What Is 4C Marketing? How to Build a Quantitative Research Model

What is the 4C Marketing model

You can think of 4C Marketing as a way to view marketing activities from the customer's perspective, rather than starting with the product and the company's sales plan. The four commonly used components are Customer Solution, Cost, Convenience, and Communication. In Vietnamese, they can be expressed respectively as the solution for the customer, the cost the customer must bear, convenience, and communication with the customer.

In a quantitative study, 4C should not appear only as four slogans. You need to turn each component into a latent variable and then measure it with several observed variables in the questionnaire. For example, if you are studying purchase decisions on an e-commerce platform, Customer Solution may reflect how well the product addresses the customer's needs, while Convenience may reflect whether searching, ordering, and receiving the product are convenient.

This approach differs from the AIDA model, because AIDA describes stages from attention to action, while 4C focuses on the value customers perceive in marketing activities. You should also distinguish 4C from the 4P Marketing Mix and 7P Marketing Mix. These three frameworks may be related, but you should not combine their component names in the same model without explaining the theoretical basis.

Because DoThesis's citation allowlist does not contain a separate original source for 4C, this article does not assign a year or author to the model's formation. When you include 4C in your thesis, check the original sources required by your lecturer, identify the definition that fits your field, and cite the source you actually read.

Components of the model

The four components below are a conceptual framework to help you start designing the model. They are not a scale that has been validated for every context. You still need to adjust the wording to match the product, customer group, and research area.

This table helps you avoid the common mistake of treating Cost as exactly the same as Price. In an actual experience, customers also consider delivery fees, waiting time, the effort required to search, and post-purchase risk. Likewise, Communication is broader than the company's advertising, because it also includes responses and dialogue.

You can also review posts in the Research model topic to compare how a marketing concept is connected to a dependent variable. When building the model, write an operational definition for each component before drafting the observed variables.

The original model and common extensions

A basic 4C model places the four components before an outcome variable, such as purchase decision, purchase intention, satisfaction, or loyalty. The conceptual form can be written as follows:

Customer Solution, Cost, Convenience, Communication → Purchase intention

This is a research framework, not a conclusion that all four factors definitely have an effect. The direction and size of the effects must be tested with your data. If the study examines customer perceptions at one point in time, use terms such as “effect”, “relationship”, or “prediction” in line with the research design, and avoid overstating the result as a certain causal relationship.

Common extensions include adding a mediator or moderator. For example, you could test whether 4C affects satisfaction and whether satisfaction is related to loyalty. Another option is to examine whether trust moderates the relationship between Communication and purchase intention. In that case, you must clearly distinguish a mediator from a moderator. What is trust commitment may help you see how a related concept is interpreted, but you should not automatically add “trust commitment” to a 4C model if your study has no supporting argument.

If you are studying technology, 4C can be placed alongside TAM, TPB, or UTAUT. Adding variables only because the model has more arrows will make the questionnaire longer and harder to explain. Every additional variable must answer three questions: what part of the problem does it explain, is there a suitable scale, and can you collect the data from the right respondents?

Common scales for each concept

There is no single set of observed variables that can be used unchanged for 4C in every industry. Look for scales in studies with a similar context, translate and adjust the meaning, and then run a pilot survey. For service quality, SERVQUAL is a separate reference developed by Parasuraman et al. (1988), but you should not call the entire SERVQUAL instrument a 4C scale.

The table below is a draft to show you how the coding could work. These are suggested wordings, not confirmed translations, and their validity has not been established in your data.

You can use a five-point or seven-point Likert scale, but the instructions must be consistent throughout the questionnaire. Likert introduced the technique for measuring attitudes in (Likert, 1932). If you have reverse-coded items, mark them immediately in the data file and check the recoding before running Cronbach's Alpha. Using observed variables instead of calling everything a “question” will make the methods chapter more precise.

Threshold checklist for testing

The thresholds below apply only to the relevant measurement-model or structural-model assessment step. Each row includes a source so that you do not copy a number without its context.

Do not use this table to force your data to meet the thresholds. If an item does not meet the threshold, review the item wording, its correlation with items measuring the same concept, the sample size, and the theoretical argument before deciding whether to remove it.

Applying the model to your study

Suppose you are studying: “The effect of marketing activities based on the 4C model on the purchase intention for cosmetics on an e-commerce platform”. The model has four independent variables: Customer Solution, Cost, Convenience, and Communication. The dependent variable is Purchase Intention.

The hypotheses could be written as follows:

• H1: Customer Solution has a positive effect on the purchase intention for cosmetics on an e-commerce platform.
• H2: Perceived reasonable Cost has a positive effect on the purchase intention for cosmetics on an e-commerce platform.
• H3: Convenience has a positive effect on the purchase intention for cosmetics on an e-commerce platform.
• H4: Communication has a positive effect on the purchase intention for cosmetics on an e-commerce platform.

You need to define “positive effect” before collecting data. The wording for Cost must be consistent. If a high Cost score means that customers consider the total cost reasonable, a positive coefficient fits H2. If a high Cost score instead means that the cost is expensive, H2 must be worded in the opposite direction or you must clearly recode the item.

Each variable should have at least several observed variables after you review the literature and adjust the context. The questionnaire should include screening questions, the necessary demographic information, and the measurement items for the concepts. Avoid adding variables just to make the model look complex. A four-variable model with clear definitions is usually easier to defend than a ten-variable model for which you cannot explain the scale sources.

