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Does the Channel Matter? : the Impacts of Review Valence and Type of Channel on Attitudes and Intention to Visit a Location: the Case of a Newly Opened Restaurant

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Does the Channel Matter?

The Impacts of Review Valence and Type of Channel on Attitudes and Intention to Visit a Location: The Case of a Newly Opened Restaurant

By

Lucie Peterková

Master’s Thesis: Graduate School of Communication

Student ID: 11571640

Supervisor: Dr. Stephan Winter

Date of completion: 29 June 2018

Word Count: 7,478 words

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Abstract

The objective of the present paper is to investigate effects of review valence and the channel of the message on attitudes and intention to visit a location, such as a restaurant. A 2x2 between-subjects experiment shows that review valence and its interaction with the channel of the message play a significant role in influencing one’s attitudes and intention. Research showed that it is useful to send reviews via personal messages on SNSs compared to public posts. A private channel is more influential than a public channel due to personalization. The present study also replicates previously made assumptions that review valence has strong effects on attitudes and intention. This research rejected the prediction that effects could be stronger for high self-monitors. On the basis of this experiment, it is claimed that electronic word-of-mouth from a reliable source has a causal effect on customer purchase decisions. This paper extends current literature by dealing with eWOM on Instagram. It suggests practical implications and foreshadows ideas for future investigation.

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Introduction

Social media is commonly used for communication with our peers. With a push of a button, a user can leave a review, which may shape the process of decision-making (Wang, Yu, & Wei, 2012). Customers strongly rely on prior experiences of others to decrease purchase risk (Bambauer-Sachse & Mangold, 2013). The investigation of particular social media sites (SNSs) opens new challenges and opportunities for marketing communication (Keegan & Rowley, 2017). Previous research shows that electronic word-of-mouth (eWOM) has an impact on one’s attitudes and purchase intention (Tsao, Hsieh, Shih, & Lin, 2015; Purnawirawan, Eisend, De Pelsmacker, & Dens, 2015). It is crucial to deepen the current knowledge of the elements that have an impact on customer's perceptions of eWOM. The way in which people share information online is transforming constantly, so it is important to monitor effects of eWOM with special attention (Srivastava & Sharma, 2017).

To the best of my knowledge, no previous studies took into consideration the influence of the interaction effect of valence and channel through which a review is received. It may be expected that people will respond to eWOM differently if they receive the recommendation from a friend via a private message than from a public post, which is less personal (Ha, Oh, & Jo, 2015). This research could help to fill this gap. Moreover, it is assumed that individuals can react to reviews in a distinctive way. It is predicted that high self-monitors, who try to act in a desirable and appropriate way, will be more influenced by others to change their attitudes or create purchase intention than low self-monitors (Snyder, 1974).

The goal of this study is to shed light on the effects of review valence and the channel of the message on attitudes and intention to visit a location, such as a restaurant. Most attention will be attributed to the interaction effect of channel and review valence. This

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research aims to find whether there is a distinct difference of perception of a review when it is received via a private message or via a public post on Instagram. Moreover, the moderation role of self-monitoring will be discussed. The following research question arises: What are the effects of review valence and channel of the message on attitudes and intention to visit a location such as a restaurant and is this moderated by self-monitoring?

Theoretical framework eWOM

EWOM is denoted as “any positive or negative statement made by potential, actual, or former customers about a product or company, which is made available to a multitude of people and institutions via the Internet” (Hennig-Thurau, Gwinner, Walsh, & Gremler, 2004, p. 39). A recent study by Pentina, Bailey and Zhang (2018) claim that 90 % of consumers worldwide use online reviews before their decision is made. People trust online recommendations from their peers. Online reviews are more persuasive than traditional advertising or product placement visible through mass media (Kim, Maslowska, & Malthouse, 2017). A consumer can share review at any place and any time (Eisingerich et al., 2015). Social media provides an optimal environment for sharing and the exchange of peers’ experiences and opinions (Liu et al., 2017). It is a relatively new marketing tool, which leans on interactions (Kim, Kim, & Heo, 2016). This setting enables consumers to share their insights with a broad audience or through customized messages (Kim & Ko, 2012).

Review valence

Review valence is connected with the direction of the message. A review can be either positive, which may be perceived as a recommendation, or negative, which may be perceived as a complaint (Cheung, Luo, Sia, & Chen, 2009). Scholars have observed the

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valence of online review extensively. Some researchers claim that negative reviews will be a cause for a greater negative effect (Hennig-Thurau, Wiertz, & Feldhaus, 2015; Cui, Lui, & Guo, 2012). On the contrary, other scientists emphasized the dominant strength of a positive review and its positive effect on attitudes and intention (Tsao et al. 2015; Kim, Brubaker, & Seo 2015). Despite the fact that the results are equivocal, most authors would agree that in an eWOM setting, positive reviews play a major role in affecting attitudes and intention (Ladhari & Michaud, 2015; Vermeulen & Seegers, 2009). In general, it is claimed that positive reviews will result in more positive attitudes and future intentions to visit the place compared to negative reviews (Purnawirawan et al., 2015).

