Acknowledgements
I started the MSc marketing in February not without difficulties. It was my first real experience abroad for a period longer than three months. A different approach not only to the lectures but also in the way of study. Moreover a new city and a new environment. This year of RUG has been really important because I learned a lot and I feel to be grown as man and as student. This master taught me to see everything from another point of view, to deal with daily problems with more confidence and finally to be able to work in an international environment. I am extremely proud of my path and I am grateful to RUG for this opportunity. I want to start by thanking my supervisor Dr. J.A. Voerman, I could not have wished for a better supervisor. Thank you for your positivity. Also when I was (too) concerned you supported me encouraging me to see the bright side. This aspect has been really important.A special thanks to my thesis group, that is because during the meeting we always had a positive and constructive atmosphere and we provided important insights.
1. Introduction
1.1 Introduction to the topic
defined as “an informal, person-to-person communication between a perceived non-commercial communicator and a receiver regarding a brand, a product, an organization, or a service”. Additionally, according to Henricks (1998) word of mouth has been defined as one of the most powerful tools in the market. Moreover, other studies showed that WoM has a strong impact on a customer’s evaluation of products and services, and an impact on future purchase decisions (e.g.; Richins, 1983; Bolen, 1994). In recent years, the internet has grown fast and the way in which consumers seek new information has changed: the traditional offline WoM evolved due to the internet’s development, and now it is more developed in the online context and can therefore be defined as online word of mouth (eWoM). Hennig-Thurau et al. (2004 p.39) define eWoM as “any
positive or negative statement made by potential, actual, or former customers about a product or company which is made available to multitude of the people and institutes via the Internet”.
A specific form of eWoM is the online customer review (OCR). According to Duan, Gu and Whinston (2008) through OCRs, customers have quick and easy access to an unprecedented amount of user-generated product information and, as said by Moe and Trusov (2011), OCRs can help customers to choose the best products which fit with their characteristics according to their idiosyncratic preferences based on other customers' experiences. Therefore, for any firm, it is crucial to be aware of the power of OCRs.
Thus, the main goal of this study is to highlight the impact of the valence of the OCRs and the role of peer opinions on the purchase intention of sportswear products. Additionally, it is interesting to investigate whether one of these two tools can overcome the other one. In this paper is hypothesized that peer opinions can overcome the OCRs because according to Smith et al (2005) when shopping objectives have a hedonic component, consumers will rely on other customer’ opinion and suggestion, and they will rely on the level of perceived rapport or closeness shared with recommenders. As will be explained in section 1,4 sportswear has a strong hedonic component so it makes sense to hypothesize that peer opinions could overcome OCRs because peers are closer to the person who receives the opinion.
brand image can be linked in a positive way to a willingness to pay premium prices and higher brand equity. Similar conclusions are provided by O’Cass & Lim (2001) in their study on brand credibility.
1.2 Valence of online customer reviews
The second reason is related to previous researches. Although all three metrics have been studied many times, for variance and volume in particular there are several exhaustive research papers, for instance, Sun (2012); Chen et al. (2011); Ho-Dac et al. (2013); Zhu & Zhang (2010), and Sen and Lerman (2007).
Therefore, considering this first independent variable, the goal of this paper is to understand the impact of the OCRs and, in particular, the valence of the OCRs on purchase intentions of the sportswear product. Sometimes in chapter two could happen that one may read about the effect of the valence of the OCRs on sales. This is premeditated, indeed in order to be more accurate and try to explain more precisely the effect exerted by the valence of the OCRs may be worth it to include also its effect on sales.
1.3 Role of peer opinions
With regards to peer opinion, the second independent variable of this study, they can be defined as opinions provided by individuals with a close relationship with the customer. In order to be more precise, according to Pilgrim and Lawrence (2001) peers are siblings, friends, acquaintances and reference groups.With regards to family members, according to Bolton et al. (2013) the influence of family members has been identified in different behavioural studies as a very important element that impacts shoppers’ behaviours due to the interdependence and interconnections among individuals or groups. Additionally, as found by Kerrane et al. (2012) when it involves soliciting advice about product or service patronage, family members are commonly among the sources of information and advice.
Considering the peer opinions as part of the communication between subjects, in the context of shopping behaviour, Moschis and Churchill (1978), defined peer communication as overt peer interactions concerning goods and services. Even though the study by Moschis and Churchill was conducted in 1978, it is still very useful. They found that positive peer communication influences a customer during his/her first interaction with the product. For instance, product evaluation of young consumers is positively affected by peer communication.
members. So it seems that peer opinions could be stronger than OCRs due to this desire to be socially accepted by reference groups and the closer relationship with the recommenders.
