# Expressions of Gratitude Applied to Business: A Lesson for Managing Online Reviews

Authors: Ana R. Farias; Cláudia Simão; Joana Reis
Journal: Human Behavior and Emerging Technologies
 (2022)

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## Abstract

Online reviews are critical for businesses to thrive, but their management is not often effective. Using data from social media platforms, with more than 600 observations of public online interactions between business owners and customers, we showed that strategic management of online reviews predicts a positive increment of online reputation. Publicly expressing gratitude (Study 1) and, specifically, directing these expressions towards beneficial online reviews (Study 2) are effective strategies supporting a general increase of the business online score. These findings identify public expressions of gratitude as a responsive, attentive gesture that signals care and consideration towards customers. Such gesture promotes the online reputation through satisfaction between business community relationships.


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## Body

## 1. Introduction

The digital market leveraged the importance of online reputation, with indicators predicting that more than 50% of consumers try a product or consume an experience based on its online rating [1]. The online reputation was initially perceived as a unidirectional process; however, since Web 2.0 emphasized social networking and content generated by users, business owners soon realized the importance of an active contribution to their own online reputation [2] and brand perceptions [3]. They moved away from the willingness to fully satisfy their customers to a more interactive business-consumer relationship. Nonetheless, what is still missing is the understanding of the most efficient way to manage and improve such digitally mediated relationships, which are often susceptible to the inexperience of business owners when facing both positive and negative online reviews. Through social psychological research insights, we aim to explore how business owners can positively invest in the relationship with their customers while at the same time improve their online reputation.Evidence indeed shows that directly engaging in digital platforms is crucial to building a relationship of trust with customers [4], and responding to online reviews is a way of publicly humanizing the business-customer interaction [5, 6]. However, it can be an advantage or a disadvantage for the relationship. Even though most responses to reviews are expressions of gratitude in the format of thank you notes [7], business owners can get emotional and angry when managing negative comments [8]. When this happens, there is a general denial of one of the most efficient ways of dealing with these online reviews and the consequences of expressions of gratitude and negative reactions to the business online reputation. Some examples of appropriate responses to negative reviews are thank you notes, apologies for shortcomings, and explanations for inconsistent performance [5].Previous evidence associates gratitude felt by consumers with the willingness to reciprocate, helping to build a trusting relationship between consumers and companies [9]. Recent research has shown that gratitude expands this dyadic relationship into a triadic cycle, through indirect reciprocity with other consumers. A recent paper found that when consumers are grateful, they exhibited more direct reciprocity by sharing positive employee-centric content [10], i.e., reviews describing positive characteristics of the employees. By advising other consumers, the initiator is creating indirect reciprocity, by contributing to the reputation of the company. On the other hand, the other consumers who read the reviews find that employee-centric reviews are rated as more useful than employee-noncentric reviews.Within a social context, gratitude is broadly defined as a general feeling of appreciation of what is valuable to the self. Grateful individuals represent themselves in a state of thankfulness and/or appreciation [11]. It is well documented that after receiving expressions of gratitude, individuals feel more socially valued [12] and prosocial [13], influencing psychological states such as generosity and cooperation. Saying thanks shows receptiveness to and acknowledgment of kindness, signaling expressers of gratitude as safe targets for future investments [14]. The high levels of trust from gratitude have been interpreted as an acknowledgment of success [15, 16], creating then the optimal circumstances for positive social interaction [13]. Applied to online communities, it stimulates participation [17]. A recent study investigating gratitude in online communities tried to understand how online gratitude expression, acknowledgments, breakdown, and reinforcement occur. Results provided a model of gratitude comprehended in a “gratitude cycle,” emphasizing the cyclic, cascading nature of gratitude: thank you notes promote online community members to be kind to each other, to be thankful for acts of kindness, and to acknowledge expressions of gratitude that are self-directed [18].All these responsiveness synonyms are crucial for both interpersonal [19, 20] and business [21, 22] interactions. Service quality is partially identified by responsiveness, a way to engage with customers signaling the willingness to help [21] and responding with gratitude from the customer perspective. For business owners, expressing messages of gratitude has a positive long-term relational effect [22]; however, it is yet underexplored how these expressions of and reactions to gratitude are perceived by the community when they are expressed publicly. For example, what should be the business strategy of whom to respond to or how to respond to the reviews? A group of researchers addressed this question by investigating the optimal response strategy under resource constraints [23]. Managerial responses to online reviews are perceived differently according to their valence: managerial responses to extremely positive reviews are perceived as promotional messages whereas managerial responses to extremely negative reviews are perceived as an attempt to influence delivery that strengthens the consumption intentions. Thus, companies should tailor their responses to negative reviews, using high-tailored responses, and limit their customization when responding to positive reviews, using low-tailored responses. According to a previous study, managerial responses have a significant and positive impact on promoting subsequent consumer reviews. The authors also found that the valence of the review plays a role in how to address them [24].Thus, in the current study, we aim to address how managerial expressions of gratitude when responding to the reviews may have a significant impact on promoting their online reputation, which is expressed in terms of online rating. We expect that responding to reviews with expressions of gratitude/thank you notes should be interpreted as a relational signal, demonstrating general responsiveness to the kindness of customers. That gesture would have positive feedback from the online community, which would be translated into an increase in the online rating or the business online reputation. On the contrary, expressions of negative reactions or no replies, as they do not signal any relational intention, should not be associated with the business online reputation.Across two independent studies using naturalistic data from digital mediums, specifically food business partners listed on one of the most popular social media platforms (http://www.zomato.com), we explored the impact of online reviews on businesses and reputation and explore how different types of interaction should be addressed depending on the valence of reviews left by customers. We randomly selected 59 (Study 1) and 86 (Study 2) food business partners, and subsequently, business owners’ responses were coded by the three authors across a set of relevant measures described in each study.


