The effect of brand transparency on consumer decision making
Master ThesisMSc Business Administration – Marketing
Iris van Dijk 11110899
February 3rd, 2017 Draft
Statement of originality
This document is written by Student Iris van Dijk who declares to take full responsibility for the contents of this document. I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in creating it. The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.
Table of Contents
Abstract 4
Introduction 5
Literature Review 8
Consumer Decision Making 8
Transparency 9
The impact of transparency 10
Price 11 Brand Equity 13 Source of information 14 Methodology 16 Design 16 Pre-test 16 Procedure 16 Price 16 Brand 17 Transparency 17 Source of transparency 18 Conclusion 18 Conjoint analysis 19 Sample 19 Research Design 20 Procedure 20 Data analysis 21 Results 22 Data screening 22 Descriptive statistics 22
Differences in relative importance of the attributes 23
Effect of transparency, price, brand and channel of information on product choice 24 Effect of the relative importance of price, brand and source of information on the utility
scores of the three levels of transparency 26
Discussion 28
General discussion 28
Theoretical and managerial implications 30
Limitations and Future Research 30
References 32
Appendix 37
Abstract
The emergence of the Internet and social media has increased consumer access to information. This has increased the need for transparency for both consumer and brands. This study examines the relationship between transparency and the consumer decision making process, in addition to that it examines the moderating effect of price, brand equity, and source of information on this relationship. This is done using a choice based conjoint analysis, this for the establishment of part utilities for the several attributes. Multiple analysis led to the findings that consumers ascribe more value to a transparent option then they do to a non-transparent option. There was however, no moderating effect found for price and brand equity. Source of information was found to have a moderating effect on the relationship between transparency and consumer decision making. When information was medium transparent and it was provided by the company’s website, participants were less likely to purchase that particular product. The same effect holds for when information is provided by a product’s packaging. It was also found that consumers ascribe more value to information provided by friends than they do to information provided by the company.
Overall, this research show that in this digital age, transparency is becoming increasingly important for consumer decision making, whilst there is a shift in credibility from brands to peers.
Introduction
In September 2015 the American Environmental Protection Agency (EPA) found that a large number of Volkswagen cars being sold in America had a “defeat device” - or software - in diesel engines that could detect when they were being tested, changing the performance accordingly to improve results (Hotten, 2015). Volkswagen subsequently had to recall nearly twenty million cars in the United States and Europe. The company has set aside more than $20 billion for costs related to the scandal and in addition to that has recorded losses, announced layoffs and has shaken up its leadership (Gates et al., 2016). The Volkswagen emission scandal is just one of many that have shown that nowadays everything that can be exposed will be exposed. The financial crisis has led to increased skepticism towards companies (Demmers et al., 2015). This together with an increased demand for transparency has caused consumers to be more critical than ever. The demand for transparency has been heightened, in part, by an increased awareness of the environment and the advanced technology in communication. Thus maintaining the secrecy of corporate wrongdoings has become very difficult and extremely risky. How does this heightened demand for and availability of transparent information influence consumers?
The rapid rise of the Internet and the subsequent emergence of Social Media have increased the availability and accessibility of information. Since the early 1990s, calls for increased transparency have risen in all sectors of society, from the government, to business and the non-profit sector (Auger, 2014). In the 2012 Edelman Trust Barometer (Edelman, 2012) transparency is ranked seventh of 16 important business attributes and second in important governmental attributes. Rawlins (2009) identifies transparency as the deliberate attempt to make available all legally releasable information—whether positive or negative in nature—in a manner that is accurate, timely, balanced, and unequivocal, for the purpose of enhancing the reasoning ability of publics and holding organizations accountable for their actions, policies, and practices. In addition to the developments in technology and communication, an increased awareness of the environment has heightened the demand for transparency (Bhaduri and Ha-Brookshire, 2011).
Unearthed information, now more than ever, can weaken iconic brands (Fournier and Avery, 2011). Recent scandals such as the Volkswagen emission scandal, the Panama Papers and the FIFA corruption scandal have shown that nowadays, everything that can be exposed will be exposed. Customers are demanding transparency as they take an increasing interest in the ethical practices of those they buy from (Baker, 2015). Facing the ticking time bomb of
publicity, where no contentious action goes unnoticed or unannounced, contemporary brands seem to have no choice but to adopt proactive positions of full disclosure (Fournier & Avery, 2011). In this era of multiple media and instantaneous news, transparency is less of an option than a necessity for organisations that wish to remain viable (Auger, 2014). One technique organisations employ to prevent negative information being released outside of their control is called ‘stealing thunder’. When an organisation steals thunder, it breaks the news about its own crisis before the crisis is discovered by the media or other interested parties (Arpan & Roskos-Ewoldsen, 2005). A proactive stance might allow an organisation to set the tone for coverage of the crisis and influence which issues will be considered in discussions of the crisis (Arpan & Roskos-Ewoldsen, 2005). Stealing thunder in a crisis situation, as opposed to allowing the information to be first disclosed by another party, was found to result in higher credibility ratings (Arpan & Roskos-Ewoldsen, 2005).
