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Factors influencing the effectiveness of online group buying in the restaurant industry
International Journal of Hospitality Management 35 (2013) 237–245
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International Journal of Hospitality Management journal homepage: www.elsevier.com/locate/ijhosman
Factors influencing the effectiveness of online group buying in the restaurant industry夽 Ziqiong Zhang a,∗ , Zili Zhang a,1 , Fan Wang b,2 , Rob Law c,3 , Dechao Li d,4 a
School of Management, Harbin Institute of Technology, China School of Business, Sun Yat-sen University, China School of Hotel and Tourism Management, Hong Kong Polytechnic University, Hong Kong d Department of Chinese and Bilingual Studies, Hong Kong Polytechnic University, Hong Kong b c
a r t i c l e Keywords: Group buying Discount Service quality Satisfaction Return intention Restaurant
i n f o
a b s t r a c t Group buying, which provides daily discounts for various services and products, is a new form of marketing that has attracted the attention of both practitioners and academic researchers. However, it is unclear what factors influence the effectiveness of a group buying promotion. This study presents an analysis of 862 restaurant group buying deals based on the secondary data collected from a restaurant guide website. The results indicate that group buying effectiveness – namely, the number of coupons sold, satisfaction improvement, and return intention of group buying customers – can be affected by the discount depth of a deal as well as the service quality and popularity of a restaurant. These findings provide useful insights regarding the elements that are necessary to make a group buying promotion work. © 2013 Elsevier Ltd. All rights reserved.
1. Introduction The deep discount group buying or deal-a-day model for e-commerce, which has exploded in popularity over the last few years, is a combination of “online platform”, “bulk purchase”, and “team buying” (Lo et al., 2012). As one example of growth, the revenue earned by the daily deal site Groupon increased 32% yearover-year to $568.6 million in the third quarter of 2012, compared with $430.2 million in the third quarter of 2011 (Groupon, 2012). Groupon offers one or more daily deals for each market in which it operates, with significant discounts such as $20 for $40 worth of catering. When purchasing a deal, consumers pay for the product up front, and then have a stipulated amount of time, perhaps up to three months, to redeem it. Some analysts have argued that when the deep discounts offered to consumers and the payouts given to the site operators are taken into consideration, group
夽 This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial-No Derivative Works License, which permits non-commercial use, distribution, and reproduction in any medium, provided the original author and source are credited. ∗ Corresponding author at: 92 West Dazhi Street, Harbin 150001, China. Tel.: +86 451 86414022; fax: +86 451 86414024. E-mail addresses: [email protected] (Z. Zhang), [email protected] (Z. Zhang), [email protected] (F. Wang), [email protected] (R. Law), [email protected] (D. Li). 1 Tel.: +86 451 86414019. 2 Tel.: +86 20 84114186. 3 Tel.: +852 34002181. 4 Tel.: +852 27667458. 0278-4319/$ – see front matter © 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.ijhm.2013.06.012
buying promotions should not be profitable (Dholakia, 2010). It is, however, the potential exposure that encourages small businesses to join in this recent Internet group buying fashion. Group buying is also expanding at a high speed in Mainland China. More than 1,000 Chinese copycats of Groupon came into existence in the twinkle of an eye in 2010. According to a recent report (Tuan800, 2012), the eighth month of 2012 brought 270,000 daily deal offers to Chinese consumers, and the turnover of that month reached 2.11 billion RMB (around 335 million USD). Daily deals on food catering are particularly popular in China, and there were about 108,000 daily deal offers for restaurants in the same month, with a turnover of RMB 0.93 billion (around USD 148 million), accounting for 44.1% of the total turnover. The number of restaurant coupons sold for an offer always far exceeds the minimum group size requirement before the deadline. Obviously, group buying can offer benefits for sellers. It brings in a lot of customers who might otherwise be unaware of a business’ existence. Nevertheless, Dholakia (2011) found that some business owners regret their group buying promotions. One of the reasons could be that in general a business cannot directly estimate how many of its group buying coupons will be sold. If a business is not well prepared for extra customers, customers’ satisfaction and intent to repurchase as well as its reputation would be negatively affected. As mentioned previously, the restaurant daily deal is a big component of group buying promotions. However, few studies have looked at the factors that can influence the effectiveness of restaurant group buying. To fill this research gap, the current study considers the effectiveness of a restaurant group buying deal to
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be represented by number of buyers, satisfaction improvement of a restaurant, and return intention of group buying customers. The study examines how the deal discount, and a restaurant’s service quality and popularity influence the deal’s effectiveness. The results of this study are expected to help restaurateurs in making decisions on if, when, and how to offer a group buying deal. 2. Background and hypotheses 2.1. Price discount Price promotions have emerged as an important marketing factor in sales promotion strategy and have drawn an increasing amount of attention from both practitioners and researchers (Nusair et al., 2010). This growing interest is a result of their ability to boost sales performance in the short run (Gijsbrechts et al., 2003; Ruiz and Descals, 2008) and to hold or increase market share (Diaz and Bailey, 2011). Toward these goals, group buying is a unique selling strategy which provides consumers a discounted price for sales promotion purposes. Price discounts are by far the most common form of sales promotion employed by firms (Palazon and Delgado-Ballester, 2009); they can stimulate store patronage and consumer purchases by increasing the value of an offer via a price reduction (Marshall and Leng, 2002). When comparing discount promotion purchases to nonpromotion purchases, researchers have verified the existence of purchase acceleration (Neslin et al., 1985) and larger sales volumes in the former (Banerjee, 2009; Gijsbrechts et al., 2003; Ruiz and Descals, 2008). That is, in response to a price discount, consumers may buy a larger quantity of the product category, or buy at an earlier time. Mulhern and Leone (1990) reported that sellers offering deeper discounts provide a stronger incentive to visit the store and/or to adjust in-store buying behavior. According to Raghubir (1998), the higher the promotional discount, the higher the saving, and the higher the probability of purchasing the product/service. Also, in an attempt to cope with information overload on group buying websites, consumers may use the discount depth as a screening device to select information worthy of their attention. We therefore hypothesize a positive influence of discount depth on the coupons sold for a group buying deal. Hypothesis 1. The discount rate has a positive effect on number of buyers of a group buying deal. Furthermore, price discounts can enhance customers’ perceptions of savings and value (Alford and Biswas, 2002; Fraccastero et al., 1993). Perceived value is generally regarded as an antecedent of satisfaction (Jamilena et al., 2012; Setijono and Dahlgaard, 2007). Thus, by reducing a customer’s economic sacrifice, price discounts can increase perceived value and, as a result, satisfaction (Campo and Yagüe, 2007). In other words, it might be expected that higher price discounts result in higher perceptions of value and customer satisfaction. As group buying deals offer good discounts, we conjecture that group buying would improve customer satisfaction with a restaurant and that the improved satisfaction tends to be greater for a group buying deal with a larger discount. Hypothesis 2. The discount rate has a positive effect on the satisfaction improvement of a group buying restaurant. 2.2. Service quality Rust and Oliver (1994) propose that overall perception of service quality is based on a customer’s evaluation of three dimensions of the service interface: product, delivery, and environment. Mohammad et al. (2005) suggest that this model is especially suitable for use in the restaurant industry, given that consumers
are experiencing food, service, and physical environment simultaneously (Wall and Berry, 2007). Previous research has identified these attributes as the top three factors contributing to the dining experience and customers’ perception of a restaurant’s quality. For example, Ryu and Han (2010) indicate that food quality is the most important attribute to customers in the quick-casual dining sector, followed by the quality of physical environment and service. Pantelidis (2010) reveals that food quality, service, and atmosphere are the most frequently cited elements based on a content analysis of customer comments on an online restaurant guide. According to Mattila (2001), the top three reasons for customers to patronize their target restaurants in the casual dining sector are food quality, service, and atmosphere. Building upon these findings, we propose the following hypothesis. Hypothesis 3. The three service quality dimensions (food, physical environment, and employee service) have a positive effect on number of buyers of a group buying deal. Researchers generally support the idea that service quality has a positive effect on consumer satisfaction (Babin et al., 2005; Caruana et al., 2000). Nevertheless, research also suggests that there is a law of diminishing satisfaction (Dermanov and Eklöf, 2001; Yang, 2003). More specifically, customers’ satisfaction with an attribute depends on the level of the attribute’s quality or fulfillment. A higher level of fulfillment is associated with higher customer satisfaction. However, similar to the utility function, the satisfaction function of an attribute’s quality follows the law of diminishing marginal satisfaction. When an attribute’s initial level of quality is high, an increase of quality will bring a smaller improvement of satisfaction compared to the improvement when an attribute’s initial quality is low. Following from these findings, we can propose the following hypothesis: Hypothesis 4. The three service quality dimensions (food, physical environment, and employee service) have a negative effect on the satisfaction improvement of a group buying restaurant. 2.3. Popularity Restaurant popularity determines how many customers regularly visit a restaurant, and can be mirrored by the crowdedness of a physical restaurant space or the online activeness of its customers. Based on the conformity literature, consumers are likely to follow the choice of others as a result of the pressure to conform to a peer group (Clark and Goldsmith, 2005; Mandrik et al., 2005; Park and Feinberg, 2010). Consumers’ behavior of product choice and usage is also influenced by concerns over what others might think of them or how others might act toward them (Miniard and Cohen, 1983). A good popularity can thus lead consumers to rationalize their purchase decisions by telling themselves that many other people also bought the same product. Hence, the more customers a restaurant has, the more likely it is that more consumers will find its group buying deal sufficiently attractive to warrant a visit. In addition, sales promotions such as coupons can prompt loyal and occasional buyers to purchase earlier (Blair, 1982). Since a popular restaurant has more routine customers than less popular restaurants, supposing the same proportion of customers originally have planned to dine and want to take advantage of a discount, the former will sell more group buying coupons. Therefore: Hypothesis 5. Restaurant popularity has a positive effect on number of buyers of a group buying deal. As a popular restaurant has many regular consumers, its accommodation capacity (e.g., the quantity of rooms, facilities, service staff, and food reserves) is usually higher than restaurants with fewer regular diners, which leads to greater flexibility when extra
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Discount rate Food
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H1(+) H2(+)
Buyer number
H3(+) Environment
H7(+)
Service quality H4(–)
Employee service
H5(+) Popularity
H8(+)
Return intention
Satisfaction improvement
H6(+)
Fig. 1. Proposed model for determinants of group buying effects.
