Not all adaptive selling to omni-consumers is influential: The moderating effect of product type

Journal of Retailing and Consumer Services ∎ (∎∎∎∎) ∎∎∎–∎∎∎

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Not all adaptive selling to omni-consumers is influential: The moderating effect of product type Yuliya Yurova a, Cindy B. Rippé b, Suri Weisfeld-Spolter a,n, Fiona Sussan c, Aaron Arndt d a

Huizenga College of Business and Entrepreneurship, Nova Southeastern University, 3301 College Ave Ft. Lauderdale, FL 33314, Unites States of America College of Business Administration, Tarleton State University, Box T-0200 Stephenville, TX 76402, United States of America c School of Advanced Studies, Center for Global Business Research, University of Phoenix, 1625 West Fountainhead Parkway Tempe, AZ 85282, Unites States of America d Strome College of Business, Old Dominion University, 2040 Constant hall Norfolk, VA 23529 United States of America b

art ic l e i nf o

a b s t r a c t

Keywords: Salesperson Adaptive selling Retailing Multichannel consumer Omni-channel Purchase intention Russia U.S. Singapore U.K. Hedonic Utilitarian

Computer-mediated technologies have resulted in a proliferation of the omni-channel consumer (OCC) who shops for products and services using mobile, online, and traditional retail channels. While OCCs may have greater access to information, they do not necessarily have access to accurate information; hence the salesperson has both a challenge as well as an opportunity to use adaptive selling techniques when selling to the OCC. To better understand under what circumstances the salesperson can best be utilized to bring about the sale with the OCC, this research develops and evaluates a model of adaptive selling behaviors when selling to omni-channel consumers around the globe. Adaptive selling behaviors are conceptualized as having two dimensions, non-interactive and interactive adaptation. The efficacy of these two types of adaptive selling behaviors depends upon product type (utilitarian, hedonic) and OCCs' perceived control over the buying situation. To test the hypotheses, survey data was collected from global OCCs in four different countries and evaluated using path analysis. Results suggest salesperson’s influence depends upon product type and salesperson's adaptive selling behavior. & 2016 Published by Elsevier Ltd.

1. Introduction The evolution of interactive digital media has made selling to consumers increasingly complex and difficult (Crittenden et al., 2010). There is a growing number of omni-channel consumers (OCC) who shop for products and services using more than one retail channel, such as brick-and-mortar retailers, catalogs, websites, and mobile devices (Verhoef et al., 2015; Kumar and Venkatesan, 2005). For example, an OCC might research a product's specifications on a mobile device, compare several brands in a retail store, and then purchase the product through the least expensive website. Unlike single channel consumers buying in a brick-and-mortar retail, OCCs learn about product specifications and pricing from a wide variety of sources (Tanner et al., 2005), and generally do not rely on the salesperson as the primary conduit of information (Crittenden et al., 2010). Indeed, a recent survey showed that 59% of consumers in a retail store preferred to n

Corresponding author. E-mail addresses: [email protected] (Y. Yurova), [email protected] (C.B. Rippé), [email protected] (S. Weisfeld-Spolter), [email protected] (F. Sussan), [email protected] (A. Arndt).

look up information on their phone rather than ask a salesperson for help (Harris Interactive, 2013). Thus, there is a need to explore the unique challenges of selling to OCCs. OCCs exhibit appreciably different search and buying behaviors from non-OCCs (Verhoef et al., 2015). Although OCCs have greater access to information than single channel consumers, they do not search the same channels for every purchase (Strebel et al., 2004). OCCs often use different strategies for gathering information for utilitarian products as compared to hedonic products (Childers et al., 2001; Pookulangara et al., 2011). For utilitarian products, OCCs search the channels that they consider to be the most useful (Childers et al., 2001). However, when shopping for hedonic products, consumers gather information from the most pleasurable channels because enjoyment of the shopping experience is more important than efficiency (Childers et al., 2001; Pookulangara et al., 2011). Consequently, some OCCs will enter a retail store already having considerable information about the purchase while other OCCs might have very little information. One aspect of adaptive selling behavior is modifying the substance, quantity, frequency, and timing of information shared based upon customer needs (Eckert, 2006). This paper posits that salespeople will need a greater ability to adapt informational content when selling to OCCs

http://dx.doi.org/10.1016/j.jretconser.2016.01.009 0969-6989/& 2016 Published by Elsevier Ltd.

