Consumers' Perceptions of Online and Offline Retailer Deception: A Moderated Mediation Analysis

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ScienceDirect Journal of Interactive Marketing 35 (2016) 16 – 26 www.elsevier.com/locate/intmar

Consumers' Perceptions of Online and Offline Retailer Deception: A Moderated Mediation Analysis Isabel P. Riquelme a & Sergio Román b & Dawn Iacobucci c,⁎ a

University Jorge Tadeo Lozano, Carrera 4 # 22-61, Bogotá, Colombia b University of Murcia, Campus de Espinardo, 30.100 Murcia, Spain c Vanderbilt University, Nashville, TN 37240, United States

Abstract This research examines the effects of consumers' perceptions of retailers' deceptive practices on their evaluations of online and offline retailers. Results from two samples of consumers (shopping in online versus offline channels) show the direct and indirect influence of consumers' perceptions of retailers' deceptive practices on consumers' evaluations, including product satisfaction, retailer satisfaction and word-of-mouth. Perceptions of deception influence retailer satisfaction through product satisfaction, and word-of-mouth through retailer satisfaction. These mediated effects are further moderated by the online vs. offline purchase channel. Implications for theory and management are discussed. © 2016 Direct Marketing Educational Foundation, Inc., dba Marketing EDGE. All rights reserved. Keywords: Deception; Channel; Online; Customer satisfaction; Word-of-mouth

Introduction Many marketing scholars have contributed to the literatures on deception in marketing (Darke and Ritchie 2007), and on differences between consumer experiences when making purchases online or offline (Wolfinbarger and Gilly 2003), yet there is little work combining the two research streams, examining how a particular retail format might modify the effects of consumers' perceptions of a company's deceptive practices. Our research involves a fairly large-scale, non-student sample field study in which we examine the effects of consumers' perceptions of marketing deception on customer satisfaction and word-of-mouth, and compare them for purchases made in physical retail outlets versus comparable purchases made online. Research has consistently shown that consumers' perceptions of deceptive practices lead to important negative consequences for the retailer, such as consumer complaints, dissatisfaction, ⁎ Corresponding author. E-mail addresses: [email protected] (I.P. Riquelme), [email protected] (S. Román), [email protected] (D. Iacobucci).

switching behavior, negative word-of-mouth, and distrust, all of which subsequently damage the company's reputation (e.g., Román 2010). Consumers' concerns about deception in online transactions seem to be growing: whereas in 2001, the Consumer Sentinel Network (www.ftc.gov) received just over 100,000 Internet fraud complaints, last year, it received over 2,500,000 complaints. Note that consumers' perceptions matter as much as the veracity of the company's deception. Obviously if an advertisement conveys a lie or an exaggeration, it is inherently deceptive. However, even if neither is true, but consumers believe the ad to be misleading, their perceptions will have similar consequences in diminished satisfaction and the generation of more negative word-of-mouth. Furthermore, the growth and prevalence of the Internet raise important research questions about how deceptive retailer actions, such as overly exaggerated claims of the features and benefits of a product, may be perceived differently online compared to the offline environment, and how the consequences of perceived deception for the consumer may differ between these two shopping channels (cf., Harris, Mohr, and Bernhardt 2009). The differences may be complex, bearing on direct and indirect effects

http://dx.doi.org/10.1016/j.intmar.2016.01.002 1094-9968/© 2016 Direct Marketing Educational Foundation, Inc., dba Marketing EDGE. All rights reserved.

I.P. Riquelme et al. / Journal of Interactive Marketing 35 (2016) 16–26

of deception on various consumer consequences differing across channels (Diallo and Lambey-Checchin 2015). In this research, we attempt to address these gaps by proposing and testing a moderated mediation model. Specifically, our model (1) explicitly disentangles direct versus indirect effects of consumer's perceptions of online retailer's deceptive practices on consumer's product satisfaction, retailer satisfaction and word-of-mouth communications, and (2) incorporates the moderating role of the purchase channel (online vs. traditional) in both the direct and indirect relationships between perceptions of deception and its consequences. In doing so, this research will provide a more comprehensive understanding of the mechanisms that lead from perceptions of deception to unfavorable outcomes. Understanding these differences should benefit retailers in several ways. First, retailers will become aware of the importance of perceived deception and its influence on multiple consumers' outcomes. Second, knowledge about channel differences will allow retailers to amend their policies in order to develop better practices that may reduce perceived deception and its negative potential consequences in each channel. The remainder of this article is organized as follows. First, we provide a brief overview of the literature, deriving our conceptual framework and hypotheses. Then, we describe our study's methodology. Using structural equation modeling, we test our hypotheses with data from online and offline consumers in the context of technological products. Finally, we discuss the results and their implications for theory and practice. Literature, Conceptual Framework, and Hypotheses In this section, we define the focal constructs of this research. They include: customers' perceptions of deception, customers' satisfaction with the product and with the retailer store, and word-of-mouth (WOM). Perceived Deception Deception is defined as one party (in our research, the retailer) taking action to intentionally cause another party (in this case, the customer) to believe something that is not necessarily true (Bok 1989). Such marketing practices are considered “unethical and unfair to the deceived” (Aditya 2001, p. 737), and raise ethical questions for companies, consumers, and policy makers. Our study focuses on consumers' perceptions of deceptive practices, specifically retailer actions that cause consumers to have incorrect beliefs about its offerings, including product characteristics, price, warranty coverage, etc., that make the product seem overly attractive so as to induce greater likelihood of purchase (Román 2010). For example, a customer may be enticed to purchase a sophisticated, expensive smartphone because it is currently “20% off,” but “only for a limited time,” when a simple, cheaper smartphone would suffice for the customer's needs. Our study is not focused on whether the retailer is being deceptive, but rather on the extent to which the customer perceives the retailers' behavior as deceptive, and how consumers respond differently to this perceived deception. For example, consider an illustration from Román (2007, p. 137) in which one