1.4 Sportswear industry
Over the last decade, the sportswear industry has radically evolved. Acceptability of casual dress on more occasions has paved the way for sportswear to move from a product line solely aimed at small and unique markets into a mainstream apparel market (Sportswear Industry Data and Company Profiles, 2004). The lines that once existed between performance and fashion, function and style, and formal and informal dress codes, have become increasingly blurred. Today, most sportswear is bought for leisure, casual or everyday use (Chi 2013). Furthermore, as shown in Ko et al. (2012), the sportswear market has significantly increased in terms of revenue and market shares. Andreff & Andreff (2009), have found something similar; the economic importance of the sports industry in general, and of the sporting goods industry in particular, has grown in recent years. This growth has been attributed to the augmented levels of sports consumption, and to increased expenditures on sportswear in particular. It seems clear that customers are willing to buy more sportswear products, or what can be defined as “athleisure” products, a term coined for clothes that are designed to be “worn both for exercising and for doing (almost) everything else” (Merriam Webster, 2015). According to Just-Style.com (2015), between 2015 and 2019, the UK sportswear market can expect a significant growth estimated to increase from GBP 6.38bn to reach GBP 8.65bn. In terms of annual rates of growth, it is between 7.7% and 8.1%. One of the major drivers of this growth is women’s sportswear which, over the last period, has radically increased also thanks to the higher number of collaborations with celebrities and the ongoing crossover between fashion and sportswear. For this reason, Nike decided to open specific women's only sportswear store to cater to this trend in London in May 2015.
Finally, to underline the importance of this industry, according to the 2018 Love List Brand Affinity Index compiled by Goldman Sachs and Conde Nast, an interesting point is the top 10 of the
Iconic Movers: Enduring Favorites with Increasing Momentum. In this ranking, it is relevant to
highlight the presence of three Athleisure brands: Nike, Adidas and Puma.
the brand possesses. The hedonic benefits result from the sensations derived from the experience of using products, based on subjective components. Sportswear products have both utilitarian and hedonic features as they strive/aim to have high functionality and aesthetically pleasing design. Yet, several studies proved the hedonic component of this industry, such as Ranebi and Thenet (2015) and Lim et al. (2016). For instance, Lim et al. (2016) find that the influence of hedonic benefits has a strong impact on the purchase intention and willingness to pay for regular sportswear brands. A concrete example of the presence of both these features is Nike. According to Bouwman (2008) Nike is a well-publicized example of a brand that made sportswear accessible to non-sportspeople by building emotional brand attachment. Like Nike, many sportswear brands aim to deliver functional and utilitarian features that are superior to their competitor brands, as well as to create strong emotional brand attachment by developing a fashion-oriented and aesthetically pleasing design.
1.5 Problem statement and research questions
Considering the two independent variables, it is interesting to compare the impact of the valence of online customer reviews with the valence of peer opinions because first of all, as mentioned above, there are findings which state that they are the most trusted source of information and additionally there are no findings which consider them at the same time on a dependent variable as purchase intention. Therefore, it would be very important try to fill this gap, providing significant insights about the different sources of information available to the customers. Moreover, it would be important try to understand whether the difference between these two variables, which is mainly the familiarity of the person who writes a review or gives you an opinion, can make the difference in the way in which this message is received and therefore alter the purchase intention of a sportswear product. This understanding would also have managerial implications.
Therefore, the research question is:
2. Theoretical framework
In this chapter, a deeper understanding of the study will be provided through a discussion of the existing literature. First of all, the mechanism of a review will be discussed, secondly the valence of OCRs and the valence peer opinions, and then the moderating role of the strength of the brand image will be analyzed. As a final point, there will be a proposition of the conceptual model. In the meantime, all hypotheses will also be introduced.2.1 Valence of a review mechanism
In this section, the aim is to provide an insight into the mechanism of a review on purchase intention1.
Regarding the overall mechanism, different studies clarify the reviews effect and how it can impact the purchase intention and the sales of a store (Chevalier and Mayzlin 2006; Mudambi and Schuff 2010 Chintagunta et al. 2010; Kostyra and Reiner 2012).
2.1.1 Valence of an online customer review
In this section, we aim to deepen our research and thus we want to shed light on the valence of OCRs.
valence, volume, and variance”. Therefore, due to the importance and reliability of these reasons, we expect that the valence of OCRs has a direct impact on the purchase intention. Referring to the different findings related to the size of the effect of positive versus negative reviews, there are on one hand studies which highlight the direct positive effect of the valence of OCRs on the sales or product choice, for instance Zhu and Zhang (2010); Moe and Trusov (2011) and Kostyra et al. (2015). A very interesting finding in this sense is that one by Clemons, Gao, and Hitt (2006). They focus their attention on the craft beer category, finding that the valence and variance (but not the volume) of ratings affect sales growth. More in depth, they find that the valence of the top quartile of ratings has the greatest effect on predicting sales growth. On the other hand there are Mizerski (1982) and Cui, Lui, and Guo (2012) who show that negative valence is always more influential compared to positive valence. Interestingly, Hao et al. (2010) find that the product type has a moderating role on the valence of OCRs, especially on the negative ones. Additionally, they found that the negative valence of an OCRs has a stronger effect, only if the product is an experience or hedonic product. Although, as said before, the foregoing empirical findings are dissimilar, for the reasons mentioned above it seems reasonable to assume in this paper that positive valence of online customer reviews increases purchase intention of the sportswear product, whereas negative valence of reviews reduce it. Therefore, the following first hypothesis has been developed to understand the direct link between valence and purchase intention:
Lilien, 2008). According to Brown and Reingen, (1987) consumer tend to have a high level of personal communication with primary reference groups while a low level of communication with secondary reference groups.