Negativity Bias and Prospect Theory

Negativity Bias and the Prospect Theory explain how the processes of decision-making can be influenced. More than half century ago, Arndt stated that negative WOM has a much stronger effect than positive WOM. To be concrete, he considered negative WOM two times more influential than positive WOM (Arndt, 1967). Some scholars found evidence that negative reviews cause greater effects and are spread with higher speed than positive reviews (Libai, Muller, & Peres, 2013; Park & Lee, 2008). The explanation for this can be found in the fact that the diagnostic value for the consumer is higher when exposed to negative information (Hennig-Thurau et al., 2015). Positive reviews are more common. Therefore, when one comes across negative reviews, it brings more attention and highly impacts an individual's decisions (Fox, Deitz, Royne, & Fox, 2018). Furthermore, Hornik, Satchi, Cesareo, and Pastore (2015) claim that negative information is spread to more recipients for a longer time and in a very detailed form in comparison to positive information.

This perspective is supported by the Prospect Theory (Kahneman & Tversky, 1979). Prospect Theory explains how people act in a situation involving a risk. It is important to

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emphasize that a decision is made in regard to the value of a loss or a gain rather than the overall outcome, i.e., "Losses loom larger than gains." (Kahneman & Tversky, 1981, p. 456). A gain or a loss is assessed through a reference point. For instance, if this point is zero, all above this point is a gain and all below is a loss. People usually overestimate unlikely probabilities and underestimate moderate or large probabilities. Decisions of people are based on subjective feelings (Jhala, Natarajan, & Pahwa, 2018). The framing of a message is essential. If a message is positively framed, people start to think about the benefits. If a message is negatively framed, people start to consider the losses (Kahneman & Tversky, 1979).

An experimental study by Ladhari and Michaud (2015) investigated the impact of comments on social media site on attitudes and intention to book a hotel. Exposure to positive comments led to more positive attitudes than in the case of negative comments. Moreover, positive feedback led to higher intention to book a hotel than negative feedback. Vermeulen and Seegers (2009) previously supported this finding. In their experiment, review valence brought awareness of a location and resulted in the intention to make a reservation at the hotel. A positive valence review created favorable attitudes and increased the number of reservations. A negative valence review led to less favorable attitudes and a lower number of reservations. An online experiment by Ballantine and Yeung (2015) showed that negative WOM results in the least favorable brand attitudes and purchase intention, whereas positive WOM results in the most favorable attitudes and purchase intention. Nieto-García and colleagues (2017) added that valence causes a positive effect on willingness to pay for a hospitality industry.

It is predicted that potential customers will stay away from a place with negative review(s). Customers would rather visit a location with positive review(s) to ensure certain gains and decrease the risk of paying for a service with a low standard. Customers will be

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more inclined to select a restaurant with reviews that provide more benefits than costs. Therefore, it was hypothesized that:

H1a: A positive review will lead to more favorable attitudes toward a location, such

as a restaurant, than a negative review.

H1b: A positive review will lead to a higher intention to visit this place than a

negative review.

Valence and channel of the message in the digital era

There are still noticeable gaps in consumer online reviews which have to be filled (Kimmel & Kitchen, 2014). SNSs created new opportunities of how a review can be transmitted. SNSs provide a landscape for sharing textual, visual or audiovisual content, which may be received through different channels. Users usually share their opinions through captions or comments, and/or pictures or video depicting an experience (Papathanassis & Knolle, 2011). This review type is very persuasive because it does not reflect the clear attempt of advertising. The information comes from a disinterested party (Landhari & Michaud, 2015). SNSs provide us with two main options of how a review can be shared. It can be sent either via a private channel or a public channel (Brown, Michinov, & Manago, 2017).

The Masspersonal Communication Model (MPCM)

The MPCM explains distinctive options of how individuals can communicate within SNSs (O´Sullivan, 2005). In the digital era, the perception of a certain channel as strictly independent is impossible. Channels overlap, and boundaries between interpersonal and mass communication have been extended. These two can even coexist simultaneously, especially

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within SNSs (O’Sullivan & Carr, 2017). One can be tagged in a personal message, which can then be shared with an entire social network via a mass communication on a "wall". Personal communication can be visible publicly with a large audience. These messages reflect the high rate of personalization that can be widely accessible (O’Sullivan & Carr, 2017).

SNSs have established new space for communication between individuals. People communicate through updating their status, leaving comments on one’s wall or private messaging (Boyd & Ellison, 2007). Therefore, O’Sullivan and Carr (2017) introduced the MPCM. This concept demonstrates that it is the sender of the message who decides what the type of communication will be. The sender decides the content of the message, selects through which channel the message will be transmitted, and decides how it will be used. This provides new insight and moves from a channel-centered to a communication-centered mindset (French & Bazarova, 2017).

