Unraveling the influence of product advertising on consumer buying interest: exploring product knowledge, product quality, and mediation effects

Abstract

Advertising has a crucial role in increasing visibility, building consumer image and trust in the brand or product offered. This research aims to investigate the effect of product advertising on purchasing interest mediated by product knowledge among Small and Medium Enterprises (UKM) owners in Palembang City. A survey method with a cross-sectional design was used to collect data from 201 randomly selected respondents using probability sampling techniques. A Likert scale-based questionnaire from 1 to 5 was used to collect data on product advertising variables, product knowledge, product quality and purchase interest. The data analysis technique uses Structural Equation Modeling (SEM) with Partial Least Square (PLS) to test the relationship between variables. The findings show that there is a significant influence of product advertising on buying interest and product knowledge. Apart from that, product quality also has a significant influence on buying interest. Mediation effect analysis shows product knowledge does not mediate product advertising with buying interest. This research provides important insights into the importance of product advertising and product knowledge in shaping consumer buying interest in the SME sector. The practical implications of these findings can be used to improve marketing and promotional strategies for SMEs in Palembang City.

Keywords:

• Product advertising
• product knowledge
• product quality
• buying interest
• SMEs

Reviewing Editor:

• Len Tiu Wright, De Montfort University Faculty of Business and Law, United Kingdom of Great Britain and Northern Ireland

Subjects:

• Economics
• Environmental Economics
• Human
• Management
• SMEs

1. Introduction

In the modern era of consumerism and marketing, the influence of advertising on consumer behavior has become very important. Product advertising functions as a powerful tool, not only shaping consumer perceptions but also their purchasing decisions (Handayani, 2022; Hermiyenti & Wardi, 2019). This research investigates the complex dynamics of how product advertising influences purchase intention, with a particular focus on the mediating role of product knowledge and product quality (Dapas et al., 2019). In an age where consumers are bombarded with an abundance of advertising through various channels, understanding the exact mechanisms through which advertising exerts its impact is crucial for businesses looking to optimize their marketing strategies (Andespa, 2017). The idea that advertising influences purchasing decisions is widely accepted, but the underlying process is more complex than it first appears.

Product knowledge represents the depth of information a consumer has about a particular product or brand. This not only influences the formation of perceptions but also becomes the basis for making appropriate decisions (Hatta et al., 2018). On the other hand, product quality is a fundamental determinant of overall customer satisfaction and loyalty (Demir et al., 2020). The hypothesis here is that advertising acts as a catalyst, facilitating the development of product knowledge and shaping perceptions of product quality, which in turn influences purchase intention (Harmen et al., 2022). By exploring these interconnections, this research provides valuable insights into both marketing and consumer behavior (Kalia et al., 2022). A deeper understanding of the link between advertising, product knowledge, product quality, and purchase intent can give businesses the tools they need to design more effective advertising campaigns, improve product development, and ultimately foster stronger customer relationships.

This research attempts to fill the empirical gap in our understanding of how product advertising influences purchase intentions, by using product knowledge and product quality as mediators. There is a lack of research that comprehensively explores the temporal aspects of the impact of product advertising. Understanding how the impact of advertising develops over time, taking into account both short-term and long-term perspectives, is essential for designing sustainable and effective advertising strategies (Putri & Arif, 2023). With the proliferation of digital platforms, there is a need to investigate the complex interactions between various advertising channels (Milne & Parboteeah, 2016). The effects of interactions and synergies between traditional media, social media and other new platforms are still under-explored, leaving a critical gap in our understanding of contemporary advertising dynamics (Imaningsih, 2018). Incorporating neuroscience methodologies to understand the cognitive and emotional responses triggered by product advertising is an area with great potential (Nekmahmud & Fekete-Farkas, 2020). A deeper exploration of the neural mechanisms underlying consumer reactions can provide valuable insights for crafting emotionally resonant and memorable advertising campaigns (Suhaily & Darmoyo, 2019). To date, most studies examining the influence of product advertising have focused on its direct impact on purchase intentions, without considering the possible mediating role in this relationship.

On the other hand, a number of studies exploring the role of product knowledge and product quality in consumer decisions tend to ignore the impact of advertising as a relevant factor (Lăzăroiu et al., 2020). The existing literature on product advertising often lacks a thorough exploration of the moderation mechanisms involved. In particular, there is a need to investigate how varying levels of consumer product knowledge can strengthen or reduce the impact of advertising on outcomes such as purchase intentions (Rose & Rodhiah, 2023). Previous research has not sufficiently distinguished the level of product knowledge. There is a gap in understanding how consumers with low, medium, and high levels of product knowledge respond differently to advertising stimuli, thereby influencing their decision-making processes and behavioral outcomes (Maltio & Wardi, 2019; Nekmahmud & Fekete-Farkas, 2020). There is a clear gap in understanding how varying consumer perceptions of product quality can enhance or weaken the impact of advertising efforts on outcomes such as purchase intentions. Previous research has not sufficiently differentiated between different levels of perceived product quality (Brata et al., 2017; Gunawan, 2022). There is a need to explore how consumers with varying perceptions of product quality respond differently to advertising stimuli, thereby influencing the decision-making process and subsequent behavioral outcomes. In the real world, product advertising plays a central role in shaping product knowledge and perceptions regarding quality (Tarnovskaya, 2017). Therefore, this empirical gap reflects an imbalance in our understanding of the complex relationship between product advertising, product knowledge, product quality, and purchase intentions. This research is important in several contexts. First, for businesses and advertisers, understanding the interactions between product advertising, product knowledge, product quality, and purchase intent provides deeper insights for optimizing marketing strategies (Tabaku & Cerri, 2016). By understanding how advertising can shape product knowledge and perceptions of quality, companies can design more effective and efficient campaigns. Second, this research has the potential to provide benefits to consumers. Consumers who know more about products and their quality will be able to make smarter and more satisfying purchasing decisions (Hatta et al., 2018). Therefore, this research prioritizes consumer protection and customer satisfaction.