Due to the rise of the Internet and the subsequent emergence of Social Media, a power shift has occurred. Labrecque et al. (2013) identified four sources of customer power, namely demand-based, information-based, network-based and crowd-based. Web 2.0 technologies have caused three effects: (1) a shift in locus of activity from the desktop to the Web, (2) a shift in locus of value production from the firm to the consumer, and (3) a shift in the locus of power away from the firm to the consumer (Berthon et al., 2012). As a result of increased access to information, the customers’ information-based power has dramatically increased. In addition to that, the rise of the internet and social media improved communication technologies. So not only can customers access information more easily, they additionally have increased access to each other which has made it easier to exchange information and opinions. This has enlarged the consumers’ crowd-based power which resides in the ability to pool, mobilize, and structure resources in ways that benefit both individuals and groups (Labrecque et al. 2013). Moreover, the increased and improved access to information has made the consumer more critical than ever. Especially for consumers who are concerned about the environment and society, information of business transparency can be important for their purchase and consumption choices (Bhaduri & Ha-Brookshire, 2011).
What other effects does transparency have on consumers, in addition to giving the consumer more power? Even though transparency is rapidly gaining popularity in marketing research, much is still to be examined with regards to its influence on consumer behaviour. This research addresses the gap in the current literature by attempting to answer the following
How does transparency affect consumer decision-making and what is the relative impact of transparency on the probability of choosing the product dependent on brand, price level and
source of transparency?
This study builds on and extends the existing transparency literature and adds to the existing theory on transparency and consumer decision making. More specifically, this research provides new insights in the effect of transparency on customer decision making, taking into account the moderating effect of price, brand and source of information. An increased understanding of this effect can greatly influence marketing practices. If transparency turns out to influence customer decision making, companies should focus more on advertising their transparency in order to increase sales.
This study will contribute to existing literature on the effects of transparency on customer decision making. In addition to that it will provide several insights on this relationship and several variables that potentially moderate this relationship, such as price, brand equity and source of information.
This article will first provide a review of the relevant literature, in order to gain a better understanding of the different concepts. Subsequently, the research design and research methods will be discussed. Thirdly, the results of the study will be explained. Finally, a discussion of the results is provided, consisting of conclusions, implications and suggestions for future research.
Literature Review
This chapter will provide an extensive overview of the existing literature on customer decision making and transparency. In the first paragraph consumer decision making is examined, including current theories on the subject. In the second paragraph transparency will be discussed on a conceptual level, including prevailing definitions and different typologies proposed in previous work. Subsequently several other factors that influence the customer decision making process will be examined, including price, brand and source of information. Finally, based on the existing literature, several hypotheses will be drafted and a conceptual model will be presented which reflects the hypotheses that have previously been drafted. Consumer Decision Making
Consumer decision making can be defined as the behaviour patterns of consumers, that precede, determine and follow on the decision process for the acquisition of need satisfying products, ideas or services (Rousseau, 2001). Both consumer behaviour and consumer decision making have gained a lot of attention from researchers in recent years. During the 1970s and early 1980s much of consumer behaviour research was focused on the consumer decision-making (Mowen, 1988). As a result, several models were developed to provide insight into the process of consumer decision making. Even though these models all differ slightly, there appears to be a general consensus that consumer decision making involves a number of different stages. The most commonly used model is that proposed by Cox, Granbois and Summers (1983). They propose a ‘Five Stage Model’ which suggests that there are five stages to the consumer decision making process; recognition of need or problem, information search, comparing alternatives, purchase and post-purchase evaluation. This model appears to be in line with the theory of reasoned action, which is based on the premise that individuals are rational and make systematic use of information available to them. The theory of planned behaviour extended the theory of reasoned action by including perceived behavioural control as a determinant of both behavioural intention and behaviour (Belleau et al., 2007). According to the theory of reasoned action, one’s behavioural intention is based on two determinants: attitude toward the behaviour and perception of social pressures to perform or not to perform the behaviour (Belleau et al., 2007). Attitude toward a behaviour is personal in nature and is the individual’s positive or negative evaluation of performing the behaviour. Furthermore,
performing an action will have positive outcome(s) will be more likely to perform the action than one who does not (Bhaduri and Ha-Brookshire, 2011).
The Five Stage Model proposed by Cox, Granbois and Summers (1983) has been subject to criticism in recent years. Several researchers have questioned the rational approach to consumer decision making, because studies show that for many products consumers spend very little time or do not even engage in some of the sequential activities suggested as being important (Rousseau, 2001). It was suggested that consumers engage in both cognitive and emotional information processing prior to a purchase (Rousseau, 2001). Cognitive information processing refers to active, effortful planning and goal directed consumer behaviour that involves mediated intellectual activity, while emotional processing refers to the evaluation of product alternatives within more abstract parameters (Rousseau, 2001).
The inconsistency in rational or subconscious decision making might be explained by the Elaboration Likelihood Model. Petty and Cacioppo (1983) suggest that there are two distinct routes to attitude change. The central route views attitude change as a result of diligent consideration of information that is central to what people feel are the true merits of its advocacy (Petty & Cacioppo, 1983). The peripheral route on the other hand views attitude change as a result of the attitude object being associated with either positive or negative cues or the person using a simple decision rule to evaluate a communication (Petty & Cacioppo, 1983). The processing route an individual takes is influenced by multiple factors of which motivation and ability to think are the two most influential.
In addition to internal factors, there are several external factors which influence the customer decision making process. There has been vast research into what these factors are and what exactly is their influence on consumer behaviour. The next section will address some of these factors which are relevant for the current research.
Transparency
Due to its growing importance, transparency has received increasing attention from various scholars. Even though these scholars do not seem to degree on one universal definition of transparency, their respective definitions have some common ground. The Merriam-Webster dictionary defines transparency as “visibility and accessibility of information especially concerning business practices”. Gower (2006) described information transparency as the attempts of organisations to have their actions and decisions “ascertainable and understandable by a party interested in those actions or decisions”. Corporate transparency is defined as the
availability of firm-specific information to those outside public trade firms (Bushman, Piotroski & Smith, 2004). The most elaborate definition of transparency comes from Rawlins (2009) who defines transparency as “the deliberate attempt to make available all legally releasable information- whether positive or negative in nature- in a manner that is accurate, timely, balanced, and unequivocal, for the purpose of enhancing the reasoning ability of publics and holding organisations accountable for their actions, policies and practices.