customers come hot on the heels of others. In general, a group buying coupon should be redeemed in a fixed period, i.e., the time from drop to expiration. This will bring a larger flow of customers in the time limitation than usual. Inman and McAlister (1994) have provided evidence that expiration dates serve to influence redemption behavior, in that the redemption rate increases markedly just prior to expiration. It could be postulated that less popular restaurants have lower capacities and less experience of handling a rapid increase in customers. The likelihood of crowding, a long waiting time, or inferior service levels would result in less improvement of customer satisfaction in those restaurants. Thus, we hypothesize a positive influence of popularity on the improvement of customer satisfaction with a group buying restaurant. Hypothesis 6. Restaurant popularity has a positive effect on the satisfaction improvement of a group buying restaurant. 2.4. Return intention The marketing literature has shown that there is a positive relationship between service quality and repurchase intentions (Baker and Crompton, 2000; Boulding et al., 1993; Cronin and Taylor, 1992; Zeithaml et al., 1996). In the restaurant context, Kim et al. (2009) found that the three restaurant quality dimensions – i.e., food, physical environment, and employee service – significantly influence customers’ intention to return to a food service establishment. Barber et al. (2011) reported a strong relationship between the cleanliness attributes of a restaurant’s interior, restrooms, and employees with customers’ repeat patronage. Moreover, through the use of dining experience scenarios, Dubé et al. (1994) showed that the higher the level of several attributes (i.e., food quality, menu variety, atmosphere, food-quality consistency, and waiting time), the higher the likelihood of repeat purchasing. However, there has been relatively little discussion of the relationship between the service quality of a restaurant and customer intention to return after consumption of a group buying offer. We expect that customers tend to revisit a high-quality restaurant than a lowquality restaurant. Therefore: Hypothesis 7. The three service quality dimensions (food, physical environment, and employee service) have a positive effect on the return intention of group buying customers. Finally, the relationship between customer satisfaction and behavioral intensions has received considerable attention in the marketing literature. Numerous empirical studies have confirmed that high levels of satisfaction result in increased loyalty and future visitation (Alegre and Cladera, 2009; Oh, 2000; Qin and Prybutok, 2009; Suh and Yi, 2006; Um et al., 2006; Yoon and Uysal, 2005). For
example, in their investigation of fast-food restaurants, Qin and Prybutok (2009) found that customer satisfaction directly and positively influences return intentions. Oh (2000) also reported that customer perceived satisfaction is an influential determinant of post-purchase intention to patronize a restaurant. Likewise, Baker and Crompton (2000) showed that improvement in satisfaction will result in retention and expansion of tourist numbers. For a group buying restaurant, there are two kinds of satisfaction: prepurchase satisfaction (or usual satisfaction) and post-purchase (or group buying satisfaction). The present study is interested in the impact of satisfaction improvement on group buying customers. When comparing group buying satisfaction to usual satisfaction, restaurants with a larger positive improvement can give customers a better impression, which may encourage their intention to visit again. Therefore, we suggest: Hypothesis 8. The improvement of satisfaction has a positive effect on the return intention of group buying customers. Fig. 1 displays the conceptual model of the relationships among price discount, restaurant service quality and popularity, and effectiveness of restaurant group buying. 3. Research setting and methodology We conducted our empirical analysis using group buying data from a leading Chinese restaurant guide website, Dianping.com, which has a group buying column offering various localized deals for major geographic markets or cities. In each geographic market, there are one or more restaurant deals of the day. A deal may be available for one or more days. Generally, each deal has a minimum threshold that must be reached for the deal to be valid, and restaurants may also set a maximum threshold to limit the number of coupons sold. Dianping.com is the largest restaurant review website in China. It offers a pre-set format for users to post independent evaluations of their experiences. As a third-party platform, Dianping.com attempts to ensure genuine reviews by requiring users to register through a valid email address and to post reviews after logging in. It now offers over 20 million independent consumer evaluations of around 1,200,000 restaurants scattered across 2300 Chinese cities. We gathered data pertaining to restaurant group buying deals published during the period June 21, 2010 to May 5, 2012 for Shanghai (http://t.dianping.com/shanghai/deallist) as Shanghai has the largest turnover of group buying among Chinese cities (Tuan800, 2012). As shown in Fig. 2, from the page of each group buying deal we collected the discount rate and the total number of buyers (or the number of coupons sold) after a group buying deal ended. This page also gives a link to the restaurant (which is on
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Fig. 2. Details of a restaurant’s daily deal on Dianping.com (http://t.dianping.com/deal/15396).
Dianping.com) that issues the deal. To obtain restaurant information, we clicked on this link and downloaded all customer reviews for that restaurant. From each customer review, we extracted the overall rating (1–5) of the restaurant and the attribute ratings (0–4) of food taste, physical environment, and employee service. The overall rating is considered the customer satisfaction with a restaurant, and the rating of an attribute is regarded as attribute performance or customer perceived quality for that particular attribute (Zhang et al., 2011). In addition, Dianping.com allows group buying customers to post feedback subsequent to consumption of a group buying meal, and shows this information on the group buying deal’s page (see Fig. 3). We recorded the number of group buying customer reviews, their satisfaction with the restaurant (on a scale of 1–5), and the percentage of group buying customers who announced their intention to revisit the restaurant in the future. Not all data were used in this study. As one of our aims is to examine what influences the number of group buying coupons sold, we dropped a deal which had a maximum limitation for the number of coupons sold and had been sold out. Furthermore, to make a comparison between group buying customers and regular customers, and to ensure there are sufficient data, we discarded a deal if it had less than 10 reviews from group buying customers or if the relevant restaurant had less than 10 reviews from regular customers. As a result, our analysis was restricted to 862 restaurant group buying deals in Shanghai, China. For each deal, we collected data on (i) discount rate, (ii) restaurant service quality, (iii) restaurant popularity, (iv) number of buyers, (v) satisfaction improvement, and (vi) return intention of group buying customers (see Table 1).
4. Analysis and results 4.1. Descriptive statistics Table 2 shows the descriptive statistics of the analyzed data. It can be viewed that group buying sometimes offers very large discounts, for up to 95% in our sample. A group buying deal was bought as many as 29,449 times, suggesting that restaurant group buying is widely accepted by China’s customers. The means of Satisfaction improvement and Return intention (0.575 and 0.763) indicate that online group buying, if adopted appropriately, can have a positive influence on customers’ satisfaction and willingness to return. In addition, following previous studies (such as Ghose and Ipeirotis, 2007; Zhang et al., 2010), we used the base-10 logarithm values of Popularity and Buyer number in the model to smooth out the distribution of the two variables.