Forms of communication can be divided according to the perceived accessibility (exclusivity) of the message and its rate of personalization. A private message and a public message can be perceived as two distinctive approaches based on a senders' actions with a message. Interpersonal communication includes a private, highly personalized message with low accessibility for others. Mass communication includes a public, highly accessible impersonal message. The mixture of these two spheres, highly personalized and highly accessible, refers to masspersonal communication (O'Sullivan & Carr, 2017). Whether a message will be sent through a private or a public channel depends on the rate of intimacy and the aim/incentive of communication (Bazarova, 2012). This study is interested in personal and a mass communication. Therefore, prototypical examples are explained strictly for a private and a public message.

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Private vs. Public Message

Private messages provide information exclusively for a receiver (Bazarova, 2012). Generally, a message sent via a private channel is directed to a well-known person. This type of message is transmitted through a chat. Private messages represent features of high personalization and low accessibility. Communication through a private message captures information that fits a recipient’s interests. They are perceived as tailored to a certain person (Lottridge & Bentley, 2018). Private messaging enables communication within a strong-ties network rather than a miscellaneous network, which consists of people without a close bond (Valkenburg & Peter, 2011). An example of this communication is a private messaging function offered by most of SNSs (O'Sullivan & Carr, 2017).

On the contrary, a message sent via a public channel contains information for a broad audience and can reach people ranging from the closest friends to complete strangers (Lottridge & Bentley, 2018). An example of a public channel providing a uniform message for a wide audience can be found on Twitter or Youtube (O'Sullivan & Carr, 2017). It is possible the received message can bear the value for all individuals in one´s social network. One identical message is spread to many recipients at a parallel time (Brown et al., 2017). In the same vein, Bazarova and Choi (2014) talk about the distinctive rate of the directness and visibility of the communicated message.

Interaction of review valence and channel of the message

Social media users use distinctive channels while sharing messages online. Online reviews received by peers via a private message can be apprehended as a sort of personalized advertisement. An experimental study by Kalyanaraman and Sundar (2006) showed that customized advertisements have higher effectiveness on attitudes than advertisements with lack of customization. Experiments conducted by De Keyzer, Dens and De Pelsmacker

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(2015) were interested in effects of personalization on one's responses to ads on Facebook. If the message was considered relevant for the recipient, brand attitudes and intention to click on the ad increased. This is in line with a study by Brown and colleagues (2017) which showed that customized emails from well-known source result in higher intention to engage with the ad compared to emails from unknown source without customization. Bizer and colleagues (2000) state that if a message is connected to the subject's interest, the persuasive effects are boosted.

Personal communication via social media can be perceived as an alternative to face-to-face communication. Individuals spend more time online and many interactions are facilitated through private messaging. A study by Meuter, McCabe and Curran (2013) compared the influence of interpersonal recommendation and mass recommendation shared publicly online on attitudes toward a company and intention to visit the company. The authors used a mixture of quantitative and qualitative methods to test a scenario of a café, situated in a location where subjects had never been. Results demonstrated that face-to-face communication had a higher influence on attitudes, and intention than eWOM reviews shared on public pages including SNS. Thus, it was hypothesized:

H2a: A positive review received via a private message will lead to more favorable

attitudes toward the restaurant and a higher intention to visit this place than a positive review received via a public message.

As a rule of thumb, negative reviews create unfavorable attitudes and decrease purchase intention (Chevalier & Mayzlin, 2006). Moreover, their interaction with a public channel can reach a mass audience and be more impactful than a private channel, which fulfills the function of interpersonal communication (O’Sullivan & Carr, 2017). In the past,

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consumers used private channels more often to complain. Nowadays, with the rise of the web 2.0, angry customers prefer to spread their negative experiences through public messages (Gensler, Völckner, Liu-Thompkins, & Wiertz, 2013). Negative eWOM in SNSs can lead, in extreme cases, to so-called online firestorms. This phenomenon includes online public discussions with a high amount of negative WOM. It is spread with incredible speed, and can be commented and shared rapidly within a short time period (Pfeffer, Zorbach, & Carley, 2014). A public channel is more influential in convincing the masses (Zeelenberg & Pieters, 2004). New technologies and innovations provide an opportunity for sharing complaints on SNSs anytime with large number of recipients (van Noort & Willemsen, 2012).

An experimental study by Lee and Cranage (2014) tested the effects of negative eWOM on consumers’ perceptions and attitude changes towards a restaurant. They found that negative reviews sent through mass communication resulted in negative changes on attitudes. A study by Kim, Wang, Maslowska, and Malthouse (2016) demonstrated that negative effects are visible for subsequent purchase behavior. An experiment showed that exposure to negative eWOM resulted in the decrease of one’s intention to spend money at a certain place. Ward and Ostrom (2006) assumed that a public negative statement is more impactful than a message shared with few individuals. This is supported by a recent case study. Jansen, Zhang, Sobel, and Chowdury (2009) evaluated more than 150 thousand posts on Twitter. Their results indicate that public negative posts are shared faster and with greater impacts than private messages. Therefore, it was predicted that:

H2b: A negative review received via a public message will lead to less favorable

attitudes toward the restaurant and a lower intention to visit this place than a negative review received via a private message.

