of zero (0) indicates the absence of some property. A value of zero (0) income in our example may indicate a person does not have a job. Another example of a ratio scale is the Kelvin temperature scale because zero (0) degrees Kelvin indicates the complete absence of heat. Finally, a nominal level measure identifies categorical differences among observations. Numerical values assigned to nominal variables have no inherent meaning, but only differentiate one type" (e.g., gender, race, religion) from another.

1.2.2 Theories and Causality

Theories should be causal in nature, meaning that an independent variable is thought to have a causal influence on the dependent variable. In other words, a change in the independent variable causes a change in the dependent variable. Causality can be thought of as the motor" that drives the model and provides the basis for explanation and (possibly) prediction.

The Basis of Causality in Theories

  • Time Ordering: The cause precedes the effect, X→Y
  • Co-Variation: Changes in X are associated with changes in Y
  • Non-Spuriousness: There is not a variable Z that causes both X and Y

To establish causality we want to demonstrate that a change in the independent variable is a necessary and sufficient condition for a change in the dependent variable (though more complex, interdependent relationships can also be quantitatively modeled). We can think of the independent variable as a treatment, τ, and we speculate that τ causes a change in our dependent variable, Y. The gold standard’’ for causal inference is an experiment where a) the level of ττ is controlled by the researcher and b) subjects are randomly assigned to a treatment or control group. The group that receives the treatment has outcome Y 1 and the control group has outcome Y 0 ; the treatment effect can be defined as τ=Y 1 -Y 0 . Causality is inferred because the treatment was only given to one group, and since these groups were randomly assigned other influences should wash out. Thus the difference τ=Y 1 -Y0 can be attributed to the treatment.

Given the nature of social science and public policy theorizing, we often can’t control the treatment of interest. For example, our case study in this text concerns the effect of political ideology on views about the environment. For this type of relationship, we cannot randomly assign ideology in an experimental sense. Instead, we employ statistical controls to account for the possible influences of confounding factors, such as age and gender. Using multiple regression we control for other factors that might influence the dependent variable. 1

1.2.3 Generation of Testable Hypothesis

Theory building is accomplished through the testing of hypotheses derived from theory. In simple form, a theory implies (sets of) relationships among concepts. These concepts are then operationalized. Finally, models are developed to examine how the measures are related. Properly specified hypotheses can be tested with empirical data, which are derived from the application of valid and reliable measures to relevant observations. The testing and re-testing of hypotheses develops levels of confidence that we can have for the core propositions that constitute the theory. In short, empirically grounded theories must be able to posit clear hypotheses that are testable. In this text, we discuss hypotheses and test them using relevant models and data.

As noted above, this text uses the concepts of political ideology and views about the environment as a case study in order to generate and test hypotheses about the relationships between these variables. For example, based on popular media accounts, it is plausible to expect that political conservatives are less likely to be concerned about the environment than political moderates or liberals. Therefore, we can pose the working hypothesis that measures of political ideology will be systematically related to measures of concern for the environment – with conservatives showing less concern for the environment. In classical hypothesis testing, the working hypothesis is tested against a null hypothesis . A null hypothesis is an implicit hypothesis that posits the independent variable has no effect (i.e., null effect) on the dependent variable. In our example, the null hypothesis states ideology has no effect on environmental concern.

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Introduction: What is Empirical Research?

Empirical research is based on observed and measured phenomena and derives knowledge from actual experience rather than from theory or belief. 

How do you know if a study is empirical? Read the subheadings within the article, book, or report and look for a description of the research "methodology."  Ask yourself: Could I recreate this study and test these results?

Key characteristics to look for:

  • Specific research questions to be answered
  • Definition of the population, behavior, or   phenomena being studied
  • Description of the process used to study this population or phenomena, including selection criteria, controls, and testing instruments (such as surveys)

Another hint: some scholarly journals use a specific layout, called the "IMRaD" format, to communicate empirical research findings. Such articles typically have 4 components:

  • Introduction : sometimes called "literature review" -- what is currently known about the topic -- usually includes a theoretical framework and/or discussion of previous studies
  • Methodology: sometimes called "research design" -- how to recreate the study -- usually describes the population, research process, and analytical tools used in the present study
  • Results : sometimes called "findings" -- what was learned through the study -- usually appears as statistical data or as substantial quotations from research participants
  • Discussion : sometimes called "conclusion" or "implications" -- why the study is important -- usually describes how the research results influence professional practices or future studies

Reading and Evaluating Scholarly Materials

Reading research can be a challenge. However, the tutorials and videos below can help. They explain what scholarly articles look like, how to read them, and how to evaluate them:

  • CRAAP Checklist A frequently-used checklist that helps you examine the currency, relevance, authority, accuracy, and purpose of an information source.
  • IF I APPLY A newer model of evaluating sources which encourages you to think about your own biases as a reader, as well as concerns about the item you are reading.
  • Credo Video: How to Read Scholarly Materials (4 min.)
  • Credo Tutorial: How to Read Scholarly Materials
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Thank you to librarians at Penn State for serving as the inspiration for this library guide

An empirical research article is a primary source where the authors reported on experiments or observations that they conducted. Their research includes their observed and measured data that they derived from an actual experiment rather than theory or belief. 

How do you know if you are reading an empirical article? Ask yourself: "What did the authors actually do?" or "How could this study be re-created?"

Key characteristics to look for:

  • Specific research questions  to be answered
  • Definition of the  population, behavior, or phenomena  being studied
  • Description of the  process or methodology  used to study this population or phenomena, including selection criteria, controls, and testing instruments (example: surveys, questionnaires, etc)
  • You can readily describe what the  authors actually did 

Layout of Empirical Articles

Scholarly journals sometimes use a specific layout for empirical articles, called the "IMRaD" format, to communicate empirical research findings. There are four main components:

  • Introduction : aka "literature review". This section summarizes what is known about the topic at the time of the article's publication. It brings the reader up-to-speed on the research and usually includes a theoretical framework 
  • Methodology : aka "research design". This section describes exactly how the study was done. It describes the population, research process, and analytical tools
  • Results : aka "findings". This section describes what was learned in the study. It usually contains statistical data or substantial quotes from research participants
  • Discussion : aka "conclusion" or "implications". This section explains why the study is important, and also describes the limitations of the study. While research results can influence professional practices and future studies, it's important for the researchers to clarify if specific aspects of the study should limit its use. For example, a study using undergraduate students at a small, western, private college can not be extrapolated to include  all  undergraduates. 
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Methods article, developing and publishing strong empirical research in sustainability management—addressing the intersection of theory, method, and empirical field.

empirical and theoretical part of the research work

  • Chair of Supply Chain Management, University of Kassel, Kassel, Germany

Students starting their research into sustainability management are often driven by a normative assumption of wanting “to do something good” or “save the world” from this or that problem. This also holds for many researchers, where the pressure to do research that has an immediate impact on the local business or natural environment is paramount. This often gets into the way of developing sound research that might pass the review process in strong academic journals. Good (empirical) research builds on the interplay of the theoretical foundation, appropriate research method, and a well-justified selection of the empirical field. The discussion paper offers some guidelines and reflections on how to do this. The core point is that academic papers get cited for their theoretical contribution, so this has to be in the foreground of research question and design. If implemented in the wrong manner upfront, this can usually not be corrected later on, preventing the research to be published in top journals. This has to do with the interplay of theory, method, and empirical field. While we would see theory as the winning factor, methods and empirical field-related choices often constitute what might be called qualifying for hygiene factors. Methods and empirical field would hardly sell the paper on their own, but if done wrongly, they will prevent it from having a chance of being accepted. The paper explores some core ideas around theory, methods, and empirical field and offers some related guidelines on how to link them. This is illustrated at some points borrowed from debates in sustainability management.

Introduction

Identifying an excellent piece of research is often quite challenging. In many cases, only the test of time will identify a paper that makes an impact on their field and even be called a seminal paper. Even when a paper is accepted after a sound review process, it is hard to predict how the research community would react and act on it. Yet, there are some guidelines, where following them, make it more likely, that a paper would pass the review process.

The related choice of (1) theory, (2) research method, and (3) empirical field are highly interrelated. Once certain investments into the research process have been made, they are often hard to change, linking this to typical aspect of the (sustainable) new product development process ( Gmelin and Seuring, 2014 ), where 80% of the decisions have been made, while only 20% of the cost or time have been incurred.

This links into a further motivation for writing this piece. Visiting different parts of the world was an opportunity for getting into contact with many highly motivated researchers. Quite so often, they were dedicated to “making an impact” with their research, a typical aspect of transdisciplinary research ( Lang et al., 2012 ). This is often demanded in emerging economies or low income countries, where funding for universities might be coupled to an expectation that it would have a positive impact on its environment. While such often-practical implications are not wrong in itself, this notion was often in the way of stepping one step back and looking at their own research in a more reflective manner. Even more, the inherent choice of theory, or a lack of theoretical grounding, the (missing) justification for the research method employed and the empirical field in total usually did not qualify that research and respective findings for publication in highly regarded academic journals. It is often hard to explain what is “wrong” in such cases. Here, we emphasize obtaining research results that are publishable in journals, which many researchers would regard as being of high standards and, usually, having sound impact factors, Citescore values, or other journal related indicators. There would be alternative measures of research impact, linking it, e.g., to transition management ( Stephens and Graham, 2010 ) and action-oriented research ( Caniglia et al., 2020 ), thereby also asking for the impact on real business contexts. The paper addresses the question: How can theory, method, and empirical field be interrelated in creating strong research questions?

The paper explores some reflections on developing strong research. Admittedly, this is a somewhat biased perspective, which is highly dependent on the authors' personal experience and perceptions. Still, taking the intersection of sustainability management and supply chain management as an example, some guidelines can be put forward. This is also, why much of this paper will use the else not very popular “I” and “we” style, emphasizing that a personal perspective is put forward. This is also the justification for citing many references from own research, being justified as they will serve as illustrative examples.

The discussion paper is structured into two parts. The first one introduces some core terminology on theory, method, and empirical field. The second part looks then at typical choices to be made when conducting research. While some overlap can hardly be avoided, the three issues are analyzed on their own; before then, their interplay is analyzed. A final note goes to the aspect journal quality, which seems to be needed for completeness. A brief conclusion ends this paper.

Core Terminology

The three core points addressed in this paper are (1) theory, (2) research methods, and (3) empirical field. Here is a brief outline, not aiming at giving full explanations, which are available in respective books and guiding papers.

As the first reference point for the term theory, the four criteria put forward by Wacker (1998) are employed:

• Definitions of core terminology:

Definitions are usually the starting point of every academic debate, so that a common ground is reached. In sustainability management, the triple bottom line approach ( Elkington, 1998 ; Dyllick and Hockerts, 2002 ; for a critical assessment Elkington, 2018 ) serves as such a foundation, but there are multiple other links, e.g., into organizational sustainability ( Lozano and Garcia, 2020 ).

• Boundaries, where a theory applies and where not:

Boundaries are often quite hard to establish and can shift over time. Much of the sustainability debate started more on the environmental management side and only gradually expanded into social arguments. At the moment, it might be hard to see what sustainability management does not cover as the term is used quite often in an encompassing manner. For the respective piece of research, clarifying the boundaries and the unit of analysis is of central relevance (see Busse et al., 2017 ) or linking it to the core or the Lakatosian “protective” belt of theory in a field (see Gold, 2014 ).

• Variables or constructs and their interrelations:

Variables or constructs are the typical operational entities used for the analysis. Quite often, they are further broken down into items or indicators. In sustainability management, the triple bottom line might rather form a concept, which is operationalized into the three dimensions (environment, economic, social). These dimensions are then further measured, by employing related indicators. This also holds for stakeholder theory-related aspects, which consist of different conceptual elements (e.g., Schaltegger et al., 2019 ).

• Predictions:

A theory should allow to make predictions by linking the constructs or variables to the real world on what could (1) be observed, or (2) might be developed, or (3) how certain things might be implemented. As one example in sustainability management, there is a wide debate on the application of environmental and social standards, how they can be developed, and what effect might be reached putting them into practice and whether they would contribute to sustainability ( De Lima et al., 2021 ). The typical prediction is that implementing the standards allows overcoming certain problems and leads to improved environmental and social, sometimes even economic, performance.

These four criteria might be complemented by the classical pieces on theory by Sutton and Staw (1995) on “What theory is not” and the comment to this by Weick (1995) on “What theory is not, theorizing is.” Taken together, these two short notes offer great advice for academic research. They point to shortcomings and an overreliance on, e.g., references, data, list of variables, or diagrams, as these are misinterpreted as theory. Such elements have to be set into their theoretical context. This emphasizes the aspect of theorizing or moving toward theory, which often does not emerge in one piece, but is crafted by multiple contributors over time ( Starbuck, 2004 ).

Research Methods or Research Process

There are multiple textbooks on all kinds of research methods. A kind of overarching picture is presented by Saunders et al. (2019) , which sums up core decisions on the research methodology in their research onion (see Figure 1 ).

www.frontiersin.org

Figure 1 . Research onion ( Saunders et al., 2019 , p. 108).

Looking in more detail at the research process, Stuart et al. (2002) provide a basic research process in five steps. While Stuart et al. (2002) use this to explain case study research, the five steps are so basic that they would apply to all fields and kinds of empirical research in the social sciences (see Figure 2 ). This might have to be modified or amended, but is a straightforward guideline ensuring that core steps of the research process are carried out and documented in a stringent manner. Developing a research question, entering the field, collecting and analyzing data, and making sense of the data by writing up offers a quite basic outline of almost any research process. Applications can be found in numerous other books and papers, so there is no further explanation of these steps needed here (see, e.g., Seuring, 2008 ).

www.frontiersin.org

Figure 2 . The five-stage research process ( Stuart et al., 2002 ).

While this will be repeated in this paper several times, it cannot be overemphasized that each choice made need to be justified. Taking the research onion ( Saunders et al., 2019 ) and the research process as a starting point and justifying each choice made will ensure that the reader can understand this quite well. The challenge might be to do so in a concise manner, keeping the overall length and composition of the paper in mind.