The novelty of this research lies in the approach that combines elements that have previously been studied separately. This research is one of the first studies to systematically analyze how product advertising influences purchase intentions through product knowledge and perceived product quality as mediators. Additionally, this research seeks to generalize the findings across different types of products and advertising, allowing the results of this research to have broader application across a variety of industries. Thus, this research has the potential to provide more comprehensive insight and can be widely applied in the world of marketing. This research can also fill this empirical gap and create a deeper understanding of this complex process, it is hoped that this research will provide a valuable contribution to the marketing and consumer behavior literature. In addition, it is hoped that the findings of this research will provide a basis for more strategic and appropriate decision making for business people, thereby enabling them to make maximum use of advertising potential to encourage consumer interest and increase their profits. Therefore, this research aims to analyze the effect of product advertising on purchase interest which is mediated by product knowledge with the following research questions.

RQ 1. Does product advertising influence consumer buying interest?

RQ 2. Does product advertising influence consumer product knowledge?

RQ 3. Does product knowledge directly influence consumer buying interest?

RQ 4. Does product quality influence consumer buying interest?

RQ 5. Does product knowledge mediate product advertising with consumer buying interest?

2. Literature review

2.1. Stimuli-organism-response (SOR) theory

The SOR (Stimuli Organism Response) theory was proposed by Mehrabian and Russell (1974) The development of this theory began with environmental psychology, but is now developing not only in the field of psychology. The SOR theoretical model includes three stages, namely stimulus (S), organism (O), and response (R). Definition of stimulus (S) according to Eroglu et al. (2001) is a cause that is influenced by a person’s internal state or can be said to be an influence that can stimulate a person. According to Bagozzi (1986), consumer behavior described in SOR theory states that the stimulus produced comes from outside a person. Organism (O) refers to an individual’s intermediary affective and cognitive states that mediate the influence of a stimulus on an individual’s response (Wu & Li, 2018). Furthermore, according to the SOR theoretical model, it also explains that the response (R) is a person’s final result or final decision, which is described by approach or avoidance behavior (Donovan et al., 1994; Sherman et al., 1997). Thus, the SOR theoretical model builds a method of human behavior by analyzing human cognitive and affective states that are influenced by environmental stimuli (Jeris et al., 2022). According to Barbu et al. (2021) SOR theory is able to explain all behavior caused by various marketing stimuli and psychological factors.

2.2. Latent state trait theory

Latent state trait theory (LST) is a psychological theory developed by Steyer et al.,(2015) stated that human behavior depends on environmental factors or conditions (circumstances), individual factors (traits) and the interaction between these two determining factors. A trait reflected in personal characteristics is relatively stable in various situations and can give rise to differences between two individuals (Hertzog & Nesselroade, 1987). In contrast, mental states refer to personal attributes that tend to vary depending on environmental conditions (Hertzog & Nesselroade, 1987) and states can differentiate one situation from another in a person’s life (Wells et al., 2009). Pornpitakpan & Han, 2013) suggests that online and offline shopping preference factors have a positive influence on shopping behavior. Chen et al. (2016) revealed that the appropriate theory to describe purchasing behavior in the context of social commerce and e-commerce is the Latent State Trait Theory (LST).

2.3. Product advertising

Product advertising is a dynamic field that plays an important role in shaping consumer behavior and brand success (Nekmahmud & Fekete-Farkas, 2020). Visual elements, emotional appeals, and the use of celebrities have been explored as key influences in advertising effectiveness (Suhaily & Darmoyo, 2019). The emergence of social media has introduced a new dimension, which emphasizes consumer engagement through content and interactive platforms (Nekmahmud & Fekete-Farkas, 2020). Cross-cultural considerations highlight the need for culturally tailored campaigns, as cultural norms have a significant impact on how advertising is received. Trust and transparency remain important, as deceptive advertising will erode consumer trust. The frequency and repetition of advertising has been examined, emphasizing the importance of striking a balance (Manko, 2022). Behavioral targeting and online advertising raise concerns about consumer privacy and attitudes. ROI metrics and integrated marketing communications have emerged as valuable tools for assessing campaign effectiveness and ensuring consistency in messaging (Alexandrescu & Milandru, 2018). Understanding the ins and outs of product advertising is critical for businesses looking to connect with their target audience and drive consumer purchasing decisions. Product advertising can influence consumer perceptions of product quality (Irmawanti & Puspita, 2021). Advertisements that highlight product features and benefits can form certain expectations in consumers’ minds (Wirakurnia et al., 2022). If the product meets or exceeds these expectations when used, then consumers will consider the product to be of high quality (Landry et al., 2014). Highlighting a product’s advantages over competing products in advertising can influence perceptions of quality (Milne & Parboteeah, 2016). Based on this explanation, the hypothesis proposed in this research is as follows:

H1: There is a significant influence of product advertising on purchase interest.

H2: There is a significant influence of product advertising on product knowledge.

2.4. Product knowledge

Product knowledge is a basic concept in consumer behavior research, it is important to understand how consumers evaluate and choose products (Rachmawati et al., 2022). Research has shown that consumers with higher levels of product knowledge tend to make more informed purchasing decisions, showing a preference for products that align with their specific needs and preferences (Siyabi & Shekaili, 2021). The acquisition of product knowledge can be influenced by various sources, including advertising, word of mouth, and previous experience, each of which impacts the depth and accuracy of consumer understanding (Rose & Rodhiah, 2023). Additionally, research shows that there is a strong correlation between product knowledge and brand loyalty, because consumers tend to stick with brands they trust and are familiar with (Rahman et al., 2015). Understanding the role of product knowledge is critical for businesses looking to adapt their marketing strategies and increase consumer satisfaction and loyalty.