Overall, there are four possible situations: when the two valences are similar (positive vs. positive; negative vs. negative) and when the two valences are opposite (positive vs. negative; negative vs positive).
When the valence of an online customer review is negative and the valence of a peer opinion is negative as well, according to Marconi (1997) and his theory about the negative information, consumers respond in a homogeneous way, thus we expect that they will be perceived both in a negative manner.
However, when the valence of OCRs is positive and the valence of peer opinions is positive we expect that the positivity of the valence of peer opinions will reinforce the positivity of the online customer review. This reinforcement is due for different reasons such as the credibility, the closeness and the trustworthiness. According to Brown and Reingen, (1987) when the source of recommendations is closer, such as primary reference group, this source is perceived as more credible and thus consumers will give more weight to any recommendation. Moreover, Brown et al. (2007) find similar conclusions regarding the important role of credibility.
2.3 The moderating role of the strength of the brand
image
In this section, the primary goal is to understand the effect of the strength of the brand image on the purchase intention and how this variable could moderate the direct effect of the other independent variables. Anselmsson et al. (2014) made an experiment on food brands. What they find is very interesting. When adding uniqueness, and strength of the brand image to their analysis, the prediction of the price premium is doubled from 23 to 46 percent depending on the food category, for instance, rice or frozen foods. This can confirm the crucial role of brand image. Referring to another market, the sports shoes one, Belén del Río et al. (2001) investigated the brand image based on the functions or benefits that the consumer associates with the brand. Brand image can exert an effect on the various customer reactions that influence the advantages that the brand can provide the firm with. In particular, in the sports shoe market, it has been observed that favourable associations of the brand can lead to obtaining a price premium and extending the brand to other product categories. Moreover, according to Richardson et al. (1994) due to the limited time and knowledge about the product to make an informed purchase decision, consumers often use the brand image as an extrinsic cue in order to maximize the choice satisfaction. Akaah and Korgaonkar (1988) find that in order to reduce the purchase risks, consumers are more willing to buy well-known brand products/brands with a favourable brand image. These findings are in line with Rao and Monroe (1988) who conclude that a brand with a more strong and favourable brand image reduces the perceived risk and it increases positive feedback from consumers. Thus, consumers generally think they can make a purchase that will reduce risks and will increase satisfaction by choosing well-known brands.Nan-Hong Lin (2015) confirmed what said above in a study on the Taiwanese market. Results confirmed that consumers’ purchase intention does get influenced by brand image. The higher the strength of the brand image, the more purchase intention there is. Thakor and Katsanis (1997) find that when consumers are evaluating the product, brand image is one of the most important cues evaluated, especially when it comes to experiential brand concept-image. That is because they feel a higher perceived value, resulting in higher purchase intention when they can choose their favourite brand.
the relationship between OCRs and sales. In both categories, cumulative positive OCRs increase and cumulative negative OCRs decrease the sales of models of weak brands. In contrast, the valence of OCRs does not have a relevant effect on the sales of models of strong brands.
For the reasons mentioned above in this section, this paper assumes that brand image might have a moderating role on the effect of the OCRs and peer opinions on the purchase intention. This moderating effect could be negative in presence of a strong brand image because the consumer may rely on the favourable image in his/her mind and therefore, be less interested in the effect of online customer reviews or peer opinions. If we consider OCRs and peer reviews as a tool to reduce uncertainty, we can assume that, in presence of a strong brand, this need for information could be mitigated by the clear and distinct image that a customer has in his/her mind. Therefore, we infer the fourth hypotheses of this paper as follows: H5: Brand strength tends to reinforce (reduce) the effect of positive (negative) valence of online customer reviews on the customer purchase intention.
Table 3.1: Study design 2x2x2 between-participants Valence of peer opinion Positive Negative valence of anonymous OCR
Positive 1)High brand image 2)Low brand image 5)High brand image 6)Low brand image Negative 3)High brand image 4)Low brand image 7)High brand image 8)Low brand image
Regarding the questionnaire in this study, 30 questions are asked and on average, it takes 5 minutes to fill in the survey. The questionnaire is designed to use Likert scale; by using the online survey platform Qualtrics, respondents can easily access the questionnaire via a shared link on social media such as Facebook, Linkedin or WhatsApp. The time period in which the data was collected, runs from the 26th of November to the 3rd of December 2018.