Empirical Field

An editorial by Crane et al. (2016) is titled: “Publishing Country Studies in Business & Society Or, Do We Care About CSR in Mongolia?” As they point out, the title is certainly not discriminating authors from Mongolia or empirical research being based on data from Mongolia, nor from any other country. Reverting the title, it is preferred to argue about the point that research findings and contributions from Pakistan, Ghana, Slovenia, or Chile should also be of interest to researchers in other parts of the world. Hence, solving a practical problem on a local level might be relevant to people in the close environment, but might not receive much interest beyond this point. Researchers often take too much for granted that what they or even more their environment views as relevant. Yet, the question is, whether this would also be seen as relevant by other researchers. Looking at the many different settings we have globally, this needs to be well-justified, thereby aiming for generalizability of the findings.

More positively spoken, it is certainly not wrong to collect data in your local environment, be this in Norway, Mexico, Indonesia, Uganda, or New Zealand. In all cases, you have to consider, which similar or divergent context would be found globally, where certain conditions would apply so that a transfer of the research might be justified. This must be reflected upon in the limitations section of the paper.

Starting and Continuing the Research

Textbooks on research methods usually contain a chapter on developing research questions. In the context of research practice, much of the research rather develops step by step. After the completion of the PhD thesis, few researchers staying in an academic environment change their topic completely. This has pros and cons at the same time:

• Pro: You can continue a well-developed stream of research, build on your knowledge in theory, method, and empirical field.

• Con: Always staying on the same direction might limit what you can achieve further. If you are in a field rather declining in relevance, when is the right time to step out?

Related to this is the question, what can better be adjusted, where a typical sequence might be: (1) empirical field, (2) theoretical foundation, or (3) methodological choice? Please note, that this sequence is used here to explain and illustrate a certain issue. This is not a sequence for the choice of research questions or topics overall. At the end of each section, we will provide a proposition for what to keep in mind.

Many pieces of research are starting in a particular empirical context. This might even be the case for large research grants, such as the ones funded by the European Union Horizon 2020 program for Innovative Training Networks. These projects often center on a rather practical or applied topic, e.g., Circular Economy (see http://www.retrace-itn.eu last access December 28, 2020) as just one example. This is justified as such topics trigger a broader research interest and most likely can have a significant impact. It is hard to devise particular hints for which empirical fields or phenomena are worth to receive related research. If you manage to be early in a field of rising attention, this might be very beneficial for receiving, later on, great recognition, and being very careful with suggestions here, many fields are worth being researched.

In recent years, many topics around sustainability and sustainable products ( Gmelin and Seuring, 2014 ; Dyllick and Rost, 2017 ; Lozano and Garcia, 2020 ) certainly have been on the rise, and their relevance is well-documented. This does not only hold for climate change or resource consumption but also cover biodiversity, marine ecosystems, plastic pollution of the environment and oceans, or working conditions in global supply chains (as rather arbitrary examples, e.g., Seuring, 2012 ; Khalid et al., 2020 ). This is a list only containing a few items, so there are certainly more topics warranting future research.

Looking to the wider business environment, the increasing use of digital technologies is certainly such a topic, where the link to their sustainability impact is evident, but not much explored so far ( Liu et al., 2020 ). This will certainly be a topic staying on the agenda for the next decade. The impact of digital technologies on businesses and society at large will be far reaching; the sustainability implications are only explored toward some first aspects. An obvious issue, such as the energy consumption of computer systems, is only the surface level, as, e.g., Corbett (2018) rightly points out.

The empirical field needs constant adjustment and will be checked for every major piece of research started. Classical choices, such as agriculture and food (industries) can hardly be wrong, as they impact on every human beings' life. Still, conducting the 200th study on why consumers do not buy more organic food, thereby relating to the attitude–behavior gap ( Aschemann-Witzel and Niebuhr Aagaard, 2014 ), might be hard to publish in leading journals. The new study would have to offer a novel contribution, moving beyond the already known elements of theory in the field. A similar example would be drivers and barriers for sustainable supply chain management. After several review papers on the topic have been published already ( Diabat and Govindan, 2011 for an early one in the field; Sajjad et al., 2015 ), there is hardly any additional insight to be gained, even if a different industry in a different country would be addressed. It might be almost trivial, but a sound reasoning of the choice of the practical problem and empirical field chosen needs to be presented. This seems to be often overlooked by taking the own research environment and the presumed relevance for granted. Putting this to test upfront would avoid many frustrations in the later publishing process. This also holds for the theoretical grounding of every piece of research, so this is explored next.

Proposition 1: Do not take your research field for granted. Make sure, your wider research community would be interested in it and try to look at topics that will stimulate more research in the future.

Theoretical Foundation

As expressed by Weick (1995) , theory is constantly evolving. Even if we have achieved or confirmed certain operationalization, there is always a next step. A simple illustration might be the value, rarity, immutability, and organization (VRIO) framework of the resource-based view ( Barney, 1991 ), which are often taken up, but alone would not suffice to make a research contribution. As a brief example, this is reflected in the move to dynamic capabilities overcoming some of the limitations of the comparatively static VRIO framework (e.g., Gruchmann et al., 2019 ).

This links to the previously introduced four elements of theory; so here, the level of variables, constructs, and their interrelations is analyzed. A typical (sub-)question might be what kind of theory might be used or whether so-called grand theories, mid-range, or local ones might be taken up. This seems rarely discussed in sustainability management, the paper by Lozano et al. (2015) rather being an exception. As an example, Lozano et al. (2015) mentioned, e.g., the agency theory, the resource-based view, or stakeholder theory, so grand theories many researchers in sustainability management and other related fields would be familiar with. In a similar line of arguments, Spina et al. (2016) assessed the use of grand theories in purchasing and supplying management research, thereby pointing to, e.g., transaction cost economics, resource-based view, contingency theory, or game theory. Swanson et al. (2020) complemented such an approach, by pointing out that grand theories might be too general to explain certain mechanisms in detail. Stank et al. (2017 , p. 7) provide characteristic features of middle-range theories, which offer good positioning also for sustainability and organizational and inter-organizational aspect-related research:

• Synthesize empirical findings that have emerged through research in a particular domain of knowledge

• Rely on a limited set of realistic assumptions appropriate for the focal domain

• Define concepts in a manner that is specific to the focal domain

• Restrict theoretical propositions regarding the relationships among concepts to the focal domain

• Make predictions that are specifically relevant to resolving theoretical and practical problems within the focal domain

• Provide a basis for potential linkages to more general theories that could potentially extend knowledge into other domains.

So, even a mid-range theory would comprise certain “accepted” variables or constructs, what need to be operationalized in conducting empirical research. This leaves open, whether a deductive, abductive, or inductive approach would be applied for doing so (see, e.g., Seuring et al., 2020 ). It might be allowed to say that the authors are quite open to all such approaches, while a paper following a typically deductive logic is often easier to comprehend and therefore easier to “sell” to reviewers and editors.

In line with the arguments already made on the empirical field, there is no right or wrong theory, but there is a sound justification for a choice of theory. Extending boundaries ( Busse et al., 2017 ) or applying or borrowing theories to new fields ( Gold, 2014 ; Stank et al., 2017 ) is often fruitful. The typical “so-what” question would still apply, requiring a justification for selecting the respective theory.

A note goes to the need for fit among empirical field or better the unit of analysis and the theory chosen. This also needs to fit to each other. This might be an issue warranting more debate, as, e.g., the dynamic capability approach has been applied to green transformation of companies ( Da Giau et al., 2020 ), social enterprises ( Ince and Hahn, 2020 ), sustainable supply chain management (e.g., Beske et al., 2014 ; Gruchmann et al., 2019 ), and sustainable innovation ( Inigo and Albareda, 2019 ). This inherently argues that dynamic capabilities, a theoretical framing developed for single companies, can be applied to the supply chain level.

Proposition 2: Carefully consider your theoretical foundation. Be aware what the core constructs or variables to analyze in your research would be. Do not easily say there is no research on the topic yet.

Methodological Choice

What has just been said for the choice of theory and early been highlighted for the empirical field also holds for the choice of an empirical (or analytical or experimental) method. The core aspect is a sound justification and being aware of the strength and weaknesses of a certain method. Many researchers are open to different research methods and methodologies, and often, they complement each other ( Seuring, 2012 ).

A first point is typically sample size, which has to be determined for a questionnaire-based survey as much as for a number of experts in a Delphi study design or interviews for data collection in case study-based research. Beyond, there are hardly further points to be made on the research methodology, which are not already explained in detail in typical textbooks (e.g., Saunders et al., 2019 ).

Proposition 3: Be aware of the strength and weaknesses of the (empirical) method chosen. Start with a challenging plan, as there cannot be too much data.

The Interplay of Empirical Field and Method

This leads to an interesting intersection with the empirical field that we briefly like to illustrate. Research in base-of-the-pyramid environments might find it hard to collect data with tools highly accepted in developed countries. Companies in such an environment might see researchers rather skeptical and mistrust them, which could be a consequence of governance issues at large, but hampering related data collection, which might only be conducted in personal interviews, thereby limiting sample size (e.g., Khalid et al., 2020 ). Of course, this limitation is hard to justify and might be rejected by reviewers. In this way, we might not be able to collect related data and miss out more inclusive research. A further aspect might be illiteracy of interviewees and the lack of trust, needing both personal contact as well as intermediaries (e.g., Yawar and Kauppi, 2018 ; Brix-Asala and Seuring, 2020 ). So, the empirical field to a certain extent determines the choice of method. Yet, turning the argument round an attempt of rather being able to conduct survey base research in such base-of-the-pyramid environments would be very welcome, thereby moving to more theory testing approaches that seem to be well-justified on a topic coming to a certain level of maturity. Returning to the previous statement on data from different countries and contexts, this will be very welcoming, if combined with a strong theoretical foundation.

Proposition 4: Is the typical unit of analysis employed in the research method applicable to the empirical field? Does the field allow access to informants that are required both in the quantity (response rate) as quality of information needed?

The Interplay of Empirical Field and Theory

It would be hard to argue in a similar manner about empirical field and theory. A core point might be that pure replication studies are often hard to publish in more organizational and management-related research, while they are of key importance, such as evident, e.g., in medicine. This might somewhat be a critical issue of our field of research, which can hardly be resolved by the individual researcher or single piece of research. As mentioned before, there is hardly a perfect choice, but a strong and convincing justification of choices being made, keeping the already mentioned boundary conditions ( Busse et al., 2017 ) in mind and making sure that the unit of analysis in the field matches the typical theoretical approach. An example can be given at the example of transaction cost theory, where the unit of analysis is the single transaction ( Williamson, 2008 ). Relating this to the four elements of theory introduced earlier, respective definitions taken up from the theoretical side must find their equivalent in the empirical field and should be applicable in the respective context. The boundaries of the theory and its application have to fit the empirical field or the other way around. It has to be checked, whether the empirical field can be analyzed with the particular theory. Extending the theory to a new empirical field might be possible, but might have to be argued for in a careful manner.

Variables or constructs need to be meaningful, which relates to face validity in the empirical research. Farmers, managers, or consumers interviewed must make sense in the eyes of the researched upon. This does not imply that the person responding to research questions would have to comprehend every part of the theory. They have to be able to respond in a meaningful manner. Particularly in research environments, where less formally educated people might serve as informants, such as the growing body of research on base of the pyramid environments (see, e.g., Rehman et al., 2020 ), this might impose challenges to data collection ( Khalid et al., 2020 ). Still, even theoretical approaches such as institutional voids can still be studied gaining insights and allowing to develop it further (e.g., Brix-Asala and Seuring, 2020 ). One practical challenge would be that researchers have to avoid being arrogant on their field of research and treat any respondent with respect. This should then allow to draw conclusions and propose predictions feeding back into the empirical context and allowing to make a contribution both on the practical as well as the theoretical side.

Proposition 5: As a thought experiment, think of what the application of the theory chosen to the empirical field might yield? Check, that the theory is applicable to the field, so the conceptual boundaries relate to each other. What expected outcomes might the research yield driving the theory development forward?

The Interplay of Theory and Method

While the intersection of theory and method is highly important for a strong contribution, it seems to be close to impossible to propose clear advice. In line with the aspects already mentioned, just replicating what others have done is often seen as highly critical. A particular challenge is emerging if established constructs are taken up from already published research. Even if these papers were published in highly reputed journals, just borrowing several constructs from one paper and others from a second one for creating a new survey might not do the job. A stronger interrelation or interaction among theory and method would be required. This is mentioned while avoiding to be simplistic and providing a “cookbook” solution. This holds for quantitative as much as qualitative research (e.g., Gehman et al., 2018 ).

One note goes to the fact that this would change over time, such as illustrated at the example of sustainable supply chain management ( Seuring, 2012 ). If a topic newly emerges, not much empirical data might be around. Then, even some initial case-based research might be a great insight already, providing thick descriptions of emerging phenomena. As the field matures, it would move to other methods, such as survey and more detailed insights on the interaction of certain constructs in the field. This can then be summarized, e.g., in a meta-analysis ( Golicic and Smith, 2013 ). Agreeing with Carter and Washispack (2018) , at such a stage, yet another literature review or bibliometric analysis pointing to the most cited papers or authors in the field would not make much a contribution anymore (e.g., Nimsai et al., 2020 ). Hence, rather in-depth analysis of certain topics in detail and stronger grounding in a theoretical base would be required. Summing up a field and providing a sound contribution would be a typical demand, such as, e.g., given in the paper by Reike et al. (2018) on the circular economy, where related activities are conceptualized into 10Rs, i.e., from re-fuse to re-mine. A second example, staying with the Circular Economy topic is the link to business models, such as systemized by Lüdeke-Freund et al. (2019) . This then leads to interesting intersections, such as the one with sustainability assessment methods ( Walzberg et al., 2021 ) or sustainable supply chain management ( Genovese et al., 2017 ).