Although there is no encompassing definition of transparency, the definitions provided above seem to agree that in order to achieve transparency the availability and accessibility of information is crucial. The advanced technology in communication, together with an increased awareness of the environment has heightened the demand for transparency (Bhaduri and Ha-Brookshire, 2011). In addition to that the rapid advancement in technology, together with the rise of the Internet and social media has provided consumers with availability and convenient access to information. This research will define transparency as the visibility and accessibility of information concerning all business practices.
The impact of transparency
Multiple studies have provided insights into how transparency influences brands and consumers. Several of these have shown that transparency can influence the customer decision making process and purchase intentions. Bhaduri and Ha-Brookshire (2011) found that transparency can affect purchase intention. In their research, when two products were of similar quality, their participants were willing to buy the product that was from a transparent apparel business. Research by Pickett-Baker and Ozaki (2008) found that respondents reported to be more likely to choose brands which they knew were manufactured by companies whose products and processes were more environmentally friendly. This shows that being transparent about businesses practices attracts consumers. Further research examined the influence of product transparency on purchase decisions. It found that for homogeneous products, product transparency is not a major driver of purchase decisions, however for differentiated products, higher product transparency will likely lead to higher prices (Granados, Gupta and Kauffman, 2010).
In addition to influencing customer decision making, transparency also appears to influence brand perception. Research by Auger (2014) found that participants perceived
implying that perceptions of integrity, respect and openness do much to curtail limitations in communicative transparency. Furthermore, participants indicated high respect for those who communicated transparently and a lack of respect for those who did not communicate transparently.
Several studies have highlighted the effect that transparency has on trust and attitude towards a brand. Kang and Hustvedt (2013) found that transparency positively affects trust and general attitude toward a brand. They also found that transparency exerts indirect, positive effects on word-of-mouth intention and purchase intention, mediated by trust and general attitude. In an effort to build trust with customers, numerous companies including major brand corporations such as Gap Inc., Nike, Macy’s, and Microsoft have expanded the scope of corporate social responsibility initiatives by being transparent about their supply chain and labour issues and/or outreach to the community (Kang and Hustvedt, 2013). Research by other scholars seems to confirm that there appears to be a relationship between transparency and brand trust. Granados, Gupta and Kauffman (2010) have found evidence that consumers may view a product with suspicion in the absence of information about salient attributes of the product. Transparency on the other hand enhances brand perception because it increases trust in the brand. This can have great benefits for brands. Trust built between consumers and a company significantly contributes to positive outcomes for the company, such as loyalty toward de the company, customer retention, product choices, purchase intention, willingness to act and overall market performance (Kang and Hustvedt, 2013).
The research described above implies that transparency influences brand perception leading to higher respect and higher perceived brand accountability. Additionally, transparency increases brand trust and consumers report that they are more willing to buy products from a transparent apparel business. Therefore, it appears that consumers ascribe more value to transparent options than to their non-transparent counterparts. This leads to the following hypothesis:
H1: Consumers ascribe more value to transparent options than to non-transparent options.
Price
Prior research indicates that perceived price (i.e., the consumer's perception of price) is formed based on the actual (objective) price and the consumer's reference price (Chang and Wildt, 1994). Perceived price can be defined as the consumer's perceptual representation or
subjective perception of the objective price of the product (Jacoby and Olson 1977). Previous studies have shown that price directly influences customer purchase intentions (Chiang and Jang, 2007; Chang and Wildt, 1994) 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). If consumers believe price and quality are positively related, it follows that they would use price as an indicator, or signal, of quality. Previous research suggests that price and quality are important determinants of shopping behaviour and product choice. although all of the participants preferred to purchase apparel from businesses that are transparent, most of them put a priority on price and quality over transparency (Bhaduri & Ha-Brookshire, 2011).
Even though there has been a heightened demand for corporate transparency by consumers, research shows that price is still considered one of the most important attributes. In a study by Boulstridge and Carrigan (2000), respondents stated that they neither favoured good behaviour nor boycotted poor behaviour by companies. In addition to that it was demonstrated that the most important influences on purchase behaviour were price, cost/value, and brand familiarity. This importance of price, quality and convenience as decision factors show that consumers are purchasing for personal reasons rather than societal (Boulstridge & Carrigan, 2000). Several studies support these findings, including research by Carrigan and Attalla (2001) which showed that although respondents stated they were unhappy with low wages being paid to people producing chocolate for them, they still would not be willing to boycott products over this. In addition to that they stated that they would not pay a price premium of around 10-15 per cent for the same chocolate if it were produced in a more socially responsible way (Carrigan & Attalla, 2001). Consumers are unwilling to undergo any extra inconvenience in order to purchase ethically, and price, value, trends and brand image remain the dominant influences over purchase choice (Carrigan & Attalla, 2001). It appears that consumers value price above knowledge and transparent information about ethical and unethical business practices. Therefore, the following is hypothesised:
H2: The relationship between brand transparency and customer decision making is moderated by price, such that consumers ascribe less value to transparency when product price is low.
