Impact of Brand Cues on Young Consumers’ Preference for Mobile Phones: A Conjoint Analysis and Simulation Modelling

Abstract

There are multiple attributes and product features which influence youth and impact their purchase decisions. The companies are assiduously trying to tap young consumers but are unable to sustain their high growth and market share. The attributes or brand cues influencing youth can be broadly classified as extrinsic cues and intrinsic cues. Extrinsic cues are peripheral or external to the product such as price, brand image, store image, whereas intrinsic cues are independent, inherent and constitute the product’s physical characteristics such as mobile camera, size, colour etc. In the present study, extrinsic cues are brand name and price while camera, RAM, Android type and battery power are intrinsic cues. To recognize the role of brand cues, a sample of 417 respondents through judgement sampling is drawn. The impact of brand name, price and technology on brand equity is empirically examined by conjoint and simulation analysis to generate information regarding consumers’ preferences towards mobile phone. The salience of study put forth the measures of enhancing mobile brand equity among young consumers. The originality of present study articulates brand equity diverge across different age groups and the young consumers obsession to spend more on consumer durable products like mobile with their desirable intrinsic and extrinsic cues.

Keywords

New product youth brand name brand equity price technology consumer preference simulation conjoint analysis

Introduction

With countless products launched every day, there exists a cut-throat competition in today’s consumer durable market. Due to the dynamic nature of consumer behaviour, manufactures and marketers keenly look for all possible ways to enhance their market share. Studies for predicting the young consumers behaviour have gained a lot of attention lately (Kirmani & Rao, 2000; Shugan, 2006), which has compelled manufactures and brand managers to come up with effective marketing strategies, new product launch, product modifications or new technology.

Technology facilitated development of several life-changing and revolutionary products; mobile phone is one of such products. The mobile obsessed youth use it not only for communication but also to access financial services (Demombynes & Thegeya, 2012; Jonathan & Camilo, 2008). Growing penetration of mobile phones has converted the market into a ‘pocket market’. This access to mobile technology benefitted the consumers in several ways. The consumers who wished to access the traditional and remote markets can now easily do so. People further value it as a means of intensifying social ties (Galperin & Mariscal, 2007). The Competition among mobile manufacturing companies promoted dispersion, innovation and price reduction (Rouvinen, 2004).

Rise in consumer awareness (Graeff & Harmon, 2002) and sophistication in technology put consumers in a difficult situation to take decisions regarding product quality, rationally (Olson, 1972). It was observed that attributes which influence youth the most, were based on different brand cues. These brand cues can be divided into intrinsic and extrinsic cues (Lee & Lou, 1996; Szbillo & Jacoby, 1974; Van Trijp & Schifferstein, 1995). Intrinsic cues are inherent and form the basis or objective nature of the product. Change in intrinsic cues lead to change in the nature of a product. On the other hand, extrinsic cues are such product attributes which can be changed without creating any change in the objective nature of the product (Veale & Quester, 2009). Simultaneously, price and brand name being extrinsic cues (Al-Swide, Huque, Hafeez, & Shariff, 2014; Estelami & Maeyer, 2004; Govers & Schoormans, 2005; Iacocca, Sawhill, & Zhao, 2015; Kulshreshtha, Bajpai & Tripathi, 2017; Padel & Foster, 2005; Round & Roper, 2015; Roy, Rabbanee, & Sharma, 2016a; Tarkiainen & Sundqvist, 2011; Wang & Yang, 2008) act as a parameter for quality check and criteria of sophisticated product (Batey, 2008; Keller, 2013; Leavitt, 1954; Rao & Monroe, 1989). The aforesaid brand cues ultimately contribute and improve brand equity of the given product. Firms get attracted towards brand equity because it leads to customer loyalty and eventually results in profitability (Passikoff, 2005). Since brand equity is the outcome of consumer perception of brand name that catches consumer’s eye, it convinces consumer to spend money for product. Brand equity has been defined as the effect of brand knowledge on consumer response for the brand. Brand equity gives value to the brand name that in turn is used for line extensions or for co-branding purposes (Rao & Ruekert, 1994).

In spite of the several researches conducted over the years on consumer behaviour and consumer psychology (Bazoche, Deola, & Soler, 2008; Follows & Jobber, 2000; Hines, Hungerford, & Tomera, 1987; Krause, 1993; Loureiro, 2003; Martin & Simintiras, 1994; Schultz, 2002), only few have explored the combined effect of brand name, price and technology on young consumers’ preferences across different age groups. Further, this research adopted a realistic decision model that made the subjects to evaluate the product as a whole (as in real life), as if in the actual purchasing situation. It forms individual decision models for each subject and allows the formation of an aggregated decision model across all the subjects (Dauda & Lee, 2016).

The first objective of this research study is to examine the impact of brand name, price and technology on brand equity of mobile phones. Second is to examine the relative importance of different prevailing attributes affecting consumer preference for mobile phones, then to suggest manufacturers and brand managers the ways to enhance brand equity for the given product.

Review of Literature

Brand equity is a complex but a vital notion in consumer marketing and is difficult to manage for both domestic and global brands. Surprisingly, the available literature on brand equity has not paid extensive attention in addressing extrinsic and intrinsic brand cues associated with managing brands (Khojastehpour, Ferdous, & Polonsky, 2015). As the taste and preferences are changing rapidly due to the availability of wide range of products, it becomes imperative for the companies to manage the brand cues, so that the companies can position themselves distinguish and portray clear image in the mind of consumers and later influence their buying behaviour. Therefore, an attempt was made to understand brand equity through brand name, price and technology. A brief discussion of brand name, technology and price is presented below.

Brand Name

Brand name is an important aspect that has impact on brand equity (Aaker, 1996; Batey, 2008; Davcik, Vinhas da Silva, & Hair, 2015; Keller, 1993, 2013; Keller & Lehmann, 2006; Peterson & Ross, 1972; Robertson, 1989) and acts as a brand messenger in building the brand’s image as a ‘unique sign’ and a ‘source of identity’ (Kapferer, 2012). A brand name helps to create brand awareness through its appropriateness, meaningfulness, simplicity and distinctiveness (Keller, 1998; Lowrey, Shrum, & Dubitsky, 2003; McCracken & Macklin, 1998; Moore & Lehmann, 1982; Robertson, 1989; Samu & Krishnan, 2010; Yorkson & Menon, 2004). It is intrinsically linked with consumer psychology leading towards brand equity (Aaker, 1991; Christodoulides & de Chernatony, 2010; French & Smith, 2013; Keller, 1993). Research identified that different brand names provide different associations to branded goods or services and impact the quantum of brand equity (Argo, Popa, & Smith, 2010; Gunasti & Ross, 2010; Lowrey et al., 2003; Luna, Carnevale, & Lerman, 2013; Samu & Krishnan, 2010); it further leads to consumers’ brand preference (Gunasti & Ross, 2010; Lowrey & Shrum, 2007; Mehrabian & Wetter, 1987). Not only brand name but also the pronunciation of a brand name affects consumers’ association with the brand (Argo et al., 2010; Guevremont & Grohmann, 2015; Klink & Athaide, 2012; Robertson, 1989). For youth and teenagers, brand name plays a significant role (Jezkova Isaksen & Roper, 2008, 2012; Phau & Leng, 2008). For instance, youth who possess well-known brand names are considered happier and socially more acceptable (Chan, 2006). The earlier discussion indicates that without brand name, development of brand equity seems to be very difficult.