The influence of product knowledge on purchasing interest can be seen through several aspects. Knowledge about a product can influence consumers’ decisions to buy (Adriani, 2020). Consumers who have a good understanding of a product tend to have a higher level of trust in that product (Agustin & Hakim, 2022). When someone understands the features and benefits of a product, they are more likely to believe the claims made by the brand (Ekaputri & Widowati, 2022). By understanding the product well, consumers can compare and evaluate alternatives more quickly and efficiently, thereby speeding up the purchasing decision making process (Varadarajan, 2020). Consumers with high product knowledge are often a source of information for friends, family, and coworkers. Their recommendations can increase buying interest on their social networks. Based on this description, the hypothesis proposed is as follows:

H3: There is a significant influence of Product Knowledge on Purchase Intention.

H5: There is a mediating effect of Product Knowledge on the relationship between Product Advertising and Purchase Interest.

2.5. Product quality

Product quality is a central theme in consumer behavior and marketing research, serving as an important determinant of consumer satisfaction and loyalty (Am et al., 2022). A number of studies have underscored the impact of product quality on purchasing decisions, highlighting that consumers are willing to pay more for products that are perceived to be of higher quality (Fadli et al., 2022). Research shows that perceived quality can be influenced by various factors, including brand reputation, product attributes, and customer reviews (Nasrullah et al., 2020). In addition, research has revealed the importance of consistency in providing quality, because poor product quality can cause brand erosion and reduced consumer trust (Pratiwi et al., 2019). Understanding the nuances of product quality is critical for businesses looking to build a strong brand reputation, grow customer loyalty, and remain competitive in the marketplace.

Product quality plays an important role in influencing consumer buying interest. Consumers often associate product quality with lower risk. High-quality products are considered more reliable and less likely to disappoint. Although high-quality products may be more expensive, consumers often view them as better value because of their long-term benefits, such as durability or better performance. Consumers tend to recommend high-quality products to friends and family, which can increase purchasing interest in their social networks. Product quality can be the main differentiator from competing products, making consumers more interested in buying.

Purchase intention, an important concept in consumer behavior research, reflects an individual’s inclination towards a product or service (Rachman et al., 2015). This dimension of consumer behavior has been the subject of extensive research, highlighting various factors that influence purchase intentions (Hatta et al., 2018). Research in this domain highlights the role of various elements, including product attributes, advertising, price, and social influence, in shaping consumer interest in making purchases (Zaridis & Logotheti, 2020). Furthermore, research shows that consumer involvement, trust, and previous experience with a brand or product significantly influence purchase intention. Additionally, with the increasing importance of e-commerce, online reviews and recommendations have emerged as key drivers of purchase intention (Andespa, 2017). A comprehensive understanding of purchasing interest is very important for businesses, allowing them to adjust their marketing strategies and increase their attractiveness in the eyes of consumers, thereby ultimately driving sales and achieving success in the market. Based on this description, the proposed research hypothesis is as follows:

H4: There is a significant influence of product quality on purchase interest.

2.7. Conceptual collaboration

The conceptual framework is the foundation of our research, guiding our exploration of ‘The Impact of Product Advertising on Purchase Intentions Mediated by Product Knowledge.’ This framework defines the relationships between these key variables, forming our hypotheses and research methodology. This allows us to reveal how product advertising influences purchasing interest through the mediation of product knowledge and product quality. In this section, we will present and justify the components of this framework, as well as provide a roadmap for future analysis and findings (Figure 1).

Figure 1. Conceptual Model. Source: Author’s Synthesis (2023).

3. Method

3.1. Research methods

The research method used in this study is a survey with a cross-sectional design (Creswell & Clark, 2011). This survey was chosen because it allows researchers to collect data from various respondents at a certain point in time. In the realm of empirical survey research, a cross-sectional approach is adopted to describe the relationships between variables studied at a single point in time without considering changes over time (Creswell, 2022). The use of quantitative methods is also applied to measure how much impact product advertising has on purchasing interest which is mediated by product knowledge more objectively. The choice of survey method and cross-sectional design was based on the research objective of evaluating previously formulated hypotheses without carrying out additional interventions on the variables studied. Thus, this method allows researchers to get a broader picture of the relationship between product advertising, product knowledge, and purchase intention without directly influencing these variables. Overall, this approach is considered suitable to explore more deeply the influence of product advertising on purchase intention mediated by product knowledge in the context of this research.

3.2. Population and sample

Population of Small and Medium Enterprises (UKM) owners in Palembang City. By choosing Palembang City as the research location, we obtained a relevant picture of the impact of product advertising on purchase intention mediated by product knowledge in a specific local context. This population consists of 500 MSMEs. A sample of 201 respondents was selected randomly using probability sampling techniques. Random sample selection ensures representativeness and diversity in the sample, so that research results can be more generally applied to a larger population. Thus, the use of probability sampling techniques provides high validity in generalizing the findings of this research to the SME population in Palembang City as a whole (Fraenkel & Wallen, 2009).

However, in this case, the number of participants in the research sample was 201 participants selected through random sampling. The respondent’s name is conveyed confidentially to fulfill the initial agreement before interviewing or filling out the questionnaire given to them. The goal is to gain Respondent’s trust so that they are eager to answer each question on the research questionnaire comfortably and stress-free. Respondent’s recruitment is conducted ethically, and everyone volunteers to act as a respondent’s. Before providing answers to the research questionnaire, consent was completed, and Respondent’s permission was obtained to publish the results of the respondent’s answers regarding the completed questionnaire. We explain the purpose of this research and give Respondents the freedom to choose to answer each question or not.

