Keywords

Product Quality, Perceived Value, Trust and Purchase Intention.

Introduction

The electronic products industry has experienced remarkable growth and innovation in recent years, with consumers confronted by a vast array of choices. In this dynamic landscape, the quality of electronic products stands out as a critical determinant of consumer preferences and behaviors. This study investigates the "Influence of Product Quality on Perceived Value, Trust, and Purchase Intention" within the context of electronic products. Quality, often synonymous with reliability, durability, and performance, has a profound impact on how consumers perceive the value of products, the trust they place in brands, and their intentions to make a purchase. Understanding these interconnections is essential for both businesses seeking to thrive in this competitive market and consumers striving to make informed choices. This study delves into these relationships, shedding light on the multifaceted dynamics between product quality, perceived value, trust, and purchase intention in the realm of electronic products.

The concept of purchasing intention has been extensively explored in the field of marketing research, garnering significant attention from scholars who are interested in understanding the relationship between purchase intention and consumer buying behavior. In addition, researchers have also examined purchase intention as a means of forecasting consumer transactions including both existing and novel goods and services. The provision of purchase intention data can assist professionals in making informed marketing choices pertaining to both new and existing products, market segmentation, and promotional strategies (Tsiotsou, 2006). Furthermore, trust has emerged as a fundamental component in establishing enduring and dependable interpersonal relationships. The utilization of personal relationships has frequently been employed as a means of illustrating the link between the brand and the consumer (Fournier, 1998). According to Aydin and Taskin (2014), the significance of brand trust is revealed in its role in fostering strong relationships and establishing enduring connections between brands and consumers.

Objective

1. To study the influence of percieved quality on percieved value.

2. To study the influence of percieved quality on trust.

3. To determine Perceived quality of electronic products influence purchase intentions.

Literature Review

In the present-day highly competitive market, consumers heavily rely on cues such as price and brand image to make inferences about the quality of the products they intend to purchase (Paulins & Ann, 2005; Oxoby & Finnigan, 2007). Marketing literature posits that these cues are observable characteristics of products or services that enable consumers to draw conclusions about unobservable attributes, such as product durability or service quality, ultimately influencing their perception of product quality (Roest & Rindfleisch, 2010).

A study conducted by Toivonen (2012) has demonstrated that the quality of tangible products can be assessed based on their technical specifications and performance aspects. However, other research, such as that conducted by Sweeney, Soutar, and Johnson (1999), has identified factors like ease of use and the suitability of product features to individual needs as significant contributors to perceived product quality. Furthermore, attributes such as product flawlessness, durability, appearance, and distinctiveness have also been associated with the concept of product quality in various studies (Bao, et al. 2011). According to Zeithaml et al. (2002), the perceived value refers to the comprehensive evaluation of the trade-off between the prominent sacrifices made and advantages received. The direct influence of perceived value on post-purchase behaviors, such as repurchase intentions and word-of-mouth, was further substantiated by Eggert and Ulaga's (2002) research findings. According to Peng et al. (2019), consumers are more likely to exhibit brand loyalty and continue patronizing the same e-store when they perceive a good value in the products or services offered.

The determinants of purchase intention in the context of e-commerce can be categorized into two distinct groups: internal factors and external ones (Ali & Bhasin, 2019; Lin et al. 2021). Internal variables are closely associated with goods and services, encompassing aspects such as product quality, product features, and perceived pricing. Extrinsic factors encompass product aspects that influence purchase intention, including trustworthiness, website quality, customer service quality, and delivery time. Numerous studies have been conducted to investigate the independent impacts of these components; nevertheless, there remains a scarcity of research that integrates these aspects collectively. Various elements, including information quality, system quality, and website quality, have been standardized. Hence, this study examines the significance of perceived pricing (an internal factor) and delivery quality (an external element) in influencing repeat purchase intention. According to Kim et al. (2017), the significance of perceived trust is believed to be greater in online market settings when compared to traditional offline markets. This is attributed to the presence of perceived risk and uncertainty that can arise in the context of online purchase. Consumer perceived value is a crucial factor that drives consumer engagement and involvement in the online shopping experience. There exists a high positive correlation between consumer perceived value and perceived trust, and this association subsequently exhibits a notable influence on consumers' inclinations to participate in online shopping activities. According to Sharma and Klein (2020), According to Chen (2012), the research findings indicate a significant positive relationship between confidence in the website and users' intents to make purchases on the site.