Illustrative output for the results table

The table below is illustrative output, not the result of a real study. Its column structure follows the tables students commonly read after running SPSS and SmartPLS, so you know which measures to record in the results file.

If you use SPSS, start with Analyze > Scale > Reliability Analysis to view Cronbach's Alpha and Corrected Item-Total Correlation. For EFA, go to Analyze > Dimension Reduction > Factor, select KMO and Bartlett, choose an appropriate extraction method, and then check Rotated Component Matrix. For regression, go to Analyze > Regression > Linear, place Purchase Intention in the Dependent box, place the four 4C components in the Independent box, and save the necessary diagnostic measures.

If you use SmartPLS 4, prepare a .csv file with each column representing an observed variable and each row representing a respondent. Draw the measurement model first, connect the latent variables to their observed variables, and then connect the four 4C variables to Purchase Intention. Run PLS-SEM Algorithm to view outer loadings, CR, and AVE, then run Bootstrapping to view the path coefficient, t-value, and p-value. For PLS-SEM, the process of evaluating the measurement model before the structural model is presented in (Hair et al., 2022).

How to test the model with SPSS or SmartPLS

SPSS is suitable when you use composite scale scores, EFA, and linear regression. After checking reliability, you will create a mean score for each 4C component and then enter those scores into the regression model. This approach is easy to follow when your study uses an observed-score regression model.

SmartPLS is suitable when 4C is treated as latent variables measured by multiple indicators and you want to assess the measurement model and structural model in the same workflow. You need to distinguish the outer loading, AVE, CR, and HTMT of the measurement section from the path coefficient, R², f², and Q² of the structural section. CFI, TLI, and RMSEA are fit indices for CB-SEM and should not be included in a guide for assessing a PLS-SEM model.

The practical workflow should be: lock the definitions and variable codes, check missing data, review reverse-coded items, assess the scale, examine discriminant validity, check VIF, run the structural model, and only then write whether the hypotheses are supported. If you use bootstrapping, 5,000 bootstrap subsamples is an option stated in (Hair et al., 2022), but you still need to report the configuration used in the methods section.

Common mistakes when using this model

Calling 4C a model proven for every industry. 4C is only a framework for building variables. The effect of each component depends on the product, market, sample, and measurement approach.

Changing Price to Cost while still asking only one question about price. Cost needs to be defined in terms of total perceived cost. If the questionnaire asks only whether “the price is reasonable”, acknowledge the narrow measurement scope instead of giving the concept an overly broad name.

Mixing 4C, 4P, and 7P without a reasoning diagram. You can compare the 7P Marketing Mix, but every component included in the model needs its own definition, scale source, and hypothesis.

Removing items just to make the results table look better. Item removal must be based on the statistic, the measurement content, and the consistency of the concept. Save each version of the data file, record the reason for removing an item, and discuss the decision with your lecturer when it changes the scale structure.

Writing the conclusion from the p-value while ignoring the coefficient. A relationship with an acceptable p-value still requires you to examine the direction and size of the path coefficient or regression coefficient. You cannot infer that the variable with the largest coefficient is the sole cause of purchase intention.

Entering the Likert direction incorrectly or coding text data incorrectly. Before running the analysis, check the minimum, maximum, missing values, and value labels. A column containing “Strongly agree” instead of numerical codes can cause an error or produce results that do not mean what you think they mean.

Frequently asked questions

Is 4C Marketing the same as the 4P Marketing Mix?

The two models are related to marketing but start from different perspectives. The 4P framework usually organizes marketing activities around Product, Price, Place, and Promotion, while 4C focuses on the customer's problem, cost, convenience, and communication. When choosing a model, follow your research question and target respondents.

How many observed variables are enough for 4C Marketing?

There is no single number for every study. The number depends on the concept definition, the scale found in the literature, and the research context. You need enough observed variables to cover the content of the concept, then assess scale reliability and validity instead of lengthening the questionnaire with items that are almost identical.

Can I run 4C Marketing in SPSS?

Yes. You can check Cronbach's Alpha, run EFA, and conduct regression in SPSS if the model is handled using composite scale scores. If you want to evaluate latent variables and structural relationships in the same workflow, SmartPLS is a more suitable option. AMOS, JASP, and R can also be used for other designs, but you need to keep the software consistent with the method stated in your proposal.

If one 4C component does not meet the threshold, should I delete it immediately?

Do not delete it immediately. Check which observed variable is causing the problem, whether the wording was misunderstood, whether a reverse-coded item has not been recoded, and whether missing data are concentrated in a particular group. Then review the theoretical argument and discuss the decision with your lecturer. Deleting an entire component changes the research question, model, and related hypotheses.

Can I add satisfaction or trust to the 4C model?

You can, if you can explain the role of the variable. Satisfaction may be a dependent variable or a mediator, while trust may be an independent variable, mediator, or moderator depending on the model. Draw the diagram, write the hypotheses, and identify the scale before adding the variable to the questionnaire.

How should I write the 4C model in my thesis?

How to write this in your thesis: “Based on the literature review and the context of [industry/market], this study proposes examining the effects of [Customer Solution, Cost, Convenience, and Communication] on [dependent variable]. Hypotheses H1 to H4 are tested using [SPSS/SmartPLS] on data collected from [survey respondents] in [research area].” Replace the bracketed sections with your actual information and add the sources you have read.

Open your data file and immediately create a table with the variable name, definition, observed-variable code, scale source, and related hypothesis. Then choose the appropriate analysis tool in M4 Data analysis with SPSS and SmartPLS to test the 4C model on your own .sav or .csv file.