From reactivity to reputation management: online consumer review systems in the restaurant industry

Figures & data

Table 1. Some contextual information of restaurants in the data.

Of respondent		Of restaurant					As of 31st May 2018 (only when N ≥ 30)
ID	Position	Location	Age	Size (#)		Tourist %	TripAdvisor		Iens		Google		Facebook
				Seats	Staff		Rating	N	Rating	N	Rating	N	Rating	N
P1	Owner	West	16	90	8	40%	4	95	–	–	3.8	124	4.4	66
P2	Owner	West	6	40	6	30–40%	4.5	43	–	–	4.8	126	4.9	109
P3	Owner/Manager	Centrum	11	30	30	30%	4	138	–	35	4.3	200	4.4	151
P4	Owner	Centrum	1.5	75	17	50%	4.5	173	–	–	4.4	138	4.8	120
P5	Owner	Centrum	7	45	22	20%	4.5	292	8.4	78	4.4	185	4.6	96
P6	Owner	Oost	0.5	140	20–5	5%	–		–	–	4.4	92	5	94
P7	Owner	Centrum	4	45	31	40–50%	4.5	126	9	288	4.6	144	4.9	118
P8	Owner	Zuid	1	130	30	30%	4.5	58	–	–	4.1	178	4.6	157
P9	Manager	Oost	2	480	43	10%	4	51	–	–	4.1	200	4.6	67
P10	Manager	Centrum	1	50	6	50%	4.5	158	–	–	4.2	126	–	–
P11	Manager	Centrum	12	30–40	15	60%	4	435	8.6	169	4.2	151	4.3	162
P12	Owner	Centrum	1	45	10	50%	5	103	9	90	4.5	69	4.9	85
P13	Manager	Zuid	5	250–260	40–50	20–30%	4	58	8.2	51	4.1	222	4.6	98
P14	Owner	Zuid	2	50	9	60%	4.5	376	–	–	4.7	203	4.8	71
P15	Management trainee	West	1	50–60	20	30–40%	4.5	95	8.7	84	4.2	112	4.7	156
P16	Owner	West	3	100	11	10–15%	4.5	38	8.7	64	4.5	102	4.8	70
P17	Owner	Centrum	12	80	5	30–40%	4.5	190	9.3	353	4.6	60	4.8	86

^a N refers to the total number of reviews (includes ratings without comments).

^b The range of ratings on Google and TripAdvisor is 1–5, on Iens it is 1–10; the high ratings of restaurants are largely inherent to OCR systems in general (see Mellet 2014, pp. 27–28).

^c The number of seats, staff and the percentage of tourists are self-reported numbers of respondents.

Restaurant P3 is owned by a student foundation, with many students working few hours, and therefore has a large number of staff in proportion to the size of the restaurant.

Table 2. Illustrative examples of codes and themes in the data.

