What drives customers’ post-purchase price search intention in the context of online price matching guarantees

Journal of Retailing and Consumer Services 54 (2020) 102015

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What drives customers’ post-purchase price search intention in the context of online price matching guarantees Hsin-Hui Lin a, Timmy H. Tseng b, Ching-Hsuan Yeh c, Yi-Wen Liao d, Yi-Shun Wang e, * a

Department of Distribution Management, National Taichung University of Science and Technology, Taiwan Department of Business Administration, Fu Jen Catholic University, Taiwan c School of Guomai Information, School of Internet Economics and Business Fujian University of Technology, China d Department of Information Management, Cheng Shiu University, Taiwan e Department of Information Management, National Changhua University of Education, No. 2, Shi-da Road, Changhua, 500, Taiwan b

A R T I C L E I N F O

A B S T R A C T

Keywords: Online price-matching guarantee (PMG) strat­ egy Post-purchase price search Customer value theory Refund length Refund scope Refund depth

Using a research model based on customer value theory, this study investigates the determinants of customer post-purchase price comparison searches in the context of online price-matching guarantees (PMG). Data collected from 222 eligible respondents are tested against the proposed research model using partial least squares structural equation modeling (PLS-SEM). The results indicate two PMG characteristics, refund length and refund scope, influence utilitarian benefit (i.e., refund depth) and hedonic benefit (i.e., playfulness). These two benefits subsequently contribute to customer perceived value, which leads to price search intentions. This study pioneers the exploration of online PMGs and the determinants of customer post-purchase price search intentions. Several important theoretical and practical implications can be drawn from the findings to guide online retailers’ PMG strategies.

1. Introduction The price-matching guarantee (PMG) is a popular retailing price ~ ez, 2007; Huang and Feng, 2020; Png and strategy (Fatas and Man Hirshleifer, 1987). It refers to retailers’ “promise [to customers] to match or beat competitive prices before and after a purchase” (Kukar-­ Kinney et al., 2007, p. 211). According to a poll by BDO USA (2015), price matching was the second most successful promotional strategy for US retailers (free shipping was number one). And PMGs are increasingly being used. For instance, in 2014, an estimated 10% of retailers adopted PMGs, growing to 18% just one year later (eMarketer, 2015). The popularity of PMGs is attributable to the many benefits they provide retailers. For example, in the pre-purchase stage, PMGs communicate a low-price image, reduce risk perception, and enhance customer pur­ chase intentions (Bashir et al., 2018. Jain and Srivastava, 2000; Kukar-Kinney and Grewal, 2006; Oghazi et al., 2018; Srivastava and Lurie, 2001, 2004). In the post-purchase stage, PMGs promise compensation for perceived monetary loss, help retailers build customer relationships, consolidate the retailer’s low-price image, and prevent customers from switching to competitors (Dutta and Biswas, 2005; Estelami et al., 2007).

The PMG pricing strategy can also influence customer search behavior. Once customers have confidence in a retailer’s low-price image (as promised by the PMG), they may forgo subsequent price searches and directly patronize the retailer (Kukar-Kinney and Grewal, 2006). However, Dutta and Biswas (2005) claimed PMGs can actually induce customers’ post-purchase search behavior as customers look for competing lower prices that they can use to claim compensation from the PMG. The authors of the current study argue that customer post-purchase search can be an effective part of customer relationship management. If customers encounter a lower price after a purchase, this may trigger cognitive dissonance (Mao and Oppewal, 2010) and perceived regret (Dutta et al., 2011). But if the store has a PMG that allows consumers to claim a refund on the price difference, this action can eliminate negative outcomes, which can strengthen customers’ trust of the retailer. This increases satisfaction, positive word-of-mouth, and repurchase in­ tentions. Additionally, if price-sensitive customers find a cheaper product at rivals during in the pre-purchase stage, they can easily switch to competitors, leading to lost opportunities to build customer re­ lationships. In this vein, retailers offering PMGs are likely to encourage post-purchase searches rather than pre-purchase searches.

* Corresponding author. E-mail address: [email protected] (Y.-S. Wang). https://doi.org/10.1016/j.jretconser.2019.102015 Received 12 May 2019; Received in revised form 18 October 2019; Accepted 2 December 2019 Available online 19 December 2019 0969-6989/© 2019 The Authors. Published by Elsevier Ltd. This is an open (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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H.-H. Lin et al.

Journal of Retailing and Consumer Services 54 (2020) 102015

length, refund scope, and refund depth are the three characteristics that retailers most often manipulate (Kukar-Kinney et al., 2007). Hence, the current authors adopted these three characteristics in this study. Refund length refers to the time period a customer has to claim a price match refund (Kukar-Kinney et al., 2007). Refund scope refers to the number of competitors that the focal firm accepts for price comparisons (Kukar-­ Kinney et al., 2007). Although Kukar-Kinney et al. (2007) defined refund depth as the amount of money that is refunded, when they tested this concept they defined it as “match the lower price versus beat the lower price by 20% for the refund depth,” which is a comparative rather than an absolute amount. As such, the current authors define refund depth as the perceived monetary merit of the PMG policy. According to Estelami et al.’s (2007) content analysis of 74 PMGs, the most frequent form of PMG compensation is exact difference compensation (i.e., the difference in price between the focal firm’s price and a competitor’s lower price). The second most common form of compensation is the competitor-beating price compensation (e.g., price difference plus an additional 10%). These three PMG characteristics significantly affect customer re­ sponses. Kukar-Kinney and Walters (2003) reported competitive scope and refund depth are positively associated with customer PMG value and patronage intention. Desmet et al. (2012) examined the effects of refund depth and suggest PMG policies featuring competitor-beating price compensation have higher perceived value and result in greater customer visit intentions compared to exact difference PMGs. Although retailers frequently adjust various PMG characteristics (e.g., 7 days or 30 days, local or national competitors, exact difference compensation or competitor-beating compensation), retailers may not know which op­ tions are optimal for each refund characteristic (Arbatskaya et al., 2004).

