Multichannel customer journeys and their determinants: Evidence from motor insurance

Journal of Retailing and Consumer Services 54 (2020) 102022

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Multichannel customer journeys and their determinants: Evidence from motor insurance Tun-I Hu, Andrea Tracogna * University of Trieste, Department of Economics, Business, Mathematics and Statistics (DEAMS), Trieste, Italy

A R T I C L E I N F O

A B S T R A C T

Keywords: Multichannel behavior Channel choice Channel synergy Customer journey Webrooming

This study focuses on channel choices in motor insurance. Our aims are twofold: to fill a gap of empirical studies on the determinants of multichannel behavior in the insurance industry and to help companies improve their retail strategies by better predicting customers’ channel decisions. The paper adopts a broad set of personal and digital channels and several dimensions of customer profiling, including psychographic and channel-experience variables. We identify four different customer journeys, based on channel combinations. Our web-based survey, which has turned out 338 valid responses, shows that the majority of insurance customers adopt multichannel search behaviors. However, although most of the search is carried out through digital media, such channels generate low search-to-purchase conversion rates. Most customer journeys are, instead, finalized in the personal channels (namely, the insurance agents), thus evidencing an interesting webrooming effect. We test our set of hypotheses on the determinants of customer journeys with a multinomial logistic regression. Our findings show that multichannel journeys can serve several purposes: they may reflect the customer need to collect more in­ formation, the customer preference for shopping innovation, and his/her preference for shopping convenience. Corporate channel management strategies and practices shall consider such determinants and be revised accordingly.

1. Introduction Empowered by the developments of the internet and digital tech­ nologies, and by the increasing efforts by retailers to provide a seamless channel experience, today’s customers are experimenting an unprece­ dented freedom to define their shopping journeys and are finding it easier than ever to switching and integrating channels, both physical and digital, across the different shopping stages, from search to pur­ chase, up to the post-purchase phase (Van Bruggen et al., 2010). Consequently, as highlighted by Verhoef et al. (2015), an increasing number of consumers have become multichannel shoppers, as they simultaneously search for information and make their purchase de­ cisions through a combination of channels that, on a specific moment, best optimizes their shopping needs. These customers require, in turn, new and specific approaches by retailers, aimed at extending the firms’ reach to as many channels as possible, so to maximize the chances to finalize the transactions with the customers (Zhang et al., 2010; Lewis et al., 2014). In response to this evolution, the academic interest on the

determinants of shopping behavior and channel choice has been recently revamped (Rangaswamy and Van Bruggen, 2005; Neslin et al., 2006; Lee and Kim, 2008), and specific calls to analysis have been made within the community, such as the one by Verhoef et al. (2007), which express a “particular need for studies considering channel choice de­ cisions for search and purchase in a multichannel environment, partic­ ularly studies that investigate interdependencies between the search and purchase decisions” (p. 130). Despite the growing academic literature on the topic, several aspects of the multichannel phenomenon appear to not be fully understood, while several open questions remain unanswered, both for scholars and firms. For instance, on the one hand, many channels are mostly used for search purposes and are not generating lots of purchases, while, on the other hand, other channels display a much higher effectiveness in con­ verting the customer shopping visits into actual purchases. The different capacity of a channel to generate purchases has been already high­ lighted in the extant literature (Verhoef et al., 2007; Weinberg et al., 2007; Verhoef et al., 2015), with specific reference to the offline/online dichotomy, envisaging a “research shopping” behavior (Verhoef et al.,

* Corresponding author. Dipartimento di Scienze Economiche, Aziendali, Matematiche e Statistiche (DEAMS), Universit� a di Trieste, Via dell’Universit� a 1, Trieste, 34123, Italy. E-mail addresses: [email protected] (T.-I. Hu), [email protected] (A. Tracogna). https://doi.org/10.1016/j.jretconser.2019.102022 Received 24 July 2019; Received in revised form 10 December 2019; Accepted 16 December 2019 0969-6989/© 2019 Elsevier Ltd. All rights reserved.

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Journal of Retailing and Consumer Services 54 (2020) 102022

year, with relatively low switching costs), with the customer having frequently the freedom to change the channel, the provider, and the policy coverage. Further, customers are typically involved in motor in­ surance purchase for several years, which means they undertake several journeys in sequence, which allows for the analysis of channel synergy effects between and within different shopping phases (Kankainen et al., 2012). Overall, the insurance market represents an ideal setting for the analysis of consumer behaviors along repeated shopping journeys and for the understanding of complex patterns of information search and their effects on the purchase channel choices. Compared to previous research on multichannel customer behavior, our paper introduces a broader set of search and purchase channels and new dimensions of customer profiling, specifically at the psychographic level. Further, differently than in the extant literature, which is adopting a typical physical (offline) vs. digital (online) channel dichotomy, our research classifies the channels available for search and purchase in two distinct “families”: personal and digital, based on their correlation with the customer preference for “personal contact”, a new psychographic variable we here introduce. Furthermore, in our paper we emphasize a number of different channel associations (both positive and negative) and focus on the possible channel synergy effects over four distinct customer journeys (which we introduce in section 2), with the ultimate goal of explaining the determinants of multichannel shopping behavior. The paper is structured as follows. In the next (second) section we provide an account of the large stream of literature on multi-/omni­ channel shopping behaviors, with the aim of highlighting the synergy effects between channels and of deriving a workable definition of customer journey. In the third section, we introduce and discuss a set of hypotheses on the determinants of customer journeys, ranging from psychographic factors to channel experience effects. In the fourth sec­ tion, we describe the methods, the sampling procedure and the mea­ surement issues of our empirical research, focusing on the motor insurance market. In the fifth section we give account of our results and test our hypotheses with a multinomial logistic regression model. The sixth section includes a thorough discussion of the research findings and concludes, deriving theoretical and managerial implications, addressing limitations and providing indications for future research.

2007) and the correlated “showrooming” and “webrooming” effects (Bell et al., 2014, 2017), whereby the customer searches offline to purchase online or searches online to purchase offline, respectively. However, little is known on the actual determinants of the above shopping behaviors and on how can retailing firms strategically manage such fundamental channel synergies (Verhoef et al., 2015). This is particularly true with reference to the multichannel behaviors and the customer journeys which can be observed in the motor insurance mar­ kets where, despite a growing tendency of customers to follow sophis­ ticated journeys, which include the use of multiple search channels simultaneously - particularly the digital channels (both corporate and independent) - there is still a clear preference by the insurance cus­ tomers to finalize their journey (i.e. to purchase the motor policy) from the personal channels, and namely from the insurance agents. A number of research questions arise: in a world where customers are free to move along different journeys, and free to follow - without im­ pediments - their preferences and needs, rather than being subject to the impositions of the sellers, what specific drivers inform their decisions, and how can companies predict the specific channels where customers will ultimately choose to collect the needed information and will finalize their purchase? More in general, in the light of the different possible combinations of search and purchase channels, which may lead to different “research shopping” outcomes, such as the showrooming and webrooming effects, what do we know about the factors/variables that influence the selection of a specific shopping journey? And what can the seller organizations do - within a multichannel context - to exert an influence on a specific shopping outcome? The implications of our research are of the utmost importance for retailers: by knowing how different customers search for information along their shopping journey, with different approaches, companies can better manage their channels and develop more effective and granular multichannel strategies, which can ultimately maximize the conversion rate from search to purchase. Further, the above questions are particu­ larly appropriate within the non-life insurance industry where - despite a long-standing evolution of its distribution channels and the pervasive introduction of digital technologies and media to support the customer information search - a majority of shopping journeys are still finalized through the personal and traditional channels, i.e. the agents (Insurance Europe, 2019). This industry has recently implemented radical changes in the articulation of the available channels, with an increasing role played by the digital channels, both corporate (e.g., corporate web sites) and independent (e.g., web aggregators). However, this increase in the available purchase options has not significantly changed the traditional preference, by the customers, to approach the corporate agent for the actual finalization of the purchase. In other words, the increase in the number of search options has had a relatively small impact at the pur­ chase channel level and has generated a peculiar “webrooming” effect, whereby insurance customers tend to extend their search to many digital and personal channels but still prefer to purchase from the agent. The full understanding of this effect requires a specific investigation on the insurance markets, carried out through a new categorization of the different customer journeys, which we introduce in the paper, and through a more granular profiling of the psychographic traits of the customer, combined with the acknowledgement of the different channel synergy effects, which we have included in our empirical model to derive and test our set of hypotheses. Our focus in motor insurance is also justified in the light of the relatively few studies which have spe­ cifically analysed customer journeys in the financial services, and namely in insurance, with few exceptions, such as Beckett et al. (2000), Black et al. (2002) and, more recently, Barwitz and Maas (2018). Furthermore, motor insurance is a peculiar service as it is subject to periodic renewal, has a significant post-purchase phase (where accident reporting, claims management, and other customer-seller interactions can occur), is characterized by experience effects (i.e., past decisions and service experience have a relevant weight in the successive channel decisions) and repetitive purchase (motor policies are renewed every

