The Influence of Product Quality, Sales Promotion, and Price on Purchase Intention: A Case Study of Indihome Users in Palembang After FMC (Fixed Mobile Convergence)�

Dyandra Dwi Arifia¹*, Indrawati²

^1,2Universitas Telkom, Indonesia

Emails: [email protected]

Abstract

The advancement of telecommunications in Indonesia provides substantial and meaningful benefits for the Indonesian people. The telecommunications industry in Indonesia is one of the fastest-growing sectors in Asia. This study aims to analyze customer feedback on telecommunication service users following the fixed mobile convergence transformation implemented by Telkom and Telkomsel. The research adopts a quantitative approach, utilizing a survey of 320 respondents selected through a non-probability purposive sampling method, focusing on individual Telkomsel telecommunications service users at GraPARI Palembang City. The data were analyzed using Structural Equation Modeling (SEM) with the Partial Least Square (PLS) technique to evaluate the model and test the hypotheses. The findings reveal that product quality, sales promotion, and price are all categorized as high. Moreover, these factors have a significant positive influence on buying interest, accounting for 50.1% of the variance. This indicates that higher product quality leads to increased buying interest, improved sales promotion enhances consumer engagement, and well-placed pricing strategies elevate customer interest in Indihome Telkomsel One. These results underline the critical role of product quality, sales promotions, and pricing strategies in driving consumer buying interest, providing actionable insights for telecommunications service providers. Companies like Telkom and Telkomsel can leverage these findings to refine their marketing strategies, focusing on enhancing product offerings, developing targeted promotions, and implementing competitive pricing to sustain and expand their market presence in the fast-evolving telecommunications industry in Indonesi.

Keywords: product quality; price; purchase interest; sales promotion

INTRODUCTION

In the current era of rapid development of information and communication technology, where technology is closely related and affects all aspects of human life regardless of space and time, and from various groups ranging from children, adolescents, to adults (Keeley & Little, 2017). At first, communication only developed through media such as newspapers, newspapers, television, and radio, but along with the rapid transformation of media, communication has now developed from the home telephone network, which from the beginning of its development from the use of cables to the current development of telecommunications has entered the fiber optic era.

The more developed telecommunications progress in Indonesia, the better it will fulfill the need for telecommunications to meet the daily needs of people who are increasingly entering the modern era (Sudarmo et al., 2021). According to data taken from Kompas.Id (2023) Indonesia's telecommunications industry is one of the fastest growing in Asia. The communication and information sector contributed IDR 748.75 trillion to the gross domestic product (GDP).

The growing internet penetration continues to encourage telecommunications companies, including Telkomsel, to provide the best quality internet products and carry out strategies to increase customer purchasing power (Yusuf & Wibowo, 2022). Telkomsel is a subsidiary of Telkom which is engaged in telecommunications for individual and corporate consumers until 2023 and is only in charge of cellular telecommunications. In May 2023, according to data taken from the company website Telkom.co.id (2023), the Telkom company obtained independent shareholder approval for the corporate action of separating the IndiHome business segment (spin-off) from Telkomsel. The separation of IndiHome from Telkom is implemented into Fixed Mobile Convergence (FMC), which is part of the company's main strategy, "Five Bold Moves," which is strategic for Telkom in supporting the creation of digital inclusion through increasing the reliability of wider and more equitable connectivity for the community. The synergy between Telkom and Telkomsel, which is the market leader in the broadband business, is expected to make it easier for people to access a variety of digital services (Asyraf & Tricahyono, 2024) . Telkomsel will have a new growth engine that will strengthen the company's performance going forward.

The official transfer of Indihome's business to Telkomsel, which has been running since Legal Day One on July 1, 2023, has made Indihome a stronger internet provider with integrated value so that it has a better image in the market. Quality improvement at Indihome is expected to increase customer satisfaction, which in turn can be a force for Indihome's buying interest (Tamon, 2023). In accordance with the results of the literature review that has been carried out and refers to the formulation of the problem, namely "How much influence do product quality, sales promotion, and price have a positive significant effect on customer buying interest in Indihome Palembang users after FMC." The purpose of this research is to empirically test by analyzing "The Effect of Product Quality, Sales Promotion, and Price on Customer Purchase Interest Case Study of Indihome Palembang Users Post FMC (Fixed Mobile Convergence)." The benefit of this research is that it is expected to be able to add to the treasury of knowledge in the field of marketing, especially those related to the development of variables and aspects in influencing customer buying interest in a product, especially in a product resulting from the latest breakthrough in the unification of two fixed and mobile services. The findings obtained in this study can also be used as a reference for future researchers (Wirtz & Daiser, 2018).

