Ways to Explore and Address Research Gaps in Digital Marketing: A PhD Researchers’ Guide

The value of identifying research gaps in the field of digital marketing cannot be overstated. As technology continues to advance and consumer behavior evolves, there is an ever-growing need for up-to-date and relevant research to guide marketing strategies. In this guide, we will delve into the significance of identifying research gaps and insights on how to uncover them through a comprehensive literature review. By following these steps, researchers can contribute to the advancement of the field and drive meaningful progress in digital marketing practices.

The current research gaps and identifying and prioritizing the gaps

  1. Privacy and Ethical Concerns: With the increasing use of data-driven marketing strategies, there is a need to address privacy and ethical concerns related to consumer data collection, usage, and targeting. Researchers can explore topics such as consumer perceptions of privacy, data protection regulations, and ethical frameworks for digital marketing practices.
  2. Personalization and User Experience: While personalization is widely adopted in digital marketing, there is a need for deeper insights into its effectiveness and impact on user experience. PhD researchers can investigate topics like personalized marketing strategies, user acceptance of personalization, and the balance between personalization and privacy concerns.

To identify and prioritize research gaps, PhD researchers can follow these steps:

  1. Literature Review: Conduct a comprehensive review of existing literature in digital marketing to identify the current state of knowledge and potential research gaps. Analyze recent studies, industry reports, and academic journals to gain insights into the latest trends and emerging challenges.
  2. Industry Engagement: Engage with industry professionals, practitioners, and experts to understand their perspectives on the existing challenges and research needs in digital marketing. Attend conferences, workshops, and seminars to connect with industry leaders and gain practical insights.

Leveraging emerging technologies like artificial intelligence and machine learning to bridge research gaps 

  1. Data Analysis and Predictive Modeling: AI and ML algorithms can be used to analyze large datasets and extract valuable insights for digital marketing research. Researchers can develop predictive models to understand consumer behavior, forecast trends, and optimize marketing campaigns based on data-driven decisions.
  2. Personalization and Recommendation Systems: AI and ML techniques enable the development of personalized marketing strategies and recommendation systems. Researchers can explore how these technologies can improve the accuracy and effectiveness of personalized marketing efforts, enhancing customer engagement and satisfaction.
  3. Sentiment Analysis and Social Media Listening: AI and ML algorithms can be applied to analyze user-generated content on social media platforms, allowing researchers to gain insights into consumer sentiments, opinions, and preferences. This can help understand brand perception, identify emerging trends, and measure the impact of marketing campaigns.

Key factors contributing to the emergence of research gaps in digital marketing and identifying and prioritizing these gaps for investigation

  1. Evolving consumer behavior: Digital marketing is heavily influenced by consumer behavior, which is constantly evolving. Changes in consumer preferences, attitudes, and purchasing patterns can create gaps in knowledge that PhD researchers can address. Analyzing consumer trends, social media usage, or the adoption of new digital platforms can help identify research gaps.

To effectively identify and prioritize research gaps, PhD researchers can adopt the following approaches:

  1. Gap analysis: Compare and contrast existing studies to identify gaps or inconsistencies in findings, methodologies, or theoretical frameworks. Look for areas where further research is needed to build upon or challenge existing knowledge.

Influence of different research methodologies (qualitative, quantitative, mixed methods, etc.) in designing a PhD research and the strengths and limitations of each methodology

1. Qualitative Research:

Qualitative research methodologies, such as interviews, focus groups, or case studies, are valuable for exploring research gaps in digital marketing. They allow researchers to delve into the subjective experiences, perceptions, and motivations of individuals or groups. Qualitative research can uncover rich insights, generate new theories, and provide an in-depth understanding of complex phenomena related to digital marketing.

Strengths:

– Provides rich and detailed data about individuals’ experiences, motivations, and behaviors.

– Allows for in-depth exploration of research gaps, providing context and meaning.

Limitations:

– Limited generalizability of findings due to the small sample sizes often used in qualitative research.