Technology

With the rise in technology, there is a corresponding rise in competition. Today, businesses have the choice of not to use the technology and perish or use it for their advantage and get the competitive edge. Therefore, it becomes pertinent for business houses to keep themselves abreast with technology (Bell, 1973; Douglas & Craig, 1989; Haines & Sharif, 2006; Hamel & Prahalad, 1994; Ohmae, 1985; Porter 1985, 1990; Schumpeter, 1942; Solow, 1956). In the twentieth century, technology—that is, intrinsic cue—started taking shape in the daily life of people, though gradually but steadily.

In the 1990s, the fear of technology, its complexity and speedy obsolesce among people was wide spread (Harper, 1994), which acted as a barrier to consumer adoption of high-tech products (Dhebar, 1996; Higgins & Shanklin, 1992). Mick and Fournier (1998) in their research work stated that consumers had both positive and negative fear for technology and concern for how they may cope with technological advancement. It was believed that technology might not harm a human; simultaneously, human must be the master not the slave of technology (Asimov, 1950).

Despite consumers’ apprehensions, companies rushed to adopt and offer technology-based services and products. The uncertainty among consumers, coupled with corporate interest to launch the technology, made the market conditions ambiguous. Soon, the consumer began evaluating new products with technology as one of the important parameters (Jun, Mazumdar, & Raj, 1999).

Later on, it was found that perception of consumers about technology became as significant as the perception about specific product’s features, image or brands (Bridges, Keller, & Sood, 2000; Park, Milberg, & Lawson, 1991); and unlike old age group consumers, younger consumers were more interested in the latest technology (Harris, Cox, Musgrove, & Ernstberger, 2016). Lee, Barker and Kandampully (2003) explored that as per the opinions and beliefs of managers, technology enhances the quality of service, contributes in lifting the overall image of the product or services and promotes customer loyalty.

Now, it is believed that technology helps firms to increase productivity, and conversely, technological innovations enhance brand equity (Chailan, 2009). According to Porter (1985), technology and technological innovations both have strategic significance and cause transformational output. Moreover, stakeholders started considering innovation in technology as driving force behind economic growth (Jie, 1988) and accepted technology as one of the critical resources (Azzone, Bertele, & Rangone, 1995).

Technology need not be novel or ground breaking but may simply bring a change in industrial practice, which in turn improves productivity (Khalil, 2000). Researchers in the field of marketing now have agreed that technological innovations are the most essential forces upsetting a firm’s competitive position (Burgelman, Maidique, & Wheelwright, 2001).

Price

Price is the most unloved variable in marketing mix (Maxwell & Estelami, 2006); however, its role in purchase decisions has received a great deal of attention (Biswas & Blair, 1991; Damay, Ezan, Gollety & Hemar, 2011; Gabor & Granger, 1964; Monroe & Lee, 1999; Urbany & Dickson, 1991). Price acts as a facilitating factor for consumers to make comparison between proposed products and subjective inferences related to the product’s quality (Erickson & Johansson, 1985; Monroe, 1973); but there is a paradox, consumers attach great importance to price for purchasing decisions despite relative price unfamiliarity (Dickson & Sawyer, 1990; Estelami & Lehmann, 2001; Gabor & Granger, 1964).

The theoretical and empirical evidence suggest that consumer use internal reference standard to make comparative judgement and choice regarding observed phenomenon (Helson, 1964), like a reference price which is an internal standard against which observed prices are compared (Mullikin, 2003); therefore, there is a difference in the consumers’ encountered and expected prices. Price also carries psychic benefits, quality and risk (Zeithaml, 1988). Similarly, consumer will buy more than usual, if the prices are reduced substantially (Smith & Sinha, 2000). Price differences affect consumer preference for the product variants within a product class, offering the same standard functionality and of approximately similar in price (Govers & Schoormans, 2005). Since consumers take price into priority and base their purchasing decision on it, price is an important attribute to be studied (Kim, Natter & Spann, 2009; Kulshreshtha, Tripathi, Bajpai & Dubey, 2017; Roy, Rabbanee, & Sharma, 2016b).

Based on this discussion, the present study seeks to examine empirically young Indian consumers’ preference towards brand name, price and technology of various global brands. In this study, rather than testing hypotheses, we explored the impact of brand name, price and technology on brand equity for the young consumers of difference age brackets by framing three research questions as follows:

RQ. 1. Does youth weigh price as the most important criteria while selecting brand?

RQ. 2. Does brand name play a significant role in building brand equity?

RQ. 3. Does a consumer evaluate mobile phones based on technology?

Methodology

In an attempt to answer such research questions, selection of an appropriate research technique is of paramount importance. Conjoint analysis, a widely used marketing research technique, was being considered for the present study to draw the inference on consumer preference (Agrawal, 2014; Ding & Keh, 2016; Gil & Sanchez, 1997; Kaynak & Kara, 2002). The following discussion highlights the rationale of using conjoint analysis to answer the aforestated research questions.

Conjoint Analysis

Conjoint analysis is a term given by Green and Srinivasan (1978) for referring number of dimensions in economics, psychology and marketing that are concerned with the quantitative description of consumer preferences. Wittink and Cattin (1989) documented a great number of conjoint applications in different sectors of industry in the USA. Indeed, since its introduction into the field of marketing, conjoint analysis has become a method for identifying and analysing the consumer preference, drawing attention of academicians and practitioners. Conjoint analysis is being developed as one of the most widely used marketing research methods to forecast and predict consumer preferences, choices and decisions (Louviere, 1994). This technique also allows responses to be segmented based on respondents’ preference structures. Market segmentation in commercial applications is found to be one of the prime reasons for applying conjoint analysis (Wittink & Cattin, 1989; Wittink, Vriens, & Burhenne, 1994).

The responses were collected through a well-developed questionnaire obtained through online and offline mode, containing 21 different product options. For each of these product options, participants were asked to rate the option as per their degree of preference, using a seven-point Likert scale. In the given questionnaire on likert scale, which ranged from 1-7 point, if the participant disliked the bundle of different attributes (product), then they could choose option 1 on the left i.e. totally unacceptable and if the respondent like the option (product) most, they could choose option on extreme right i.e. 7 in questionnaire, which stands for perfectly acceptable. If they were indifferent among options, then they could select the middle point on the Likert scale.