At the moment, the intersection of information technology, operations, and sustainability might be such a new field ( Bai et al., 2020 ; Liu et al., 2020 ). Such topics might justify more conceptual analysis, such as Saberi et al. (2019) on Blockchains in supply chains, but would certainly also benefit from data collected in the field. In this respect, the interplay of theory and method develops over time (e.g., Seuring, 2012 ; or see the editorial by Boyer and Swink, 2008 ).

Proposition 6: Assess the intersection of theory and method. Does this promise to match each other and yield insights driving theory development forward?

Reflecting on the six propositions should allow to link empirical field, method, and theory to each other, so that the hint given in proposition 6 would also relate to the intersection of all three topics.

Challenging Journal Quality and Research Impact

Much of this discussion paper pointed toward publishing in top journals, while avoiding to clarify this term itself. There is much debate on journal quality and journal rankings, which is only mentioned to guide the interested researcher further into the topic. Grey (2010 , p. 683) argued that “the constitution of journals as ‘top journals' is clearly an accomplishment of power. There is a circularity, in which to publish in the ‘best' journals, one must produce the ‘right kind' of work.” As a consequence, PhD students sometimes limit their research choice by what might have a chance to be published in the “right” journal. This can be quite critical and might not trigger really interesting research, but rather confirm what we know already and follow in the already beaten path. This has a lot to do with how research performance is measured, an issue also attracting increasing attention (e.g., Aguinis et al., 2020 ).

It is admitted that this discussion paper takes a single-sided research-driven perspective. Hence, a second brief note is made on the point that this can be comprehended quite differently. As one example, e.g., Nicholls-Nixon et al. (2011) point out that many scholars in Latin America have advocated practical impact as their political, economic, and social contexts suffer from institutional voids and related uncertainties. In line with this, the survey of Bartunek et al. (2006) asked what makes management research interesting. Specifically, they compared the reasons for rating an article as interesting following the perspective of Revista de Administração de Empresas (RAE) and Academy of Management Journal (AMJ) board members. For RAE members, it was found that impact (including practical impact) was most important. The overlap between the perspective of AMJ and RAE board members concerns research quality, including well-crafted theory, good technical or method jobs, etc. The authors conclude that this variance of results points to the likelihood that readers in different parts of the world have diverse criteria for scholarly interest. There is no single conventional norm to which all scholars should ascribe in common terms and with mutual understanding, but rather a multi-vocality of aspirations in doing research. This should be kept in mind developing and evaluating research in the way it is promoted in this paper.

The starting point for this discussion paper is the creation of strong research questions or expressing it differently making sound choice on theory, method, and empirical field. While the single topics can already be challenging for themselves, strong research builds on a sound choice and justification of their interplay. The starting observation that this is often taken too easy and a sound planning of a respective research process often neglected results from interaction with many researchers and students in different contexts.

The paper alone will not be able to address all issues and provide detailed guidelines. There is more hope that pointing to the necessity for a sound interplay would make researchers be aware of their choice and drive them to (a) put more time into the research plan and (b) offer better justification in their later writing.

We may be allowed a last word and a kind of a warning. In some cases, this paper has pointed to several examples from research in a very brief manner. This should usually be avoided, as it does not offer a deeper analysis and links it to the overall debate in the paper. In this paper, such references serve an illustrative basis, connecting the arguments made to the wider literature in organizational sustainability. In this way, the discussion paper partly does wrong what it aims to criticize. So allow us the encouraging words: Keep writing just better.

Data Availability Statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.

Author Contributions

SS: Conceptualizing and writing the paper. TS and MS: reviewing and improving the paper, helping to develop the ideas upfront and pointing to the need for it. All authors: contributed to the article and approved the submitted version.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

We are thankful to Sadaf Aman, Felipe Alexandre de Lima, Lara Schilling, and Jayani Sudusinghe, all at the Chair of Supply Chain Management, University of Kassel, Kassel, Germany, for their critical feedback on the paper.

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Keywords: theory, method, empirical research, sustainability management, relevance of research

Citation: Seuring S, Stella T and Stella M (2021) Developing and Publishing Strong Empirical Research in Sustainability Management—Addressing the Intersection of Theory, Method, and Empirical Field. Front. Sustain. 1:617870. doi: 10.3389/frsus.2020.617870

Received: 15 October 2020; Accepted: 31 December 2020; Published: 05 February 2021.

Reviewed by:

Copyright © 2021 Seuring, Stella and Stella. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Stefan Seuring, seuring@uni-kassel.de

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5.5 Developing a theoretical framework

Social work researchers develop theoretical frameworks based on social science theories and empirical literature. A study’s theory describes the theoretical foundations of the research and consists of the big-T theory(ies) that guide the investigation. It provides overarching perspectives, explanations, and predictions about the social problem and research topic.

In deductive research (e.g., quantitative research), researchers create a theoretical framework to explain the thought process behind the study’s research questions and hypotheses. The theoretical framework includes the constructs of interest in the study and the associations the researchers expect to find. These constructs and their relations are based on the broader theory, but likely do not entail all the components of the theory.  The theoretical framework is specific to a particular study or analysis and provides the rationale for the research question(s). In inductive studies such as grounded theory, a theoretical framework can be the final result of the research.  In this case, the theoretical framework is also a combination of concepts and their associations, but it is derived from the data collected during the research. This contrasts to theoretical frameworks in deductive research, which are created before collecting data and derive from theories and other empirical findings.

In Chapter 8, we will develop your quantitative theoretical framework further, identifying associations or causal relations in a research question. Developing a quantitative theoretical framework is also instructive for revising and clarifying your working research question and identifying concepts that serve as keywords for additional literature searching. But first, we will consider identifying your theory. The greater clarity you have with your theoretical perspective, the easier each subsequent step in the research process will be. Getting acquainted with the important theoretical concepts in a new area can be challenging. While social work education provides a broad overview of social theory, you will find much greater fulfillment out of reading about the theories related to your topic area. We discussed some strategies for finding theoretical information in Chapter 3 as part of literature searching. To extend that conversation a bit, some strategies for searching for theories in the literature include:

  • Consider searching for these keywords in the title or abstract, specifically
  • Looking at the references and cited by links within theoretical articles and textbooks
  • Looking at books, edited volumes, and textbooks that discuss theory
  • Talking with a scholar on your topic, or asking a professor if they can help connect you to someone
  • It is helpful when authors are clear about how they use theory to inform their research project, usually in the introduction and discussion section.
  • For example, from the broad umbrella of systems theory, you might pick out family systems theory if you want to understand the effectiveness of a family counseling program.

It’s important to remember that knowledge arises within disciplines, and that disciplines have different theoretical frameworks for explaining the same topic. While it is certainly important for the social work perspective to be a part of your analysis, social workers benefit from searching across disciplines to come to a more comprehensive understanding of the topic. Reaching across disciplines can provide uncommon insights during conceptualization, and once the study is completed, a multidisciplinary researcher will be able to share results in a way that speaks to a variety of audiences. A study by An and colleagues (2015) [1] uses game theory from the discipline of economics to understand problems in the Temporary Assistance for Needy Families (TANF) program. In order to receive TANF benefits, mothers must cooperate with paternity and child support requirements unless they have “good cause,” as in cases of domestic violence, in which providing that information would put the mother at greater risk of violence. Game theory can help us understand how TANF recipients and caseworkers respond to the incentives in their environment, and highlight why the design of the “good cause” waiver program may not achieve its intended outcome of increasing access to benefits for survivors of family abuse.

Of course, there are natural limits on the depth with which student researchers can and should engage in a search for theory about their topic. At minimum, you should be able to draw connections across studies and be able to assess the relative importance of each theory within the literature. Just because you found one article applying your theory (like game theory, in our example above) does not mean it is important or often used in the domestic violence literature. Indeed, it would be much more common in the family violence literature to find psychological theories of trauma, feminist theories of power and control, and similar theoretical perspectives used to inform research projects rather than game theory, which is equally applicable to survivors of family violence as workers and bosses at a corporation. Consider using the Cited By feature to identify articles, books, and other sources of theoretical information that are seminal or well-cited in the literature. Similarly, by using the name of a theory in the keywords of a search query (along with keywords related to your topic), you can get a sense of how often the theory is used in your topic area. You should have a sense of what theories are commonly used to analyze your topic, even if you end up choosing a different one to inform your project.

empirical and theoretical part of the research work

Theories that are not cited or used as often are still immensely valuable. As we saw before with TANF and “good cause” waivers, using theories from other disciplines can produce uncommon insights and help you make a new contribution to the social work literature. Given the privileged position that the social work curriculum places on theories developed by white men, students may want to explore Afrocentricity as a social work practice theory (Pellebon, 2007) [2] or abolitionist social work (Jacobs et al., 2021) [3] when deciding on a theoretical framework for their research project that addresses concepts of racial justice. Start with your working question, and explain how each theory helps you answer your question. Some explanations are going to feel right, and some concepts will feel more salient to you than others. Keep in mind that this is an iterative process. Your theoretical framework will likely change as you continue to conceptualize your research project, revise your research question, and design your study.

By trying on many different theoretical explanations for your topic area, you can better clarify your own theoretical framework. Some of you may be fortunate enough to find theories that match perfectly with how you think about your topic, are used often in the literature, and are therefore relatively straightforward to apply. However, many of you may find that a combination of theoretical perspectives is most helpful for you to investigate your project. For example, maybe the group counseling program for which you are evaluating client outcomes draws from both motivational interviewing and cognitive behavioral therapy. In order to understand the change happening in the client population, you would need to know each theory separately as well as how they work in tandem with one another. Because theoretical explanations and even the definitions of concepts are debated by scientists, it may be helpful to find a specific social scientist or group of scientists whose perspective on the topic you find matches with your understanding of the topic. Of course, it is also perfectly acceptable to develop your own theoretical framework, though you should be able to articulate how your framework fills a gap within the literature.

Much like paradigm, theory plays a supporting role for the conceptualization of your research project. Recall the ice float from Figure 5.1. Theoretical explanations support the design and methods you use to answer your research question. In projects that lack a theoretical framework, you may see the biases and errors in reasoning that we discussed in Chapter 1 that get in the way of good social science. That’s because theories mark which concepts are important, provide a framework for understanding them, and measure their interrelationships. If research is missing this foundation, it may instead operate on informal observation, messages from authority, and other forms of unsystematic and unscientific thinking we reviewed in Chapter 1.

Theory-informed inquiry is incredibly helpful for identifying key concepts and how to measure them in your research project, but there is a risk in aligning research too closely with theory. The theory-ladenness of facts and observations produced by social science research means that we may be making our ideas real through research. This is a potential source of confirmation bias in social science. Moreover, as Tan (2016) [4] demonstrates, social science often proceeds by adopting as true the perspective of Western and Global North countries, and cross-cultural research is often when ethnocentric and biased ideas are most visible . In her example, a researcher from the West studying teacher-centric classrooms in China that rely partially on rote memorization may view them as less advanced than student-centered classrooms developed in a Western country simply because of Western philosophical assumptions about the importance of individualism and self-determination. Developing a clear theoretical framework is a way to guard against biased research, and it will establish a firm foundation on which you will develop the design and methods for your study.

Key Takeaways

  • Just as empirical evidence is important for conceptualizing a research project, so too are the key concepts and relationships identified by social work theory.
  • Using theory your theory textbook will provide you with a sense of the broad theoretical perspectives in social work that might be relevant to your project.
  • Try to find small-t theories that are more specific to your topic area and relevant to your working question.

TRACK 1 (IF YOU ARE CREATING A RESEARCH PROPOSAL FOR THIS CLASS):

In Chapter 2, you developed a concept map for your proposal.

  • Take a moment to revisit your concept map now as your theoretical framework is taking shape. Make any updates to the key concepts and relationships in your concept map.

If you need a refresher, we have embedded a short how-to video from the University of Guelph Library (CC-BY-NC-SA 4.0) that we also used in Chapter 2.

TRACK 2 (IF YOU AREN’T CREATING A RESEARCH PROPOSAL FOR THIS CLASS):

You are interested in researching bullying among school-aged children, and how this impacts students’ academic success.

  • Find two theoretical frameworks that have been used in published articles on this topic. Identify similarities and differences between the frameworks.

5.6 Designing your project using theory and paradigm

Learning Objectives

Learners will be able to…

  • Apply the assumptions of each paradigm to your project
  • Summarize what aspects of your project stem from positivist, constructivist, or critical assumptions

In the previous sections, we reviewed the major paradigms and theories in social work research. In this section, we will provide an example of how to apply theory and paradigm in research. This process is depicted in Figure 5.2 below with some quick summary questions for each stage. Some questions in the figure below have example answers like designs (i.e., experimental, survey) and data analysis approaches (i.e., discourse analysis). These examples are arbitrary. There are a lot of options that are not listed. So, don’t feel like you have to memorize them or use them in your study.

A linear process moving from initial research questions (defining the purpose of research and its context), then moving to paradigmatic questions of ontology and epistemology which help us refine research questions; then moving to methodology, methods, and data analysis.

This diagram (taken from an archived Open University (UK) course entitled E89 ​- Educational Inquiry ) ​ shows one way to visualize the research design process. While research is far from linear, in general, this is how research projects progress sequentially. Researchers begin with a working question, and through engaging with the literature, develop and refine those questions into research questions (a process we will finalize in Chapter 9). But in order to get to the part where you gather your sample, measure your participants, and analyze your data, you need to start with paradigm. Based on your work in section 5.3, you should have a sense of which paradigm or paradigms are best suited to answering your question. The approach taken will often reflect the nature of the research question; the kind of data it is possible to collect; and work previously done in the area under consideration. When evaluating paradigm and theory, it is important to look at what other authors have done previously and the framework used by studies that are similar to the one you are thinking of conducting.

Once you situate your project in a research paradigm, it becomes possible to start making concrete choices about methods. Depending on the project, this will involve choices about things like:

  • What is my final research question?
  • What are the key variables and concepts under investigation, and how will I measure them?
  • How do I find a representative sample of people who experience the topic I’m studying?
  • What design is most appropriate for my research question?
  • How will I collect and analyze data?
  • How do I determine whether my results describe real patterns in the world or are the result of bias or error?