Brand Equity
There is a large number of authors who have researched the different aspects of brand equity. These researchers also provided a number of different definitions of brand equity. Faircloth, Capella and Alford (2001) define brand equity as the biased behaviour a consumer has for a branded product versus an unbranded equivalent. Farquhar (1989) defines brand equity as the ‘added value’ with which a brand endows a product. According to Yoo, Donthu and Lee (2000) brand equity is the incremental utility or value added to a product by its brand name.
Brand equity has been examined from two different perspectives; the financial perspective and the customer based perspective. The financial perspective on brand equity deals with the value brand equity creates for the firm.
Brand equity is regarded as a very important concept in business practice as well as in academic research because marketers can gain competitive advantage through successful brands. The competitive advantage of firms that have brands with high equity includes the opportunity for successful extensions, resilience against competitors’ promotional pressures, and creation of barriers to competitive entry (Lassar, Mittal and Sharma, 1995).
Brand equity is thought to exist of two components, brand strength and brand value (Lassar, Mittal and Sharma, 1995). Brand strength consists of the brand associations held by customers, while brand values are the gains that accrue when brand strength is leveraged to obtain superior current and future profits (Srivastava and Shocker, 1991).
Research has indicated that brand equity has an influence on customer purchase intentions. Cobb-Walgren et al. (1995) indicated that higher equity brands generate greater purchase intention. Chang and Liu (2009) state that as brand equity is reflected in brand preference, it could be inferred that brand preference would be reflected in purchase or usage intention. In addition to that, Chang and Liu (2009) found that a direct positive impact of brand equity on brand preference was supported. Therefore, they concluded that brands with higher levels of brand equity would generate higher levels of customer brand preference, which in turn was associated with more willingness to continue using the brand. Therefore, the following is hypothesised:
H3: The relationship between brand transparency and consumer decision making is moderated by brand, such that consumers ascribe less value to transparency when brand equity is high.
Source of information
Over the past few years there has been a steep decline in trust in global institutions. There is a profound crisis in trust, which has its origins in the Great Recession of 2008 (Edelman, 2017). Like the second and third waves of a tsunami, ongoing globalization and technological change are now further weakening people’s trust in global institutions, which they believe have failed to protect them from the negative effects of these forces (Edelman, 2017). With the emergence of Social Media, it is now easier than ever for consumers to gain information. However, growing Internet use for research and information has also raised concerns about the quality of information people are obtaining and how they are assessing its credibility (Greer, 2003). Credibility, along with liking, and representativeness is one of four criteria that influence attitudes toward print and online news (Greer, 2003). Media experts define a credible source as one that is seen as providing correct information and as willing to release that information without bias (Greer, 2003).
A report by Edelman (2017) showed that sixty-two percent of those questioned think that a company’s social media is more credible than its advertising. In addition to that fifty-one percent of those questioned believes that personal experience is more credible than data. While research by Greer (2003) showed that information from sources rated as high in expertise leads to the greatest attitude change among those receiving the message; low-expertise sources typically produce no changes in attitudes. However, a more recent report by Edelman (2016) shows that seventy-five percent of people indicated that peers help them make decisions and help them overcome concerns. In addition to that the Edelman Trust Barometer (Edelman, 2017) shows that people now find peers as credible as experts. They find peers more credible than for example CEO’s and boards of directors. Therefore, the following is hypothesised:
H4: Consumers ascribe more value to information provided by friends than they do to information provided by companies. The relationship between brand transparency and consumer decision making is moderated by source of information.
An illustration of the hypothesized relationship between brand transparency and customer decision making is presented in the conceptual model below.
Methodology Design
An online survey was used to pre-test the attributes and levels that were later to be used in a conjoint analysis. A choice based conjoint analysis was used in order to examine the effects of transparency, price and brand on customer decision making. The choice of the additional attributes price (Moon, Chadee & Tikoo, 2008) and brand (Chang & Liu, 2009; Spears & Singh, 2004) is based on previous research on consumer decision making.
This chapter will further explain the methodology. First the pre-test is discussed, providing in-depth explanations as to the attributes and levels that were selected. Subsequently the conjoint experiment will be explained, providing details on the sample, procedure and interpretation.
Pre-test Procedure
A pre-test was administered to determine the levels of the attributes. As previously mentioned, the choice of attributes was based on previous research. After answering a number of demographic questions, the respondents were asked to answer several questions about their habits and choices regarding coffee consumption. The data was collected through an online survey using Qualtrics software. The survey was spread using social media as a communication tool. The participants were collected through non-random sampling techniques, which included self-selection and the snowball effect. The survey was written in Dutch and therefore the sample existed of solely Dutch speaking participants. A total of N = 20 respondents participated in the pre-test of which 17 completed the survey. 70% of the participants were female.
Price
Participants were asked to indicate how much they pay on average per cup of coffee, what they considered to be cheap for a cup of coffee and the maximum price they were willing to pay per cup of coffee. They were asked to answer these questions on a line scale ranging from €0,00 to €1,00 with 10 cent intervals. The chosen prices were based on the median of the three line scales.
Table 1: The median of the three line scales for the attribute price for a cup of coffee Median
Considered as a cheap price 0.33
Considered as an average price 0.44
Considered as the maximum price 0.51
Brand
Subsequently participants were asked to indicate which coffee brands they purchase or consume. Next, participants were asked to answer five questions regarding brand loyalty, based on a seven point Likert scale. The chosen brand levels were based on the mean of the loyalty scales. Four of the presented brands had the highest means, two of them were picked based on the ability to be interpreted very distinct by customers.