2. Literature review A research focused on multichannel behavior and management shall start with a clarification of the key terms employed. By channel we here mean “a customer contact point or a medium through which the firm and the customer interact” (Neslin et al., 2006, p. 96). Channels are here considered, broadly speaking, as media for interaction and not just as pure settings for the finalization of a transaction. Customer-seller in­ teractions progress along time, over the shopping journey, encompass­ ing different phases: need recognition, information search, evaluation of alternatives, purchase decision, and after-sales service (post-purchase decision) (Engel et al., 1990; Howard, 1989). Thus, several different channels can be leveraged at every stage of a specific shopping journey and this is the essence of multichannel shopping. 2.1. From multichannel to omnichannel shopping There is not full consensus, in the academic literature, on the level where to apply the multichannel construct, and on what exactly a multichannel shopping behavior is. For some scholars, multichannel shopping simply means using different channels for different purchases, either within the same product category or for different products (Kumar and Venkatesan, 2005; Weinberg et al., 2007; Cho and Workman, 2011; Konus, Neslin and Verhoef 2014; Cambra-Fierro et al., 2016; Harris et al., 2018). For an increasing number of scholars, however, the multichannel construct refers to the customer behavior as expressed along the whole journey, i.e., over the different shopping phases, from search to purchase. For instance, Schoenbachler and Gordon (2002) 2

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Journal of Retailing and Consumer Services 54 (2020) 102022

posit that the multichannel consumers use the different channels ac­ cording to their preferences, perceived convenience and product avail­ ability which can be different in the different shopping phases. Wind and Mahajan (2002) define a multichannel behavior as the combination of various channels and approaches, such as searching online to buy off­ line, searching offline to buy online and everything in between. Along this same line, Verhoef et al. (2007) show that consumers’ search pref­ erences need not to be the same as their purchase preferences. They introduce the concept of the “research shoppers”, which research the product in one channel (e.g., the Internet), and then purchase it through another channel (e.g., the store). Likewise, McGoldrick and Collins (2007) refer to multichannel customers as those who use a variety of channels to research their products, before committing to the purchase. This same emphasis on the whole shopping process can be found in Zhang et al. (2010) who define the multichannel shoppers as those consumers who use multiple channels along the shopping process. More recently, Ripp� e et al. (2015) relate the multichannel construct to “a more knowledgeable consumer”, who gains information about the product by surfing and switching between channels, such as brick and mortar stores, web sites, mobile devices, and other emerging shopping outlets. The natural evolution of this stream of literature has been the omnichannel model of shopping (Yurova et al., 2017). Piotrowicz and Cuthbertson (2014) describe the omnichannel customers as those who move freely between the online channels, the mobile devices, and the physical store, all within a single transaction process. For Juane­ da-Ayensa et al. (2016) these customers use the new technologies to search for information, offer opinions, explain experiences, make pur­ chases, and “talk to the brand”. The line of separation between the multi-channel and the omni-channel behaviors is blurred. The differ­ ence, according to Saghiri et al. (2018), is that - in an omnichannel approach - a customer can interact with the product and the firm in all ways and in all locations. Within this perspective, an increasing number of consumers seamlessly move, over their customer journeys, between the available channels, searching for information and making their purchase decisions through the channels that best optimize their pur­ chase needs (Verhoef et al., 2015). In sum, the shift to omnichannel shopping implies that, rather than considering the search and purchase phases as separate moments, customers (and sellers) integrate channel usage along the whole shopping journey. Under this perspective, search and purchase decisions, rather than being independent stages, become complementary and synergistic moments within the customer journeys.

channel lock-in or channel spillovers: the choice of a channel in one stage of the shopping process affects that channel’s utility and the likelihood of choosing that channel in another stage of the shopping process for the same product (Anderl et al., 2016; Frasquet et al., 2019), which implies some stickiness of channel decisions (Melis et al., 2015). Along this similar vein is the “augmentation effect” described by Barwitz and Maas, 2018, whereby customers use a broad set of channels to augment the information collected and then select one channel for their purchase. 2.3. From channel decisions to customer journeys In the light of the above, we can assume that customers will choose the channels that best exploit the available positive synergies, while minimizing their negative effects. However, such channel decisions are not taken independently, at each stage of the shopping process, but are combined by the customer into his/her preferred shopping journey. One of the earliest formal definitions of customer journey is that of Zomerdijk and Voss (2010) who refer to it as a series of touchpoints that involve all activities and events related to the delivery of a service to a customer. The concept has then been further developed by Lemon and Verhoef (2016) who split this journey into three linearly sequential stages (i.e., pre-purchase, purchase, and post-purchase), which transpire during the current journey and also during past and future shopping experiences. Notably, the authors call for a need to identify specific ways in which customers deviate from their habitual or expected customer journeys and suggest that “researchers could evaluate not only the journeys themselves but also what drives these journeys” (p. 88) especially within the framework of multi- and omnichannel marketing. Understanding the specific determinants of shopping journeys may allow marketers to strategically intercept consumers at each particular stage of the shopping process and maximize customer value. And this is, indeed, the goal of our paper, where we aim at analyzing the different customer journeys, thus highlighting the possible synergies and com­ plementarities between the search and the purchase phases and identi­ fying the main determinants of such journeys. Following Lee et al. (2018), we are fully aware that shopping today does not always adhere to a linear process as shoppers do not always finalize their journeys with a purchase and, instead, switch back and forth between shopping stages. However, for the purposes of our research, we are here limiting our focus on those customer journeys who are finalized with an actual purchase. Further, in consideration of the variety of channels which can be utilized by customers to collect information and make the purchase, we intro­ duce a specific classification of channels, grouping them into two fam­ ilies: personal and digital. The personal channels are chosen by customers expressing a preference for personal contact with the seller. Digital channels are, instead, chosen when the interaction level with the seller is less important and typically occurs via digital technologies. Further, we introduce a new pattern of search, which occurs both in personal and digital channels and that we call “mixed search”. Our main goal would be that of understanding the determinants of the different search pattern and the implications in terms of purchase behaviors. Finally, as in previous studies, that have clustered customers into different groups, based on their channel choices (Thomas and Sullivan, 2005; McGoldrick and Collins, 2007; Konus, Verhoef, and Neslin 2008; De Keyser, Schepers and Konus, 2015; Park an Kim, 2018; Nakano and Kondo, 2018; Frasquet et al., 2019), we here introduce four customer journeys which differently combine the personal and digital channels and segment customers in distinct groups:

2.2. Channel synergy effects and research shopping The multi- and omni-channel literature has also had the merit to highlight the many possible channel synergy effects taking place along the shopping journey. First, there exist positive synergies between channels at the same stage of the shopping process; using the words of Verhoef et al. (2007): “channel synergy means that higher attitudes toward search or purchase on channel A translate into higher attitudes toward search or purchase on channel B” (p. 132). Second, channel synergies can also be negative, as witnessed by the “research shopping” phenomenon (Verhoef et al., 2007), where customers research the product in one channel, and then purchase it through another channel. Here, the offline search and online purchases become complementors and the online activities may partially substitute for the experiential shopping in the physical store (Pauwels et al., 2011). This interplay between traditional retail and e-commerce channels generates what is also called a “showrooming effect”, where the information is searched and collected offline while the fulfillment occurs online (Bell et al., 2014). As a more recent evolution of the channel synergy phenomenon, Bell et al. (2017) describe the “webrooming” effect, where information is collected online but fulfillment occurs offline. Channel synergy effects occur also at the temporal level, i.e., the previous use of a channel in­ creases the likelihood of using that channel again in the future (Gensler et al., 2012). Verhoef, Neslin, and Vroomen (2007) call these effects as

- Journey 1. Is adopted by customers who focus their search and pur­ chase in the personal channels. We call this journey as the “fully personal” journey. - Journey 2. Is undertaken by customers who adopt a mixed search approach and purchase in the personal channels. We call this journey as the “mixed search and personal purchase” journey. 3

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service, prompt service and personal attention. Verhoef, Neslin, and Vroomen (2007) define the service quality as the consumers’ perception of the delivered channel service, including the perception of getting good service, excellent help, and good personal advice. Within the present research, although it is possible for a company to provide customer supportive digital channels, we expect that a consumer expecting a high service quality (i.e., a customized and personal ser­ vice), will rather prefer a personal channel instead of receiving a stan­ dardized service from a digital one. However, we believe it is still to be ascertained whether a multichannel approach (and related journeys) is positively or negatively associated to the preference for service quality. Thus, the following hypothesis is advanced:

- Journey 3. Is undertaken by customers who adopt a mixed search approach and purchase in the digital channels. We call this journey as the “mixed search and digital purchase” journey. - Journey 4. Is adopted by customers who focus their search and pur­ chase in the digital channels. We call their journey as the “fully digital” journey. Journeys 2 and 3 represent, in our research, the archetypical multichannel customer journeys. Our research is aimed at identifying their determinants and at defining the profile of the customers under­ taking such journeys. 3. Hypotheses development

H1c. The preference for service quality affects the selected customer journey.

