Factors Influencing Purchase Intention Towards Smartphone Brands in Indonesia

Factors Influencing Purchase Intention Towards Smartphone Brands in Indonesia

Aeshah Mohd Ali¹, Mohd Khairi Ismail², Francisco Owen Widodo³, Robertus Randy Gosal⁴, Quratul’ain Syahirah Awang ali^5*, Nor Aziyatul Izni⁶

¹Arshad Ayub Graduate Business School, University technology MARA, Shah Alam, Malaysia

²Faculty of Business and Management, University technology MARA, Cawangan Terengganu, Terengganu, Malaysia

^3,4Ungku Aziz Centre for Development Studies (UAC), University of Malaya

⁵University Sultan Zainal Abidin, Kampus Gong Badak, Terengganu, Malaysia

⁶Centre of Foundation Studies, University technology MARA, Cawangan Selangor, Kampus Dengkil, Dengkil, Selangor, Malaysia

[1]Corresponding author

DOI: https://dx.doi.org/10.47772/IJRISS.2025.90300387

Received: 14 March 2024; Accepted: 20 March 2025; Published: 23 April 2025

ABSTRACT

Smartphones have become one of the important things needed in this era. As a result of the important usage of smartphone, many individuals have some criteria for the type of smartphones they want to buy. Some of them prefer to buy a flagship smartphone that give the users the best experience or they will buy it based on their needs which the smartphone just enough for the daily activities. In this case, there are 3 brands that has a quite good sales percentage in the ASEAN market which Oppo, Apple, and Samsung. The purpose of this research is to examine factors influencing purchase intention towards Oppo, Apple, and Samsung smartphone brands in Java Island, Indonesia. This research was done on the people living on Java Island by distributing 25 questions through questionnaire to 150 people living on Java Island. The quantitative descriptive analysis approach was utilized in this study, and the Smart PLS software was used to conduct the analysis. According to the study’s findings, marketing activity, product features, price, lifestyle, and product quality all have an effect on purchase intention towards Oppo, Apple, Samsung smartphone brands in Java Island, Indonesia. It is expected that the result gleaned from this study’s findings would assist the readers in factors influence purchase intention. Additionally, it is intended that this research would serve as a guide and reference for future reviews.

Keywords: Purchase Intention, Product Quality, Product Features, Indonesian Smartphone

INTRODUCTION

Abdullah et al. (2023) reported that Oppo led the Indonesian smartphone market in the first quarter of 2022 with a 22.3% market share, followed by Samsung at 17.7%. Apple, despite its global brand strength, captured only 18% of the Indonesian market during the same period. Oppo secured its dominance in the middle to lower market segments with its “Reno” and “A” series, while Samsung positioned itself as the leader in the premium segment with its Galaxy S22 Ultra. However, despite Oppo’s lead, Samsung’s market share was close, suggesting increasing competition between the two brands. At the global level, data from Canalys (2022) indicated that Samsung held the largest market share in the first quarter of 2022, accounting for 24% of worldwide smartphone shipments, with Apple following at 18%. Oppo ranked fourth globally, after Xiaomi, with a 9% market share.

In the second quarter of 2022, the Indonesian smartphone market experienced further shifts. According to the International Data Corporation (IDC) report titled Indonesia’s Smartphone Market Declined 10 Percent YoY in 2Q22 (September 19, 2022), Oppo maintained its dominance with a 20.6% market share, marginally ahead of Samsung at 20.2%. However, Apple’s presence remained weak, with Statcounter (2022) reporting that it held only a 9.23% market share in Indonesia as of April 2022. Meanwhile, Canalys (2022) data for the second quarter of 2022 confirmed Samsung’s continued leadership in the global market, holding a 21% share, followed by Apple at 16%, with Oppo remaining in fourth place at 10%.