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Self-monitoring

Review perception and consumer behavior may differ across individuals. People make decisions based on their own thoughts or are influenced by opinions of others (Snyder, 1974). It is important to investigate if the perception of a review will differ based on one’s self-monitoring. Self-monitoring can be described as the degree of changing and monitoring a behavior to meet the perceived appropriateness of a situation judged by others (Parks-Leduc, Pattie, Pargas, & Eliason, 2014).

This concept was introduced by Snyder (1974) and refers to distinctive levels of self-control and self-observation. Self-monitoring shows the extent to which individuals can be depicted as so-called social chameleons, which refers to the ability to adapt to a situation and change own behavior (Parks-Leduc et al., 2014). High self-monitors adjust their actions in order to be understood positively by others. Low self-monitors act based on their own will despite the information on SNSs (He et al., 2014). High self-monitors observe others and replicate their behavior. Low self-monitors are less interested in the expression of others, and less influenced by them (Snyder, 1974). Low monitors count on their own beliefs and attitudes (He et. al, 2014).

A study by Rosenberg and Egbert (2011) employed a cross-sectional survey to investigate personal traits and secondary goals to reveal how Facebook users present themselves on the SNS. Results showed that individuals who care about the opinions of others are the ones who copy desirable behaviors of their peers. This is in line with an experiment by Pentina and colleagues (2018). Research found that influence of review valence might be stronger when a person is easily influenced by communication with others. Moreover, high self-monitors are more likely to change their own intentions in order to adapt

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to public opinions than low self-monitors (Aizen, Timko, & White, 1982). A study by Hillhouse, Turrisi, & Kastner (2000) tested tanning salon behavior among young people. Their survey demonstrated that high self-monitors paid more attention to opinions of others when deciding whether to use a tanning salon than low self-monitors. It was predicted that a post shared with an entire network will evoke a higher desire to imitate the behavior. Consequently, it will lead to a conversion of attitudes, and subsequent intentions.

H3: High self-monitors exposed to a positive review via a public vs. private message

will show more favorable attitudes and a higher intention to visit a location, such as a restaurant, than low self-monitors.

Method

In order to test the hypotheses and answer the research question, this research adopts 2 x 2 factorial design, with the valence of review as the between-subjects variable (2 levels, namely: positive and negative), the type of channel as the between-subjects variable (2 levels, namely: private message and public message) and control group (flyer). The experimental design has been selected according to high internal validity and capability to reveal a causal relationship. Randomization is an essential feature of this experimental design. This step might cancel out third-party explanations and contribute to equality between groups. Random assignment helps to keep unsystematic variation to a minimum (Field, 2013). This research is conducted through an online survey depicting the case of a newly opened restaurant.

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Sample and data collection

The convenience sample was selected in consideration to time restriction and easier accessibility of participants. The sample includes the general public above 18 years old. Respondents were recruited through an online post on SNS; the survey link relocated them to the research survey. A between-subjects factorial design with inclusion of a control group needs a larger sample size. To ensure statistically valid results, it was concluded to assign at least forty subjects into each condition (40), with a minimum of two hundred participants overall (n = 200). Taking into account a possible non-response rate or drop out, this number should be adequate for analysis of variance (Bryman, 2016).

Data collection took place for a week, in the period from the 26th of April until the 2nd of May 2018. A self-selected sample of 340 respondents between the age 18 and 74 answered the survey questions which captured the influence of valence of the review and distinctive channel of the message on attitudes and intention to visit a location, such as a restaurant. All respondents had to agree with informed consent, be 18 years old or older, feel confident with the use of English language, and understand the treatment in the intended way. 59 participants were excluded from the research because they did not meet one or more conditions. Therefore, the final sample consists of 281 participants.

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The average age of individuals who took part in this research is 28 (SD = 7.20, n = 281). The sex distribution of our sample consists of 196 females (70%), and 85 males (30%). Additionally, participants indicated their country of origin. Individuals came from 43 countries spread all around the world. However, the most represented countries were the Czech Republic (35%), Netherlands (9%) and Germany (6%). A randomization check confirmed that participants were randomly assigned and equally distributed to the experimental conditions. There were no significant differences in age (p = .590), sex (p = .148) and country of origin (p = .590).