In this empirical study, 417 participants from North India were selected with various backgrounds. The responses collected through judgemental sampling included demographic breakdown of respondents as 168 females and 249 males, with 139 aged less than 20 years, 173 aged between 20 and 25 years, and 105 aged between 25 and 29 years. The sample size was in line with what is considered as sufficient for this type of study (Hair, Black, Babin, & Anderson, 2010; Orme, 2006). Subsequently, conjoint analysis was executed on the three groups and also on the combined response of the respondents (n = 417).

On the basis of the discussion with expert panel (including retailers, marketers, academicians and consumers, i.e., youth), six relevant attributes with three different levels of each were identified including brand name, RAM (determines the processing speed of mobile), camera, Android type, price and battery power (Table 1). Out of these six attributes, brand name and price were taken as extrinsic cues (Veale & Quester, 2009) and RAM, camera, Android type, and battery power were taken as intrinsic cues.

The different levels of attributes stated earlier (3 × 3 × 3 × 3 × 3 × 3) resulted in 729 combinations. Collecting responses for these combinations were not feasible, so, with the help of orthogonal design, 729 combinations were being reduced to 18 combinations combined with 3 simulation cases. Here, it is important to mention that these simulation cases were also obtained through expert panel discussion. To minimize bias, participants were not informed about the purpose of the test until completion. From the obtained preference data, the conjoint analysis was performed through SPSS v.20. To examine the effect of various attributes such as technology, brand name and price on brand equity of well-known global brands, three famous brands Samsung, Motorola and Xiaomi were selected. The rationale of selecting these three brands lies in their familiarity among the Indian consumers and their potential to penetrate in the market in terms of high sales volume across the country.

Table 1.

Model Description

Attributes	N of Levels	Relation to Ranks or Scores
RAM	3	Discrete
Camera	3	Discrete
Android type	3	Discrete
Brand name	3	Discrete
Price	3	Discrete
Battery power	3	Discrete

Source: Authors’ own.

Analysis and Results

The study was carried out by dividing groups into five clusters, that is, combined groups that include all respondents followed by teenagers group, young adults group, adults group and finally, the simulation analysis as follows.

Combined Group

The result obtained through conjoint analysis showed that the respondents (n = 417) in the age group ranged from 13 years to 29 years, referred to as young consumers in this study, were well informed and stuck to extrinsic product cues (Table 2). In the list of extrinsic cues, brand name with relative importance value (18.153) was the first and foremost attribute appraised. Brand name has always been the matter of prime importance for consumers and marketers. By the unique feature of conjoint analysis, the likability of consumers’ preference for specific brand name was also identified (Table 2). Samsung with utility estimate (0.141) was found to be the most preferred brand name, though consumers were also having the option of choosing Xiaomi and Motorola.

Table 2.

Relative Importance Values

Attributes (Including Intrinsic and Extrinsic Cues)	Teenagers Group (n = 139)	Ranking	Young Adults Group (n = 173)	Ranking	Adults Group (n = 105)	Ranking	Combined Group (n = 417)	Ranking
RAM	18.2	1	14.982	5	14.692	5	15.980	4
Camera	16.583	4	15.701	4	15.306	4	15.873	5
Android type	15.606	5	14.870	6	14.686	6	15.060	6
Brand Name	15.444	6	18.378	2	20.768	1	18.153	1
Price	16.997	3	17.615	3	18.397	2	17.658	2
Battery power	17.17	2	18.453	1	16.150	3	17.277	3

Source: Authors’ own.

Note: Values/Output by SPSS version 20 generated through conjoint analysis run.

Price with the relative importance value 17.658 has emerged out as the second most preferred attribute, and as discussed earlier, is an extrinsic cue. Battery power, RAM, camera and Android type were discovered as third, fourth, fifth and sixth important attributes with relative importance scores as 17.277, 15.980, 15.873 and 15.060, respectively. As discussed earlier, these attributes were referred as intrinsic attributes. Respondents under combined groups indicated more importance to extrinsic cues.

Teenager Group

The inter-group study suggested that the first group (n = 139) which comprised teenagers, gave more importance to the intrinsic attribute (see Table 2) like RAM with relative importance percentage as 18.2. In terms of relative importance, battery power with 17.17 percentage has secured second place. The maximum battery options, that is, 4001–5000 mAh with 0.118 utility score occupied top scores in the Table 4, confirming clear preference by teenagers. As an extrinsic cue, price with relative importance percentage 16.997 has secured third place in Table 2. However, the youth of this group chose the second highest price band (0.072) out of three listed price levels. Other important attributes such as camera (16.583), Android type (15.606) and brand (15.444) secured their respective places in the table after price. Researchers have encountered with a very important result as brand name securing the last place in the table.

Young Adults Group

The second group of young adults (n = 173) consisted of youngsters in their early 20s exhibited clear inclination towards battery power (18.453) an intrinsic cue, not so different from the first group. This group has indicated second and third priority to brand name (18.378 per cent) and price (17.615 per cent), respectively, included as extrinsic cues for the present study (see Table 2). While examining the level of preference, Samsung again secured first place with a score 0.144, followed by Xiaomi (0.028). Price score (0.049) was highest for middle range price as exhibited in Table 4. Camera (15.701 per cent), RAM (14.982 per cent) and Android type (14.870 per cent) occupied third, fourth and fifth priority, respectively (see Table 2) in terms of relative importance.

Adults Group

In the third group of adults (n = 105), the extrinsic cue brand name was the most preferred attribute over others with 20.768 per cent for Samsung, endorsing strong influence of brand name on consumer preference. Price (18.397 per cent) secured second place, followed by battery power, camera and RAM (16.150 per cent, 15.306 per cent and 14.692 per cent relative importance value, respectively (see Table 2). In the light of age group of consumers, it was observed that young consumers prefer extrinsic attributes of the product taken for the present study.

Simulation Result

Simulation analysis has been applied in marketing research to evaluate the potential and viability of new product concepts for consumers (Bogue & Sorenson, 2009; Childs, Thompson, Berry, & Drake, 2007; Choi, Shin, & Lee, 2013; Raz, Piper, Haller, Dussart, & Giboreau, 2008). Under simulation analysis, the maximum utility model tends to exaggerate the market share of a specific product (assigns a very high percentage of market share to the first choice), while the Bradley–Terry–Luce (BTL) and logit models tend to underestimate the market share of the first choice. The maximum utility model is the probability of choosing the most preferred profile. It is based on the concept that the product, which provides consumers the highest utility will be selected. The BTL computes the probability of choosing the most preferred profile by dividing the profile’s utility by the sum of all the simulation total utilities. Finally, the logit model is similar to BTL but uses the natural log of the utilities instead of the utilities.