Please cite this article as: Yurova, Y., et al., Not all adaptive selling to omni-consumers is influential: The moderating effect of product type. Journal of Retailing and Consumer Services (2016), http://dx.doi.org/10.1016/j.jretconser.2016.01.009i

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Y. Yurova et al. / Journal of Retailing and Consumer Services ∎ (∎∎∎∎) ∎∎∎–∎∎∎

than when selling to single channel consumers. A significant challenge to selling to OCCs is the practice of “showrooming,” which is the practice of “using mobile technology while in-store to compare products for potential purchase via any number of channels,” (Rapp et al., 2015, p. 360). Although showrooming causes some salespeople to disengage, other salespeople feel it is an opportunity to ask customers if they have additional questions and discuss unclear information (Rapp et al., 2015). Customers with mobile devices can access information and opinions from a variety of sources, including friends, competitors, consumer-to-consumer reviews, and even other channels at the focal retailer. Hence, salespeople must not only adapt their communication style and presentation based upon the customer's behaviors (i.e., traditional adaptive selling) but also based upon the information the customer is accessing from channels outside of the store during the in-store sales encounter. Consequently, the need for salespeople to adapt their selling is greater when selling to OCCs than when selling to single channel consumers. Simintiras et al. (2013) developed an innovative model explaining the conditions in which retail salespeople are more likely to practice adaptive selling behaviors; however, they did not address the unique challenges of adaptive selling to OCCs. Consequently, the first purpose of this study is to address this gap by developing and testing a framework of adaptive selling behaviors when interacting with OCCs. Definitions of adaptive selling almost always specify adaptation both “during a customer interaction or across customer interactions” (Weitz et al., 1986, p. 175); hence, there are two categories of adaptive behaviors, adaption between customers and adaption during a customer interaction. Although McFarland et al. (2006) and Hall et al. (2015) investigated adaption of tactics across customer interactions and Arndt et al. (2014) examined the adaption of tactics during customer interactions, no research was found to have separately investigated the efficacy of both categories of adaption. As a second contribution, and consistent with the core definition of adaptive selling and the unique characteristics of OCCs, it is proposed that adaptive selling behaviors to OCCs requires two subdimensions: (1) non-interactive adaptive selling behavior, which is the attempt to evaluate the informational needs of each individual OCC, and (2) interactive adaptive selling behavior, which is the willingness to adjust selling style, presentation, and solutions during an interaction with an OCC. We investigate the efficacy of each dimension on customer purchase intention. Furthermore, because OCCs may use different search strategies for utilitarian and hedonic products (Childers et al., 2001; Pookulangara et al., 2011), the model is tested separately for both types of goods. Additionally, people with greater perceived control over the sales encounter feel empowered to influence the outcome of the encounter (Hui and Bateson, 1991). OCCs who believe that they know more about a purchase than salespeople tend to perceive themselves as having more control over the sales encounter (Rippé et al., 2015). Thus, the influence of perceived control is explored for each product type. Finally, research has shown that OCCs are a growing global phenomenon (e.g., Schlager and Maas, 2013) so it is important to test the model in multiple cultures, as it is expected that the model will be generalizable across national cultures. Park and Deitz (2006, p. 10) studied the adaptive selling behaviors (ASB) of Korean automobile salespeople and explained, “We believe firmly that the efficacy of ASB is a function of the selling situation and not national culture.” Therefore, despite macro- and micro- levels of differences between countries and consumer behavior, it is suggested that OCCs' purchase intention behavior is universally impacted by salespersons' adaptive selling practices. Accordingly, as a final contribution, this research collects samples from OCCs in two western and two non-western countries to

increase the generalizability and robustness of the model with regards to the influence of adaptive selling in different contexts. The next section develops the theoretical model and specifies the hypotheses. Then the empirical study in which a structured survey was administered to a sample of 340 OCCs from the U.S., the United Kingdom, Russia, and Singapore is discussed. Using the data, the theoretical model is evaluated. Finally, the results and implications of the findings are examined.

2. Adaptive selling behavior Adaptive selling is a tool exclusive to the channel of selling. While other channels can supply general product information that is easily available, they cannot replace a salesperson's skills of persuasion, adaptability, and capability (Ahearne and Rapp, 2010) which facilitate connections with the OCC through verbal and nonverbal communication. Researchers generally define adaptive selling as having two components, adaption between customers and adaption during customer transactions (Franke and Park, 2006; Weitz et al., 1986). A growing body of research has begun to look at these two categories of adaption separately. For example, McFarland et al. (2006, p. 112) used “theory to pinpoint the appropriate tactics for different buyers,” and Arndt et al. (2014) showed that strategies used to build rapport are not necessarily effective at addressing customer objections. Thus, adaptation between customers and adaption within a sales encounter are different behaviors that may have separate antecedents and consequences. It is therefore proposed that adaptive selling behaviors should be categorized as (1) non-interactive and (2) interactive adaptive selling behaviors. Prior to entering a retail store, OCCs have the opportunity to learn about products and services from a range of sources, from technological sources such as websites and mobile devices to conventional sources such as word of mouth and catalog (Kumar and Venkatesan, 2005). Yet, OCCs seldom use every source available. According to Strebel et al. (2004) information channels act as substitutes for one another where OCCs select the most desirable channels to gather information and forgo less desirable ones. Accordingly, OCCs vary greatly in how much knowledge they already possess and what information they would gather from the retail channel. Salespeople with greater non-interactive adaptive selling behavior attempt to customize their information presentation to match the OCC’s informational needs. In the business-to-business context, McFarland et al. (2006) demonstrated that salespeople who matched their selling style to customer buying styles had greater performance than salespeople who failed to match the customer's buying style. Thus, salespeople who offer the right amount of information to customers should be more effective than those who offer more or less information than customers want. Consistant with their findings, it is proposed that salespeople with greater non-interactive adaptive selling behaviors should be more effective than salespeople who provide the same information to all OCCs. More formally: H1a. : The more OCCs perceive that retail salespeople are using noninteractive adaptive selling behaviors the greater OCC purchase intention. Although research has shown that salespeople who are able to adapt their presentation during the customer interaction are more effective than less adaptive salespeople (Franke and Park, 2006), it is proposed that adaption during customer interaction may be particularly important when selling to OCCs because of showrooming behavior. Showrooming complicates the selling processes in several ways. First, customers may become aware of additional