– Subjectivity and potential researcher bias can impact data collection and analysis.

2. Quantitative Research:

Quantitative research methodologies, such as surveys, experiments, or statistical analysis, focus on numerical data and objective measurements. They provide a structured approach to exploring and addressing research gaps in digital marketing. Quantitative research allows for the identification of patterns, relationships, and statistical significance, enabling researchers to make generalizations about a larger population.

Strengths:

– Allows for large-scale data collection and analysis, providing robust statistical evidence.

– Enables measurement and comparison of variables, relationships, and trends.

Limitations:

– May overlook contextual nuances and subjective experiences related to research gaps.

– Limited scope for capturing complex and nuanced phenomena in digital marketing.

3. Mixed Methods Research:

Mixed methods research combines qualitative and quantitative approaches, offering a comprehensive and balanced perspective. By integrating both methodologies, researchers can address research gaps in digital marketing more comprehensively, capturing both the depth of qualitative insights and the breadth of quantitative analysis.

Strengths:

– Allows for a more holistic understanding of research gaps, combining the strengths of qualitative and quantitative approaches.

– Provides opportunities for triangulation, where data from different sources and methods reinforce each other.

Limitations:

– Requires expertise in both qualitative and quantitative research methods.

– Time-consuming and resource-intensive due to the need for data collection and analysis using both approaches.

Strategies employed by the PhD researchers

  1. Data Analytics and Big Data: Leveraging data analytics and big data techniques allows researchers to analyze large datasets and extract meaningful patterns and trends. By uncovering actionable insights from vast amounts of digital marketing data, researchers can develop more targeted and personalized marketing approaches.
  2. Collaboration and interdisciplinary approach: PhD researchers often collaborate with other researchers, or industry experts to gain diverse perspectives and expertise. They may also adopt an interdisciplinary approach by integrating knowledge from different fields such as marketing, psychology, information system or computer science to provide a holistic understanding of digital marketing phenomena.
  3. Publication and dissemination: Lastly, PhD researchers aim to disseminate their findings through academic publications in peer-reviewed journals, conference presentations, or book chapters. By sharing their research with the academic community and industry professionals, they contribute to the collective knowlege in digital marketing and inspire further research.

Impact of cultural, social, and economic factors on the identification and exploration of research gaps 

  1. Literature Review and Cross-Cultural Studies: Conducting a comprehensive literature review is crucial for understanding existing research within different cultural, social, and economic contexts. Researchers should explore studies conducted in diverse regions and markets to identify how digital marketing practices vary across cultures and economies. Cross-cultural studies enable researchers to compare findings, identify patterns, and uncover unique research gaps specific to different contexts.
  2. Data Collection and Analysis: When collecting data, researchers should consider cultural, social, and economic nuances. This includes tailoring survey questions, interview protocols, and experimental designs to account for context-specific factors. Researchers can also leverage secondary data sources, such as government reports, market research studies, and social media analytics, to gain insights into the specific characteristics of the target region or market.
  3. Collaboration with Local Experts: Collaborating with local experts, practitioners, or scholars who possess in-depth knowledge of the specific region or market being studied is invaluable. These experts can provide insights into cultural norms, societal values, and economic dynamics that may impact digital marketing practices. Their guidance can help researchers refine their research questions, methodologies, and interpretations.

Ethical considerations and challenges 

  1. Informed Consent: Respecting the rights and privacy of participants is crucial. Obtain informed consent from individuals participating in your research, clearly explaining the purpose, procedures, risks, and benefits involved. Ensure that participants understand their rights and have the option to withdraw at any time.
  2. Data Privacy and Security: Digital marketing often involves collecting and analyzing vast amounts of personal data. Researchers must handle this data responsibly, ensuring appropriate security measures are in place to protect participants’ privacy. Adhere to legal and ethical guidelines, such as data anonymization and storage encryption.
  3. Transparency and Honesty: Maintain transparency throughout your research process. Clearly communicate your objectives, methodologies, and potential biases. Provide accurate and unbiased information in reporting research outcomes to avoid misleading or deceptive practices.