Researchers have increasingly investigated customers’ post-purchase search behaviors. Results from these studies show refund value is the main motivation behind customer post-purchase price searches (Este­ lami et al., 2007). For example, Dutta and Biswas (2005) found cus­ tomers with high value-consciousness perform greater post-purchase searches for greater monetary value than those with low value-consciousness. However, to the best of the current authors’ knowledge, prior studies have not empirically investigated how benefit and cost, two determinants of value, contribute to customer’s PMG value perception and search behavior. To fill this void, the authors use customer value theory to explore post-purchase price search behavior. Customers calculate the likelihood of finding a cheaper price within the constraints of the PMG, such as refund length and refund scope. The greater likelihood of finding a cheaper price, the more potential value customers perceive, which motivates post-purchase price search behaviors. Following traditional retailers (i.e., brick-and-mortar stores), many e-tailers have adopted PMGs strategy as well. For instance, the online travel booking site Expedia.com guarantees to refund customers the difference between its price and any competitor’s lower price, plus an additional $50 travel coupon if customers find a lower price for the same product within 24 h of booking. Online computer retailer Dell offers customers identically priced and configured computers and electronic products if customers discover a competitor’s advertised price is cheaper. More recently, eBay launched a price-matching guarantee for over 50,000 items in its competition with Amazon, Walmart, and other online retailers (Perez, 2017). However, e-tailers offering PMGs face many challenges because the pervasiveness of mobile technologies (e.g., smartphones and tablets) enables instantaneous online price compari­ sons. Furthermore, in the highly competitive e-retailing environment, price is often the key differentiating factor. Thus, there is a higher possibility of customers claiming PMG refunds in the online context compared to traditional contexts. Prior studies have examined the relationship between PMGs and search behavior. Dutta and Biswas (2005) found the presence of a low-price guarantee stimulates customers’ post-purchase price search behaviors in physical retail contexts. Kukar-Kinney and Grewal (2007) found opposite results in online contexts; a PMG does not significantly increase post-purchase price searches. The mixed results indicate further investigations are needed to clarify the effects. To sum up, the goal of the current study is to form a comprehensive understanding of the relationships between PMG characteristics, search benefits (i.e., utilitarian and emotional benefits), search cost, perceived value, and customers’ post-purchase price search intentions in eretailing contexts. In the following sections, the authors first develop several hypotheses and then test them using partial least squares structural equation modeling (PLS-SEM) on a sample of 222 re­ spondents. Next, results are discussed, which show two PMG charac­ teristics (i.e., refund length and refund scope) are positively related to search benefits and negatively related to search costs. Only search benefits contribute to perceived value, which induces search intentions. This paper concludes with discussion of theoretical and practical im­ plications and suggestions for future research.

2.2. Theoretical foundation of PMG policies in the pre-purchase stage Researchers investigating how PMG policies influence customer re­ sponses have frequently used Spence’s (1974) signaling theory, which assumes the existence of asymmetric information between the parties involved in a transaction. Signals are used to reduce asymmetric infor­ mation effects (Srivastava and Lurie, 2004). In retailing contexts, a customer with limited information about the unobservable attributes of a product (e.g., product quality) might use an observable attribute to infer unobservable attributes (i.e., halo effect). Retailers often use price, an observable product attribute, to signal product quality, which is often not immediately observable. Price matching guarantees also function as quality signals at the store/retailer level. Retailers that honor their PMGs are able to establish a proven low-price store image, even though they may suffer financial losses (d’Astous and Gu� evremont, 2008; Lurie and Srivastava, 2005). However, once customers trust a retailer’s PMG, they increase patronage and may even discontinue future price search behavior because they assume the retailer has the lowest prices. Studies that have examined the link between PMG characteristics and customer responses have focused on refund depth. Kukar-Kinney and Grewal (2006) pointed out deep refunds are costly to PMG retailers. To avoid these costs, PMG retailers need accurate price information, which increases customer confidence in the low price and increases the likelihood of buying. Other researchers doubt the positive relationship between PMG characteristics and customer responses. Based on the assimilation-contrast theory (Sherif and Hovland, 1961), Kukar-Kinney and Walters (2003) argued customers may have expectations associated with refund depth. A PMG with greater depth activates the assimilation effect and builds consumer confidence in the guarantee. Conversely, the contrast effect causes consumers to doubt retailers that have PMGs exceeding consumer expectations (i.e., has high refund depth).

2. Literature review 2.1. PMG characteristics Retailers often manipulate PMG characteristics in order to differen­ tiate themselves from competitors. According to Sivakumar and Wei­ gand (1996), the main PMG characteristics include the following: the amount of money refunded (i.e., refund depth), time limitation (i.e., refund length), geographical limitation (i.e., refund scope), mode of refund (e.g., cash or credit), items covered (i.e., identical or similar items), prices covered (e.g., sales prices or advertised prices), and burden of proof (i.e., by customers or by the PMG retailers). Refund

2.3. Theoretical foundation of PMG policies in the post-purchase stage Although a PMG can signal a retailer’s low-price business model, 2