Please cite this article as: Yurova, Y., et al., Not all adaptive selling to omni-consumers is influential: The moderating effect of product type. Journal of Retailing and Consumer Services (2016), http://dx.doi.org/10.1016/j.jretconser.2016.01.009i

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information during the sales encounter that changes customer attitude towards the product or service. Second, salespeople may find information they provided to customers earlier contradicted by outside sources. Contradictions could reduce trust in the salesperson even though research has shown repeatedly that information available online is not necessarily accurate (Weiss et al., 2008). When salespeople are contradicted by an online source, they would have to adapt their strategy to prevent the loss of customer trust. Third, showrooming can introduce outside opinions into a sales conversation. For example, customers can take pictures of products on mobile devices, send them to and elicit the opinions of their friends. Furthermore, electronic word-of-mouth (eWOM) from competitor websites and online customer reviews may offer advice and suggestions, with some eWOM being perceived as more credible and persuasive than others (Weisfeld-Spolter et al., 2014). Thus, salespeople must be able to adapt during the sales interactions and handle objections based upon the opinions and eWOM of influential parties who are not physically present. Although addressing customer objections, such as those caused by showrooming, can be challenging, it can also be an opportunity for building customer rapport (Arndt et al., 2014; Campbell et al., 2006). For example, Arndt et al. (2014) found that customers were more satisfied and had stronger perceptions of relationship building when salespeople successfully addressed their concerns. Accordingly, salespeople who have higher interactive adaptive selling behaviors should have better performance than salespeople who are less able to adapt their presentation. H1b. : The more OCCs perceive that retail salespeople are using interactive adaptive selling behaviors the greater their purchase intention. Hedonic and utilitarian products are two general categories of products purchased by consumers, often motivated by different needs, and resulting in different emotional feelings and levels of involvement (Babin et al., 1994). Utilitarian buying motives include variety and convenience-seeking, searching for quality of merchandise, and reasonable price rates, while hedonic buying motives are related to emotional needs of individuals for enjoyable and interesting shopping experiences (Babin et al., 1994). For utilitarian products, OCCs are primarily oriented towards making an efficient and effective purchase (Babin et al., 1994). Accordingly, salespeople must be able to quickly contribute informational value to the purchase experience or else OCCs will consider them to be irrelevant. Conversely, for hedonic products, the ability to touch the product and interact with the salespeople, can add value to the consumer, and in fact, may be a reason that the OCC purchases in-store rather than online (Alba et al., 1997). Even if salespeople do not immediately contribute informational value they can still add to the shopping experience (Cox et al., 2005). Therefore, non-interactive adaptive selling should be more important for utilitarian products than for hedonic goods while interactive is more important for hedonic goods than utilitarian goods. Taken together: H2a:. Non-interactive adaptive selling behaviors have a larger impact on an OCC’s purchase intention when purchasing a utilitarian product than when purchasing a hedonic product. H2b:. Interactive adaptive selling behaviors have a larger impact on an OCC's purchase intention when purchasing a hedonic product than when purchasing a utilitarian product. Furthermore, OCCs also differ in the extent to which they perceive themselves to be in control when speaking to the salesperson. According to Kidwell and Jewell (2010) people who perceive themselves as being more in control are less motivated to

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engage in deliberative processing when formulating purchase intention. In other words, control diminishes the importance of events that occur during the retail transaction. The way consumers feel about and respond to service encounters depends largely on their perceptions of control (Hui and Bateson, 1991). Accordingly, OCCs who enter a store with a higher initial purchase intention and higher perceived control will have a greater intention to buy, regardless of the actions of the salesperson (Rippé et al., 2015). H3a.. OCCs' perceived control positively impact purchase intention. H3b.. OCCs' initial purchase intention is positively related to OCCs' purchase intention. Nonetheless, adaptive selling behaviors still play a role even when perceived control is high. According to Kirmani and Campbell (2004), customers can choose to either engage with salespeople to achieve their consumption goals or avoid salespeople. For utilitarian products, customers are focused on selecting the most optimal product choice as efficiently as possible (Strebel et al., 2004). Hence, customers who are buying utilitarian products will be more likely to avoid salespeople unless the salesperson is an efficient and effective source of information. This is particularly true for customers with a higher perceived control in the sales interaction because those customers feel more comfortable disengaging from the salesperson. Thus, when buying utilitarian products, customers with high perceived control will be more likely to disengage from salespeople unless the salesperson can immediately offer relevant information. Consequently, non-interactive adaptive selling behaviors are valuable when selling to OCCs shopping for utilitarian products who have high perceived control. However, if the OCC withdrawals from the sales encounter, the ability to practice interactive adaptive selling is irrelevant because the OCC has already withdrawn from the salesperson. Conversely, OCCs shopping for hedonic products are unlikely to enter a retail store unless they feel that the retail store is an enjoyable shopping channel. Accordingly, perceived control should have less effect on whether salespeople can immediately offer relevant information. Still, OCCs with higher perceived control may be more comfortable voicing objections and actively directing the sales interaction. Furthermore, OCCs with higher perceived control should be more likely to break off a sales interaction if the salesperson does not practice sufficient interactive adaptive selling behaviors. Hence, salespeople selling to OCCs shopping for hedonic goods have a greater need to practice interactive adaptive selling behavior when OCC's have high perceived control. H4a.. The interaction between non-interactive adaptive selling and perceived control is stronger for utilitarian than hedonic products. H4b.. The interaction between interactive adaptive selling and perceived control is stronger for hedonic than utilitarian products.