The third quarter of 2022 saw a further decline in Indonesia’s smartphone market. Muslim (2022) found that smartphone sales in Indonesia fell to 8.1 million units, representing a 12.4% decline compared to Q3 2021 and a 14.6% decrease from Q2 2022. According to Statcounter (2022), Samsung overtook Oppo in September 2022, securing a 20.85% market share, while Oppo trailed slightly at 20.43%. Apple’s market share remained modest at 10.46%. Globally, Canalys (2022) reported a 9% contraction in the smartphone market as consumers reduced spending. Despite these challenges, Samsung maintained its position as the leading global smartphone brand with a 22% share, supported by aggressive promotional strategies. Apple was the only major vendor to achieve positive growth, increasing its global market share to 18% due to strong demand for iPhones. Oppo, in contrast, adopted a cautious expansion strategy amid uncertainties in its domestic market, retaining a 10% global share. By the fourth quarter of 2022, Oppo reclaimed its lead in Indonesia, with a 20.79% market share in December, followed by Samsung at 20.48% and Apple at 10.61% (Statcounter, 2022). However, on a global scale, Apple emerged as the dominant smartphone brand in Q4 2022, holding a 23% market share, ahead of Samsung at 19%, while Oppo maintained its fourth-place ranking at 10% (Counterpoint, 2023). In terms of global smartphone shipments, Apple led with 70 million units, followed by Samsung with 58.3 million, and Oppo with 29.6 million units.

In Indonesia, Oppo continued to dominate the smartphone market as of January 2023. According to Statista Research Department (Market Share of Leading Mobile Vendors Across Indonesia as of January 2023), Oppo held a 21% market share, followed closely by Samsung at 20.5%. Meanwhile, Apple maintained a significantly lower share, at 10.91%, as reported by Statcounter (Mobile Vendor Market Share Indonesia Feb 2022 – Feb 2023). Historically, Oppo has maintained its leadership in Indonesia’s smartphone market, though its market share has been gradually declining. Abdullah et al. (2021) found that in the first quarter of 2022, Oppo led with 22.3%, followed by Samsung at 17.7%, while Apple captured 18% (Taylor et al., 2023). Going further back, in Q1 2021, Oppo had an even stronger lead with a 22.9% market share, followed by Apple at 16% and Samsung at 12.6% (Abdillah et al., 2023).

The Indonesian smartphone market has experienced dynamic shifts in brand leadership, with Oppo consistently securing the top position in market share from 2021 to early 2023. However, despite its dominance, Oppo’s market share has shown a gradual decline, indicating potential shifts in consumer preferences and competition from Samsung and Apple. Prior studies have primarily focused on market share distribution, brand positioning, and price sensitivity in Indonesia’s lower to middle-income segment (Wakhono, 2022; Barjasteh-Asgari et al., 2023). While Oppo benefits from competitive pricing and feature-rich products, Samsung and Apple continue to challenge its position, particularly in the premium market segment. Existing literature lacks an in-depth exploration of the key determinants influencing consumer purchase intentions for these leading smartphone brands, particularly in Java Island, Indonesia. Therefore, this study aims to bridge this gap by analyzing the factors that drive consumer preferences for Oppo, Apple, and Samsung, considering pricing strategies, product quality, promotional efforts, and socio-economic influences.

LITERATURE REVIEW

Purchase Intention

The interest to buy is a motivator that stays in a consumer’s mind, and when the need arises, the consumer is likely to act on it. Although the purchase may not always happen in the future, measuring the level of interest in a purchase is generally done to improve the accuracy of predicting the actual purchase itself as mentioned by Amri and Prihandono (2019). The consumer’s intention to buy a product or services determined by their attitudes and other factors that affecting the purchase intention, before they actually make the purchase (Khan et al., 2023). Hidayat et al. (2021) stated that previous research has indicated a direct correlation between an increase in purchase intention and an increase in the likelihood of making a purchase. When consumers have a positive purchase intention towards a particular brand, positive brand engagement can further encourage the purchase.