To derive our set of hypotheses we have referred to the broad liter­ ature on channel choice determinants within a multichannel perspec­ tive. Our main references have been the influential studies by Verhoef et al. (2007), Venkatesan et al. (2007) and Ansari et al. (2008) which, by adopting a multichannel perspective and focusing on dimensions such as channel adoption, channel choice, and channel usage, have analysed and explained the different shopping behaviors across channels, in the different phases of the customer journey. Other studies on multichannel customer segmentation have also raised our attention, such as Konus, Verhoef and Neslin (2008). Our first set of hypotheses refer to the psychographic traits of cus­ tomers. Below, we describe the specific variables and their expected association with the multichannel customer journeys, which are formulated in the form on non-directional hypotheses. Shopping enjoyment. It relates to the fun and excitement derived to the customer from trying new experiences, custom designing products and taking part to an enriching customer journey (Forsythe et al., 2006). People who enjoy shopping spend extra time on participating in the journey, including searching and purchasing from different channels (Konus, Verhoef, and Neslin 2008). Verhoef, Neslin, and Vroomen (2007) show empirically that there is an association between the pref­ erence for shopping enjoyment and the selection of multiple channels. In other words, the preference for shopping enjoyment is here posited to be associated with multichannel shopping journeys.

Shopping convenience. Verhoef, Neslin, and Vroomen (2007) define shopping convenience in terms of the perceived ease, effort, and speed of a specific channel where consumers can gather product information and products can be purchased. Accordingly, Choudhury and Karahanna (2008) define convenience as a consumer’s perception of the time and effort required to interact through a channel. Frambach, Roest, and Krishnan (2007) claim that the perceived convenience has a positive effect on consumer’s channel choice. Similarly, Juaneda-Ayensa et al. (2016) identify effort expectancy as a major determinant of the con­ sumers’ use of different channels during the shopping process. Whether the need for shopping convenience can be better served by a multi­ channel journey, is still to be clarified. The following hypothesis is then advanced: H1d. The preference for shopping convenience affects the selected customer journey. Price consciousness. Lichtenstein, Netemeyer, and Burton (1990) define price consciousness as the degree to which consumers have a preference for paying low prices. The perceived price level affects channel choice: the higher the price, the lower the likelihood for a channel to be chosen (Balasubtamanian, Raghunathan, and Mahajan 2005; Venkatesan et al., 2007; Verhoef et al., 2007; Konus, Verhoef, and Neslin 2008; Gensler et al., 2012). It is no surprise that consumers will prefer to pay for a lower price if the product/service remains the same; and to guarantee paying the lowest price, consumers will likely search from several channels, either personal or digital. Our research will investigate this expected association, which is expressed by the following hypothesis:

H1a. The preference for shopping enjoyment affects the selected customer journey; Need for information. Prior to making a purchase decision, consumers may want to collect information on product features, price offers, customer reviews, payment methods, delivery options, and alternatives available. Therefore, a channel could be selected based on the richness of the information provided for the shopping decision – i.e., a channel which provides the right quality, quantity, and accessibility of infor­ mation and helps comparing alternatives is preferable for consumers (Alba et al., 1997; Hoque and Lohse, 1999; Ratchford et al., 2001; Verhoef et al., 2007). In line with the above, Choudhury and Karahanna (2008) posit that the capacity of a channel to provide information and explanations to customers is to be considered an important factor for channel choice. Within our research, we can here expect that the use of multiple channels for information search (and the related customer journeys) may better serve the need for information.

H1e.

Price consciousness affects the selected customer journey.

Shopping innovation. Midgley and Dowling (1978) define shopping innovation as the customer’s preference to try new and different prod­ ucts or channels and seek out new experiences. Goldsmith and Hofacker (1991) describe the shopping innovators as those customers who are the first to buy a new product, to be knowledgeable about the product itself, and more likely to talk to others about the product. In the context of this research, Konus, Verhoef, and Neslin (2008) link innovation search to channel selection by positing that consumers with high preferences for innovation would require more extensive search, i.e. a multichannel approach. Along the same line, Juaneda-Ayensa et al. (2016) indicate that innovative consumers have a stronger propensity to search (and purchase) using different channels. This is the hypothesis we aim at investigating.

H1b. The preference (need) for information affects the selected customer journey. Preference for service quality. Parasuraman, Zeithaml, and Berry (1988) define service quality as the seller’s “willingness to help cus­ tomers and provide prompt service” (p.23). Conversely, we can define the preference for service quality as the customer’s expectation to be helped by the seller and to receive prompt support. The study of Baker et al. (2002) label “interpersonal service quality” as the quality of the interactions between store employees and customers, based on how well the customer is treated, and on whether he/she is receiving high-quality

H1f. The preference for shopping innovation affects the selected customer journey. Preference for personal contact. For the purposes of our research, we introduce a new psychographic variable, which, in the context of shopping behaviors, is defined as the preference of customers for interacting with the sales organization (i.e., the provider of goods and/ or services) at a personal level (i.e., face to face, verbally, via written 4

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Journal of Retailing and Consumer Services 54 (2020) 102022

H2a. The purchase channel at T0 affects the selected customer journey at T1;

communications between specific individuals) rather than impersonally (i.e., through generic interactions with the company, especially ICTmediated). Our expectation is that the customer showing a higher preference for personal contact will prefer personal channels both at the search and purchase level. This will likely impact on the preferred customer journey. Thus, the following hypothesis is advanced:

H2b.

The purchasing cost at T0 affects the selected customer journey at T1;

H2c. The interaction of the customer with the seller in the post-purchase phase at T0 affects the selected customer journey at T1. Other determinants of customer journeys can be found within the domain of situational and sociodemographic variables. The situational variables are “all those factors, particular to a time and place of obser­ vation, which do not follow from a knowledge of personal (intra­ individual) or stimulus (choice alternative) attributes” (Belk, 1974, p. 157). Three variables have a particular significance for our study: time pressure, distance to store and shopping level (Kleijnen et al., 2007; Konus, Verhoef, and Neslin 2008; Chocarro et al., 2013; Leenders et al., 2019). We have not developed specific hypotheses for these variables, which will be used as controls in our logistic regression model. Further, consistently with a rich literature, including Pauwels and Dans (2001), Inman et al. (2004), Gupta et al. (2004), Neslin et al., (2006), Verhoef et al. (2007), Ansari et al. (2008), and Yu et al. (2011), we have considered a set of sociodemographic variables, such as gender and age, which we will use as control variables, too. In the following Fig. 1 we are summarizing our model and the set of hypotheses formulated.

H1g. The preference (need) for personal contact affects the selected customer journey. Customer journeys are not only determined by the psychological traits of customers. Our second set of hypotheses link our dependent variable (the selected customer journey) to the covariates pertaining to the “channel synergy” effects and namely to the different impact that the previous period channel choices and experiences had on the current period decisions. Here, the main studies we refer to are represented by Verhoef et al. (2007), Pauwels et al. (2011), Gensler et al. (2012) and also by the more recent Bell et al. (2014, 2017). We have considered how the past channel experience effects (at T0) can have an impact on the current shopping behaviors (at T1). With reference to our empirical setting (motor insurance) we refer, in particular, to the past (T0) pur­ chase decision and to the past (T0) post-purchase phase. Considering the past purchase decision, we introduce two variables: the “purchase channel at T0” and the “purchasing cost at T0”. Indeed, we expect that the past purchase channel at T0 may generate a lock-in effect at T1, i.e., a propension to replicate the previous channel decisions. In turn, we expect that the level of purchasing cost at T0 may determine a higher (lower) level of effort in the search for cheaper alternatives in the next period (T1). Further, with reference to the post-purchase phase at T0 we focus on the events that may generate some further interaction between the seller and the customer. Within the context of our study, motor in­ surance, we mainly refer to the “reporting of accidents” (claims) and to the “change of the insured car”. We expect that the reporting of an ac­ cident may direct the customer towards new channels, either digital or personal, and this may have consequences on the policy renewal de­ cisions at T1. In turn, the change of the car may expose the customer to new channels (e.g., the car dealer) and this may eventually lead to a revision of the past channel decisions. We therefore develop the following three hypotheses:

4. Methods 4.1. Sampling and data collection To test the above hypotheses, we have carried out a web-based survey. The data were collected from January 2019 to February 2019. For convenience reasons, the sampling population was represented by all the MBA graduates of an Italian Business School which received their degree from 1991 to 2018. A total of 2950 graduates, 39.29% female and 60.71% male, with an average age of 38 and from 95 different na­ tionalities, have received via email a link to a web-based questionnaire. 59 mails didn’t reach the target. Thus, the actual recipients of the invitation have been 2891. A total of 554 questionnaires have been fully or partially filled, with a gross response rate of 19.2%. Because of the