3. Methodology 3.1. Study design To test the hypotheses depicted in Fig. 1, an online questionnaire was administered to a sample of consumers in four countries-U.S., U.K., Russia and Singapore. This multi-country design approach was chosen because OCCs are similar in their information search behaviors across cultures (Rippé et al., 2015) and adaptive selling behaviors are similar across nations and cultures (Park and Deitz 2006). Given that there should not be cross-cultural differences between OCCs and adaptive selling, the data were pooled together to assess and generalize the conceptual model.

Please cite this article as: Yurova, Y., et al., Not all adaptive selling to omni-consumers is influential: The moderating effect of product type. Journal of Retailing and Consumer Services (2016), http://dx.doi.org/10.1016/j.jretconser.2016.01.009i

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Fig. 1. Hypothesized model of adaptive selling, perceived control, product type and purchase intention.

The questionnaire distributed in Russia was translated using a back translation procedure with two independent bilingual experts to ensure semantic equivalence of the constructs (Brislin, 1970) and then verified by a Russian-native researcher. For OCCs measurements, respondents answered three questions about their comparison shopping and information search: (1) “I compare the prices of different stores through the Internet, mobile devices, catalogs, retail stores, or in other ways before finally deciding where to do my shopping,” (2) “I shop back and forth between online and retail stores before choosing where I will make a purchase,” and (3) “I make an extra effort in the beginning of purchasing a product to search for information about the product.” This measurement was used previously in Rippé et al. (2015) multi-channel consumer classification and is similar to Johnson et al. (2006) OCCs and non-OCCs categorization. Respondents who answered 4 or higher were classified as OCCs. For hedonic versus utilitarian product classification (Kushwaha and Shankar, 2013), respondents were asked to recall their latest visit to a retail store within the last month and to recount the product for which they were shopping. Three researchers independently reviewed the answers given by respondents and classified them as hedonic or utilitarian according to Kushwaha and Shankar’s definitions (2013, p.70). 3.2. Measurement scales Existing scales were used to measure initial purchase intention (IPI), actual purchase intention (API), interactive adaptive selling (IAS) and non-interactive adaptive selling (NAS), and perceived control (PC). IPI and PI were measured using three items (e.g., “I would purchase the product”) of the behavioral intention scale (Ajzen and Fishbein, 1980). For IAS, the scale of adaptive selling (Robinson et al., 2002) was modified to reflect the views of the consumer as opposed to the seller as suggested by Pettijohn et al. (2000). The questions “When the salesperson’s approach does not work, he changes it to another approach”, “The salesperson likes to experiment with different sales approaches”, and “The salesperson

can easily use a wide variety of selling approaches” were used to measure the salesperson interactive selling approaches. A single question “The salesperson understands how one customer differs from another” was used to measure NAS. Four questions from Collier and Sherrell (2010) were used to measure PC depicting the degree of control the customer feels in a particular buying situation. Demographic data of age, gender, and education were also collected but had no statistically significant effect and thus their results not reported here. Harman's single factor test failed to detect substantial common method variance in both samples, and no difference was reported in the variation of scales, 5 vs. 7 point, used in the survey.

4. Results 4.1. Sample From a total of 407 usable surveys collected, 340 were identified as OCCs (sample size by country: U.S. ¼97; U.K. ¼94; Russia ¼71; Singapore¼78). From the 340, 107 shopped for a utilitarian product and 233 for a hedonic product. About 67% of respondents were females; 42% were 26–34 years old, 32% were 35 years or older, and 70% had a 2-year college or advanced degree. 4.2. Estimation procedure Structural equation modeling utilizing Partial Least Squares Path Modeling (PLSPM) was used to test the hypotheses. PLSPM relaxes requirements for sample size and distributional assumptions allowing for distribution-free bootstrap tests to be conducted when testing hypotheses (Chin and Newsted, 1999). Our data was not normally distributed, thus justifying the use of PLSPM method. We first estimate the hedonic and utilitarian sub-samples separately to ensure the group invariance. After that, we test the moderating effect of interactive and non-interactive adaptive