Table 2: The descriptive statistics of the average loyalty scores of the different brands
N M SD
Kannis & Gunnik 2 3.80 0.28
DE 5 4.88 0.77 NES 4 6.10 0.38 Senseo 1 4.80 Starbucks 6 5.07 0.59 Nescafe 0 Illy 2 4.60 0.28 Huismerk 4 5.15 0.72 Lavazza 3 4.33 0.81 Transparency
Participants were presented with four different conditions containing different product information. In each following condition, the product information got more elaborate, with the most elaborate information representing the highest level of transparency. Participants were asked to judge how transparent they perceived the information to be on a seven point Likert scale. The transparency levels were chosen based on a repeated measures ANOVA, which indicated that these three conditions were interpreted by the participants on the intended level of transparency, F(4, 2547) = 1080.99, p <.001. The fourth condition showed lower levels of
perceived transparency contrary which was contrary to what was expected. Therefore, the first three conditions were chosen as attribute level.
Table 3: The descriptive statistics of the transparency of the different brands
Condition M SD
Lowest Transparency 1.59 .60
Low to Medium Transparency 2.12 .90
Medium to High Transparency 3.53 1.14
Maximum Transparency 3.00 1.09
Source of transparency
Finally, participants were asked to judge how transparent they perceived product information to be when it originated from different sources. They were asked to judge this on a seven point Likert scale. The levels that were selected represented the lowest score on transparency, the average score of transparency and the highest score on transparency based on their means tested using a repeated measures ANOVA which showed significant differences in these mean scores, F(4, 3396) = 258.06, p <.001.
Table 4: The descriptive statistics of the transparency of the different brands
Source of information M SD
Website of the product 2.59 1.09
Store of the product 3.47 1.33
From the packaging of the product 3.59 1.14
News website 3.35 1.24
A friend, family or collegue 4.18 1.43
Conclusion
Based on the analyses of the pre-test data, the chosen levels of each attribute are presented in Table 5.
Table 5: The chosen levels of each attribute in the conjoint analysis
Attribute Levels
Price (per cup of coffee) 1. € 0.30 2. € 0.40 3. € 0.50
Brand 1. Nespresso
2. Store brand
Transparency 1. Made with fresh beans
2. Made with fresh beans from Brazil, Colombia and Ethiopia
3. Made with fresh beans from Brazil, Colombia and Ethiopia and UTZ certified
Source of information 1. packaging of the product 2. brand website
3. a friend
Conjoint analysis
Sample
The data was collected through an online survey, using Sawtooth Software. The survey was spread using social media as a communication tool. The participants were collected through non-probability sampling techniques, these included self-selections and the snowball effect. The sample was made up entirely of Dutch speaking participants, due to the fact that both surveys were written in Dutch. A total of 184 respondents participated in the conjoint experiment of which 112 completed the survey. The average age in the conjoint experiment was M = 40 and 70% of the participants were female. No names or personal data were collected or used in the report.
Research Design
Currently, conjoint analysis is the most used experimental choice analysis to measure consumer preferences and buying attention. In a conjoint analysis, the attributes of products are randomly combined and used as a manipulation condition within the experiment.
An attribute is a characteristic of a product (e.g. color), made up of various levels (there must be at least two for each attribute) or degrees of that characteristic (e.g. red, yellow, blue). The underlying theory of conjoint analysis holds that buyers view products as composed of various attributes and levels. Buyers place a certain utility (value) on each of those characteristics, and can determine the overall utility of any product by summing up the value of its parts (levels). In conjoint experiments, respondents express their preferences for products described by varying levels of attributes. By observing how respondents evaluate products in response to changes in the underlying attribute levels, we can estimate the impact (utility) each attribute level has upon overall product preference.
The collected data are used for parameter estimation with model reflecting relations between profile evaluation and the values of attributes that characterize them. These models describe the choice as depending of the attributes and their parameters, called utilities. The model used for conjoint studies is also called logit model is used in discrete choice models. The utilities scores are calculated using a HB logit regression analysis, every attribute level in a conjoint project is assigned a utility (Formula 1). The higher the utility, the more desirable the attribute level. Levels that have higher utilities have a larger positive impact on
influencing respondents to choose products.
(1) The logit regression equation for calculating the utility score
𝑃 𝑃𝑟𝑜𝑑𝑢𝑐𝑡 𝑐ℎ𝑜𝑖𝑐𝑒 = 1 = 𝐵/+ 𝑒𝑓𝑓𝑒𝑐𝑡 𝑜𝑓 𝑃𝑟𝑖𝑐𝑒 𝑓𝑎𝑐𝑡𝑜𝑟 + 𝑒𝑓𝑓𝑒𝑐𝑡 𝑜𝑓 𝐵𝑟𝑎𝑛𝑑 𝑓𝑎𝑐𝑡𝑜𝑟 + 𝑒𝑓𝑓𝑒𝑐𝑡 𝑜𝑓 𝑇𝑟𝑎𝑛𝑠𝑝𝑎𝑟𝑒𝑛𝑐𝑦 𝑓𝑎𝑐𝑡𝑜𝑟 + 𝑒𝑓𝑓𝑒𝑐𝑡 𝑜𝑓 𝑆𝑜𝑢𝑟𝑐𝑒 𝑓𝑎𝑐𝑡𝑜𝑟
+ 𝑖𝑛𝑡𝑒𝑟𝑎𝑐𝑡𝑖𝑜𝑛 𝑒𝑓𝑓𝑒𝑐𝑡𝑠 + ℰ
Procedure
This conjoint analysis contained four attributes: price, brand, transparency and source of information. These attributes were randomly combined and assignment into 20 tasks for
information, which resulted in an equal 33% distribution of each of the levels. The price attribute consisted of two levels, which we both equally (50%) presented. For every task, participant had to choose one of 3 different products with different combinations of attributes. The software generated random combinations of levels of the product attributes. After completing the twenty tasks, participants were asked several demographic questions including age, level of education and income. In addition to that participants were asked questions on their habits of checking product packaging for information on clues regarding its level of environmental consciousness and Fairtrade information.