Fig. 1. Research model. 5

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Journal of Retailing and Consumer Services 54 (2020) 102022

nature of the product selected for this research (motor insurance), two filter questions were made prior to the entry of the questionnaire, in order to avoid any non-qualified responses: Do you and/or your family have (own, possess, drive) a car? With reference to the cars you and/or your family have, have you been involved in the purchase of the current or the previous insurance policy? A negative answer to any questions would direct the “un-qualified” respondents to the ending page. The submitted questionnaire was structured in three parts: the first part included sociodemographic questions (age, gender, education level, employment status, household size, number of cars owned/used, internet access status, birth country, citizenships, resident country and city population). The second part asked respondents to self-evaluate based on the psychographic and situational variables introduced in the previous sections. Five-point multi-item Likert scales were here applied, where 1 represented “strongly disagree” and 5 represented “strongly agree”. Part three was focused on the motor insurance customer journey; information was collected both for the previous (T0) and for the current insurance period (T1). We have assumed that, being motor insurance a complex service, consumers tend to recall the past decisions beyond the current shopping choices (Buhler et al., 2016; Crosby et al., 1990).; this may reduce their recall bias. The questionnaire was submitted via Computer Assisted Self-Interviewing (CASI), where members were contacted via email, and invited to self-compile a web-based, closed-question, English structured questionnaire. The choice of CASI is justified by the extreme expensiveness of the face-to-face alternative and by the presumed respondents’ preference for the self-compilation of the questionnaire. In turn, this survey mode has its limitations: in particular, the possibility of a significant number of survey and item non-responses and the likely slowness of the question­ naires’ collection process. After cleaning the dataset for the non-qualified responses and for any incomplete questionnaire, 338 full responses were used for data anal­ ysis, with a net response rate of 11.7%. The respondents hold 42 different nationalities and are mostly similar to the survey population in terms of age (40 vs. 38, and gender (male: 70.4% vs. 60.7%). Due to the peculiar survey population (MBA graduates) we have also considered a possible sampling bias, as the population attitudes towards multi­ channel shopping are likely to be different than in the general popula­ tion, because of the significant differences in the level of education, age range (from 25 to 68 in our sample), product knowledge (a number of graduates have taken a specific degree in Insurance at the Business School) and disposable income. However, as the purpose of our research is not to make specific inferences on the general population, but mostly to test a set of associations and correlations among channels and possible causation effects between psychographic variables and shopping be­ haviors, we do not consider such biases as real issues affecting the outcomes of our research.

insurance period). With reference to the independent variables, we have adopted several multi-item scales which we have developed from the literature (see Section 2 above). The only exception has been our new variable, the preference (need) for personal contact. We have measured it through a multi-item scale composed of the following assertions: When shopping, I like to deal with a human. When shopping, I like to have a personal inter­ action with the seller. When shopping, I like to interact face to face. Overall, 30 items for the 7 psychographic variables were included in the questionnaire. To validate the scales, a principal-component analysis (PCA) has been applied to examine if items are grouped to the corre­ sponding variables and if the number of factors is the same as expected. The extraction of components was based on eigenvalues greater than 1. In total, 7 components were obtained with a total variance explained of 68.1%. The items of each component are in line with the corresponding variables. To reduce collinearity between variables, we rotated the components through a Varimax method with Kaiser Normalization to obtain a rotated orthogonal factor score for each component. Following Hair et al. (2016) we also calculated the composite reliability value (CR) and the average variance extracted (AVE). Generally, factor loadings above 0.5, Cronbach’s Alpha exceeding 0.7, AVE above 0.5 and CR values above 0.7 are considered satisfactory (Fornell and Larcker, 1981; Nunnally and Bernstein, 1994). Table 1 shows the correlation matrix for the construct validity test where all the above conditions are met. Another group of measures pertained to the “channel experience” variables, which consider what has happened in the previous period (T0), both at the purchase and post-purchase phases. The “past purchase channel” was reported based on the five channels utilized for the current purchase decisions. The “cost of the insurance policy at time T0” is the cost of the previously purchased policy; here, we have used its natural logarithm transformation. With reference to the post-purchase phase, the “reporting of an accident” variable is a binary variable reflecting the case when the insured car has been subject to a claim in the previous period, while the “change of the insured car” is a binary variable reflecting the fact that the previous insured car (at T0) is not the same as the currently insured car (at T1). As regards the control variables, we have measured the “distance to store” in terms of the distance of the respondent from the insurance agent. The distance has been measured by the time needed to reach the closest agent through the normal means of transportation. We have converted it into a binary variable, where we set 0 for a distance shorter than 30 min, and 1 for a distance longer than 30 min. The “shopping level” is a measure of the shopping intensity of the customer. It ranges from 1 (“I am a light shopper”) to 5 ("I am a heavy shopper"). The var­ iable of “time pressure” is measured with two items, “I am always busy” and “I usually find myself pressed for time” (Konus, Verhoef, and Neslin 2008) by applying five-point multi-item Likert scales, where 1 repre­ sents “strongly disagree” and 5 represents “strongly agree”.

4.2. Measurements

5. Results

Our dependent variables are represented by the channels actually selected by the insurance customers along the search and purchase phases and their combination at a specific time (which we here call the “customer journey”). We assumed that only one channel can be selected for purchase, at each renewal period; we allowed respondents, instead, to select more than one channel for information search. Five types of channels have been mapped: insurance agent, corporate call center, corporate website, corporate mobile app, online web aggregator (i.e., price comparison site). The channels used for search include all of the above and also the social networks and any personal contacts (friends, relatives, family etc.). Further, within each search channel type, we have reported the cases of multiple search, i.e., the search from two or more channels of the same type (e.g. a multiple search on several corporate web sites). Just for the purchase decisions, and with the aim of exploring any channel lock-in effect, we asked respondents to report both the channels utilized in T0 (previous insurance period) and T1 (current

Table 2, below, reports the motor insurance customer preferences in respect to the search channels and the purchase decisions at the current time (T1). We can see that the prevalent purchase channel is the insurance agent, with percentages above 54%. The second major purchase channel are the corporate websites (22.8%), followed by the web aggregators (13.3%) and the corporate call centers (8.6%). As for the search chan­ nels, our data show that the policyholders tend to search on several channels prior to making their purchase decision. This is evident from the cumulative percentage of all search channels, which is higher than 100%. Overall, respondents have used an average of 2.63 types of channels and 4.04 specific channels to search for information. We also note that the conversion rates (the percentage of search channel activ­ ities which are actually converted into a purchase) are significantly different in the different channels. Indeed, while some search channels 6

T.-I. Hu and A. Tracogna

Journal of Retailing and Consumer Services 54 (2020) 102022

Table 1 Correlation matrix for the construct validity test. N

Variable

α

CR

AVE.

1

2

3

1 2 3 4 5 6 7

Enjoyment Information Service Convenience Price Innovativeness Personal contact

0.887 0.856 0.865 0.805 0.757 0.810 0.927

0.900 0.888 0.901 0.865 0.824 0.855 0.931

0.601 0.614 0.752 0.616 0.543 0.542 0.818

1 .313a .014 .005 .121b .370a .057

1 .050 .042 .204a .100 -.067

4

1 -.015 .059 .019 .376a

5

1 .221a .098 -.165a

6

1 .017 -.179a

1 .030

7

Mean

Stand. dev.

1

3.063 3.897 2.455 3.832 3.712 2.695 3.015

0.798 0.633 0.761 0.681 0.622 0.678 0.852

Note: α ¼ Cronbach’s Alpha. CR ¼ Composite reliability. AVE ¼ Average variance extracted. KMO measure of sampling adequacy: 0.815. Bartlett’s test of sphericity: Chi square 5195.382, df435, p < 0.000. a Correlation is significant at the 0.01 level (2-tailed). b Correlation is significant at the 0.05 level (2-tailed). Table 2 Search and purchase channels at T1 (N ¼ 338). Agency