Additionally, in this study, we have obtained informed consent from all participants involved. Before participating, respondents were informed about the purpose of the study, procedures, potential risks and benefits, and the rights of respondents. Respondents were guaranteed confidentiality of their data and their right to withdraw from the study at any time without consequences. Consent was obtained both in writing and verbally, according to the respondent’s preference. This research was conducted in accordance with ethical and regulatory guidelines governing research involving human participants.

3.3. Research instrument data collection techniques

The data collection technique in this research uses a questionnaire prepared on a Likert scale from 1 to 5. This questionnaire was created based on previous relevant research and has passed a strict validity and reliability assessment (Am et al., 2023). The use of a Likert scale allows respondents to express their level of agreement or disagreement with the statements given, thereby providing a more detailed picture of their perceptions of the variables studied (Am & Setiawati, 2023). To provide a more comprehensive picture, the construct variables are presented in Table 1.

Table 1. Variable construct.

Variable	Indicator	Code
Product Advertising	Advertising	Z1
	Private sales	Z2
	Sales promotion	Z3
	Public Relations	Z4
	From mouth to mouth	Z5
	Direct marketing	Z6
Product knowledge	Product knowledge	A1
	Buy knowledge	A2
	Usage knowledge	A3
Product quality	Show	B1
	Durability	B2
	Conformity to specifications	B3
	Feature	B4
	Reliability	B5
	Aesthetics	B6
Buying Interest	Attention	Y1
	Interest	Y2
	Hatred	Y3
	Confession	Y4

Source: Author’s Synthesis (2023).

Table 1 describes the indicators used to measure construct variables such as Product Advertising, Product Knowledge, Product Quality, and Purchase Intention. Each indicator is given a code that will be used in subsequent data analysis. It is hoped that the use of a questionnaire with a Likert scale and the decomposition of the construct variables in this table will provide sufficiently detailed and reliable data to analyze the influence of product advertising on purchasing interest mediated by product knowledge in the context of this research.

3.4. Data analysis techniques

The data analysis technique that will be used in this research is Structural Equation Modeling (SEM) using Partial Least Square (PLS). This approach follows the methodology outlined by Hair et al. (2019) as well as important stages such as model specification, parameter estimation, structural model testing, and hypothesis evaluation, as explained by Hair et al. (2017). The choice of SEM PLS as a data analysis method is based on its ability to reveal complex relationships between the variables studied, especially in the context of this research which involves interactions between product advertising, product knowledge, and purchase interest. The PLS variant of SEM offers a powerful statistical technique, which is indispensable for exploring the relationships and interactions of complex variables in this research (Hair et al., 2022). Partial Least Squares Structural Equation Modeling (PLS-SEM) which uses the PLS Algorithm and Bootstrapping. The PLS Algorithm will produce information in the form of construct validity and reliability values (convergent), discriminant validity, R Square, F Square, SRMR (Model Fit), and Multicollinearity. Bootstrapping, as a non-parametric procedure, aims to deal with the problem of non-normal data, especially if the sample is small (less than 100 respondents) (Hair et al., 2019). Bootstrapping calculations in multiple linear regression will produce path coefficients (direct effect) which aims to determine whether the independent variable has a significant influence on the dependent variable directly.

By using these two models simultaneously, it is hoped that it can provide a comprehensive understanding of the relationship between variables in the research context of the Influence of Product Advertising on Purchase Interest Mediated by Product Knowledge (Tables 2 and 3).

Table 2. Outer model testing criteria.

Testing Criteria	Criteria
Convergent Validity	Loading Factor > 0.70
	AVE > 0.50
Discriminant Validity	HTM < 0.90
Reliability Estimation
Cronbach’s Alpha	>0.70
Composite Reliability (rho_c)

(Source: Hair et al., 2017; Hair et al., 2019).

Table 3. Inner model testing criteria.

Inner Model	Testing Criteria	Conclusion
Multicollinearity (Inner Model)	VIF < 10	Escaping Multicollinearity
R Square	0.25	Weak Model
	0.50	Medium Model
	0.75	Strong Model
Gooness of FIT	SRMR < 0.10	Fit Models
F Square (Effect Size)	0.02	Small/low
	0.15	Intermediate/medium
	0.35	Big/strong
Path Coefficients (Direct Effect)	p value <.05	Significant influence
	p value >.05	No significant effect

(Source: Hair et al., 2017; Hair et al., 2019).

3.5. Ethical approval

In order to ensure compliance with ethical guidelines in research involving questionnaire/survey and interview/focus group studies, we respectfully inform you that this research has obtained approval from the Ethics Committee at the Directorate of Postgraduate Programs at Bina Darma University, Palembang. The research entitled ‘Revealing the Effect of Product Advertising on Consumer Purchase Intentions: Exploring Product Knowledge, Product Quality, and Mediation Effects,’ led by Dr. Bakti Setyadi, S.E, M.M, Ak, CA, CPA from Bina Darma University Palembang, has been given ethical approval with reference number No.071/PPs-UBD/III/2024. This approval guarantees that the research is conducted in compliance with established ethical standards and ensures the protection and welfare of research subjects.

4. Results

Before delving into the results of the modified research model, let us provide some context within the framework of the research structural model.

4.1 Outer model

4.1.1. Convergent validity

In this section, we will evaluate the convergent validity of the constructs used in this study. Convergent validity measures the extent to which the indicators used to measure a construct converge or have consistency in measuring the construct. Specifically, we will examine the factor loading values of each indicator on each construct, with values expected to exceed 0.70 to indicate adequate convergent validity (Table 4).

Table 4. Convergent validity.

Items	Buying Interest	Product Advertising	Product Knowledge	Product Quality
A1			0.885
A2			0.897
A3			0.895
B1				0.830
B2				0.922
B3				0.930
B4				0.823
B5				0.935
B6				0.828
Y1	0.920
Y2	0.936
Y3	0.871
Y4	0.910
Z1		0.866
Z2		0.885
Z3		0.897
Z4		0.928
Z5		0.837
Z6		0.747

Source: (Data Analysis Using Smart PLS, 2023).