In this study, a logit model was adopted for conducting further analysis, since it is a more probabilistic method and tends to produce more realistic estimates of purchase behaviour over maximum utility and the BTL models (Green & Srinivasan, 1978; Luce & Tukey, 1964). Out of the three simulated items, the simulation result depicted that second simulated combination ID no. 52 with maximum utility value of 51.20 per cent, BTL value of 35.90 per cent and logit value of 42.20 per cent (Table 3) was preferred the most by teenager group respondents.

Young adults group again gave priority to I.D. no 52 with 48.8%, 36.3%, 45.0% under Maximum utility, Bradley-Terry-Luce and Logit. Similarly, adults group weigh high to I.D 52 as Maximum utility, Bradley-Terry-Luce and Logit were 42.1%, 36.1% and 40%, respectively. Therefore, all the three models gave similar prediction (Table 3) in favour of ID no. 52, followed by ID no. 51 and ID no. 53.

Discussion and Conclusion

Factors that determine brand equity have been an important research avenue to understand consumer preference in the past decades. This study sought to advance that stream of interest by investigating the extent to which brand name, technology and price combine to influence brand equity.

In fact, the results supported the research questions that type of technology and brand name affects brand equity. As expected, consumer preference for brand equity was positively related to brand name and technology. Thus, when the importance attached to the brand name and technology is high, consumers are more likely to buy the same brand.

This is a particularly noteworthy finding, given that brand equity can move positively or negatively based on the intrinsic cues such as type, level and duration (time/age) of technology. Moreover, brand equity has a significant association with brand name, which in turn relates to price and results in consumer preference.

Table 3.

Preference Probabilities of Simulations

Card Number and ID		Teenagers Group			Young Adults Group			Adults Group			Combined Group
Card Number	ID	Maximum Utility^a(%)	Bradley–Terry–Luce (%)	Logi t (%)	Maximum Utility^a(%)	Bradley–Terry–Luce (%)	Logit (%)	Maximum Utility^a (%)	Bradley–Terry–Luce (%)	Logit (%)	Maximum Utility^a (%)	Bradley–Terry–Luce (%)	Logit (%)
1	51	27.50	32.60	30.80	27.5	32.8	29.0	43.4	35.9	41.1	32.6	33.8	33.6
2	52	51.20	35.90	42.20	48.8	36.3	45.0	42.1	36.1	40.0	47.5	36.1	42.4
3	53	21.20	31.50	26.90	23.8	30.9	26.0	14.5	27.9	18.9	19.9	30.1	24.0

Source: Authors’ own.

Note: Values/Output by SPSS version 20 generated through conjoint analysis run.

This finding supported the research questions 2 and 3 that overall increase in the brand name value and technology lead to higher brand equity. Simultaneously, consumers paid more attention to the intrinsic cue technology, which included Android type, battery capacity, camera megapixel and RAM. Desire to keep several mobile applications, frequent selfies and games generated the importance for RAM and technology. It is worth mentioning here that battery backup is one of the most important aspects and because of this reason, smart watches are not able to make the significant presence over the market, especially in India. Thus, our results showed that other than the price parameter, brand name and technology were important considerations that affected the purchasing decisions of the consumers. These findings are consistent with the available extensive literature emphasizing the role of brand name, price and technology in consumer information search and processing (Denstadli, Julsrud, & Hjorthol, 2012; Dholakia, 2001).

Table 4.

Conjoint Analysis Utilities Score: Inter-group Comparison

Utilities
Attributes (Including Intrinsic and Extrinsic Cues) with Specific Details of Each		Teenagers Group		Young Adults Group		Adults Group		Combined Group
		Utility Estimate	Std. Error	Utility Estimate	Std. Error	Utility Estimate	Std. Error	Utility	Std. Error
RAM	1 GB	–0.028	0.127	0.082	0.165	–0.018	0.169	0.013	0.131
	2 GB	–0.099	0.127	–0.118	0.165	0.013	0.169	–0.069	0.131
	3 GB	0.126	0.127	0.036	0.165	0.004	0.169	0.056	0.131
Camera	2–2.9 MP	–0.124	0.127	–0.085	0.165	0.132	0.169	–0.028	0.131
	3–4.9 MP	–0.019	0.127	–0.051	0.165	–0.018	0.169	–0.03	0.131
	5 MP and above	0.143	0.127	0.136	0.165	–0.114	0.169	0.058	0.131
Android type	KitKat	–0.074	0.127	–0.143	0.165	–0.07	0.169	–0.096	0.131
	Lollipop	0.101	0.127	0.157	0.165	0.088	0.169	0.116	0.131
	Marshmallow	–0.028	0.127	–0.014	0.165	–0.018	0.169	–0.02	0.131
Brand name	Samsung	0.072	0.127	0.144	0.165	0.211	0.169	0.141	0.131
	Motorola	–0.09	0.127	–0.172	0.165	–0.18	0.169	–0.147	0.131
	Xiaomi	0.018	0.127	0.028	0.165	–0.031	0.169	0.006	0.131
Price	Rs 5,000–10,000	–0.032	0.127	–0.035	0.165	0.061	0.169	–0.003	0.131
	Rs 10,001–18,000	0.072	0.127	0.049	0.165	0.294	0.169	0.136	0.131
	Rs 18,001–25,000	–0.04	0.127	–0.014	0.165	–0.355	0.169	–0.133	0.131
Battery power	2,500–3,000 mAh	–0.036	0.127	–0.031	0.165	–0.009	0.169	–0.025	0.131
	3,001–4,000 mAh	–0.082	0.127	–0.218	0.165	–0.083	0.169	–0.129	0.131
	4,001–5,000 mAh	0.118	0.127	0.249	0.165	0.092	0.169	0.154	0.131

Source: Authors’ own.

Note: Values/Output by SPSS version 20 generated through conjoint analysis run.

This study finds that price also has significant relationship with brand equity, but not always. Contrary to what was predicted, all groups with different age respondents chose the middle price. When further investigated, it was observed that Indian consumers traditionally are concerned for the price and are willing to spend less. Indian consumers still chose for higher price (not the highest) because now the youth is probably of the opinion that the performance of technology gets better with higher price. So, the better option for the consumers was to go for middle price that offers them brand name, technology and quality at a reasonable cost (Figure 1). Therefore, research question 1 is not supported. In previous researches, it was well established that brand name was an essential communicator of meaning (Klink, 2000, 2003; Klink & Athaide, 2012), and it augmented marketing offers with benefits even beyond the basic function of the core product or service (cf. Aaker, 1991; del Rio, Vazquez, & Iglesias, 2001; Klink, 2000; Kotler, 1991). Brand name symbolizes the sum of the attributes that make up the brand (Blackett, 1988). Brand name was found to have a significant impact on consumers’ attitudes (Haubl, 1996) as well as ‘influence consumers’ evaluation of and purchase intentions towards a product’ (Ahmed, Johnson, Ling, Fang, & Hui, 2004, p. 102). The importance of brand name can be assessed by the fact that regardless of the reasons, the practice of changing or substituting brand names is extremely risky for companies (Kapferer, 2007). Consumers may face difficulty in recognizing their usual product or doubt its quality, which could provoke them to stop buying that product. Moreover, failure to handle brand name properly may result in brand equity loss that further leads to decreased market share; this is all because of consumers’ failure to recognize the product (Delassus & Descotes, 2012).