After the initial introduction, the respondents will be randomly assigned to one of the eight conditions of the experiment in order to ensure the validity of the test (Aronson et al 1998). At the end of the survey, some questions are asked in order to collect information about the age, gender, country, current occupation, level of English, and language chosen for filling the questionnaire (to make sure about the language used to fill the questionnaire in case of particular results, maybe due to the translation of the questions). Additionally, in order to stimulate the participation and the commitment, as reward it will be raffled one €30- gift card. This could create more engagement and it could be an incentive to complete the survey. This gift card is offered on Amazon or bol.com (the double choice is due to the fact that if the winning respondent comes from a different country than the Netherlands, bol.com could be not very interesting/stimulating. Moreover, because the survey will be shared on social media it is impossible to be sure from which country respondents will come from). Table 3.2a: Descriptive statistics of the sample Table 3.2b: Country of origins of the sample
3.3 Procedure
The survey was introduced by a welcome text: “Dear Sir or Madam, welcome to this study. My name is Stefano and I am currently working on my Master Thesis. You are invited to participate in a research study as part of my Master Thesis.Condition N Gender
Male Female Other
order to participate in this contest, you will have to submit your email address on the final page of this survey. This email address will be processed apart from your answers to maintain anonymity in your answers. Thank you for participating, I really appreciate your help! Kind regards, Stefano Giolito. Please click the next button to get started.”
Then there is the introduction of the stimuli and the respondents started his/her questionnaire.
3.4 Manipulation of the independent variables and
moderator
A critical issue is the manipulation of the experimental variables. The two independent variables and the moderator are manipulated in different ways. Overall these manipulations lead to 8 different scenarios (appendix A). Before to show the stimuli, at the beginning of the survey, a fictitious scenario is described in order to frame participants into a certain setting. “Please imagine that you are looking for a t-shirt, with a very simple and plain design. First of all, you go online and check the online customer reviews to have a first idea and then your best friend gives you an opinion about it."
3.4.1 Valence of online customer reviews by unknown customer
The first IV, the valence of online customer reviews by an unknown customer is manipulated through the usage of the typical Amazon reviews page. Regarding the valence of the online customer reviews, according to Kostyra et al. (2015) considering the positive valence, the higher is the product rating the better it is. Indeed, a high rating increases the customer choice probability. On the other way around, when the product rating is low, there is less chance to occur in product choice. Moreover, according toCampbell (2017) it is possible to say that above 4 out 5 stars, the rating is perceived as high. According to his study, the most common filter applied is to see only products with 4-star ratings and higher, while at the same time, 1 star is always considered as negative (but anyway trustworthy).
3.4.2 Valence of the peer opinions
The second IV, the valence of the peer opinions, is introduced after the images mentioned above. Also in this case there are two sentences, one for each condition. Two related to Nike and two related to Hazzys sport. There is a brief introduction: “Now imagine that your best friend, who has your same clothes preferences, says that:” • Positive valence: “I strongly recommend you that t-shirt because not only it has a good quality- price ratio, but it is also quite cool, it fits you very well.” • Negative valence: “I do not recommend you that t-shirt because not only it does not have a good quality- price ratio, but it is also ugly. It fits you badly.”3.4.3 The strength of the brand image
Considering the brand image, according to the 2018 Love List Brand Affinity Index compiled by Goldman Sachs and Conde Nast, in the top 10 of the Iconic Movers: Enduring Favorites withIncreasing Momentum Nike ranks as third, so we can assume that its strength is asserted.
Additionally, this is confirmed by another study by Forbes (2012); they rank Nike as the world's most valuable sports brands.
An example of a possible stimulus can be as follow:
“Please imagine that you are looking for a t-shirt, with a very simple and plain design.
First of all you go online and check the online customer reviews to have a first idea and
then your best friend gives you an opinion about it
Now imagine that your best friend, who has your same clothes preferences, says that:
“I strongly recommend you that t-shirt because not only it has a
good quality- price ratio, but it is also quite cool, it fits you very well.”
