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Study on Perceived Quality and Perceived Fair Price
Available online at www.sciencedirect.com
ScienceDirect Procedia Economics and Finance 15 (2014) 1304 – 1309
Emerging Markets Queries in Finance and Business
Study on perceived quality and perceived fair price Lucian Pitica, Stelian Bradb, Diana Piticb a Technical University, B-dul Muncii nr. 103-105, Cluj-Napoca 400641, Romania BabeЮ - Bolyai University, Teodor Mihali Street 58-60, Cluj –Napoca 400591, Romania
b
Abstract Does improved quality really pay off? This paper focuses on the relationship between perceived quality and perceived fair price as key component for customer value. A quantitative relationship between quality level and perceived fair price level is established in order to determine value perception by the customer. Based on empirical data, factors influencing the perceived quality and their importance are determined and quality levels are calculated. Client perceived fair price is also collected and linked to different quality levels. A retail business is used as case study to suggestively illustrate the theoretical approach. © 2014 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license © 2013 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Emerging (http://creativecommons.org/licenses/by-nc-nd/3.0/). Markets Queries Finance and local organization underinresponsibility of Business the Emerging Markets Queries in Finance and Business local organization Selection and peer-review Keywords: perceived quality, perceived fair price, value perception
1. Introduction In today’s economy customer value creation is a wide spread concern. Although there are several approaches to achieve this goal, some companies consider quality as the central piece for customer value creation. Numerous references in literature support the tight relationship between perceived value and perceived quality (Graf & Maas, 2008; Gallarza, Saura, & Holbrook, 2011; Hua, Kandampullyb, & Juwaheerc, 2009; Beneke, Flyn, Greig & Mukaiva, 2013). Some authors established there is a link between these two (Kotri, 2006; Gallarza, Saura, & Holbrook, 2011; Arslan & Altuna, 2010), while others only suggest a positive influence of perceived quality on perceived value (Yee & San, 2011). A recent research also shows a strong relationship between perceived price and perceived value (Beneke, Flyn, Greig & Mukaiva, 2013). The present research is aimed at determining a quantitative relationship between perceived quality and perceived fair price as determinants of customer value.
2212-5671 © 2014 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/). Selection and peer-review under responsibility of the Emerging Markets Queries in Finance and Business local organization doi:10.1016/S2212-5671(14)00592-9
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Lucian Pitic et al. / Procedia Economics and Finance 15 (2014) 1304 – 1309
2. Research objectives The scope of this paper is to determine a link between quality perception and fair price perception. Considering that customer quality perception is influenced by a set of product characteristics, determining these characteristics and their influence on client perception of quality is also a goal of this research. After establishing a perceived quality hierarchy for the analyzed products, clients’ perception on fair price related to quality is gathered. With this information, a quantitative link is established between perceived quality and perceived fair price. 3. Research methodology For achieving the goal of this research paper a step-by-step methodology has been developed, based on a prior research done by the authors (Pitic & Brad, 2011). Step one involves selecting relevant characteristics of the analyzed product. All characteristics that are relevant to client perception of quality have to be considered. For each characteristic a set of relevant values or value ranges has to be defined. As a next step, a conjoint analysis is performed by using the selected characteristics from the first step. The result of the conjoint analysis should be a percentage of importance for each characteristic, revealing how important a characteristic is for client perceived quality. Another important result from the conjoint analysis, which will be used in step three, is the relative importance for each value of the characteristic. Step number three involves the development of a hierarchy of quality levels. The perceived quality level will be calculated using the selected attribute values from step 1 and the percentages of influence on the perception of quality determined in step 2. In order to set up different levels of perceived quality, the relative perceived quality for each product is being determined by using formula 1. Each quality level can contain multiple products that fit the values of those characteristics for that level. n
QPi = ¦ I k ⋅ aik k =1
(1)
QPi – determined perceived quality of the product n – number of characteristics considered for the given product I – perceived importance of the characteristic (in percentages) ai – relative importance (in percentages) Step number 4 involves the development and application of a questionnaire on a representative number of potential buyers to determine a fair price level for each customer perceived quality level defined in step 3. Also a polynomial regression is applied on the results in order to attain a function describing the relationship between the perceived fair price and product’s perceived quality. If there is any segmentation criteria used, a function can be determined for each segment to highlight the differences between them. 4. Case study In order to have a clearer perspective on the use of the proposed methodology, a simple case study is presented. The products analyzed are electronic cigarettes, also known as personal vaporizers. This study has been made in collaboration with a company that is specialized in selling this type of goods. First phase consisted in consulting a number of specialists from the company that are responsible for these products. As a result, the relevant characteristics were chosen and a set of possible values have been established, as shown in table 2. Table 2. Relevant characteristics to the perceived quality assessment of an electronic cigarette
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Lucian Pitic et al. / Procedia Economics and Finance 15 (2014) 1304 – 1309
Nr.