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study participant argued that she had perceived an online retailer to be unfair and deceptive because “it shows photographs of a woman, before and after having taken a weight loss product for 10 months, making her lose a total of 59 k (130 lb).” Even though this might have been true, the participant perceived that “the images were manipulated,” and that “the claim was an exaggeration.” Note also that the reverse may be true—not only might there not be intended deception but the customer perceives it anyway (incorrectly), it can also be the case that deception is so smoothly executed that it is not noticed or perceived by the customer. In such as situation, with real but unsensed deception, downstream effects such as a dampening of satisfaction presumably would not occur. In our research, we focus on the consumers' perceptions, whether they experience practices they believe to be deceptive. Numerous marketing scholars have studied the antecedents and consequences of a marketer's unethical behavior in traditional retail settings (e.g., Diallo and Lambey-Checchin 2015; Ingram, Skinner, and Taylor 2005; Román and Ruiz 2005). In this research, we are investigating whether it might be the case that, given various characteristics of online commerce, it might make the perpetration of online deception even easier or the perception of online deception more pervasive (Román 2010). For example, the Internet is inherently an environment in which the consumer cannot physically experience the product, but nevertheless consumers must make decisions about products relying upon online representations, thus it may be easier for an unethical retailer to provide deceptive information about the product online as compared to offline (Aditya 2001). Customer Satisfaction with the Product and Retailer Satisfaction is a purchase outcome in which consumers compare rewards and costs with anticipated consequences. Many marketing research studies focus on a single omnibus indicator of customer satisfaction, defined as an individual's subjectively derived favorable evaluation of an outcome or experience associated with consuming a product (Cronin, Brady, and Hult 2000; Westbrook 1981). Other researchers find it useful to distinguish elements of customer satisfaction (Oliver and Swan 1989), such as satisfaction with the product and satisfaction with the retailer. In our research we examine the influence of perceived deception on both customers' satisfaction with the product as well as their satisfaction with the retailer company. We distinguish these related constructs shortly, regarding their respective roles as mediators, moderated by the online or offline channel distinction. The incorporation in this research of these two distinct objects of consumer satisfaction (i.e., product and retailer satisfaction) into the analysis of deception consequences allows us to examine the fuller and richer theoretical context of these mediated and moderated conceptual relationships. Word-of-mouth Harrison-Walker (2001, p. 63) defined WOM as “informal, person-to-person communication between a perceived

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non-commercial communicator and a receiver regarding a brand, a product, an organization or a service.” Marketers are naturally interested in promoting positive WOM, such as recommendations to others, and that is the behavioral intention we study in this research (Brown et al. 2005). Our study examines how perceived deception may negatively influence customer satisfaction and subsequent WOM, an important post-purchase behavior. WOM represents a long-term commitment of a customer to the organization (Brown et al. 2005; De Matos and Rossi 2008), and it has been shown to influence attitudes, behavioral intentions, and actual purchase both in offline and online contexts, often being more influential than other marketer-controlled sources (Chen and Xie 2008; Román and Cuestas 2008). Thus, an important managerial contribution is enhancing retailers' understanding of how perceived deception may negatively influence WOM. Hypothesis Development The model's network of constructs presented in Fig. 1 is rooted in the cognitive–affective–conative loyalty framework. Oliver (1999, p. 35) proposed that the “analysis needed to detect true brand loyalty requires researchers to assess consumer beliefs, affect, and intention within the traditional consumer attitude structure.” According to this traditional attitude structure's order of cognitive–affective–conative responses, perceived deception and product satisfaction should precede company satisfaction, which in turn precedes loyalty responses (WOM communication). Specifically, our framework contends that perceived deception has a direct influence on product satisfaction (H1) and retailer satisfaction (H2). Hypotheses H3 and H4 capture the effects of product satisfaction on retailer satisfaction, and retailer satisfaction on WOM. In the sections that follow, we explain our reasoning for the theoretical predictions we derive. Hypotheses H1 through H4 are represented in Fig. 1, and we illustrate Hypotheses H5 and H6 shortly. We build on the expectancy disconfirmation paradigm (e.g., Oliver 1997) to support the influence of deception on consumers' product satisfaction (H1). This theory posits that consumers make a comparison between product expectations and performance that will result in either confirmation or