Search channel Purchase channel

Corporate call center

Corporate website

Corporate mobile app

Web aggregator

Social network

Friends and personal contacts

N

%

N

%

N

%

N

%

N

%

N

%

N

%

217 183

64.20 54.14

61 29

18.05 8.58

200 77

59.17 22.78

45 4

13.31 1.18

201 45

59.47 13.31

36

10.65

128

37.87

Search-to-Purchase conversion rate

84.33

47.53

38.50

are, by nature, exclusively for search (such as friends, personal contacts, and social networks), other channels (such as the web aggregators, or the mobile apps) display a below-average conversion rate, which means they are more for search than for purchase, while other channels (namely, the insurance agencies) show a much higher conversion rate (above 80%). Table 3 provides evidence on the tendency of customers to use more than one search channel of the same type (i.e., two or more agents, two or more websites … etc.). Our data show that most insurance customers are indeed multi­ channel shoppers, i.e., are using two or more search channels before finalizing their purchase decision. However, in line with the above­ mentioned different search-to-purchase conversion rates, the data show that the "search intensity" is much higher for the digital channels (corporate web sites, web aggregators, social networks) than for the personal channels (agents and call centers). In particular, most digital channel types tend to be activated multiple times (i.e., with a high “search intensity”) while the personal channels (agents and corporate call centers) tend to be mostly activated once. This combination of low conversion rates and high intensity, by the customer, in the use of the digital channels represents a peculiar, and somehow counterintuitive, finding, which requires a special analytical explanation. Further, our data show significant synergy effects between channels. Indeed, Table 4 shows that most correlations between search channels are positive, i.e. the use of one channel is positively associated with the use of another channels, with the exception of the agency channel, where all the other channels (except friends and social networks) are negatively correlated to it. These positive channel synergies at the search level mostly reflect an “augmentation effect”, whereby customers

Agency Corporate call center Corporate website Corporate mobile app Web aggregator Social networks Friends

Used only once

Used twice or more

N

Percent

N

Percent

144 43 53 20 81 17 52

66 70 27 44 40 47 41

73 18 147 25 120 19 76

34 30 74 56 60 53 59

22.38

augment the available information through multiple channel search, before selecting the purchase channel. This also highlights – in motor insurance - a typical “webrooming” effect, where the customers collect information on several digital channels before finalizing the purchase in the traditional (personal) channels. As for the reported negative synergy between the agents and the other channels, we can here suggest that the former , by its very nature, represents a very information-rich channel, which may easily satisfy all the information needs of the customers, thus substituting the other channels. In the following analysis, based on the positive and negative corre­ lation with the “personal contact” construct, and in preparation to the further analysis of the determinants of the selected customer journeys, we have grouped the search and purchase channels into two families: personal and digital. In particular, we have included the agency, the corporate call center and the friends in the personal channel, and the corporate website, the corporate mobile app, the web aggregator and the social networks in the digital channel. Further, consistently with our classification of customer journeys, we have defined a specific pattern of multichannel shopping behavior (“mixed search”) which includes those respondents (more than 50% in motor insurance) who search both in personal and digital channels. The results of our classification are re­ ported in Table 5. As for the purchase channel, 211 respondents (62.4% of the total) used the personal channels, while 37.6% (127 out of 338) the digital channels. On the search side, 22.5% (76 out of 338) of re­ spondents used only the digital channel, 24% (81 out of 338) only the personal channel, and the majority of respondents (53.5%, 181 out of 338) adopted a “mixed search” approach, i.e., have searched both on the digital and the personal channels. Based on the above, we have classified the sample respondents into four clusters, reflecting their selected customer journey at T1: 1. “fully personal” journey (24.0%, 81 out of 338). 2. “mixed search and personal purchase” journey (38.5%, 130 out of 338). 3. “mixed search and digital purchase” journey (15.0%, 51 out of 338). 4. “fully digital” journey (22.5%, 76 out of 338). We can note that the majority of the motor in­ surance customers have adopted a “mixed search” approach (journeys 2 and 3). Within the “mixed search” journeys, we observe that 71.8% (130 out of 181) of customers eventually purchase their motor policy in the personal channels, while only 28.2% (51 out of 181) finalize their purchase on the digital channel. It is this research shopping (webrooming) effect, with the purchase mostly finalized on the personal

Table 3 Search channel intensity (N ¼ 338). Search Channel

8.87

7

T.-I. Hu and A. Tracogna

Journal of Retailing and Consumer Services 54 (2020) 102022

Table 4 Search channel correlations. Agency Agency Corporate call center Corporate website Corporate mobile app Web aggregator Social networks Friends a b

Corporate Call center

1

0.115a 0.269b 0.071 0.177b 0.098 0.214b

1 0.092 0.224b 0.043 0.187b 0.141b

Corporate website

Corporate mobile app

Web aggregator

Social network

Friends

1 0.272b 0.356b 0.092 0.140a

1 0.253b 0.316b 0.125a

1 0.168b 0.135a

1 0.284b

1

Correlation is significant at the 0.05 level (2-tailed). Correlation is significant at the 0.01 level (2-tailed).

to a “fully personal” journey (our reference category), any increases in the “need for information” (Exp(B) ¼ 1.468), in the preference for “shopping convenience” (Exp(B) ¼ 1.336), in the intensity of the “shopping level” (Exp(B) ¼ 1.821) and any decrease in the “need for personal contact” (Exp(B) ¼ 0.772) and in the age of the customer (Exp (B) ¼ 0.170), are increasing the odds that a specific customer follows a “mixed search and personal purchase” journey. In other words, the odds to carry out a mixed search and to finalize the purchase on personal channels (as opposed to a journey which is only focused on the personal channels) are higher in the presence of younger customers, who are more intensively involved in shopping activities, and which show higher needs for “information” and “convenience” and a lower preference for “personal contact”. This is consistent with the hypotheses H1b (infor­ mation), H1d (convenience) and H1g (personal contact) and is in line with the findings of previous research, where motor insurance con­ sumers are found to browse and surf on comparison websites in the search phase (webrooming), but rather prefer to use a personal channel for the purchase stage (Barwitz and Maas, 2018). Our multinomial regression provides also interesting insights on the other customer journeys. In particular, when we focus on the journeys that are finalized with a digital purchase (“mixed search and digital purchase” and “fully digital”) we see they are highly affected by channel experience effects (and namely by the purchase channel selected at T0 and by the event of reporting an accident). In particular, the purchase channel selected at T0 becomes the most relevant explanatory variable to infer the customer journey at T1, thus, supporting H2a. Further, we find that a higher preference for “shopping innovation” (Exp(B) ¼ 1.790) and a lower preference for “personal contact” (Exp(B) ¼ 0.662) increase the odds of following a multichannel “mixed search and digital purchase” journey (this is supporting H1f and H1g), while a lower preference for “personal contact” (Exp(B) ¼ 0.539) and the lack of “reporting of accidents at T0” (Exp(B) ¼ 0.235) will direct the customer towards a fully digital journey (which supports H1g and H2c). Hy­ potheses H1a, H1c, H1e, and H2b are not supported in this study, which means that shopping enjoyment, service quality, price consciousness, the cost of the insurance policy at T0 and the change of the insured car do not show significant effects on motor insurance customer journeys.

Table 5 Cross-tabulation between search and purchase channel at T1. Purchase channel at T1

Search channel at T1

Digital Personal Mixed

Total

Personal

Digital

Total

0 81 130

76 0 51

76 (22.5%) 81 (24.0%) 181 (53.5%)

211 (62.4%)

127 (37.6%)

338

Pearson Chi-Square ¼ 181.836, Sig. ¼ 0.000.

channels, and namely in the agent channel, that raises key questions: what accounts for the different finalization of the “mixed search” customer journeys? And, further, what are the determinants of the fully digital and fully personal journeys? The last step of our analysis is aimed at answering to these questions. This is done by carrying out a multi­ nomial logistic regression. In so doing, we will test the hypotheses introduced in section 3. The regression results, where the “fully per­ sonal” journey has been used as the reference category, are reported in Table 6. The results of our regression can be interpreted as follows. In relation Table 6 Customer journey determinants: Multinomial logistic regression.

Intercept Age Gender Enjoyment Information Innovativeness Convenience Personal contact Service Price Time pressure Shopping level Distance to store Digital purchase at T0 Purchasing cost at T0 Change of car at T0 Reporting of Accident at T0

Mixed search and personal purchase

Mixed search and digital purchase

Fully digital journey

Sig.

Sig.

Exp (B)

Sig.