How to find out the Research gap in Literature review

  1. Identify the existing knowledge: Start by thoroughly reviewing the existing literature in your field of study. Understand the key concepts, theories, and findings that have been explored by previous researchers. This will provide you with a solid foundation and help you identify the existing knowledge base.
  2. Analyze the limitations: As you delve deeper into the literature, pay close attention to the limitations or gaps that are mentioned by researchers. These limitations could be related to methodology, sample size, geographical scope, or other factors. By understanding these limitations, you can identify potential areas for further research.
  3. Look for inconsistencies or conflicting findings: Literature often contains inconsistencies or conflicting findings. These discrepancies can point to research gaps that need to be addressed. Analyze the differing perspectives, methodologies, or contextual factors that may contribute to these inconsistencies. This analysis can provide valuable insights into areas where further research is needed.

Hence, the value of identifying research gaps in digital marketing is paramount for the growth and development of the field. PhD researchers have a unique opportunity to contribute to the existing body of knowledge by exploring uncharted territories and filling the voids in current research. Through a meticulous literature review, researchers can pinpoint areas that have not been adequately addressed or require further investigation. By doing so, they can pave the way for innovative strategies and approaches that align with the evolving needs of consumers and the dynamic digital landscape. Ultimately, understanding how to find out the research gap in a literature review enables researchers to make significant contributions to the field of digital marketing and enhance our understanding of effective marketing practices in the digital realm.

Solving the statistical juggle: Anova, Ancova,Manova, Mancova

It is challenging to keep the difference between the four statistical techniques aligned. These four similar but still different techniques are ANOVA, ANCOVA, MANOVA, MANCOVA. Before we start to appreciate the differences between these four techniques, it is helpful to review the similarities between them.

Almost every researcher would feel trapped when it comes This statistical soup of the four techniques. Most novice researchers feel confused and trapped when it comes to making logical comparisons between ANOVA, ANCOVA, MANOVA, MANCOVA. Let us understand the analogy between them.

ANOVA

 The expansion of the term ANOVA is Analysis of Variance. It is the fundamental element of the four techniques. In ANOVA there is only one dependent variable. In statistics, when there is a comparison between two or more than two means at the same time, the technique used for comparison is ANOVA. The values and results given by ANOVA are used to find if there is any relationship between the different variables. If we have to find out if the means of two or more groups are equal, then ANOVA comes to our rescue through a test known as T-test. ANOVA as a statistical technique is extremely helpful in avoiding TYPE 1 Error, especially when one has to carry out multiple, two sample tests. Another very useful feature of ANOVA is that it can compare the scale or interval variables, also known as the continuous variables. There are three distinct models in ANOVA:

  • Fixed Effect Model: This is subjected to one or more than one treatment to identify whether the value of the response is changing.
  • Random Effect Model: When the treatment that is applied to the subject is not fixed the random effect model is used.
  • Mixed Effect Model: This Model has got dual effects, the fixed as well as the random effects and is applied to experimental factors.

Primarily, we see two types of ANOVA being used, One way ANOVA and two-way ANOVA. In one-way ANOVA the levels are compared. We can also call them as groups. But they are of a single factor and are based on a single continuous response variable.

In the case of two-way ANOVA, it compares the levels of two or more factors for the mean differences on a single continuous response variable. In application, the use of one-way ANOVA is more common in practice. So, whenever the term ANOVA is used without specifications, by default the interpretation is one way ANOVA only.