disconfirmation. Confirmation occurs when product performance meets consumers' prior expectations, whereas a discrepancy between expectations and performance leads to positive or negative disconfirmation. Positive disconfirmation takes place when product performance exceeds prior expectations and results in satisfaction. Consumers' expectations regarding the product are thought to be affected by several factors, including consumers' prior knowledge and experience, as well as information provided by the retailer. Certain types of products, such as technological ones (e.g., personal computers, electronic products, smartphones), the focus of our research, are complex and rapidly changing due to innovations, all of which enhance the importance of sellers' information as a source of knowledge for consumers. Consistent with past findings, we expect perceptions of deception to have a negative influence on product satisfaction, in part by the company having provided unrealistic expectations (Román 2010). In addition, we expect this effect to be stronger when consumers shop online for several reasons: 1) the online environment allows deceptive retailers to manipulate product information more easily than in traditional stores, 2) the relatively uncertain and impersonal nature of the Web diminishes consumers' ability to detect deception, 3) although the Internet offers more product information that may help buyers make more informed decisions, in the online channel consumers cannot physically experience the product, making the online shopping environment seem inherently riskier (Mathwick, Malhotra, and Rigdon 2001), thus consumers' expectations are more dependent upon and susceptible to the information provided by the retailer, and 4) online consumers must search for information, implying a greater amount of time and cognitive effort (Park, Hill, and Bonds-Raacke 2015). Given that the opportunities for deception are greater online, it is also more likely that consumers experience more negative surprises when they shop online than when they do it offline, thus leading to less product satisfaction due to deception. Stated formally: H1. Perceived deception will have a negative influence on product satisfaction. In particular, this effect will be stronger when consumers shop online than when shopping at traditional stores.

Fig. 1. Conceptual model and hypotheses H1–H4.

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Attribution theory (Folkes 1984; Weiner 1985) provides a potential explanation for the expected different influences of perceived deception on customer satisfaction with the online vs. traditional retailer. Consider that a negative shopping experience may influence consumer satisfaction judgments about the retailer differently depending on the different attributions that a consumer makes after the negative shopping experience. Specifically, according to locus-of-causality posited in attribution models, a dissatisfied consumer experience may be mainly attributed to external causes (e.g., retailer-related causes) or internal ones (e.g., consumer-related causes). For example, a consumer may believe that he or she has received a product that does not meet his or her previous expectations because he/she did not spend enough time evaluating shopping alternatives. Such a scenario would constitute an internal attribution. By comparison, a consumer may blame a bad purchase decision on the retailer's behavior during the purchase process if he/she believes that the retailer used misleading tactics in order to sell the product, and this would comprise an example of an external attribution. External attributions of bad consumer experiences lead to higher dissatisfaction with the company and less with the product, so we anticipate effects on retailer satisfaction level, but not on product satisfaction. When comparing across channels, research suggests that consumers are more likely to blame themselves when they shop online than when they shop at traditional stores because consumers perform more of the shopping process for themselves, and thus have more control over this process (Harris, Mohr, and Bernhardt 2009; Singh, Ratchford, and Prasad 2014). Stated formally: H2. Perceived deception will have a negative influence on retailer satisfaction. In particular, this effect will be stronger for consumers shopping at traditional stores than when shopping online. Most scholars posit a causal direction of product satisfaction having an effect on company satisfaction rather than the reverse (Chiou and Droge 2006; Oliver 1999; Westbrook 1981), and we predict this effect as well. We anticipate that this effect will be stronger in the traditional channel as compared to the online one. When consumers are shopping online, they are more likely to make internal attributions with an implication that, when satisfied with a purchase, online shoppers are likely to attribute the success to themselves, obversely translating into a weaker influence of product satisfaction on retailer satisfaction. Accordingly, we formulate the hypothesis: H3. Product satisfaction will have a positive influence on retailer satisfaction. In particular, this effect will be stronger when consumers shop at traditional stores than when shopping online. The positive influence of customer satisfaction with the retailer on WOM has been documented in the traditional retail context (e.g., Anderson 1998; Richins 1983), and in the online environment (e.g., Holloway, Wang, and Parish 2005). We expect consumer satisfaction with the retailer to have a stronger effect on WOM in the online context compared to the offline one. When a consumer feels satisfied with a specific retailer,

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he or she is likely motivated to share the experience by the desire to help other consumers in their purchase decisions. Given that online shopping entails a higher level of uncertainty and perceived risks than traditional shopping (Biswas and Biswas 2004), online WOM is likely perceived as more useful. In addition, when consumers have a satisfactory shopping experience, they may want to share this positive experience with the aim of enhancing their image and projecting themselves as intelligent shoppers, and doing so is facilitated with the many online sites on which consumers may offer feedback. Finally, some studies suggest that the online medium itself might reinforce the relationship between satisfaction and loyalty (Chu et al. 2010; Danaher, Wilson, and Davis 2003; De Matos and Rossi 2008), and we examine the extent to which an analogous relationship holds for WOM as well. Accordingly, we propose the following: H4. Retailer satisfaction will have a positive influence on WOM. In particular, this effect will be stronger when consumers shop online than in traditional stores. Next we derive hypotheses H5 and H6 which involve moderated mediation effects. Our theory predicts an indirect effect of perceived deception on retailer satisfaction through product satisfaction (H5), and on WOM through retailer satisfaction (H6). Furthermore, we expect that the purchase channel will moderate the strength of these indirect relationships, thereby fully demonstrating a pattern of moderated mediation. For online purchases, for many purposes, one website can seem nearly interchangeable with another, so whether a customer is satisfied with an online retailer is determined primarily by whether the customer is satisfied with the product purchased and received by that retailer. In turn, product satisfaction is driven by (lack of) perceived deception. Alternatively, understood from another angle, given the time and cognitive effort required to make an online purchase, the customer is likely to be more satisfied with the product if the product performs to expectations, in turn if the expectations had been properly calibrated by (lack of) deception. If the online information source seems transparent and the product is in accord with expectations, a downstream effect is that the customer considers the online retailer satisfactory. In sum: H5. The purchase channel will moderate the negative indirect effect of perceived deception on retailer satisfaction, through product satisfaction, such that this mediation effect will be more pronounced (stronger negative indirect effects) in the online channel than in the traditional one (Fig. 2). Our conceptual framework also contends that perceived deception will have an indirect negative influence on WOM through retailer satisfaction, which is expected to mediate this perceived deception–WOM link. We expect the indirect effects of perceived deception on WOM to be stronger in the traditional shopping channel. In traditional stores, there are numerous sources for perceived deception. One source is any advertisement or packaging information, just as in online purchasing. However, a perhaps more salient source is the store's frontline personnel. Customers may sense deception in