Please cite this article as: Yurova, Y., et al., Not all adaptive selling to omni-consumers is influential: The moderating effect of product type. Journal of Retailing and Consumer Services (2016), http://dx.doi.org/10.1016/j.jretconser.2016.01.009i

Y. Yurova et al. / Journal of Retailing and Consumer Services ∎ (∎∎∎∎) ∎∎∎–∎∎∎ Table 1 Measurement model. Constructs and instrument variables

Table 2 Descriptive statistics, correlations, and discriminant validity. Utilitarian (n¼ 107) SL

OCC's Initial Purchase Intention (IPI) IPI1. How likely IPI2. How certain IPI3. How definite

OCC's Perceived Control (PC) PC1. Feel in control during the interaction PC2. Being in charge PC3. Feel decisive PC4. Feel in control over purchasing decision

AVEa

.916** .928** .909**

.843

.682**

.629

Rhob

.941

.870

.733** .888** .850**

Salesperson's Interactive Adaptive Selling (IAS) IAS1. Changes selling .906** approach IAS2. Experiments with .901** sales approaches IAS3. Uses variety of selling .909** approaches

OCC's Actual Purchase Intention (PI) PI1. How likely PI2. How certain PI3. How definite

5

.961** .962** .905**

Hedonic (n¼ 233) SL

AVEa

.931** .945** .909**

.862

.794**

.671

Rhob

.949

.891

.902** .782** .793**

.820

.932

.928**

.851

.945

.916**

.960

.952** .925** .879**

Mean

SD

AVE IPI

Hedonic (n ¼233) IPI 5.22 PC† 5.27 †† NAS 5.05 IAS 4.69 PI 5.84

.99 1.05 1.27 1.21 1.04

.86 .67

Utilitarian (n ¼107) IPI 5.17 PC† 5.38 NAS†† 4.84 IAS 4.42 PI 5.72

1.04 .89 1.35 1.24 1.05

.82 .89

AL

SIG

1

.928 .319 1 .818 .078 .270 1 .002 .255 .540 1 .923 .348 .341 .417 .237 .918

1

.915 .386 1 .795 .271 .337 1 .094 .274 .511 1 .906 .511 .404 .432 .202 .943

.85 .84

.84 .63

PC

Square root of AVE

Notes. Correlations in italic are insigificant at 5% level for a two-sided test; Discriminant validity of a measurement model requires correlations between the constructs to be smaller than the square root of AVE. † To make the descriptive statistics comparable between constructs, all items were transformed to a 7-point scale; †† Non-Interactive Adaptive(NAS) variable consisted of one item and its AVE is not reported.

.923**

.890

Correlations

.845

.942

Notes: SL standardized loading. a AVE average variance extracted-percentage of variance of item explained by the latent variable. b Rho ρ composite reliability (Wertz et al., 1974). ** po .05 for a two-tailed test.

selling by adding interaction terms (Henseler and Fassott, 2010). Finally, we test the effect of the product type by comparing path estimates for the utilitarian and hedonic models. The analysis was conducted using Addinsoft XLSTAT 2014.2.02. 4.3. Measurement model Table 1 reports the results of the measurement model in terms of its reliability, convergent validity, and consistency. Item reliability is confirmed with individual items in each construct reporting acceptable standardized loading. No significant crossloadings between items of dissimilar constructs were identified. All constructs had values of Dillon Goldstein's composite reliability indicator greater than .70 (Fornell and Larcker, 1981) and explained more than 50% of variance extracted; therefore, construct reliability and convergent validity were established. Variable means, standard deviations and correlations are summarized in Table 2. The square root of average variance extracted (AVE) for all latent constructs exceeded the inter-construct correlations indicating adequate discriminant validity of the measurement models for both product categories (Fornell and Larcker, 1981). Further, a multi-group test for PLS SEM was conducted to verify the generalizability of the conceptual model and no significant and meaningful differences were found among the four countries, therefore these results were not reported. 4.4. Structural model The relations between actual purchase intention (PI) of OCCs and their initial purchase intention (IPI) and perceived control (PC)

as well as the direct effects of the salespersons' interactive adaptive selling (IAS) and non-interactive adaptive selling (NAS) on the purchasing intention was estimated first (Model 1). From Table 3, both IPI and PC have significant positive effects onto PI for both hedonic and utilitarian products supporting H3a and H3b. Furthermore, NAS had a significant positive effect on OCC purchase intention for all types of products, thus supporting H1a. However, IAS had significant positive direct effects onto PI for only hedonic but not utilitarian products, not supporting H1b. Both IAS and NAS had a significant impact on OCC's PI when purchasing a hedonic product, and it was greater when the salesperson used IAS as compared to when purchasing a utilitarian product, confirming the moderating effect of product type on the impact of adaptive selling, thus supporting H2b but not H2a (see Table 3). The moderating effects of IAS and NAS were examined next by introducing interaction terms between these two variables and perceived control (Model 2). IAS did not moderate PC for either product. However, NAS moderates the relations between PC onto PI for utilitarian products: the effect of PC on PI is significantly higher for OCCs who are exposed to NAS, supporting H4a but not H4b.

