The Relationships between Perceived Quality, Perceived Value, and Purchase Intentions A Study in Internet Marketing

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1 The Relationships between Quality, Value, and Purchase Intentions A Study in Internet Marketing Man-Shin Cheng, National Formosa University, Taiwan Helen Cripps, Edith Cowan University, Australia Cheng-Hsui Chen, National Yunlin University of Science & Technology, Taiwan Abstract The purpose of this paper is to examine the relationships between price, perceived quality, perceived value, and purchase intentions in internet marketing. When someone browses the web pages on the internet, they can not touch or try the physical products. Although many scholars have studied the topics, few authors studied these relationships in the context of internet. The data was collected by experimental design using undergraduate internet marketing students. The students were given a questionnaire that contained questions 15 on three classes products of based on price for a 1 Gigabyte MP3 player. The products were sold in Taiwan Yahoo s web pages. The students were assigned to browse only the web pages for the three products. In total 246 responses were collected and analysed. The results indicate that there is a positive relationship between perceived quality and perceived value. Although, price is positively associated with perceived quality, price is negatively associated with perceived value. Moreover, perceived value is positively associated with purchase intentions. Introduction The customer s perception of quality is very important to a company as positive perceived quality and perceived value are areas where companies can achieve and sustain success in a competitive marketplace. Although many scholars have studied the relationships between perceived quality, perceived value and purchase intentions (Dodds, Monroe and Grewal, 1991; Mazumder, 1993; Groth, 1995; McLeon, 2002), few authors studied these relationships in the context of internet marketing. When someone browses the web pages on the internet, they can not touch or try the physical products. The consumer evaluates the product with some attributes, such as price, brand, pictures, information from other users on the internet. So the perceived quality and value from consumer s view is very important to anyone who will sell products on internet. Increasingly businesses have realized that adopting e-business practices to gain efficiencies and competitive advantages is now more important than ever (Kleindl, 2003). The purpose of this paper is to examine the relationships between price, perceived quality, perceived value, and purchase intentions in internet marketing. Related Literature Traditionally, price has operated as the major determinant of buyer choice. Although nonprice factors have become more important in recent decades, price still remains one of the most important elements determining market share and profitability (Kotler, 2003). Consumers use price as an indicator of product quality because they believe that market prices are determined by the forces of competitive supply and demand (Grewal et al., 1998).

2 Quality has long been recognized as an important strategic weapon, particularly in developing defensive marketing strategies (Sweeney, Soutar and Johnson, 1999). Quality can be defined broadly as superiority or excellence (Zeithaml, 1988). By extension, perceived quality can be defined as the consumer s judgement about a product s overall excellence or superiority; it also can be viewed as a global assessment that in some cases resembles attitude, and a judgement usually made within a consumer s evoked set (Zeithaml, 1988). This perspective is similar to the user-based approach of Garvin (1983) and differs from product-based and manufacturing-based approaches. Garvin (1983) discussed product-based quality and manufacturing-based quality. Product-based quality refers to amounts of specific attributes or ingredients of a product. Manufacturing-based quality involves conformance to manufacturing specifications or service standards. The possibility of a perceived quality to the price mapping phenomenon is illustrated by Monroe (1973). Dodds, Monroe and Grewal (1991) found that price had a negative effect on a product s value for money, but a positive effect on perceived product quality. This finding of a dual role price is suggested by Monroe (1990). Researchers have posited that value is an evaluation that balances what consumers receive in an exchange versus what they give up (Grewal et al., 1998; Zeithaml, 1988). Kotler (2003) discussed perceived value from customer s perspectives. Customer perceived value is the difference between the prospective customer s evaluation of all the benefits and all the costs of an offering and the perceived alternatives. Many scholars have studied the topic of value and there are a number of ways it is can be defined or measure. McLeon (2002) defined value as the total worth of a product for a consumer, and calculated value with the equation: value = benefit / (cost + price). Mazumder (1993) computed it with the equation: perceived value = perceived benefits perceived sacrifices. Sweeney, Soutar, and Johnson (1999) investigated that the quality led to perceived value. Dodds, Monroe and Grewal (1991) found that perceived quality had a significant effect on perceived value. Marketing scholars have developed a stages model of the buying decision process. Kotler (2003) suggested that the consumer passed through five stages: problem recognition, information search, evaluation of alternatives, purchase decision, and post-purchase behaviour. In the evaluation stage, the consumer forms preferences among the brands in the choice set. The consumer may also form an intention to buy the most preferred brand. Purchase intention has been widely used in the literature as a predictor of subsequent purchase (Grewal et al., 1998). Dodds, Monroe, and Grewal (1991) defined purchase intention as the likelihood that the buyer intended to purchase the product. Although scholars suggested that quality perception was a critical element in purchase decisions (Richardson, Jain, and Dick, 1996), Dodds, Monroe and Grewal (1991) found that perceived quality had a significant effect on perceived value. value has been assumed to influence behavioural and consumption decisions through attitudes (Kim et al., 2002). So, perceived value may intervene between perceived quality and purchase intention. Research framework and hypotheses The research framework is in Figure 1. When a consumer evaluates alternatives, price is a key factor in the information search stage of consumer buying process in internet marketing. In order to control other variables regarding consumers experience with the product category, prior knowledge, prior experience of shopping online, we assigned the three web pages for this experiment. When someone browsed the web pages on the internet, they could not touch or try the physical products. Because the three products were not well-known brands, and the