3.5 Operationalization of the variables
Table 3.5: Variables used for measurementConstruct Reference Items Scale Eigen- value Cronbach’s Alpha
D.V. à Purchase intention (Shukla , 2010) (PI1) I would buy this product/brand rather than any other brands available (PI2) I am willing to recommend others to buy this product/brand (PI3) I intend to purchase this product/brand in the future 7- point Likert scale: Ranging from strongly disagree (1) to strongly agree (7) 2,391 ,872 Manipulation
check (IV1) // I perceived the valence of the review as positive
7- point Likert scale: Ranging from strongly disagree (1) to strongly agree (7) // // Manipulation
check (IV2) // I perceived the valence of opinion of my friend as positive
(II4) I frequently gather information from friends or family about a product before I buy. strongly agree (7) Other variableà brand dependence Bristow et al. (2002) (BD1) When it comes to buying a sportswear product, I rely on brand names to help me choose among alternative products. (BD2) I would be more likely to purchase a sportswear product that had a well-known brand name (BD3) Brand name would not play a significant role in my decision of which sportswear product to purchase (ATTENTION CHECK) (BD4) When faced with deciding among two or more brands of sportswear products, I depend on the brand name of each product to help me make a choice (BD5) If faced with choosing between two sportswear products with similar features, I would select the better-known brand name (BD6) The brand name of a sportswear product is important to me when deciding which product to purchase (BD7) Regardless of what features a competing brand of sportswear product may offer, I would buy the brand of sportswear product that I most trust 6- point Likert scale: 1=strongly disagree; 2=disagree ; 3=slightly disagree; 4 slightly agree; 5=agree; 6= strongly agree 3,259 ,828 Other variableà General attitude towards rating Park et al. (2007) 1. When I buy a product on-line, I always read reviews that are presented on the Web site. 2. When I buy a product on-line, the reviews presented on the Web site are helpful for my decision making 3. When I buy a product on-line, the reviews presented on the Web site make me confident in purchasing the product 4. If I do not read the reviews presented on the Web site when I buy a product on-line, I worry about my decision 5. When I buy a product on-line, reading the reviews presented on the Web site impose a burden on me 6. When I buy a product on-line, reading the reviews presented on the Web site irritates me 6- point Likert scale: 1=strongly disagree; 2=disagree ; 3=slightly disagree; 4 slightly agree; 5=agree; 6= strongly agree 2,244 ,823 Control variable
à Age // How old are you? Age Type the age // //
Control variable à Current occupation // • Employed full time • Employed part time • Unemployed looking for a job • Unemployed NOT looking for a job • Student with a job • Student without a job • Retired 7 options // // Control variable à English skills // How would you describe your English skills? -No English skills/ Beginner -Basic communic ation skills -Good command -Very good command -Native speaker // // àBD3 is the attention check
3.5.1 Dependent variable measurement
The operationalization table displays the steps through which participants answered the different items in the online survey. The survey begins with the welcome text, then there is a brief description of the scenario, after which, participants were randomly exposed to one of the eight possible conditions. Immediately after being exposed to the two stimuli, participant’s purchase intention of the sportswear product was measured using a 3- item measurement scale. Participants had to indicate the degree of (dis)agreement on a 7-point Likert scale on 3 statements about their purchase intention about to the product that they saw in the foregoing stimulus. This scale is based on the studies of Shukla, (2010) and Gillani (2012). Fortunately, the dependent variable questions passed factor and reliability analysis. Regarding the factor analysis, considering Eigenvalues for the dependent variable, only the first component has an initial eigenvalue (2,391) greater than 1. The first component explains 79,715% of the total variance in that construct. This means that the first component explains the majority of the variance within this set of data.3.5.2. Manipulation check
Three manipulation checks were asked to the respondents to all respondents in order to check whether the stimuli related to the two IVs and the brad image were correctly perceived. According to Malhotra, (2010) the manipulation check is a secondary evaluation of the manipulation in the experiment. These questions were asked immediately after the three questions about the purchase intention in order to be sure that the participant still had a clear idea about the images previously seen. One can read the questions asked in the table above. Regarding the manipulation check of the brand image, the scale is based on the studies of Davis et al., (2009). For the brand image, we run also F.A. and Reliability analysis. Regarding the factor analysis, considering Eigenvalues for the brand image, only the first component has an initial eigenvalue (1,994) greater than 1. The first component explains 66,467% of the total variance in that construct. This means that the first component explains the majority of the variance within this set of data. Regarding the reliability analysis, the Cronbach’s alpha score of the moderator variable shows that 74,70 % of the variance in the composite scores associated with these items is reliable variance.
3.5.3 Other variables
After the manipulation checks, all control variables included in this research and mentioned in table 3,5 are measured.majority of the variance within this set of data. However, by looking at the scree plot generated by SPSS, we see that the elbow in the graph occurs at item number 3. Additionally, by checking the communalities the first factor (Q26 – inf1) its communality is below the cut-off point of 0,4.
As one can see, now the Eigenvalues, again only the first component has an initial eigenvalue (2,290) greater than 1. The first component explains 76,318% of the total variance in that construct. In addition, by looking at the scree plot generated by SPSS, we see that now the elbow in the graph occurs at item number 2. Regarding the reliability analysis, the Cronbach’s alpha score of the informational influence shows that 84,40% of the variance in the composite scores associated with these items is reliable variance. Moreover, if we look at the “Cronbach’s alpha if item deleted” we can see that we would be punished by deleting one item because “Cronbach’s alpha if item deleted” is always lower than the Cronbach’s alpha score. Regarding the Brand dependency, in the Factor Analysis, considering Eigenvalues only the first component has an initial eigenvalue (3,418) greater than 1. The first component explains 48,830% of the total variance in that construct. This means that the first component does not explain the majority of the variance within this set of data. Additionally, by checking the communalities, the factor brD7 has a communality which is below the cut-off point of 0,4. Therefore, we rerun the F.A. without those factors which are below the cut-off point.