5. Discussion, limitations and future research This research develops and tests a cross-culturally generalizable model for using adaptive selling behaviors with OCCs. OCCs are different from single channel consumers in that they possess greater variability in the amount and quality of information they possess prior to interacting with salespeople. Although the results demonstrate that both non-interactive and interactive adaptive selling behaviors influence the purchase intention of OCCs, they have different effects based on product type and OCCs' perceived control. Hence, it is important to distinguish between non-interactive and interactive adaptive selling behavior. Salespeople who practice non-interactive adaptive selling behaviors make an effort to customize the informational needs of each individual OCC. Because channels offer different information and OCCs' actual search behaviors vary between buying situations (Pookulangara et al., 2011), OCCs enter retailers with very different

Please cite this article as: Yurova, Y., et al., Not all adaptive selling to omni-consumers is influential: The moderating effect of product type. Journal of Retailing and Consumer Services (2016), http://dx.doi.org/10.1016/j.jretconser.2016.01.009i

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Table 3 Structural model of purchase intention. Utilitarian (n¼ 107) Model 1 Path

a

Hedonic (n¼ 233) Model 2

Model 1

Hypotheses results Model 2

SE

Path

SE

Path

SE

Path

SE

PC  4PI IPI  4PI NAS  4PI

.218** .270** .232**

.04 .05 .04

.159** .196** .169**

.04 .05 .02

.201** .202** .247**

.03 .04 .03

.200** .200** .248**

.03 .05 .03

IAS  4 PI

.111 ns

.06

.083 ns

.04

.141**

.04

.143**

.04

.184**  .155ns

.04 .11

 .133 ns  .095 ns

.09 .11

Interaction terms: NAS  PC  4PI IAS  PC  4PI Goodness-of-fit: Absolute GoF b Relative GoF c R²

.520 .825 .346

.438 .742 .397

.468 .926 .275

H3a H3b H1a H2a H1b H2b

supported supported supported not supported not supported supported

H4a supported H4b not supported

.473 .881 .320

Notes: Path¼ Standardized Path Coefficient¼ SE ¼ Standard Error. **

po .05 for a two-tailed test. All path estimates were significant at 5% level based on a bootstrap with 1000 resamplings and 100 iterations (Chin and Newsted, 1999); bootstrapped standard errors of the estimates were included for completeness. b Tenenhaus et al. (2004). c Vinzi et al. (2010, pp. 58–59). a

knowledge bases. Findings show that salespeople who are able to adapt the informational content of their sales presentation to the OCCs’ needs are more effective than those who present the same information to all buyers. Non-interactive adaptive selling behaviors are particularly important for OCCs who are shopping for utilitarian goods and have greater perceived control over their purchase intention. If salespeople present information that is irrelevant or unwelcome to these consumers, they will disengage from salespeople and further sales communications will be dismissed, even if the salesperson uses interactive adaptive selling. Conversely, OCCs with less perceived control will not be as willing to disengage, and then interactive adaptive selling behaviors will still be useful. Salespeople who practice interactive adaptive selling behaviors adjust their selling style, presentation, and solutions during interactions. The findings indicate that interactive adaptive selling behaviors have a greater influence on OCCs making hedonic purchases than utilitarian purchases because salespeople have the opportunity to make the shopping experience more pleasurable. Contrary to expectations, this effect is not stronger for customers who have a greater sense of control. This suggests that interactive adaptive selling behaviors are broadly beneficial when selling hedonic goods to OCCs. The findings that interactive and non-interactive adaptive selling behaviors are differentially useful is a key contribution for theory and practice because it means that there are multiple types of adaptive selling behaviors, and they are not equally effective in every selling situation. When studying OCCs, researchers need to measure both types of adaptive selling behaviors. Traditional measures of adaptive selling behaviors do not distinguish between types of adaptive behaviors, even though the efficacy of different types of behaviors varies by sales context. A limitation of this research is that NAS was measured from the perspective of customers. Although it is often relatively clear whether salespeople are using a standard scripted approach, customers cannot be certain unless they stay in the store long enough to witness salespeople sell to other customers. Further research should attempt to measure adaptive behaviors from the perspective of objective third-parties.

While salespeople can impact customer service and overall profits, there is a gap in understanding why consumers consult with salespeople (Haas and Kenning, 2014) in today's high-tech world. This research provides guidance to practitioners. By training salespeople in non-interactive adaptive selling behavior, retailers can directly influence OCC’s purchase intention. This means that salespeople should be trained in asking probing questions (i.e. “What kind of research have you done before coming to the store?”) to uncover the OCC's knowledge levels. Then, salespeople can use that information to adjust the information they share. Furthermore, salespeople may need to re-ask these questions as the OCC consults with mobile devices during the sales interaction. This research also shows that product types matters. Retailers can utilize interactive adaptive selling to influence OCC’s purchase of hedonic products. This will allow the salesperson to address customer’s concerns or to correct misinformation from the OCC who visits the retail store with information that may not be accurate. Perceived control is also important to the OCC's purchase intention. Salespeople can use interactive adaptive selling as a technique to bolster the OCC's perceptions of control by adapting to the customer’s personality and buying style. Because the interaction for non-interactive (interactive) adaptive selling and perceived control was stronger for utilitarian (hedonic) products, salespeople can add value to the customer by providing a tangible experience with a hedonic product using interactive adaptive selling. In a similar way, salespeople can capitalize on consumer's utilitarian motivation by using non-interactive adaptive selling to provide information about rates, quality, and functional options. It should be noted that this model was developed specifically for OCCs rather than single channels consumers. Nevertheless, it is possible that the model will hold in other sales contexts where customers have greater access to information, such as first time sales meetings with well-informed business customers. Future research should investigate the non-interactive and interactive adaptive selling behavior framework in other sales settings such as B2B settings.