Characteristic
Characteristics-possible values
1
Size (Length)
Small ~ 10 cm Medium ~ 13 cm Large ~17 cm
2
Battery life in use
For ~ 400 puffs For ~ 700 puffs For ~ 1100 puffs
3
Possibility of front USB charging during use
Yes No
4
Color
Black, silver or white Colored
5
Variable battery voltage
Yes No
For the second step a conjoint analysis was performed by questioning a number of 62 potential customers of a retail chain specialized in the sale of these products. The results are shown in table 3. Table 3. The results of the conjoint analysis
Nr.
Characteristic
Importance
CharacteristicsPossible values
Relative importance
Relative importance expressed in percentages (%)
1
Size (Length)
14.71%
Small ~ 10 cm
0.298
71.8%
Medium ~ 13 cm
0.415
100%
Large ~17 cm
0.291
70.12%
For ~ 400 puffs
0.090
17.68%
For ~ 700 puffs
0.334
65.61%
For ~ 1100 puffs
0.509
100%
Yes
0.479
100%
No
0.236
49.27%
Black, silver or white
0.330
97.92%
Colored – Various colors
0.337
100%
Yes
0.358
100%
No
0.308
86.03%
2
3
4
5
Battery life in use
Possibility of front USB charging during use
Color
Variable battery voltage
49.70%
28.83%
0.83%
5.93%
Based on the results from step 2 quality levels have been calculated for a number of 36 combinations of characteristics matching the products that are up for sale in the shops where the analysis has been conducted.
Lucian Pitic et al. / Procedia Economics and Finance 15 (2014) 1304 – 1309
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All possible theoretical combinations of characteristics that have no corresponding actual product up for sale have been disregarded. In figure 1, the quality levels for the analyzed combinations corresponding to actual products are represented.
Fig. 1. Quality levels
A total of nine quality levels have been defined, where Q9 corresponds to the highest level of perceived quality and Q1 to the lowest. A comparison of characteristics and their values for the two extremities is presented in table 3. A quick analysis of the data presented in table 3 and 4 reveals that battery life and USB charge during use weigh the most in quality perception, size has some influence while variable battery voltage and color have almost no influence at all. It is also important to note that the combination that would sum up to 100% perceived quality is not amongst the products that are in the shop’s offering. Table 4. Comparison between the highest and lowest quality levels Perceived Quality level
Q1
Q9
Size (length)
Small ~ 10 cm
Medium ~ 13 cm
Battery life in use
For ~ 400 puffs
For ~ 1100 puffs
Possibility of front USB charging during use
No
Yes
Color
Any
Any
Variable battery voltage
No
Yes
For each quality level the average perceived quality has been calculated. Table 4 shows the average perceived value for each quality level.