Product Quality

Kotler and Keller (2022) defined quality as the overall set of features and characteristics of a product or service that depend on its ability to fulfil the stated or implied needs of consumers. A company is considered to deliver quality when its products or services meet or exceed customer expectations. A company that consistently satisfies most of its customers’ needs is considered a quality company, although there is a distinction between compatibility quality and performance (or level) of quality. Additionally, consumers evaluate the quality and durability of a product, and prefer products that can be used for an extended period of time (Amri & Prihandono, 2019)

Marketing Activity

Axmedov and Bikuzin (2022) noted that in accordance with the principles of marketing, human activity is focused on fulfilling needs and fulfilling requirements through exchange, where all participants of the market work together based on shared interests to shape and fulfil demand. This process enables consumers to select from a diverse array of products. Certain authorities in the field suggest that the chief objective of the marketing system is to offer the greatest possible variety of goods, thereby affording consumers an extensive selection. However, the true aim of the system should be to assist consumers in finding a product that is completely tailored to their specific needs.

Product Features

Numerous advanced smartphones are currently available in the market, and people choose specific features based on their needs and preferences. Abdullah et al. (2021) stated product features are defined as the characteristics or attributes of a product that provide benefits to the end-user and differentiate it from similar products offered by competitors in the market. Product characteristics encompass the various attributes of a product that can fulfil the desires and requirements of consumers by means of owning, utilizing, and applying the product (Armstrong et al., 2020). These attributes, in conjunction with the quality and functionality of the product, can influence its adoption and usage (Aufegger et al., 2021).

Price

The seller determines the price of a product which is the amount paid by the customer. This price may depend on several factors, such as the cost of the product, place of sale, promotional expenses, taxes, and shipping costs. The price can have a negative impact on the perceived value, trust, perceived quality, brand image, and purchase decision. This is supported by research conducted by Aufegger et al. (2021). Octaviani et al. (2024) explicated that individuals use the process of price to select, arrange, and comprehend data to form a significant understanding of the world. In the context of products or services, price is the monetary value that is assigned to them.

Lifestyle

Based on the high number of social media users in Indonesia, including on platforms like Facebook and other social media sites, it appears that the use of social media has become a part of the daily lifestyle of many Indonesians. The concept of lifestyle refers to how a person lives their life and is demonstrated through their favored activities, interests, and opinions, as explained by Brivio et al. (2023) and Kotler and Armstrong (2018). Shopping, work, and entertainment are some examples of activities that reflect a consumer’s lifestyle as mentioned by Qazzafi (2020). Individuals who lead a contemporary lifestyle tend to purchase new and costly products and prioritize the product’s appearance. Conversely, those who have a more traditional lifestyle prioritize the functionality of a product over its latest design or performance, and may not be inclined to purchase designer goods (Amri & Prihandono, 2019). A unique lifestyle is characterized by distinct needs and desires (Fuchs et al., 2021). Solomon (2020) asserts that examining a person’s lifestyle can provide valuable insights into their spending habits.

Development of Hypothesis

Marketing activity on purchase intention

Studies conducted by Kuncoro and Kusumawati (2021) and Wang et al. (2019), have shown that social media marketing has a significant impact on brand preference. Through frequent exposure to social media marketing activities, e-commerce platforms can differentiate themselves from their competitors and create brand preference among consumers who compare various e-commerce platforms to find one that best fits their needs. The assertion that brand preference has an impact on purchase intention is supported by Briliana and Andrianto (2019) and Gomez-Rico et al. (2023). These researchers suggest that when customers prefer a particular brand, they are more likely to purchase it over competing brands. Brand preference is formed based on past experiences, and this factor plays an important role in shaping purchase decisions.

H1 : There is a relationship between Marketing Activity and Purchase Intention towards Oppo, Apple, and Samsung smartphone brand in Java Island, Indonesia.