## 2. Study 1

Study 1 tested whether business owners’ responses expressing gratitude, but not reacting negatively, would positively predict an increase in the business online reputation.


### 2.1. Method

We used G∗Power software [25] to calculate the sample size required to detect a medium effect based on a coefficient of determination of 20% (r2=0.2). The Determine button calculated automatically ρ from the coefficient of determination r2 as a medium effect size (ρ=0.45). To achieve 95% power, α level of 5% (two-tailed), the estimated sample size was 59 cases. Therefore, we randomly selected 59 food business partners listed on one of the most popular social media platforms (http://www.zomato.com), from which we coded and analyzed data (all data have been made publicly available via Open Science Framework and can be accessed at https://osf.io/nur5t/). Our sample was comprised mainly of casual dining restaurants (67%) and fine dining restaurants (17%). The remaining 16% was divided into food court establishments, cafes, pastry-cafes, teahouses, and general bars, lounge bars, or wine bars.


#### 2.1.1. Procedure and Measurements

We gathered data from food business partners that are listed on the Zomato platform. Therefore, we selected each food business partner that appeared in every 15th case. The procedure went on until we got to the size of 59 completed cases. In the following subsection, we present the measures collected from the platform (see Table1 for a summary).Table 1
List of measures: Study 1.
MeasureDescriptionOnline reputationOnline rating of each business partner, ranging from 1 (very poor) to 5 (excellent)Expressions of gratitudeNumber of times business owners responded by expressing their gratitude to popular reviewsNegative reactionsNumber of times business owners responded negatively or defensively to popular reviewsNumber of most popular reviewsShow up first in the feed and were rated by other online users as usefulTotal number of reviewsTotal number of people who rated the business partner(1) Online Reputation. Online reputation was measured through the online rating of each business partner, ranging from 1 (very poor) to 5 (excellent). Customers generally attribute this rating after using the service, and it should translate into how much they enjoyed the experience.(2) Expressions of Gratitude. This variable was measured by counting the number of times business owners responded by expressing their gratitude for popular reviews. To be coded as an expression of gratitude, the interaction had to explicitly say “thanks” or express any appreciation about the review.(3) Negative Reactions. Negative reactions were accessed by counting the number of times business owners responded negatively or defensively to popular reviews. To be coded as a negative reaction, the interaction had to explicitly express a negative reaction to a review or counterexplain defensively the details of any experience customers shared or negatively criticized.(4) Number of Popular Reviews. Each business partner has a different number of its most popular reviews. Those are the reviews that show up first in the feed and were rated by other online users as useful. Consequently, these popular reviews automatically appear online when users want to check a business partner’s reviews.(5) Number of Popular Reviews. The total number of reviews measures the total number of people who rated the business partner.