Data analysis
As described earlier, the utilities scores for all levels of attributes are calculated using the HB logit regression analysis within the Sawtooth software. After that, these utility scores, demographic variables and the control variables were combined and analyzed in SPSS 24 (IBM, 2015). First, the descriptive statistics and Pearson correlation were calculated. Next, a repeated measures ANOVA was performed to test whether there was a difference in relative importance of the four attributes. To test the conceptual model a hierarchical logistic
regression was performed, which included the main effect of transparency, price, brand and source of information in model 1 and the interactions in model 2 on the probability of
choosing the product (dichotomous) as dependent variable. Lastly, three multiple regressions were performed with the relative importance of price, brand and source of information of the utility scores of each level of transparency. For all tests a significance level of 5% was used.
Results Data screening
To exclude the availability of order effect on the probability of choice, a chi square of independence test was performed. The chi square test was not significant, c2 (19) = 24.82, p =.17. Based on these results, we can conclude that there was no order effect on probability of choice across the 20 tasks.
Descriptive statistics
Before the conjoint analysis experiment, participants answered three questions with on demographics as well on their critical attitude towards product description, as well how they considered themselves as environmentally conscious and related to fair trade consuming. The descriptive statistics are presented in Table 6. This table also contains the utility scores as calculated by Sawtooth for the different levels per attribute. A utility score is a measure of relative desirability or worth; the higher the utility, the more likely the item is to be chosen as best.
Table 6: Descriptive statistics for the control variables and utility score for each level of the attributes M SD Min Max Critical 3.92 1.40 1 7 Environmentally conscious 3.67 1.49 1 7 Societally conscious 3.5 1.58 1 7 Lowest Price 1.78 1.86 -1.66 5.42 Average Price 0.28 0.39 -0.64 1.29 Highest Price -2.06 1.70 -5.37 1.16 Nespresso 0.49 1.79 -3.65 4.36 Store brand -0.49 1.79 -4.36 3.65
Made with fresh beans -1.23 1.27 -3.62 1.57
Made with fresh beans from Brasil, Colombia and Ethiopia
-0.32 0.53 -1.69 1.11
Product packaging 0.18 0.64 -1.25 1.94
Brand website -0.32 0.78 -2.38 1.35
Friends 0.14 1.17 -2.99 3.44
Differences in relative importance of the attributes
In order to compare the attributes on the influence on the choice probability, the relative importance (RI) needs to be calculated. The relative importance characterizes the importance of each attribute by considering how much difference each attribute could make in the total utility of a product. The range in the utility scores in the attribute determines the maximum difference an attribute could make on the choice each utility score is divided by this maximum difference to get the relative importance in percentage. The descriptive statistics and Pearson correlations between the relative difference scores are displayed in Table 7. As expected from a score that represents a percentage, all the relative importance scores are significantly negatively correlated. This means that if one attributes becomes more important, the other attributes are decreasing in importance. As shown in Table 2, the RI of brand and the RI of price have a strong negative correlation, which indicates that the brand and price “share” the same relative importance, so one can replace the other. The same holds for transparency and channel of information. This means that if the transparency has a high level of importance, the source of information becomes less important.
Table 7: Descriptive statistics and Pearson correlations between the relative importance score (in %) M SD 2 3 4 1.RI price 0.33 0.23 1 2. RI brand 0.22 0.18 -.43** 1 3. RI transparency 0.28 0.19 -.58** -.26** 1 4. RI source of information 0.17 0.14 -.32** -.29** -.06 Note: *p<.05; **p<.01; ***p<.001
Next, a repeated measures ANOVA was used to test the difference in the four RI mean scores. The sphericity did not meet the assumption, Mauchly test was significant. Therefore, the Huyn feldt correction was used. Based on the test value of this correction, there is a significant differences in two or more of the four means, F(2.58,333)= 12.12, p<.001. The
pairwise comparisons with Bonferroni correction are displayed in Table 8. This difference in mean RI was present between price and brand (p<.001), price and source of information (p<.001), and between transparency and source of information (p<.001). Price was
significantly the most important attribute (compared to brand and source of information) for choosing a product. Transparency was the second most important attribute.
Table 8: Pairwise comparisons between the relative importance of the four attributes
Pairwise comparison Difference in M SE p
Price Brand 0.12 0.03 0.00
Price Transparency 0.05 0.04 0.78
Price Source of information 0.16 0.03 0.00
Brand Transparency -0.06 0.03 0.18
Brand Source of information 0.05 0.03 0.29
Transparency Source of information 0.11 0.02 0.00
Effect of transparency, price, brand and channel of information on product choice A hierarchical logistic regression was performed with the choice of product (0 = no, 1 = yes) as dependent variable. The first model was used to test the main effect of the
attributes. Next, the interaction terms between price, brand and channel of information with transparency were added to investigate the moderated effects of these three attributes on the effect of transparency on the probability of choice. The highest level of the attribute is chosen the reference categories.
Model 1 with the main effects of the attributes had a percentage explained variance of 12% (R2 = .12). There is a significant increase in probability of choice for an average priced product versus the expensive product. The probability of choice increased even more when presented a low priced versus the expensive product.