Product features on purchase intention

Abdullah et al. (2021) found that with the emergence of numerous high-tech features in smartphones, people tend to select smartphones that cater to their specific needs and preferences while another study conducted by the same researcher revealed that college students mainly consider the appearance, size, and menu structure of a cell phone when making a purchase. Product features are important determinants of consumer behavior and purchase intention. Studies have shown that product features can influence consumer perceptions of product quality, value, and satisfaction (Liang et al., 2020). For example, Cabrera and Villarejo (2018) and Luo et al. (2022) found that product features such as design, functionality, and performance significantly influenced consumers’ perceived quality of a product, which in turn influenced their purchase intention.

H2 : There is a relationship between Product Features and Purchase Intention towards Oppo, Apple, and Samsung smartphone brand in Java Island, Indonesia.

Price on purchase intention

A number of studies including Yuan et al. (2019), Suhud and Willson (2019) and Zhao et al. (2021) have investigated the impact of price on consumer behavior. These studies suggest that price is an important factor that can affect consumer behavior and purchase decisions. Abdullah et al. (2021) suggest that the price of a phone is a crucial factor that influences consumers’ purchasing decisions. Despite the importance of other marketing mix factors, companies cannot ignore the role of price in consumers’ decision-making processes (Asdi & Putra, 2020)

H3 : There is a relationship between Price and Purchase Intention towards Oppo, Apple, and Samsung smartphone brand in Java Island, Indonesia.

Lifestyle on purchase intention

Zollo et al. (2021) highlight that consumers’ purchase intentions are becoming more closely linked to their lifestyles in various ways, with a particular emphasis on healthy lifestyles. According to Park (2019), smartphones have become a ubiquitous device that can be used anytime and anywhere. Smartphones are no longer seen as just a communication tool, but rather as a hub for making powerful decisions about trends and fashion, and as a tool to interact with others, which are deeply embedded in the users’ lifestyles (Lee et al., 2019).

H4 : There is a relationship between Lifestyle and Purchase Intention towards Oppo, Apple, and Samsung smartphone brand in Java Island, Indonesia.

Product quality mediates marketing activity towards purchase intention

Social networking sites are platforms that enable users to increase brand recognition, shape brand image, and express their preferences for particular brands, making them a significant source of social influence that can influence consumer decision-making (Siddiqui et al., 2021; Ruiz-Mafe et al., 2018). According to Frey and Van de Rijt (2021) and Teo et al. (2019) people are susceptible to social influence because they learn to depend on others’ opinions and judgments as evidence. Consequently, the literature suggests that social influence on social networking sites may have an impact on consumers’ perceptions of product quality and purchase intention. Consumers are likely to give positive reviews and recommendations for high-quality products, resulting in an increase in purchase intention.

H1b: Product Quality mediates the relationship between marketing activity toward purchase intention for Oppo, Apple, and Samsung smartphones brand in Java Island, Indonesia.

Product quality mediates product features towards purchase intention

In the market, a product is a fundamental component that delivers value to customers, as defined by Kotler et al. (2020). The quality of a product is a critical factor that influences consumers’ purchasing decisions since it impacts the product’s performance, which ultimately affects the customer’s perceived value and satisfaction, according to Alam and Noor (2020).

H2b: Product Quality mediates the relationship between product features toward purchase intention for Oppo, Apple, and Samsung smartphones brand in Java Island, Indonesia.

Product quality mediates price towards purchase intention

According to Kotler et al. (2020), the price of a product or service refers to the amount of money that consumers exchange for the benefits they receive from it. While a company’s policies may influence pricing, there are various factors to consider, such as the product’s specifications and advantages. As Yahya et al. (2022) note, the price of a product is subjective and depends on the perceived value it provides to customers, as well as other market factors like competition, demand, and production costs. Ultimately, a company’s pricing strategy should align with its marketing objectives, target audience, and overall business goals.  Sinaga et al. (2023) stated price influences the purchase intention.

H3b: Product Quality mediates the relationship between price toward purchase intention for Oppo, Apple, and Samsung smartphones brand in Java Island, Indonesia.