4.2. Reliability and correlation analysis According to previous research such as Wang et al. (2011), data were analyzed using a two-step approach in which the measurement of the latent construct is confirmed first and then the structural model is tested. Service quality is the only latent construct in this study, which was measured by the qualities of food, physical environment and employees’ service. There was sufficient evidence of reliability of Service quality based on the estimate of internal consistency (Cronbach’s alpha coefficient = 0.881). The item reliabilities, squared multiple correlations of three individual items, ranged from 0.452 to 0.908, indicating an acceptable level of reliability.
Z. Zhang et al. / International Journal of Hospitality Management 35 (2013) 237–245 Table 1 Summary of variables and variable measures.
signs is opposite to the expectation, and it is necessary to further examine the structural model.
Variable
Variable measure
Discount rate
Discount rate is a discounted rate that a restaurant offers on a food package in a group buying promotion. Service quality of a restaurant is operationalized with three indicators: food taste, physical environment, and employee service. Dianping.com allows diners to input ratings on food taste, physical environment, and employee service in a review. We collected customer reviews posted on a restaurant’s webpages before the restaurant issued a group buying deal and calculated the average value of all customer ratings on each attribute. The average rating is regarded as the performance or quality of an attribute in a restaurant (Zhang et al., 2011). Popularity is defined in this study as the welcome level of a restaurant. The more popular a restaurant is, the more patrons it has. Furthermore, it could be assumed that as more customers eat in a restaurant, the number of online customer reviews increases. Therefore, following the method used in recent studies (Kovac¸s and Johnson, 2012), we counted the number of reviews posted before a restaurant issued a group buying deal and used it as a proxy measure for the restaurant’s popularity. Buyer number is the number of times that a group buying deal was purchased. Satisfaction improvement is defined as group buying customer satisfaction minus original satisfaction with a restaurant. For each deal, the former is measured by averaging the satisfaction ratings of group buying customers. The latter is measured by averaging the overall ratings posted on the restaurant’s webpages prior to the issue of a group buying deal. Return intention is the percentage of customers who would like to visit the restaurant again out of all group buying customers of a deal.
Service quality
Popularity
Buyer number Satisfaction improvement
Return intention
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We adopted Pearson’s bivariate correlation analysis to evaluate a possible correlation between the studied variables (see Table 3). The Pearson correlation coefficients showed that (i) Buyer number is positively correlated with Popularity, (ii) all study variables except Buyer number are significantly correlated with Satisfaction improvement, and (iii) all variables are significantly correlated with Return intention. However, the direction of some coefficients’
4.3. Structural modeling To test the research hypotheses, structural equation modeling (SEM) was performed using AMOS software (version 17.0). Unbiased and maximum likelihood estimation covariances (as input and to be analyzed) analysis properties were selected in AMOS. Overall, the model’s fit was acceptable. The observed chi-square 2 /df = 5.7 and the RMSEA (root mean square error of approximation) = 0.074. A general rule of thumb is that a RMSEA ≤ 0.05 indicates a close approximate fit, and values between 0.05 and 0.08 suggest a reasonable error of approximation (Browne and Cudeck, 1992). Other additional goodness-of-fit indices are the NFI (normed fit index) = 0.982, the NNFI (non-normed fit index) = 0.963, the GFI (goodness-of-fit index) = 0.983, the AGFI (adjusted goodness-of-fit index) = 0.946, and the CFI (comparative fit index) = 0.985. While the overall model tests support the proposed conceptualization with the sample, the hypotheses refer to the significance tests of the specified paths. The hypothesized path diagram with standardized path coefficients and squared multiple correlations are illustrated in Fig. 4. Fig. 4 shows that Discount rate has a significant positive influence on Buyer number (ˇ = 0.101, P = 0.003), which supports H1. However, there is a negative relationship between Discount rate and Satisfaction improvement (ˇ = −0.061, P = 0.049). This indicates that the larger a deal discount, the less improvement there is in satisfaction; thus, H2 is not supported. The results also show that the latent variable Service quality is well represented by the observed variables Food (ˇ = 0.953, P < 0.001), Environment (ˇ = 0.672, P < 0.001), and Employee service (ˇ = 0.796, P < 0.001). In addition, Service quality has a significant positive influence on Buyer number (ˇ = 0.082, P = 0.013), which supports H3, and has a significant negative influence on Satisfaction improvement (ˇ = −0.619, P < 0.001), thereby supporting H4. Popularity has a positive impact on Buyer number (ˇ = 0.362, P < 0.001) and Satisfaction improvement (ˇ = 0.142, P < 0.001), supporting H5 and H6. Also, Hypotheses 7 and 8 are supported, the results showing strong relations between Service quality and Return intention (ˇ = 0.919, P < 0.001), and between Satisfaction improvement and Return intention (ˇ = 0.991, P < 0.001).
Table 2 Descriptive statistics.
Discount rate Food Environment Employee service Popularity Buyer number Satisfaction improvement Return intention
N
Minimum
862 862 862 862 862 862 862 862
0.100 0.571 0.643 0.429 10.000 21.000 −1.030 0.290
Maximum
Mean
Std. deviation
0.950 3.077 3.500 3.420 17,488.000 29,449.000 2.110 1.000
0.584 2.014 1.984 1.953 523.643 1734.716 0.575 0.763
0.138 0.328 0.456 0.431 1151.267 2510.800 0.359 0.125
Table 3 Correlations.
Discount rate Service quality Popularity Buyer number Satisfaction improvement Return intention **
Discount rate
Service quality
Popularity
Buyer number
Satisfaction improvement
Return intention
1 0.035 −0.373** −0.029 −0.152** −0.314**
1 −0.147** −0.014 −0.585** 0.233**
1 0.321** 0.203** 0.302**
1 0.026 0.158**
1 0.469**
1
Correlation is significant at the 0.01 level. N = 862.
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Fig. 3. Customer feedback on a group buying deal on Dianping.com (http://t.dianping.com/deal/15396).
5. Discussion Overall, most of the empirical results confirmed the established hypotheses, but some were also unexpected. This section presents a summary of the main findings and some comments in this regard. As expected, generous discounts can bring in group buying customers. This result is in accord with the findings of previous studies that stores offering larger discounts can generate more customer traffic and higher sales (Banerjee, 2009; Gijsbrechts et al., 2003; Ruiz and Descals, 2008), and indicates that customers are likely to link group buying with getting a good deal for their money. Another novel – and to some extent unexpected – result of this study is the negative effect of discounts on satisfaction improvement. We have found that larger discounts tend to attract more buyers. However, the resources (such as waiters, cooks, and facilities) of a given restaurant are limited and it may be hard for
Discount rate
0.101**
Food
-0.061*
0.953*** R2=0.908 Environment
R2=0.633
Buyer number
0.082* 0.672***
R2=0.452 Employee service
some restaurants to accommodate extra group buying customers while maintaining regular service standards, thereby resulting in a negative relation between discount rate and satisfaction improvement. Additionally, offering discount may have negative effects on the consumers’ perception of quality and internal reference prices (Grewal et al., 1998; Raghubir and Corfman, 1999). When a price discount is too large to be considered bona fide, consumers may be suspicious of the sale prices, in that they may view the lower selling prices, rather than the higher initial price, as the true value of the item. Previous studies have found that consumer perception of the magnitude of a price reduction tends to be less than the actual reduction advertised by retailers (Chen et al., 1998). Moreover, consumers discount the price discounts offered, and this discounting effect increases significantly with the increase in advertised discounts (Marshall and Leng, 2002). For these reasons, the discount rate of a group buying deal is proven to be negatively related to satisfaction improvement with a restaurant.