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their interpersonal dealings with the store's staff. If a customer were to perceive such deception, presumably the deception would impact the customer's opinion of the store that tolerates or encourages such positioning, rather than the product itself, which may well have been somewhat tangential in the interpersonal dealings with the store's staff, even in a pre-purchase state. Hence, we expect perceptions of deception to (negatively) impact retailer satisfaction and in turn impact downstream behavioral intentions, such as WOM in our study (or other related constructs such as loyalty intentions in other studies). Thus, we expect the following: H6. The purchase channel will moderate the negative indirect effect of perceived deception on WOM communication, through retailer satisfaction, such that this mediation effect will be more pronounced (stronger negative indirect effects) in the traditional channel than in the online one (Fig. 3).

surveyed them using our questionnaire. Study participants were asked to consider their most recent (within 6 months) purchase of technological equipment (PC, electronics, etc.) when making the rating judgments (e.g., whether the store or website “misrepresents product characteristics,” whether the consumer was “satisfied with my decision to purchase from this website/store,” all items appear in Table 2 presented shortly). Interviews typically lasted 15 minutes. Quota sampling was applied to obtain approximately evenly distributed numbers of respondents in the two shopping contexts. The final sample consisted of data from 409 consumers (208 who shopped online and 201 who shopped at traditional store). The sample profiles are shown in Table 1. The samples show reasonably good variability across gender and age, and the online and offline samples are not significantly different. Education shows a slight tendency for online respondents to be somewhat more educated.

Method

Instrument Development

Data Collection and Sample

All measurements were based on previous studies. Pre-tests of six in-depth interviews and a survey on a convenience sample of 60 consumers were conducted to assess the research variables and refine the survey. Respondents were asked to point out any questions they found confusing, irrelevant, or repetitive. Minor changes were made, and final items are shown in the Table 2 (shown shortly). Based on the results of the pre-tests conducted, we slightly modified Román's (2010) four-item perceived deception scale to measure online and offline perceived deception. Items refer to consumer beliefs about whether the online/offline retailer uses deceptive or misleading practices with the intent to persuade consumers to purchase the retailer's offerings. Importantly, these items are focused on consumer's perceptions of online retailer's deceptive practices rather than on the act of deceiving itself (Román 2007, 2010). Customer satisfaction with the product was measured using three items, a short form of a standard scale that has been successfully used in prior studies (Oliver 1999), e.g., “I am satisfied with my decision to buy this product.” Similarly, customer satisfaction with the retailer was measured using three items as a previously used short form of the scale from Anderson and Srinivasan (2003). Finally, consumers' WOM intentions were measured using a three-item scale adapted from Brown et al. (2005) and Maxham (2001). All scales were measured with 7-point Likert items.

The retail sector of technological products (e.g., personal computers, electronic products) was chosen as the context of this study for several reasons. First, technological products are among the most commonly physical-product category purchased through the Internet (Comscore Report 2013), which facilitated data collection in the online context. Second, they are characterized by product attributes for which a great deal of information can be gathered prior to purchase. Finally, technological products are generally high-involvement items, and consumers are typically engaged in a problem-solving task of moderate to high complexity. A survey was chosen as a data collection instrument. We hired a marketing research firm to assist with the data collection through personal interviews. As in previous research (Frambach, Roest, and Krishnan 2007; Román 2010), trained interviewers randomly selected respondents among individuals who passed the data collection point located on the pedestrian walkway in three major metropolitan cities. Respondents were screened for their age and their level of purchase experience with technological products. An invitation followed if the respondent proved to be eligible for the study (that is, he/she must be over 18 years, and should have purchased at least one technological product online or offline in the last six months). Then, study participants were taken to the company office, where specialist interviewers

Fig. 2. Conceptual model and hypothesis H5.

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Fig. 3. Conceptual model and hypothesis H6.