Figure 1.

Source: Authors’ own.

As assumed, consumers with age understood the value of brand name more, and it was quite visible in the result of all the three groups and combined result (Figure 1). In the given study, teenagers opted for RAM and battery power over brand name, one of the reasons could be that teenagers play heavy games and carry several mobile applications. Young adults group, adults group and combined group demonstrated the importance of brand name (Table 2). The utility score obtained by utility value showed that brands having strong brand name and brand equity were preferred the most (Table 4). Here, Samsung was the most preferred brand name, leaving two other brand names behind. Previous studies also supported the impact of brand name on brand outcomes such as greater brand recall, liking and recognition (Schloss, 1981; Vanden Bergh, Adler, & Oliver, 1987; xref ref-type="bibr" rid="bibr128-0973258617722422">Vanden Bergh, Collins, Schultz, & Adler, 1984). This was the reason respondents unanimously preferred the brand with highest brand equity, especially those who did not have expertise of the field purchased the goods on the basis of brand name, thus supporting our research question 2.

The relationship between technology and brand equity, though significant, had given unexpected results in this study. Out of the three Android types, that is, KitKat, Marshmallow and Lollipop, presently Marshmallow is the latest in India. However, respondents preferred one level older technology, that is, Lollipop (Table 4); the simulation results also supported the contention in favour of Android type technology (Table 3). As the youth is fond of using several apps and mobile games, which occupy more mobile space, therefore, they always prefer mobile with higher RAM capacity and battery backup. Further, investigation suggested that the latest Android type technology, at the time of its launch might not be compatible with the already existing popular games and mobile applications (Figure 1). Thus, our third research question is partially supported. One of the major findings in this research is that though in the early age, the youth paid relatively less importance to brand name (15.444 per cent), but with the rise in age, they paid more importance to it, which was endorsed by the increase in importance score for brand name (Table 2). The probable reason might be that consumer at later age started attaching the quality of goods with better brand name.

To summarize, it is quite clear that young generation in the present study paid attention to both intrinsic and extrinsic cues (Table 2). No single factor is effective for consumer preference but the brand cues as the whole defines it. Managers can use these findings while making strategies related to brand equity and eventually enhance market share. However, different brand-related issues might have different solutions.

Managerial Implications

This research offers important implications for managers. It has been identified that there are two kinds of consumers, namely (a) those who prefer extrinsic cues and (b) those who prefer intrinsic cues, with different priority within the segment. So, managers should be aware of the consumer segment they are catering to. It was observed that there were several attributes and levels preferred by consumers of different age. Therefore, the marketers may also work upon offering customized mobile phones by assembling the attributes and levels preferred by the consumers. Customization of mobile phones as a new concept may be introduced in India, and this is very likely that this concept may come up as a revolutionary step in mobile industry. Youth prefers branded products due to the assurance of quality, perfection, workmanship and status, etc., but managers need to be careful that consumer creates the much negative image, if the branded product does not deliver as per the expectation.

Consumer once satisfied with a brand, later purchases the brand name. Brand name is crucial for brand equity, and therefore, marketers should be cautious in keeping up the brand name. Youth prefers latest technology, but very latest technology may not be compatible to existing mobile applications and games as emphasized earlier. In light of this observation, technology managers must maintain the balance between the latest and newly launched technology to harness the market optimally.

Limitations and Directions for Future Research

Firstly, this research was conducted only on high-involvement product, thus, the future research can include comparison between low-involvement and high-involvement product to measure the brand equity. Secondly, this research was based on judgemental sampling and might be subject to bias, eventually limits the generalizability. Thirdly, this research was conducted on Indian consumers only. A comparative study of Indian and foreign consumers can be conducted. Another limitation of this study was its sample that consists more male respondents than female (168 female in comparison of 249 male). Hence, the study can be replicated with more representative sample.

Footnotes

Acknowledgements

The authors want to thank editor Manisha Pathak-Shelat, anonymous reviewers and member of expert panel including a teenager Mr. Sarthak Sharma, New Delhi, for their valuable suggestions and feedbacks.

References

Aaker

(1991). Managing brand equity. New York, NY: Macmillan.

Aaker

(1996). Building strong brands. New York, NY: The Free Press.

Agrawal

(2014). Analytics based decision making. Journal of Indian Business Research, 6(4), 332–340.

Ahmed

Johnson

J. P.

Ling

C. P.

Fang

T. W.

& Hui

A. K.

(2004). Country-of-origin and brand effects on consumers’ evaluations of cruise lines. International Marketing Review, 19(3), 279–302.

Al-Swide

Huque

S. M. R.

Hafeez

M. H.

& Shariff

M. N. M.

(2014). The role of subjective norms in theory of planned behavior in the context of organic food consumption. British Food Journal, 116(10), 1561–1580.

Argo

J. J.

Popa

& Smith

M. C.

(2010). The sound of brands. Journal of Marketing, 74(4), 97–109.

Asimov

(1950). I, robot. New York, NY: Gnome Press.

Azzone

Bertele

& Rangone

(1995). Measuring resources for supporting resource based competition. Management Decision, 33(9), 57–62.

Batey

(2008). Brand meaning. New York, NY: Psychology Press.

10.

Bazoche

Deola

& Soler

(2008). An experimental study of wine consumers’ willingness to pay for environmental characteristics. Paper presented at the 12th Congress of the European Association of Agriculture Economists. Retrieved 12 June 2016 , from www.ageconsearch.umn.edu/bitstream/43651/2/007.pdf

11.

Bell

(1973). The coming of post-industrial society: A venture in social forecasting. New York, NY: Basic Books.

12.

Biswas

& Blair

E. A.

(1991). Contextual effects of reference prices in retail advertisements. Journal of Marketing, 55(3), 1–12.

13.

Blackett

(1988). Researching brand names. Marketing Intelligence & Planning, 6(3), 5–8.

14.

Bogue

& Sorenson

(2009). A case for market-oriented development of functional beverages. Agro Food Industry Hi-Tech, 20(5), 15–17.

15.

Bridges

Keller

K. L.

& Sood

(2000). Communication strategies for brand extensions: Enhancing perceived fit by establishing explanatory links. Journal of Advertising, 29(4), 1–11.

16.