### 2.2. Results

To test our main hypothesis that the number of expressions of gratitude from the business owners was positively associated with the restaurant online reputation, we first conducted a correlation analysis. Because values of both variables assume different scales, the Spearman coefficient was selected. As predicted, there is a positive correlation between the number of expressions of gratitude and the business online reputation,ρ=0.44,p<0.001,r2=0.19. The relation between the negative reactions and business online reputation was not statistically significant, ρ=0.20,p=0.126,r2=0.04.Following this, we conducted a regression analysis to make a prediction of how much an expression of gratitude would increase the online reputation rating of a business partner after controlling for the negative reactions, the number of popular reviews, and the total number of reviews. Results revealed that expressions of gratitude significantly increased the online reputation of a food business partner,β=0.45,p=0.024, even after controlling for negative reactions, β=−0.01,p=0.958; number of popular reviews, β=−0.10,p=0.666; and total number of reviews, β=0.27,p=0.083.


## 2.1. Method

We used G∗Power software [25] to calculate the sample size required to detect a medium effect based on a coefficient of determination of 20% (r2=0.2). The Determine button calculated automatically ρ from the coefficient of determination r2 as a medium effect size (ρ=0.45). To achieve 95% power, α level of 5% (two-tailed), the estimated sample size was 59 cases. Therefore, we randomly selected 59 food business partners listed on one of the most popular social media platforms (http://www.zomato.com), from which we coded and analyzed data (all data have been made publicly available via Open Science Framework and can be accessed at https://osf.io/nur5t/). Our sample was comprised mainly of casual dining restaurants (67%) and fine dining restaurants (17%). The remaining 16% was divided into food court establishments, cafes, pastry-cafes, teahouses, and general bars, lounge bars, or wine bars.


### 2.1.1. Procedure and Measurements

We gathered data from food business partners that are listed on the Zomato platform. Therefore, we selected each food business partner that appeared in every 15th case. The procedure went on until we got to the size of 59 completed cases. In the following subsection, we present the measures collected from the platform (see Table1 for a summary).Table 1
List of measures: Study 1.
MeasureDescriptionOnline reputationOnline rating of each business partner, ranging from 1 (very poor) to 5 (excellent)Expressions of gratitudeNumber of times business owners responded by expressing their gratitude to popular reviewsNegative reactionsNumber of times business owners responded negatively or defensively to popular reviewsNumber of most popular reviewsShow up first in the feed and were rated by other online users as usefulTotal number of reviewsTotal number of people who rated the business partner(1) Online Reputation. Online reputation was measured through the online rating of each business partner, ranging from 1 (very poor) to 5 (excellent). Customers generally attribute this rating after using the service, and it should translate into how much they enjoyed the experience.(2) Expressions of Gratitude. This variable was measured by counting the number of times business owners responded by expressing their gratitude for popular reviews. To be coded as an expression of gratitude, the interaction had to explicitly say “thanks” or express any appreciation about the review.(3) Negative Reactions. Negative reactions were accessed by counting the number of times business owners responded negatively or defensively to popular reviews. To be coded as a negative reaction, the interaction had to explicitly express a negative reaction to a review or counterexplain defensively the details of any experience customers shared or negatively criticized.(4) Number of Popular Reviews. Each business partner has a different number of its most popular reviews. Those are the reviews that show up first in the feed and were rated by other online users as useful. Consequently, these popular reviews automatically appear online when users want to check a business partner’s reviews.(5) Number of Popular Reviews. The total number of reviews measures the total number of people who rated the business partner.


## 2.1.1. Procedure and Measurements

We gathered data from food business partners that are listed on the Zomato platform. Therefore, we selected each food business partner that appeared in every 15th case. The procedure went on until we got to the size of 59 completed cases. In the following subsection, we present the measures collected from the platform (see Table1 for a summary).Table 1
List of measures: Study 1.
MeasureDescriptionOnline reputationOnline rating of each business partner, ranging from 1 (very poor) to 5 (excellent)Expressions of gratitudeNumber of times business owners responded by expressing their gratitude to popular reviewsNegative reactionsNumber of times business owners responded negatively or defensively to popular reviewsNumber of most popular reviewsShow up first in the feed and were rated by other online users as usefulTotal number of reviewsTotal number of people who rated the business partner(1) Online Reputation. Online reputation was measured through the online rating of each business partner, ranging from 1 (very poor) to 5 (excellent). Customers generally attribute this rating after using the service, and it should translate into how much they enjoyed the experience.(2) Expressions of Gratitude. This variable was measured by counting the number of times business owners responded by expressing their gratitude for popular reviews. To be coded as an expression of gratitude, the interaction had to explicitly say “thanks” or express any appreciation about the review.(3) Negative Reactions. Negative reactions were accessed by counting the number of times business owners responded negatively or defensively to popular reviews. To be coded as a negative reaction, the interaction had to explicitly express a negative reaction to a review or counterexplain defensively the details of any experience customers shared or negatively criticized.(4) Number of Popular Reviews. Each business partner has a different number of its most popular reviews. Those are the reviews that show up first in the feed and were rated by other online users as useful. Consequently, these popular reviews automatically appear online when users want to check a business partner’s reviews.(5) Number of Popular Reviews. The total number of reviews measures the total number of people who rated the business partner.