Stimuli

The participants were asked to imagine that they are sitting in their living room and scrolling down on Instagram. Suddenly, they either came across a public post or a private message from their close friend Emma. She shares her positive or negative experience. The control group should imagine the same scenario. While scrolling down on Instagram, they found a flyer from a new restaurant in town. Taking into account comparability, the same person communicated the same message across experimental conditions. The scenarios were artificially created, so there is a need to keep the content and setting as realistic as possible (Ramirez, Mukherjee, Vezzoli, & Kramer, 2015).

The stimuli were created with approximately the same strength and the keywords for each condition were selected with great caution. For instance, if the positive stimulus was given, the subjects came across words such as cheap, yummy and high quality. If the negative stimulus was received, the subjects saw words such as expensive, disgusting and low quality. To arouse the recognition of a public post on Instagram, the hashtag #bestrestaurantever or #worstrestaurantever was added to the end of the review. To arouse the recognition of a private message on Instagram, the greeting and sentence referring to the previous

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conversation were included before the embedded review. The variation between experimental conditions and control group is shown in Appendix A.

Procedure

This study was introduced to subjects as a research project dealing with personal experiences with reviews on social media. Participants were encouraged to read the informed consent. Before the experiment, individuals had to provide a consent that they fulfill all conditions, which is necessary for further participation. Subsequently, participants were randomly assigned to one experimental condition or control group. Then, the outcome variables were measured. It was evaluated if the respondents held favorable or unfavorable attitudes towards the restaurant and if there was an intention to visit the place. The manipulation check included questions asking if the received review was perceived as negative or positive and if participants perceived the review to be rather private or public. After the manipulation check, participants indicated to what extent they agree or disagree with statements measuring the self-monitoring. Ultimately, three socio-demographic questions capturing one's age, sex and country of origin were asked. The very last part consisted of acknowledgment for the participation and debriefing. The entire research took about ten minutes.

Measurement

Attitudes toward a newly opened restaurant. This outcome variable was measured

through three affective attitude scales and three cognitive attitudes scales. Items were measured with a 7-point Likert scale ranging from 1 (strongly disagree) to 7 (strongly agree). The scale was inspired by previous studies dealing with attitudes in a restaurant setting (Arora & Singer, 2006; Hwang & Ok, 2013). Six items were used to indicate attitudes toward

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a newly opened restaurant (M = 4.02, SD = 1.49). The scale measuring attitudes toward the restaurant was gathered through averaging the items and the scale proved to be reliable (Cronbach’s alpha = .94). A principal axis factor analysis showed that the items loaded on one factor = 4.69 explaining 78% of the variance in the original items. The following statements provide an example of the used items: “I would really like to try food from this restaurant.” or “You think that if you decide to visit this restaurant, you will have an overall good experience.”

Intention to visit this restaurant. This is the second outcome variable which was

measured on a scale adapted from a prior study interested in eating initiatives for children (Lee, Conklin, Bordi, & Cranage, 2016). The wording was changed slightly. Participants should answer on a 7-point Likert scale ranging from 1 (strongly disagree) to 7 (strongly agree) to the extent they were willing to visit this restaurant. Four items measured the intention to visit this restaurant (M = 3.76, SD = 1.61). The items loaded on one factor = 3.36 explaining 84% of the variance in the original items. The created scale was reliable (Cronbach’s alpha = .94). Participants should indicate to what extent they agree or disagree on items such as: “I would select this restaurant to have a meal here.” or “This restaurant would be my first choice for eating out with my friend(s).”

Self-monitoring. Moderation of self-monitoring (M = 3.93, SD = .69) was measured

through 18-items on a revised self-monitoring scale invented by Snyder and Gangestad (1986). Instead of a 4-point scale, which was originally used, this study employed a 7-point scale ranging from 1 (I strongly disagree) to 7 (I strongly agree). This provides more

possibilities to express true beliefs. The scale was reliable (Cronbach’s alpha = .75). Most of the items had to be recoded because of reversed wording. Examples of used items

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are: “I’m not always the person I appear to be” or “I find it hard to imitate the behavior of other people (reversed coded).”

Results

Manipulation checks

Firstly, it was checked if the manipulation was perceived in an intended way. The analysis employed review valence as the independent variable and the perception of the valence as the dependent variable. The Levene’s F test indicated roughly equal differences between groups, F = .62, p = .432. The independent samples t-test found that the positive valence condition (M = 4.33, SD = .73) was perceived differently from the negative valence condition (M = 1.58, SD = .78) based on the received treatment, t (221) = 27.07, p = .001, d = 3.64, 95% CI [2.55; 2.95]. This indicates that manipulation of the valence of the review was successful.

Secondly, to evaluate if the manipulation worked for the channel of the message, the study ran another independent samples t-test using the channel of the message as the independent variable, and the perception of the channel as the dependent variable. The Levene’s F test found non-significant results, F = 3.04, p = .083. The independent samples t-test revealed that participants perceived the private channel (M = 3.97, SD = .57) differently than the public channel (M = 1.87, SD = .60) based on the assigned treatment, t (220) = 27.07, p = .001, d = 3.59, 95% CI [1.94; 2.25]. To conclude, the manipulation worked in a desirable way.