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Journal of Retailing and Consumer Services 54 (2020) 102015

opportunism, a turbulent environment, and high pre-purchase search costs (e.g., an urgent purchase with high time costs) may mean the PMG is an imperfect signal, resulting in customers not always getting the lowest price. Therefore, customer search behavior, which often occurs at a reduced frequency in the presence of a PMG in the pre-purchase stage, is more likely to occur in the post-purchase stage (Dutta and Biswas, 2005; Jiang et al., 2017) and be influenced by contextual and individual factors. Kukar-Kinney and Grewal (2007) stated price-searching behavior in the post-purchase stage is the result of the potential price difference and/or extra compensation from the retailers, which in­ dicates price-searching primarily depends on customer value perception. Customer value theory has been used to explain various customer behaviors like product purchase (Kim et al., 2011), store patronage and repatronage (Babin et al., 2016), and brand loyalty (Yeh et al., 2016). Customers typically arrive at a perceived value using cost-benefit eval­ uations that are trade-offs (Cai and Xu, 2006). Zeithaml (1988) defined perceived value as customers’ overall assessment of the pro­ duct/service’s net utility based on perceptions of what is given and what is received. Any perceived benefit positively contributes to perceived value, while perceived costs negatively affect customers’ value percep­ tion. This definition originates from Dodds and Monroe’s (1985) study, which extended the notion of customer value from benefit to net benefit. Dodds and Monroe (1985) argued that perceived price is positively related to perceived quality and perceived sacrifice. Perceived value is the antecedent of willingness to buy, which is a joint consideration of perceived quality and perceived sacrifice. Later, Zeithaml (1988) augmented Dodds and Monroe’s (1985) model with the concepts perceived quality and perceived sacrifice: the former is the result of extrinsic attributes (e.g., brand and reputation) and intrinsic attributes (e.g., texture and taste), while the latter includes the monetary and nonmonetary price. The nature of perceived benefit and perceived sac­ rifice varies across studies, including monetary/nonmonetary elements or process/outcome elements (Cai and Xu, 2006). For example, Kim et al. (2007) proposed perceived value is composed of benefits (i.e., usefulness and enjoyment) and sacrifices (i.e., technicality and perceived fee). The benefit–sacrifice trade-off has been widely used in conceptualizing perceived value of products and/or shopping.

and Kim, 2001). Playfulness is a short-term psychological state that arises from the interaction experience between an individual and an object/event (Ahn et al., 2007; Shang et al., 2005). According to the experiential view of consumption (Holbrook and Hirschman, 1982) and the hedonic consumption literature (Hirschman and Holbrook, 1982), some individuals consume for enjoyment or fun (Gupta and Kim, 2010) rather than just to gain utilitarian benefits. Playful experience is one important part of hedonic consumption (Holbrook et al., 1984; Bilgihan, 2016). It is intrinsically motivated and involves spending time on ac­ tivities that are enjoyed for their own sake (Holbrook et al., 1984). In the PMG context, consumers might be curious if a lower price is available, and they may derive enjoyment from price searching behaviors. Therefore, playfulness can be a hedonic benefit in terms of process and outcome. As with refund depth, the current authors specify playfulness as a type of benefit that is determined by both refund length and refund scope. Srivastava and Lurie (2001) examined the effects of PMGs under different search cost conditions. They found PMGs drive customers to purchase without a pre-purchase search if the search cost is high. The authors argue that PMG characteristics are associated with customer perceptions of search cost, and refund length and refund depth are the drivers of search cost. Finally, by considering search benefit and search cost together, customers might form a net value perception, which in­ fluences search intentions. The research model indicating all hypothe­ sized relationships is shown in Fig. 1.

3. Hypotheses development

H2a. Refund scope is positively related to refund depth.

3.2. The hypothesized relationships Refund length is the amount of time customers are allowed to claim refunds. Refund scope is the number of competing retailers that qualify for customer price comparisons. Both PMG characteristics affect the potential for a successful refund: a longer refund length and broader refund scope both tend to increase the perceived utilitarian merit of the PMG (i.e., a better refund). If there is a greater refund length and refund scope, it is more likely that customers perceive more utilitarian benefit. Thus, the authors posit the following hypotheses: H1a. Refund length is positively related to refund depth. Price match guarantees are designed to provide utilitarian benefit, but the search mechanism also provides a playful experience (Koufaris, 2002). Alford and Biswas (2002) indicated customers derive emotional and entertainment value from shopping for lower prices. In this vein, PMGs with a longer refund length and a broader refund scope should increase the chance of customers’ finding lower prices during post-purchase price searches. This creates feedback that can further drive playful experiences during post-purchase price searches. Further­ more, a longer refund length and broader refund scope afford customers additional opportunities and variety during the search for alternatives. A greater the variety of choices increases the playful experience (Chung and Tan, 2004). Based on the above concepts, the current authors pro­ pose the following hypotheses:

3.1. Research model This study explores customers’ post-purchase searching behavior within the online PMG shopping context. Based on customer value theory, the authors contend search benefit and search cost determine customer perceived value, and perceived value subsequently impacts search intentions. Both utilitarian benefits and hedonic benefits comprise search benefits (Babin et al., 1994). In line with Kukar-Kinney and Walters’ (2003) study, the current authors adopt the three PMG characteristics related to customer benefit beliefs. Kukar-Kinney et al. (2007) showed refund depth is more important than refund length or refund scope. Refund length and refund scope are strongly related to the eligibility of a lower competing price for a price comparison refund and less concerned with compensation magnitude compared to refund depth. Refund length and refund scope provide price protection for customers, and convey lower perceived price risk. Therefore, the current authors specify refund depth as the utilitarian benefit customers may gain from post-purchase searching, which directly contributes to cus­ tomers’ perceived value. Refund length and refund scope, which are helpful in terms of the eligible refund, are specified as antecedents of refund depth. Playfulness is a multi-dimensional construct comprising concentra­ tion, curiosity, and enjoyment. It is defined as the degree to which the individual’s attention is focused, the individual is curious regarding an interaction, and finds an interaction enjoyable and interesting (Moon

H1b.

Refund length is positively related to playfulness.

H2b.

Refund scope is positively related to playfulness.

Research by Srivastava and Lurie (2001) shows high search costs can prevent customers from correctly assessing the PMG signal. Search costs are lower for PMGs with longer refund length and broader refund scope because consumers have more time and flexibility to assess competitors’ prices. Therefore, the relationships between refund length, refund scope, and search cost are hypothesized as follows: H1c. Refund length is negatively related to search cost. H2c. Refund scope is negatively related to search cost. 3

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Journal of Retailing and Consumer Services 54 (2020) 102015

Fig. 1. Research model.