Results Instrument Validation Prior to testing the research hypotheses, the constructs were assessed for convergent and discriminant validity via confirmatory factory analysis (CFA) using linear structural relation models (LISREL 8.80). We checked the unidimensionality of each construct. Both online (χ 2(59) = 114.88 p b .01; GFI = .92; AGFI = .88; NNFI = .97; CFI = .97; RMSEA = .07; RMSR = . 05) and offline (χ2 (59) = 101.46 p b .01; GFI = .93; AGFI = .89; NNFI = .98; CFI = .98; RMSEA = .06; RMSR = .05) measurement models had a reasonably good fit. In addition, the observed normed χ2 for both online and offline models was 1.95 and 1.72 respectively, smaller than the 3 recommended by Fornell and Larcker (1981), indicating a good model fit. Following the procedures suggested by Bagozzi and Yi (1988) and Fornell and Larcker (1981), convergent validity was assessed by verifying the significance of the t-values associated with the parameter estimates. Table 2 shows that the path Table 1 Respondent characteristics (%). Demographic variable

Gender Male Female Age b20 20–35 36–50 N50 Education Low (bH.S.) Middle (high school) High (college degree) Occupation Employed full-time Self-employed Students Others (retired, stay-at-home, unemployed) ⁎ p b .05.

Whole sample (N = 409)

Online sample (n = 208)

Offline sample (n = 201)

χ2

59.4 40.6

58.2 41.8

60.7 39.3

0.27

9.0 48.9 33.3 8.8

8.7 50.5 32.2 8.7

9.5 47.3 34.3 9.0

0.44

12.0 46.2 41.8

7.7 47.6 44.7

16.4 44.8 38.8

7.52 ⁎

47.7 11.0 19.1 22.2

50.0 10.6 17.3 22.1

45.3 11.4 20.9 22.4

1.24

loadings for all of the questions were positive and statistically significant (p b .01) for both datasets. In Table 2, the reliability of the measures was also confirmed with the composite reliability index being higher than the recommended level of .60 (Bagozzi and Yi 1988) and the average variance extracted was higher than the recommended level of .50 (Bagozzi and Yi 1988, p. 80) for all latent constructs in both subsamples. Discriminant validity was tested by comparing the average variance extracted by each construct to the shared variance between the construct and all other variables (Fornell and Larcker 1981). As shown in Table 3, for each comparison, the explained variance exceeded shared variances, in both samples, thus confirming discriminant validity. Tests of Moderating Hypotheses Hypotheses H1 through H4 are moderated effects, whereby the relationships between perceived deception and its consequences are expected to differ according to the shopping channel (online versus offline). To establish whether these hypothesized differences were statistically different, multi-group analyses were performed using LISREL 8.8, in a series of nested models to examine group differences. To do so, we first seek to establish that the measurement model was invariant across the two samples, online versus offline. This test is conducted by evaluating the two samples nested in a hierarchical sequence with a less restrictive model, namely M1 is estimated with all the factor loadings freed across the two samples, used as a baseline for the evaluation of the more restrictive model, where M2 is estimated with all the factor loadings constrained to be equal across the two samples. These constraints increased the χ2 value from 247.14 to 267.33, on 13 degrees of freedom. The χ2 difference was not statistically 2 = 20.19; p N .05), so full metric invariance significant (Δχ13 was supported. Thus, we may test for differences in structural coefficients between the online and offline contexts. A third structural model was then estimated with all parameters (direct and indirect structural path coefficients) freed across the two samples (M3) and compared with a nested model (M4) in which all of these parameters were constrained to being equal across the online–offline samples (Iacobucci, Saldanha, and Deng 2007) The resulting χ2 difference test with 4 degrees of freedom was significant (Δχ42 = 12.89; p b .05), indicating that the structural path coefficients varied, as predicted, across contexts. Next, each

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Table 2 Construct measurement summary: results of convergent validity tests. Constructs and items

Online sample (n = 208)

Offline sample (n = 201)

Stdzd loading (t-Value)

AVE

CR

Stdzd loading (t-Value)

AVE

CR

Perceived deception 1) This web site/store exaggerates the benefits and characteristics of its offerings. 2) It misrepresents product characteristics 3) It uses misleading tactics to convince consumers to buy its products. 4) This site/store attempts to persuade you to buy things that you do not need

0.85 (14.81) 0.93 (17.25) 0.88 (15.68) 0.80 (13.66)

0.75

0.92

0.86 (14.90) 0.88 (15.61) 0.87 (15.33) 0.87 (15.22)

0.76

0.93

Satisfaction with the product 1) I am satisfied with my decision to buy this product. 2) Purchasing this product has been a good choice. 3) In general, this product meets my expectations.

0.87 (15.08) 0.80 (13.53) 0.90 (16.10)

0.74

0.89

0.79 (12.78) 0.89 (15.05) 0.85 (14.18)

0.71

0.88

Satisfaction with the retailer 1) I am satisfied with my decision to purchase from this website/store. 2) My choice to purchase from this website/store was a wise one. 3) I am happy I made my purchase at this website/store.

0.76 (12.37) 0.84 (14.09) 0.86 (14.63)

0.68

0.86

0.81 (13.66) 0.92 (16.61) 0.87 (15.11)

0.76

0.90

Consumer's word-of-mouth 1) I would recommend the website/store to someone who seeks my advice. 2) I would encourage friends and relatives to do business with this website/store. 3) I could say positive things about the website/store to other people.