Hypothesis Development

The concept of perceived value has gained popularity in marketing literature as a means to explain consumer buying behavior. Additionally, a growing body of empirical research suggests that businesses can gain a competitive advantage by providing superior value to their customers ( Kuo, Deng & Wu, 2009; Lu & Hsiao, 2010). Consequently, as noted by Levenburg (2005) and Sanchez-Fernandez and Iniesta-Bonillo (2009), perceived value has become a crucial strategic consideration for both retailers and marketers. While there isn't a unanimous definition of customer value in existing literature, several common perspectives suggest that customer perceived value may encompass aspects such as monetary worth, quality, and expected benefits—whether psychological or economic (Lu & Hsiao, 2010).

Various behavioral outcomes are associated with customer perceived value, including customer satisfaction, trust, and purchase intention (Sanchez-Fernandez & Iniesta Bonillo, 2007; Chen, Shang, & Lin, 2008). In this current study, perceived value is defined as the consumer's overall evaluation of a product (or service) based on their perceptions of what they receive compared to what they give (Chen & Chen, 2010).

H1: Perceived quality of electronic products significantly influence perceived value.

Empirical findings from the body of research show that trust is crucial in helping buyers overcome their perceptions of danger and uncertainty when using and accepting the things they purchase. The present study utilizes the definition of trust offered by Chinomona and Cheng (2013), despite the fact that trust is a diverse and comprehensive concept that has been thoroughly investigated across numerous domains. According to this study, "the readiness of one party to expose themselves to the actions of another party, grounded in the expectation that the other party will carry out a specific action of significance to the trustor, regardless of their ability to supervise or control that other party" (Chinomona & Cheng, 2013) is the definition of trust. Brand loyalty, emotional attachment to the brand, the intention to make a purchase, and impulsive buying are among the behavioral effects of trust that have been noted in the literature on retailing, particularly in the context of brand trust (Hong & Cho, 2011; Chinomona, 2013).

H2: Perceived quality of electronic products significantly influence trust.

Purchase intention signifies the likelihood that consumers will intend or be inclined to acquire a specific product or service in the future. An escalation in purchase intention corresponds to an uptick in the probability of making a purchase (Carrillat et al., 2009). As per Hong and Cho (2011), purchase intention effectively serves as a robust predictor of actual purchase behavior. The existing body of research identifies common determinants of consumers' purchase intentions, including factors like brand reputation, satisfaction with the brand, brand loyalty, and the quality of the product or brand (Bian & Forsythe, 2012; Diallo, 2012). In the present study, purchase intention is defined within the study's specific context as the willingness of consumers to formulate plans for the acquisition of a particular product (Carrillat et al., 2009).

H3: Perceived quality of electronic products significantly influence their purchase intentions.

Research Methodology

The present study utilised a blend of descriptive and inferential statistics to achieve the research aims. The descriptive statistics were calculated utilising various variables including the mean, standard deviation, percentage, and frequency. The sample size for the study is 327 and area of study is Chenn

The main instruments utilised in this research encompass the Statistical Package for the Social Sciences (SPSS) and AMOS version 24. In order to ascertain the underlying structure of a particular dataset, an initial exploratory factor analysis (EFA) was conducted. After the elements of the study were completed, Confirmatory Factor Analysis (CFA) was employed to evaluate the suitability of the suggested scale for the investigation. The investigation was ended by employing Structural Equation Modelling (SEM), a multivariate technique that enables the estimation of relationships between all variables in the study by simultaneously assessing multiple regression equations.

Results and Data Analysis

The data presented in the table 1 provides insights into the demographic characteristics of the study's participants. In terms of gender, the sample is fairly balanced, with 58% being male and 42% female. Regarding marital status, a majority of the participants are married, constituting 67% of the sample, while 33% are unmarried.