Benefit is a positive antecedent of perceived value and cost is a negative antecedent (Zeithaml, 1988). For example, Forsythe et al. (2006) found that four perceived benefits (i.e., shopping convenience, product selection, ease/comfort of shopping, and hedonic/enjoyment) have positive effects on the relative advantage of Internet shopping and search intentions. The three perceived risks (i.e., costs or potential los­ ses), including financial risk, product risk, and time/convenience risk, are negatively related to customer search behaviors. Kukar-Kinney and Walter (2003) suggested PMG net value is assessed in terms of benefits and costs. In the context of e-tailer PMGs, greater PMG refund depth and greater playfulness correspond to increased perceived utilitarian and hedonic merits, respectively. Dutta and Biswas (2005) noted that refund depth is positively associated with post-purchase price search behaviors for value-motivated customers, indicating refund depth might be posi­ tively related to perceived value. Furthermore, if customers believe the search costs (e.g., the time and physical cost) are high, they tend to perceive the transaction as a high potential loss (i.e., sacrifice). The authors develop the following related hypotheses: H3.

Refund depth is positively related to perceived value.

H4.

Playfulness is positively related to perceived value.

H5.

Search cost is negatively related to perceived value.

purchasing an item from an e-tailer with a PMG were qualified to participate. The qualified respondents reported their most recent online shopping experience at an e-tailer (generically referred to as the “X estore” hereafter). Prior to answering the survey items, respondents were allowed to visit the X e-store website in order to refamiliarize themselves with the retailer’s PMG policy. Data was collected over three months, from August 2013 to October 2013. A total of 256 responses were collected. Unqualified or incomplete responses were excluded, resulting in a total of 222 usable responses (a response rate of 86.72%). Since the respondents had similar knowledge and/or experience as individuals in the target market (i.e., online retail shoppers), any inferences drawn from the study findings should be generalizable (Highhouse and Gil­ lespie, 2009). Sample characteristics and store characteristics are summarized in Table 1. Of the respondents, 57.14% were female. Most respondents were under 30 (96%). Additionally, 61% of respondents reported visiting X e-store 1–9 times per month, 28% reported 10–29 times, and 11% reported visiting more than 30 times per month. Most (73%) re­ spondents reported making a purchase at X e-store at least once per month. Based on the average monthly visit rate and purchase frequency, respondents were very experienced with online shopping at e-tailers that have PMGs. Most (69%) respondents spent less than 500 (in NTD) at X estore per month. All of the respondents were cross-over customers, buying at both online and offline retailers. As for store characteristics, the most frequent retail purchases were on online shopping platforms (e.g., Yahoo!, PChome Online, Rakuten), book stores (e.g., Books.com), drug stores (e.g., Watsons), hypermarkets (e.g., Carrefour), and game trading platforms (e.g., 8591.com). Just over half (53.2%) were business-to-customer (B2C) transactions and 46.8% customer-to-customer (C2C) transactions. Most of the reported online transactions were at general grocery stores (77.5%), and 22.5% were at specialty stores (e.g., books, drugs, or in-game virtual items). Most of the stores were purely online (89.3%), while 10.7% were so-called clickand-mortar stores.

Prior studies have found evidence that perceived value is positively correlated to search intentions. To et al. (2007) showed the utilitarian and hedonic motivation that are derived from value antecedents (e.g., convenience and adventure) positively contribute to online search in­ tentions. In the context of e-tailer PMGs, if customers believe a post-purchase price search offers more net benefit (i.e., value), they have higher search intentions. Dutta and Biswas (2005) found high value-seeking customers have high intention to engage in post-purchase search behaviors. Building on these previous findings, the current au­ thors propose the following hypothesis: H6.

Perceived value is positively related to search intentions.

4. Methods

4.2. Measures

4.1. Data collection

A total of 22 items were used to measure the seven constructs in the research model. The measures for refund length, refund scope, and refund depth were developed from Kukar-Kinney et al.’s (2007) study, and each of the three constructs contained three items. The items measuring refund depth captured transaction utility. The measures for playfulness were adapted from Ahn et al. (2007), who defined playful­ ness using the dimensions of concentration, entertainment, and

A web-based, self-reported survey was employed to collect data. The survey was hosted on a well-known social networking website in Taiwan where surveys are one of the major services provided. Both members and non-members of the website could view and respond to the ques­ tionnaire, but only people who indicated they had previous experience 4

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Journal of Retailing and Consumer Services 54 (2020) 102015

Table 1 Sample characteristics and store characteristics. Variable Sample characteristics Gender Age Average monthly visits to X e-store with a PMG Purchase frequency

The average expenditure at X e-store (in NTD) Store characteristics Business type

Retailing type

Table 2 Results of convergent validity and reliability.

Level

Proportion (%)

Male Female Under 20 21–30 Over 30 1-9 times 10-29 times 30 times and above More than three times a month Two times a month One time a month Once every two to five months Once every six months to a year Less than 500 500–1000 1000–2000 More than 2000

42.86 57.14 64.00 32.00 4.00 61.00 28.00 11.00 30.00

Business to customer (B2C) Customer to customer (C2C) Sells various product types Sells a specific product type