The brand Nespresso had a higher chance of being chosen versus store brand. The attribute transparency also showed a significant main effect. A higher transparency level was positively correlated with the probability of choice. From the levels of the source of
information, it can be concluded that friends as source have the highest probability of choice, while the website had the lowest probability. There was no significant difference between
Model 2 included the interactions with transparency to test the moderation effects of price, brand and source of information. Based on the results from model 2, only source of information has an influence on the relation between transparency and the choice probability. The positive effect of a higher transparency level on the choice probability is decreased in strength in both the website as well as packaging as source of information. The results are displayed in Table 9.
Table 9: Results from the hierarchical logistic regression on the choice of product
B SE Wald p Exp(B) R2
Model 1 .12
Lowest priced vs highest priced 1.30 0.06 412.35 0.00 3.67 Average priced vs highest priced 0.67 0.07 100.78 0.00 1.95 Nespresso vs store brand 0.29 0.05 33.74 0.00 1.34 Low transparency vs high -1.04 0.06 286.47 0.00 0.35 Medium transparency vs high -0.78 0.06 175.38 0.00 0.46 Packaging vs friend -0.06 0.06 0.84 0.36 0.95 Website of product vs friend -0.23 0.06 14.66 0.00 0.79
Constant -1.22 0.07 287.84 0.00 0.30
Model 2 .13
Lowest priced vs highest priced 1.16 0.10 144.70 0.00 3.18 Average priced vs highest priced 0.60 0.10 38.46 0.00 1.83 Nespresso vs store brand 0.26 0.08 11.84 0.00 1.30 Low transparency vs high -1.05 0.17 39.02 0.00 0.35 Medium transparency vs high -0.83 0.16 27.55 0.00 0.44 Packaging vs friend 0.05 0.09 0.29 0.59 1.05 Website of product vs friend -0.09 0.09 0.91 0.34 0.91 Brand x Low transparency -0.14 0.12 1.22 0.27 0.87 Brand x Medium transparency 0.20 0.12 2.77 0.10 1.22 Lowest priced x Low transparency 0.31 0.16 3.59 0.06 1.36 Lowest priced x Medium transparency 0.20 0.15 1.76 0.18 1.22 Average priced x Low transparency 0.12 0.17 0.49 0.48 1.13 Average priced x Medium transparency 0.14 0.16 0.79 0.38 1.15 Packaging x Low transparency -0.06 0.15 0.18 0.67 0.94
Packaging x Medium transparency -0.28 0.14 3.99 0.05 0.75 Website x Low transparency -0.21 0.15 1.89 0.17 0.81 Website x Medium transparency -0.28 0.14 3.72 0.05 0.76
Constant -1.21 0.10 150.76 0.00 0.30
Effect of the relative importance of price, brand and source of information on the utility scores of the three levels of transparency
Three multiple regression were performed with the relative importance scores of price, source of information and brand on each of the utility scores of one of the levels of transparency. The results are summarized in Table 10.
For the low transparency level, the model explained 65% of the variance. The relative importance score of price, source of information and brand all showed a significant positive relation with the utility score of transparency. The higher the utility, the more desirable the attribute level. Levels that have higher utilities have a larger positive impact on influencing respondents to choose products. This means that a higher importance of price, source of information and brand was related to a higher utility score of the low level of transparency. Within the level of low transparency, the RI of price, source of information and brand was important for the choice of a product.
For the medium transparency level, the model explained 28% of the variance. The relative importance score of price, source of information and brand all showed a significant positive relation with the utility score of transparency, but these relations were weaker within this level of transparency than within the low transparency level. This means that within the level of medium transparency, the RI of price, source of information and brand was less important for the choice of a product.
For the highest transparency level, the model explained 72% of the variance. The relative importance score of price, source of information and brand all showed a significant negative relation with the utility score of transparency, which is contrary to the other two levels of the transparency attributes. This means that within the level of high transparency, the higher the RI of price, source of information and brand, transparency became less important for the choice of a product.
Table 10: Multiple regressions with the relative importance scores of price, source of information and brand as predictors for the utility scores of each of the levels of transparency
Utility scores B SE Beta t p R2
Low transparency .65 Constant -5.18 0.30 -17.58 0.00 RI price 5.44 0.40 0.79 13.66 0.00 RI source of information 6.55 0.63 0.71 10.40 0.00 RI brand 4.72 0.49 0.68 9.62 0.00 Medium transparency .28 Constant -1.30 0.18 -7.28 0.00 RI price 0.92 0.24 0.35 3.79 0.00 RI source of information 1.60 0.38 0.37 4.18 0.00 RI brand 1.87 0.30 0.52 6.28 0.00 High transparency .72 Constant 6.49 0.31 21.22 0.00 RI price -6.36 0.41 -0.83 -15.39 0.00 RI source of information -8.15 0.65 -0.77 -12.48 0.00 RI brand -6.59 0.51 -0.78 -12.96 0.00
Discussion
The final chapter of this thesis will provide an elaboration on the findings of this research. A general discussion of the results is provided after which the theoretical and managerial implications of these results will be presented. Finally, the limitations of the study will be discussed and suggestions for future research will be presented.
General discussion
The aim of this research was to contribute to existing literature on brand transparency. This was done by examining how transparency affects consumer decision-making and what the relative effect of transparency is on the probability of choosing the product dependent on brand, price level and source of transparency. This study adds to current literature through a number of important findings.
First, the results show that transparency has a significant main effect on customer decision making. A higher transparency level was positively correlated with the probability of choice. This confirms hypothesis 1, which states that consumers ascribe more value to transparent options than to non-transparent options. This finding corresponds with previous research by Bhaduri and Ha-Brookshire (2011) who found that transparency can affect purchase intention. In their research, when two products were of similar quality, their participants were willing to buy the product that was from a transparent apparel business. It is also in line with previous research in which respondents reported to be more likely to choose brands which they knew were manufactured by companies whose products and processes were more environmentally friendly (Pickett-Baker & Ozaki, 2008).