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Lucian Pitic et al. / Procedia Economics and Finance 15 (2014) 1304 – 1309
Table 5. Perceived quality levels Perceived Quality level
Value range for perceived quality
Average perceived quality of the quality level
Q1
38.65% - 40.63%
38.65%
Q2
42.8% - 43.63%
43.22%
Q3
57.43% - 58.26%
57.84%
Q4
62.23% - 63.06%
62.56%
Q5
66.62% - 67.45%
67.04%
Q6
76.85% - 81.25%
79.42%
Q7
82.8% - 84.5%
83.45%
Q8
93.95% - 94.77%
94.36%
Q9
98.34% - 99.17%
98.76%
Based on the defined levels of quality a new questionnaire has been drawn up in order to link customer perceived fair price to quality level. Respondents were asked to indicate a fair price for each quality level. The questionnaire has been applied to 65 potential customers of the same retail chain. Responses to this questionnaire are graphically represented in figure 2.
Fig. 2. Perceived fair price in correlation to quality level
By applying a polynomial regression on the gathered data, a function has been determined that correlates perceived quality level land perceived fair price. It can be observed that the slope of the curve is starting to decrease once the quality level is over 90%, thus leading us to conclude that in this case the difference in quality between the top two quality levels is not proportionally translated into an increase in value.
Lucian Pitic et al. / Procedia Economics and Finance 15 (2014) 1304 – 1309
5. Conclusions This paper shows that it is possible to link perceived fair price and perceived quality. By applying the proposed methodology in practice, useful information regarding products quality, as viewed by customers, can be obtained. By associating fair price to quality highlights what client value. This research also highlights that perceived quality is not necessarily directly proportional to perceived fair price. The results lead us to suggest that not all increases in quality proportionally contribute to increasing customer value. While in this case the last step towards top quality is not so valued in term of fair price, with other products there can be a different situation. Further research is still necessary in order to establish whether this function pattern is similar to other products. By performing this analysis on companies’ offering one can easily see where they “missed a spot”. The perceived quality gap between products in Q2 and Q3 is 13.8%, indicating a lack of offering for a certain segment. It can also be noted that there is no product up for sale that matches the combination of characteristics that give 100% quality. Although the majority of responses do not consider top quality to be worth that much more, there are some responses, as seen in figure two, that suggest there is a category of clients that would consider it fairly to pay much more for top quality. By using this methodology and product profit margin analysis companies’ could adapt they offerings to better fit the market and increase their profits. The selection of criteria for quality perception assessment can constitute an issue for the presented methodology. If relevant criteria or their values are not properly chosen different results can be obtained for the same products. Also top quality is limited to the best possible combination of values for the selected criteria, excluding therefore other client wishes from the analysis. Improving this methodology in order to include possible client wishes could be a subject for further research.
Acknowledgements This paper was supported by the project "Improvement of the doctoral studies quality in engineering science for development of the knowledge based society-QDOC” contract no. POSDRU/107/1.5/S/78534, project co-funded by the European Social Fund through the Sectorial Operational Program Human Resources 2007-2013. All authors are principal authors of the paper. The first author is the corresponding author.
References Arslan, F. M., & Altuna, O. K. (2010). The effect of brand extensions on product brand image. Journal of Product & Brand Management, 19(3), 170-180. 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, Vol. 22 Iss: 3 Gallarza, M. G., Saura, I. G., & Holbrook, M. B. (2011, iulie 26). The value of value: Further excursions on the meaning and role of customer value. Journal of Consumer Behaviour, 179-191. Graf, A., & Maas, P. (2008). Customer value from a customer perspective: a comprehensive review. JOURNAL FÜR BETRIEBSWIRTSCHAFT, 58(1), 1-20. Hua, H.-H., Kandampullyb, J., & Juwaheerc, T. D. (2009). Relationships and impacts of service quality, perceived value, customer satisfaction, and image: an empirical study. The Service Industries Journal, 29(2), 111-125. Kotri, A. (2006). Analzying customer value using conjoint analysis: the example of a packaging company. Takenin may 2, 2012, from http://www.mtk.ut.ee/orb.aw/class=file/action=preview/id=207677/febawb46.htm Pitic, S. L., & Brad, S. (2011). Methodology for value-to-money modeling. Business Trends – Reviewed conference proceeding, 1-5 Yee, C. J., & San, N. C. (2011). Consumers’ Perceived Quality, Perceived Value and Perceived Risk - Towards Purchase Decision on Automobile. American Journal of Economics and Business Administration, 47-57.