0.77 (12.74) 0.95 (17.23) 0.86 (14.78)

0.74

0.89

0.71 (11.08) 0.92 (16.30) 0.87 (14.85)

0.70

0.87

AVE average variance extracted; CR scale composite reliability.

hypothesis was tested, and as Table 4 indicates, the paths for H1 and H2 were in the directions predicted and they differed between online and offline contexts, significant in one shopping channel but not both. As predicted in the first part of H3, product satisfaction has a significant positive influence on retailer satisfaction, but the result held true in both shopping channels, thus the channel moderator does not hold for H3. Finally, the expected relationship between retailer satisfaction and consumer's WOM was confirmed in both shopping channels, and it was stronger in the online channel, thus, H4 was also supported. Structural path estimations for direct and indirect effects in both shopping channels are shown in Fig. 4. Tests of Moderated Mediation Hypotheses Hypotheses H5 and H6 contended the moderated mediation effects proposed in our research model. To test these relationships, we used a multigroup SEM analysis by (1) fitting one model via SEM, so the direct and indirect paths are fit simultaneously and freed across the two subsamples (online vs. offline shoppers) so as to estimate effects while partialling out, or statistically controlling for all other effects; (2) comparing this model with one in which the direct and indirect paths are constrained to be equal across the two subsamples; and (3) computing the Sobel (1982) z-test to check explicitly the relative sizes of the indirect (mediated) vs. direct paths in each group on analysis (Baron and Kenny 1986; Iacobucci, Saldanha, and Deng 2007). The first and second steps were addressed in the tests of moderating hypotheses (Table 4), thus we turn now to the third step to further validate findings of this moderated mediation relationships. We conducted a statistical significance test based on Sobel (1982) exact standard error for indirect effects to compute a z

statistic for the indirect effect. This z-test allows us to determine the relative sizes of the indirect (mediated) vs. direct paths in each group. Results in Tables 5 and 6 show that the indirect and negative effect of perceived deception on retailer satisfaction through product satisfaction was stronger and significant in the online group (indirect effect = − 0.11; p b .05), and weaker and not significant in the offline group (indirect effect = − 0.05; p N .05), which supports H5. Moreover, as the direct influence of perceived deception on retailer satisfaction in the online channel was not significant (β = − 0.09; p N .05), we can conclude from these results that product satisfaction fully mediates the relationship between perceived deception and retailer satisfaction in this channel. In contrast, in the traditional channel the indirect effect was not significant and, thus, in this channel we can conclude that perceived deception only influences retailer satisfaction directly. Finally, results for H6 are as predicted (see Table 6), whereby the indirect effects of perceived deception on WOM through retailer satisfaction were stronger and significant in the traditional channel (indirect effect = − 0.12; p b 0.05), but weaker and not significant in the online one (indirect effect = − 0.06; p N 0.05), which support H6. The insignificant direct influence of perceived deception on WOM in the traditional channel (rival models) indicates that such influence is fully mediated by the negative effect of deception in retailer satisfaction. Rival Models To test the robustness of a model, it is generally agreed that researchers should compare the fit of rival models. Accordingly, we tested two alternative models intended to be meaningful and not just “straw” hypotheses. For example, we have posited product satisfaction as an antecedent of retailer satisfaction, yet

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Table 3 Mean, SD, correlations and shared variances between latent variables. Construct

1. Perceived deception 2. Satisfaction with the product 3. Satisfaction with the retailer 4. Consumer's WOM

Online sample (n = 208) Mean

sd

1

3.61 5.43 5.35 5.22

1.27 0.83 0.72 0.88

− 0.26* − 0.10 − 0.09

Offline sample (n = 201) 2

3

4

Mean

sd

1

0.07

0.01 0.35

0.01 0.00 0.41

3.87 5.58 5.36 5.19

1.35 0.82 0.79 0.81

− 0.10 − 0.19* − 0.02

0.59* 0.05

0.64*

2

3

4

0.01

0.04 0.29

0.00 0.00 0.32

0.54* 0.04

0.57*

Shared variances of multi-item measures are reported in the upper half of the both matrix. Correlations are reported in the lower half of the both matrix: *p b .05.

one might argue the opposite directionality, so we fit a model in which the direction of the path between them was reversed. The resulting test statistics (χ2(148) = 275.74; GFI = .91; CFI = .96; NNFI = .96; RMSEA = .06) were borderline acceptable, and inferior to our model. In addition, we have posited a fully mediating role of retailer satisfaction in the perceived deception–WOM link, yet one might argue that perceived deception might also have a direct impact on consumer's behavioral intentions. Accordingly, we added a direct path from perceived deception to WOM and from product satisfaction to WOM. This model had a χ2(144) value of 264.37. The decrease of the chi-square (from the χ2 value of 271.04 in M3) was not significant (χ2(4) = 6.67; p N .05), yet none of the new paths were significant, indicating that these paths were not necessary or meaningful empirically. This benchmark model offers strong support in favor of our model—this alternative model contains more paths and therefore could very well have fit much better. Yet it did not. In short, we believe that our model should serve well as an appropriate basis for further research.