## 2.2. Results

To test our main hypothesis that the number of expressions of gratitude from the business owners was positively associated with the restaurant online reputation, we first conducted a correlation analysis. Because values of both variables assume different scales, the Spearman coefficient was selected. As predicted, there is a positive correlation between the number of expressions of gratitude and the business online reputation,ρ=0.44,p<0.001,r2=0.19. The relation between the negative reactions and business online reputation was not statistically significant, ρ=0.20,p=0.126,r2=0.04.Following this, we conducted a regression analysis to make a prediction of how much an expression of gratitude would increase the online reputation rating of a business partner after controlling for the negative reactions, the number of popular reviews, and the total number of reviews. Results revealed that expressions of gratitude significantly increased the online reputation of a food business partner,β=0.45,p=0.024, even after controlling for negative reactions, β=−0.01,p=0.958; number of popular reviews, β=−0.10,p=0.666; and total number of reviews, β=0.27,p=0.083.


## 3. Study 2

Taking into account that perceptions of gratitude are only triggered by benefits [19], we tested if expressions of gratitude would be more effective when targeting positive (beneficial) ratings. Thus, we predicted that only when gratitude was expressed towards beneficial ratings (vs. negative, nonbeneficial ratings) would it increase the online reputation.


### 3.1. Method

As in Study 1, we used G∗Power software [25] to calculate the minimum sample size required. Based on the inputs from Study 1, to detect again a medium effect size (f=0.28) with 95% power, α level of 5%, and considering 4 predictors, the number of cases should not be less than 72 cases. We collected a few more observations and ended up with 86 cases from different restaurants (all data have been made publicly available via Open Science Framework and can be accessed at https://osf.io/nur5t/). Our sample was comprised mainly of casual dining restaurants (63%) and diners (15%). The remaining 22% was divided into food court establishments, cafes and pastry-cafes, fine dining restaurants, fast meals, or wine bars.


#### 3.1.1. Procedure and Measurements

Similar to Study 1, we randomly selected 86 different food business partners that are listed on the Zomato platform and coded their data. In the following subsection, we present the measures collected from the platform (see Table2 for a summary).Table 2
List of measures: Study 2.
MeasureDescriptionOnline reputationOnline rating of each business partner, ranging from 1 (very poor) to 5 (excellent)Expressions of gratitudeBeneficial expressions of gratitude are the number of times a business partner responded to a positively rated review (3-5 stars). Nonbeneficial expressions of gratitude are the number of times a business partner responded to a negatively rated review (1-2 stars). Responses expressing gratitude were coded as in Study 1Negative reactions(1)Beneficial negative reactions take into account the number of times a business partner responded to a positively rated review (3-5 stars), whereas (2) nonbeneficial negative reactions concern the number of times a business partner responded to negatively rated reviews (1-2 stars). Responses expressing negative reactions were coded as in Study 1(1) Online Reputations. Online reputation was measured, as in Study 1, by the average rating of each business partner, ranging from 1 (very poor) to 5 (excellent).(2) Expressions of Gratitude. The measure was subdivided into two levels of interest, namely, (1) beneficial expressions of gratitude reflecting the number of times a business partner responded to a positively rated review (3-5 stars) and (2) nonbeneficial expressions of gratitude reflecting the number of times a business partner responded to a negatively rated review (1-2 stars). Responses expressing gratitude were coded as in Study 1.(3) Negative Reactions. (1) Beneficial negative reactions take into account the number of times a business partner responded to a positively rated review (3-5 stars), whereas (2) nonbeneficial negative reactions concern the number of times a business partner responded to negatively rated reviews (1-2 stars). Responses expressing negative reactions were coded as in Study 1.