RESEARCH METHODS

The type of research conducted is descriptive research with a quantitative approach. The data collection technique used in this study is the distribution of questionnaires directly by the author to Indihome customers at GraPARI in the Palembang area, who are the target respondents. The population in this study comprises all Indihome customers at GraPARI Palembang City. The sample is determined using the Non-probability Sampling method, which does not provide equal opportunities for each element of the population to be selected as a sample member (Indrawati, 2015). The data analysis technique employed in this research includes descriptive statistical analysis to interpret and summarize the collected data.

RESULT AND DISCUSSION

In this study, SmartPLS was used to conduct two types of model testing, namely the measurement model (outer model) and the structural model (inner model). The process begins with testing the measurement model, which aims to determine validity and reliability. This model links reflective indicators with latent variables using three measurement methods (Fleuren et al., 2018). After confirmatory factor analysis was conducted, all indicators were declared valid and reliable. Next, testing was conducted on the overall structural model. Testing the structural model involves evaluating the percentage of variance (R2) for endogenous latent variables affected by exogenous latent variables (Adusei & Gyapong, 2017). This test also includes the analysis of the t values obtained from the bootstrap to determine the significance of the effect.

Measurement Model (Outer Model) Testing

The measurement model measurement model (outer model) is a model that connects latent variables with manifest variables (Latan et al., 2017). Evaluation of the measurement results of the measurement model (outer model) is through confirmatory factor analysis (CFA by testing the validity and reliability of latent constructs) (Hair et al., 2020). The measurement model test consists of convergent validity, discriminant validity, and reliability tests.

The following is the data obtained from respondents through a questionnaire in this study processed using SmartPLS 4.1.0.8 and stated with the following results:

Figure 1. Outer Model Results

Source: Data processing results, 2024

Validity Test

The validity test is a measuring instrument that is tested for the level of effectiveness of the measuring media in obtaining valid data (Janna & Herianto, 2021). The validity test is used to measure whether the questionnaire instrument used in this study will be feasible and able to present accurate data. There are two types of models in conducting validity tests, namely Convergent Validity and Discriminant Validity.

Table 1. Convergent Validity Test

Variables	Dimensions	Item	Factor loading	Description
Product Quality (X1)	Performance	QP1	0.846	Valid
	Performance	QP2	0.749	Valid
	Reliability	QP3	0.767	Valid
	Reliability	QP4	0.752	Valid
	Conformance	QP5	0.838	Valid
		QP6	0.824	Valid
		QP7	0.823	Valid
		QP8	0.869	Valid
	Durability	QP9	0.779	Valid
	Durability	QP10	0.775	Valid
	Perceived Quality	QP11	0.771	Valid
	Perceived Quality	QP12	0.780	Valid
Sales Promotion (X2)	Prices Packs	PO1	0.811	Valid
		PO2	0.805	Valid
		PO3	0.816	Valid
	Rebates	PO4	0.740	Valid
		PO5	0.804	Valid
		PO6	0.819	Valid
	Point of sale display	PO7	0.763	Valid
		PO8	0.743	Valid
		PO9	0.772	Valid
Price (X3)	Price affordability	PR1	0.806	Valid
	Price affordability	PR2	0.772	Valid
	Price affordability of product quality	PR3	0.775	Valid
	Price affordability of product quality	PR4	0.781	Valid
	Affordability of benefits	PR5	0.787	Valid
	Affordability of benefits	PR6	0.765	Valid
	Price affordability of competitiveness	PR7	0.758	Valid
	Price affordability of competitiveness	PR8	0.801	Valid
Purchase Intention (Y)	Transactional Interest	PI1	0.789	Valid
	Transactional Interest	PI2	0.813	Valid
	Explorative Interest	PI3	0.790	Valid
	Referential Interest	PI4	0.769	Valid
	Interests Preferences	PI5	0.754	Valid
	Interests Preferences	PI6	0.760	Valid

Source: Data processing results, 2024

The table above provides information about the loading factor value for each manifest variable; the loading factor value of all indicators on latent variables shows> 0.6 so all indicators are declared valid, and most of the values exceed 0.70 so that they are categorized as having a high correlation.

Table 2. Average Variance Extracted (AVE)

	Average Variance Extracted (AVE)
Product Quality (X1)	0.638
Sales Promotion (X2)	0.619
Price (X3)	0.609
Purchase Intention (Y)	0.607

Source: Data processing results, 2024

From the table presented it can be seen that all variables have an Average Variance Extracted (AVE) value greater than 0.50, the value specified as the minimum limit. This shows that all variables are valid in explaining their latent variables, which means that the use of variables has met the established AVE criteria.

Table 3. Fornell-Lacker Criterion

	X1	X2	X3	Y
X1	0.799
X2	0.606	0.787
X3	0.496	0.575	0.781
Y	0.557	0.606	0.613	0.779

Source: Data processing results, 2024

Based on the results of the table above show that the loading value of each indicator item on its construct is greater than the cross-loading value (Ab Hamid et al., 2017). Thus, it can be concluded that all constructs or latent variables already have good discriminant validity, whereas the construct indicator block is better than other block indicators.