Exp (B)

Exp (B)

0.200 0.052a 0.132 0.419 0.019b 0.116 0.062a 0.038b 0.934 0.275 0.262 0.016b 0.705 0.258

0.170 1.714 1.181 1.468 1.319 1.336 0.772 0.987 0.841 0.823 1.821 0.858 2.620

0.694 0.355 0.175 0.115 0.260 0.025b 0.360 0.099a 0.907 0.818 0.164 0.520 0.652 0.000c

0.274 2.140 1.611 1.344 1.790 1.253 0.662 0.971 0.945 0.797 1.272 1.311 103.341

0.550 0.796 0.053a 0.149 0.996 0.204 0.885 0.045b 0.770 0.551 0.213 0.930 0.297 0.000c

1.534 3.757 1.634 0.999 1.460 0.958 0.539 0.918 0.842 0.689 0.964 0.418 1894.8

0.573

1.112

0.962

0.987

0.310

0.710

0.846 0.697

1.088 1.155

0.386 0.362

1.765 0.567

0.634 0.053a

1.448 0.235

6. Discussion and conclusions Our research was aimed at responding to a call for further studies on multichannel shopping behavior (Verhoef et al., 2007) through an investigation of channel interdependencies and the testing of a set of hypotheses on the determinants of multichannel customer journeys. Our research interest has been initially raised by the empirical observation of the non-life insurance markets, where - despite a long-standing evolu­ tion of the distribution channels and the pervasive introduction of dig­ ital technologies and media to support the customer information search a majority of shopping journeys are still finalized through the personal and traditional channels, i.e. the agents (Insurance Europe, 2019). Little is currently known – in this industry - about this “webrooming” effect (searching online and buying offline), as the insurers can only collect

Reference category: fully personal journey (search and purchase on personal channels). -2 Log Likelihood ¼ 554.732, Chi-Square ¼ 344.869, Sig. ¼ 0.000. a Coefficient is significant at the 0.10 level (2-tailed). b Coefficient is significant at the 0.05 level (2-tailed). c Coefficient is significant at the 0.01 level (2-tailed). 8

T.-I. Hu and A. Tracogna

Journal of Retailing and Consumer Services 54 (2020) 102022

data related to the purchases but have little visibility and knowledge about the search efforts which brought a customer to the purchase. The empirical evidence we have collected is quite meaningful in this respect: more than 84% of the searches through the agent channels are also finalized with a purchase, where only 22% of the searches through web aggregators and 9% through corporate web apps, are eventually generating a purchase transaction. In other words, despite a growing tendency of customers to follow sophisticated search patterns, by using multiple search channels simultaneously and taking full advantage of the newly developed digital channels (both corporate and independent), customers show an enduring preference to finalize their journey (i.e. to purchase the motor policy) from the personal channels, and namely from the insurance agents. Following the above, our main research aim has been to confirm, understand and derive theoretical and managerial implications from this relatively unexpected and underexplored phenomenon. Our specific research questions have been derived from the above­ mentioned research aim: in a world where customers are free to move along different journeys, and free to follow - without impediments - their preferences and needs, rather than being subject to the impositions of the sellers, what are the specific drivers of their decisions, and how can companies predict the specific channels where customers will ultimately finalize their purchase? And in the light of the different possible com­ binations of search and purchase channels, which may lead to different “research shopping” outcomes, such as the showrooming and webrooming effects, what are the factors/variables that mostly influ­ ence the selection of a specific shopping journey? And what can the seller organizations do - within a multichannel context - to exert a higher influence on a specific shopping outcome? The above questions have been addressed in this paper through a new categorization of the different customer journeysand a more gran­ ular profiling of the psychographic traits of the customer, combined with the acknowledgement of the different channel synergy effects, which we have included in our empirical model to derive and test our set of hypotheses. Based on our findings, we have collected clear evidence that most insurance customers adopt multichannel behaviors, i.e., they purchase from one specific channel only after having extensively searched from a set of alternative channels. However, most of the customer journeys, although initiated through a mixed search, are still finalized in the personal channels (namely, the agents). This is interesting when asso­ ciated to the enormous efforts made by the insurance companies to develop their digital channels and also in the light of the fact that a growing number of insurance customers are utilizing multiple digital channels for their information search. This combination of low con­ version rates and high intensity, by the customer, in the use of digital channels represents a peculiar, and somehow counterintuitive, finding, which justifies our analytical effort. The testing of our hypotheses has confirmed that multichannel journeys have different psychographic determinants: on the one side, they may reflect the customer need to collect more information, relevant for the shopping decision; on the other side, they reflect the customer preference for shopping innovation, i.e., the willingness to search and purchase new products and to have new experiences; further, they may be driven by the preference for convenience (need to minimize the time and effort required for shopping); lastly, a multichannel journey may be positively associated with customer’s high shopping level. Finally, with reference to the enduring tendency - within a multichannel journey - to finalize the purchase with an insurance agent, as opposed to a digital channel, the discriminant variable appears to be the customers’ prefer­ ence for shopping convenience. Overall, our findings have evident implications for retail strategies and management. While our data confirm that insurance customers keep buying mostly from the traditional channels (with the agents holding the highest share: i.e. more than 54% of the cases), they also show very varied and intense search patterns beneath the purchase level,

particularly in the digital channel. Webrooming effects, whereby digital channels are not generating lots of purchases but are nevertheless acti­ vated several times, mostly for information search, and in combination with other, potentially competing, channels, shall be properly managed by insurers. This is a first important indication for the insurers, who are currently engaged in a significant effort to transfer the selling trans­ actions towards the digital channels. In the light of our findings, these efforts might be less effective than expected and must be carefully addressed and assessed. In other words, if read through the lenses of the finalized transactions (actual purchases), insurers may be led to the conclusion that the digital channels are much less productive than the personal ones. However, this conclusion is wrong, as it doesn’t take into account the significant positive synergies between channels and the instrumental role of the digital search channels for the subsequent purchase decisions. The clear implications of the above is the need, for the insurance companies, to better understand the variety of multichannel customer behaviors in the different shopping phases (search and purchase) and to provide the customers with a complete freedom to walk across the different channels available and to finalize their purchases wherever they prefer, without any friction and any burden in terms of incremental transaction costs, thus maximizing their shopping convenience. To cope with the evolving customer’s expectations in term of information availability and richness, it is important that the insurers quickly adopt a truly multichannel approach in retailing, providing the customers the needed information and purchasing options throughout all the available channels. In other words, companies shall not focus only on the channels where most of the purchases are finalized, and must, instead, leverage on all the options available for providing customers with seamless transition opportunities between channels, aimed at reducing any possible negative channel synergies and lock-in effects (Homburg et al., 2017; Verhoef et al., 2007). The flexibility allowed to the customers must, at the same time, be complemented with a clear understanding of the specific functions and features of the different channels within a corporate multiple channel retail strategy. In particular, insurers shall be able to optimize their multichannel strategy by defining the quantity and quality of informa­ tion available on each channel, determining the right level of comple­ mentarity among channels as well as the right channel ownership structure, the width and depth of the assortment of services available and the level of customer service provided (i.e., personal vs. virtual assistance). Furthermore, insurers must also encourage, through a new design of the economic incentive systems, the traditional channels (agents) to develop stronger positive synergies with the new digital channels, so to allow customers to maximize shopping convenience also along “digitally finalized” or “fully digital” journeys, thus leveraging in full the huge search potential offered by the new channels. At the moment, the insurance agents seem not to have a clear incentive in reversing this (for them) favorable situation, which may impede the effective implementation, by the insurance companies, of newly conceived omnichannel strategies. Overall, it is becoming more and more important for insurers, and for retailers of any market, to provide customers with a consistent and integrated journey, where the different channels are not left to the customer’s independent and individual integration but are, rather, combined and put at work synergistically by the retailers and fully aligned to the expected customer experience and shopping performance, in accordance to her/his personal preferences and traits. Further, our findings highlight the possibility for the insurers to better profile and segment their customers, either current or potential. Through this segmentation, which can be based on our set of psycho­ graphic variables, and may also include our other covariates (channel experience, situational and demographic), insurance companies will better predict their customers’ behavior, even at an early stage of the shopping journey. By surveying existing, new and potential customers, based on channel/product experience and psychographic traits, and 9

T.-I. Hu and A. Tracogna

Journal of Retailing and Consumer Services 54 (2020) 102022

using the available customer relationship data (CRM) for an early-stage segmentation of customers, companies would be able to predict shop­ ping behaviors, so to better target and reinforce their marketing stra­ tegies and channel management practices, thus identifying the best actions aimed at improving the overall search-to-purchase conversion rates and, eventually, increasing the customer retention rates, which are historically low in the industry. As most research, ours too suffers of several limitations, starting from the survey population selected (MBA graduates), which is far from reflecting the general population of insurance customers. However, our intention wasn’t to make any general inference on the profile and behavior of insurance customers. Rather, our aim was to shed light on the diverse search channels available and to identify a possible frame­ work for the classification of customer journeys and the understanding of their determinants. In this respect, our findings, although provisional, offer interesting empirical evidence to the academic community which could be useful also in other industry settings. In particular, the fact that a majority of shopping journeys are still finalized through the personal and traditional channels, i.e. the agent - which we might have explained with the intrinsic complexity of the products and with the need to refer to a trustable intermediary – has been surprisingly detected also within our sample of more educated persons. These results have further increased our determination to search and identify other possible ex­ planations for this webrooming effect, which we have found in the psychographic traits of customers (and the channel experience effects), rather than in the personal level of education and disposable income. However, for the specific nature of the sample utilized, we are also aware that our findings cannot be fully generalized. Other limitations pertain to the methodology utilized: compared to our cross-sectional study, a panel-based, longitudinal study would have better addressed our research on shopping journeys, but at the expense of its feasibility. Another limitation is related to the evolutionary nature of customer behaviors and to the ongoing changes in the shopping channels. We know that channel choices change over time (Valentini et al., 2011) and that the drivers of such evolution shall not be found only at the behavioral level, but also pertain to the exogenous evolution of tech­ nological and sociological variables. Such dimensions have not been explicitly considered in our research. Further, our research was mostly aimed at describing multichannel shopping behavior and its de­ terminants, and wasn’t directed to the assessment of the desirability of such behaviors, particularly from the perspective of the seller: How is a multichannel journey related to the seller’s economic performance? Is such a shopping behavior beneficial for the seller? Recent studies maintain that multichannel shopping is associated with higher customer profitability and to higher brand loyalty (Frasquet and Miquel, 2017). Future research could further explore tthe above questions, in relation to the different customer journeys and to different products and markets.