The data collection phase can begin once these decisions are made. It can be very tempting to start collecting data as soon as possible in the research process as this gives a sense of progress. However, it is usually worth getting things exactly right before collecting data as an error found in your approach further down the line can be harder to correct or recalibrate around.

Designing a study using paradigm and theory: An example

Paradigm and theory have the potential to turn some people off since there is a lot of abstract terminology and thinking about real-world social work practice contexts. In this section, I’ll use an example from my own research, and I hope it will illustrate a few things. First, it will show that paradigms are really just philosophical statements about things you already understand and think about normally. It will also show that no project neatly sits in one paradigm and that a social work researcher should use whichever paradigm or combination of paradigms suit their question the best. Finally, I hope it is one example of how to be a pragmatist and strategically use the strengths of different theories and paradigms to answering a research question. We will pick up the discussion of mixed methods in the next chapter.

Thinking as an expert: Positivism

In my undergraduate research methods class, I used an open textbook much like this one and wanted to study whether it improved student learning. You can read a copy of the article we wrote on based on our study . We’ll learn more about the specifics of experiments and evaluation research in Chapter 13, but you know enough to understand what evaluating an intervention might look like. My first thought was to conduct an experiment, which placed me firmly within the positivist or “expert” paradigm.

Experiments focus on isolating the relationship between cause and effect. For my study, this meant studying an open textbook (the cause, or intervention) and final grades (the effect, or outcome). Notice that my position as “expert” lets me assume many things in this process. First, it assumes that I can distill the many dimensions of student learning into one number—the final grade. Second, as the “expert,” I’ve determined what the intervention is: indeed, I created the book I was studying, and applied a theory from experts in the field that explains how and why it should impact student learning.

Theory is part of applying all paradigms, but I’ll discuss its impact within positivism first. Theories grounded in positivism help explain why one thing causes another. More specifically, these theories isolate a causal relationship between two (or more) concepts while holding constant the effects of other variables that might confound the relationship between the key variables. That is why experimental design is so common in positivist research. The researcher isolates the environment from anything that might impact or bias the cause and effect relationship they want to investigate.

But in order for one thing to lead to change in something else, there must be some logical, rational reason why it would do so. In open education, there are a few hypotheses (though no full-fledged theories) on why students might perform better using open textbooks. The most common is the access hypothesis , which states that students who cannot afford expensive textbooks or wouldn’t buy them anyway can access open textbooks because they are free, which will improve their grades. It’s important to note that I held this theory prior to starting the experiment, as in positivist research you spell out your hypotheses in advance and design an experiment to support or refute that hypothesis.

Notice that the hypothesis here applies not only to the people in my experiment, but to any student in higher education. Positivism seeks generalizable truth, or what is true for everyone. The results of my study should provide evidence that  anyone  who uses an open textbook would achieve similar outcomes. Of course, there were a number of limitations as it was difficult to tightly control the study. I could not randomly assign students or prevent them from sharing resources with one another, for example. So, while this study had many positivist elements, it was far from a perfect positivist study because I was forced to adapt to the pragmatic limitations of my research context (e.g., I cannot randomly assign students to classes) that made it difficult to establish an objective, generalizable truth.

Thinking like an empathizer: constructivism

One of the things that did not sit right with me about the study was the reliance on final grades to signify everything that was going on with students. I added another quantitative measure that measured research knowledge, but this was still too simplistic. I wanted to understand how students used the book and what they thought about it. I could create survey questions that ask about these things, but to get at the subjective truths here, I thought it best to use focus groups in which students would talk to one another with a researcher moderating the discussion and guiding it using predetermined questions. You will learn more about focus groups in Chapter 18.

Researchers spoke with small groups of students during the last class of the semester. They prompted people to talk about aspects of the textbook they liked and didn’t like, compare it to textbooks from other classes, describe how they used it, and so forth. It was this focus on  understanding and subjective experience that brought us into the constructivist paradigm. Alongside other researchers, I created the focus group questions but encouraged researchers who moderated the focus groups to allow the conversation to flow organically.

We originally started out with the assumption, for which there is support in the literature, that students would be angry with the high-cost textbook that we used prior to the free one, and this cost shock might play a role in students’ negative attitudes about research. But unlike the hypotheses in positivism, these are merely a place to start and are open to revision throughout the research process. This is because the researchers are not the experts, the participants are! Just like your clients are the experts on their lives, so were the students in my study. Our job as researchers was to create a group in which they would reveal their informed thoughts about the issue, coming to consensus around a few key themes.

empirical and theoretical part of the research work

When we initially analyzed the focus groups, we uncovered themes that seemed to fit the data. But the overall picture was murky. How were themes related to each other? And how could we distill these themes and relationships into something meaningful? We went back to the data again. We could do this because there isn’t one truth, as in positivism, but multiple truths and multiple ways of interpreting the data. When we looked again, we focused on some of the effects of having a textbook customized to the course. It was that customization process that helped make the language more approachable, engaging, and relevant to social work practice.

Ultimately, our data revealed differences in how students perceived a free textbook versus a free textbook that is customized to the class. When we went to interpret this finding, the remix  hypothesis of open textbook was helpful in understanding that relationship. It states that the more faculty incorporate editing and creating into the course, the better student learning will be. Our study helped flesh out that theory by discussing the customization process and how students made sense of a customized resource.

In this way, theoretical analysis operates differently in constructivist research. While positivist research tests existing theories, constructivist research creates theories based on the stories of research participants. However, it is difficult to say if this theory was totally emergent in the dataset or if my prior knowledge of the remix hypothesis influenced my thinking about the data. Constructivist researchers are encouraged to put a box around their previous experiences and beliefs, acknowledging them, but trying to approach the data with fresh eyes. Constructivists know that this is never perfectly possible, though, as we are always influenced by our previous experiences when interpreting data and conducting scientific research projects.

Thinking like an activist: Critical

Although adding focus groups helped ease my concern about reducing student learning down to just final grades by providing a more rich set of conversations to analyze. However, my role as researcher and “expert” was still an important part of the analysis. As someone who has been out of school for a while, and indeed has taught this course for years, I have lost touch with what it is like to be a student taking research methods for the first time. How could I accurately interpret or understand what students were saying? Perhaps I would overlook things that reflected poorly on my teaching or my book. I brought other faculty researchers on board to help me analyze the data, but this still didn’t feel like enough.

By luck, an undergraduate student approached me about wanting to work together on a research project. I asked her if she would like to collaborate on evaluating the textbook with me. Over the next year, she assisted me with conceptualizing the project, creating research questions, as well as conducting and analyzing the focus groups. Not only would she provide an “insider” perspective on coding the data, steeped in her lived experience as a student, but she would serve as a check on my power through the process.

Including people from the group you are measuring as part of your research team is a common component of critical research. Ultimately, critical theorists would find my study to be inadequate in many ways. I still developed the research question, created the intervention, and wrote up the results for publication, which privileges my voice and role as “expert.” Instead, critical theorists would emphasize the role of students (community members) in identifying research questions, choosing the best intervention to used, and so forth. But collaborating with students as part of a research team did address some of the power imbalances in the research process.

Critical research projects also aim to have an impact on the people and systems involved in research. No students or researchers had profound personal realizations as a result of my study, nor did it lessen the impact of oppressive structures in society. I can claim some small victory that my department switched to using my textbook after the study was complete (changing a system), though this was likely the result of factors other than the study (my advocacy for open textbooks).

Social work research is almost always designed to create change for people or systems. To that end, every social work project is at least somewhat critical. However, the additional steps of conducting research with people rather than on people reveal a depth to the critical paradigm. By bringing students on board the research team, study had student perspectives represented in conceptualization, data collection, and analysis. That said, there was much to critique about this study from a critical perspective. I retained a lot of the power in the research process, and students did not have the ability to determine the research question or purpose of the project. For example, students might likely have said that textbook costs and the quality of their research methods textbook were less important than student debt, racism, or other potential issues experienced by students in my class. Instead of a ground-up research process based in community engagement, my research included some important participation by students on project created and led by faculty.

Designing research is an iterative process

I hope this conversation was useful in applying paradigms to a research project. While my example discusses education research, the same would apply for social work research about social welfare programs, clinical interventions, or other topics. Paradigm and theory are covered at the beginning of the project because these assumptions will structure the rest of the project. Each of the research steps that occur after this chapter (e.g., forming a question, choosing a design) rely upon philosophical and theoretical assumptions. As you continue designing a project, you may find yourself shifting between paradigms. That is normal, as conceptualization is not a linear process. As you move through the next steps of conceptualizing and designing a project, you’ll find philosophies and theories that best match how you want to study your topic.

Viewing theoretical and empirical arguments through this lens is one of the true gifts of the social work approach to research. The multi-paradigmatic perspective is a hallmark of social work research and one that helps us contribute something unique on research teams and in practice.

  • Multi-paradigmatic research is a distinguishing hallmark of social work research. Understanding the limitations and strengths of each paradigm will help you justify your research approach and strategically choose elements from one or more paradigms to answer your question.
  • Paradigmatic assumptions help you understand the “blind spots” in your research project and how to adjust and address these areas. Keep in mind, it is not necessary to address all of your blind spots, as all projects have limitations.

Post-awareness check (Emotion)

Of the introduced social science paradigms, which would you say aligns with your current perspective on your research topic?

  • Sketch out which paradigm applies best to your project. Second, building on your answer to the exercise in section 6.3, identify how the theory you chose and the paradigm in which you find yourself are consistent or are in conflict with one another. For example, if you are using systems theory in a positivist framework, you might talk about how they both rely on a deterministic approach to human behavior with a focus on the status-quo and social order.
  • Select one paradigm and one theoretical framework. How does your selected theoretical framework align with your paradigm? How could the theory and paradigm together inform the overall research design?
  • An, S., Yoo, J., & Nackerud, L. G. (2015). Using game theory to understand screening for domestic violence under the TANF family violence option.  Advances in Social Work ,  16 (2), 338-357. ↵
  • Pellebon, D. A. (2007). An analysis of Afrocentricity as theory for social work practice.  Advances in Social Work ,  8 (1), 169-183. ↵
  • Jacobs, L. A., Kim, M. E., Whitfield, D. L., Gartner, R. E., Panichelli, M., Kattari, S. K., ... & Mountz, S. E. (2021). Defund the police: Moving towards an anti-carceral social work.  Journal of Progressive Human Services ,  32 (1), 37-62. ↵
  • Tan, C. (2016). Investigator bias and theory-ladenness in cross-cultural research: Insights from Wittgenstein. Current Issues in Comparative Education ,  18 (1), 83-95. ↵

a network of linked concepts that together provide a rationale for a research project or analysis; theoretical frameworks are based in theory and empirical literature

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Theoretical Frame for the Research—Research Design and Methodology

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Hagemann, M. (2023). Theoretical Frame for the Research—Research Design and Methodology. In: A Leadership Paradigm Shift to ‘Eclectic Leadership’. Gabler Theses. Springer Gabler, Wiesbaden. https://doi.org/10.1007/978-3-658-41578-5_3

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Color and psychological functioning: a review of theoretical and empirical work

In the past decade there has been increased interest in research on color and psychological functioning. Important advances have been made in theoretical work and empirical work, but there are also important weaknesses in both areas that must be addressed for the literature to continue to develop apace. In this article, I provide brief theoretical and empirical reviews of research in this area, in each instance beginning with a historical background and recent advancements, and proceeding to an evaluation focused on weaknesses that provide guidelines for future research. I conclude by reiterating that the literature on color and psychological functioning is at a nascent stage of development, and by recommending patience and prudence regarding conclusions about theory, findings, and real-world application.

The past decade has seen enhanced interest in research in the area of color and psychological functioning. Progress has been made on both theoretical and empirical fronts, but there are also weaknesses on both of these fronts that must be attended to for this research area to continue to make progress. In the following, I briefly review both advances and weaknesses in the literature on color and psychological functioning.

Theoretical Work

Background and recent developments.

Color has fascinated scholars for millennia ( Sloane, 1991 ; Gage, 1993 ). Theorizing on color and psychological functioning has been present since Goethe (1810) penned his Theory of Colors , in which he linked color categories (e.g., the “plus” colors of yellow, red–yellow, yellow–red) to emotional responding (e.g., warmth, excitement). Goldstein (1942) expanded on Goethe’s intuitions, positing that certain colors (e.g., red, yellow) produce systematic physiological reactions manifest in emotional experience (e.g., negative arousal), cognitive orientation (e.g., outward focus), and overt action (e.g., forceful behavior). Subsequent theorizing derived from Goldstein’s ideas has focused on wavelength, positing that longer wavelength colors feel arousing or warm, whereas shorter wavelength colors feel relaxing or cool ( Nakashian, 1964 ; Crowley, 1993 ). Other conceptual statements about color and psychological functioning have focused on general associations that people have to colors and their corresponding influence on downstream affect, cognition, and behavior (e.g., black is associated with aggression and elicits aggressive behavior; Frank and Gilovich, 1988 ; Soldat et al., 1997 ). Finally, much writing on color and psychological functioning has been completely atheoretical, focused exclusively on finding answers to applied questions (e.g., “What wall color facilitates worker alertness and productivity?”). The aforementioned theories and conceptual statements continue to motivate research on color and psychological functioning. However, several other promising theoretical frameworks have also emerged in the past decade, and I review these frameworks in the following.

Hill and Barton (2005) noted that in many non-human animals, including primate species, dominance in aggressive encounters (i.e., superior physical condition) is signaled by the bright red of oxygenated blood visible on highly vascularized bare skin. Artificial red (e.g., on leg bands) has likewise been shown to signal dominance in non-human animals, mimicking the natural physiological process ( Cuthill et al., 1997 ). In humans in aggressive encounters, a testosterone surge produces visible reddening on the face and fear leads to pallor ( Drummond and Quay, 2001 ; Levenson, 2003 ). Hill and Barton (2005) posited that the parallel between humans and non-humans present at the physiological level may extend to artificial stimuli, such that wearing red in sport contests may convey dominance and lead to a competitive advantage.