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Hypotheses tests

Effects of review valence on attitudes toward a newly opened restaurant. A

one-way ANOVA tested whether a positive review will lead to more favorable attitudes toward a location, such as a restaurant, than a negative review. This analysis showed statistically significant results between groups, F(2, 278) = 153.13, p = .001, η2 = .52, which refers to the strong effects of this variable. However, the Levene’s F test revealed significant result, Levene’s F (2, 278) = 6.89, p = .001. Therefore, the assumption of equal variances within the sample was violated. The ANOVA procedure has been shown to be robust toward this violation. Compared to the participants exposed to the control group (M = 4.12, SD = 1.10), and to the negative valence condition (M = 2.66, SD = 1.15), participants exposed to the positive valence condition showed more favorable attitudes toward a newly opened restaurant (M = 5.09, SD = .88). A Bonferroni Post Hoc Test found a significant difference between subjects assigned to the positive and negative review valence conditions (Mdifference= 2.43, p = .001). Participants who were exposed to the positive review expressed more favorable attitudes than the control group (Mdifference= .97, p = .001). People exposed to negative review depicted more unfavorable attitudes than the control group (Mdifference= -1.46, p = .001). The valence of the review accounted for 52% of the variance in the attitudes toward a newly opened restaurant. A positive review results in more favorable attitudes than a negative review. Thus, H1a was supported.

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Effects of review valence on intention to visit this restaurant. A one-way ANOVA

was conducted to investigate whether the intention to visit the restaurant is higher for participants exposed to a positive review than for participants exposed to a negative review. The ANOVA revealed significant results, demonstrating the significant differences between previously tested groups, F(2, 278) = 147.52, p = .001, η2 = .52. The Levene’s F test of homogeneity found non-significant results, Levene’s F (2, 278) = 1.62, p = .201. Participants in the control group (M = 4.00, SD = 1.26) and in the negative valence condition (M = 2.27, SD = 1.13) scored lower on the intention scale than participants in the positive valence condition (M = 4.86, SD = 1.05). A Bonferroni Post Hoc Test showed that there is a significantly higher intention to visit this restaurant after exposure to the positive review than after exposure to the negative review (Mdifference= 2.59, p = .001). Participants who read the positive recommendation showed a higher intention to visit the restaurant than participants who saw the flyer (Mdifference= .85, p = .001). Participants who received the negative review had a lower intention to visit the restaurant than participants who were exposed to the flyer (Mdifference= -1.73, p = .001). Thus, H1b was supported.

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Interaction effect between the channel of the message and review valence. To test

whether the interaction between the channel of the message and the review valence matters, a one-way multivariate analysis of variance (MANOVA) was employed. It was predicted that a positive recommendation of the restaurant gathered through a private channel would result in more favorable attitudes toward the restaurant and a higher intention to visit this place than a positive recommendation received via a public channel (H2a). On the contrary, a negative review through a public channel would lead to lower attitudes toward the restaurant and lower intention to visit the restaurant than a negative review received via a private channel (H2b). A MANOVA was carried out to assess the influence of exposure to the same valence and the distinctive channel. Statistically significant results were obtained, Pillai’s trace, V = .05, F(3, 219) = 5.85, p = .003.

The MANOVA found a significant interaction effect of the valence and channel for attitudes toward a newly opened restaurant, F(1, 219) = 4.22, p = .041, η2 = .02, which stands for a small effect. This effect is slightly stronger for the intention to visit the restaurant, F(1, 219) = 12.11, p = .001, η2 = .05. A private positive review (M = 5.20, SD = .14) led to more favorable attitudes than a public positive review (M = 5.02, SD = .12). Moreover, a private positive review (M = 5.20, SD = .14) resulted in a higher intention to visit the restaurant than a public positive review (M = 4.60, SD = .13). Therefore, H2a was supported. On the other hand, it was revealed that a public negative review (M = 2.85, SD = .14) did not lead to less favorable attitudes than a private negative review (M = 2.45, SD = .15). A public negative review (M = 2.43, SD = .15) did not decrease the intention to visit this place more than a private negative review (M = 2.09, SD = .16). Therefore, H2b was rejected.