Product quality mediates lifestyle towards purchase intention

According to Anwar et al. (2023) and Chen et al. (2017) the quality of a smartphone can significantly impact a user’s lifestyle. With a high-quality smartphone, users are able to seamlessly integrate their devices into their daily routines, resulting in greater efficiency and convenience. The quality of a product can play a critical role in shaping a positive user experience, resulting in greater brand loyalty and repurchase intention. Furthermore, Dianawati et al. (2018) discovered that product quality significantly impacts customers’ overall satisfaction, which can have profound effects on their lifestyle and purchasing behaviors.

H4b: Product Quality mediates the relationship between lifestyle toward purchase intention for Oppo, Apple, and Samsung smartphones brand in Java Island, Indonesia.

Theoretical/Conceptual Framework and Hypothesised Research Model

Figure 1: Conceptual Framework (product quality adapted from Amri and Prihandono (2019); marketing activity adapted from Sawaftah et al. (2020); product features, price and purchase intention adapted from Al Rashid et al. (2020).

Fig. 1. Hypothesised Research model

METHODOLOGY

The study employed a quantitative research method and focused on individuals residing in Java Island, Indonesia. G-Power was used to calculate the minimum sample size (n = 138). Out of 200 respondents approached, 150 completed the survey within the stipulated time frame. To reduce potential response bias and enhance the reliability of the data, the survey was designed to be self-administered, and anonymity was ensured. Respondents were informed that their participation was voluntary and that their responses would remain confidential. This encouraged participants to answer honestly without fear of judgment or repercussion. In this study, hypothesis testing was used to examine the relationships between independent variables, a mediator, and the dependent variable. The objective was to determine whether these factors significantly influenced Indonesian consumers’ purchase intentions toward smartphone brands. A structured survey was developed using Google Forms, suitable for the study’s target population individuals aged 20 and above familiar with online survey platforms. The questionnaire included three screening questions and two demographic questions, followed by 35 closed-ended questions. These comprised five items each for the independent variables (Marketing Activity, Product Features, Price, and Lifestyle), one mediator variable (Product Quality), and one dependent variable (Purchase Intention). All survey questions were presented in English.

The questionnaire was distributed via social media platforms including Facebook, WhatsApp, and Instagram over the course of one month, from May 1 to May 31, 2023. Data analysis was conducted using the Statistical Package for Social Science (SPSS) version 24 and SmartPLS version 4.0. SPSS was utilized for data entry, coding, normality testing, and descriptive statistics. Frequencies, percentages, mean scores, and standard deviations were calculated to summarize the quantitative data. Partial Least Squares – Structural Equation Modeling (PLS-SEM) was employed to assess both the Reflective Measurement Model and the Structural Measurement Model. The Reflective Measurement Model evaluated indicator reliability, internal consistency, and discriminant validity, while the Structural Measurement Model analyzed coefficients of determination (R²), effect sizes (F²), and predictive relevance (Q²). While the study focused on specific variables, it is acknowledged that other factors such as after-sales service, brand reputation, and technological advancements could further enrich the analysis and provide a more holistic understanding of consumer behavior. Future research should consider including these variables to build a more comprehensive model of purchase intention.

RESULT

Respondents’ Profiles

In this study, 150 respondents of people living on Java Island were collected through the questionnaire via Google Form. There are a few sections divided in the questionnaire, in which the first section, required respondents to answer a few basic information about themselves. The question included the gender; age; educational level of the respondents. The demographic profile of respondents (n = 150) as shown in Table 1. From the data collected, it can be examined that most of the respondents are males (50.7%), followed by females (49.3%). On the other hand, most of these respondents were 20-25 years old (61.3%), followed by 26-30 years old (28%) and above 30 years old in the third position with 10.7%. For the educational level, most of these respondents were pursuing or graduated from Bachelor’s Degree (56%). Followed by diploma (16%), and senior high school (28%). Lastly for the budget, most of the respondents (48.7%) spend around Rp 10.000.000 – Rp 20.000.000, followed by under Rp 10.000.000 with 42%, and above Rp 20.000.000 with 9.3%.