Other theorists have also utilized a comparative approach in positing links between skin coloration and the evaluation of conspecifics. Changizi et al. (2006) and Changizi (2009) contend that trichromatic vision evolved to enable primates, including humans, to detect subtle changes in blood flow beneath the skin that carry important information about the emotional state of the conspecific. Increased red can convey anger, embarrassment, or sexual arousal, whereas increased bluish or greenish tint can convey illness or poor physiological condition. Thus, visual sensitivity to these color modulations facilitates various forms of social interaction. In similar fashion, Stephen et al. (2009) and Stephen and McKeegan (2010) propose that perceivers use information about skin coloration (perhaps particularly from the face, Tan and Stephen, 2012 ) to make inferences about the attractiveness, health, and dominance of conspecifics. Redness (from blood oxygenization) and yellowness (from carotenoids) are both seen as facilitating positive judgments. Fink et al. (2006) and Fink and Matts (2007) posit that the homogeneity of skin coloration is an important factor in evaluating the age, attractiveness, and health of faces.

Elliot and Maier (2012) have proposed color-in-context theory, which draws on social learning, as well as biology. Some responses to color stimuli are presumed to be solely due to the repeated pairing of color and particular concepts, messages, and experiences. Others, however, are presumed to represent a biologically engrained predisposition that is reinforced and shaped by social learning. Through this social learning, color associations can be extended beyond natural bodily processes (e.g., blood flow modulations) to objects in close proximity to the body (e.g., clothes, accessories). Thus, for example, red may not only increase attractiveness evaluations when viewed on the face, but also when viewed on a shirt or dress. As implied by the name of the theory, the physical and psychological context in which color is perceived is thought to influence its meaning and, accordingly, responses to it. Thus, blue on a ribbon is positive (indicating first place), but blue on a piece of meat is negative (indicating rotten), and a red shirt may enhance the attractiveness of a potential mate (red = sex/romance), but not of a person evaluating one’s competence (red = failure/danger).

Meier and Robinson (2005) and Meier (in press ) have posited a conceptual metaphor theory of color. From this perspective, people talk and think about abstract concepts in concrete terms grounded in perceptual experience (i.e., they use metaphors) to help them understand and navigate their social world ( Lakoff and Johnson, 1999 ). Thus, anger entails reddening of the face, so anger is metaphorically described as “seeing red,” and positive emotions and experiences are often depicted in terms of lightness (rather than darkness), so lightness is metaphorically linked to good (“seeing the light”) rather than bad (“in the dark”). These metaphoric associations are presumed to have implications for important outcomes such as morality judgments (e.g., white things are viewed as pure) and stereotyping (e.g., dark faces are viewed more negatively).

For many years it has been known that light directly influences physiology and increases arousal (see Cajochen, 2007 , for a review), but recently theorists have posited that such effects are wavelength dependent. Blue light, in particular, is posited to activate the melanopsin photoreceptor system which, in turn, activates the brain structures involved in sub-cortical arousal and higher-order attentional processing ( Cajochen et al., 2005 ; Lockley et al., 2006 ). As such, exposure to blue light is expected to facilitate alertness and enhance performance on tasks requiring sustained attention.

Evaluation and Recommendations

Drawing on recent theorizing in evolutionary psychology, emotion science, retinal physiology, person perception, and social cognition, the aforementioned conceptualizations represent important advances to the literature on color and psychological functioning. Nevertheless, theory in this area remains at a nascent level of development, and the following weaknesses may be identified.

First, the focus of theoretical work in this area is either extremely specific or extremely general. A precise conceptual proposition such as red signals dominance and leads to competitive advantage in sports ( Hill and Barton, 2005 ) is valuable in that it can be directly translated into a clear, testable hypothesis; however, it is not clear how this specific hypothesis connects to a broader understanding of color–performance relations in achievement settings more generally. On the other end of the spectrum, a general conceptualization such as color-in-context theory ( Elliot and Maier, 2012 ) is valuable in that it offers several widely applicable premises; however, these premises are only vaguely suggestive of precise hypotheses in specific contexts. What is needed are mid-level theoretical frameworks that comprehensively, yet precisely explain and predict links between color and psychological functioning in specific contexts (for emerging developments, see Pazda and Greitemeyer, in press ; Spence, in press ; Stephen and Perrett, in press ).

Second, the extant theoretical work is limited in scope in terms of range of hues, range of color properties, and direction of influence. Most theorizing has focused on one hue, red, which is understandable given its prominence in nature, on the body, and in society ( Changizi, 2009 ; Elliot and Maier, 2014 ); however, other hues also carry important associations that undoubtedly have downstream effects (e.g., blue: Labrecque and Milne, 2012 ; green: Akers et al., 2012 ). Color has three basic properties: hue, lightness, and chroma ( Fairchild, 2013 ). Variation in any or all of these properties could influence downstream affect, cognition, or behavior, yet only hue is considered in most theorizing (most likely because experientially, it is the most salient color property). Lightness and chroma also undoubtedly have implications for psychological functioning (e.g., lightness: Kareklas et al., 2014 ; chroma: Lee et al., 2013 ); lightness has received some attention within conceptual metaphor theory ( Meier, in press ; see also Prado-León and Rosales-Cinco, 2011 ), but chroma has been almost entirely overlooked, as has the issue of combinations of hue, lightness, and chroma. Finally, most theorizing has focused on color as an independent variable rather than a dependent variable; however, it is also likely that many situational and intrapersonal factors influence color perception (e.g., situational: Bubl et al., 2009 ; intrapersonal: Fetterman et al., 2015 ).

Third, theorizing to date has focused primarily on main effects, with only a modicum of attention allocated to the important issue of moderation. As research literatures develop and mature, they progress from a sole focus on “is” questions (“Does X influence Y?”) to additionally considering “when” questions (“Under what conditions does X influence Y and under what conditions does X not influence Y?”). These “second generation” questions ( Zanna and Fazio, 1982 , p. 283) can seem less exciting and even deflating in that they posit boundary conditions that constrain the generalizability of an effect. Nevertheless, this step is invaluable in that it adds conceptual precision and clarity, and begins to address the issue of real-world applicability. All color effects undoubtedly depend on certain conditions – culture, gender, age, type of task, variant of color, etc. – and acquiring an understanding of these conditions will represent an important marker of maturity for this literature (for movement in this direction, see Schwarz and Singer, 2013 ; Tracy and Beall, 2014 ; Bertrams et al., 2015 ; Buechner et al., in press ; Young, in press ). Another, more succinct, way to state this third weakness is that theorizing in this area needs to take context, in all its forms, more seriously.

Empirical Work

Empirical work on color and psychological functioning dates back to the late 19th century ( Féré, 1887 ; see Pressey, 1921 , for a review). A consistent feature of this work, from its inception to the past decade, is that it has been fraught with major methodological problems that have precluded rigorous testing and clear interpretation ( O’Connor, 2011 ). One problem has been a failure to attend to rudimentary scientific procedures such as experimenter blindness to condition, identifying, and excluding color deficient participants, and standardizing the duration of color presentation or exposure. Another problem has been a failure to specify and control for color at the spectral level in manipulations. Without such specification, it is impossible to know what precise combination of color properties was investigated, and without such control, the confounding of focal and non-focal color properties is inevitable ( Whitfield and Wiltshire, 1990 ; Valdez and Mehrabian, 1994 ). Yet another problem has been the use of underpowered samples. This problem, shared across scientific disciplines ( Maxwell, 2004 ), can lead to Type I errors, Type II errors, and inflated effect sizes ( Fraley and Vazire, 2014 ; Murayama et al., 2014 ). Together, these methodological problems have greatly hampered progress in this area.

Although some of the aforementioned problems remain (see “Evaluation and Recommendations” below), others have been rectified in recent work. This, coupled with advances in theory development, has led to a surge in empirical activity. In the following, I review the diverse areas in which color work has been conducted in the past decade, and the findings that have emerged. Space considerations require me to constrain this review to a brief mention of central findings within each area. I focus on findings with humans (for reviews of research with non-human animals, see Higham and Winters, in press ; Setchell, in press ) that have been obtained in multiple (at least five) independent labs. Table ​ Table1 1 provides a summary, as well as representative examples and specific references.

Research on color and psychological functioning.

In research on color and selective attention, red stimuli have been shown to receive an attentional advantage (see Folk, in press , for a review). Research on color and alertness has shown that blue light increases subjective alertness and performance on attention-based tasks (see Chellappa et al., 2011 , for a review). Studies on color and athletic performance have linked wearing red to better performance and perceived performance in sport competitions and tasks (see Maier et al., in press , for a review). In research on color and intellectual performance, viewing red prior to a challenging cognitive task has been shown to undermine performance (see Shi et al., 2015 , for a review). Research focused on color and aggressiveness/dominance evaluation has shown that viewing red on self or other increases appraisals of aggressiveness and dominance (see Krenn, 2014 , for a review). Empirical work on color and avoidance motivation has linked viewing red in achievement contexts to increased caution and avoidance (see Elliot and Maier, 2014 , for a review). In research on color and attraction, viewing red on or near a female has been shown to enhance attraction in heterosexual males (see Pazda and Greitemeyer, in press , for a review). Research on color and store/company evaluation has shown that blue on stores/logos increases quality and trustworthiness appraisals (see Labrecque and Milne, 2012 , for a review). Finally, empirical work on color and eating/drinking has shown that red influences food and beverage perception and consumption (see Spence, in press , for a review).

The aforementioned findings represent important contributions to the literature on color and psychological functioning, and highlight the multidisciplinary nature of research in this area. Nevertheless, much like the extant theoretical work, the extant empirical work remains at a nascent level of development, due, in part, to the following weaknesses.

First, although in some research in this area color properties are controlled for at the spectral level, in most research it (still) is not. Color control is typically done improperly at the device (rather than the spectral) level, is impossible to implement (e.g., in web-based platform studies), or is ignored altogether. Color control is admittedly difficult, as it requires technical equipment for color assessment and presentation, as well as the expertise to use it. Nevertheless, careful color control is essential if systematic scientific work is to be conducted in this area. Findings from uncontrolled research can be informative in initial explorations of color hypotheses, but such work is inherently fraught with interpretational ambiguity ( Whitfield and Wiltshire, 1990 ; Elliot and Maier, 2014 ) that must be subsequently addressed.

Second, color perception is not only a function of lightness, chroma, and hue, but also of factors such as viewing distance and angle, amount and type of ambient light, and presence of other colors in the immediate background and general environmental surround ( Hunt and Pointer, 2011 ; Brainard and Radonjić, 2014 ; Fairchild, 2015 ). In basic color science research (e.g., on color physics, color physiology, color appearance modeling, etcetera; see Gegenfurtner and Ennis, in press ; Johnson, in press ; Stockman and Brainard, in press ), these factors are carefully specified and controlled for in order to establish standardized participant viewing conditions. These factors have been largely ignored and allowed to vary in research on color and psychological functioning, with unknown consequences. An important next step for research in this area is to move to incorporate these more rigorous standardization procedures widely utilized by basic color scientists. With regard to both this and the aforementioned weakness, it should be acknowledged that exact and complete control is not actually possible in color research, given the multitude of factors that influence color perception ( Committee on Colorimetry of the Optical Society of America, 1953 ) and our current level of knowledge about and ability to control them ( Fairchild, 2015 ). As such, the standard that must be embraced and used as a guideline in this work is to control color properties and viewing conditions to the extent possible given current technology, and to keep up with advances in the field that will increasingly afford more precise and efficient color management.

Third, although in some research in this area, large, fully powered samples are used, much of the research remains underpowered. This is a problem in general, but it is particularly a problem when the initial demonstration of an effect is underpowered (e.g., Elliot and Niesta, 2008 ), because initial work is often used as a guide for determining sample size in subsequent work (both heuristically and via power analysis). Underpowered samples commonly produce overestimated effect size estimates ( Ioannidis, 2008 ), and basing subsequent sample sizes on such estimates simply perpetuates the problem. Small sample sizes can also lead researchers to prematurely conclude that a hypothesis is disconfirmed, overlooking a potentially important advance ( Murayama et al., 2014 ). Findings from small sampled studies should be considered preliminary; running large sampled studies with carefully controlled color stimuli is essential if a robust scientific literature is to be developed. Furthermore, as the “evidentiary value movement” ( Finkel et al., 2015 ) makes inroads in the empirical sciences, color scientists would do well to be at the leading edge of implementing such rigorous practices as publically archiving research materials and data, designating exploratory from confirmatory analyses, supplementing or even replacing significant testing with “new statistics” ( Cumming, 2014 ), and even preregistering research protocols and analyses (see Finkel et al., 2015 , for an overview).

In both reviewing advances in and identifying weaknesses of the literature on color and psychological functioning, it is important to bear in mind that the existing theoretical and empirical work is at an early stage of development. It is premature to offer any bold theoretical statements, definitive empirical pronouncements, or impassioned calls for application; rather, it is best to be patient and to humbly acknowledge that color psychology is a uniquely complex area of inquiry ( Kuehni, 2012 ; Fairchild, 2013 ) that is only beginning to come into its own. Findings from color research can be provocative and media friendly, and the public (and the field as well) can be tempted to reach conclusions before the science is fully in place. There is considerable promise in research on color and psychological functioning, but considerably more theoretical and empirical work needs to be done before the full extent of this promise can be discerned and, hopefully, fulfilled.