R2=0.121
Service quality
0.919***
Return intention
-0.619*** 0.796***
0.362*** Popularity
0.142***
Satisfaction improvement
0.991***
R2=0.764
R2=0.414
Fig. 4. The hypothesized path diagram with standardized path coefficients and R2 . Note: ***P < 0.001, **P < 0.01, and *P < 0.05
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Customer-perceived service quality is another important determinant of customers’ participation in restaurant group buying. The results reveal that daily deals for better quality restaurants tend to attract more customers. In addition, the satisfaction improvement through group buying promotion is lower in high-quality restaurants than that in low-quality restaurants. The results indicate that, to enhance satisfaction, group buying may be more effective for low-quality restaurants than for high-quality restaurants, which provides further evidence for the law of diminishing marginal satisfaction in the restaurant industry. As such, large discount group buying does not seem to be a good way for high-quality restaurants to try to improve satisfaction. Popular restaurants’ daily deals are appealing to customers, as we expected. This study measured restaurant popularity using the number of online customer reviews received. When more reviews are offered, consumers perceive the set of review information to be more informative. Previous studies have suggested that the more informative an information set is, the more favorable the associations that consumers will have with the product being reviewed, which results in increased behavioral intention (Park et al., 2007; Petty and Cacioppo, 1984). This finding offers observation of herd behavior, where everyone is doing what everyone else is doing (Banerjee, 1992). Our results also show that the group buying model is more applicable to popular restaurants in terms of satisfaction improvement. Popular restaurants normally have heavy customer traffic. We conjecture that they would be more experienced and more prepared to deal with the increased traffic on redemption dates and thus make customers feel more satisfied when compared with less popular restaurants. Previous research results concerning the determinants of customer intention to return to a restaurant suggest that the effective ways of ensuring customers’ loyalty are to improve service quality and customer satisfaction (Baker and Crompton, 2000). The present study looked at the influential factors of group buying customers’ return intention and confirms the positive role of service quality and satisfaction improvement in retaining group buying customers’ loyalty.
6. Implications 6.1. Theoretical implications This paper contributes to the literature in several ways. Firstly, group buying can be viewed as a unique type of sales promotion, which is offered to a group of consumers who may not be related in any way before forming the buying group. This study is one of the very few attempts to investigate the roles of discount depth and restaurant characteristics in restaurant group buying, which contributes to the literature on group buying and sales promotion in general, and particularly in the hospitality industry. Additionally, despite the emergent Web 2.0 phenomenon and the unprecedented growth of social media in recent years, very little attention has been given to the marketing applications of these phenomena in hospitality (Line and Runyan, 2012). In traditional promotional events, customers cannot easily get quality information of products prior to consumption, and their decisionmaking may largely depend on the depth of discount. Moreover, they could only make their feedback known by a few people around. This study, however, examines the impact of social media on customers’ purchase behavior. More specifically, customers can scrutinize service quality and popularity of a restaurant through electronic word-of-mouth to decide whether participating in a specific online group buying deal. Our findings extend the existing literature regarding the role of discount in online settings and provide a new view point for the linkage among discount,
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quality perception, and customer behaviors in the foodservice sector. In addition, most existing group buying research has employed questionnaire survey or mathematical modeling methods (e.g., Dholakia, 2010, 2011; Jing and Xie, 2011). In contrast to the traditional methods, this study relies on online diners’ voluntarily provided reviews to measure customer perceptions of attributes, customer satisfaction, and return intention. The use of these data, collected directly from a restaurant guide site, provides an opportunity to triangulate our findings with those obtained using other data-collection methods like surveys. 6.2. Practical implications Having analyzed the results, we now offer some specific meaningful conclusions and suggestions for restaurant practitioners. First, as discount depth can affect the number of coupons sold, launching deep discount group buying may be a good way for restaurants, particularly new or struggling ones, to solicit business and advertise their brands. Nevertheless, when setting a discount rate, restaurateurs should consider their accommodation capacity. If a restaurant handles too many appointment requests on a day and makes most of its consumers feel under-served and unsatisfied, the restaurant is likely to get criticized in many review websites, as group buying consumers are usually frequent users of social media. The restaurant can try to control the traffic of group buying customers by imposing restrictions on a group buying deal, e.g., the discount rate, sale days, or duration of redemption. It should simultaneously prepare extra foodstuffs and employees for peak times. Second, a group buying promotion is likely to draw many buyers for a high-quality restaurant, and these customers may exhibit a high tendency to visit again. However, customer satisfaction improvement is lower in a high-quality restaurant than in a lowquality restaurant. Thus, high-quality restaurants are not advised to offer excessively big discounts that may result in insufficient revenues, uncovered costs, as well as an insignificant contribution to customer satisfaction. In addition, popular restaurants can achieve greater group buying effectiveness in terms of the number of buyers and satisfaction improvement, and thus may want to boost sales by issuing a group buying deal. In contrast, the daily deals of less popular or small restaurants may not be attractive to customers, but they can still adopt group buying to advertise brand and attract new customers to try them by designing appealing features and setting good discounts. If a reasonable proportion of the people who come in response to such a promotion are converted into regular customers, long-term sales and profitability can increase. Furthermore, information about a group buying deal will remain on the website after the end of a promotion, which produces a longer-term effect of advertising. Nevertheless, small restaurants have to pay attention to their accommodation capacity to avoid the increased numbers of visitors having an effect on service quality and customer satisfaction. Lastly, restaurant managers must understand the importance of service quality and group buying satisfaction, which lead directly to favorable outcomes through increased revisiting of a restaurant. Hence, they should not much impair the quality of the discounted food. For instance, a local restaurant plans to provide a 50% off deal for advertising purposes. This deal may be sold in the thousands, which means that the restaurant needs to serve a huge number of customers in certain months with little profit. Yet, the restaurant must attempt to ensure the quality of both meal and service. Otherwise, group buying customers may have no intention of coming again. Restaurants with limited accommodation capacity are advised to set a maximum of buyers for a group buying deal to
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achieve exposure aim and to increase customer repatronage intentions. 7. Conclusions and limitations The group buying model in web-based selling is still a new concept to many restaurants and customers. Even though it represents an interesting business model, this alone is not sufficient to justify its viability as group buying firms face the challenges of designing a deal and achieving the desired goals. This study developed a broad model or concept and, based on data from a daily deal website, illustrated the interrelationships between group buying discount, restaurant quality and popularity, number of coupons sold, satisfaction improvement, and customers’ intention to return. Based on the findings, suggestions on modifying group buying offers are provided to better balance customer attraction with positive outcomes for restaurants offering them. Despite its contributions, this study is not free from limitations. One limitation is its research setting, which was restricted to one Chinese-language website. Thus, the empirical findings should be interpreted as primarily applicable to restaurants in China. Future research could extend the current study to group buying promotions around the world to improve the external validity and to examine the differences across heterogeneous social and cultural segments. Another limitation of the present study is that the sample was limited to customers who posted online reviews. This may not provide a complete picture of customer opinions. Yet, with many promotions aimed at encouraging people to post comments, review websites represent a broad swath of customer opinions. Nevertheless, future studies could survey a more universal population of customers to determine whether our findings remain consistent. Finally, this study examines several key factors of group buying effectiveness. Future studies could extend the models to include additional variables that are related to group buying. Acknowledgements The authors would like to thank the anonymous reviewers for providing constructive comments on an earlier version of this paper. The work presented in this paper was funded by the National Natural Science Foundation of China (71101039 and 71203042), and a grant funded by The Hong Kong Polytechnic University. References Alegre, J., Cladera, M., 2009. Analysing the effect of satisfaction and previous visits on tourist intentions to return. European Journal of Marketing 43 (5), 670–685. Alford, B.L., Biswas, A., 2002. The effects of discount level, price consciousness and sale proneness on consumers’ price perception and behavioral intention. Journal of Business Research 55 (9), 775–783. Babin, B.J., Lee, J.-K., Kim, E.-J., Griffin, M., 2005. Modeling customer satisfaction and word-of-mouth: restaurant patronage in Korea. Journal of Services Marketing 19 (3), 133–139. Baker, D.A., Crompton, J.L., 2000. Quality, satisfaction and behavioral intentions. Annals of Tourism Research 27 (3), 785–804. Banerjee, A., 1992. A simple model of herd behavior. Quarterly Journal of Economics 107 (3), 797–817. Banerjee, S., 2009. Effect of product category on promotional choice: comparative study of discounts and freebies. Management Research News 32 (2), 120–131. Barber, N., Goodman, R.J., Goh, B.K., 2011. Restaurant consumers repeat patronage: a service quality concern. International Journal of Hospitality Management 30 (2), 329–336. Blair, K.C., 1982. Coupon design, delivery vehicle, target market affect conversion rate: research. Marketing News 15 (24), 1–2. Boulding, W., Kalra, A., Staelin, R., Zeithaml, V.A., 1993. A dynamic process model of service quality: from expectations to behavioral intentions. Journal of Marketing Research 30 (1), 7–27. Browne, M.W., Cudeck, R., 1992. Alternative ways of assessing model fit. Sociological Methods Research 21 (2), 230–258. Campo, S., Yagüe, M.J., 2007. The formation of the tourist’s loyalty to the tourism distribution channel: how does it affect price discounts? International Journal of Tourism Research 9 (6), 453–464.