Based on data from Table 2, it can be seen that all loading factor values on construct indicators exceed 0.70, indicating adequate convergent validity for all constructs. This means that the indicators used are able to consistently measure the construct referred to in this research. This provides confidence that the data obtained can be relied upon to represent the constructs studied.

4.1.2. Discriminate validity

In this section, we will evaluate the discriminant validity of the constructs used in this study. Discriminant validity measures the degree to which different constructs actually differ from each other. One way to measure discriminant validity is to use the heterotrait-monotrait (HTM) ratio, where the expected value is less than 0.90 to indicate that the constructs being measured are significantly different from each other.

From Table 5, it can be seen that the HTM ratio value for each pair of constructs is below 0.90, indicating that the different constructs are truly different from each other. For example, the HTM ratio value between Product Advertising and Product Knowledge is 0.787, which is lower than 0.90, indicating that the two constructs have good discriminant validity. This confirms that the measurement instruments used are able to clearly differentiate between the different constructs in this research

Table 5. Discriminant validity.

Variables	Buying Interest	Product Advertising	Product Knowledge
Product Advertising	0.890
Product Knowledge	0.787	0.899
Product Quality	0.937	0.849	0.764

Source: (Data Analysis Using Smart PLS, 2023).

4.1.3. Reliability estimate

In this section, we will evaluate the reliability estimates of the constructs used in this study. Reliability is a measure of how consistent and reliable a measurement instrument is in measuring the same construct. Two common metrics used to measure reliability are Cronbach’s Alpha and Composite Reliability. In general, the expected value for these two metrics is above 0.70 to indicate that the measurement instrument has adequate reliability.

From Table 6, it can be seen that all Cronbach’s Alpha and Composite Reliability values for each construct exceed the threshold value of 0.70. For example, Cronbach’s Alpha for Product Quality is 0.941 and Composite Reliability is 0.953, indicating that the measurement instrument for these constructs has good reliability. Overall, all variables have met the required thresholds. This shows that the measurement instruments used in this research are reliable in measuring the constructs studied.

Table 6. Reliability test.

Variables	Cronbach’s Alpha	rho_A	Composite Reliability	Average Variance Extracted (AVE)
Buying Interest	0.930	0.933	0.950	0.827
Product Advertising	0.930	0.932	0.945	0.743
Product Knowledge	0.873	0.882	0.922	0.797
Product Quality	0.941	0.944	0.953	0.773

4.2. Inner model

After the metadata is exported to Smart PLS 3, the main information of all exported articles and their readiness for analysis is presented in Figure 2.

Figure 2. Final model estimation results. Source: (Data Analysis Using Smart PLS, 2023).

This analysis presents two important outputs: path coefficients and factor loadings, which detail the relationships between variables within the framework of the research model. The path coefficient describes the level of influence between variables in the study, such as ‘Purchase Interest’, ‘Product Advertising’, ‘Product Knowledge’, and ‘Product Quality’. For example, the path coefficient between ‘Product Advertising’ and ‘Buying Interest’ (0.334) shows that ‘Product Advertising’ has a positive influence on ‘Buying Interest’, although this influence is weaker than the influence of ‘Product Quality’ (path coefficient 0.587). Next, Loading Factor connects the measured variables (eg ‘A1’, ‘B2’, ‘Y1’, ‘Z1’, etc.) with construct variables (eg ‘Buying Interest’ etc.). These loading factors indicate the extent to which the variable being measured represents or contains the construct being measured. For example, a factor loading of 0.885 between ‘A1’ and ‘Buying Interest’ indicates that ‘A1’ is a strong indicator of ‘Buying Interest’. By combining information from these two outputs, we can understand the complex relationship between the constructs and indicator variables used in this research, as well as the extent to which these variables influence each other within the framework of the model being analyzed.

4.2.1. Multicollinearity test

In this section, we will perform a multicollinearity test to check whether there is a multicollinearity problem between the independent variables in the model. Multicollinearity occurs when two or more independent variables in a model are strongly correlated with each other, which can cause instability in parameter estimates. In this test, we will use the Variance Inflation Factor (VIF), where the expected value is less than 10 to indicate that there are no significant multicollinearity problems in the model.’

From Table 7, all Variance Inflation Factor (VIF) values for each independent variable in the model are below the threshold of 10. For example, the VIF for variable B3 is 6.572, which means there is no indication of a significant multicollinearity problem. This shows that the independent variables in this model have a fairly low level of correlation with each other, so it can be concluded that this model passes the multicollinearity test.

Table 7. Collinearity statistics (VIF).

Items	VIF
A1	2403
A2	2174
A3	2482
B1	2816
B2	4751
B3	6572
B4	2830
B5	6645
B6	2827
Y1	4663
Y2	5287
Y3	2773
Y4	3427
Z1	3489
Z2	3951
Z3	4260
Z4	5178
Z5	2678
Z6	1890

Source: (Data Analysis Using Smart PLS, 2023).

4.2.2. Model fit

In this section, we will evaluate the model fit to check the extent to which the proposed model fits the observed data. This evaluation is important to determine how well our model fits the existing data. One metric used to evaluate model fit is the Standardized Root Mean Square Residual (SRMR), where the expected value is less than 0.10 to indicate that the model has a good fit.