1309
ScienceDirect Procedia Economics and Finance 15 (2014) 1304 – 1309
Emerging Markets Queries in Finance and Business
Study on perceived quality and perceived fair price Lucian Pitica, Stelian Bradb, Diana Piticb a Technical University, B-dul Muncii nr. 103-105, Cluj-Napoca 400641, Romania BabeЮ - Bolyai University, Teodor Mihali Street 58-60, Cluj –Napoca 400591, Romania
b
Abstract Does improved quality really pay off? This paper focuses on the relationship between perceived quality and perceived fair price as key component for customer value. A quantitative relationship between quality level and perceived fair price level is established in order to determine value perception by the customer. Based on empirical data, factors influencing the perceived quality and their importance are determined and quality levels are calculated. Client perceived fair price is also collected and linked to different quality levels. A retail business is used as case study to suggestively illustrate the theoretical approach. © 2014 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license © 2013 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Emerging (http://creativecommons.org/licenses/by-nc-nd/3.0/). Markets Queries Finance and local organization underinresponsibility of Business the Emerging Markets Queries in Finance and Business local organization Selection and peer-review Keywords: perceived quality, perceived fair price, value perception
1. Introduction In today’s economy customer value creation is a wide spread concern. Although there are several approaches to achieve this goal, some companies consider quality as the central piece for customer value creation. Numerous references in literature support the tight relationship between perceived value and perceived quality (Graf & Maas, 2008; Gallarza, Saura, & Holbrook, 2011; Hua, Kandampullyb, & Juwaheerc, 2009; Beneke, Flyn, Greig & Mukaiva, 2013). Some authors established there is a link between these two (Kotri, 2006; Gallarza, Saura, & Holbrook, 2011; Arslan & Altuna, 2010), while others only suggest a positive influence of perceived quality on perceived value (Yee & San, 2011). A recent research also shows a strong relationship between perceived price and perceived value (Beneke, Flyn, Greig & Mukaiva, 2013). The present research is aimed at determining a quantitative relationship between perceived quality and perceived fair price as determinants of customer value.
2212-5671 © 2014 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/). Selection and peer-review under responsibility of the Emerging Markets Queries in Finance and Business local organization doi:10.1016/S2212-5671(14)00592-9
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Lucian Pitic et al. / Procedia Economics and Finance 15 (2014) 1304 – 1309
2. Research objectives The scope of this paper is to determine a link between quality perception and fair price perception. Considering that customer quality perception is influenced by a set of product characteristics, determining these characteristics and their influence on client perception of quality is also a goal of this research. After establishing a perceived quality hierarchy for the analyzed products, clients’ perception on fair price related to quality is gathered. With this information, a quantitative link is established between perceived quality and perceived fair price. 3. Research methodology For achieving the goal of this research paper a step-by-step methodology has been developed, based on a prior research done by the authors (Pitic & Brad, 2011). Step one involves selecting relevant characteristics of the analyzed product. All characteristics that are relevant to client perception of quality have to be considered. For each characteristic a set of relevant values or value ranges has to be defined. As a next step, a conjoint analysis is performed by using the selected characteristics from the first step. The result of the conjoint analysis should be a percentage of importance for each characteristic, revealing how important a characteristic is for client perceived quality. Another important result from the conjoint analysis, which will be used in step three, is the relative importance for each value of the characteristic. Step number three involves the development of a hierarchy of quality levels. The perceived quality level will be calculated using the selected attribute values from step 1 and the percentages of influence on the perception of quality determined in step 2. In order to set up different levels of perceived quality, the relative perceived quality for each product is being determined by using formula 1. Each quality level can contain multiple products that fit the values of those characteristics for that level. n
QPi = ¦ I k ⋅ aik k =1
(1)
QPi – determined perceived quality of the product n – number of characteristics considered for the given product I – perceived importance of the characteristic (in percentages) ai – relative importance (in percentages) Step number 4 involves the development and application of a questionnaire on a representative number of potential buyers to determine a fair price level for each customer perceived quality level defined in step 3. Also a polynomial regression is applied on the results in order to attain a function describing the relationship between the perceived fair price and product’s perceived quality. If there is any segmentation criteria used, a function can be determined for each segment to highlight the differences between them. 4. Case study In order to have a clearer perspective on the use of the proposed methodology, a simple case study is presented. The products analyzed are electronic cigarettes, also known as personal vaporizers. This study has been made in collaboration with a company that is specialized in selling this type of goods. First phase consisted in consulting a number of specialists from the company that are responsible for these products. As a result, the relevant characteristics were chosen and a set of possible values have been established, as shown in table 2. Table 2. Relevant characteristics to the perceived quality assessment of an electronic cigarette
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Nr.