53.20

Constructs and items Refund length (α ¼ 0.76, CR ¼ 0.86, AVE ¼ 0.67) RL1 The refund length period of X e-store’s PMG policy is long enough if I find a lower price at another e-store. RL2 X e-store’s PMG promises a sufficient refund length period. RL3 The refund length period of X e-store’s PMG is sufficient. Refund scope (α ¼ 0.83, CR ¼ 0.90, AVE ¼ 0.75) RS1 Most e-stores are included in the scope of X e-store’s PMG policy. RS2 Most e-stores and physical stores are included in the scope of X e-store’s PMG policy. RS3 All e-stores and physical stores in the country are included in the scope of X e-store’s PMG policy. Refund depth (α ¼ 0.72, CR ¼ 0.84, AVE ¼ 0.64) RD1 The refunded amount of money of X e-store’s PMG policy is high if I find lower price at other e-stores. RD2 The refunded amount of money of X e-store’s PMG policy is higher than others if I find lower price at other e-stores. RD3 The refunded amount of money of X e-store’s PMG policy is higher than price differences if I find lower price at other e-stores. Playfulness(α ¼ 0.79, CR ¼ 0.88, AVE ¼ 0.70) PL1 It would be enjoyable to validate whether the prices of X e-store’s PMG products are the lowest.d PL2 To validate whether the prices of X e-store’s PMG products are the lowest would lead to my exploration of this topic. PL3 To validate whether the prices of X e-store’s PMG products is lowest would arouse my imagination. PL4 To validate whether the prices of X e-store’s PMG products are the lowest would be fun. PL5 To validate whether the prices of X e-store’s PMG products are the lowest would make me not realize the time elapsed.d PL6 To validate whether the prices of X e-store’s PMG products are the lowest would be immersive.d Search cost (α ¼ 0.70, CR ¼ 0.83, AVE ¼ 0.63) SC1 To validate whether the prices of X e-store’s PMG products are the lowest would require a lot of money. SC2 To validate whether the prices of X e-store’s PMG products are the lowest would require a lot of time. SC3 To validate whether the prices of X e-store’s PMG products are the lowest would require a lot of physical and mental effort. Perceived value (α ¼ 0.75, CR ¼ 0.89, AVE ¼ 0.80) PV1 To validate whether the prices of X e-store’s PMG products are the lowest may lead to additional benefits. PV2 To validate whether the prices of X e-store’s PMG products are the lowest is worthwhile. Search intention (α ¼ 0.74, CR ¼ 0.88, AVE ¼ 0.79) SI1 I would like to validate whether the prices of X estore’s PMG products are the lowest. SI2 Once X e-store hits the market with PMG products in the future, I would like to validate whether their prices are the lowest.

26.00 17.00 11.00 16.00 69.00 19.00 7.00 5.00

46.80 77.50 22.50

Notes: 1 USD≒30NTD.

curiosity. Each of these three dimensions was measured with two items. Three items adapted from Jones et al. (2000) were included to measure search cost. Finally, two items each for perceived value and search in­ tentions were adapted from Biswas et al. (2002). The items measuring perceived value captured total utility. All 22 item responses were reported on7-point Likert scales anchored at 1 (strongly disagree) and 7 (strongly agree). Before finalizing the survey tool, a small group of three e-commerce users and two PMG ex­ perts evaluated the initial survey tool as a pretest. Following Hardesty and Bearden (2004), these members were provided the definitions of key research constructs and were asked to indicate whether the items in the initial pool reflected the desired construct. Based on the feedback gained from the pretest, all the 22 items adequately represented the intended constructs. Hence, the scales used in this study achieved face validity. Any ambiguous items (those having confusing or inappropriate wording) were identified and modified according to the consensus of the members in the pretest. Table 2 displays the finalized construct items. 5. Results Covariance-based structural equation modeling (CB-SEM) and par­ tial least squares structural equation modeling (PLS-SEM) are two key approaches to examine structural relationships among constructs (Hair et al., 2016). PLS-SEM examines both the psychometric properties of the constructs and the validation of the research model simultaneously. Compared with CB-SEM, PLS-SEM is variance-based and less restrictive in terms of sample size, measurement scales, and data distribution (Rose et al., 2012). The choice of PLS-SEM or CB-SEM depends on the goal of the research. If the research goal is to identify key driver constructs, PLS-SEM should be selected (Hair et al., 2011). Since the aim of the current research was to examine the determinants of customers’ post-purchase price search intentions in the PMG context, PLS-SEM was selected to analyze the data. A normality test was conducted on all the items for the key constructs. All the items significantly deviated from

Loadings

Tvalues

0.79

15.80

0.84

17.73

0.84

27.76

0.86

32.33

0.89

59.86

0.85

35.45

0.81

24.26

0.83

32.92

0.75

16.01

–

–

0.84

29.73

0.79

16.36

0.88

46.83

–

–

–

–

0.81

21.70

0.82

26.46

0.75

15.79

0.89

54.99

0.89

51.46

0.90

62.36

0.88

45.17

Note: d indicates the item was deleted due to the item loading being lower than the 0.70 threshold (Hair et al., 2016). The deletion of these items did not change the hypothesis test results.

normality as revealed by both the Kolmogorov-Smirnov test (ps < .001) and the Shapiro-Wilk test (ps < .001; Hair et al., 2016). These results indicated a PLS-SEM as a nonparametric approach was the more appropriate choice (Hair et al., 2016).

5

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Journal of Retailing and Consumer Services 54 (2020) 102015

5.1. Measurement model

5.3. Mediation analysis

The results of the reliability, convergent validity, and discriminant validity tests are reported in Tables 2 and 3. The composite reliability (CR) values for all constructs in the measurement model exceeded 0.80, and the Cronbach’s alpha values were larger than 0.7, suggesting the reliability of the measures was acceptable. The average variance extracted (AVE) for each construct was above the recommended threshold of 0.50 (Fornell and Larcker, 1981), indicating more than half of the variance observed in the items was accounted for by the hy­ pothesized constructs. As shown in Table 3, the square root of the AVE of each construct was much larger than the correlation of the specific construct with any of the other constructs in the model, which supports discriminant validity (Chin, 1998). In addition, discriminant validity was supported by the fact that no cross-loading occurred (Hair et al., 2016).