Table 1. Respondents’ profiles

Evaluation of Measurement Model

Internal consistency, composite reliable and average variance extracted

In measurement model, the researcher will use internal consistency, composite reliability, convergent validity, and discriminant validity. According to Daud et al. (2018), values of Cronbach’s alpha greater than 0.6 are considered acceptable. Table 2 shows the results of the internal consistency and composite reliability. Cronbach’s alpha value for marketing activity was 0.841, for product features it was 0.803, for price it was 0.635, for lifestyle it was 0.844, for product quality it was 0.819, and for purchase intention it was 0.706. Additionally, this value from each variable may be recognised as acceptable, which shows that the variables are internally consistent.

Table 2. Internal Consistency & Composite Reliability

Hair et al. (2019) mentioned that higher values are frequently associated with higher levels of dependability in composite reliability. According to the above table, the values for marketing activity is 0.849, product features is 0.806, price is 0.637, lifestyle is 0.846, product quality is 0.821, and purchase intention is 0.719. All other variables, with the exception of price, had values above the cutoff point of 0.7 (Hair et al., 2019). However, it revealed that the composite reliability has higher levels of dependability, can be regarded as having strong reliability for the majority of variables, and has met the requirements.

When the other loading of each item is more than 0.70, the validity of convergent items is supported. Additionally, each construct’s average variance extracted (AVE) (see Table 2) is equal to or higher than 0.50 (Hair et al., 2014). The variables for marketing activity, product features, price, lifestyle, product quality, and purchase intention may be regarded as valid and convergent based on the data that has been computed on SmartPLS. However, an acceptable AVE value of 0.50 or above denotes that the construct adequately explains at least 50% of the variation in the study’s items (Hair et al., 2019). According to the AVE table above (see Table 2), the values for marketing activity (0.676), product features (0.560), price (0.578), and lifestyle (0.683) may all be regarded as acceptable. Additionally, a score of 0.581 for product quality and a value of 0.532 for purchase intention are seen as satisfactory.

Convergent Validity

Table 3 shows the results of convergent validity. Convergent validity in SMART PLS is established when the constructs effectively represent their indicators. Key criteria include an Average Variance Extracted (AVE) of at least 0.50, indicating that the construct explains more than 50% of the variance of its indicators, and outer loadings of indicators should be ≥ 0.70, though values between 0.40 and 0.70 may be acceptable under specific conditions. Additionally, composite reliability (CR) should be ≥ 0.70 to ensure internal consistency, complemented by Cronbach’s alpha of ≥ 0.70. These metrics are typically reviewed after running the PLS algorithm, with convergent validity confirmed if all conditions are met. Since all the values exceed 0.70, the model demonstrates strong convergent validity.

Table 3. Convergent validity

Discriminant Validity

The Heterotrait-Monotrait ratio of correlation (HTMT) is a suggested alternative method for evaluating discriminant validity (Henseler et al., 2015). This method relies on a multitrait-multimethod matrix as the measurement’s basis. In order to show discriminant validity between the two reflective ideas, the HTMT value must be less than 0.9 (Henseler et al., 2015). Based on Table 4, all variables have met the requirement for discriminant validity for HTMT with the exception of price towards purchase intention (1.017) and product quality towards purchase intention (1.058). However, HTMT calculations show that the discriminant validity for the relationship between price and purchase intention and purchase intention and product quality is invalid.

Table 4. Discriminant Validity

Structural Model Analysis

In this structural model analysis, the researcher will explain further regarding Collinearity Assessment, Path Coefficient Assessment on Relationships Between Constructs, and Direct Relationship of Independent Variables and Dependent Variable. Based on Collinearity Assessment, all the value are accepted.

Path coefficient assessment on relationships between constructs

According to Mohamed et al. (2018), path coefficient values around or more than 0.5 were thought to indicate big impact sizes, values around 0.3 were thought to indicate medium effect sizes, and values between and below 0.1 were seen to indicate minor effect sizes. When assuming a significance level of 5%, the p value must be smaller than 0.05 to conclude that the relationship under consideration is significant at a 5% level.