Characteristic
Characteristics-possible values
1
Size (Length)
Small ~ 10 cm Medium ~ 13 cm Large ~17 cm
2
Battery life in use
For ~ 400 puffs For ~ 700 puffs For ~ 1100 puffs
3
Possibility of front USB charging during use
Yes No
4
Color
Black, silver or white Colored
5
Variable battery voltage
Yes No
For the second step a conjoint analysis was performed by questioning a number of 62 potential customers of a retail chain specialized in the sale of these products. The results are shown in table 3. Table 3. The results of the conjoint analysis
Nr.
Characteristic
Importance
CharacteristicsPossible values
Relative importance
Relative importance expressed in percentages (%)
1
Size (Length)
14.71%
Small ~ 10 cm
0.298
71.8%
Medium ~ 13 cm
0.415
100%
Large ~17 cm
0.291
70.12%
For ~ 400 puffs
0.090
17.68%
For ~ 700 puffs
0.334
65.61%
For ~ 1100 puffs
0.509
100%
Yes
0.479
100%
No
0.236
49.27%
Black, silver or white
0.330
97.92%
Colored – Various colors
0.337
100%
Yes
0.358
100%
No
0.308
86.03%
2
3
4
5
Battery life in use
Possibility of front USB charging during use
Color
Variable battery voltage
49.70%
28.83%
0.83%
5.93%
Based on the results from step 2 quality levels have been calculated for a number of 36 combinations of characteristics matching the products that are up for sale in the shops where the analysis has been conducted.
Lucian Pitic et al. / Procedia Economics and Finance 15 (2014) 1304 – 1309
1307
All possible theoretical combinations of characteristics that have no corresponding actual product up for sale have been disregarded. In figure 1, the quality levels for the analyzed combinations corresponding to actual products are represented.
Fig. 1. Quality levels
A total of nine quality levels have been defined, where Q9 corresponds to the highest level of perceived quality and Q1 to the lowest. A comparison of characteristics and their values for the two extremities is presented in table 3. A quick analysis of the data presented in table 3 and 4 reveals that battery life and USB charge during use weigh the most in quality perception, size has some influence while variable battery voltage and color have almost no influence at all. It is also important to note that the combination that would sum up to 100% perceived quality is not amongst the products that are in the shop’s offering. Table 4. Comparison between the highest and lowest quality levels Perceived Quality level
Q1
Q9
Size (length)
Small ~ 10 cm
Medium ~ 13 cm
Battery life in use
For ~ 400 puffs
For ~ 1100 puffs
Possibility of front USB charging during use
No
Yes
Color
Any
Any
Variable battery voltage
No
Yes
For each quality level the average perceived quality has been calculated. Table 4 shows the average perceived value for each quality level.
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Lucian Pitic et al. / Procedia Economics and Finance 15 (2014) 1304 – 1309
Table 5. Perceived quality levels Perceived Quality level
Value range for perceived quality
Average perceived quality of the quality level
Q1
38.65% - 40.63%
38.65%
Q2
42.8% - 43.63%
43.22%
Q3
57.43% - 58.26%
57.84%
Q4
62.23% - 63.06%
62.56%
Q5
66.62% - 67.45%
67.04%
Q6
76.85% - 81.25%
79.42%
Q7
82.8% - 84.5%
83.45%
Q8
93.95% - 94.77%
94.36%
Q9
98.34% - 99.17%
98.76%
Based on the defined levels of quality a new questionnaire has been drawn up in order to link customer perceived fair price to quality level. Respondents were asked to indicate a fair price for each quality level. The questionnaire has been applied to 65 potential customers of the same retail chain. Responses to this questionnaire are graphically represented in figure 2.