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Self-monitoring. To check, whether self-monitoring may affect the attitudes and

intention to visit the restaurant, a linear regression analysis was employed. It was predicted that high self-monitors in the positive valence condition who received a review via a public vs. private message would show more favorable attitudes and a higher intention to visit the restaurant than low self-monitors. To test this hypothesis (H3), most attention was directed to an interaction of the channel (private vs. public; independent variable) and self-monitoring (high vs. low; moderator). Two linear regression analyses were applied in order to see the predictors of both dependent variables: attitudes toward a newly opened restaurant (M = 4.02, SD = 1.49) and the intention to visit this restaurant (M = 3.76, SD = 1.61). With regard to the hypothesis, only cases exposed to the positive review were selected. Before statistical analyses, the variables were scanned to reach the conditions of linearity, normally distributed residuals, independence, homoscedasticity, non-auto and multicollinearity. The conditions of multicollinearity and normally distributed residuals were violated for both outcome variables. A linear regression analysis was applied to investigate if self-monitoring can moderate attitudes toward a newly opened restaurant. To answer H3, channel, self-monitoring, and the interaction of the channel of the message and self-monitoring were selected as predictors. The overall model was non-significant, F(3, 122) = .41, p = .746. This regression model does not include any significant predictor of the outcome. The created dummy variable, which represented the main effect of the public channel, did not significantly predict the outcome, b* = -.14, t = -.28, p = .777. Self-monitoring is not a significant predictor of attitudes toward a newly opened restaurant, b* = -.07, t = -.23, p = .817, 95%. The interaction of high self-monitors in the public channel did not show a statistically significant effect, b* = .06, t = .10, p = .918. To conclude, self-monitoring cannot be considered a significant moderator.

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Another linear regression analysis was conducted to find out whether self-monitoring could predict the intention to visit the restaurant. The same predictors were entered into the analysis. Similarly as in the results presented above, the overall model showed a non-significant result, F(3, 122) = 3.75, p = .083. All variables showed non-non-significant results. Self-monitoring did not predict the intention to visit this restaurant, b* = -.09, t = -.68, p = .496 and its interaction with channel was non-significant, b* = .10, t = .21, p = .833. Both analyses did not significantly predict the outcome. Therefore, H3 was rejected.

Discussion

The aim of the current study was to test the effects of review valence and the interaction effects of valence and channel of the message in the social media landscape. The study worked with previously made assumptions that review valence strongly impacts the process of decision-making. The first two hypotheses served as the replication of these effects, which have been already shown by scholars (Purnawirawan et al., 2015; Ladhari & Michaud, 2015; Ballantine & Yeung, 2015). Specifically, it was confirmed that a positive review will create more favorable attitudes toward a location, such as a restaurant than a negative review. Furthermore, a positive review led to a higher intention to visit the restaurant than a negative review. This research found that review valence strongly impacts subsequent attitudes and intention.

These findings are in line with Prospect Theory and Negativity Bias. Individuals behave in a certain way in which prospect is brought into their lives and risk is at a minimum. If a review is framed as a gain, one expects to get a good-quality service, which will be worth the money. If the review is framed as a loss, one becomes risk-aversive and usually is not willing to spend the money for a low-quality service or a product. Losses loom larger than gains (Kahneman & Tversky, 1979). A negative review has negative impacts on attitudes and

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intention to visit a newly opened restaurant. The likelihood of positive attitudes and willingness to visit the restaurant increases after exposure to a positive recommendation. This is in line with previous findings (Ballantine & Yeung, 2015; Vermeulen & Seegers, 2009). People paid slightly more attention to a negative review. This may cause higher impacts on the process of decision-making (Hornik et al., 2015; Fox et al. 2018).

Furthermore, this research found that the interaction of the channel and the review valence has an influence on attitudes and intention to visit a specific location. Results showed that a positive review received via a private channel leads to more favorable attitudes toward a location and higher intention to visit a location than a positive review received via a public channel. In contrast, the study hypothesized that a negative review received via a public channel would lead to less favorable attitudes and a lower intention to visit a newly opened restaurant. Results demonstrated that this effect is reversed because it was the private negative review that was more influential.

With regards to our findings, the private channel influenced our participants more than the public channel, no matter the provided valence of the review. It might be assumed that the private channel served as a more personalized recommendation. Our results confirm statements that personalized advertisements on social media can serve as a persuasive tool (Ha et al., 2015; Kohli, Suri, & Kapoor, 2015). Meuters and colleagues (2013) expressed that traditional WOM has greater effects on subsequent attitudes and intention than eWOM. Private messages on SNSs can be perceived as an extension of personal WOM in an online setting. MPCM explains the process in which channels overlap and fulfill the function of a personal and mass channel simultaneously. The digital era goes beyond the general classification of mass and personal communication. However, it is still useful to differentiate between mass and personal communication based on a sender's actions with the message

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(O’Sullivan & Carr, 2017). This is supported by the results of this current study where research showed differences between personal and mass communication. It is in line with the thoughts of Ha and colleagues (2015) who found that personalized private messages are more influential than public messages, which represent a lack of personalization. Customization of messages brings more positive evaluations and increases purchase intention (Kalyanaraman & Sundar, 2006, De Keyzer et al., 2015).