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Contents lists available at SciVerse ScienceDirect
International Journal of Hospitality Management journal homepage: www.elsevier.com/locate/ijhosman
Factors influencing the effectiveness of online group buying in the restaurant industry夽 Ziqiong Zhang a,∗ , Zili Zhang a,1 , Fan Wang b,2 , Rob Law c,3 , Dechao Li d,4 a
School of Management, Harbin Institute of Technology, China School of Business, Sun Yat-sen University, China School of Hotel and Tourism Management, Hong Kong Polytechnic University, Hong Kong d Department of Chinese and Bilingual Studies, Hong Kong Polytechnic University, Hong Kong b c
a r t i c l e Keywords: Group buying Discount Service quality Satisfaction Return intention Restaurant
i n f o
a b s t r a c t Group buying, which provides daily discounts for various services and products, is a new form of marketing that has attracted the attention of both practitioners and academic researchers. However, it is unclear what factors influence the effectiveness of a group buying promotion. This study presents an analysis of 862 restaurant group buying deals based on the secondary data collected from a restaurant guide website. The results indicate that group buying effectiveness – namely, the number of coupons sold, satisfaction improvement, and return intention of group buying customers – can be affected by the discount depth of a deal as well as the service quality and popularity of a restaurant. These findings provide useful insights regarding the elements that are necessary to make a group buying promotion work. © 2013 Elsevier Ltd. All rights reserved.
1. Introduction The deep discount group buying or deal-a-day model for e-commerce, which has exploded in popularity over the last few years, is a combination of “online platform”, “bulk purchase”, and “team buying” (Lo et al., 2012). As one example of growth, the revenue earned by the daily deal site Groupon increased 32% yearover-year to $568.6 million in the third quarter of 2012, compared with $430.2 million in the third quarter of 2011 (Groupon, 2012). Groupon offers one or more daily deals for each market in which it operates, with significant discounts such as $20 for $40 worth of catering. When purchasing a deal, consumers pay for the product up front, and then have a stipulated amount of time, perhaps up to three months, to redeem it. Some analysts have argued that when the deep discounts offered to consumers and the payouts given to the site operators are taken into consideration, group
夽 This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial-No Derivative Works License, which permits non-commercial use, distribution, and reproduction in any medium, provided the original author and source are credited. ∗ Corresponding author at: 92 West Dazhi Street, Harbin 150001, China. Tel.: +86 451 86414022; fax: +86 451 86414024. E-mail addresses: [email protected] (Z. Zhang), [email protected] (Z. Zhang), [email protected] (F. Wang), [email protected] (R. Law), [email protected] (D. Li). 1 Tel.: +86 451 86414019. 2 Tel.: +86 20 84114186. 3 Tel.: +852 34002181. 4 Tel.: +852 27667458. 0278-4319/$ – see front matter © 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.ijhm.2013.06.012
buying promotions should not be profitable (Dholakia, 2010). It is, however, the potential exposure that encourages small businesses to join in this recent Internet group buying fashion. Group buying is also expanding at a high speed in Mainland China. More than 1,000 Chinese copycats of Groupon came into existence in the twinkle of an eye in 2010. According to a recent report (Tuan800, 2012), the eighth month of 2012 brought 270,000 daily deal offers to Chinese consumers, and the turnover of that month reached 2.11 billion RMB (around 335 million USD). Daily deals on food catering are particularly popular in China, and there were about 108,000 daily deal offers for restaurants in the same month, with a turnover of RMB 0.93 billion (around USD 148 million), accounting for 44.1% of the total turnover. The number of restaurant coupons sold for an offer always far exceeds the minimum group size requirement before the deadline. Obviously, group buying can offer benefits for sellers. It brings in a lot of customers who might otherwise be unaware of a business’ existence. Nevertheless, Dholakia (2011) found that some business owners regret their group buying promotions. One of the reasons could be that in general a business cannot directly estimate how many of its group buying coupons will be sold. If a business is not well prepared for extra customers, customers’ satisfaction and intent to repurchase as well as its reputation would be negatively affected. As mentioned previously, the restaurant daily deal is a big component of group buying promotions. However, few studies have looked at the factors that can influence the effectiveness of restaurant group buying. To fill this research gap, the current study considers the effectiveness of a restaurant group buying deal to
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be represented by number of buyers, satisfaction improvement of a restaurant, and return intention of group buying customers. The study examines how the deal discount, and a restaurant’s service quality and popularity influence the deal’s effectiveness. The results of this study are expected to help restaurateurs in making decisions on if, when, and how to offer a group buying deal. 2. Background and hypotheses 2.1. Price discount Price promotions have emerged as an important marketing factor in sales promotion strategy and have drawn an increasing amount of attention from both practitioners and researchers (Nusair et al., 2010). This growing interest is a result of their ability to boost sales performance in the short run (Gijsbrechts et al., 2003; Ruiz and Descals, 2008) and to hold or increase market share (Diaz and Bailey, 2011). Toward these goals, group buying is a unique selling strategy which provides consumers a discounted price for sales promotion purposes. Price discounts are by far the most common form of sales promotion employed by firms (Palazon and Delgado-Ballester, 2009); they can stimulate store patronage and consumer purchases by increasing the value of an offer via a price reduction (Marshall and Leng, 2002). When comparing discount promotion purchases to nonpromotion purchases, researchers have verified the existence of purchase acceleration (Neslin et al., 1985) and larger sales volumes in the former (Banerjee, 2009; Gijsbrechts et al., 2003; Ruiz and Descals, 2008). That is, in response to a price discount, consumers may buy a larger quantity of the product category, or buy at an earlier time. Mulhern and Leone (1990) reported that sellers offering deeper discounts provide a stronger incentive to visit the store and/or to adjust in-store buying behavior. According to Raghubir (1998), the higher the promotional discount, the higher the saving, and the higher the probability of purchasing the product/service. Also, in an attempt to cope with information overload on group buying websites, consumers may use the discount depth as a screening device to select information worthy of their attention. We therefore hypothesize a positive influence of discount depth on the coupons sold for a group buying deal. Hypothesis 1. The discount rate has a positive effect on number of buyers of a group buying deal. Furthermore, price discounts can enhance customers’ perceptions of savings and value (Alford and Biswas, 2002; Fraccastero et al., 1993). Perceived value is generally regarded as an antecedent of satisfaction (Jamilena et al., 2012; Setijono and Dahlgaard, 2007). Thus, by reducing a customer’s economic sacrifice, price discounts can increase perceived value and, as a result, satisfaction (Campo and Yagüe, 2007). In other words, it might be expected that higher price discounts result in higher perceptions of value and customer satisfaction. As group buying deals offer good discounts, we conjecture that group buying would improve customer satisfaction with a restaurant and that the improved satisfaction tends to be greater for a group buying deal with a larger discount. Hypothesis 2. The discount rate has a positive effect on the satisfaction improvement of a group buying restaurant. 2.2. Service quality Rust and Oliver (1994) propose that overall perception of service quality is based on a customer’s evaluation of three dimensions of the service interface: product, delivery, and environment. Mohammad et al. (2005) suggest that this model is especially suitable for use in the restaurant industry, given that consumers
are experiencing food, service, and physical environment simultaneously (Wall and Berry, 2007). Previous research has identified these attributes as the top three factors contributing to the dining experience and customers’ perception of a restaurant’s quality. For example, Ryu and Han (2010) indicate that food quality is the most important attribute to customers in the quick-casual dining sector, followed by the quality of physical environment and service. Pantelidis (2010) reveals that food quality, service, and atmosphere are the most frequently cited elements based on a content analysis of customer comments on an online restaurant guide. According to Mattila (2001), the top three reasons for customers to patronize their target restaurants in the casual dining sector are food quality, service, and atmosphere. Building upon these findings, we propose the following hypothesis. Hypothesis 3. The three service quality dimensions (food, physical environment, and employee service) have a positive effect on number of buyers of a group buying deal. Researchers generally support the idea that service quality has a positive effect on consumer satisfaction (Babin et al., 2005; Caruana et al., 2000). Nevertheless, research also suggests that there is a law of diminishing satisfaction (Dermanov and Eklöf, 2001; Yang, 2003). More specifically, customers’ satisfaction with an attribute depends on the level of the attribute’s quality or fulfillment. A higher level of fulfillment is associated with higher customer satisfaction. However, similar to the utility function, the satisfaction function of an attribute’s quality follows the law of diminishing marginal satisfaction. When an attribute’s initial level of quality is high, an increase of quality will bring a smaller improvement of satisfaction compared to the improvement when an attribute’s initial quality is low. Following from these findings, we can propose the following hypothesis: Hypothesis 4. The three service quality dimensions (food, physical environment, and employee service) have a negative effect on the satisfaction improvement of a group buying restaurant. 2.3. Popularity Restaurant popularity determines how many customers regularly visit a restaurant, and can be mirrored by the crowdedness of a physical restaurant space or the online activeness of its customers. Based on the conformity literature, consumers are likely to follow the choice of others as a result of the pressure to conform to a peer group (Clark and Goldsmith, 2005; Mandrik et al., 2005; Park and Feinberg, 2010). Consumers’ behavior of product choice and usage is also influenced by concerns over what others might think of them or how others might act toward them (Miniard and Cohen, 1983). A good popularity can thus lead consumers to rationalize their purchase decisions by telling themselves that many other people also bought the same product. Hence, the more customers a restaurant has, the more likely it is that more consumers will find its group buying deal sufficiently attractive to warrant a visit. In addition, sales promotions such as coupons can prompt loyal and occasional buyers to purchase earlier (Blair, 1982). Since a popular restaurant has more routine customers than less popular restaurants, supposing the same proportion of customers originally have planned to dine and want to take advantage of a discount, the former will sell more group buying coupons. Therefore: Hypothesis 5. Restaurant popularity has a positive effect on number of buyers of a group buying deal. As a popular restaurant has many regular consumers, its accommodation capacity (e.g., the quantity of rooms, facilities, service staff, and food reserves) is usually higher than restaurants with fewer regular diners, which leads to greater flexibility when extra
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Discount rate Food
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H1(+) H2(+)
Buyer number
H3(+) Environment
H7(+)
Service quality H4(–)
Employee service
H5(+) Popularity
H8(+)
Return intention
Satisfaction improvement
H6(+)
Fig. 1. Proposed model for determinants of group buying effects.