Anderl, E., Becker, I., von Wangenheim, F., Schumann, J.H., 2016. Mapping the customer journey: lessons learned from graph-based online attribution modeling. Int. J. Res. Mark. 33 (3), 457–474. https://doi.org/10.1016/j.ijresmar.2016.03.001. Ansari, A., Mela, C.F., Neslin, S.A., 2008. Customer channel migration. J. Mark. Res. 45 (1), 60–76. https://doi.org/10.1509/jmkr.45.1.060. Baker, J., Parasuraman, A., Grewal, D., Voss, G.B., 2002. The influence of multiple store environment cues on perceived merchandise value and patronage intentions. J. Mark. 66 (2), 120–141. https://doi.org/10.1509/jmkg.66.2.120.18470. Balasubramanian, S., Raghunathan, R., Mahajan, V., 2005. Consumers in a multichannel environment: product utility, process utility, and channel choice. J. Interact. Mark. 19 (2), 12–30. https://doi.org/10.1002/dir.20032. Barwitz, N., Maas, P., 2018. Understanding the omnichannel customer journey: determinants of interaction choice. J. Interact. Mark. 43, 116–133. https://doi.org/ 10.1016/j.intmar.2018.02.001. Beckett, A., Hewer, P., Howcroft, B., 2000. An exposition of consumer behaviour in the financial services industry. Int. J. Bank Mark. 18 (1), 15–26. https://doi.org/ 10.1108/02652320010315325. Belk, R.W., 1974. An exploratory assessment of situational effects in buyer behavior. J. Mark. Res. 11 (2), 156–163. https://doi.org/10.1177/002224377401100206. Bell, D.R., Gallino, S., Moreno, A., 2014. How to win in an omnichannel world. MIT Sloan Manag. Rev. 56, 45–53. Bell, D.R., Gallino, S., Moreno, A., 2017. Offline showrooms in omnichannel retail: demand and operational benefits. Manag. Sci. 64 (4), 1629–1651. https://doi.org/ 10.1287/mnsc.2016.2684. Black, N.J., Lockett, A., Ennew, C., Winklhofer, H., McKechnie, S., 2002. Modelling consumer choice of distribution channels: an illustration from financial services. Int. J. Bank Mark. 20 (4), 161–173. https://doi.org/10.1108/02652320210432945. Buhler, P., Eling, M., Maas, P., Milanova, V., 2016. The Consumer’s View of Consumer Protection; an Empirical Study of the Swiss Insurance Market. Institute of Insurance Economics, St. Gallen. Cambra-Fierro, J., Kamakura, W.A., Melero-Polo, I., Sese, F.J., 2016. Are multichannel customers really more valuable? An analysis of banking services. Int. J. Res. Mark. 33 (1), 208–212. https://doi.org/10.1016/j.ijresmar.2015.12.007. Cho, S., Workman, J., 2011. Gender, fashion innovativeness and opinion leadership, and need for touch: effects on multi-channel choice and touch/non-touch preference in clothing shopping. J. Fash. Mark. Manag.: Int. J. 15 (3), 363–382. https://doi.org/ 10.1108/13612021111151941. Chocarro, R., Corti~ nas, M., Villanueva, M.-L., 2013. Situational variables in online versus offline channel choice. Electron. Commer. Res. Appl. 12 (5), 347–361. https://doi. org/10.1016/j.elerap.2013.03.004. Choudhury, V., Karahanna, E., 2008. The relative advantage of electronic channels: a multidimensional view. MIS Q. 32 (1), 179–200. https://doi.org/10.2307/ 25148833. Crosby, L.A., Evans, K.R., Cowles, D., 1990. Relationship quality in services selling: an interpersonal influence perspective. J. Mark. 54 (3), 68–81. https://doi.org/ 10.2307/1251817. De Keyser, A., Schepers, J., Konus¸, U., 2015. Multichannel customer segmentation: does the after-sales channel matter? A replication and extension. Int. J. Res. Mark. 32 (4), 453–456. https://doi.org/10.1016/j.ijresmar.2015.09.005. Engel, J.F., Blackwell, R.D., Miniard, P.W., 1990. Consumer Behavior. Dryden Press. Fornell, C., Larcker, D.F., 1981. Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res. 18 (1), 39–50. https://doi.org/ 10.1177/002224378101800104. Forsythe, S., Liu, C., Shannon, D., Gardner, L.C., 2006. Development of a scale to measure the perceived benefits and risks of online shopping. J. Interact. Mark. 20 (2), 55–75. https://doi.org/10.1002/dir.20061. Frambach, R.T., Roest, H.C.A., Krishnan, T.V., 2007. The impact of consumer Internet experience on channel preference and usage intentions across the different stages of the buying process. J. Interact. Mark. 21 (2), 26–41. https://doi.org/10.1002/ dir.20079. Frasquet, M., Miquel, M.-J., 2017. Do channel integration efforts pay-off in terms of online and offline customer loyalty? Int. J. Retail Distrib. Manag. 45 (7/8), 859–873. https://doi.org/10.1108/IJRDM-10-2016-0175. Frasquet, M., Leva, M., Ziliani, C., 2019. Understanding complaint channel usage in multichannel retailing. J. Retail. Consum. Serv. 47, 94–103. https://doi.org/ 10.1016/j.jretconser.2018.11.007. Gensler, S., Leeflang, P., Skiera, B., 2012. Impact of online channel use on customer revenues and costs to serve: considering product portfolios and self-selection. Int. J. Res. Mark. 29 (2), 192–201. https://doi.org/10.1016/j.ijresmar.2011.09.004. Goldsmith, R.E., Hofacker, C.F., 1991. Measuring consumer innovativeness. J. Acad. Mark. Sci. 19 (3), 209–221. https://doi.org/10.1177/009207039101900306. Gupta, A., Su, B., Walter, Z., 2004. An empirical study of consumer switching from traditional to electronic channels: a purchase-decision process perspective. Int. J. Electron. Commer. 8 (3), 131–161. https://doi.org/10.1080/ 10864415.2004.11044302. Hair, J.F., Hult, G.T.M., Ringle, C., Sarstedt, M., 2016. A primer on partial least squares structural equation modeling (PLS-SEM), Second ed. SAGE, Los Angeles. Harris, P., Dall’Olmo Riley, F., Hand, C., 2018. Understanding multichannel shopper journey configuration: an application of goal theory. J. Retail. Consum. Serv. 44, 108–117. https://doi.org/10.1016/j.jretconser.2018.06.005. Homburg, C., Jozi�c, D., Kuehnl, C., 2017. Customer experience management: toward implementing an evolving marketing concept. J. Acad. Mark. Sci. 45 (3), 377–401. https://doi.org/10.1007/s11747-015-0460-7. Hoque, A.Y., Lohse, G.L., 1999. An information search cost perspective for designing interfaces for electronic commerce. J. Mark. Res. 36 (3), 387–394. https://doi.org/ 10.1177/002224379903600307.

Declaration of competing interest None. Acknowledgements None. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. References 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. https://doi.org/ 10.1177/002224299706100303.