3 web page pictures just showed the products simply, customer evaluated the product quality with its price. Therefore the higher price of the product, the higher quality that may be perceived by the customer. This conceptualization leads to the following hypothesis: H1: There is a positive relationship between price and perceived quality. Past research has suggested that perceived quality is a key determinant of consumers judgments of value (Grewal et al., 1998). Sweeney, Soutar, and Johnson (1999) investigated hypothesis that the quality led to perceived value. Dodds, Monroe and Grewal (1991) found that perceived quality had a significant effect on perceived value. So, when the perceived quality of a product is high, its perceived value is high. quality H2 value Price H1 H3 H4 Purchase intention Figure 1 Research Framework value is the difference between the prospective customer s evaluation of all the benefits and all the costs of an offering and the perceived alternatives (Kotler, 2003). The price of product is the cost to consumer when they pay for product. When the price is higher, the cost is higher. And then, perceived value of product in internet marketing will be lower. Thus, we test the following hypotheses: H2: There is a positive relationship between perceived quality and perceived value. H3: There is a negative relationship between price and perceived value. Past research had found that purchase intention was positively associated with perceived value (Dodds, Monroe and Grewal, 1991; Grewal, Monroe, and Krishnan, 1998). When the perceived value of a product is higher than another product providing the same function, the consumer may prefer this product than others. So, we hypothesize that: H4: There is a positive relationship between perceived value and purchase intentions. Methodology These hypotheses were tested using causal modelling. This study used a 3-class experimental design. The price was measured by 3 classes where (3 = high price; 2 = medium price; 1 = low price). quality (five items), perceived value (five items), and purchase intentions (five items) were measured using five-point Likert-type scales where (1 = very low; 5 = very high). The items were modified from previous study (Dodds, Monroe, and Grewal, 1991).