Similar to Ohiomah et al.’s (2019) study, the current authors con­ ducted an additional mediation analysis to understand the relationships between the focal constructs in this study. Zhao et al.’s (2010, p. 201) procedure for mediation analysis was followed. The statistical signifi­ cance of both direct effects and indirect effects were examined based on 95% bias-corrected confidence intervals. If 95% confidence intervals of the direct effects/indirect effects include zero, the direct effects/indirect effects are statistically insignificant. The results of the mediation anal­ ysis are shown in Table 4. For direct effects, both refund length and refund scope were found to be irrelevant to search intentions. The results of indirect effect revealed that refund length and refund scope impact search intentions via refund depth → perceived value and via playfulness → perceived value paths. Also, refund scope was indirectly related to search intentions via search cost → perceived value path, but this was not the case for refund length. In sum, except for the relationship of refund length, playfulness, perceived value, and search intentions, all the other 5 relationships were significant and fully mediated (Zhao et al., 2010). The results of the mediation analysis show a broader refund scope causes customers to perceive higher refund depth (utilitarian benefit) and playfulness (he­ donic benefit) and lower search cost, which then leads to higher perceived value and greater search intentions. Similar to refund scope, a longer refund length produces stronger refund depth (utilitarian benefit) and playfulness (hedonic benefit), which results in higher perceived value and greater search intentions. In other words, PMG characteristics influence perceived benefit and cost perception and produce a calcu­ lated value perception, which determines post-purchase price search intentions.

5.2. Structural model Based on a two-step estimation process and a bootstrapping resam­ pling technique, the standardized path coefficients and respective sig­ nificance are shown in Fig. 2. The coefficient estimation was performed according to the path weighting scheme, while significance testing was performed using 1000 bootstrapped samples with the no sign change option (Tenenhaus et al., 2005). The paths of the control variables on the endogenous constructs were included in PLS-SEM. Two store charac­ teristics (i.e., business type and retailing type) were used as control variables in the analysis. Business type (β ¼ 0.06, p ¼ .31) and retailing type (β ¼ 0.02, p ¼ .76) showed insignificant influence on search intentions. Refund length and refund scope were significantly related to refund depth, playfulness, and search cost (βRL→RD ¼ 0.29, βRL→PL ¼ 0.22, βRL→SC ¼ 0.18, βRS→RD ¼ 0.38, βRS→PL ¼ 0.38, and βRL→PL ¼ 0.37, respectively), supporting H1a, H1b, H1c, H2a, H2b, and H2c. Refund depth and playfulness were positively related to perceived value (βRD→PV ¼ 0.30 and βPL→PV ¼ 0.30), supporting H3 and H4. Search cost was not significantly associated with perceived value (βSC→PV ¼ 0.13), so H5 was not supported. Finally, perceived value showed a positive relationship with search intention (βPV→SI ¼ 0.69), supporting H6. The authors examined the coefficient of determination (R2) to assess the precision of the predicted endogenous constructs. For the estab­ lishment of the predictive power of the model, the R2 values should be greater than 0.67 (substantial), 0.33 (moderate), and 0.19 (weak), as suggested by Chin (1998). The R2 values for refund depth, playfulness, search cost, perceived value, and search intention were 32.1%, 26.4%, 22.8%, 33.6%, and 48.1%, respectively, showing a moderate predictive power. To understand the strength of the independent variables, the authors also performed effect size analyses.1 The effect sizes of refund length on refund depth, playfulness, and search cost were 0.10, 0.06, and 0.03, respectively, suggesting the strength of refund length on the three con­ structs was small. Additionally, the effect sizes of refund scope on refund depth, playfulness, and search cost were 0.17, 0.16, and 0.15, respec­ tively. The strength of refund scope on the three constructs was medium. Finally, the effect sizes of refund depth and playfulness on perceived value were 0.10 and 0.10, respectively; each had a small influence on perceived value.

1

The formula for effect size analyses is:f 2 ¼

R2included R2excluded 1 R2included

6. Discussion Prior studies on PMG policies have increased understanding of how PMGs influence customer responses in the pre-purchase stage (e.g., Moorthy and Zhang, 2006). However, customer responses in the post-purchase stage have received less attention. The current research helps fill this knowledge gap by examining how three PMG character­ istics influence customer post-purchase price search behavior, and it also delineates the psychological processes underlying it. Customer value theory accounts for the benefit and cost reasoning in post-purchase price search behavior. The findings indicate refund length and refund scope facilitate utilitarian and hedonic search benefits (i.e., refund depth and playfulness) and search cost. Both refund depth and playfulness increase perceived value, which in turn promotes post-purchase price searching behavior. An unsupported empirical finding is search cost does not affect perceived value. This finding might be attributable to the different contexts of previous research and the current research. Prior studies focused on offline retail contexts; the current study focused on online retail contexts. Internet search costs are relatively low because cus­ tomers can obtain price information more efficiently and with fewer physical and financial costs (Kim et al., 2012). Many price comparison websites (e.g., Google Shopping, NexTag, PriceGrabber) enable conve­ nient and efficient price searches. Furthermore, Lee et al. (2003) argued that a market price can be evaluated in terms of price level, price adjustment, and price dispersion. By comparing online and offline CD markets, they found CD prices in online markets were lower, adjusted less, and less dispersed. Their findings also suggest search costs in online contexts are lower than in offline contexts. In this vein, low search costs may play a weak role in terms of perceived value. The insignificant relationship between search cost and perceived value may indicate, for online customers, search benefit rather than search cost dominates the value perception in the post-purchase stage.

(Hair et al., 2016).