Conflict of Interest Statement

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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  • Published: 03 April 2024

The effect of job satisfaction and moonlighting intentions with mediating and moderating effects of commitment and HR practices an empirical study

  • K. D. V. Prasad   ORCID: orcid.org/0000-0001-9921-476X 1 , 2 ,
  • Sripathi Kalavakolanu 2 , 3 ,
  • Tanmoy De   ORCID: orcid.org/0000-0002-2605-677X 2 , 4 &
  • V. K. Satyaprasad 2 , 4  

Humanities and Social Sciences Communications volume  11 , Article number:  483 ( 2024 ) Cite this article

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  • Business and management

Moonlighting as a practice, the limelight was gained during the COVID-19 pandemic due to remote work involving flexible work, which saved employees’ commuting time to the office and has become a potential source of income for individuals seeking other jobs. The authors examined the phenomenon of moonlighting by assessing the relationships between job satisfaction, organizational commitment, and moonlighting intentions. The authors also examined the mediating effects of employee organizational commitment and economic intentions and the moderating role of human resource practices on the relationship between job satisfaction and moonlighting intentions. The data were gathered for five reflective constructs of this empirical study—job satisfaction, organizational commitment, human resources practices, economic intentions, and moonlighting intentions—by surveying IT-enabled industry employees in Hyderabad. The data from 311 valid responses were subjected to structural equation modeling analysis using IBM AMOS version 28. The model-fit indices from SEM analysis indicate excellent model fit. The structural model from SEM analysis reveals that 50% of the variance in moonlighting is accounted for by job satisfaction and organizational commitment. The factor of job satisfaction is statistically significant and influences the moonlighting intentions of employees in IT-enabled industries. Job satisfaction has a positive impact on organizational commitment, and when organizational commitment increases, moonlighting intentions decrease. Organizational commitment partially mediates moonlighting intentions through job satisfaction. The study also assessed the moderating role of human resource practices on the relationship between job satisfaction and moonlighting intentions. The moderation analysis results reveal statistically significant and positive moderating effects of human resource practices on intentions to moonlight through job satisfaction. The slope analysis indicated that human resource practices strengthen the positive relationship between job satisfaction and moonlighting.

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Introduction

Moonlighting refers to holding a second job, typically in addition to a full-time job. This is done for various reasons, including financial needs, economic causes, career advancement, or personal fulfillment. Moonlighting can take many forms, such as working part-time on weekends, freelancing in a field related to one’s full-time job, or starting a side business. Moonlighting can positively and negatively affect an individual’s work-life balance, job performance, and overall well-being. Most employees moonlight in their free time after their first job to supplement their income (Ashwini et al., 2017 ). It can be an opportunity to gain new skills, increase income, and build professional networks, but it can also lead to burnout and negatively impact one’s health and relationships.

There are a variety of factors that can lead employees and individuals to have moonlighting intentions. Financial needs are one of the most common reasons for moonlighting. Many individuals may be unable to make ends meet with their full-time job alone and may turn to a second job to supplement their income (Rispel et al., 2014 ). Some individuals may commit to career advancement to gain additional skills or experience that can help them advance their primary career (George and George, 2022 ). Akmaliah and Hisyamuddin ( 2009 ) noted that the entrepreneurial aspirations of individuals who want to start their own business and who are moonlighting can provide a way to test water and gain experience in their desired field. Personal fulfillment is one of the reasons individuals have moonlighting intentions and may take on a second job simply because they enjoy the work or find it fulfilling (Bell and Roach, 1998 ).

In a study of teachers, the authors observed that teachers may take up moonlighting if they are not satisfied with their current job and want to explore new opportunities or gain additional skills to find a better job in the future (Raffel and Groff, 1990 ). The cost of living in certain regions can be very high, and moonlighting can be a way for people to make ends meet (Ballou, 1995 ). In today’s economy, many people face job insecurity, moonlighting can provide an additional source of income, and a safety net in the case of job loss and lack of job security may be one of the reasons for employee intentions toward moonlighting (Kisumano and Wa-Mbaleka, 2017 ). Flexibility is another reason for moonlighting, as some individuals who have job flexibility may moonlight for additional income (Kimmel, 1995 ).

The part-time job, freelancing, side business, self-employment, gig economy with working short-term contracts, moonlighting in the same type of employment, and different types of moonlighting that employees take. It is worth noting that some people may engage in more than one type of moonlighting, depending on their circumstances and goals. Additionally, these types may overlap with each other. Additional income, career development, flexibility, diversification of income, and a sense of accomplishment are some of the advantages of moonlighting. Some disadvantages of moonlighting include time constraint fatigue, burnout, potential conflicts of interest, income tax issues, and the risk of job loss.

However, moonlighting can also create conflicts of interest, mainly if the second job is in a similar area to the primary job. Additionally, it can also lead to time management and work-life balance issues (Baldwin Jr. and Daugherty, 2002 ). Overall, moonlighting can be a valuable opportunity for individuals to gain additional skills and income, but it is crucial to consider the potential implications and manage time and responsibilities effectively (Sussman, 1998 ). Moonlighting can also have implications for an individual’s employer and colleagues. Employers may have policies that prohibit or limit moonlighting, while others may support it. Some employers may see this as a way for employees to gain additional skills and experience to benefit the company (Lyle, 2015 ). However, if an employee is exhausted from working multiple jobs, this may negatively impact their job performance and productivity. Additionally, moonlighting can create conflicts of interest, mainly if the second job is in a field similar to or related to the full-time job (Jamal, 1986 ).

In another study, the authors evaluated the positive impacts of moonlighting on workers who had second jobs that were different from their primary jobs. They emphasized the method of increasing salaries heavily. From a training standpoint, worker turnover affects the wages of individuals in primary occupations (Panos et al. ( 2014 ). Campion et al. ( 2020 ) presented an integrative systematic review of multiple job holding tasks and performed a coherent synthesis of multiple job holding tasks. The authors presented the financial reasons—pay off debts, meet regular expenses, future savings; career development—an opportunity to enhance and learn new skills; and heterogeneous job models; psychological fulfillment—work enjoyment, desire to mix with other groups, to balance negative primary job experiences, work-life balance and flexibility—the components critically reviewed. The authors opined that multiple job-holding intentions vary from person to person based on one’s needs.

Ashwini et al. ( 2017 ) investigated the intentions of middle-level employees working for selected information technology companies in southern India to engage in moonlighting. They researched the different aspects of the workforce that contribute to the phenomenon of moonlighting and concluded that the lack of proactive retention benefits for committed employees who are experienced and loyal leads to a loss of organizational commitment on the part of those workers, leading them to seek secondary job holding to pursue their ambitions. According to George and George ( 2022 ), IT workers may switch jobs to obtain new skills to switch careers. Secondary jobs can help people shift occupations or foster entrepreneurship. This notion emphasizes investing in moonlighting rather than consumption. This approach is similar to that of employment diversity but considers the financial benefits of moonlighting.

Methodological choices

Several previous studies and methods for assessing moonlighting intentions and factors associated with employee moonlighting intentions have been reported. Šťastný et al. ( 2021 ) examined the factors that cause Czech teachers of lower secondary schools to commit private tuition and a paid job to moonlighting. The results revealed that economic factors are the main cause of male teachers’ responses to moonlighting. Male teachers were more likely to moonlight if they were part-time, had less professional experience, had more significant household financial burdens, or were less satisfied with their pay. Using structural equation modeling analysis, Sai Manogna and Swamy ( 2023 ) examined the influence of organizational commitment dimensions on secondary hiring decisions made by teachers in higher education. The study’s findings suggest a negative correlation between organizational commitment and intentions to moonlight, and higher education institutions can lessen faculty members’ intentions to moonlight by creating an environment that encourages organizational commitment. This study emphasizes that it is essential to comprehend the factors that lead to moonlighting intentions to assist organizations in putting in place procedures and policies that lessen the possibility that employees will moonlight. Seema and Sachdeva ( 2020 ) reported that financial or economic strain has been the most discussed or studied motive in the recent past. The PLS-SEM results demonstrate the critical roles that organizational commitment and entrepreneurial motivation play in motivating employees to moonlight. Using SEM analysis, Seema et al. ( 2021 ) reported the relationship between employee job satisfaction and moonlighting intentions, with organizational commitment serving as a mediator. While job satisfaction has a very strong positive impact on organizational commitment, job satisfaction also has a mediating effect on the relationship between job satisfaction and intentions to moonlight. Furthermore, there is a noteworthy inverse relationship between organizational commitment and moonlighting intentions.

The authors followed the methods of Seema et al. ( 2021 ) in the present study to assess the relationships among job satisfaction, organizational commitment and moonlighting intentions. The mediating and moderating effects of employee organizational commitment, economic intentions, and human resource practices were examined by surveying information technology-enabled employees. This model fits well with the study we carried out; however, the authors used IBM SPSS AMOS for structural equation modeling analysis.

Several studies have been carried out in the area of moonlighting. Although employees may moonlight for various reasons, the most explored and researched motivations in the literature are financial strain, economic needs and causes. However, no researcher has reported the association between economic intentions and moonlighting. This paper aims to empirically investigate the relationship between job satisfaction and moonlighting intentions through multiple mediation analyses with organizational commitment and economic intentions as mediators. The moderating role of human resource practices in the IT-enabled industry is also examined. The primary data from 311 IT-enabled industry professionals were gathered using reliability-tested scales. IBM SPSS 29 and AMOS 28 ver. software were used for structural equation modeling analysis to test the statistical significance of the differences. This study contributes to identifying employee multitasking capabilities and predicting employee well-being by studying whether a second job has an additive or interactive effect on employee well-being. Employee retention and turnover, employee job satisfaction, and, in some cases, employee conditions were identified.

Literature review

Sai Manogna and Swamy ( 2023 ), in the context of intended moonlighting, investigated the influence of organizational commitment factors on secondary employment decisions among higher education professors. The study’s findings suggest that there is a negative correlation between organizational commitment dimensions and intentions to moonlight and that higher education institutions can lessen faculty members’ intentions to moonlight by creating an environment that fosters organizational commitment. The associations between job happiness and moonlighting intentions, as well as the mediating effect of organizational commitment on this relationship, were investigated by Seema et al. ( 2021 ). According to the study, job satisfaction has a very high positive impact on organizational commitment, and organizational commitment has a mediating effect on the relationship between job satisfaction and intentions to moonlight. Furthermore, there is a noteworthy inverse link between Organizational Commitment and Moonlighting Intentions. An empirical study examined the relationship between two nonfinancial factors—organizational commitment and entrepreneurial motivation—and IT professionals’ inclinations to moonlight. The PLS-SEM results show that entrepreneurial motivation and organizational commitment are critical factors that influence employees’ aspiration to moonlight (Seema and Sachdeva, 2020 ). Because of globalization and technological growth, the environment and scenario under which firms function have drastically changed. Numerous effects of this transition have been felt by the economy. The idea of moonlighting has gained traction because of the current state of the economy and the volatility of job prospects. The authors concluded that employees’ organizational engagement has decreased as a result of moonlighting (Joseph and Ambily, 2019 ).

In an empirical study, the authors examined how job satisfaction differed between moonlighters and nonmoonlighters in relation to job stress and teachers’ well-being in Canadian colleges. The majority of the findings were consistent with the energizing/opportunity theory of moonlighters rather than the depriving/constraining hypothesis, which holds that job satisfaction has a statistically significant impact on employees’ intentions to moonlight (Jamal et al. 1998 ). The impact of moonlighting on teachers’ job satisfaction at public universities in Punjab and the Federal Capital was investigated by Ara and Akbar ( 2016 ). Four factors were examined to determine the causes of moonlighting among university teachers: extra income, denied promotions, skill diversity, and job autonomy. Overall, the study’s main conclusions showed that moonlighting has a large impact on job satisfaction. The impacts of job satisfaction, organizational citizenship behavior, turnover intention, and moonlighting intentions on an organization’s success were examined by the authors in an empirical study. The authors found a negative relationship between IT professionals’ intentions toward moonlighting and job satisfaction. The authors suggest that people who are happy in their current position are less likely to look for new work. Furthermore, the research findings indicate that turnover intention is considerably negatively impacted by job satisfaction and organizational commitment. Thus, by improving job satisfaction, organizational commitment, and person-organization fit, organizations can reduce turnover intention. In an empirical study, the authors examined the connection between job satisfaction and IT professionals’ intentions to moonlight within the theoretical framework of the “Attitudes and Alternatives Model” (AAM) of Withdrawal Cognitions. This model explains turnover as a result of generalized dissatisfaction, along with its associated antecedents and potential consequences. The results demonstrated that people’s intentions to put in more hours were significantly and negatively influenced by job satisfaction (Malodia and Butail ( 2024 ).

The difficulties that managers and organizations face as a result of employees who intend to moonlight were studied by Banerjee ( 2012 ). The author proposed that moonlighting needs to be controlled and regulated to avoid ambiguities. For employees to understand the repercussions of breaking policies, they must be implemented, and their terms must be included in the employment contract. Appropriate policies should be formulated to synergize moonlighting practices with mainstream practices to ensure stability in future work interactions (Bakare, 2021 ). Llobet and Fito ( 2013 ) studied theoretical frameworks for organizational behavior and job satisfaction concerning changes in social and economic situations in most Western nations. The study reported that workers’ identification with the organization and their impression of job satisfaction are the major variables in organizational adaptation and retention. The organization must identify and embrace these important aspects to implement an effective human resource policy.

Research gap

Despite significant research on employee moonlighting intentions, there may be a lack of understanding of the underlying mechanisms of how organizations should allow/disallow moonlighting practices. There is a need to identify the causes and mechanisms that lead to an employee’s moonlighting intentions. The authors observed inconsistent findings, and there may be a need for further research to clarify or resolve these inconsistencies. As new human resource practices and policies emerge, there may be limited research on their applications, benefits, and potential drawbacks. Furthermore, the authors could identify gaps, that moonlighting phenomenon was not researched with economic intentions and human resource practices. These factors influence the moonlighting intentions of employees in general or of IT-enabled industry employees in particular. Therefore, the researchers assessed the relationship between job satisfaction and moonlighting intentions—the mediating and moderating effects of employee organizational commitment, economic intentions, and human resource practices—for the employees of the information technology-enabled industry.

Objective of the study

To investigate the phenomenon of “moonlighting” (i.e., working a second job or additional source of income) among employees in the IT-enabled industry in and around Hyderabad, an Indian Metro. The study also examines the influence of job satisfaction, human resource practices, economic intention, organizational commitment, and moonlighting intentions.