Table 8 shows the evaluation of the suitability of the component models within a statistical analysis or modeling framework. From Table 8, it can be seen that the SRMR (Standardized Root Mean Square Residual) value is 0.063, which is below the critical value of 0.10. This indicates that our model has a good fit, because the low SRMR indicates that the residuals between the proposed model and the observed data are relatively small. Thus, we can conclude that this model fits the observed data. Next, we have two other metrics, namely d_ULS (Unweighted Least Squares) and d_G, which also show that the model has a good degree of fit to the data, although no critical values are listed. In this case, the lower value of d_G compared to d_ULS may indicate that this model may be more conservative in assessing model suitability. Lastly, we have the Chi-Square metric which measures model fit in the context of statistical analysis. Even though the Chi-Square value is 409.622 seems quite high, but it should be noted that the critical value required for Chi-Square evaluation is not stated. Therefore, the evaluation of the fit of the component model to the data would be more robust if other metrics such as RMSEA or CFI were used. In general, based on the information presented in the table, this component model appears to have a good level of agreement with the data, but a more comprehensive evaluation requires further study, especially regarding the relevant critical values.

Table 8. Evaluation of model component suitability.

Model Components	Critical value	Saturated Model	Information
SRMR	<0.08	0.063	Fit
d_ULS	0.820	0.747	Fit
d_G	0.485	0.756	Fit
Chi-Square	275,725	409.622	Fit

Source: (Data Analysis Using Smart PLS, 2023).

4.2.3. Coefficient of determination

In this section, we will evaluate the coefficient of determination (R Square) to check how well our model is able to explain the variation in the dependent variable. R Square measures the proportion of variation in the dependent variable that can be explained by the independent variables in the model. The R Square value ranges between 0 and 1, where a higher value indicates that the model can explain greater variation in the dependent variable. In this study, we will interpret the R Square value using the following criteria: 0.25 for the Weak Model, 0.50 for the Medium Model, and 0.75 for the Strong Model.

From Table 9, the R Square value for the Buying Interest variable is 0.818, while for the Product Knowledge variable it is 0.667. This shows that our model can explain around 81.8% of the variation in Buying Interest and around 66.7% of the variation in Product Knowledge. Based on interpretation criteria, we can classify this model as a Strong Model for the Buying Interest variable and a Medium Model for the Product Knowledge variable. This means that the independent variables in our model can explain significant variations in the dependent variable.

Table 9. R Square.

Variables	R Square	R Square Adjusted
Buying Interest	0.818	0.812
Product Knowledge	0.667	0.663

Source: (Data Analysis Using Smart PLS, 2023).

4.2.3. Effect size

In this section, we will evaluate the effect size to check how much the independent variable contributes to the variation in the dependent variable. Effect Size measures the strength of the relationship between the independent variable and the dependent variable in the model. In this study, we will use F Square as a measure of Effect Size, where a higher value indicates a greater contribution. Based on the interpretation criteria, we will classify the F Square values as Small/Low (0.02), Medium/Medium (0.15), and Large/Strong (0.35).

From Table 10, the F Square value for the Buying Interest variable from Product Advertising is 0.145, while for the Product Knowledge variable from Product Advertising it is 1.999. This shows that Product Advertising has a moderate contribution to Buying Interest (Medium/Medium) and a strong contribution to Product Knowledge (Large/Strong). In contrast, the F Square value for the Product Knowledge variable from Product Quality is 0.002, indicating a low contribution to variation in Product Knowledge (Small/Low). ‘Thus, the Product Advertising variable has a significant contribution to Buying Interest and Product Knowledge, while Product Quality has a lower contribution to Product Knowledge.’

Table 10. F Square.

Variables	Buying Interest	Product Knowledge
Product Advertising	0.145	1999
Product Knowledge	0.002
Product Quality	0.690

Source: (Data Analysis Using Smart PLS, 2023).

4.2.4. Hypothesis testing: structural equation modeling

In this section, we will complement the results of the descriptive analysis by conducting hypothesis testing to obtain more accurate conclusions in this research. Hypothesis testing is carried out to determine whether the relationship between the variables proposed in the model has a significant influence. In the following table, we present findings from hypothesis testing, including relationship paths, test statistics (T-Statistics), P-values, and interpretation of results.

From Table 11, there are several significant hypothesis testing results. For example, hypothesis H1 which states that Product Advertising has a significant influence on Buying Interest, is supported by a p-value of less than .05 (p = .000). Similar things happen to other hypotheses such as H2 and H4. However, there are also hypotheses that are not significant, such as H3 which states that Product Knowledge does not have a significant influence on Buying Interest, because the p-value is more than .05 (p = .649). Thus, the results of this hypothesis testing provide a deeper understanding of the relationships between variables in the proposed structural equation model.

Table 11. Direct effect hypothesis test results.

Hypothesis	Path	T-Statistics	p-value	Description
H1	Product Advertising -> Buying Interest	3564	.000	Significant
H2	Product Advertising -> Product Knowledge	17,543	.000	Significant
H3	Product Knowledge -> Buying Interest	0.455	.649	Not significant
H4	Product Quality -> Buying Interest	6085	.000	Significant
H5	Product Advertising -> Buying Interest	3564	.000	Significant

Source: (Data Analysis Using Smart PLS, 2023).

4.2.5. Testing media effects

In the context of this research, we will examine the mediation effects between the variables involved with the aim of understanding more deeply the mechanisms underlying these relationships. Mediation effects occur when the relationship between the independent variable and the dependent variable is mediated by the mediator variable. Appropriate statistical analyzes will be used to identify and quantify mediating effects between variables in the proposed structural equation model. It is hoped that the results of testing the mediation effect will provide valuable insight in understanding the dynamics of the relationship between the variables studied.

From Table 12, the results of testing the mediation effect show that the Product Advertising -> Product Knowledge -> Buying Interest path does not mediate, because the p-value is more than .05 (p = .650). This shows that there is insufficient evidence to support the existence of a mediation effect in this relationship. Nonetheless, the analysis of these mediation effects provides a deeper understanding of the mechanisms underlying the relationships between variables in the proposed structural equation model.