The suggested critical values of effect size are 0.02 (small), 0.15 (medium), and 0.35 (large). 6

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Journal of Retailing and Consumer Services 54 (2020) 102015

Table 3 Correlation matrix and descriptive statistics of the constructs. RL RS RD PL SC PV SI

M

SD

RL

RS

RD

PL

SC

PV

SI

4.55 4.66 4.74 4.70 3.29 4.82 4.86

0.84 0.83 0.73 0.84 0.77 0.83 0.86

0.82 0.43 0.45 0.38 0.34 0.33 0.36

– 0.86 0.50 0.47 0.45 0.36 0.36

– – 0.80 0.48 0.33 0.49 0.45

– – – 0.84 0.35 0.49 0.48

– – – – 0.79 0.33 0.35

– – – – – 0.89 0.69

– – – – – – 0.89

Note: 1RL: refund length; RS: refund scope; RD: refund depth; PL: playfulness; SC: search cost; PV: perceived value; SI: search intention. 2The values on the diagonal (in bold) are the square root of the average variance extracted (AVE).

Fig. 2. Results of the hypotheses tests.

6.1. Theoretical implications

context (Arbatskaya et al., 2004). Prior studies show search cost is negatively related to PMG accep­ tance. When search costs are high, customers are likely to avoid searching and instead base their purchase decisions on the PMG signal (Srivastava and Lurie, 2001). It is worth noting the argument that search cost reduces search intentions was developed in offline contexts. The current authors contend the joint evaluation of search benefit and search cost determines search intentions. Search cost in online contexts is relatively low, which should induce high online search intentions. The empirical results of this study show that search benefit may be more influential than search cost in terms of affecting search intentions via perceived value. Furthermore, the insignificant relationship between search cost and perceived value may indicate that, for online customers, search benefit is dominant and influences post-purchase price search intentions. Finally, this study outlined several of the many PMG characteristics. Kukar-Kinney et al. (2007) identified refund length, refund scope, and refund depth as three common PMG characteristics, and contended that refund length and refund scope are less relevant to direct benefits. The current study’s empirical results are additional supporting evidence that refund length and refund scope are the antecedents of utilitarian (i.e., refund depth) and hedonic benefits (playfulness). These two benefit beliefs positively influence search intentions. While refund length and refund scope may determine the likelihood of finding a lower price, the

The current results provide four theoretical contributions. First, though an e-tailer’s use of a PMG can act as a signal that causes cus­ tomers to forgo price searching in the pre-purchase stage because they assume the PMG price is lowest, the characteristics associated with PMGs (e.g., longer refund periods) may encourage customers price search in the post-purchase stage in order to maximize value. Based on customer value theory, the current authors found customers’ postpurchase price search behaviors are contingent on a trade-off between perceived benefit and perceived cost (i.e., perceived value). The higher the perceived value, the greater the post-purchase searching intentions. The current results are also evidence supporting prior studies that argue value perception motivates customer price searching in the postpurchase stage. Additionally, Kukar-Kinney and Grewal (2006) suggested more PMG studies should focus on customer responses in the context of clicks-and-mortar shopping. As with bricks-and-mortar shopping, the current authors assumed PMGs are very important to customers in on­ line shopping contexts and the results support this view. As mentioned earlier, though the price dispersion may be low amongst online retailers, customers are motivated to search for competitive prices after shopping at an e-tailer that offers a PMG. As such, the “buy now, search later” philosophy or “buy now” effect may still exist in the clicks-and-mortar 7

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Journal of Retailing and Consumer Services 54 (2020) 102015

detrimental to low-priced retailers if the market is characterized by a high degree of price variation, a large number of competing retailers, and frequent price changes. To reduce the price variation level in the market, EDLP retailers should set the refund scope to cover only those competitors that also used EDLP strategies. Conversely, hi-lo e-tailers should not encourage their customers to conduct post-purchase price comparison searches because their customers have a greater chance of encountering lower prices elsewhere, which may result in hi-lo retailers refunding significant amounts of money even though their temporary promotion prices might be lower than EDLP retailers.

Table 4 Significance analysis of the direct and indirect effects. Paths

Direct effects Refund length → Search intention Refund scope → Search intention Indirect effects Refund length → Refund depth → Perceived value → Search intentiona Refund length → Playfulness → Perceived value → Search intentionb Refund length → Search cost → Perceived value → Search intention Refund scope → Refund depth → Perceived value → Search intentiona Refund scope → Playfulness → Perceived value → Search intentionb Refund scope → Search cost → Perceived value → Search intention

Coefficients

95% Biased corrected confidence intervals (C.I.)

Type of mediation

0.09

[-0.02, 0.20]

0.05

[-0.08, 0.18]

0.06*

[ 0.03, 0.11]

Full mediation

0.05*

[ 0.01, 0.10]

Full mediation

0.02

[-2.21E-04, 0.05]

No mediation

0.08*

[ 0.04, 0.13]

Full mediation

0.08*

[ 0.04, 0.14]

Full mediation

0.03*

[ 4.27E-04, 0.08]

Full mediation

6.3. Limitations and future research When generalizing our findings, three limitations should be noted. First, some possible factors that may influence post-purchase search were not considered in the model. For example, Kukar-Kinney and Grewal (2007) found store reputation moderated the relationship be­ tween PMG policy presence and customers’ store price perceptions. Customers give more credence to PMGs issued by reputable retailers, which in turn reduces customer post-purchase price search behaviors. Similarly, Cheema and Papatla (2010) reported experienced customers may collect information easily and the search cost may be low for them. For inexperienced customers, the higher search cost may result in less perceived value and discourage post-purchase searching. Taken together, future studies need to consider additional possible factors and control for their effects on PMG policies and customers’ post-purchase search intentions. Second, in Kukar-Kinney and Walters’ (2003) study, the effect of refund depth on PMG value was questionable. On one hand, refund depth impacted PMG value directly and positively: when refund depth is great, customers evaluate the PMG policy as valuable. On the other hand, refund depth may impact PMG value via PMG believability. If refund depth is great, customers doubt the PMG is realistic, and the relationship between refund depth and PMG believability is negative. Low PMG believability in turn leads to a lower PMG value. A joint consideration of these two paths may reveal that the effect of refund depth on PMG value is paradoxical. While the results of this study support the notion that refund depth is perceived as a utilitarian benefit, and contributes to perceived value (i.e., PMG value) as well as post-purchase searching, the paradox in the relationship between refund depth and PMG value requires additional investigation in future studies. Finally, this study recruited respondents with purchase experience of products covered by PMGs. Some respondents may have recently pur­ chased a product but not yet searched for other prices, which means the PMG refund length was still valid at the time of data-collecting. Also, respondents were not required to have post-purchase search experience. To increase sample representativeness, additional respondent qualifi­ cations should be considered in future studies.