### 3.2. Results

We tested whether expressions of gratitude would only be positively associated with the online reputation when business owners responded expressing gratitude towards beneficial reviews but not when they responded expressing gratitude towards nonbeneficial reviews. Again, we also predicted that negative reactions, whether beneficial or nonbeneficial, would not positively contribute to the online business reputation. Therefore, we conducted a multiple regression analysis entering online reputation as the dependent variable, beneficial and nonbeneficial expressions of gratitude, and beneficial and nonbeneficial negative reactions. Results revealed that beneficial expressions of gratitude were the only significant predictor of online business reputation,β=0.29,p=0.037. Neither nonbeneficial expressions of gratitude (β=−0.04,p=0.764), nor beneficial negative reactions (β=0.17,p=0.190), nor nonbeneficial negative reactions (β=0.06,p=0.582) were statistically significant.


## 3.1. Method

As in Study 1, we used G∗Power software [25] to calculate the minimum sample size required. Based on the inputs from Study 1, to detect again a medium effect size (f=0.28) with 95% power, α level of 5%, and considering 4 predictors, the number of cases should not be less than 72 cases. We collected a few more observations and ended up with 86 cases from different restaurants (all data have been made publicly available via Open Science Framework and can be accessed at https://osf.io/nur5t/). Our sample was comprised mainly of casual dining restaurants (63%) and diners (15%). The remaining 22% was divided into food court establishments, cafes and pastry-cafes, fine dining restaurants, fast meals, or wine bars.


### 3.1.1. Procedure and Measurements

Similar to Study 1, we randomly selected 86 different food business partners that are listed on the Zomato platform and coded their data. In the following subsection, we present the measures collected from the platform (see Table2 for a summary).Table 2
List of measures: Study 2.
MeasureDescriptionOnline reputationOnline rating of each business partner, ranging from 1 (very poor) to 5 (excellent)Expressions of gratitudeBeneficial expressions of gratitude are the number of times a business partner responded to a positively rated review (3-5 stars). Nonbeneficial expressions of gratitude are the number of times a business partner responded to a negatively rated review (1-2 stars). Responses expressing gratitude were coded as in Study 1Negative reactions(1)Beneficial negative reactions take into account the number of times a business partner responded to a positively rated review (3-5 stars), whereas (2) nonbeneficial negative reactions concern the number of times a business partner responded to negatively rated reviews (1-2 stars). Responses expressing negative reactions were coded as in Study 1(1) Online Reputations. Online reputation was measured, as in Study 1, by the average rating of each business partner, ranging from 1 (very poor) to 5 (excellent).(2) Expressions of Gratitude. The measure was subdivided into two levels of interest, namely, (1) beneficial expressions of gratitude reflecting the number of times a business partner responded to a positively rated review (3-5 stars) and (2) nonbeneficial expressions of gratitude reflecting the number of times a business partner responded to a negatively rated review (1-2 stars). Responses expressing gratitude were coded as in Study 1.(3) Negative Reactions. (1) Beneficial negative reactions take into account the number of times a business partner responded to a positively rated review (3-5 stars), whereas (2) nonbeneficial negative reactions concern the number of times a business partner responded to negatively rated reviews (1-2 stars). Responses expressing negative reactions were coded as in Study 1.


## 3.1.1. Procedure and Measurements

Similar to Study 1, we randomly selected 86 different food business partners that are listed on the Zomato platform and coded their data. In the following subsection, we present the measures collected from the platform (see Table2 for a summary).Table 2
List of measures: Study 2.
MeasureDescriptionOnline reputationOnline rating of each business partner, ranging from 1 (very poor) to 5 (excellent)Expressions of gratitudeBeneficial expressions of gratitude are the number of times a business partner responded to a positively rated review (3-5 stars). Nonbeneficial expressions of gratitude are the number of times a business partner responded to a negatively rated review (1-2 stars). Responses expressing gratitude were coded as in Study 1Negative reactions(1)Beneficial negative reactions take into account the number of times a business partner responded to a positively rated review (3-5 stars), whereas (2) nonbeneficial negative reactions concern the number of times a business partner responded to negatively rated reviews (1-2 stars). Responses expressing negative reactions were coded as in Study 1(1) Online Reputations. Online reputation was measured, as in Study 1, by the average rating of each business partner, ranging from 1 (very poor) to 5 (excellent).(2) Expressions of Gratitude. The measure was subdivided into two levels of interest, namely, (1) beneficial expressions of gratitude reflecting the number of times a business partner responded to a positively rated review (3-5 stars) and (2) nonbeneficial expressions of gratitude reflecting the number of times a business partner responded to a negatively rated review (1-2 stars). Responses expressing gratitude were coded as in Study 1.(3) Negative Reactions. (1) Beneficial negative reactions take into account the number of times a business partner responded to a positively rated review (3-5 stars), whereas (2) nonbeneficial negative reactions concern the number of times a business partner responded to negatively rated reviews (1-2 stars). Responses expressing negative reactions were coded as in Study 1.