4 The items of perceived quality measured were: (1)the likelihood that the product would be reliable; (2)the workmanship of product; (3)The sense of product quality; (4)the likelihood that this product is dependable; (5)this product would seem to be durable. The items of perceived value measured were: (1)the value of product for the money; (2)the price of the product is economical; (3)the product is considered to be a good buy; (4)the price of the product is acceptable; (5)this product appears to be a bargain. The items of purchase intentions measured were: (1) the likelihood of purchasing this product; (2)if I were going to buy this product, I would consider buying this model at the price; (3)at the price shown, I would consider buying the product; (4)the probability that I would consider buying the product is high; (5)my willingness to buy the product is high. The data was collected by an experimental design from 82 university students in internet marketing class. The students were given a questionnaire that contained 15 questions on three classes of products based on price for a 1 Gigabyte MP3 player. The products were sold in Taiwan Yahoo s web pages (http://buy.yahoo.com.tw/). All of the three products were not well-known brands. The price of the first product was NT$5890 ($245.90AUS). The prices of the second and third products were NT$3990 ($166.55AUS) and NT$2290 ($95.60AUS) respectively. In order to control variables which were not in the framework (e.g., brands of the products, and web design, such as font used, pictures, colour, technical details, etc.), the students were assigned to browse only the web pages for the three products. For each of the three products the students browsed the web pages and answered the 15 items for perceived value, quality and purchase intentions, which resulted in 246 responses. Research Results The data was analysed in two stages. The measurement model was assessed to confirm that the scales were unidimensional and reliable. When the reliability of the measures had been established, the structural model was tested using SAS causal modelling procedures. The reliability of the instrument (Frankfort-Nachmias and Nachmias, 1996; Kerlinger, 1992) was tested using a Cronbach α to check the reliability of perceived quality, perceived value, and purchase intention. Cronbach α of perceived quality, and purchase intentions were 0.91, Cronbach α of perceived value was 0.84 when the first item (the value of product for the money) and the last item (this product appears to be a bargain) of the construct were deleted (Cronbach α was 0.69 when the construct included these two items). This study used confirmatory factor analysis to test validity. The overall fit of the model was determined initially by examining the χ2 statistics for this study, which was significant (χ 2= , d.f.=62, p<0.0001). A significantχ2 statistics may indicate an inadequate fit, but this statistics is sensitive to sample size and model complexity; therefore, rejection of a model on the basis of this evidence alone is inappropriate (Bagozzi and Yi, 1988; Bearden, Sharma, and Teel,1982; Marsh, Balla, and McDonald, 1988). For this reason, many researchers supplement the chi-square test with a number of other stand-alone goodness of fit indexes. In some cases, these indexes may reveal a relatively good fit even when the chi-square test suggests rejection of the model (Hatcher, 2003). Accordingly, other measures of fit were also applied: Bentler and Bonett s (1980) normed fit index (NFI), Tucker and Lewis s (1973) nonnormed fit index (NNFI), Bentler s (1990) comparative fit index (CFI), goodness of fit index (GFI), GFI adjusted for degrees of freedom (AGFI), root mean square residual (RMR). NFI (=0.9062), NNFI (=0.9091), and CFI (=0.9278) were greater than 0.9. Although GFI (=0.8647) and AGFI (=0.8014) were not greater than 0.9, they were near 0.9. RMR (=0.0753) was

5 smaller than These indexes showed that this model was adequate. The assessment of the measurement properties of all scales indicated that the factor loadings were all higher than 0.5 and significant (p<.05) for each item respectively, which satisfied the criteria for convergent validity. Then, the structural model (Figure 1) was used to test hypotheses. The χ2 statistics for the overall fit of the model was significant (χ2= , d.f.=74, p<0.0001). Other measures of fit were listed below: NFI was ; NNFI was ; CFI was ; GFI was ; AGFI was ; RMR was Both of NFI, NNFI, and CFI were greater than 0.9, although GFI and AGFI were not greater than 0.9. RMR was smaller than These indexes showed that this model was adequate. The results are showed in Figure 2. The relationship between price and perceived quality was significant. The direct effect was So, H1 hypothesis was supported. The relationship between perceived quality and perceived value was significant, and its direct effect was Hence H2 was supported. H3 was supported because the direct effect between price and perceived value was Finally, H4 was also supported because the direct effect between perceived value and purchase intentions was quality value Price Figure 2 Results Purchase intentions Discussion and Conclusion The central finding is that price has a negative direct effect on perceived value in internet marketing context. Price has a positive direct effect on perceived quality, and perceived quality has a positive direct effect on perceived value. This due role for price supports Monroe (1990). The research also finds that perceived value has a positive direct effect on purchase intentions in internet marketing. So if the price is high then the perceived quality is high and so too the perceived value and the purchase intentions. When consumers browse the web pages on the internet, they may use prices to evaluate the products which is not wellknown. Higher price means higher quality. The higher-quality-product shows higher perceived value and increases the consumer s purchase intention. When perceived quality is taken out of the process if the price is high then the perceived value is low which may reduce the purchase intentions. So, one should use the price carefully when he or she sells products in internet marketing. Despite being set in the virtual realm where direct contact with the products is limited these results support those of previous research (Grewal et al., 1998; Sweeney et al., 1999; Dodds et al., 1991). The implication is that consumers may believe that an internet advertisement portraying high quality is likely to positively impact the products perceived value and increase purchase intentions. An enterprise should re-enforce a high quality image of their products for consumers browsing and purchasing via the internet. Many constructs may be considered in this framework. In the future, perceived sacrifices may be examined as a mediator between price and perceived value in internet marketing. Other attributes, such as brand, web pages design, may also be tested whether the direct effects on perceived quality and perceived value exist or not.