The distribution of participants across age groups shows a diverse representation. The largest age group is those aged between 36 and 45, making up 48% of the sample, followed by those in the 25-35 age bracket at 31%. Participants under 25 and those above 45 represent smaller proportions at 17% and 4%, respectively.When it comes to education qualifications, the sample exhibits diversity as well. The majority of participants have completed their graduation (48%), followed by post-graduates at 27%. A smaller percentage holds diplomas (3%), while 22% fall into the 'other' category, signifying a range of educational backgrounds.

Normality and Multicollinearity

Prior to undertaking structural equation modelling (SEM), it is imperative to assess the normality assumption of the data by scrutinising the skewness and kurtosis values. According to the findings presented in Table 2, the observed values fall within the range of -2 and +2. This suggests that the data satisfies the normalcy assumption, as stated by Hair et al. (2010).

It is important to acknowledge that high multicollinearity among variables can distort the obtained results. To assess multicollinearity, variance inflation factors (VIF) were utilized as a diagnostic tool. The observed VIF values as reported by Kock (2015), were all below the threshold of 3.3. Thus there is no multicollinearity in data.

Scale Validation

For scale validation we have performed both exploratory and confirmatory factor analysis (CFA) . The sample adequacy was tested using Kaiser- Meyer-Olkin for current sample KMO = 0.912 with significant Bartlett’s sphericity test confirmed sample is good enough to run the exploratory factor analysis. EFA was conducted using principal component analysis with Varimax rotations. Factors having Eigen values above 1 were considered for analysis and finally 13 factors were extracted which are able to explain 75.84% of total variance.

After exploratory factor analysis, CFA was performed on four factors, by considering all exogenous constructs. The fit indices of the measurement model indicates that good indicators such as Chi-square/degree of freedom (CMIN/df) =1.330 {<=3}, Adjusted Goodness of Fit Index (AGFI) = 0.947 {<=0.80}: Comparative Fit Index (CFI) = 0.992 {<=0.90} and Normalized Fit Index (NFI) = 0.969 {<=0.90} all are above the threshold criteria. However, the bad indicator measured using Root Mean Square of Approximation (RMSEA) value = 0.032 is also below 0.08 within threshold limit (Hair et al., 2010) Figure 1.

Figure 1 CFA Analysis Model

The presented table offers a comprehensive overview of the results derived from a factor analysis conducted on various constructs within the study. Four key constructs are examined: "Perceived Quality," "Trust," "Perceived Value," and "Purchase Intention." Each construct comprises multiple items or questions, and the table provides valuable insights into their loadings, skewness, kurtosis, and alpha values.For "Perceived Quality," represented by items PQ1, PQ2, PQ3, and PQ4, the analysis reveals high loadings, indicating a strong association between these items and the construct. The skewness and kurtosis values suggest slight leftward skew and peakedness in the distribution of these items. Importantly, the alpha value of 0.825 indicates good internal consistency among the perceived quality items, confirming their reliability.The construct "Trust," consisting of items T1, T2, and T3, also exhibits strong loadings, emphasizing their relevance to trust measurement. Skewness and kurtosis values describe the distribution characteristics of these trust items. The alpha value of 0.784 reflects a high level of internal consistency among the trust items, reinforcing their reliability.

Moving to "Perceived Value," represented by items PV1, PV2, and PV3, the analysis indicates robust loadings, signifying their alignment with the perceived value construct. Skewness and kurtosis values offer insights into the distribution patterns of these items. Notably, the alpha value of 0.818 attests to the internal consistency of the perceived value items, underscoring their reliability.

Lastly, the construct "Purchase Intention," comprising items PI1, PI2, and PI3, demonstrates strong loadings, confirming their connection to the purchase intention construct. Skewness and kurtosis values characterize the distribution of these items. The alpha value of 0.828 highlights the high internal consistency among the purchase intention items, further affirming their reliability.

These findings from the factor analysis validate the constructs and their respective items within the study, providing assurance of their reliability and relevance for measuring perceived quality, trust, perceived value, and purchase intention. Additionally, the distribution characteristics assessed through skewness and kurtosis values offer insights into the data's normality.

The data presented in the table 3 yields several notable inferences about the relationships between the constructs: "Perceived Quality," "Trust," "Perceived Value," and "Purchase Intention."