As one can see, now the Eigenvalues, again only the first component has an initial eigenvalue (3,259) greater than 1. The first component explains 54,323% of the total variance in that construct. In addition, by looking at the scree plot generated by SPSS, we see that now the elbow in the graph occurs at item number 2. As a final check, we look at the communalities. This final condition is also satisfied in this case. All these indicators confirm that a single-factor component is the most appropriate for further testing.
In order to be more accurate, we decide to go for 1 factor. This deliberate decision is because of often happens that the same question asked in a negative way would lead to a different result, thus even if these two items are reversed, it could be that these two questions rephrased would lead to different scores. Therefore, only factor 1 is considered. Regarding the reliability analysis, the Cronbach’s alpha score of the general attitude toward reviews shows that 81,10% of the variance in the composite scores associated with these items is reliable variance. However, if we look at the “Cronbach’s alpha if item deleted” we can see that gatr4 has “Cronbach’s alpha if item deleted” higher than Cronbach’s alpha score (,823> ,810). Thus, we decide to delete gatr4 and this decision makes sense also if one checks table 3,5. That because the first three items gatr1, gatr2 and gatr3 have a similar sentence. (gatr1 à When I buy a product on-line, I always read reviews that are presented on the Web site. gatr2 à When I buy a product on-line, the reviews presented on the Web site are helpful for my decision making. gatr3 àWhen I buy a product on-line, the reviews presented on the Web site make me confident in purchasing the product).
3.6 Results of manipulation check
An independent T-test is used to determine whether the sample means are statistically different from each other. Since the manipulation check questions of the IVs consisted of single item measurement scale, no internal validity tests are necessary on these variables. Regarding the brand image, factor analysis and reliability analysis were run in order to check first of all if the items are likely to factor well and additionally their internal consistency. Moreover, the 3 items were combined together by summing them together and then dividing per 3 as the number of the items, this because in this way the scale of the new variable computed reflects the original scale of the items. Table 3.6.1: Manipulation check of the valence of the online customer reviewN Mean T statistic df Sig. (2 tailed) Negative 141 3,60 -8,680 245,788 ,000
Table 3.6.2: Manipulation check of the valence of the peer opinions
N Mean T statistic df Sig. (2 tailed) Negative 141 3,90 -8,164 245,600 ,000 Positive 150 5,41 Overall F 47,818 ,000 Overall Significance Table 3.6.3: Manipulation check of the brand image
N Mean T statistic df Sig. (2 tailed) Negative 141 3,7264 -8,729 289 ,000 Positive 150 4,8200 Overall F ,195 ,659 Overall Significance The results show a significant difference between the different conditions. According to Malhotra, (2010) this can be seen by the high t-statistics. This means that participants actually perceived the uncertain reward scenarios as uncertain, so the manipulation applied in this research was proven to be successful.
3.7 Plan of analysis
As a first step, it is important to check that the dataset is suitable for factor analysis. In order to do that, according to Malhotra (2010) we use the KMO and the Bartlett's test of sphericity and the communalities.
The Kaiser-Meyer-Olkin Measure of sampling adequacy is an index used to examine the appropriateness of factor analysis. High values (between 0.5 and 1.0) indicate factor analysis is appropriate. Values below 0.5 imply that factor analysis may not be appropriate. Regarding the Bartlett's test of sphericity, it is a test statistic used to examine the hypothesis that the variables are uncorrelated in the population. It needs to be significant at a value which is less than .05 (p < .05) in order to perform factor analysis. According to Malhotra (2010) principal component analysis method is used. In this method of factor analysis, the total variance in the data is considered. According to Malhotra (2010) then the factors are rotated after extraction. We use the VARIMAX rotation. Rotation does not change the solution, but it makes the results more interpretable. Rotating the factors also ensures that they are orthogonal, which eliminates problems of multicollinearity in later regression analyses which is often a common issue.
According to Malhotra (2010 p.606) “the communalities measure the percentage of variance in a given variable explained by all the extracted factors.” The criteria for the communalities is that they have to be above 0,4. This final condition is also satisfied in all cases.
means that questions are internally consistent. Moreover, SPSS provides another function, which is Alpha if item deleted. It allows you to see what could happen in terms of Cronbach's alpha score if one deletes that specific item. One can see the Cronbach’s alpha scores per construct in the operationalization table above (table 3.5). As one can see, all the values are above the 0.60 and therefore they satisfy the criteria.
3.7.3 Analysis of variance (ANOVA)
Before to run the multiple regressions, according to Malhotra (2010) in order to test the main effects of the independent variables and their interactions with each other, an analysis of variance is conducted. Analysis of variance is used as a test of means for two or more populations.