The last prediction of this research claimed that a positive review received through private vs. public channel would produce more favorable attitudes and a higher intention to visit the restaurant for so-called high-self monitors than low self-monitors. Based on previous findings, it was suggested that self-monitoring could serve as a significant moderator (Gangestad & Snyder, 2000; Hillhouse et al., 2000). However, this assumed effect was not confirmed. Some studies did not find significant results as well. For example, might be mentioned a research by Wolfe, Lennox, and Hudiburg (1983) who investigated self-monitoring as a possible moderator of the self-reported alcohol and marihuana use. Furthermore, Aijzen (2005) stated that if one evaluates attitude-behavior relationships, results do not always show significant differences between low and high self-monitors. With regard to research interested in attitudes and intention, this could serve as an additional justification why this study unable to provide significant findings.

Limitations

The framework of the research could be stated as the limitation. The restriction to one single scenario including one review for each participant could be insufficient for revealing desired effects. Instagram is fueled by an interactive character, which was suppressed in this study by the use of one static screenshot. Moreover, the quality of pictures was not the same.

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The positive valence condition used a high-quality picture. The negative valence condition employed a low-quality picture. This could result in biased perception of review valence. Additionally, this research did not implement a pre-test which could have speculated to what extent participants were familiar with Instagram. It would be helpful to see a comparison of people using this social network and those who do not have an account. Lastly, the sampling method and the fact that the majority of participants were women could be misrepresenting.

Future research

Future research should select a distinctive sampling method that better reflects the entire population. An inclusion of a mixture of quantitative and qualitative methods could be useful. Interviews may shed light on the in-depth perspective of the receiver and clarify personal feelings. Future research could investigate whether distinctive formats of a review, such as picture or video, can strengthen the relationship between valence and channel. Another suggestion is to examine Instagram Stories. A review could be displayed in the "stories" where it disappears within 24 hours. Expressing a review via pictures, text and video in an enjoyable way could make the persuasive intent less visible. Receivers might be influenced unconsciously while observing one’s leisure time. It could be appealing to specify the population and focus on a certain group, or differences between age groups such as Millennials and Generation X. While this study showed a review from a close friend, future research could display a review that comes from an influencer or different source. Additionally, it could be useful to check whether results will vary after exposure to more than one review.

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Practical implications

Until now, marketers were trying to find an effective way how to create eWOM within consumers' social network. The number of reviews on Instagram is constantly growing. For instance, the hashtag #foodreview was used more than 400 thousand times (Instagram, 2018). Sharing food or restaurant experiences on Instagram serves as an ongoing trend, as specific restaurants are usually tagged or mentioned in posts (Chung et al., 2017). Therefore, it is crucial to find the “recipe” in which eWOM has the highest effects on attitudes and intention to visit a location such as a restaurant.

An artificial case of a newly opened restaurant was used, but it is important to emphasize that this information can be transmitted to any other business. Marketers should thoroughly monitor the activity of their customers on Instagram and create a supportive environment for sharing reviews via interpersonal communication including private messaging. Personalization of a message may rapidly underline the impacts of communication. The right message that reaches the right audience can be very effective (Postma & Brokke, 2002). This research can serve as an inspiration for marketers to encourage their customer to share adequate user-generated content.

It is obvious that people cannot be forced to share reviews on SNSs. However, a message that seems to be useful or interesting for others has a higher probability to be shared (Prasetio, Hurriyati, & Sari, 2017). It is important to keep the aim of persuasion hidden. The private message has to be perceived as the common communication between friends, as this space is an inappropriate area for advertisements (Kelly, Kerr, and Drennan, 2010). When people are aware of personalized techniques, they perceive this as disturbing their privacy. SNSs provide a suitable landscape to share personalized messages. Using friends to send a recommendation will make the effects of persuasion less visible (Tucker, 2014).

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Finding the formula of how to unconsciously attract future customers can create an appealing brand, which then can eventually enhance sales. Reviews shared on SNSs can emphasize brand awareness and can lead to willingness to visit the location (Vermeulen & Seegers, 2009). Last but not least, customers can gather more personalized messages referring to locations they might be interested in. Sharing a review via SNSs that are popular among peers can emphasize the favorable impacts of personalized messages (De Keyzer et al., 2015).

Conclusion

The present study brings new insights into the investigation of electronic word-of-mouth in Web 2.0. It shows that it is not only review valence that can influence customers' purchase behavior. The channel of the message deserves considerable attention. A private personal message combined with either positive or negative review valence may play a major role in influencing one's attitudes and intention to visit a location, such as a restaurant. A plausible explanation for this effect can be that receiving personalized advertising usually creates more favorable reactions than advertising without personalization. Personal relevance of the message plays a crucial role in message perception, subsequent attitudes and intention (De Keyzer et al., 2015). Messages or comments on SNS are perceived as more credible because we know the recipient (Chu & Kim, 2011). To understand how this process works in detail, further investigation is needed. Future researchers should shed more light on the channel of the message in the social media landscape. Specifically, on Instagram, there are myriad possibilities for influencing the opinion of peers through eWOM. It is important to know how the ways we receive messages influence individual perceptions.

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Appendix A

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Positive review received via public channel

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