Firstly, looking at the means of these constructs, we observe that the participants, on average, have given relatively positive scores to all four constructs. "Trust" received the highest mean score of 3.56, suggesting a relatively high level of trust in the context under study. "Perceived Value" follows closely with a mean score of 3.43, indicating that participants perceive a favorable value proposition. "Perceived Quality" has a mean score of 3.21, reflecting a moderately positive perception. Lastly, "Purchase Intention" registers a mean score of 3.33, suggesting a reasonable intention to make a purchase.

Secondly, the Variance Inflation Factor (VIF) values for "Perceived Quality," "Trust," and "Perceived Value" all fall below critical thresholds, indicating that multicollinearity concerns are not significant among these constructs. This implies that they can be considered as distinct and relatively independent factors within the study.

Thirdly, the correlation matrix reveals valuable insights into the associations between these constructs. Notably, "Perceived Quality" and "Trust" exhibit a moderate positive correlation (0.528**), indicating that as perceived quality increases, so does trust in the product or brand. Additionally, "Perceived Quality" has moderate positive correlations with "Perceived Value" (0.397**) and "Purchase Intention" (0.349**), suggesting that higher perceived quality tends to be associated with more favorable perceptions of value and a greater intention to purchase.

Furthermore, "Trust" and "Perceived Value" show a positive correlation (0.377**), suggesting that trust and the perception of value tend to align positively in the eyes of the participants. Finally, the strongest correlation is between "Perceived Value" and "Purchase Intention" (0.437**), indicating that as participants perceive greater value, their intention to make a purchase becomes more pronounced.

The finding of correlation table indicates that perceived quality positively related with perceived value, trust and purchase intention. All the correlation coefficients values are significant as p value less than 0.05.

Reliability and Validity

The data derived from the measurement model were employed to evaluate the dependability and accuracy of the measurements. Composite reliability and Cronbach's alpha coefficients are frequently utilised to evaluate the dependability of measurement devices in academic research. The values of both indices surpassed the recommended threshold of 0.70, as established by Nunnally and Bernstein (1994). In this work, the criterion used to evaluate the validity of our measurements is the average variance extracted (AVE) values beyond 0.5. Furthermore, we have shown convergent validity by verifying that all observed variables were suitably loaded onto their corresponding constructs, as evidenced in Table 4. In conclusion, the discriminant validity was evaluated using the Fornell and Larcker (1981) criterion. Based on this particular criterion, we conducted a comparison between the average variance extracted (AVE) values of all constructs and the maximum shared variances (MSV). The fulfilment of the discriminant validity requirement can be inferred from the observation that all the average variance extracted (AVE) values exceed the maximum shared variance (MSV).

The provided statistics reveal the reliability and validity of the constructs in the study, which include "Trust," "Perceived Quality," "Perceived Value," and "Purchase Intention." High Construct Reliability (CR) scores for each construct signify strong internal consistency, indicating that the measurement items reliably assess their respective constructs. The Average Variance Extracted (AVE) values demonstrate good construct validity, indicating that these constructs capture a significant proportion of the variance in the data, relative to measurement error. Additionally, the Mean Shared Variance (MSV) highlights moderate shared variance among some constructs, particularly between "Trust," "Perceived Quality," and "Perceived Value," suggesting some overlap in these dimensions. Overall, these findings provide a robust foundation for subsequent analyses and insights into the interrelationships among these constructs in the study.

Hypothesis Testing using SEM

The utilisation of structural equation modelling (SEM) entails the application of the maximum likelihood estimation approach, which is widely employed and favoured for conducting hypothesis testing (Bunch, 2013). The β values of each path in the standardised regression coefficients, along with their related p values, will be utilised as evidence to either validate or refute the specified hypotheses.

The adoption of a research hypothesis is determined by specific criteria, namely a critical ratio (CR) or T-value that exceeds 1.96, and a p-value for the corresponding path that is less than 0.05 at a 5% level of significance.

The findings of structural model as depicted in figure 2 and path coefficients table 5 indicate that perceived quality has significant positive effect on perceived value of electronic products. The β = 0.361, CR = 5.769 and p = 0.000, since p value is less than 0.05 and T value above 1.96, provided sufficient evidence to accept hypothesis H1.

Figure 2 Casual Structure

The impact of perceived quality on trust is positive and significant having β = 0.482 with p =0.000. The p value for this relationship is less than 0.05, thus hypothesis H2 was accepted.