customers come hot on the heels of others. In general, a group buying coupon should be redeemed in a fixed period, i.e., the time from drop to expiration. This will bring a larger flow of customers in the time limitation than usual. Inman and McAlister (1994) have provided evidence that expiration dates serve to influence redemption behavior, in that the redemption rate increases markedly just prior to expiration. It could be postulated that less popular restaurants have lower capacities and less experience of handling a rapid increase in customers. The likelihood of crowding, a long waiting time, or inferior service levels would result in less improvement of customer satisfaction in those restaurants. Thus, we hypothesize a positive influence of popularity on the improvement of customer satisfaction with a group buying restaurant. Hypothesis 6. Restaurant popularity has a positive effect on the satisfaction improvement of a group buying restaurant. 2.4. Return intention The marketing literature has shown that there is a positive relationship between service quality and repurchase intentions (Baker and Crompton, 2000; Boulding et al., 1993; Cronin and Taylor, 1992; Zeithaml et al., 1996). In the restaurant context, Kim et al. (2009) found that the three restaurant quality dimensions – i.e., food, physical environment, and employee service – significantly influence customers’ intention to return to a food service establishment. Barber et al. (2011) reported a strong relationship between the cleanliness attributes of a restaurant’s interior, restrooms, and employees with customers’ repeat patronage. Moreover, through the use of dining experience scenarios, Dubé et al. (1994) showed that the higher the level of several attributes (i.e., food quality, menu variety, atmosphere, food-quality consistency, and waiting time), the higher the likelihood of repeat purchasing. However, there has been relatively little discussion of the relationship between the service quality of a restaurant and customer intention to return after consumption of a group buying offer. We expect that customers tend to revisit a high-quality restaurant than a lowquality restaurant. Therefore: Hypothesis 7. The three service quality dimensions (food, physical environment, and employee service) have a positive effect on the return intention of group buying customers. Finally, the relationship between customer satisfaction and behavioral intensions has received considerable attention in the marketing literature. Numerous empirical studies have confirmed that high levels of satisfaction result in increased loyalty and future visitation (Alegre and Cladera, 2009; Oh, 2000; Qin and Prybutok, 2009; Suh and Yi, 2006; Um et al., 2006; Yoon and Uysal, 2005). For
example, in their investigation of fast-food restaurants, Qin and Prybutok (2009) found that customer satisfaction directly and positively influences return intentions. Oh (2000) also reported that customer perceived satisfaction is an influential determinant of post-purchase intention to patronize a restaurant. Likewise, Baker and Crompton (2000) showed that improvement in satisfaction will result in retention and expansion of tourist numbers. For a group buying restaurant, there are two kinds of satisfaction: prepurchase satisfaction (or usual satisfaction) and post-purchase (or group buying satisfaction). The present study is interested in the impact of satisfaction improvement on group buying customers. When comparing group buying satisfaction to usual satisfaction, restaurants with a larger positive improvement can give customers a better impression, which may encourage their intention to visit again. Therefore, we suggest: Hypothesis 8. The improvement of satisfaction has a positive effect on the return intention of group buying customers. Fig. 1 displays the conceptual model of the relationships among price discount, restaurant service quality and popularity, and effectiveness of restaurant group buying. 3. Research setting and methodology We conducted our empirical analysis using group buying data from a leading Chinese restaurant guide website, Dianping.com, which has a group buying column offering various localized deals for major geographic markets or cities. In each geographic market, there are one or more restaurant deals of the day. A deal may be available for one or more days. Generally, each deal has a minimum threshold that must be reached for the deal to be valid, and restaurants may also set a maximum threshold to limit the number of coupons sold. Dianping.com is the largest restaurant review website in China. It offers a pre-set format for users to post independent evaluations of their experiences. As a third-party platform, Dianping.com attempts to ensure genuine reviews by requiring users to register through a valid email address and to post reviews after logging in. It now offers over 20 million independent consumer evaluations of around 1,200,000 restaurants scattered across 2300 Chinese cities. We gathered data pertaining to restaurant group buying deals published during the period June 21, 2010 to May 5, 2012 for Shanghai (http://t.dianping.com/shanghai/deallist) as Shanghai has the largest turnover of group buying among Chinese cities (Tuan800, 2012). As shown in Fig. 2, from the page of each group buying deal we collected the discount rate and the total number of buyers (or the number of coupons sold) after a group buying deal ended. This page also gives a link to the restaurant (which is on
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Fig. 2. Details of a restaurant’s daily deal on Dianping.com (http://t.dianping.com/deal/15396).
Dianping.com) that issues the deal. To obtain restaurant information, we clicked on this link and downloaded all customer reviews for that restaurant. From each customer review, we extracted the overall rating (1–5) of the restaurant and the attribute ratings (0–4) of food taste, physical environment, and employee service. The overall rating is considered the customer satisfaction with a restaurant, and the rating of an attribute is regarded as attribute performance or customer perceived quality for that particular attribute (Zhang et al., 2011). In addition, Dianping.com allows group buying customers to post feedback subsequent to consumption of a group buying meal, and shows this information on the group buying deal’s page (see Fig. 3). We recorded the number of group buying customer reviews, their satisfaction with the restaurant (on a scale of 1–5), and the percentage of group buying customers who announced their intention to revisit the restaurant in the future. Not all data were used in this study. As one of our aims is to examine what influences the number of group buying coupons sold, we dropped a deal which had a maximum limitation for the number of coupons sold and had been sold out. Furthermore, to make a comparison between group buying customers and regular customers, and to ensure there are sufficient data, we discarded a deal if it had less than 10 reviews from group buying customers or if the relevant restaurant had less than 10 reviews from regular customers. As a result, our analysis was restricted to 862 restaurant group buying deals in Shanghai, China. For each deal, we collected data on (i) discount rate, (ii) restaurant service quality, (iii) restaurant popularity, (iv) number of buyers, (v) satisfaction improvement, and (vi) return intention of group buying customers (see Table 1).
4. Analysis and results 4.1. Descriptive statistics Table 2 shows the descriptive statistics of the analyzed data. It can be viewed that group buying sometimes offers very large discounts, for up to 95% in our sample. A group buying deal was bought as many as 29,449 times, suggesting that restaurant group buying is widely accepted by China’s customers. The means of Satisfaction improvement and Return intention (0.575 and 0.763) indicate that online group buying, if adopted appropriately, can have a positive influence on customers’ satisfaction and willingness to return. In addition, following previous studies (such as Ghose and Ipeirotis, 2007; Zhang et al., 2010), we used the base-10 logarithm values of Popularity and Buyer number in the model to smooth out the distribution of the two variables.