BASIS FOR COMPARISONONE WAY ANOVATWO WAY ANOVA
MeaningOne way ANOVA is a hypothesis test, used to
test the equality of
three or more
population means
simultaneously using
variance.
Two way ANOVA is a
statistical technique
where in, the
interaction between
factors, influencing
variables can be
studied.
Independent VariableOneTwo
ComparesThree or more levels of one factor.Effect of multiple
levels of two factors.
Number of
Observations
Need not to be the
same in each group.
Need to be equal in
each group
Design of experimentsNeed to satisfy only
two principles
All three principles
needs to be satisfied

Let us understand better by looking at examples of one way and two-way ANOVA

Example of one-way ANOVA

A class of 90 students is randomly split into three different groups. All the three groups are assigned for a test after fifteen days. The test will be for the same syllabus and a common question paper. However, the studying technique given to all the three groups is different. The purpose of the study will be to determine if the technique of study has any impact on the study scores. One-way ANOVA will work here to identify if there is a statistically significant difference between the mean scores of these three student groups. This will help us know if the technique of study has any impact on the scores of the test.

Example of Two Way ANOVA: This is used to determine how two factors impact a response variable, and to find out whether there is an interaction between the two factors on the response variable.

From your research you want to know if your level of fitness regime (no regime, light, moderate or heavy regime) and gender (Male/female) has some kind of impact on weight loss. Here there are two factors involved in the study. These two factors are fitness regime and gender and the response variable here is weight loss. Here two-way ANOVA is conducted to identify the impact of type of fitness regime as well as gender on the weight loss and to know if there exists an interaction re relationship between the independent and dependent variables which are type of exercise, gender, and weight loss. The below flowchart explains this better.

ANCOVA

A layman or someone who has no understanding of statistical techniques would express his understanding of the difference between ANOVA and ANCOVA as the letter “c”. But if they are two different words with different spellings, even if with the slightest of variation then it is with a purpose.  Both these techniques are different from each other. ANCOVA is different from ANOVA for it has a single continuous response variable. ANCOVA can make an explicit distinction and comparison between the response variable with both continuous independent variable and factor. The continuous independent variable in ANCOVA is called a covariate. ANCOVA is not limited to comparative analysis but is also seen in getting used with a single response variable, continuous independent variable with no factors attached.  This kind of analysis has another nomenclature, which is regression.

Example of ANCOVA:

Taking the same example forward we used a one-way ANOVA, the class of 90 students being split into three groups of 30 students in each group and each group used a different study technique for the same exam to be taken after a period of one month.  But if we want to further account for the grade the student already has in the class, the current grade is used as a covariate. ANCOVA is applied to determine if there is a statistically significant difference between the mean scores of the three groups.

This test not only allows us to know if the studying technique has an impact on the scores of the test but it also tests the same after the influence of the covariate has been removed.

 Thus, if we find that there is a statistically significant difference in exam scores between the three studying techniques, we can be sure that this difference exists even after accounting for the student’s current performance or grades in the class, which means their present performance in the class is satisfactory or not.

Let us summarize the difference between ANOVA and ANCOVA from this tabular representation

ParticularsANOVAANCOVA
Stands forAnalysis of VarianceAnalysis of Covariance
MeaningIt is a statistical
method to test the
variance or differences between the means of
three or more groups
It evaluates the mean
of a dependent
variable based on a
categorical
independent variable while considering and controlling the effects of covariates
UsesCan blend linear and
nonlinear models
A linear model is used alone
InvolvesCategorical
independent variables
Categorical and metric independent variables
CovariateNeglects the influence of covariatesConsiders and controls the effect of covariates

MANOVA

In statistics, MANOVA contains multiple dependent variables. It helps to identify the difference between two or more than two dependent variables at the same time. It helps to surface out the interactions between dependent and independent variables. MANOVA is nothing but another type of ANOVA and it has two or more continuous variables. If the researcher wants to compare two or more continuous response variables with the help of a single response factor, then one-way MANOVA works fine. The need for two-way MANOVA arises when two or more continuous variables are there and they have to be compared with at least two factors. In ANOVA a T test is used when calculating or working with a single response variable or binary factor. But a T test does not have the capability to calculate the distinctions for more than one response variable at the same time. That is the reason it is not being used in MANOVA.