## 3.2. Results

We tested whether expressions of gratitude would only be positively associated with the online reputation when business owners responded expressing gratitude towards beneficial reviews but not when they responded expressing gratitude towards nonbeneficial reviews. Again, we also predicted that negative reactions, whether beneficial or nonbeneficial, would not positively contribute to the online business reputation. Therefore, we conducted a multiple regression analysis entering online reputation as the dependent variable, beneficial and nonbeneficial expressions of gratitude, and beneficial and nonbeneficial negative reactions. Results revealed that beneficial expressions of gratitude were the only significant predictor of online business reputation,β=0.29,p=0.037. Neither nonbeneficial expressions of gratitude (β=−0.04,p=0.764), nor beneficial negative reactions (β=0.17,p=0.190), nor nonbeneficial negative reactions (β=0.06,p=0.582) were statistically significant.


## 4. General Discussion

Expressing other-praising behaviors is closely related to the perception that the expresser has a responsive attitude. Specifically unique to the emotion of gratitude is the link between other-praising and relationship promotion [26]. Data from more than 600 naturalistic observations from 145 business owners to their customers’ reviews suggest that praising their reviews with appreciation significantly increased the consumers’ satisfaction, or so to say their online reputation, particularly when gratitude expressions are directed towards beneficial reviews. We believe that expressing gratitude towards nonbeneficial reviews may not be very effective, as it may send the message that the expression is not genuine and therefore not appreciated.These results tie well with previous indicators wherein online reputation is an important factor of online brand implementation [1] and reinforce previous studies that demonstrate the dynamic and bidirectional character of this same reputation [2]. Bearing in mind that this dynamic is essential, namely, in strengthening online brand trust [4], it is no less true that the nature of the interaction predicts in diversified ways its online reputation [7, 8]. Thus, this study provides fundamental indicators for a more robust understanding of the best online positioning when it comes to responding to reviews.The current research extends the existing research by addressing how managerial thank you notes significantly promote business online reputation, expressed in terms of online rating. These results go beyond previous studies by exploring the most efficient way to manage and improve digitally mediated relationships. It is therefore fundamental to further study the positivity of expressing gratitude on online brand perception [13–18], as demonstrated by the present study, where it is identified that this positive relationship between public expression of gratitude and business online score is especially effective when applied to beneficial online reviews.The current study has limitations that must be acknowledged. Our sample comprised naturalistic data that allows direct observation and could provide a first-hand look at a specific behavior. However, due to the specific nature of our sample, our studies have limitations: difficulty to control outside variables or account for potential impacts of the environment/context, etc. Thus, future studies should seek other methods and data. We also explored just one brand type context, and future research could seek to expand the contexts extending our findings to different kinds of businesses. Although this paper makes innovative and informative contributions to this research field, as a limitation, it is essential to explore the effect of other isolated or combined response strategies and verify and evaluate their effect.It is also important to highlight that business insights often advise business owners to address negative reviews by providing the context of the visitor’s experience and sharing the rest of the story (e.g., TripAdvisor Insights [27]). However, our data show that it may not be an effective strategy; adopting a defensive attitude towards reviews does not promote consumers’ satisfaction. When sharing in the public sphere, only indicators of responsiveness from owners seem to promote the business reputation.


## 5. Conclusion

Data from more than 600 naturalistic observations from 145 business owners to their customers’ reviews suggest that praising their reviews with appreciation significantly increased the consumers’ satisfaction or so to say their online reputation, particularly when gratitude expressions are directed towards beneficial reviews. We believe that expressing gratitude towards nonbeneficial reviews may not be very effective, as it may send the message that the expression is not genuine and therefore not appreciated.



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