Please cite this article as: Yurova, Y., et al., Not all adaptive selling to omni-consumers is influential: The moderating effect of product type. Journal of Retailing and Consumer Services (2016), http://dx.doi.org/10.1016/j.jretconser.2016.01.009i

Y. Yurova et al. / Journal of Retailing and Consumer Services ∎ (∎∎∎∎) ∎∎∎–∎∎∎

References Ahearne, M., Rapp, A., 2010. The role of technology at the interface between salespeople and consumers. J. Pers. Sell. Sales. Manag. 30 (2), 111–120. http: //dx.doi.org/10.273/PSS0885-3134300202. Ajzen, I., Fishbein, M., 1980. Understanding Attitudes and Predicting Social Behavior. Prentice-Hall, Englewood Cliffs, NJ. Alba, J., Lynch, J., Weitz, B., Janiszewski, C., Lutz, R., Sawyer, A., Wood, S., 1997. Interactive home shopping: consumer, retailer, and manufacturer incentives to participate in electronic marketplaces. J. Mark. 61 (3), 38–53. Arndt, A., Evans, K., Landry, T.D., Mady, S., Pongpatipat, C., 2014. The impact of salesperson credibility-building statements on later stages of the sales encounter. J. Pers. Sell. Sales. Manag. 34 (1), 19–32. Babin, B.J., Darden, W.R., Griffin, M., 1994. Work and/or fun: Measuring hedonic and utilitarian shopping value. J. Consum. Res. 20 (4), 644–656. Brislin, R.W., 1970. Back-translation for cross-cultural research. J. Cross-Cult. Psychol. 1 (3), 185–216. Campbell, K.S., Davis, L., Skinner, L., 2006. Rapport management during the exploration phase of the salesperson–customer relationship. J. Pers. Sell. Sales. Manag. 26 (4), 359–370. Childers, T.L., Carr, C.L., Peck, J., Carson, J., 2001. Hedonic and utilitarian motivations for online retail shopping behavior. J. Retail. 77 (4), 511–535. Chin, W.W., Newsted, P.R., 1999. Structural equation modeling analysis with small samples using Partial Least Squares. In: Hoyle, R.H. (Ed.), Statistical Strategies for Small Sample Research. Sage, Thousand Oaks, CA, pp. 307–342. Collier, J.E., Sherrell, D.L., 2010. Examining the influence of control and convenience in a self-service setting. Journal of the Academy of Marketing Science 38 (4), 490–509. Cox, A.D., Cox, D., Anderson, R.D., 2005. Reassessing the pleasures of store shopping. J. Bus. Res. 58 (3), 250–259. Crittenden, V.L., Peterson, R.A., Albaum, G., 2010. Technology and business-toconsumer selling: contemplating research and practice. J. Pers. Sell. Sales. Manag. 30 (2), 103–110. Eckert, J.A., 2006. Adaptive selling behavior: Adding depth and specificity to the range of adaptive outputs. Am. J. Bus. 21 (1), 31–40. Franke, G.R., Park, J.-E., 2006. Salesperson adaptive selling behavior and customer orientation: a meta-analysis. J. Mark. Res. 43 (4), 693–702. Fornell, C., Larcker, D.F., 1981. Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res. 18 (1), 39–50. Haas, A., Kenning, P., 2014. Utilitarian and hedonic motivators of shoppers' decision to consult with salespeople. J. Retail. 90 (3), 428–441. Hall, Z.R., Ahearne, M., Sujan, H., 2015. The importance of starting right: the influence of accurate intuition on performance in salesperson–customer interactions. J. Mark. 79 (3), 91–109. Harris Interactive, 2013. Showrooming not slowing down. Harris Poll, Retrieved from 〈http://www.harrisinteractive.com/NewsRoom/HarrisPolls〉. Hensler, J., and Fassott, G., 2010. Testing moderating effects in PLS path models: An illustration of available procedures. In V. Esposito Vinzi, W. Chin, J. Henseler, and H. Wang (Eds.),Handbook Partial Least Squares. Heidelberg: Springer. Hui, M.K., Bateson, J.E., 1991. Perceived control and the effects of crowding and consumer choice on the service experience. J. Consum. Res. 18 (2), 174–184. Johnson, K.K., Yoo, J.J., Rhee, J., Lennon, S., Jasper, C., Lynn Damhorst, M., 2006. Multi-channel shopping: channel use among rural consumers. Int. J. Retail. Distrib. Manag. 34 (6), 453–466. Kidwell, Blair, Jewell, Robert D., 2010. “The Motivational Impact of Perceived Control on Behavioral Intentions,”. J. Appl. Soc. Psychol. 40 (9), 2407–2433. Kirmani, A., Campbell, M.C., 2004. Goal seeker and persuasion sentry: how