Illustrative empirical examples	Example codes	Themes
‘Yeah, yeah. All the time. I get push messages and I respond to them as soon as I can.’ (Quote from P6)	Alert-based checking of OCRs	Checking OCRs sporadically
‘But sometimes I think it's unreasonable when people come here and something happened and they're not happy. And then they do not speak here, but they post something online. So, sometimes it's used like a punishment for the business.’ (P4)	Customers use OCRs as a punishment tool	Disputed legitimacy of OCRs
‘Obviously, we mostly communicate through WhatsApp (…) with all the chefs and with all the staff. When a good review pops up, then I often make a picture and send it and thumbs-up to motivate our staff basically.’ (P8)	Patterns of staff management	Managing staff loosely
‘Uh … no, no. I don't think that's fair. Um … because it's kind of like tips, you know. If we get really good tips, that's great. If we get really shitty tips, that sucks but that doesn't mean we were doing a bad job. It's really subjective. Ah! That's the word, subjective.’ (P11)	Reasons for no stricter staff management
‘We are now, I think, listed in TripAdvisor in the 194 (…). Our goal is to be in the top 100. You can also use it as a motivation tool: ‘Let's get into the top 100 with real reviews, with real responses, with real good experiences and then we do the job.’’ (P1)	Patterns of goal setting	Setting goals on OCR systems
‘The secret is hard work by all of us. (…) So, what we deliver here is what we aim for and the reviews come along.’ (P17)	Reasons for no direct efforts to reach the goal
‘Hum … yeah, I think, me and my co-partner, if we read the review and we think: 'Well, yeah. I think they're true.' Then, we're gonna take a look at it.’ (P12)	Patterns of using OCRs for improvements selectively	Adopting feedback selectively
‘Online reviews are, most of them, are not very useful. (…) But most of the times, it's just very general stuff like “I didn't like it there” or “the atmosphere wasn't good”.’(P3) ‘Like, I saw one today from someone who said: "oh, it's really expensive. 2-stars." Yeah, and then two pages later, there's someone who says: "oh, it's really reasonably priced." So, it's just … yeah, I don't think it's really important, in my opinion. I won't use it that much.’ (P15)	Reasons for no comprehensive feedback adoption
‘When people pay they get a little card here with a reference like: “Hey, maybe you can share what you think on TripAdvisor.” I always ask them to write something with it.’ (P4)	Mobilizing consumers for OCRs	Managing online reputation indirectly
‘I think a comment must be something that the person wants to put on the Internet and it's not that I ask for it or … For example, restaurants when you're paying they're giving a card so “Please put nice things on TripAdvisor” we don't do that sort of things, no, no. They have to do it themselves.’ (P5)	Reluctance in soliciting OCRs
‘Often I do not respond to good reviews. You're used to getting good reviews. I would say 80–90% of the reviews are good. (…) But obviously, when there's a valid problem or comment or whatever, you have to kind of respond to that. We try to do that as soon as possible.’ (P8)	Patterns of responding to OCRs	Managing online reputation directly
‘If he would be sitting here, you could talk to him like: “I’m sorry about it. Here's a cup of coffee.” (…) But you can't because he's gone. So that's the big difference. Then you have to make sure that what you write is for other people that are going to read that, to influence the other people that they will come to you and not think “oh, the food is bad there”.’ (P1)	Responding to OCRs ‘on stage’

Table 3. Overview of response strategies with different motives towards reputation management.

Case No.	Motives	Example reviews	Example responses
Case 1	A. Signaling professionalism to the crowd	[OCR on Google Maps] ★☆☆☆☆	[Response] Dear [reviewer], thank you very much for rating us. In order to understand your experience and rating in more detail would you mind to contact me via [email address] or via [phone number]? It would mean a lot to us to make it right. (OCR and response retrieved from Google Maps on 28 June 2018)
Case 2	B. Negotiating online reputation - B.1. Explaining the situation - B.2. Explaining measures taken - B.3. Contesting devaluations	[OCR on TripAdvisor (trimmed)] ★☆☆☆☆ Stay away! I found metal shape in my salad! Hey I have been at [restaurant] and ordered a salad. Suddenly, I felt something sharp in my mouth!!!! It was a sharp piece of metal!! (…) I definitely don’t think you should it there! A big piece of sharp metal could have cut my throat!!! Do not recommend!!!	[Response (trimmed)] First of all I would like to thank you for visiting our restaurant. (…) What you found in your salad was most likely a part of a metal sponge which we use to clean our pans- however we are now going to change to different types of sponges which will never leave pieces behind. On behalf of [restaurant] and our team, I sincerely apologize for your experience, and hope to welcome you another time. (OCR and response retrieved from TripAdvisor on 8 June 2018)
Case 3	C. Promoting oneself - C.1. Advertising - C.2. Imprinting a positive image	[OCR on Google Maps] ★☆☆☆☆ Cod was very tasty! However chips were not great, mushy peas were cold and came in a very small 5cm diameter 1 cm deep dish. I've had many better fish 'n chips. Not really recommended.	[Response (trimmed)] (…) We are very sorry the Mushy Peas came out cold, that should not happen. Thank you for telling us straight away, so we were given the chance to make it up straight away and get you a portion of hot Mushy Peas. (…) Thanks for leaving a review, always appreciated. Glad you like a fresh piece of Cod! (OCR and response retrieved from Google Maps on 8 June 2018)

Mellet, K, et al., 2014. A “democratization” of markets? Online consumer reviews in the restaurant industry. Valuation Studies, 2 (1), 5–41.

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