Based on the Table 5, the variable with the greatest association or the variable with the medium impact size to buy intention, according to the data in the previous table, is the product quality variable, with a correlation value of 0.457. Furthermore, marketing activity, which has a score of -0.113, is the factor that has the lowest or smallest impact on purchase intention. The t-statistics value are 1.914, 2.152, 2.537, 2.883, and 4.796 consecutively where the value is >1.96 with significant level of =0.05, indicating that there are other variables of variable value of marketing activity, product features, price, and lifestyle which have a link with purchase intention. P value for price towards purchase intention is 0.011, P value for lifestyle towards purchase intention is 0.004, and P value for product features towards purchase intention is 0.031. However, certain P values, such marketing activity towards purchase intention’s 0.056, are erroneous.

Table 5.  Path Coefficient Assessment on Relationships Between Constructs 

Direct relationships of independent variables and dependent variables

A mediator mediates the relationship between independent variables and dependent variable, as shown in Table 5 above, which also showed all evaluations between independent variables and dependent variable. A lifestyle independent variable has a 0.246 correlation coefficient and a high association to purchase intention. The coefficient indicates that there is a considerable association between lifestyle and purchase intention. Additionally, it demonstrates that in Indonesia’s Java Island, lifestyle has a favourable influence on influencing purchase intention. Furthermore, Mohamed  et al. (2018) state that figures around and below 0.1 are indicative of tiny impact sizes, which makes the association between marketing activity and purchase intention the weakest. Marketing activity only has a value of -0.113, according to the Table 5. Moreover, with a correlation value of 0.227, the association between price and purchase intention is the second strongest relationship. It may be characterised as the relationship between price and purchase intention significantly impacting purchase intention in Java Island, Indonesia. The correlation value for the relationship between product features and purchase intention is 0.170. It demonstrates that while this link has a positive influence, it is not sufficiently potent to change consumers’ intentions to make smartphone purchases on Indonesia’s Java Island.

Indirect relationship of independent variables and dependent variables

Product quality becomes the strongest mediator for the relationship between product features and purchase intention with a correlation coefficient of 0.178 (Table 5). It demonstrates that a strong association between product features and purchase intention may be mediated by product quality. Next, with a correlation value of 0.157, product quality continues to play a major role as a mediator in the relationship between price and purchase intention.  Additionally, with a correlation value of 0.067, product quality serves as a good mediator in the relationship between marketing activity and purchase intention. In addition, product quality is the weakest mediator for the relationship between lifestyle and purchase intention. Only a 0.042 correlation coefficient indicates that product quality is an ineffective intermediary in the relationship between lifestyle and purchase intention.

Coefficient of determinant (R2)

As can be seen in the Table 6, the Purchase Intention R-Square value is 0.758, which denotes a favourable association. This outcome indicates that 0.758 or 75.8% of marketing activity, product features, price, and lifestyle have an impact on purchase intention. However, other factors beyond the scope of the study have an impact on the remaining values, which are 24.2%.  The value of R-Square for product quality is 0.616, which indicates a positive relationship. This finding shows that product quality mediates marketing activity, product features, price, lifestyle on purchase intention is 0.616 or 61.6%. However, 38.4% of the remaining values are impacted by factors beyond the scope of the study.

Table 6.  Coefficient of Determinant

Goodness of fit (GoF)

The measurement model and structural approach are both evaluated as part of the overall model’s Goodness of Fit (GoF) Index. R2 is the main metric used to assess the model’s explanatory capacity because PLS does not produce general goodness of fit indices (Wasko et al., 2005). The goodness of fit (GoF) index is a different diagnostic tool provided by Tenenhaus et al. (2005) to evaluate the model fit. The geometric mean of the average communality and the average R2 (for endogenous constructs) are used to calculate the GoF measure. Based on [48], describe using the cut-off values of GoFsmall 0.1, GoFmedium 0.25, and GoFlarge 0.36 to evaluate the findings of the GoF study. For the model used in this study, a GoF value of 0.642 is calculated, which indicates a very good model fit.