7

consumer targets respond to interpersonal marketing persuasion. J. Consum. Res. 31 (3), 573–582. Kumar, V., Venkatesan, R., 2005. Who are the multichannel shoppers and how so they perform? Correlates of multichannel shopping behavior. J. Interact. Mark. 19 (2), 44–62. Kushwaha, T., Shankar, V., 2013. Are multichannel customers really more valuable? The moderating role of product category characteristics. J. Mark. 77, 67–85. McFarland, R.G., Challagalla, G.N., Shervani, T.A., 2006. Influence tactics for effective adaptive selling. J. Mark. 70 (4), 103–117. Park, J.E., Deitz, G.D., 2006. The effect of working relationship quality on salesperson performance and job satisfaction: Adaptive selling behavior in Korean automobile sales representatives. Journal of Business Research 59 (2), 204–213. Pettijohn, C.E., Pettijohn, L.S., Taylor, A.J., Keillor, B.D., 2000. Adaptive selling and sales performance: an empirical examination. J. Appl. Bus. Res. 16 (1), 91–112. Pookulangara, S., Hawley, J., Xiao, G., 2011. Explaining multi-channel consumer's channel-migration intention using theory of reasoned action. Int. J. Retail. Distrib. Manag. 39 (3), 183–202. Rapp, A., Baker, T.L., Bachrach, D.G., Ogilvie, J., Beitelspacher, L.S., 2015. Perceived customer showrooming behavior and the effect on retail salesperson self-efficacy and performance. J. Retail. 91 (2), 358–369. Rippé, C.B., Weisfeld-Spotler, S., Yurova, Y., Sussan, F., 2015. Is there a multichannel consumer. Int. Mark. Rev. 32 (3/4), 329–349. Robinson, L., Marshall, G.W., Moncrief, W.C., Lassk, F.G., 2002. Toward a shortened measure of adaptive selling. J. Pers. Sell. Sales. Manag. 22 (2), 111–119. Schlager, T., Maas, P., 2013. Fitting international segmentation for emerging markets: Conceptual development and empirical illustration. Journal of International Marketing 21 (2), 39–61. Simintiras, A.C., Ifie, K., Watkins, A., Georgakas, K., 2013. Antecedents of adaptive selling among retail salespeople: a multilevel analysis. J. Retail. Consum. Serv. 20 (4), 419–428. Strebel, J., Erdem, T., Swait, J., 2004. Consumer search in high technology markets: exploring the use of traditional information channels. J. Consum. Psychol. 14 (1), 96–104. Tanner, J.F., Ahearne, M., Leigh, T.W., Mason, C.H., Moncrief, W.C., 2005. CRM in sales-intensive organizations: a review and future directions. J. Pers. Sell. Sales. Manag. 25 (2), 170–180. Tenenhaus, M., Amato, S., Vinzi, V.E., 2004. A global goodness-of-fit index for PLS Structural Equation Modelling. Proceedings of The XLII SIS Scientific Meeting. CLEUP, Padova, pp. 739–742. Verhoef, P.C., Kannan, P.K., Inman, J.J., 2015. From multi-channel retailing to omnichannel retailing: introduction to the special issue on multi-channel retailing. J. Retail. 91 (2), 174–181. Vinzi, V.E., Trinchera, L., Amato, S., 2010. PLS path modeling: From foundations to recent developments and open issues for model assessment and improvement. In: Vinzi, V.E., Chin, W.W., Hensler, J., Wang, H. (Eds.), Handbook of Partial Least Squares. Concepts, Methods and Applications. Springer-Verlag, Berlin, Heidelberg, pp. 47–82. Weisfeld-Spolter, S., Sussan, F., Gould, S., 2014. An integrative approach to eWOM and marketing communications. Corp. Commun.: Int. J. 19 (3), 260–274. Weiss, A.M., Lurie, N.H., MacInnis, D.H., 2008. Listening to strangers: whose responses are valuable, how valuable are they, and why? J. Mark. Res. 45, 425–436. Weitz, B.A., Sujan, H., Sujan, M., 1986. Knowledge, motivation, and adaptive behavior: a framework for improving selling effectiveness. J. Mark. 50 (4), 174–191. Wertz, C., Linn, R., Jöreskog, K., 1974. Intraclass reliability estimates: Testing structural assumptions. Educational and Psychological Measurement 34 (1), 25–33.

Please cite this article as: Yurova, Y., et al., Not all adaptive selling to omni-consumers is influential: The moderating effect of product type. Journal of Retailing and Consumer Services (2016), http://dx.doi.org/10.1016/j.jretconser.2016.01.009i