6 References Bagozzi, R. P., and Yi, Y., 1988, "On the evaluation of structural equation models", Journal of the Academy of Marketing Science, Spring, Vol.16, pp Bearden, S. S., Sharma, S., and Teel, J. E., 1982, "Sample size effects on chi-square and other statistics used in evaluating causal models", Journal of Marketing Research, November, Vol.19, pp Bentler, P. M., 1990, "Comparative fit indexes in structural models", Psychological Bulletin, Vol.107, No.2, pp Bentler, P. M., and Bonett, D. G., 1980, "Significance tests and goodness of fit in the analysis of covariance structures", Psychological Bulletin, Vol.8, No.3, pp Dodds, W. B., Monroe, K. B., and Grewal, D., 1991, "Effects of price, brand, and store information on buyers product evaluations", Journal of Marketing Research, August, Vol.28, No.3, pp Frankfort-Nachmias, C., and Nachmias, D., 1996, Research Methods in the Social Sciences, 5th ed., N.Y.: St. Martin's Press. Garvin, D. A., 1983, "Quality on the line", Harvard Business Review, Vol.61, pp Grewal, D., Krishnan, R., Baker, J., and Borin, N., 1998, "The effect of store name, brand name and price discounts on consumers evaluations and purchase intentions", Journal of Retailing, Vol.74, No.3, pp Grewal, D., Monroe, K. B., and Krishnan, R., 1998, "The effects of price-comparison advertising on buyers perceptions of acquisition value, and behavioral intentions", Journal of Marketing, Vol.62, No.2, pp Groth, J., 1995, "Important factors in the sale and pricing of services", Management Decision, Vol.33, No.7, pp Hatcher, L., 2003, A Step-by-Step Approach to Using SAS for Factor Analysis and Structural Equation Modeling, 6th ed., Cary,NC:SAS Institute Inc. Kerlinger, F. N., 1992, Foundations of Behavioral Research, 3th ed., Florida: Harcourt Brace Company. Kim, J. O., Forsythe, S., Gu, Q., and Moon, S. J., 2002, "Cross-cultural consumer values, needs and purchase behavior", The Journal of Consumer Marketing, Vol.19, No.6, pp Kleindl, B. A., 2003, Strategic electronic marketing managing e-business, 2 nd ed., Thomson Learning. Kotler, P., 2003, Marketing Management, 11 th ed., Prentice Hall.

7 Marsh, H. W., Balla, J. R., and McDonald, R. P., 1988, "Goodness-of-fit indexes in confirmatory factor analysis: the effects of sample size", Psychological Bulletin, Vol.103, No.3, pp Mazumder, T., 1993, "A value-based orientation to new product planning", The Journal of Consumer Marketing, Vol.10, No.1, pp McLeon, R., 2002, "Getting to value", Management Quarterly, Fall, Vol.43, No.3, pp Monroe, K. B., 1973, "Buyers subjective perceptions of price", Journal of marketing, February, Vol.10, pp Monroe, K. B., 1990, Pricing: making profitable decisions, 2 nd ed., McGraw-Hill. Richardson, P. S., Jain, A. K., and Dick, A., 1996, "Household store brand proneness: a framework", Journal of Retailing, Vol.72, No.2, pp Sweeney, J. C., Soutar, G. N., and Johnson, L. W., 1999, "The role of perceived risk in the quality-value relationship: a study in a retail environment", Journal of Retailing, Vol.75, No.1, pp Tucker, L. R., and Lewis, C., 1973, "A reliability coefficient for maximum likelihood factor analysis", Psychometrika, Vol.38, No.1, pp Zeithaml, V. A., 1988, "Consumer perceptions of price, quality, and value: a means-end model and synthesis of evidence", Journal of Marketing, July, Vol.52, No.3, pp.2-22.

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