Burgelman

R. A.

Maidique

M. A.

& Wheelwright

S. C.

(2001). Strategic management of technology and innovation (3rd ed.). Boston, MA: McGraw-Hill Irwin.

17.

Chailan

(2009). Brand architecture and brands portfolio: A clarification. EuroMed Journal of Business, 4(2), 173–184.

18.

Chan

(2006). Young consumers and perception of brands in Hong Kong: A qualitative study. Journal of Product & Brand Management, 15(7), 416–426.

19.

Childs

J. L.

Thompson

J. S.

Berry

L. T. K.

& Drake

(2007). Consumer perception of whey and soy protein in meal replacement products. Journal of Sensory Studies, 23(3), 320–339.

20.

Choi

J. Y.

Shin

& Lee

(2013). Strategic demand forecasts for the tablet PC market using the Bayesian mixed logit model and market share simulations. Behaviour & Information Technology, 32(11), 1170–1190.

21.

Christodoulides

& de Chernatony

(2010). Consumer-based brand equity. International Journal of Market Research, 52(1), 43–66.

22.

Damay

Ezan

Gollety

& Hemar

V. N.

(2011). Social interactions as support for learning about food: The case of the French school cafeteria. Young Consumers, 12(2), 110–120.

23.

Dauda

S. Y.

& Lee

(2016). Quality of service and customer satisfaction: A conjoint analysis for the Nigerian bank customers. International Journal of Bank Marketing, 34(6), 841–867.

24.

Davcik

N. S.

Vinhas da Silva

& Hair

J. F.

(2015). Towards a unified theory of brand equity: Conceptualisations, taxonomy and avenues for future research. Journal of Product & Brand Management, 24(1), 3–17.

25.

del Rio

A. B.

Vazquez

& Iglesias

(2001). The role of the brand name in obtaining differential advantages. Journal of Product & Brand Management, 10(6), 452–465.

26.

Delassus

V. P.

& Descotes

R. M.

(2012). Brand name substitution and brand equity transfer. Journal of Product & Brand Management, 21(2), 117–125.

27.

Demombynes

& Thegeya

(2012). Kenya’s mobile revolution and the promise of mobile savings (World Bank Policy Research Working Paper No. 5988). Washington, DC: World Bank.

28.

Denstadli

J. M.

Julsrud

T. E.

& Hjorthol

R. J.

(2012). Videoconferencing as a mode of communication: A comparative study of the use of videoconferencing and face-to-face meetings. Journal of Business and Technical Communication, 26(1), 65–91.

29.

Dhebar

(1996). Speeding high tech producer: Meet the balking consumer. Sloan Management Review, 37(2), 37–49.

30.

Dholakia

U. M.

(2001). A motivational process model of product involvement and consumer risk perception. European Journal of Marketing, 35(11/12), 1340–1362.

31.

Dickson

P. R.

& Sawyer

A. G.

(1990). The price knowledge and search of supermarket shoppers. Journal of Marketing, 54(3), 42–53.

32.

Ding

& Keh

H. T.

(2016). A re-examination of service standardization versus customization from the consumer’s perspective. Journal of Services Marketing, 30(1), 16–28.

33.

Douglas

S. P.

& Craig

C. S.

(1989). Evolution of global marketing strategy: Scale, scope and synergy. Columbia Journal of World Business, 24(3), 47–58.

34.

Erickson

G. M.

& Johansson

J. K.

(1985). The role of price in multi-attribute product evaluations. Journal of Consumer Research, 12(2), 195–199.

35.

Estelami

& Lehmann

D. R.

(2001). The impact of research design on consumer price recall accuracy: An integrative review. Journal of the Academy of Marketing Science, 29(1), 32–49.

36.

Estelami

& Maeyer

P. D.

(2004). Product category determinants of price knowledge for durable consumer goods. Journal of Retailing, 80(2), 129–137.

37.

Follows

S. B.

& Jobber

(2000). Environmentally responsible purchase behaviour: A test of a consumer model. European Journal of Marketing, 34(5/6), 723–746.

38.

French

& Smith

(2013). Measuring brand association strength: A consumer based brand equity approach. European Journal of Marketing, 47(8), 1356–1367.

39.

Gabor

& Granger

C. W. J.

(1964). Price sensitivity of the consumer. Journal of Advertising Research, 4(4), 40–44.

40.

Galperin

& Mariscal

(2007). Mobile opportunities: Poverty and telephony access in Latin America and the Caribbean-regional report. Retrieved 15 May 2016 , fromhttp://www.dirsi.net/files/trinidad_tobago_final.pdf

41.

Gil

J. M.

& Sanchez

(1997). Consumer preferences for wine attributes: A conjoint approach. British Food Journal, 99(1), 3–11.

42.

Govers

P. C. M.

& Schoormans

J. P. L.

(2005). Product personality and its influence on consumer preference. Journal of Consumer Marketing, 22(4), 189–197.

43.

Graeff

T. R.

& Harmon

(2002). Collecting and using personal data: Consumers’ awareness and concerns. Journal of Consumer Marketing, 19(4), 302–318.

44.

Green

P. E.

& Srinivasan

(1978). Conjoint analysis on consumer research: Issues and outlook. Journal of Consumer Research, 5(2), 103–123.

45.

Guevremont

& Grohmann

(2015). Consonants in brand names influence brand gender perceptions. European Journal of Marketing, 49(1/2), 101–122.

46.

Gunasti

& Ross

W. T.

(2010). How and when alphanumeric brand names affect consumer preferences. Journal of Marketing Research, 47(6), 1177–1192.

47.

Haines

& Sharif

N. M.

(2006). A framework for managing the sophistication of the components of technology for global competition. Competitiveness Review: An International Business Journal, 16(2), 106–121.

48.

Hair

J. F.

Black

W. C.

Babin

B. J.

& Anderson

R. E.

(2010). Multivariate data analysis (p. 292). Upper Saddle River, NJ: Pearson.

49.

Hamel

& Prahalad

C. K.

(1994). Competing for the future. Harvard Business Review, 72(4), 122–128.

50.

Harper

(1994). Technophobes of the world unite. Industrial Distribution, 83(3), 37.

51.

Harris

Cox

K. C.

Musgrove

C. F.

& Ernstberger

K. W.

(2016). Consumer preferences for banking technologies by age groups. International Journal of Bank Marketing, 34(4), 587–602.

52.

Haubl

(1996). A cross-national investigation of the effects of country of origin and brand name on the evaluation of a new car. International Marketing Review, 13(5), 76–97.

53.

Helson

(1964). Adaptation-level theory: An experimental and systematic approach to behavior. New York, NY: Harper and Row.

54.

Higgins

S. H.

& Shanklin

W. L.

(1992). Seeking mass market acceptance for high-technology consumer products. Journal of Consumer Marketing, 9(1), 5–14.