3.7.4 Regression analysis
According to Malhotra (2010) regression analysis is a powerful and flexible procedure for analyzing associative relationships between a metric dependent variable and one or more independent variables. In this study, we are interested not only in the direct effects but also in possible interactions and in the effects of possible other variables that could influence the dependent variable. There are 6 main models:
Model number Description of the model Formula of the regression
Model 1 Base model with only the main effects on the DV Y = βo + β1 * Ev1 + β2 * Ev2 + β3 * Ev3
Model 2 Base model with the main effects on the DV and
also the interaction of the main effects Y = βo + β1 * Ev1 + β2 * Ev2 + Ev1*Ev2 +Ev1*Ev3 + Ev2*Ev3
Model 3 Base model with the main effects and the interaction of these effects, complemented by the main effect of general attitude toward reviews and its interaction with the valence of OCRs
Y = βo + β1 * Ev1 + β2 * Ev2 + Ev1*Ev2 +Ev1*Ev3 + Ev2*Ev3 + CO1 + CO1 * Ev1
Model 4 Base model with the main effects and the interaction of these effects, complemented by the main effect of informational influence and its interaction with the valence of peer opinions
Y = βo + β1 * Ev1 + β2 * Ev2 + Ev1*Ev2 +Ev1*Ev3 + Ev2*Ev3 + CO2 + CO2 * Ev2
Model 5 Base model with the main effects and the interaction of these effects, complemented by the main effect of brand dependency and its interaction with the strength of the brand image
Y = βo + β1 * Ev1 + β2 * Ev2 + Ev1*Ev2 +Ev1*Ev3 + Ev2*Ev3 + CO3 + CO3 * Ev3
Final model Base model with the main effects and the interaction of these effects, complemented by the main effects of continuous variables and their interactions with the different EVs.
In chapter four one can see the output of the regression analysis. Interpretations are made base on the judgment of some observations such as F-stat, R square and adjusted R square. Moreover, for significant variables, Standardized Betas are taken into account. Because they are standardized, they took into account all different scales measure and therefore one can easily judge the relative importance between variable.
3.7.5 Mean centering
4. Results
In figure 4.1, the main effect of the valence of OCRs is clearly visible. It seems clear that the influence of the valence of OCRs is less strong than the valence of peer opinions. The two lines provide a clear perspective. This means that the effect of the valence of peer opinions is stronger. Thanks to the graph one can see what is explained in section 2,3. When the valence of peer opinions is positive, the effect of the negative valence of OCRs is marginal. Conversely, when the valence of peer opinions is negative, it prevails on the positive valence of OCRs. By looking at the red line, one can see that is below 3,50 which means that it is perceived more as negative instead than positive (exactly as the valence of the peer opinions). Moreover, when the valence of peer opinions is positive, one can see the big gap between the positive or the negative valence of OCRs (more than 1 point).
4.2 Regression analyses
Table 4.2: Multiple regression analyses a. Dependent variable: purchase intention b. *** p-value < .01, **p-value < .05, * p-value < .10 Model 1 Model 1 shows the main effects of the valence of the OCRs, the valence of peer opinions and the brand image on the DV purchase intention. This model serves as the base model of this research. The results indicate that the overall model is significant (F = 35,247 , p < .01). This model explains about 35 percent of the variance of the purchase intention. The R2 is ,269. The R2 adjusted is ,262.
The valence of OCRs has a significant direct effect on the purchase intention of sportswear products (β = ,217, p < .01), moreover the other IV, valence of peer opinions has a stronger direct effect (β =
,448, p < .01). Finally, brand image is not significant (β = ,039, p > .10). This first model provides important results because it shows that not all the IVs are significant.
Model 2
Model 2 shows the main effects on the purchase intention and their interaction with each other. The results indicate that the overall model is significant (F = 18,578, p < .01). The R2 is ,282.
Comparing this model with the previous one (we use the R2 adjusted) there is a small rise (from
model 1 R2 adjusted= ,262 to model 2 R2 adjusted= ,267). This is important because by adding
variables, thus making the model more precise and accurate, the explanatory power of the model is higher than before. The valence of OCRs is not significant anymore (β = ,131, p >.10), however the valence of peer opinions is significant (β = ,408, p < .01). However, it is important to highlight that the interaction effect is significant (β = ,180, p < .05). Regarding the moderation, the interaction between the moderator and the valence of OCRs is not significant (β = -,029, p >.10) and the interaction between the moderator and the valence of peer opinions is not significant as well (β = -,074 p > .10). Moreover, the brand image does not exert any significant direct effect on the purchase intention (β = ,096, p > . 10).