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T.-I. Hu and A. Tracogna

Journal of Retailing and Consumer Services 54 (2020) 102022 Park, J., Kim, R.B., 2018. A new approach to segmenting multichannel shoppers in Korea and the U.S. J. Retail. Consum. Serv. 45, 163–178. https://doi.org/10.1016/j. jretconser.2018.09.007. Pauwels, K., Dans, E., 2001. Internet marketing the news: leveraging brand equity from marketplace to marketspace. J. Brand Manag. 8 (4), 303–314. https://doi.org/ 10.1057/palgrave.bm.2540030. Pauwels, K., Leeflang, P.S.H., Teerling, M.L., Huizingh, K.R.E., 2011. Does online information drive offline revenues? Only for specific products and consumer segments! J. Retail. 87 (1), 1–17. https://doi.org/10.1016/j.jretai.2010.10.001. Piotrowicz, W., Cuthbertson, R., 2014. Introduction to the special issue information technology in retail: toward omnichannel retailing. Int. J. Electron. Commer. 18 (4), 5–16. https://doi.org/10.2753/JEC1086-4415180400. Rangaswamy, A., Van Bruggen, G.H., 2005. Opportunities and challenges in multichannel marketing: an introduction to the special issue. J. Interact. Mark. 19 (2), 5–11. https://doi.org/10.1002/dir.20037. Ratchford, B.T., Talukdar, D., Lee, M.-S., 2001. A model of consumer choice of the internet as an information source. Int. J. Electron. Commer. 5 (3), 7–21. https://doi. org/10.1080/10864415.2001.11044217. Ripp�e, C.B., Sussan, F., Yurova, Y., Weisfeld-Spolter, S., 2015. Is there a global multichannel consumer? Int. Mark. Rev. 32 (3/4), 329–349. https://doi.org/ 10.1108/IMR-10-2013-0225. Saghiri, S.S., Bernon, M., Bourlakis, M., Wilding, R., 2018. Omni-channel logistics special issue. Int. J. Phys. Distrib. Logist. Manag. 48 (4), 362–364. https://doi.org/10.1108/ IJPDLM-05-2018-361. Schoenbachler, D.D., Gordon, G.L., 2002. Multi-channel shopping: understanding what drives channel choice. J. Consum. Mark. 19 (1), 42–53. https://doi.org/10.1108/ 07363760210414943. Thomas, J.S., Sullivan, U.Y., 2005. Managing marketing communications with multichannel customers. J. Mark. 69 (4), 239–251. https://doi.org/10.1509/ jmkg.2005.69.4.239. Valentini, S., Montaguti, E., Neslin, S.A., 2011. Decision process evolution in customer channel choice. J. Mark. 75 (6), 72–86. https://doi.org/10.1509/jm.09.0362. Van Bruggen, G.H., Antia, K.D., Jap, S.D., Reinartz, W.J., Pallas, F., 2010. Managing marketing channel multiplicity. J. Serv. Res. 13 (3), 331–340. https://doi.org/ 10.1177/1094670510375601. Venkatesan, R., Kumar, V., Ravishanker, N., 2007. Multichannel shopping: causes and consequences. J. Mark. 71 (2), 114–132. https://doi.org/10.1509/jmkg.71.2.114. Verhoef, P.C., Neslin, S.A., Vroomen, B., 2007. Multichannel customer management: understanding the research-shopper phenomenon. Int. J. Res. Mark. 24 (2), 129–148. https://doi.org/10.1016/j.ijresmar.2006.11.002. 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. https://doi.org/10.1016/j.jretai.2015.02.005. Weinberg, B.D., Parise, S., Guinan, P.J., 2007. Multichannel marketing: mindset and program development. Bus. Horiz. 50 (5), 385–394. https://doi.org/10.1016/j. bushor.2007.04.002. Wind, Y., Mahajan, V., 2002. Convergence marketing. J. Interact. Mark. 16 (2), 64–79. https://doi.org/10.1002/dir.10009. Yu, U.-J., Niehm, L.S., Russell, D.W., 2011. Exploring perceived channel price, quality, and value as antecedents of channel choice and usage in multichannel shopping. J. Mark. Channels 18 (2), 79–102. https://doi.org/10.1080/ 1046669X.2011.558826. Yurova, Y., Ripp�e, C.B., Weisfeld-Spolter, S., Sussan, F., Arndt, A., 2017. Not all adaptive selling to omni-consumers is influential: the moderating effect of product type. J. Retail. Consum. Serv. 34, 271–277. https://doi.org/10.1016/j. jretconser.2016.01.009. Zhang, J., Farris, P.W., Irvin, J.W., Kushwaha, T., Steenburgh, T.J., Weitz, B.A., 2010. Crafting integrated multichannel retailing strategies. J. Interact. Mark. 24 (2), 168–180. https://doi.org/10.1016/j.intmar.2010.02.002. Zomerdijk, L.G., Voss, C.A., 2010. Service design for experience-centric services. J. Serv. Res. 13 (1), 67–82.

Howard, J.A., 1989. Consumer Behavior in Marketing Strategy. Prentice Hall. Inman, J.J., Shankar, V., Ferraro, R., 2004. The roles of channel-category associations and geodemographics in channel patronage. J. Mark. 68 (2), 51–71. https://doi.org/ 10.1509/jmkg.68.2.51.27789. Insurance Europe, 2019. Key facts 2019. Brussels. Accessed on, at. https://www.insuran ceeurope.eu/sites/default/files/attachments/European%20insurance%20-%20Key %20facts.pdf. (Accessed 7 December 2019). Juaneda-Ayensa, E., Mosquera, A., Sierra Murillo, Y., 2016. Omnichannel customer behavior: key drivers of technology acceptance and use and their effects on purchase intention. Front. Psychol. 7, 1–11. https://doi.org/10.3389/fpsyg.2016.01117, 1117. Kankainen, A., Vaajakallio, K., Kantola, V., Mattelm€ aki, T., 2012. Storytelling group – a co-design method for service design. Behav. Inf. Technol. 31 (3), 221–230. https:// doi.org/10.1080/0144929X.2011.563794. Kleijnen, M., Ruyter, K. de, Wetzels, M., 2007. An assessment of value creation in mobile service delivery and the moderating role of time consciousness. J. Retail. 83 (1), 33–46. https://doi.org/10.1016/j.jretai.2006.10.004. Konus¸, U., Verhoef, P.C., Neslin, S.A., 2008. Multichannel shopper segments and their covariates. J. Retail. 84 (4), 398–413. https://doi.org/10.1016/j.jretai.2008.09.002. Konus¸, U., Neslin, S.A., Verhoef, P.C., 2014. The effect of search channel elimination on purchase incidence, order size and channel choice. Int. J. Res. Mark. 31 (1), 49–64. https://doi.org/10.1016/j.ijresmar.2013.07.008. Kumar, V., Venkatesan, R., 2005. Who are the multichannel shoppers and how do they perform? Correlates of multichannel shopping behavior. J. Interact. Mark. 19 (2), 44–62. https://doi.org/10.1002/dir.20034. Lee, H., Kim, J., 2008. The effects of shopping orientations on consumers’ satisfaction with product search and purchases in a multi-channel environment. J. Fash. Mark. Manag.: Int. J. 12 (2), 193–216. https://doi.org/10.1108/13612020810874881. Lee, L., Inman, J.J., Argo, J.J., B€ ottger, T., Dholakia, U., Gilbride, T., et al., 2018. From browsing to buying and beyond: the needs-adaptive shopper journey model. Journal of the Association for Consumer Research 3 (3), 277–293. https://doi.org/10.1086/ 698414. Leenders, M.A.A.M., Smidts, A., Haji, A.E., 2019. Ambient scent as a mood inducer in supermarkets: the role of scent intensity and time-pressure of shoppers. J. Retail. Consum. Serv. 48, 270–280. https://doi.org/10.1016/j.jretconser.2016.05.007. Lemon, K.N., Verhoef, P.C., 2016. Understanding customer experience throughout the customer journey. J. Mark. 80 (6), 69–96. https://doi.org/10.1509/jm.15.0420. Lewis, J., Whysall, P., Foster, C., 2014. Drivers and technology-related obstacles in moving to multichannel retailing. Int. J. Electron. Commer. 18 (4), 43–68. https:// doi.org/10.2753/JEC1086-4415180402. Lichtenstein, D.R., Netemeyer, R.G., Burton, S., 1990. Distinguishing coupon proneness from value consciousness: an acquisition-transaction utility theory perspective. J. Mark. 54 (3), 54–67. https://doi.org/10.1177/002224299005400305. McGoldrick, P.J., Collins, N., 2007. Multichannel retailing: profiling the multichannel shopper. Int. Rev. Retail Distrib. Consum. Res. 17 (2), 139–158. https://doi.org/ 10.1080/09593960701189937. Melis, K., Campo, K., Breugelmans, E., Lamey, L., 2015. The impact of the multi-channel retail mix on online store choice: does online experience matter? J. Retail. 91 (2), 272–288. https://doi.org/10.1016/j.jretai.2014.12.004. Midgley, D.F., Dowling, G.R., 1978. Innovativeness: the concept and its measurement. J. Consum. Res. 4 (4), 229–242. https://doi.org/10.1086/208701. Nakano, S., Kondo, F.N., 2018. Customer segmentation with purchase channels and media touchpoints using single source panel data. J. Retail. Consum. Serv. 41, 142–152. https://doi.org/10.1016/j.jretconser.2017.11.012. Neslin, S.A., Grewal, D., Leghorn, R., Shankar, V., Teerling, M.L., Thomas, J.S., Verhoef, P.C., 2006. Challenges and opportunities in multichannel customer management. J. Serv. Res. 9 (2), 95–112. https://doi.org/10.1177/ 1094670506293559. Nunnally, J.C., Bernstein, I.H., 1994. Psychometric Theory, third ed. McGraw-Hill. Parasuraman, A., Zeithaml, V.A., Berry, L.L., 1988. SERVQUAL: a multiple-item scale for measuring consumer perceptions of service quality. J. Retail. 64 (1), 12–40.

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