Theoretical framework and hypothetical model

The theoretical framework was developed following the model of Seema et al. ( 2021 ), who studied job satisfaction, moonlighting intentions, and the mediating effect of organizational commitment. Tjahjanto and Riady ( 2015 ) examined the turnover intentions of outsourced employees by assessing job satisfaction and organizational commitment as mediators in the service industry in Jakarta. The authors reported insignificant results for the intervening variables of organizational commitment and job satisfaction on the turnover intentions of employees. Palumbo ( 2020 ) investigated the effects of working from home on the mediating effects of work-related well-being and highlighted the limitations and implications for organization and employee well-being; home-based work or flexible working can be allowed if there is any mutually beneficial situation. The authors followed these studies and presented their hypothetical model and theoretical relationships and mediating and moderating frameworks in Figs. 1 – 4 .

figure 1

Theoretical framework: Moonlighting.

figure 2

Researcher hypothetical framework.

figure 3

Theoretical model and relationships among variables (author creation) adopted from Metselaar et al., 2023 ).

figure 4

Moderation model adopted from Hair et al. ( 2021 ).

To better understand this phenomenon, this study modeled the moderating effects of human resource practices on moonlighting intentions through job satisfaction by adopting the model (Fig. 4 ) of Hair et al. ( 2021 ). The moderating effect (P3) is represented by an arrow pointing at the effect P1 linking job satisfaction and moonlighting intentions. Furthermore, when the moderating impact is included in an SEM path model, there is also a direct relationship (P2) from the moderator to the endogenous construct of moonlighting intentions.

Research hypotheses

H 1 : Job satisfaction is statistically significant and influences the organizational commitment of employees working in IT-enabled industries.

H 2 : Organizational commitment is statistically significant and influences the moonlighting intentions of employees working in IT-enabled industries.

H 3 : Job satisfaction is statistically significant and influences the moonlighting intentions of employees working in IT-enabled industries.

H 4 : Job satisfaction is statistically significant and influences the economic intentions of employees working in IT-enabled industries.

H 5 : Economic intentions have consequences for the moonlighting intentions of employees working in IT-enabled industries.

H 6 : Organizational commitment mediates the relationship between job satisfaction and the moonlighting intentions of employees working in an IT-enabled industry.

H 7 : Economic intentions mediate the relationship between job satisfaction and the moonlighting intentions of employees working in IT-enabled industries.

H 8 : Human resource practices moderate the relationship between job satisfaction and the moonlighting intentions of employees working in IT-enabled industries.

Data collection and research instruments

The data were gathered using standardized, tested, and published scales that were modified to suit the study and were used to measure job satisfaction and organizational commitment. The job satisfaction scale was adapted from Huddleston and Good ( 1999 ). A modified organizational commitment scale was adapted from Mowday et al. ( 1979 ) to measure the construct of organizational commitment. The economic intentions scale was developed by appropriately modifying the statements to suit the study following the interitem moonlighting scale developed by Jehan et al., 2021 . Human resource practices were developed by adopting appropriate items from Moonlighting practices (Puja Khatri and Khushboo, 2014 ) and Demo et al. ( 2012 ) from items of the Human Resources Policies and Practice Scale (2012). All the statements are Likert-type measures ranging from strongly agree 5 to strongly disagree 1.

All the items of the scales were tested to determine whether these scales measure the unobservable constructs that the study needs to measure; the scales are valid and measure the intended constructs consistently and precisely (reliable). The authors carried out a pilot study and assessed interrater reliability, test-retest reliability, and internal consistency reliability by assessing Cronbach’s alpha statistic. Further factor analysis was carried out on the data, and only those items/statements with factor loadings >0.6 were considered for the study. Seema and Sachdev’s ( 2020 ) Moonlighting Intentions Scale was adapted and modified to measure the intentions of employees to moonlight. The reliability of the economic intentions scale and human resource practices scale were 0.86 and 0.87, respectively.

Sample size estimation

The present study targeted employees from the IT-enabled industry and sample subjects from Hyderabad, an Indian Metro. The IT-enabled employees’ population in Hyderabad Metro is not known, the authors followed the Cochran ( 1977 ) formula and estimated the required sample size to be 385. The questionnaire was developed and published online, and the link was shared with 800 employees working in the IT-enabled industry. Three hundred and fifty responses were received; the study considered only 311 valid responses, 39 were discarded because of respondents’ misbehavior, and some of the responses were incomplete. Another school of thought concerning the sample size was to have a minimum sample size of 50 + 5x, where x represents several statements. The present study included 21 statements, and the required sample size was calculated based on formula 155 (James Gaskin, 2020 ). Therefore, the valid data from 311 subjects in this study are far higher than the minimum sample size required to subject the data to structural equation modeling analysis using the IBM AMOS version 28.

The demographic characteristics of the participants are presented in Table 1 .

Data analysis

The data were subjected to factor analysis using IBM SPSS version 29 and structural equation modeling (SEM) using IBM AMOS version 28 to test the authors’ hypothetical framework. The outer measurement and inner structural models were examined. In the present study, the outer measurement model consisted of five reflective latent constructs with 22 indicators. However, one item, ECON4, was dropped from the study because it was not appropriately worded to measure economic intentions. Using IBM AMOS, researchers have established methods for measuring absolute path coefficients in several types of research studies and organizational psychology studies with small and large sample sizes, including nonnormal and normal data (Hair et al., 2016 ; Hair et al., 2013 ).

Before proceeding with the data analysis, the normality of the data was assessed by the Shapiro‒Wilk test ( p  > 0.05), and kurtosis and skewness were assessed. The data are normal, as the skewness values are within the recommended range of −2 to +2 and the kurtosis is between −7 and +7 (Hair et al., 2010 ). After the assessment of data normality, exploratory factor analysis (EFA) was carried out to uncover the underlying structure and patterns of the set of observed variables. EFA analysis is needed to simplify complex datasets by identifying common underlying factors that explain the observed relationships between variables.

Results and discussion

In the following sections, the results of the factor analysis, SEM analysis, the measurement structural model, mediation analysis, and moderation analysis are presented along with the testing of hypotheses.

Factor analysis

The data were subjected to factor analysis, which grouped the 21 variables into 5 components based on their shared variance. The Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy is a statistic that measures the suitability of data for factor analysis. A value of 0.796 (Table 2 ) indicates that the data are suitable for factor analysis. All five components explained 71.986% of the variance, which is greater than the recommended value of 60% (Hair et al., 2016 ).

In factor analysis, the “sphericity” assumption states that the correlation matrix of observed variables is an identity matrix, meaning that variables are uncorrelated and therefore not suitable for factor analysis. To evaluate whether this assumption was met, Bartlett’s test was carried out. The p value from Bartlett’s test is low (less than 0.05), indicating that the correlations between variables are significantly different from zero, and the data are appropriate for factor analysis. The factor loadings for all the constructs and their items are greater than the recommended value of 0.6 (Chin et al., 2008 ). Therefore, a measurement model was constructed.

Tables 2a – e present the respective items for the adopted and modified statements from the original instruments and outer loadings, which are measured using IBM-AMOS for the outer measurement model.

Measurement model

CFA was computed using AMOS to test the measurement model. As part of the CFA, factor loadings were assessed for each item. The model fit measures were used to assess the model’s overall goodness of fit (CMIN/df, GFI, CFI, TLI, SRMR, and RMSEA); all the values were within their respective recommended and common acceptance levels (Ullman and Bentler, 2012 ; Hu and Bentler, 1998 ; Bentler, 1990 ). The five-factor model (moonlighting intentions, job satisfaction, organization commitment, human resource practices and economic intentions) fit the data well (Table 3 ). The model-fit indices are “CMIN/DF 2.268, CFI 0.941, IFI 0.936, TLI 0.924, NFI 0.914, RMSEA 0.05 and SRMR 0.048”. The factor loading values (Kline, 2011 ) are excellent, acceptable, and nonnegative, and all are greater than 0.5, with an average factor loading >0.7 for all the five constructs; additionally, the model has an excellent fit, as presented in Table 3 (Byrne, 2013 ). The measurement model is presented in Fig. 5 .

figure 5

Measurement model with constructs.

The construct reliability was assessed using Cronbach’s alpha and composite reliability. The Cronbach’s alpha for each construct in the study was measured are above the recommended value of >0.70 (Nunnally and Bernstein, 1994 ). The composite reliabilities ranged from 0.811 to 0.901, above the recommended and benchmark values of 0.70 (Hair et al., 2010 ). Therefore, construct reliability was established (Table 4 ).

The convergent validity of the scale items was estimated using the average variance extracted (AVE) (Fornell and Larcker, 1981 ). The AVE values were above the threshold of 0.50 (Fornell and Larcker, 1981 ). Hence, the scales used in this empirical study have convergent validity(Table 4 ).

Discriminant validity illustrates how a specific construct varies from other constructs and explains how closely correlated the measures should be (Anderson and Gerbing, 1988 ). Discriminant validity was assessed in the present study using the Fornell–Larcker criterion and the Heterotrait–Monotrait (HTMT) ratio. According to the Fornell and Larcker criterion, discriminant validity is established when the square root of the AVE for a construct is greater than its correlation with the other constructs in the study. However, the Fornell and Larcker criterion has recently been criticized, and a new method for assessing discriminant validity, the HTMT ratio, has been increasingly utilized. In the present study, discriminant validity was established using the Fornell and Larcker criterion. However, discriminant validity was also assessed using the HTMT ratio, and all ratios were less than the required limit of 0.85 (Henseler et al., 2015 ). Therefore, discriminant validity was established (Tables 5 and 6 ).

After assessing the factor loadings, the CR and AVE were measured. The CRs for all five constructs are >0.5 (Hair et al., 2013 ), indicating the reliability of the latent constructs (Table 4 ).

Heterotrait–Monotrait (HTMT) ratio analysis

The HTMT analysis examines the ratio of trait correlations between two constructs. If the HTMT value is <0.90, discriminant validity is established between two constructs. In the following table, all the values are <0.90; therefore, discriminant validity is established (Hensler et al., 2015 ):

According to the model’s assessment, the recommended cutoff value of 0.7 loading constitutes a good outer measurement (Chin et al., 2008 ; Hair et al., 2013 ). However, one loading on the construct job satisfaction OC9 (0.66) was retained for the analysis because the respective construct’s average loading is >0.7 and the AVE for every construct is >0.5 (Hair et al., 2013 ).

Structural model

A structural model generated through AMOS was used to test the relationships between job satisfaction and organizational commitment and between job satisfaction and moonlighting intentions (Fig. 6 ). A good fitting model is accepted if the CMIN/df is <5, the GFI is >0.90 (Hair et al., 2010 ), the Tucker and Lewis indices are >0.90 (1973), and the confirmatory fit index (CFI) (Bentler, 1990 ) is >0.90 (Hair et al., 2010 ). In addition, an adequate-fitting model was accepted if the AMOS computed value of the standardized root mean square residual (RMR) was <0.05 and the root mean square error approximation (RMSEA) ranged between 0.05 and 0.08 (Hair et al., 2010 ). The indices indicated in Table 3 fall within the acceptable range.

figure 6

Structural model (Testing of Hypotheses H1 and H2).

The squared multiple correlation was 0.50 for moonlighting, which indicates that 50% of the variance in moonlighting is accounted by job satisfaction and organizational commitment (Fig. 6 ).

Common method bias

Common method bias (CMB) is inflation or rare case depletion of the true correlation between the observable variables in the study. In most of the cases, the respondents responded to questions consisting of independent and dependent variables at the same time, and there was a chance for artificial inflation of covariance. The study estimated common method bias using Harman’s single factor test and included common method latent factor methods.

Harman’s single factor test: The indicators were loaded to one factor in this test, and confirmatory factor analysis was performed to assess the model fit. The model fit was verified and observed, with no common method bias.

Latent Common Method Factor: In this procedure, we used a latent variable that has a direct relationship with all the construct indicators in the model. A latent construct was drawn and labeled as a common method. The model included a directo relationship from the unobserved common method latent construct to every indicator in the model. After drawing a path from the common method construct to all the indicators in the model, all the relationships from the method factor are constrained to be equal to examine whether there is any common influence across all the indicators. The model was run with the latent common method variable, which has a direct relationship with all the factors, and the chi-square value of this CFA model was noted. The observed chi-square value is 391.912, with 174 degrees of freedom. The original model chi-square without a latent factor is 396.443 with 175 degrees of freedom. The difference between the chi-square values is 4.531, indicating that there is common method bias; however, this is not a substantial concern in this study because the CMB is very low and has not affected the outcome of the study. The results are presented in Tables 7 and 8 .

Testing of hypotheses

The study assessed the impact of H 1 : job satisfaction → organizational commitment, H 2 : organizational commitment → moonlighting intentions, H 3 : job satisfaction → moonlighting intentions, H 4 : job satisfaction → economic intentions, and H 5 : economic intentions → moonlighting intentions. The impact of job satisfaction on organizational commitment is positive and statistically significant ( β  = 0.346; t  = 4.180). p  < 0.001), supporting H 1 (Table 8 ).

The impact of organizational commitment on moonlighting intentions is statistically significant and negative ( β  = −0.117, t  = −2.673, p  < 0.05) (Table 8 ). If organizational commitment decreases by one unit, moonlighting intentions increase by 0.117 units. Hence, H 2 is supported.

Furthermore, the impact of job satisfaction is positive and statistically significant ( β  = 0.741, t  = 9.713, p  < 0.001), supporting H 3 (Table 8 ).

Similarly, the impact of job satisfaction on economic intentions, β  = 0.090, t  = 1.742, p > 0.05, is not supported. The impact of economic intentions on moonlighting intentions ( β  = 0.058, t  = 0.876, p  > 0.05) does not support H 5 .

These findings indicate that the hypothetical framework model developed by the authors is supported by enough empirically and statistically significant evidence of path coefficients and coefficients of determination (0.73 for organizational commitment and 0.50 for moonlighting intentions, Fig. 6 ), where the value of organizational commitment is greater than the recommended value of 0.26 (Cohen, 1992 ), indicating a good structural model.