online channel, the moderated mediation revealed that perceived deception influenced retailer satisfaction through product satisfaction. By comparison, in the traditional channel, perceived deception impacted WOM, being fully mediated by retailer satisfaction and, given that the relationship between retailer satisfaction and WOM was weaker in this channel as compared to the online one, perceived deception had a stronger negative indirect effect on WOM in this channel. This research provides important theoretical and managerial contributions. Theoretical Contributions The results of this research indicate that the conceptual model fits well and outperforms the competing models. The findings also support the heretofore-untested indirect effects that perceived deception has on retailer satisfaction via product satisfaction in the online channel, as well as the mediating role of retailer satisfaction in the link from perceived deception to WOM for the traditional retail channel. Collectively, the results both support and build on the extant literature. Our findings indicate that perceived deception directly and indirectly leads to both product and retailer satisfaction, and indirectly to WOM, and that this pattern of relationships depends upon the retail context. In addition, the two shopping channels analyzed in this study allow us to highlight some important nuances regarding prior theorizing. Specifically, our results showed a different pattern of the relationships among deception and the two types of satisfaction

Discussion and Conclusions This research investigates the influence of customers' perceptions of retailers' deceptive practices on the customers' satisfaction with the product, satisfaction with the retailer, and subsequently WOM, with a particular focus the different effects associated with online or in-store shopping. In the Table 4 Model comparison and parameter estimates. Model

χ2

df

p-Value

GFI

NNFI

CFI

RMSEA

M3: Unrestricted (All structural relationships free) M4: Restricted (Structural relationships invariant) Difference in χ2

271.04

148

0.00

0.92

0.98

0.98

0.06

283.93

152

0.00

0.91

0.97

0.97

0.06

12.89

4

0.01

Conclusion: structural paths vary with channel

Paths 1–4 compared with restricted model Free path H1: Perceived deception → Satisfaction with the product H2: Perceived deception → Satisfaction with the retailer H3: Satisfaction with the product → Satisfaction with the retailer H4: Satisfaction with the retailer → consumer's WOM ns not significant. ⁎ p b .05. ⁎⁎ p b .01.

Chi-square difference (Δdf = 1) Δχ2 Δχ2 Δχ2 Δχ2

= = = =

8.98 ⁎⁎ 8.72 ⁎⁎ 1.88 (ns) 4.20 ⁎

Stdzd path coefficients Online context β = − 0.21 (t β = − 0.09 (t γ = 0.59 (t β = 0.64 (t

= = = =

Offline context − 3.24) − 1.21) 8.09) 8.83)

β = − 0.10 (t β = − 0.19 (t γ = 0.55 (t β = 0.56 (t

= = = =

− 1.55) − 2.42) 7.83) 7.02)

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Fig. 4. Structural model (standardized coefficients for online/offline context). ns = not significant. 1Indirect effect of perceived deception on consumer's satisfaction with the retailer through product satisfaction. 2Indirect effect of perceived deception on consumer's WOM through retailer satisfaction.

addressed in this study between the online and traditional channels. Consistent with prior evidence found in traditional settings, perceived deception had a strong and negative influence on consumer satisfaction with the retailer, however, previous studies did not include product satisfaction in their models, so our results expand these previous findings and show more complex inter-relationships among deception, and product and retailer satisfaction, especially as they differ across the channel medium. The inclusion of product satisfaction as a distinct construct that is related to yet different from retailer satisfaction allowed findings from this study to also add to the prior literature by showing that the way in which perceived deception negatively impacts satisfaction differs online compared to the offline context. As predicted, perceived deception was found to have no direct influence on WOM neither online nor offline (in testing the rival models). Rather, this expected negative relationship between perceived deception and WOM was fully mediated by retailer satisfaction in the traditional shopping channel, and was stronger there than in the online channel. The mediating role of retailer satisfaction between perceived deception and WOM is important, clarifying somewhat the nature of the relationships among these perceptions, satisfaction and behavioral intentions.

Some studies have shown that consumers' initial evaluations (e.g., perceived service quality) will affect behavior (i.e., customer loyalty, WOM, price premiums, and repurchase intentions) only through customer satisfaction, whereas other studies argued that these initial consumer evaluations might have a direct impact on consumers' behavioral intentions. This lack of general agreement has thus motivated numerous calls for further research into these complex inter-relationships between consumer satisfaction, loyalty and their antecedents in different purchase contexts. Findings from our research begins to tie both literatures together, showing that in the traditional channel the relationship between perceived deception and WOM is indirect and conducted through the negative direct effect that deception has on retailer satisfaction. Thus, these findings highlight the importance of studying motivations and means for how consumers may generate WOM regarding their consumption experiences across different retail contexts. In addition, the marketing literature has long stated the expected positive influence of satisfaction on favorable behavioral intentions in the traditional channel context. Yet, to the best of our knowledge, our study is one of the first to show the strong and positive influence of retailer satisfaction on consumers' WOM in the online context. Our results are not only consistent with

Table 5 Moderated mediated results with channel as moderator: perceived deception → product satisfaction → retailer satisfaction.

Table 6 Moderated mediated results with channel deception → retailer satisfaction → WOM.

Moderator (purchase channel)

Path

SE

z

Moderator (purchase channel)

Path

SE

z

Online channel Traditional channel

− 0.11 − 0.05

0.04 0.03

− 2.93 ⁎ − 1.68

Online channel Traditional channel

− 0.06 − 0.12

0.04 0.05

−1.08 −2.44 ⁎

⁎ p b .05.