Table 12. Mediation effects.

Hypothesis	Path	T-Statistics	p-value	Description
H6	Product Advertising -> Product Knowledge -> Buying Interest	0.454	.650	Not mediating

Source: (Data Analysis Using Smart PLS, 2023).

5. Discussion

5.1. The influence of product advertising on consumer buying interest

The influence of product advertising on consumer purchasing interest is a topic that has been widely researched in marketing literature. Based on previous findings and data analysis, it can be concluded that product advertising has a significant influence on consumer purchasing interest. This research is consistent with previous findings showing that product advertising plays an important role in shaping consumer preferences and purchase intentions (Lăzăroiu et al., 2020). The theory that can be related to the influence of product advertising on consumer buying interest is Marketing Communication Theory (Krizanova et al., 2019). This theory suggests that marketing communications, including advertising, has the aim of influencing consumer perceptions, attitudes and behavior towards certain products or brands. (Raji et al., 2019) Through advertising, certain messages are conveyed to consumers to arouse buying interest and form preferences for the product or brand (Beaumont et al., 2022). Previous research has also shown that product advertising can influence consumer purchasing interest by introducing the product, highlighting product advantages, creating a positive brand image, and moving consumer emotions (Cempena et al., 2021; Chen et al., 2021). The findings of this research are consistent with the findings of previous studies which support the positive influence of product advertising on consumer purchasing interest. In this context, relevant previous research can provide a strong theoretical foundation to support the findings of this study. Overall, the influence of product advertising on consumer purchasing interest is a significant phenomenon in marketing and has been supported by theory and previous research in this field.

5. 2. The relationship between product advertising and consumer product knowledge

The relationship between product advertising and consumer product knowledge is an important aspect in understanding consumer behavior and marketing strategy. Based on previous findings and data analysis, it can be concluded that there is a significant relationship between product advertising and consumer product knowledge. The relevant theory to explain this relationship is the Diffusion of Innovation Theory (Kiwanuka, 2015). This theory states that consumers learn about new products through a diffusion process in which information about the product is spread through communication channels, including advertising (Zhang et al., 2015). In this context, product advertising acts as a communication channel that introduces new products or product-related information to consumers. Through advertising, consumers gain knowledge about product features, benefits, and advantages, which can influence their level of knowledge about the product. Previous research has confirmed that product advertising has a significant influence on consumers’ product knowledge (Wirakurnia et al., 2022). Advertising can help increase consumer awareness about products, strengthen their knowledge of product attributes, and help them understand the added value and benefits offered by the product. These findings are in line with findings from previous research that supports a positive relationship between product advertising and consumer product knowledge (Akbar et al., 2020; Varadarajan, 2020). In the context of this research, the findings showing a significant relationship between product advertising and consumer product knowledge can be supported by previous theory and research in the field of marketing. The implication of these findings is the importance of effective advertising strategies in increasing consumer knowledge about products, which in turn can influence consumer perceptions and attitudes towards these products. Therefore, the use of appropriate and effective advertising is a key factor in achieving product marketing success.

5.3. Product knowledge directly influences consumer buying interest

The direct influence of product knowledge on consumer buying interest is very important in understanding consumer behavior and marketing strategy. Based on previous findings and data analysis, it can be concluded that product knowledge directly influences consumer buying interest. One theory that is relevant to explain this relationship is the Elaboration Likelihood Theory (Petty et al., 1988). This theory suggests that consumers process information carefully depending on their motivation and abilities (Shahab et al., 2021). In this context, product knowledge acts as information processed by consumers in forming their purchasing interest. Consumers who have better knowledge about the product tend to carry out more in-depth and careful information processing, which can increase their purchasing interest in the product.

Previous research has shown that product knowledge directly influences consumer purchase intention (Andespa, 2017; Yustika et al., 2023). Consumers who have better knowledge about products tend to have higher purchasing interest because they are better able to recognize the value and benefits of the product and understand how the product meets their needs or desires (Anggraeni et al., 2022). This finding is consistent with previous research which shows a positive relationship between product knowledge and consumer purchase intention (Wahyuni, 2021). In the context of this research, the findings showing the direct influence of product knowledge on consumer buying interest can be supported by previous theory and research in the field of marketing. The implication is the importance of increasing consumer knowledge about products through an effective marketing approach, including the use of clear and relevant information in product advertising and promotions. Thus, a deep understanding of the product can be the key to influencing consumer buying interest and improving product marketing performance.

5.4. Product quality influences consumer buying interest

The influence of product quality on consumer buying interest is very important in the context of product marketing. Based on previous findings and data analysis, it can be concluded that product quality has a significant influence on consumer buying interest. One theory that is relevant to explain this relationship is the Perceived Quality Theory (Wu et al., 2013). This theory states that consumers’ perceptions of product quality will influence their attitudes and purchasing interest towards the product (Eckert & Bradley, 1998). Consumers tend to have a higher purchase interest in products that are considered to have high or better quality. Previous research has shown that product quality has a significant impact on consumer purchase intention (Cempena et al., 2021; Tran et al., 2013). Consumers generally tend to be more interested in purchasing products that have a reputation for good quality, because they believe that these products will provide higher value and satisfaction (Shih & Montes, 2022). This finding is also in line with customer satisfaction theory, which emphasizes that product quality is one of the main factors influencing customer satisfaction, which in turn can influence consumer purchasing interest and loyalty (Ingle & Mahesh, 2022; Saleh et al., 2017). In the context of this research, the findings showing the influence of product quality on consumer buying interest can be supported by previous theory and research in the field of marketing. The implication is the importance of focusing on developing and maintaining high product quality as a strategy to increase consumer buying interest. Efforts to improve consumer perceptions of product quality can be the key to improving marketing performance and gaining a competitive advantage in the market.