Mediation analysis

The influence between two constructs follows an indirect path through a third variable called a mediator. At this point, the third variable affects the influence of the two constructs. A mediating variable is also referred to as an intervening variable. The direct effect, indirect effect, and total effect need to be assessed to measure the effect of mediating variables.

Multiple mediation analysis

This study examined the effect of more than one mediator on the dependent variable, namely, moonlighting intentions. The authors followed the procedure of Preacher and Hayes ( 2008 ), who assessed indirect effects, if any, in multiple mediator models. This study examined the indirect effect of the mediator variables of organizational commitment and economic intentions on moonlighting intentions. Several studies have reported the mediating effects of organizational commitment on several study variables. A study by Gamal et al. ( 2022 ) with a sample of 86 employees reported that work satisfaction is a mediator that improves the effect of the work environment on employee performance. Additional income, job dissatisfaction, diversified skills, and autonomy are factors that impact moonlighting intentions, with autonomy serving as a mediator (Ara and Akbar, 2016 ). Holding multiple jobs and motives were studied by Dickey et al. ( 2011 ), who indicated that financial reasons are one of the reasons for moonlighting intentions. In a study on the effects of moonlighting on job satisfaction among academic staff and medical doctors in Nigeria, multivariate analysis of variance revealed that moonlighting intentions are statistically significant and positive; however, academic staff moonlight more than doctors (Adelugba et al., 2020 ). Based on the literature, the authors included organizational commitment and economic causes, and multiple mediation analysis was carried out using IBM SPSS Amos ver. 28. The authors used the estimands function of IBM SPSS Amos to estimate the mediating roles of job satisfaction and employee performance.

Results of mediation analysis

This study assessed the mediating role of organizational commitment and economic intentions on the relationship between job satisfaction and moonlighting intentions. The results revealed a statistically significant indirect effect of organizational commitment on the relationship between job satisfaction and moonlighting intentions ( β  = −0.039, t  = −1.982, p  < 0.05), indicating that an increase in one unit of job satisfaction decreases 0.039 units of moonlighting intentions, supporting H 6 : Organizational Commitment mediates moonlighting intentions through the job satisfaction of employees working in IT-enabled companies (Table 9 , Fig. 7 ).

figure 7

JS Job satisfaction, OC Organizational commitment, ML Moonlighting intentions, ECON Economic intentions.

Analyzing the mediating role of economic intentions on the linkages between job satisfaction and moonlighting intentions ( β  = 0.38, t  = 1.245, p  > 0.05) and hence H 7 is not supported. The mediation analysis summary is presented in Table 10 .

Moderation analysis

This study assessed the moderating effect of human resource practices (HRPs) on the relationship between job satisfaction and moonlighting intentions. The results reveal a statistically significant positive moderating impact of human resource practices on the relationship between job satisfaction and moonlighting intentions ( β  = 0.306, t  = 7.128, p  < 0.01), supporting H 8 : Human resource practices moderate moonlighting intentions through the job satisfaction of employees working at IT-enabled companies (Table 11 ).

A simple slope analysis (Fig. 8 ) indicates that the steep line for low human resource practices indicates that if human resource practices are not employee friendly, the impact of job satisfaction on moonlighting intentions is much weaker than that for high-level and employee-friendly human resource practices. At a high level of employee-friendly human resource practices, the positive relationship between job satisfaction and moonlighting intentions is strengthened.

figure 8

Moderation-Slope Analysis.

Discussion and conclusions

The associations among job attitudes, turnover intentions, and their effects on moonlighting intentions were studied decades ago by March and Simon ( 1958 ). Several efforts have been made to study the influence of employee moonlighting intentions in the context of job satisfaction on teaching staff. Additional income, job dissatisfaction, diversified skills, and autonomy are factors that impact moonlighting intentions (Ara and Akbar, 2016 ). Holding multiple jobs and motives for doing so were studied by Dickey et al. ( 2011 ), who indicated that financial reasons are one of the reasons for moonlighting intentions. In a study on the effects of moonlighting on job satisfaction among academic staff and medical doctors in Nigeria, multivariate analysis of variance revealed that moonlighting intentions are statistically significant and positive; however, academic staff moonlight more than doctors (Adelugba et al. 2020 ). In a study on moonlighting intentions and their association with job satisfaction and organizational commitment among university teachers in the Punjab province of Pakistan, the authors reported a statistically significant association between moonlighting and job satisfaction and organizational commitment. Furthermore, a study revealed statistically significant group differences between moonlighting teachers and nonmoonlighting teachers (Ara and Akbar, 2016 ). Seema and Sachdeva ( 2020 ) reported that financial or economic strain has recently been the motivation of MSOTs. The authors presented the SEM results, which demonstrated the critical roles of organizational commitment and entrepreneurial motivation as the main factors motivating employees to moonlight. Using SEM analysis, Seema et al. ( 2021 ) reported the relationship between employee job satisfaction and moonlighting intentions, with organizational commitment serving as a mediator. While job satisfaction has a very strong positive impact on organizational commitment, job satisfaction also has a mediating effect on the relationship between job satisfaction and intentions to moonlight. Furthermore, there is a noteworthy inverse relationship between Organizational Commitment and Moonlighting Intentions. Our results are in line with those of previous studies in which organizational commitment partially mediated employee-moonlighting intentions through job satisfaction, and the direct and indirect effects were statistically significant. However, the results reveal that economic intentions have no mediating role in the moonlighting intentions of employees through job satisfaction.

(Sai Manogna and Swamy, 2023 ) reported a negative correlation between aspects of organizational commitment and intentions to moonlight and that higher education institutions can lessen faculty members’ intentions to moonlight by creating an environment that encourages organizational commitment. Remote work and the pandemic also enhanced the moonlighting intentions of employees, which affected human resource practices in organizations. The flexible working hours and remote working options offered by IT and IT-enabled companies are among the reasons for moonlighting. The economic well-being, professional advancement, and multitasking capabilities of employees influence their moonlighting intentions, in turn affecting their human resource practices and policies. Our results are in line with these studies, as evidenced by the outcomes reported. Our study reports that job satisfaction, human resource practices, and organizational commitment are statistically significant and influence the moonlighting intentions of IT-enabled employees. The mediating effect of organizational commitment through job satisfaction and moonlighting intentions is also statistically significant and influences the moonlighting intentions of IT-enabled industry employees. Our study revealed that the economic intentions of employees in an IT-enabled industry are a weak predictor of their moonlighting intentions. Furthermore, the COVID-19 pandemic has enhanced the moonlighting intentions of employees, particularly those working in information technology and information technology-enabled companies, where employees are asked to work remotely with flexible working hours.

Policy perspectives

The authors suggest that before allowing or stopping moonlighting practices, the impacts of such practices need to be examined, as moonlighting has both advantages and disadvantages. Moonlighting by an employee can contribute to identifying an employee’s multitasking capabilities and predicting employee well-being by helping him or her study whether a second job has an additive or interactive effect on employee well-being. Other useful aspects include identifying employee retention and turnover, employee job satisfaction, and, in some cases, employee conditions. It is important to consider the effects of factors such as organizational branding, which increase the risk of training and development. The cost factors of not employing gig workers for routine tasks, revenue losses for not allowing employees to moonlight, and employee turnover intentions need to be studied carefully. There is also a need to develop comprehensive human resource practices and policies to address moonlighting issues globally.

Indian scenario

In India, there is no legal definition or regulation of moonlighting. Legal action may be taken since this action is viewed as a breach of trust. Workers in India are required to work a minimum of eight hours a day under labor laws. Any further work is considered over time and needs to be compensated accordingly. There is no explicit law in India that addresses moonlighting. However, it might be subject to legal repercussions under many statutes, including the Employment Contract Act, the Shops and Establishments Act, and the Industrial Disputes Act. The Industrial Employment (Standing Orders) Act of 1946 permits dual employment. However, under the Factories Act of 1948, dual employment was prohibited. According to the Factories Act of 1948, an employer cannot require or let an adult employee work in the factory on days when they have already worked in another workplace. The prohibition provided by the Occupational Safety, Health, and Working Conditions (OSH) Code is restricted to simultaneous employment in a mine or factory and is largely equivalent to that outlined in the Factories Act. However, now the scenario is different.

In India, during the pandemic, many workers who were working from home took on-site jobs without receiving permission from their parent company. Some IT companies, such as InfoSys, Wipro, and TCS, are considering framing policies on moonlighting. The authors suggest that the policy should consider moonlighting by their respective employees, who do not directly conflict with their main employment and hinder the respective employees’ companies. The policy could include employees preferring moonlighting, possibly during weekends. However, the work must adhere to the terms of the service agreement and not be done by competitors. Before moonlighting, employees should provide written consent. One must be forthright about it. It is acceptable to obtain written authorization; however, some procedures must be followed.

Developed countries

The employee doing a side job should consider the tax perspectives of the host countries because the second job alters an employee’s tax status. The companies permitting moonlighting should expressly note to the payroll department the first employer and the second employer. Employees are encouraged to voluntarily disclose and report their second income while moonlighting. Certain tools and techniques should be developed and deployed to detect and mitigate the risks associated with employees having more than one job, such as data leakage, behavioral issues, and the abuse of intellectual property. Policies should be framed to address issues such as conflicts of interest, impact on primary job performance, misuse of primary company resources, fatigue, and absenteeism.

The findings of this study have implications for organizations, as they suggest that increasing employees’ affective and normative commitments decreases their intentions to engage in moonlighting. Additionally, the study highlights the importance of understanding the antecedents of moonlighting intentions, as it can help organizations implement policies and practices that reduce the likelihood of employee moonlighting. The authors suggest encouraging employee moonlighting if it does not impede the routine work of the employee. The multitasking capabilities of employees can be identified through moonlighting, which is useful for the organization. The study also suggested that IT-enabled companies should provide more opportunities within the organization for career advancement and fair compensation to reduce the need for employees to engage in moonlighting.

Limitations

This study was carried out by deploying a structured questionnaire measuring five factors—human resource practices, job satisfaction, organizational commitment, employee economic intentions, and moonlighting intentions. The model maintained a good fit and supported 6 hypotheses. Although the results can be generalized to some extent, they must be interpreted carefully before any generalizations can be made. More similar types of studies are needed with the inclusion of serial mediations and moderation for a better comprehension of the moonlighting intentions of employees. The other limitations are as follows:

The study and related research will be limited to the IT companies in India.

Confined by time and resource conditions, one cannot conduct research with larger sample pools/datasets, so the sample results cannot be generalized; however, the results may provide insight for further studies in similar industries.

This study focused on examining the effect of job satisfaction on the moonlighting intentions of employees and the mediating effects of organizational commitment and employee economic intentions among information technology professionals. As a result, the findings may not necessarily apply to other industries that are relatively different, such as the manufacturing industry.

The study included a sample of 311 employees.

The study utilized questionnaires as survey instruments for data collection.carries the risk of personal bias and researcher constraints.

The reliability and consistency of the data depend largely on the information provided by the respondents.

Data availability

The datasets generated during the research and analyzed during the current study are available and can be downloaded by clicking on the following link. The data was available on the online data repositories Figshare at https://figshare.com/articles/dataset/Moonlighting_dataset_for_HSSCOMMS/25358803 .

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The authors thank all the IT-enabled sector employees in Hyderabad for responding to our survey.

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KP: study design, data analysis, and drafting of the manuscript; SK: overall manuscript editing, addressing the reviewers’ concerns, formulation of the study; TD: Data collection, cleaning, and data analysis; VKSP: Theoretical framework, questionnaire development, and publishing of the questionnaire; KP, SK, TP, and VKSP conceptualized the contributions. All the authors contributed to the article and approved the submitted version.

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Ethical review and approval were not needed for the study, as the authors followed the following steps: Informed consent from participants. The purpose of the survey is clearly explained. Participants were fully informed about the purpose of the survey. They had a clear idea of how their data would be used and the extent of their involvement. This allowed the participants to agree and voluntarily participate and provide honest feedback. The participants fully ensured the confidentiality and anonymity of the responses. The questions were framed carefully to avoid causing distress or discomfort to the participants.

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Prasad, K.D.V., Kalavakolanu, S., De, T. et al. The effect of job satisfaction and moonlighting intentions with mediating and moderating effects of commitment and HR practices an empirical study. Humanit Soc Sci Commun 11 , 483 (2024). https://doi.org/10.1057/s41599-024-02974-x

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  21. PDF Conceptualizing the Pathways of Literature Review in Research

    selected by the research. Therefore, LR forms strong theoretical foundations and empirical evidences for the research work. It also justifies that the researcher has obtained required knowledge base in the study area. This also convinces the readers that the researcher has developed fundamentals of the study.

  22. A Review of the Empirical Literature on Meaningful Work: Progress and

    This scale was reviewed by Both-Nwabuwe, Dijkstra, and Beersma (2017) and found to have strong psychometric properties. Research within this tradition has drawn on theories of pro-social behavior in explaining the greater good motivations associated with meaningful work (e.g., Grant, 2007).

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    Nevertheless, much like the extant theoretical work, the extant empirical work remains at a nascent level of development, due, in part, to the following weaknesses. First, although in some research in this area color properties are controlled for at the spectral level, in most research it (still) is not.

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    4.3. Method(ology) used and theoretical framing of research. For any type of research, the application of an (overall) guiding or underlying research methodology and the grounding of the research in relevant theory and/or related work are fundamental (see also Section 2) and therefore need to be reported.

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    In an empirical study, the authors examined the connection between job satisfaction and IT professionals' intentions to moonlight within the theoretical framework of the "Attitudes and ...

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    2. Theory. This study integrates the Work Adjustment Theory (WAT) (Dawis & Lofquist, Citation 1984) and the Dynamic Capability Theory (DCT) (Teece, Citation 2007) to provide insights into JP in changing environments.WAT posits that work is an interaction between individuals and their environment, which demands ongoing adaptation to ensure mutual satisfaction, also termed 'work adjustment'.