Example of One-Way MANOVA:

Like explained above, MANOVA is used when one factor and two response variables. So let us understand this better with an example. We want to understand how the level of academic qualification of a person (high School, Undergraduate, Masters, Doctoral degree) impacts the annual income and the amount of student loan debt. Here there is a single factor which is the level of qualification of a student and two response variables which are the annual income and student loan or debt, so one-way MANOVA will be used here.

Two-Way MANOVA Example: Here we intend to find out  how level of education and gender has an impact on both annual income and amount of student loan debt. In this case, we have two factors (level of education and gender) and two response variables (annual income and student loan debt), so we need to conduct a two-way MANOVA.

MANCOVA

Like the slight variation in spelling that exists between ANOVA and ANCOVA, the similar difference exists between MANOVA and MANCOVA. “C” Here also the addition of C makes the application of two words distinct. “C” stands for covariance.  Both MANOVA and MANCOVA show two or more response variables but the main difference between them is the characteristics of the Independent Variables. MANCOVA compares two or more continuous response variables by levels of factor variables along with a covariate.

Assumptions

The Assumptions of MANCOVA are the same as the assumptions for MANOVA, with an inclusion of a few more for covariance. As one can imagine for a complex test in comparison to a relatively easier one, such as the Z test, these assumptions are lengthy and somewhat complex. This is one reason why these tests are nearly always performed using software, as most statistical software will test for these assumptions before running the test.

  • Covariates and dependent variables are Assumptions
  • The assumptions for MANCOVA are the same as the assumptions for MANOVA, with the addition of a couple more for covariance. As you would expect with a complex test (compared to a much simpler test like a z-test), these assumptions are lengthy and somewhat complex. This is one reason why these tests are nearly always performed using software, as most statistical software will test for these assumptions before running the test.
  • Covariates and dependent variables are continuous and ratio/ ordinal.
  • Covariance matrices should be equal (reduces Type I error).
  • Independent variables are categorical.
  • Independence of variables: the variables do not influence each other.
  • Random sampling: the data was collected using a random selection method.
  • Normality: the dependent variables follow a (multinomial) normal distribution for each group.
  • Absence of multicollinearity — the dependent variables shouldn’t be significantly correlated.
  • Homogeneity of variance between groups.
  • Covariance Matrices should be equal
  • The independent variables are categorical
  • The variables are independent of each other and do not influence each other
  • The sampling technique used is simple random sampling
  • There is normal distribution of the dependent variable for each of the groups
  •  There should not be a lot of correlation between the dependent variables: multicollinearity should not be there
  • There should be homogeneity between the groups

Example One-way MANCOVA

In MANCOVA, which is like MANOVA, the only difference being that we add a covariate. So, taking the same example we took for MANOVA, we intend to find out how a student’s level of education impacts both their annual income and amount of student loan debt. In addition to that we also want to include the annual income of the parents into consideration. So, here we have one factor (level of education), one covariate (annual income of the students’ parents) and two response variables (annual income of student and student loan debt), so we need to conduct a one-way MANCOVA.

 Example Two-Way MANCOVA

We want to know how a student’s level of education and their gender impacts both their annual income and amount of student loan debt. However, we want to account for the annual income of the students’ parents as well. In this case, we have two factors (level of education and gender), one covariate (annual income of the students’ parents) and two response variables (annual income of student and student loan debt), so we need to conduct a two-way MANCOVA.

Conclusion

Easy hack, how to distinguish one test from the other 

MANCOVA, MANOVA, ANOVA, ANCOVA: it can all get a little confusing to remember and distinguish one from the other. However, all the tests can be thought of as variants of the MANCOVA, if you register that the “M” in MANCOVA stands for Multiple and the “C” stands for Covariates. Tests can be thought of as a MACOVA…

  • ANOVA: … without multiple dependent variables and covariates (hence the missing M and C).
  • ANCOVA: …without multiple dependent variables (hence the missing M).
  • MANOVA: …without covariates (hence the missing C).