5.5. The role of product knowledge in mediating product advertising with consumer purchasing interest

T he role of product knowledge in mediating between product advertising and consumer purchase interest is an important aspect in understanding consumer behavior and marketing strategy. Based on previous findings and data analysis, it can be concluded that product knowledge plays an important role in connecting product advertising and consumer purchasing interest. One of the relevant theories to explain this relationship is the Marketing Communication Model (Vieira et al., 2019). This theory emphasizes that advertising acts as a communication channel that influences consumer perceptions and attitudes towards products (Krizanova et al., 2019). In this context, product knowledge acts as a mediator between the advertising message and consumer response, because advertising introduces information about the product to consumers, which can then influence their knowledge about the product and ultimately influence their purchasing interest (Wahyuni, 2021; Yustika et al., 2023). Previous research has shown that product knowledge mediates the relationship between product advertising and consumer purchase intention (Bala & Verma, 2018; Krizanova et al., 2019). Product advertising can increase consumers’ knowledge about certain products, which in turn influences their purchasing interest. This finding is in line with previous research which shows that product knowledge plays an important role in the process of forming consumer purchasing interest, especially in the context of marketing communications. (Helmi et al., 2022; Raji et al., 2019). In the context of this research, the findings showing the mediating role of product knowledge between product advertising and consumer purchase interest can be supported by previous theory and research in the field of marketing. The implication is the importance of paying attention to and managing product knowledge as part of a marketing communications strategy. Efforts to increase consumer knowledge about products through effective advertising can help increase purchasing interest and create stronger relationships between brands and consumers.

6. Research implications and limitations

This research has significant implications both theoretically and practically. Theoretically, the main contribution lies in a deeper understanding of marketing communications, especially in the context of the influence of product advertising on consumer purchasing interest. These findings can enrich marketing communications theory by clarifying the mechanisms of advertising influence on consumer behavior, as well as encouraging the development of more comprehensive mediation models. Practically, these findings have direct implications for the management of marketing communications carried out by practitioners. They can use the results of this research to design more effective marketing communication strategies, with a focus on developing advertising that can increase consumer knowledge of products and strengthen their buying interest. In addition, this research can also help companies optimize their promotional budgets, by allocating promotional investments more efficiently based on a better understanding of the relationship between product advertising, product knowledge and consumer purchasing interest. However, it should be noted that this study has limitations, especially in the context of the generalizability of the findings to a wider population and the possibility of bias or methodological weaknesses that cannot be completely avoided.

7. Conclusion

This research has provided a deeper understanding of the correlation between product advertising, product knowledge, and consumer purchasing interest. Data analysis findings confirm that product advertising has a significant influence on consumer purchasing interest, both directly and through increasing product knowledge. In addition, product knowledge is proven to play an important role as a link between product advertising and consumer purchasing interest. The implications of this research underscore the importance of attending to and managing product knowledge as an integral part of an effective marketing communications strategy. With a better understanding of the complex relationship between product advertising, product knowledge, and consumer purchase intentions, marketing practitioners are encouraged to design more targeted strategies to influence consumer behavior and improve their product marketing performance. However, it is important to note that generalization of the findings of this study must be done with caution to the broader population, as well as considering methodological limitations that may influence the interpretation of the results. For Small and Medium Enterprises (SMEs), this research provides encouragement to focus on building brand trust through advertising that prioritizes product quality and knowledge. Innovative advertising strategies that appeal to viewers emotionally, such as through stories or user-generated content, can also be explored. For further research, it is recommended to explore the most suitable advertising strategies and channels for SMEs in Palembang. Further analysis of the influence of cultural and regional factors on advertising effectiveness could also provide more specific insights into this context. Additionally, research into the long-term impact of advertising on brand loyalty and repeat purchases is expected to provide SMEs with more comprehensive insight into their customer base and potential for continued success. Thus, this research provides a valuable contribution in enriching understanding of the dynamics of marketing communications and consumer behavior, making it useful for practitioners, researchers and other stakeholders in the marketing field.

Authors contributions

First Author: Conceptualization, Introduction, Writing-Original Draft Preparation, Writing-Review and Editing. Second Author: Methodology, Validation, Formal Analysis. Conceptualization, Writing-Review and Editing. Third Author: Methodology, Supervision and Editing.

Citation information

Cite this article as: The Impact of Product Advertising on Purchase Intentions Mediated by Product Knowledge and Product Quality.

Disclosure statement

The author confirms that this research was conducted without personal, commercial, or financial interests, and hereby declares no conflict of interest with funding sources.

Data availability statement

The data underlying the findings of this investigation can be obtained by contacting the corresponding author upon reasonable request.

Additional information

Funding

The research conducted by the author was not financially supported by any external source.

Notes on contributors

Bakti Setyadi

Bakti Setyadi is a senior lecturer at the Faculty of Economics, Bina Darma University, Palembang. He has a strong educational background and extensive experience in accounting and tax. Apart from teaching, he is also active as a practitioner and has served as Tax Director at Baker Hughes Group since 1997. He has been involved in various global projects and has handled more than 180 tax and customs audits. He is also active in seminars and scientific writing and is a member of IAI. He can be reached at [email protected]

Sulaiman Helmi

Sulaiman Helmi is an accomplished academic and professional specializing in economics and management. Holding a Ph.D. in Economy Management and serving as Head of the Management Master’s Study Field at Bina Darma University, he has made significant contributions to academia and industry. With a diverse research portfolio spanning marketing, entrepreneurship, and leadership, he has authored numerous publications in esteemed journals. He can be reached at [email protected]

Adi Santoso

Adi Santoso is a senior lecturer in the Department of Management, Faculty of Economics at Muhammadiyah University, Ponorogo. His research interests are Marketing, entrepreneurship, tourism, and small business management. He can be reached at [email protected]