Fig. 2. Perceived fair price in correlation to quality level
By applying a polynomial regression on the gathered data, a function has been determined that correlates perceived quality level land perceived fair price. It can be observed that the slope of the curve is starting to decrease once the quality level is over 90%, thus leading us to conclude that in this case the difference in quality between the top two quality levels is not proportionally translated into an increase in value.
Lucian Pitic et al. / Procedia Economics and Finance 15 (2014) 1304 – 1309
5. Conclusions This paper shows that it is possible to link perceived fair price and perceived quality. By applying the proposed methodology in practice, useful information regarding products quality, as viewed by customers, can be obtained. By associating fair price to quality highlights what client value. This research also highlights that perceived quality is not necessarily directly proportional to perceived fair price. The results lead us to suggest that not all increases in quality proportionally contribute to increasing customer value. While in this case the last step towards top quality is not so valued in term of fair price, with other products there can be a different situation. Further research is still necessary in order to establish whether this function pattern is similar to other products. By performing this analysis on companies’ offering one can easily see where they “missed a spot”. The perceived quality gap between products in Q2 and Q3 is 13.8%, indicating a lack of offering for a certain segment. It can also be noted that there is no product up for sale that matches the combination of characteristics that give 100% quality. Although the majority of responses do not consider top quality to be worth that much more, there are some responses, as seen in figure two, that suggest there is a category of clients that would consider it fairly to pay much more for top quality. By using this methodology and product profit margin analysis companies’ could adapt they offerings to better fit the market and increase their profits. The selection of criteria for quality perception assessment can constitute an issue for the presented methodology. If relevant criteria or their values are not properly chosen different results can be obtained for the same products. Also top quality is limited to the best possible combination of values for the selected criteria, excluding therefore other client wishes from the analysis. Improving this methodology in order to include possible client wishes could be a subject for further research.
Acknowledgements This paper was supported by the project "Improvement of the doctoral studies quality in engineering science for development of the knowledge based society-QDOC” contract no. POSDRU/107/1.5/S/78534, project co-funded by the European Social Fund through the Sectorial Operational Program Human Resources 2007-2013. All authors are principal authors of the paper. The first author is the corresponding author.
References Arslan, F. M., & Altuna, O. K. (2010). The effect of brand extensions on product brand image. Journal of Product & Brand Management, 19(3), 170-180. 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, Vol. 22 Iss: 3 Gallarza, M. G., Saura, I. G., & Holbrook, M. B. (2011, iulie 26). The value of value: Further excursions on the meaning and role of customer value. Journal of Consumer Behaviour, 179-191. Graf, A., & Maas, P. (2008). Customer value from a customer perspective: a comprehensive review. JOURNAL FÜR BETRIEBSWIRTSCHAFT, 58(1), 1-20. Hua, H.-H., Kandampullyb, J., & Juwaheerc, T. D. (2009). Relationships and impacts of service quality, perceived value, customer satisfaction, and image: an empirical study. The Service Industries Journal, 29(2), 111-125. Kotri, A. (2006). Analzying customer value using conjoint analysis: the example of a packaging company. Takenin may 2, 2012, from http://www.mtk.ut.ee/orb.aw/class=file/action=preview/id=207677/febawb46.htm Pitic, S. L., & Brad, S. (2011). Methodology for value-to-money modeling. Business Trends – Reviewed conference proceeding, 1-5 Yee, C. J., & San, N. C. (2011). Consumers’ Perceived Quality, Perceived Value and Perceived Risk - Towards Purchase Decision on Automobile. American Journal of Economics and Business Administration, 47-57.
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