Finally, purchase intention of electronic products is significantly influenced by perceived quality. The β value for this path is 0.309 with p =0.000, which is less than 0.05. Therefore, results confirm the acceptance of hypothesis of H3.

The analysis results indicate significant relationships between the dependent variables ("Perceived Value," "Trust," and "Purchase Intention") and the independent variable "Perceived Quality." The standardized regression weights (β) provide insights into the strength of these relationships.The standardized regression weight (β) for "Perceived Quality" in relation to "Perceived Value" is 0.361. This positive and statistically significant relationship (p < 0.001) suggests that as perceived quality increases, perceived value also tends to increase. Therefore, Hypothesis 1 (H1) is supported.The standardized regression weight (β) for "Perceived Quality" in relation to "Trust" is 0.482. This positive and statistically significant relationship (p < 0.001) indicates that higher perceived quality is associated with greater trust in the product or brand. Thus, Hypothesis 2 (H2) is supported.The standardized regression weight (β) for "Perceived Quality" in relation to "Purchase Intention" is 0.309. This positive and statistically significant relationship (p < 0.001) implies that an increase in perceived quality is linked to a higher intention to purchase. Consequently, Hypothesis 3 (H3) is supported.

The analysis results provide strong empirical support for the hypotheses. Perceived quality is found to be a significant predictor of perceived value, trust, and purchase intention, suggesting its pivotal role in shaping consumer perceptions and intentions within the context of the study.

The findings of the study examining the "Influence of Product Quality on Perceived Value, Trust, and Purchase Intention" in the context of electronic products carry several important implications.Firstly, for businesses operating in the electronic products industry, the study underscores the pivotal role of product quality as a cornerstone of success. Maintaining and improving the quality of electronic products is not merely a matter of meeting technical standards but a strategic imperative. High-quality products not only enhance perceived value but also foster trust in the brand. Therefore, companies should prioritize quality control and innovation to ensure that their electronic products not only meet but exceed consumer expectations.Secondly, the study emphasizes the interconnected nature of perceived quality, trust, and purchase intention. Businesses should recognize that these factors are intertwined in the minds of consumers. By delivering consistent quality, companies can build trust over time, which, in turn, boosts purchase intention. This implies that a holistic approach to customer satisfaction, where product quality is at the core, can yield substantial benefits in terms of brand loyalty and market competitiveness.For consumers, the study serves as a valuable guide in the decision-making process when considering electronic products. It encourages consumers to prioritize product quality as a primary criterion when evaluating options. Recognizing that higher quality often leads to increased perceived value and trust can empower consumers to make more informed and satisfying choices.Lastly, the study opens avenues for further research in the field of consumer behavior and marketing. Researchers can delve deeper into the specific mechanisms through which product quality influences perceived value, trust, and purchase intention, exploring potential moderating factors and examining these relationships in diverse market contexts.The implications of this study illuminate the central significance of product quality in the realm of electronic products, offering valuable insights for businesses, consumers, and future research endeavors. Understanding and leveraging the dynamics between product quality, perceived value, trust, and purchase intention can be a key determinant of success in the electronic products industry.

Conclusion

In conclusion, this study has unveiled the pivotal role that product quality plays in shaping consumer perceptions and behaviors in the electronic products industry. The findings have provided empirical evidence that higher product quality significantly influences perceived value, trust, and purchase intention. Businesses operating in this sector are urged to prioritize quality as a strategic imperative, recognizing that it not only enhances perceived value but also fosters trust, ultimately driving purchase decisions and brand loyalty. For consumers, this study reinforces the importance of considering product quality as a central criterion when making choices in the electronic products market. As electronic products continue to evolve, their quality remains a steadfast anchor in the decision-making process. Additionally, this research serves as a stepping stone for future investigations, encouraging further exploration of the intricate mechanisms and potential moderating factors underlying these relationships. In a world inundated with electronic options, understanding the profound impact of product quality is not merely a matter of preference but a strategic imperative for both businesses and consumers alike.

References

Ali, A., & Bhasin, J. (2019). Understanding customer repurchase intention in E-commerce: Role of perceived price, delivery quality, and perceived value. Jindal Journal of Business Research, 8(2), 142–157.