4.2. Reliability and correlation analysis According to previous research such as Wang et al. (2011), data were analyzed using a two-step approach in which the measurement of the latent construct is confirmed first and then the structural model is tested. Service quality is the only latent construct in this study, which was measured by the qualities of food, physical environment and employees’ service. There was sufficient evidence of reliability of Service quality based on the estimate of internal consistency (Cronbach’s alpha coefficient = 0.881). The item reliabilities, squared multiple correlations of three individual items, ranged from 0.452 to 0.908, indicating an acceptable level of reliability.
Z. Zhang et al. / International Journal of Hospitality Management 35 (2013) 237–245 Table 1 Summary of variables and variable measures.
signs is opposite to the expectation, and it is necessary to further examine the structural model.
Variable
Variable measure
Discount rate
Discount rate is a discounted rate that a restaurant offers on a food package in a group buying promotion. Service quality of a restaurant is operationalized with three indicators: food taste, physical environment, and employee service. Dianping.com allows diners to input ratings on food taste, physical environment, and employee service in a review. We collected customer reviews posted on a restaurant’s webpages before the restaurant issued a group buying deal and calculated the average value of all customer ratings on each attribute. The average rating is regarded as the performance or quality of an attribute in a restaurant (Zhang et al., 2011). Popularity is defined in this study as the welcome level of a restaurant. The more popular a restaurant is, the more patrons it has. Furthermore, it could be assumed that as more customers eat in a restaurant, the number of online customer reviews increases. Therefore, following the method used in recent studies (Kovac¸s and Johnson, 2012), we counted the number of reviews posted before a restaurant issued a group buying deal and used it as a proxy measure for the restaurant’s popularity. Buyer number is the number of times that a group buying deal was purchased. Satisfaction improvement is defined as group buying customer satisfaction minus original satisfaction with a restaurant. For each deal, the former is measured by averaging the satisfaction ratings of group buying customers. The latter is measured by averaging the overall ratings posted on the restaurant’s webpages prior to the issue of a group buying deal. Return intention is the percentage of customers who would like to visit the restaurant again out of all group buying customers of a deal.
Service quality
Popularity
Buyer number Satisfaction improvement
Return intention
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We adopted Pearson’s bivariate correlation analysis to evaluate a possible correlation between the studied variables (see Table 3). The Pearson correlation coefficients showed that (i) Buyer number is positively correlated with Popularity, (ii) all study variables except Buyer number are significantly correlated with Satisfaction improvement, and (iii) all variables are significantly correlated with Return intention. However, the direction of some coefficients’
4.3. Structural modeling To test the research hypotheses, structural equation modeling (SEM) was performed using AMOS software (version 17.0). Unbiased and maximum likelihood estimation covariances (as input and to be analyzed) analysis properties were selected in AMOS. Overall, the model’s fit was acceptable. The observed chi-square 2 /df = 5.7 and the RMSEA (root mean square error of approximation) = 0.074. A general rule of thumb is that a RMSEA ≤ 0.05 indicates a close approximate fit, and values between 0.05 and 0.08 suggest a reasonable error of approximation (Browne and Cudeck, 1992). Other additional goodness-of-fit indices are the NFI (normed fit index) = 0.982, the NNFI (non-normed fit index) = 0.963, the GFI (goodness-of-fit index) = 0.983, the AGFI (adjusted goodness-of-fit index) = 0.946, and the CFI (comparative fit index) = 0.985. While the overall model tests support the proposed conceptualization with the sample, the hypotheses refer to the significance tests of the specified paths. The hypothesized path diagram with standardized path coefficients and squared multiple correlations are illustrated in Fig. 4. Fig. 4 shows that Discount rate has a significant positive influence on Buyer number (ˇ = 0.101, P = 0.003), which supports H1. However, there is a negative relationship between Discount rate and Satisfaction improvement (ˇ = −0.061, P = 0.049). This indicates that the larger a deal discount, the less improvement there is in satisfaction; thus, H2 is not supported. The results also show that the latent variable Service quality is well represented by the observed variables Food (ˇ = 0.953, P < 0.001), Environment (ˇ = 0.672, P < 0.001), and Employee service (ˇ = 0.796, P < 0.001). In addition, Service quality has a significant positive influence on Buyer number (ˇ = 0.082, P = 0.013), which supports H3, and has a significant negative influence on Satisfaction improvement (ˇ = −0.619, P < 0.001), thereby supporting H4. Popularity has a positive impact on Buyer number (ˇ = 0.362, P < 0.001) and Satisfaction improvement (ˇ = 0.142, P < 0.001), supporting H5 and H6. Also, Hypotheses 7 and 8 are supported, the results showing strong relations between Service quality and Return intention (ˇ = 0.919, P < 0.001), and between Satisfaction improvement and Return intention (ˇ = 0.991, P < 0.001).
Table 2 Descriptive statistics.
Discount rate Food Environment Employee service Popularity Buyer number Satisfaction improvement Return intention
N
Minimum
862 862 862 862 862 862 862 862
0.100 0.571 0.643 0.429 10.000 21.000 −1.030 0.290
Maximum
Mean
Std. deviation
0.950 3.077 3.500 3.420 17,488.000 29,449.000 2.110 1.000
0.584 2.014 1.984 1.953 523.643 1734.716 0.575 0.763
0.138 0.328 0.456 0.431 1151.267 2510.800 0.359 0.125
Table 3 Correlations.
Discount rate Service quality Popularity Buyer number Satisfaction improvement Return intention **
Discount rate
Service quality
Popularity
Buyer number
Satisfaction improvement
Return intention
1 0.035 −0.373** −0.029 −0.152** −0.314**
1 −0.147** −0.014 −0.585** 0.233**
1 0.321** 0.203** 0.302**
1 0.026 0.158**
1 0.469**
1
Correlation is significant at the 0.01 level. N = 862.
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Fig. 3. Customer feedback on a group buying deal on Dianping.com (http://t.dianping.com/deal/15396).
5. Discussion Overall, most of the empirical results confirmed the established hypotheses, but some were also unexpected. This section presents a summary of the main findings and some comments in this regard. As expected, generous discounts can bring in group buying customers. This result is in accord with the findings of previous studies that stores offering larger discounts can generate more customer traffic and higher sales (Banerjee, 2009; Gijsbrechts et al., 2003; Ruiz and Descals, 2008), and indicates that customers are likely to link group buying with getting a good deal for their money. Another novel – and to some extent unexpected – result of this study is the negative effect of discounts on satisfaction improvement. We have found that larger discounts tend to attract more buyers. However, the resources (such as waiters, cooks, and facilities) of a given restaurant are limited and it may be hard for
Discount rate
0.101**
Food
-0.061*
0.953*** R2=0.908 Environment
R2=0.633
Buyer number
0.082* 0.672***
R2=0.452 Employee service
some restaurants to accommodate extra group buying customers while maintaining regular service standards, thereby resulting in a negative relation between discount rate and satisfaction improvement. Additionally, offering discount may have negative effects on the consumers’ perception of quality and internal reference prices (Grewal et al., 1998; Raghubir and Corfman, 1999). When a price discount is too large to be considered bona fide, consumers may be suspicious of the sale prices, in that they may view the lower selling prices, rather than the higher initial price, as the true value of the item. Previous studies have found that consumer perception of the magnitude of a price reduction tends to be less than the actual reduction advertised by retailers (Chen et al., 1998). Moreover, consumers discount the price discounts offered, and this discounting effect increases significantly with the increase in advertised discounts (Marshall and Leng, 2002). For these reasons, the discount rate of a group buying deal is proven to be negatively related to satisfaction improvement with a restaurant.