Table 7. Goodness of Fit (GoF) r

DISCUSSION

Table 8 illustrates the results of hypotheses testing, focusing on the factors influencing purchase intention. The first hypothesis, which suggests that marketing activity directly impacts purchase intention, was not supported. This indicates that marketing efforts alone may not significantly influence consumers’ intention to purchase a product. Other factors might overshadow marketing activities in driving purchase behavior. On the other hand, the second hypothesis, which examines the effect of product features on purchase intention, was supported. This confirms that well-designed and appealing product features play a significant role in shaping consumers’ purchase decisions. Similarly, the third hypothesis found that price significantly impacts purchase intention, highlighting that consumers are sensitive to pricing and consider it a critical factor when deciding to buy. The fourth hypothesis, which explores the relationship between lifestyle and purchase intention, was also supported. This suggests that consumers’ lifestyles are closely aligned with their buying behavior and preferences, making lifestyle a crucial factor in influencing purchase decisions. Influence of marketing activities and the interaction between lifestyle and product quality on purchase intention may require further exploration in future studies.

Table 8. Hypotheses Supports

When examining the mediating role of product quality, the results were mixed. The hypothesis that product quality mediates the relationship between marketing activity and purchase intention was not supported. This implies that the impact of marketing activities on purchase intention does not depend on perceived product quality. Conversely, the hypothesis that product quality mediates the effect of product features on purchase intention was supported. This indicates that while product features attract consumers, perceived product quality enhances their intent to purchase. The results also supported the hypothesis that product quality mediates the relationship between price and purchase intention. This finding suggests that consumers perceive higher-quality products as justifying their price, strengthening the price-purchase intention link. However, the hypothesis that product quality mediates the relationship between lifestyle and purchase intention was not supported. This indicates that lifestyle factors influence purchase decisions independently of product quality. In summary, the findings highlight the importance of product features, price, and lifestyle in driving purchase intention. Additionally, product quality plays a significant mediating role in enhancing the effects of product features and price on purchase intention.

CONCLUSION

This study aimed to examine whether marketing activity, product features, price, lifestyle, and product quality significantly influence consumers’ intentions to purchase smartphones in Java Island, Indonesia. The findings revealed that these independent variables do have a meaningful impact on purchase intention, with respondents highlighting specific factors such as performance expectations, ease of use, and social influence as key motivators in smartphone adoption and usage. However, this research is not without limitations. The geographic scope was confined to Java Island, which may not fully capture the behavioral diversity across Indonesia’s broader population. Additionally, while the sample size of 150 respondents is adequate for exploratory analysis, it may not sufficiently represent the range of income levels, demographic segments, and cultural differences present in the country. The study’s reliance on self-reported questionnaire data also introduces potential response biases, limiting the objectivity of the results.

Moreover, this research primarily employed quantitative methods, which, although statistically insightful, may have overlooked the deeper psychological and emotional motivations behind consumer decisions. The exclusion of relevant variables such as after-sales service, brand reputation, and technological innovation further narrows the scope of the analysis. Future studies are encouraged to expand the geographic scope beyond Java Island to include other major regions such as Sumatra, Kalimantan, Sulawesi, and Papua to improve generalizability. Researchers should also consider increasing sample size and applying a mixed-methods approach that incorporates qualitative insights. Furthermore, the inclusion of additional variables such as income level, user experience, brand loyalty, and cultural influences would contribute to a more comprehensive understanding of consumer behavior. In conclusion, by addressing these limitations and exploring a broader array of influencing factors, future researchers can develop a more holistic model of purchase intention for smartphone brands such as OPPO, Apple, and Samsung in Indonesia. These insights could be instrumental in helping smartphone companies tailor their marketing strategies to better meet the diverse needs and preferences of their target consumers.

Ethical Considerations

This study was conducted in accordance with ethical guidelines and informed consent was obtained from all participants prior to their involvement in the study. The authors declare no conflicts of interest relevant to this research.

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