Table 4. Factor Cross-Loading Test Results

	X1	X2	X3	Y
QP1	0.846	0.503	0.418	0.474
QP2	0.749	0.488	0.373	0.393
QP3	0.767	0.472	0.407	0.444
QP4	0.752	0.446	0.356	0.420
QP5	0.838	0.505	0.387	0.473
QP6	0.824	0.480	0.396	0.452
QP7	0.823	0.464	0.346	0.459
QP8	0.869	0.511	0.398	0.486
QP9	0.779	0.466	0.362	0.438
QP10	0.775	0.451	0.394	0.410
QP11	0.771	0.494	0.455	0.403
QP12	0.780	0.523	0.468	0.471
PO1	0.491	0.811	0.449	0.472
PO2	0.513	0.805	0.420	0.495
PO3	0.553	0.816	0.452	0.544
PO4	0.431	0.740	0.447	0.443
PO5	0.454	0.804	0.512	0.481
PO6	0.478	0.819	0.465	0.494
PO7	0.439	0.763	0.438	0.440
PO8	0.480	0.743	0.447	0.441
PO9	0.439	0.772	0.445	0.468
PR1	0.402	0.482	0.806	0.495
PR2	0.387	0.459	0.772	0.429
PR3	0.383	0.472	0.775	0.463
PR4	0.385	0.424	0.781	0.483
PR5	0.400	0.465	0.787	0.505
PR6	0.411	0.451	0.765	0.465
PR7	0.375	0.378	0.758	0.458
PR8	0.360	0.460	0.801	0.523
PI1	0.471	0.471	0.458	0.789
PI2	0.445	0.489	0.468	0.813
PI3	0.411	0.467	0.441	0.790
PI4	0.411	0.479	0.506	0.769
PI5	0.423	0.442	0.488	0.754
PI6	0.441	0.484	0.503	0.760

Source: Data processing results, 2024

Based on the PLS software results table above, it can be seen that the cross loading factor correlation value of each latent construct for the corresponding indicator is higher than other constructs, so it can be concluded that the indicators used to measure latent variables have met the requirements.

Table 5. HTMT Test Results

	X1	X2	X3	Y
Product Quality X1
Sales Promotion X2	0.647
Price X3	0.536	0.629
Purchase Intention Y	0.612	0.674	0.687

Source: Data processing results, 2024

In the table above, it can be seen that the HTMT value is below 0.85, so it can be stated that all constructs pass the HTMT test.

Reliability Test

Table 6. Cronbach's Alpha and Composite Reliability Results

	Cronbach's Alpha	rho_A	Composite Reliability
X1	0.923	0.925	0.937
X2	0.904	0.906	0.926
X3	0.851	0.855	0.900
Y	0.865	0.871	0.908

Source: Data processing results, 2024

Based on the table above, it can be seen that all variables in this study have a score greater than 0.70 so that they are reliable. Then each item in the validity and reliability test can be used as an outer testing model to determine each indicator has a good consistency and trust value.

Structural Model Testing (Inner Model)

This structural modeling is to test the effect of one latent variable on other latent variables. Testing is done by looking at the path value to see whether the effect is significant or not based on the t value of the path value (t value can be obtained by doing boothstraping) (Streukens & Leroi-Werelds, 2016). The following is a picture of the bootstrapping results conducted in this study:

Figure 2. Boothstraping

Source: Data processing results, 2024

R square test

The influence of the dependent variable can be displayed by the R-square value (Ozili, 2023). The following is the acquisition of the R-square value.

Table 7. Results of R Squares

	R Square
Purchase Intention	0,501

Source: Data processing results, 2024

Through the coefficient of determination (R-square) value contained in the table above, it can be seen that the Rsquare value of the Purchase Interest variable is 0.501, which indicates that the Purchase Interest variable can be explained by 50.1% by the variables of Product Quality, Sales Promotion and Price. While the remaining 49.9% can be explained by other variables not examined.

Q2 Test/Blindfolding

Table 8. Construct Crossvalidated Redundancy

	SSO	SSE	Q� (=1-SSE/SSO)
Product Quality	2560.000	2560.000
Sales Promotion	1920.000	1920.000
Price	1280.000	1280.000
Purchase Intention	1280.000	692.131	0.459

Source: Data processing results, 2024

The qualified Q square value is greater than 0 and vice versa. Based on the results of the above calculations, it can be seen that the Q2 value for the variables of Product Quality, Sales Promotion, and Price and Purchase Intention has a value greater than 0, so it can be said that the model has predictive relevance.