R2=0.121
Service quality
0.919***
Return intention
-0.619*** 0.796***
0.362*** Popularity
0.142***
Satisfaction improvement
0.991***
R2=0.764
R2=0.414
Fig. 4. The hypothesized path diagram with standardized path coefficients and R2 . Note: ***P < 0.001, **P < 0.01, and *P < 0.05
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Customer-perceived service quality is another important determinant of customers’ participation in restaurant group buying. The results reveal that daily deals for better quality restaurants tend to attract more customers. In addition, the satisfaction improvement through group buying promotion is lower in high-quality restaurants than that in low-quality restaurants. The results indicate that, to enhance satisfaction, group buying may be more effective for low-quality restaurants than for high-quality restaurants, which provides further evidence for the law of diminishing marginal satisfaction in the restaurant industry. As such, large discount group buying does not seem to be a good way for high-quality restaurants to try to improve satisfaction. Popular restaurants’ daily deals are appealing to customers, as we expected. This study measured restaurant popularity using the number of online customer reviews received. When more reviews are offered, consumers perceive the set of review information to be more informative. Previous studies have suggested that the more informative an information set is, the more favorable the associations that consumers will have with the product being reviewed, which results in increased behavioral intention (Park et al., 2007; Petty and Cacioppo, 1984). This finding offers observation of herd behavior, where everyone is doing what everyone else is doing (Banerjee, 1992). Our results also show that the group buying model is more applicable to popular restaurants in terms of satisfaction improvement. Popular restaurants normally have heavy customer traffic. We conjecture that they would be more experienced and more prepared to deal with the increased traffic on redemption dates and thus make customers feel more satisfied when compared with less popular restaurants. Previous research results concerning the determinants of customer intention to return to a restaurant suggest that the effective ways of ensuring customers’ loyalty are to improve service quality and customer satisfaction (Baker and Crompton, 2000). The present study looked at the influential factors of group buying customers’ return intention and confirms the positive role of service quality and satisfaction improvement in retaining group buying customers’ loyalty.
6. Implications 6.1. Theoretical implications This paper contributes to the literature in several ways. Firstly, group buying can be viewed as a unique type of sales promotion, which is offered to a group of consumers who may not be related in any way before forming the buying group. This study is one of the very few attempts to investigate the roles of discount depth and restaurant characteristics in restaurant group buying, which contributes to the literature on group buying and sales promotion in general, and particularly in the hospitality industry. Additionally, despite the emergent Web 2.0 phenomenon and the unprecedented growth of social media in recent years, very little attention has been given to the marketing applications of these phenomena in hospitality (Line and Runyan, 2012). In traditional promotional events, customers cannot easily get quality information of products prior to consumption, and their decisionmaking may largely depend on the depth of discount. Moreover, they could only make their feedback known by a few people around. This study, however, examines the impact of social media on customers’ purchase behavior. More specifically, customers can scrutinize service quality and popularity of a restaurant through electronic word-of-mouth to decide whether participating in a specific online group buying deal. Our findings extend the existing literature regarding the role of discount in online settings and provide a new view point for the linkage among discount,
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quality perception, and customer behaviors in the foodservice sector. In addition, most existing group buying research has employed questionnaire survey or mathematical modeling methods (e.g., Dholakia, 2010, 2011; Jing and Xie, 2011). In contrast to the traditional methods, this study relies on online diners’ voluntarily provided reviews to measure customer perceptions of attributes, customer satisfaction, and return intention. The use of these data, collected directly from a restaurant guide site, provides an opportunity to triangulate our findings with those obtained using other data-collection methods like surveys. 6.2. Practical implications Having analyzed the results, we now offer some specific meaningful conclusions and suggestions for restaurant practitioners. First, as discount depth can affect the number of coupons sold, launching deep discount group buying may be a good way for restaurants, particularly new or struggling ones, to solicit business and advertise their brands. Nevertheless, when setting a discount rate, restaurateurs should consider their accommodation capacity. If a restaurant handles too many appointment requests on a day and makes most of its consumers feel under-served and unsatisfied, the restaurant is likely to get criticized in many review websites, as group buying consumers are usually frequent users of social media. The restaurant can try to control the traffic of group buying customers by imposing restrictions on a group buying deal, e.g., the discount rate, sale days, or duration of redemption. It should simultaneously prepare extra foodstuffs and employees for peak times. Second, a group buying promotion is likely to draw many buyers for a high-quality restaurant, and these customers may exhibit a high tendency to visit again. However, customer satisfaction improvement is lower in a high-quality restaurant than in a lowquality restaurant. Thus, high-quality restaurants are not advised to offer excessively big discounts that may result in insufficient revenues, uncovered costs, as well as an insignificant contribution to customer satisfaction. In addition, popular restaurants can achieve greater group buying effectiveness in terms of the number of buyers and satisfaction improvement, and thus may want to boost sales by issuing a group buying deal. In contrast, the daily deals of less popular or small restaurants may not be attractive to customers, but they can still adopt group buying to advertise brand and attract new customers to try them by designing appealing features and setting good discounts. If a reasonable proportion of the people who come in response to such a promotion are converted into regular customers, long-term sales and profitability can increase. Furthermore, information about a group buying deal will remain on the website after the end of a promotion, which produces a longer-term effect of advertising. Nevertheless, small restaurants have to pay attention to their accommodation capacity to avoid the increased numbers of visitors having an effect on service quality and customer satisfaction. Lastly, restaurant managers must understand the importance of service quality and group buying satisfaction, which lead directly to favorable outcomes through increased revisiting of a restaurant. Hence, they should not much impair the quality of the discounted food. For instance, a local restaurant plans to provide a 50% off deal for advertising purposes. This deal may be sold in the thousands, which means that the restaurant needs to serve a huge number of customers in certain months with little profit. Yet, the restaurant must attempt to ensure the quality of both meal and service. Otherwise, group buying customers may have no intention of coming again. Restaurants with limited accommodation capacity are advised to set a maximum of buyers for a group buying deal to
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achieve exposure aim and to increase customer repatronage intentions. 7. Conclusions and limitations The group buying model in web-based selling is still a new concept to many restaurants and customers. Even though it represents an interesting business model, this alone is not sufficient to justify its viability as group buying firms face the challenges of designing a deal and achieving the desired goals. This study developed a broad model or concept and, based on data from a daily deal website, illustrated the interrelationships between group buying discount, restaurant quality and popularity, number of coupons sold, satisfaction improvement, and customers’ intention to return. Based on the findings, suggestions on modifying group buying offers are provided to better balance customer attraction with positive outcomes for restaurants offering them. Despite its contributions, this study is not free from limitations. One limitation is its research setting, which was restricted to one Chinese-language website. Thus, the empirical findings should be interpreted as primarily applicable to restaurants in China. Future research could extend the current study to group buying promotions around the world to improve the external validity and to examine the differences across heterogeneous social and cultural segments. Another limitation of the present study is that the sample was limited to customers who posted online reviews. This may not provide a complete picture of customer opinions. Yet, with many promotions aimed at encouraging people to post comments, review websites represent a broad swath of customer opinions. Nevertheless, future studies could survey a more universal population of customers to determine whether our findings remain consistent. Finally, this study examines several key factors of group buying effectiveness. Future studies could extend the models to include additional variables that are related to group buying. Acknowledgements The authors would like to thank the anonymous reviewers for providing constructive comments on an earlier version of this paper. The work presented in this paper was funded by the National Natural Science Foundation of China (71101039 and 71203042), and a grant funded by The Hong Kong Polytechnic University. References Alegre, J., Cladera, M., 2009. Analysing the effect of satisfaction and previous visits on tourist intentions to return. European Journal of Marketing 43 (5), 670–685. Alford, B.L., Biswas, A., 2002. The effects of discount level, price consciousness and sale proneness on consumers’ price perception and behavioral intention. Journal of Business Research 55 (9), 775–783. Babin, B.J., Lee, J.-K., Kim, E.-J., Griffin, M., 2005. Modeling customer satisfaction and word-of-mouth: restaurant patronage in Korea. Journal of Services Marketing 19 (3), 133–139. Baker, D.A., Crompton, J.L., 2000. Quality, satisfaction and behavioral intentions. Annals of Tourism Research 27 (3), 785–804. Banerjee, A., 1992. A simple model of herd behavior. Quarterly Journal of Economics 107 (3), 797–817. Banerjee, S., 2009. Effect of product category on promotional choice: comparative study of discounts and freebies. Management Research News 32 (2), 120–131. Barber, N., Goodman, R.J., Goh, B.K., 2011. Restaurant consumers repeat patronage: a service quality concern. International Journal of Hospitality Management 30 (2), 329–336. Blair, K.C., 1982. Coupon design, delivery vehicle, target market affect conversion rate: research. Marketing News 15 (24), 1–2. Boulding, W., Kalra, A., Staelin, R., Zeithaml, V.A., 1993. A dynamic process model of service quality: from expectations to behavioral intentions. Journal of Marketing Research 30 (1), 7–27. Browne, M.W., Cudeck, R., 1992. Alternative ways of assessing model fit. Sociological Methods Research 21 (2), 230–258. Campo, S., Yagüe, M.J., 2007. The formation of the tourist’s loyalty to the tourism distribution channel: how does it affect price discounts? International Journal of Tourism Research 9 (6), 453–464.
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