Secondly, the results showed that there was a significant increase in probability of choice for an average priced product versus the expensive product. The probability of choice increased even more when presented a low priced versus the expensive product. This shows that price has a significant main effect on consumer decision making. The lower the price, the more likely a product is to be chosen. However, no moderating effect of price on the relationship between brand transparency and consumer decision making was found. This means that hypothesis 2 is rejected. Consumers do not make a decision for a certain product because of an interaction effect between price and transparency. This is in contrast with previous research which indicated that consumers prioritise price over transparent brand
Thirdly, the results showed participants ascribed higher value to a brand with high brand equity than to a brand with low equity. The brand Nespresso had a higher chance of being chosen than an anonymous store brand. This is in line with previous research which found that higher equity brands generate greater purchase intention (Cobb-Walgren et al, 1995; Chang and Liu, 2009). However, no moderating effect of brand equity on the relationship between brand transparency and consumer decision making was found. This means that hypothesis 3 is rejected. Consumers do not make a decision for a certain product because of an interaction effect between brand equity and transparency. This indicates that brand does not compensate for low transparency. Therefore, brand equity does not seem to have the halo effect is indicated in previous research. This could be caused by unfamiliarity of participants with the brand Nespresso. Perhaps for the participants in this study Nespresso does not have a high enough brand equity in order for it to have a moderating effect on the relationship between transparency and customer decision making.
Fourthly, a moderating effect of source of information on the relationship between transparency and consumer decision making was found. When information was medium transparent and it was provided by the company’s website, participants were less likely to purchase that particular product. The same effect holds for when information is provided by a product’s packaging. In addition to that it was found that friends as a source have the highest probability of choice, while website of the brand had the lowest probability. In addition to that there was no significant difference between friends or packaging, therefore it appears that both are perceived equally credible by participants. This means that hypothesis 4 is supported and consumers ascribe more value to information provided by friends than they do to information provided by the company. While it might seem surprising that consumers put more faith in peers than in experts, this is actually in line with reports by Edelman (Edelman, 2016; 2017) which show a declining trust in global institutions. These reports also indicate an increase perceived credibility of peers as a source of information.
Finally, the results of this research show support for a moderating effect of source of information on the relationship between transparency and consumer decision making. When information is provided by a company website or product packaging and information is medium transparent consumers are less likely to choose that particular product. No moderating effect was found for price and brand equity on the relationship between transparency and consumer decision making. However, it was found that consumers ascribe which more value to transparent options than to non-transparent options. Therefore, it can be argued that transparency influences consumer decision making.
Theoretical and managerial implications
The findings of this research have a number of theoretical and managerial implications. This study is one of the first to examine the effect of transparency on consumer decision making, whilst including the moderating effects of price, brand equity and source of information. Up until this point most research has focused solely on the effect of transparency on consumer decision making, however, it has failed to include several, possible moderators such as price, brand equity and source of information. This research has attempted to fill that gap, by including these moderating factors in a choice based conjoint analysis. A conjoint analysis is a universally employed analysis in marketing to determine part utilities of several attributes. However, a moderating effect was only found for source of information, whilst the proposed moderating effect of price and brand equity was not found.
However, what was found is that consumers ascribe more value to a transparent option, then they do to a non-transparent option. This adds to previous research which found that transparency affects purchase intentions (Bhaduri & Ha-Brookshire, 2011; Pickett-Baker & Ozaki, 2008). In addition to that this research provides support for a trend in declining trust in global institutions and an increase in credibility of peers over experts.
These findings have several implications for brand managers. This research provides additional proof for the influence of transparency on consumer decision making. This research adds to previous studies which have shown that transparency affects purchase intentions (Bhaduri & Ha-Brookshire, 2011; Pickett-Baker & Ozaki, 2008). This indicates that brands need to consider transparency as a tool to increase sales.
Additionally, this research shows that brands need to carefully consider their credibility amongst consumers. There is a decline in trust in global institutions and an increase of perceived credibility of peers (Edelman, 2016; 2017). In order for brands to remain in control over their own brand information, they need to consider what they can do in order to increase their credibility. In this age where consumers get more and more of their information online it is important for brands that the information that is provided is correct and does not damage the brand and its image.
Limitations and Future Research
In spite of careful consideration, this study has several limitations, like all other empirical research. However, these limitations can be used as improvements and opportunities
First of all, this research is set in an experimental setting. While this provides a good opportunity to gain more insights in the relationship between transparency and customer decision making, it is not very realistic. Customer decision making in more realistic settings is likely to differ from this experimental setting, due to increased variety of attributes and perceived consequences.
Secondly, participants were provided with twenty tasks they had to complete in the conjoint experiment. However, this might be too many tasks for such an experiment. While 184 participants started the survey, only 112 completed it. This is a relatively high fallout rate, which in future research might be decreased by providing the participants with less tasks. In addition to that the sample was made up entirely of Dutch participants. This might affect the generalisability of the results.
Finally, the participants were provided with two levels for the attribute brand: Nespresso and an anonymous store brand. The results showed that participants chose Nespresso relatively often in comparison to the store brand. This might be the result of bias in favour of Nespresso. Nespresso has a higher potential for brand recall than an anonymous store brand. Future research might need to use more levels for brand as an attribute, or use all anonymised brands in order to prevent this bias from occurring.
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