⁎ p b .05.

as

moderator:

perceived

I.P. Riquelme et al. / Journal of Interactive Marketing 35 (2016) 16–26

previous findings in traditional settings (e.g., Anderson 1998; Richins 1983), but also show that the relationship that exists between retailer satisfaction and consumer's WOM can be stronger in the online environment as compared to the traditional one. Our findings also indicate that the online medium may intensify the relationship between satisfaction and consumer behavioral intentions, in our case, operationalized as WOM (Chu et al. 2010; Danaher, Wilson, and Davis 2003). For example, our findings might suggest that the higher levels of perceived risks and uncertainly usually associated with online shopping (Biswas and Biswas 2004) could intensify the desire to offer WOM as a result of a satisfactory experience with an online retailer, given that consumers may perceive that engaging in WOM communications is more useful for helping other consumers who shop online than for helping others who shop at traditional stores. Moreover, it is also plausible that satisfying online shopping experiences may be more strongly linked with WOM communications, as sharing positive experiences in the online channel may contribute to enhancing one's personal image (i.e., being a knowledgeable online shopper). Managerial Implications There are a number of managerial implications derived from this study. Our results highlight the negative consequences of perceived deception on consumer satisfaction and WOM to both online and traditional retailers. Accordingly, retailers need to pay close attention to consumers' perceptions of deception, and different recommendations can be made based on the different ways in which deception is perceived in each shopping channel. In the online channel, our results indicate that perceived deception negatively affects retailer satisfaction mainly through its negative impact on product satisfaction. Thus, online retailers should pay special attention to those practices that may lead to unrealistic or misleading expectations about their products. Specifically, derived from our conceptualization and measurement of deception, online retailers are encouraged to be especially careful with information content and also with information presentation in their websites. Information content should be reliable and entirely honest in order to clarify consumers' expectations and lessen consumers' perceptions of deception. This transparency means avoiding the common practice in the online setting of using misleading “special price discounts” or presenting an offer with an initial price that seems to be more attractive than it actually is, leading consumers to feel deceived when such attractive initial price is far from the real price finally required at the end of the purchase process. Regarding information presentation, online retailers need to pay particular attention to the influential power inherent in their visual displays and their capability to distract the consumers' attention from pertinent information. In the traditional retail setting, the primary negative effects of perceived deception are directly related to retailer satisfaction, indicating that consumers are likely to blame the retailer or its frontline staff when they feel that have been deceived. Traditional retailers should have a strong additional motivation

25

to avoid deception because, as our findings also showed, its indirect negative effects on consumer's WOM were stronger in this context. Accordingly, in this context managers should place special emphasis on their frontline employees' ethical orientation, as they are, for most retail firms, the most visible representatives for the company and the main vehicle of information exchange between the company and their customers. Therefore, we recommend that managers: (1) design training programs to help employees identify potential deceptive behaviors and appropriate responses for avoiding these actions; and (2) design compensation and evaluation plans that would motivate and reward ethical behavior. Our findings also revealed that WOM communication was significantly and strongly associated with increased satisfaction, especially in the online medium. Moreover, results from this study also suggest that when deception occurs in online shopping, consumers seem to not engage in WOM communications among their acquaintances, but it is likely that this negative experience leads to more negative outcomes for the retailer, that is, to spread negative WOM on the Internet, where negative comments have a significantly increased scope and diffusion. While obviously we would endorse a blanket statement that marketers should eliminate or minimize deceptive practices, and any ambiguities that might lead to perceptions of deceptions, this research indicates that engaging in anything resembling perceived deception can be particularly damaging online. Therefore, derived from our results, we encourage retailers to both avoid online deception and to allocate their online marketing efforts between satisfaction and WOM initiatives. This research demonstrated the moderated mediation effects of consumers' perceptions of retailers' deceptive practices on their evaluations of product satisfaction, retailer satisfaction and word-of-mouth. In online shopping, perceived deception affected retailer satisfaction through product satisfaction, whereas in the traditional store shopping contexts, perceived deception affected WOM through retailer satisfaction. Acknowledgments The authors are grateful to the Editor and Reviewers for their helpful suggestions regarding this research. This research was supported by the grant ECO2012-35766 from the Spanish Ministry of Economics and Competitiveness and by the Fundación Séneca-Agencia de Ciencia y Tecnología de la Región de Murcia (Spain), under the II PCTRM 2007–2010. References Aditya, Ram.N. (2001), “The Psychology of Deception in Marketing: A Conceptual Framework for Research and Practice,” Psychology and Marketing, 18, 7, 735–61. Anderson, Eugene W. (1998), “Customer Satisfaction and Word of Mouth,” Journal of Service Research, 1, 1, 5–17. Anderson, Rolph.E. and Srini S. Srinivasan (2003), “E-Satisfaction and E-Loyalty: A Contingency Framework,” Psychology and Marketing, 20, 2, 123–38. Bagozzi, Richard P. and Youjae Yi (1988), “On the Evaluation of Structural Equation Models,” Journal of the Academy of Marketing Science, 16, 1, 74–94.

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