Indexed at, Google Scholar , Cross Ref

Asshidin, N. H. N., Abidin, N., & Borhan, H. B. (2016). Perceived quality and emotional value that influence consumer’s purchase intention towards American and local products. Procedia Economics and Finance, 35, 639–643.

Indexed at, Google Scholar,  Cross Ref

Bao, Y., Bao, Y., & Sheng, S. (2011). Motivating purchase of private brands: Effects of store image, product signatureness, and quality variation. Journal of Business Research, 64(2), 220–226.

Indexed at, Google Scholar, Cross Ref

Beneke, J., Flynn, R., Greig, T., & Mukaiwa, M. (2013). The influence of perceived product quality, relative price and risk on customer value and willingness to buy: a study of private label merchandise. Journal of Product & Brand Management, 22(3), 218–228.

Indexed at, Google Scholar , Cross Ref

Bian, Q., & Forsythe, S. (2012). Purchase intention for luxury brands: A cross cultural comparison. J ournal of Business Research, 65(10), 1443–1451.

Indexed at, Google Scholar, Cross Ref

Carrillat, F. A., Jaramillo, F., & Mulki, J. P. (2009). Examining the impact of service quality: A meta-analysis of empirical evidence. The Journal of Marketing Theory and Practice, 17(2), 95–110. ‘

Indexed at, Google Scholar

Chen, K.-J., Lin, J.-S. (elaine), & Shan, Y. (2021). Influencer marketing in China: The roles of parasocial identification, consumer engagement, and inferences of manipulative intent. Journal of Consumer Behaviour, 20(6), 1436–1448.

Indexed at, Google Scholar , Cross Ref

Gylling, C., Elliott, R., & Toivonen, M. (2012). Co‐creation of meaning as a prerequisite for market‐focused strategic flexibility. European Journal of Marketing, 46(10), 1283–1301.

Indexed at, Google Scholar , Cross Ref

Hakim, L., Susanti, N., & Ujianto. (2017). Influence of customer relationship management, brand equity, perceived product quality, perceived price on customer value and purchase intention (studies Philips light bulb in Batam). International Journal of Economics and Finance, 9(7), 122.

Indexed at, Google Scholar, Cross Ref

Hong, I. B., & Cho, H. (2011). The impact of consumer trust on attitudinal loyalty and purchase intentions in B2C e-marketplaces: Intermediary trust vs. seller trust. International Journal of Information Management, 31(5), 469–479.

Indexed at, Google Scholar, Cross Ref

Li, Y., & Peng, Y. (2021). Influencer marketing: purchase intention and its antecedents. Marketing Intelligence & Planning, 39(7), 960–978.

Indexed at, Google Scholar , Cross Ref

Oxoby, R. J., & Finnigan, H. (2007). Developing heuristic-based quality judgments: Blocking in consumer choice. Psychology & Marketing, 24(4), 295–313.

Indexed at, Google Scholar , Cross Ref

Roest, H., & Rindfleisch, A. (2010). The influence of quality cues and typicality cues on restaurant purchase intention. Journal of Retailing and Consumer Services, 17(1), 10–18.

Indexed at, Google Scholar , Cross Ref

Sharma, V. M., & Klein, A. (2020). Consumer perceived value, involvement, trust, susceptibility to interpersonal influence, and intention to participate in online group buying. Journal of Retailing and Consumer Services, 52(101946), 101946.

Indexed at, Google Scholar , Cross Ref

Tsiotsou, R. (2006). The role of perceived product quality and overall satisfaction on purchase intentions. International Journal of Consumer Studies, 30(2), 207–217.

Indexed at, Google Scholar , Cross Ref

Wang, Z., & Kim, H. G. (2017). Can social media marketing improve customer relationship capabilities and firm performance? Dynamic capability perspective. Journal of Interactive Marketing, 39, 15–26.

Google Scholar

Received: 05-Jul-2023, Manuscript No. AMSJ-23-13975; Editor assigned: 07-Jul-2023, PreQC No. AMSJ-23-13975(PQ); Reviewed: 08-Aug-2023, QC No. AMSJ-23-13975; Revised: 10-Aug-2023, Manuscript No. AMSJ-23-13975(R); Published: 11-Aug-2023