55.

Hines

Hungerford

& Tomera

(1987). Analysis and synthesis of responsible environmental behavior: A meta-analysis. Journal of Environmental Education, 18(2), 1–8.

56.

Iacocca

Sawhill

& Zhao

(2015). Why brand drugs priced higher than generic equivalents. International Journal of Pharmaceutical and Healthcare Marketing, 9(1), 3–19.

57.

Jezkova Isaksen

& Roper

(2008). The impact of branding on low-income adolescents: A vicious circle? Psychology & Marketing, 25(11), 1063–1087.

58.

Jezkova Isaksen

& Roper

(2012). The commodification of self-esteem: Branding and British teenagers. Psychology & Marketing, 29(3), 117–135.

59.

Jie

T. H.

(1988). Technological innovation in Korea: An empirical investigation into the effect of government innovation incentive policies, market pressure and competition, and firm’s organization structure to the technological innovation behavior in Korean firms. Dissertation Abstracts International, 50(02), 541A.

60.

Jonathan

& Camilo

(2008). Mobile banking and economic development: Linking adoption, impact and use. Asian Journal of Communication, 18(4), 318–322.

61.

Jun

S. Y.

Mazumdar

& Raj

S. P.

(1999). Effects of technological hierarchy on brand extension evaluation. Journal of Business Research, 46(1), 31–43.

62.

Kapferer

J. N.

(2007). Les marques, capital de l’entreprise (4th ed.). Paris: Les Editions d’Organisation.

63.

Kapferer

J. N.

(2012). The new strategic brand management. London: Kogan Page.

64.

Kaynak

& Kara

(2002). Consumer perceptions of foreign products. European Journal of Marketing, 36(7/8), 928–949.

65.

Keller

K. L.

(1993). Conceptualizing, measuring, and managing customer-based brand equity. Journal of Marketing, 57(1), 1–10.

66.

Keller

K. L.

(1998). Strategic brand management: Building, measuring and managing brand equity. Englewood Cliffs, NJ: Prentice Hall.

67.

Keller

K. L.

(2013). Strategic brand management: Building, measuring and managing brand equity. Essex: Pearson Education.

68.

Keller

K. L.

& Lehmann

D. R.

(2006). Brands and branding: Research findings and future priorities. Marketing Science, 25(6), 740–759.

69.

Khalil

(2000). Management of technology: The key to competitiveness and wealth creation (p. 265). Boston, MA: McGraw-Hill, Inc.

70.

Khojastehpour

Ferdous

A. S.

& Polonsky

(2015). Addressing the complexities of managing domestic and multinational corporate brands. Corporate Communications: An International Journal, 20(1), 48–62.

71.

Kim

J. Y.

Natter

& Spann

(2009). Pay what you want: A new participative pricing mechanism. Journal of Marketing, 73(1), 44–58.

72.

Kirmani

& Rao

A. R.

(2000). No pain. No gain: A critical review of the literature on signaling unobservable product quality. Journal of Marketing, 64(2), 66–79.

73.

Klink

R. R.

(2000). Creating brand names with meaning: The use of sound symbolism. Marketing Letters, 11(1), 5–20.

74.

Klink

R. R.

(2003). Creating meaningful brands: The relationship between brand name and brand mark. Marketing Letters, 14(3), 143–157.

75.

Klink

R. R.

& Athaide

G. A.

(2012). Creating brand personality with brand names. Marketing Letters, 23(1), 109–117.

76.

Kotler

(1991). Marketing management: Analysis, planning, implementation, and control (7th ed.). Englewood Cliffs: Prentice-Hall.

77.

Krause

(1993). Environmental consciousness. Environment and Behavior, 25(1), 126–142.

78.

Kulshreshtha

Bajpai

& Tripathi

(2017). Consumer preference for electronic consumer durable goods in India: A conjoint analysis approach. International Journal of Business Forecasting and Marketing Intelligence, 3(1), 13–37.

79.

Kulshreshtha

Tripathi

Bajpai

& Dubey

(2017). Discriminating market segments using preferential green shift: A conjoint approach. Foresight, 19(4), 386–408. DOI: 10.1108/FS-02-2017-0007

80.

Leavitt

H. J.

(1954). A note on some experimental findings about the meanings of price. The Journal of Business, 27(3), 205–210.

81.

Lee

S. C.

Barker

& Kandampully

(2003). Technology, service quality, and customer loyalty in hotels: Australian managerial perspectives. Managing Service Quality, 13(5), 423–432.

82.

Lee

& Lou

C.-C.

(1996). Consumer reliance on intrinsic and extrinsic cues in product evaluations: A conjoint approach. Journal of Applied Business Research, 12(1), 21–30.

83.

Loureiro

(2003). Rethinking new wines: Implications of local and environmentally friendly labels. Food Policy, 28(5/6), 547–560.

84.

Louviere

J. J.

(1994). Conjoint analysis. In Bagozzi

(Ed.), Advanced methods of marketing research (pp. 223–259). Cambridge, MA: Blackwell Business.

85.

Lowrey

T. M.

& Shrum

L. J.

(2007). Phonetic symbolism and brand name preference. Journal of Consumer Research, 34(3), 406–414.

86.

Lowrey

T. M.

Shrum

L. J.

& Dubitsky

T. M.

(2003). The relation between brand-name linguistic characteristics and brand-name memory. Journal of Advertising, 32(3), 7–17.

87.

Luce

R. D.

& Tukey

J. W.

(1964). Simultaneous conjoint measurement: A new type of fundamental measurement. Journal of Mathematical Psychology, 1(1), 1–27.

88.

Luna

Carnevale

& Lerman

(2013). Does brand spelling influence memory? The case of auditorily presented brand names. Journal of Consumer Psychology, 23(1), 36–48.

89.

Martin

& Simintiras

(1994). Determination of green purchase behavior: A review of the literature and an agenda for further research. In Bell

(Ed.), Marketing: Unity and Diversity (Proceedings of the 1994 Marketing Educators Group Conference, pp. 628–637). Coleraine, Northern Ireland: University of Ulster.

90.

Maxwell

& Estelami

(2006). Introduction. Journal of Product & Brand Management, 15(6), 355–357.

91.

McCracken

J. C.

& Macklin

M. C.

(1998). The role of brand names and visual cues in enhancing memory for consumer packaged goods. Marketing Letters, 9(2), 209–226.

92.

Mehrabian

& Wetter

R. D.

(1987). Experimental test of an emotion-based approach to fitting brand names to products. Journal of Applied Psychology, 72(1), 125–130.

93.

Mick

D. G.

& Fournier

(1998). Paradoxes of technology: Consumer cognizance, emotions, and coping strategies. Journal of Consumer Research, 25(2), 123–141.

94.

Monroe

K. B.

(1973, February). Buyers’ subjective perceptions of price. Journal of Marketing Research, 10(1), 70–80.