Path Coefficient

The results of testing Path Coefficients on each variable are shown in the table below:

Table 9. Path Coefficient

Influence	Path Coefficient
Product Quality -> Purchase Intention	0.219
Sales Promotion -> Purchase Intention	0.274
Price -> Purchase Intention	0.347

Source: Data processing results, 2024

Based on the table above, it can be seen that the smallest path value is the influence between product quality on purchase intention of 0.219. While the largest path value is the influence between price and purchase intention of 0.347.

F2 Effect Size Test

The F2 effect size value according to Ghozali PLS (2020), if ≥ 0.02 indicates a small effect size, ≥ 0.15 indicates a medium effect size, and≥ 0.35 indicates a large effect size. as follows:

�

Table 10. F2 Effect Size Test

Influence	Effect Size Value	Description
Product Quality� Purchase Intention	0.058	Small
Sales Promotion� Purchase Intention	0.080	Small
Price� Purchase Intention	0.153	Small

Source: Data processing results, 2024

Based on the table above, it can be seen that the price variable has the greatest influence on buying interest by having an effect size value of 0.153.

Goodness of Fit Evaluation

To validate the overall model, Goodness of Fit (GoF) is used. The following are the results of the Goodness of Fit evaluation in this study:

Table 11. Goodness of Fit (GoF)

	Saturated Model	*Estimated Model*
SRMR	0.047	0.047
NFI	0.835	0.835

Source: Processed data, 2024

Based on the results in Table 4.12, the SRMR value of the model used in this study can be said to be good because the SRMR value is less than 0.10, so the model is suitable for use in this study (Ghozali, 2021: 78). Then the results of the NFI of the model used are close to 1, which means it has a fairly good fit (Ghozali, 2021: 78).

Product Quality on Indihome Telkomsel One Products

Based on descriptive analysis, the product quality variable has an average of 1302.9 or 81.4%, which is in the interval of 68.01%-84%. From these data, it can be concluded that the product quality variable is included in the "High" category (Marian et al., 2014). The indicator with the lowest score is question X1.4, namely the reliability dimension with the statement item "Comfort is presented by Indihome Telkomsel One product with a good and stable internet network in all weather conditions." which scored 1282 with a percentage of 77.7%.

Sales Promotion on Indihome Telkomsel One Products

Based on descriptive analysis, the sales promotion variable has an average of 1314.2 or 82.1%, which is in the interval of 68.01%-84%. From this data, it can be concluded that the sales promotion variable is included in the "High" category. The indicator with the lowest score is the point of sale display dimension with question item X2.20, "The standee in front of the GraPARI outlet counter displays promotions that influence the intention to buy Indihome Telkomsel One products." which scored 1293 or 78.4%.

Price on Indihome Telkomsel One Products

Based on descriptive analysis, the price variable has an average of 1313.0 or 82.1%, which is in the interval of 68.01%-84%. From this data, it can be concluded that the price variable is included in the "High" category. The indicator with the lowest score is in the price affordability dimension with question item X3.23, namely "I agree that the discounted price given by Indihome Telkomsel One is the best price." it has a score of 1288 with a percentage of 78.1%.

Purchase Interest in Indihome Telkomsel One Products

Based on the descriptive analysis, the purchase interest variable has an average score of 1343.0, with a percentage of 83.9%, which falls within the interval of 68.01%�84%, categorizing it as "High." The indicator with the lowest score is in the exploratory interest dimension, specifically in question item Y.32: "I will look for information about other Telkomsel One Indihome Products after using the product," which scored 1323, representing 80.2%. These findings align with previous research by [Author/Research], which emphasizes that high levels of purchase interest are often influenced by customers' positive experiences with the product and their tendency to explore similar offerings. This supports the notion that exploratory interest, while crucial, may vary based on individual preferences and the perceived value of additional products (Dastane & Haba, 2023).

CONCLUSION

Based on the results of research conducted on the variables of product quality, sales promotion, and price, it has been empirically proven that these variables play a critical role in increasing buying interest in Indihome Telkomsel One product. The implications for both practical and theoretical aspects should, therefore, emphasize these three variables. The findings indicate that the price aspect has the greatest influence on increasing buying interest, with a coefficient value of 0.347 and a t-statistics value of 5.129. This is followed by sales promotion with a coefficient value of 0.274 and a t-statistics value of 4.427, and product quality with a coefficient value of 0.219 and a t-statistics value of 3.496. Future research contributions should explore additional variables beyond product quality, sales promotion, and price, such as customer satisfaction, brand trust, or digital marketing strategies, to provide a more comprehensive understanding of factors influencing purchase intention. Furthermore, longitudinal studies could examine the sustainability of these effects over time.

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Copyright holders:

Dyandra Dwi Arifias, Indrawati (2024)

First publication right:

AJEMB - American Journal of Economic and Management Business