Model 3
Model 3 shows the main effects on the purchase intention, their interactions with each other and in addition the effect of the variable general attitude toward reviews. The results indicate that the overall model is significant (F = 14,441, p < .01). The R2 is ,291. The R2 adjusted is ,271. The valence
intention (β = ,081, p > .10). However, it is important to highlight that the interaction effect between valence of OCRs and valence of peer opinions is significant (β = ,184, p < .05). Regarding the moderation, the interaction between the brand image and the valence of OCRs is not significant (β = - ,007, p >.10) as the interaction between the brand image and the valence of peer opinions (β = -,074 p > .10). Finally, informational influence is not significant (β = - ,035, p >.10), but interestingly the interaction between this control variable and the valence of peer opinions is significant (β = ,174, p <.05). Model 5 Model 5 shows the main effects on the purchase intention, their interactions with each other and in addition the effect of the variable brand dependency. The results indicate that the overall model is significant (F = 15,045, p < .01). The R2 is ,299. The R2 adjusted is ,279. The valence of OCRs is not
significant (β = ,115, p >.10) but the valence of peer reviews is significant (β = ,413, p < .01). Moreover, as in the foregoing models, the brand image does not exert any significant direct effect on the purchase intention (β = ,067, p > .10). Also in this model, it is relevant to highlight that the interaction effect between valence of OCRs and valence of peer opinions is significant (β = ,165, p < .10). Regarding the moderation, the interaction between the brand image and the valence of OCRs is not significant (β = - ,011, p >.10) and in this model the interaction between the brand image and the valence of peer opinions is not significant as well (β = -,041 p > .10). Finally, brand dependency is not significant (β = ,032, p >.10) and the interaction between this variable and the brand image is not significant as well (β = ,111, p >.10). Final model The final model shows the main effects on the purchase intention, their interactions with each other and in addition, the effect of the variables general attitude toward reviews, informational influence and brand dependency and their interaction with the IVs. The results indicate that the overall model is significant (F = 11,764, p < .01). The R2 is ,337. The R2 adjusted is ,308. The valence
variable and the valence of peer opinions is significant (β = ,168, p<.05). Finally, brand dependency is not significant (β = ,025, p >.10) and its interaction with the brand image is not significant (β = ,102, p >.10).
4.3 Discussion of results
The first IV, the valence of OCRs is probably the most complicated variable to interpret in the results. To test the HP1 it is possible to look at the ANOVA table or the regression analyses. In the base model and in the ANOVA analysis, the valence of OCRs has a significant direct effect on the dependent variable. This means that when the valence of OCRs is positive, consumers have a higher level of purchase intention of sportswear products, conversely, if the valence of OCRs is negative, this reduces the purchase intention. So, although in the other regression analyses the valence of OCRs is not significant, we consider HP1 only partially supported because in the base model and in the ANOVA analysis as well it exerts a significant direct effect on the purchase intention and this aspect have to be considered.Regarding the second IV, the valence of peer opinions, results are definitely clearer than in the previous case. Also in this case it is possible to look at the ANOVA table or the regression analyses. What is important to mention is that the valence of peer opinions has the strongest direct effect on the DV. This means that consumers rely more upon their peers than the online customer reviews. The direct effect is always stronger than that one exerted by the valence of OCRs. Thus, we support HP2.
As we hypothesized, due to the closer relationship with the person who provides the opinion, the valence of peer opinions has a stronger direct effect on the purchase intention than the valence of OCRs, thus also HP3 can be considered as supported.
Another interesting point is the absence of a direct effect of the brand image on the purchase intention, the HP7, this because this direct effect is never significant. Thus, according to our results, it seems that the strength of the brand image does not exert any type of effect on the purchase intention of sportswear products. Therefore, HP6 is rejected.
a friend, according to our results seems clear that consumer will more prone to be influenced by his/her friend’s suggestion. Thus, HP4 is considered supported.
4.4 Hypotheses overview
Table 4.4: Hypotheses overview
HYPOTHESIS N.
HYPOTHESIS DESCRIPTION
SUPPORTED/
REJECTED
precious tool that can help a firm to increase not only the purchase intention but also the product attitude (Wang et al. 2012). Thirdly, the brand image does not influence purchase intention. However, according to the literature mentioned in section 2,4 we still believe that sportswear firms should dedicate efforts to maintaining and improving their brand image. Favourable brand associations are fundamental in order to be well positioned in the consumers’ minds.
5.3 Limitations and recommendations for future
researches
This research contains some limitations that have to be taken into account. These limitations are defined below, including future research directions. Although the manipulation checks in chapter 3 report positive results, thus respondents perceived the stimuli correctly, the main limitation could be that this study lack in the presence of pre-tests. However, this deliberate choice has been thought carefully when faced a trade-off: on one hand through a pre-test one can be sure that stimuli will lead to the desired perception, but on the other hand, a pre-test requires at least 20 or 30 respondents. Thus, to have a more significant sample we preferred to avoid the pre-test using literature, and having later, in the regression analyses, a more considerable sample which leads to a higher explanatory power and more reliable results. Thus, we think that a possible future direction may be to try to repeat this study including some pre-tests of the stimuli and/or trying to change the stimuli in order to understand if this would lead to different results. This would be important in order to clarify also the reason why our HP1 is only partially supported, although we think it could be mainly due to the stimuli, however this point deserves a proper focus in future studies.In addition, when using an online questionnaire to test the behavior after a fictitious scenario, respondents may have difficulties in understanding the situation. Moreover, for many respondents it was the first time that they answer to a questionnaire like this one, therefore it may be that they provided answers with a low level of awareness. Furthermore, although the questionnaire has been carefully translated in Italian, it may be that some respondents were not familiar with some specific words and this could have created confusion. For future researches, we think it could be interesting to repeat the survey but only in English trying to avoid possible misunderstanding due to translation.
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