Note: * significant at 95% bias-corrected C.I. a denotes the utilitarian route of post-purchase price search. b denotes the hedonic route of post-purchase price search.

money customers receive from a PMG reimbursement and the playful­ ness experienced during the search process may be more influential in terms of eliciting search intentions. 6.2. Managerial implications Drawing on the findings of the current research, the authors suggest online retailers use PMGs to establish post-purchase relationships with customers, which induce post-purchase price search behavior. Based on the current results, online retailers can motivate customers to conduct post-purchase price search by using either utilitarian appeals (i.e., get money back or increase perceived monetary benefits) or hedonic ap­ peals (i.e., price search is playful and interesting). Furthermore, both utilitarian and hedonic appeals should come with a longer refund length and a broader refund scope. A large refund offer (i.e., refund depth) can enhance the value of the PMG, but it can also decrease its believability (Kukar-Kinney and Walters, 2003). As such, online retailers should use high levels of refund length and refund scope to increase the perceived chance of successfully claiming a refund, with a moderate refund depth (match the competitor’s price rather than a price match plus an extra 20% compensation) to increase the offer’s believability. Retailer type also has to be considered when implementing PMGs because the PMG might not be equally effective for different retailers. Hoch et al. (1994) argued that retailers can be divided into two types: everyday low price (EDLP) retailers and hi-lo retailers. For EDLP re­ tailers, they want to eliminate price uncertainty, so they charge a lower price on an everyday basis. Hi-lo retailers charge a higher price but run promotions frequently to temporarily decrease prices below the EDLP level. The current authors propose EDLP retailers that have PMGs should encourage customer post-purchase searching behavior because cus­ tomers are less likely to encounter a cheaper price at a competitor’s store. Over time, customers learn it is relatively hard to find a lower price elsewhere, which consolidates the retailers’ low-price image and helps develop the customer-retailer relationship. However, Dutta and Biswas (2005) warned post-purchase search for lower prices may be

About the authors Hsin-Hui Lin is a Professor and Head in the Department of Distri­ bution Management at National Taichung University of Science and Technology, Taiwan. She received her Ph.D. in Business Administration from National Taiwan University of Science and Technology. Her cur­ rent research interests include electronic commerce, service marketing, and customer relationship management. Her work has been published in academic journals such as Academy of Management Learning & Education, Information & Management, Information Systems Journal, International Journal of Information Management, International Journal of Service In­ dustry Management, Internet Research, Managing Service Quality, Journal of Service Theory and Practice, Service Business, Service Industries Journal, Computers in Human Behavior, British Journal of Educational Technology, Journal of Business Economics and Management, Journal of Global Infor­ mation Management, Information Systems and e-Business Management, and Journal of Electronic Commerce Research. 8

Journal of Retailing and Consumer Services 54 (2020) 102015

H.-H. Lin et al.

Timmy H. Tseng is an Assistant Professor in the Department of Business Administration at Fu Jen Catholic University, Taiwan. He received his PhD in Marketing from National Chengchi University, Taiwan. His current research interests include experiential marketing, branding strategy, e-marketing, and service marketing. His work has been published in Computers in Human Behavior, European Journal of Marketing, Internet Research, Information Technology and Management, and Journal of Business Economics and Management. Ching-Hsuan Yeh is an Associate Professor in the School of Guomai Information and the School of Internet Economics and Business at Fujian University of Technology, China. He was a post-doctoral fellow in the Department of Information Management at National Changhua Uni­ versity of Education, Taiwan. He received his Ph.D. in Business Administration from National Chi Nan University, Taiwan. His current research interests focuses on e-commerce, online consumer behavior, and international marketing. He has published in such journals as Journal of Business Research, International Journal of Information Man­ agement, Internet Research, Journal of Business-to-Business Marketing, and Journal of Travel & Tourism Marketing. Yi-Wen Liao is an Associate Professor in the Department of Infor­ mation Management at Cheng Shiu University, Taiwan. She received her Ph.D. in MIS from National Sun Yat-sen University, Taiwan. Her current research interests include electronic commerce, online consumer behavior, e-learning, and customer relationship management. Her work has been published in International Journal of Information Management, Internet Research, Computers in Human Behavior, Government Information Quarterly, British Journal of Educational Technology, and Journal of Educational Computing Research. Yi-Shun Wang is a Distinguished Professor in the Department of Information Management at the National Changhua University of Edu­ cation, Taiwan. He received his Ph.D. in MIS from National Chengchi University, Taiwan. His current research interests include information and educational technology adoption strategies, Internet entrepreneur­ ship education, IS success models, online user behavior, knowledge management, and e-learning. He has published papers in journals such as Information Systems Journal, Information & Management, International Journal of Information Management, Academy of Management Learning and Education, Journal of Business Research, International Journal of Service Industry Management, Tourism Management, International Journal of Hospitality Management, Journal of Global Information Management, Ser­ vice Industries Journal, Managing Service Quality, Journal of Business Eco­ nomics and Management, Computers & Education, British Journal of Educational Technology, Government Information Quarterly, Internet Research, Online Information Review, Computers in Human Behavior, Interactive Learning Environments, International Journal of HumanComputer Interaction, Information Technology and People, Information Technology and Management, Journal of Educational Computing Research, Journal of Electronic Commerce Research, among others. He is currently serving as the Chairman for the Research Discipline of Applied Science Education in the Ministry of Science and Technology of Taiwan.

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