95.

Monroe

K. B.

& Lee

A. Y.

(1999). Remembering versus knowing: Issues in buyers’ processing of price information. Journal of Academy of Marketing Science, 27(7), 207–225.

96.

Moore

W. L.

& Lehmann

D. R.

(1982). Effects of usage and name on perceptions of new products. Marketing Science, 1(4), 351–370.

97.

Mullikin

J. L.

(2003). Beyond reference price: Understanding consumers’ encounters with unexpected prices. Journal of Product & Brand Management, 12(3), 140–153.

98.

Ohmae

(1985). Triad power. New York, NY: Free Press.

99.

Olson

J. C.

(1972). Cue utilization of the quality perception process: A cognitive model and an empirical test. West Lafayette, IN: Purdue University.

100.

Orme

B. K.

(2006). Getting started with conjoint analysis. Madison, WI: Research Publishers.

101.

Padel

& Foster

(2005). Exploring the gap between attitudes and behaviour: Understanding why consumers buy or do not buy organic food. British Food Journal, 107(8), 606–625.

102.

Park

W. C.

Milberg

& Lawson

(1991). Evaluation of brand extensions: The role of product feature similarity and brand concept consistency. Journal of Consumer Research, 18(2), 185–193.

103.

Passikoff

(2005). Brand expectations of US youth. Young Consumers, 6(3), 26–29.

104.

Peterson

R. A.

& Ross

(1972). How to name new brands. Journal of Advertising Research, 12(6), 29–34.

105.

Phau

& Leng

Y. S.

(2008). Attitudes toward domestic and foreign luxury brand apparel: A comparison between status and non status seeking teenagers. Journal of Fashion Marketing and Management: An International Journal, 12(1), 68–89.

106.

Porter

M. E.

(1985). Technology and competitive advantage. The Journal of Business Strategy, 5(3), 60–78.

107.

Porter

M. E.

(1990). The competitive advantage of nations. Harvard Business Review, 68(2), 73–93.

108.

Rao

A. R.

& Monroe

K. B.

(1989). The effect of price, brand name, and store name on buyers’ perceptions of product quality: An integrative review. Journal of Marketing Research, 26(3), 351–317.

109.

Rao

A. R.

& Ruekert

R. W.

(1994). Brand alliances as signals of product quality. Sloan Management Review, 36(1), 87–97.

110.

Raz

Piper

Haller

Dussart

& Giboreau

(2008). From sensory marketing to sensory design: How to drive formulation using consumers’ input? Food Quality and Preference, 26(8), 765–771.

111.

Robertson

(1989). Strategically desirable brand name characteristics. Journal of Consumer Marketing, 6(4), 61–71.

112.

Round

& Roper

(2015). Untangling the brand name from the branded entity. European Journal of Marketing, 49(11/12), 1941–1960.

113.

Rouvinen

(2004). Diffusion of digital mobile telephony: Are developing countries different? (Discussion Paper 901). The Research Institute of the Finnish Economy. Retrieved 16 May 2016 , from http://www.sciencedirect.com/science/article/pii/S0308596105001059.

114.

Roy

Rabbanee

F. K.

& Sharma

(2016a). Exploring the interactions among external reference price, social visibility and purchase motivation in pay-what-you-want pricing. European Journal of Marketing, 50(5/6), 816–837.

115.

Roy

Rabbanee

F. K.

& Sharma

(2016b). Antecedents, outcomes, and mediating role of internal reference prices in pay-what-you-want (PWYW) pricing. Marketing Intelligence & Planning, 34(1), 117–136.

116.

Samu

& Krishnan

(2010). Brand related information as context: The impact of brand name characteristics on memory and choice. Journal of the Academy of Marketing Science, 38(4), 456–470.

117.

Schloss

(1981). Chickens and pickles. Journal of Advertising Research, 21(6), 47–49.

118.

Schultz

P. W.

(2002). Knowledge, information, and household recycling: Examining the knowledge-deficit model of behavior change. In Dietz

& Stern

D. C.

(Eds), New tools for environmental protection: Education, information, and voluntary measures (pp. 67–82). Washington, DC: National Academy Press.

119.

Schumpeter

J. A.

(1942). Capitalism, socialism and democracy. New York, NY: Harper.

120.

Shugan

S. M.

(2006). Who is afraid to give freedom of speech to marketing folks? Marketing Science, 25(5), 403–410.

121.

Smith

M. F.

& Sinha

(2000). The impact of price and extra product promotions on store preference. International Journal of Retail & Distribution Management, 28(2), 83–92.

122.

Solow

R. M.

(1956). A contribution to the theory of economic growth. The Quarterly Journal of Economics, 70(1), 65–94.

123.

Szbillo

G. J.

& Jacoby

(1974). Intrinsic versus extrinsic cues as determinants of perceived product quality. Journal of Applied Psychology, 59(1), 74–78.

124.

Tarkiainen

& Sundqvist

(2011). Subjective norms, attitudes and intentions of Finnish consumerism buying organic food. British Food Journal, 39(1), 399–414.

125.

Urbany

J. E.

& Dickson

P. R.

(1991). Consumer normal price estimation: Market versus personal standards. Journal of Consumer Research, 18(1), 45–51.

126.

Van Trijp

H. C. M.

& Schifferstein

H. J. J.

(1995). Sensory analysis in marketing practice: Comparison and integration. Journal of Sensory Studies, 10(2), 127–147.

127.

Vanden Bergh

Adler

& Oliver

(1987). Linguistic distinction among top brand names. Journal of Advertising Research, 27(4), 39–44.

128.

Vanden Bergh

Collins

Schultz

& Adler

(1984). Sound advice on brand names. Journalism Quarterly, 61(4), 835–840.

129.

Veale

& Quester

(2009). Tasting quality: The roles of intrinsic and extrinsic cues. Asia Pacific Journal of Marketing and Logistics, 21(1), 195–207.

130.

Wang

& Yang

(2008). Does country-of-origin matter in the relationship between brand personality and purchase intention in emerging economies? Evidence from China’s auto industry. International Marketing Review, 25(4), 458–474.

131.

Wittink

D. R.

& Cattin

(1989). Commercial use of conjoint analysis: An update. Journal of Marketing, 53(3), 91–96.

132.

Wittink

D. R.

Vriens

& Burhenne

(1994). Commercial use of conjoint analysis in Europe: Results and critical reflections. International Journal of Research in Marketing, 11(1), 41–52.

133.

Yorkson

& Menon

(2004). A sound idea: Phonetic effects of brand names on consumer judgments. Journal of Consumer Research, 31(1), 43–51.

134.

Zeithaml

V. A.

(1988). Consumer perceptions of price, quality, and value: A means-end model and synthesis of evidence. Journal of Marketing, 52(3), 2–22.