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Since the beginning of the 21st century, there have been multiple economic analysts who point to the relevance of services as the ability to add value to a brand (Vargo and Lusch, 2008). This feature allows companies to develop a differential competitive strategy (Ali et al., 2018), based on an innovative service offering (VI DEC Congress, 2019) offered in a unique (Blut et al., 2014) and customized way (Bilgihan et al., 2015). This new customer centricity vision (Kohli et al., 2019) implies a great knowledge of their behavior and needs (De Mooij, 2019), including affective ones (The Human Brand Report, Grass Roots, 2018). In this new interconnected socioeconomic context, it reveals that the role of consumers has changed substantially and consumer participation has become key to the development of products and services of mutual value as a source of innovative ideas and brand value (Martínez-Cañas et al., 2016). Research reveals that personalization, excellence in supply and service, and speed of delivery are critical in consumers’ value perception (Deloitte, 2017). In this way, digital channels offer companies a great opportunity to create functional value to consumers but also emotional value that as a differential tool (Zhu and Gao, 2019) can consolidate company–consumer relationship (Karjaluoto et al., 2012) since they allow the implementation of a space in which the relationship is different, even extraordinary (Carù and Cova, 2013), based on the generation of experiences and co-creation of solutions (García Haro, 2018).

Although digital reality is a fact (PWC, 2017), it is not easy to implement value self-services in all sectors and consolidate the adoption and dissemination of the web channel among customers (Nicoletti, 2016). In the case of insurance, the provision of services concerns the possibility of risk of human life, and/or the most precious assets of customers made an object of insurance (García, 2010), and where confidence that the company will meet one’s needs is the key (Capgemini, 2020). This makes the customer’s perception of this product high risk and seeks non-virtual channels (Minsait, Asociación para el Desarrollo de la Experiencia de Cliente (DEC), 2019) as a means of relationship with insurers. Guaranteeing this trust from a digital service is difficult since the private area must satisfy two profiles of well-differentiated behaviors: (1) the rational profile that prevails when contracting the insurance, where the information (Chang et al., 2014) and transaction security minimize the perceived risk (Currás-Pérez and Sánchez-García, 2012; Thakur and Srivastava, 2014) and (2) the emotional profile experienced when requesting the contracted service: reporting a mishap in the customer’s assets including the death of loved ones. In these cases, the moment of the client’s truth (IZO, 2019) on the digital channel must behave with sufficient quality and warmth (Grass Roots, 2018) that it would have received from an agent (Palm, 2016).

In short, this additional service provided by the digital channel, added to the main service, increases the value of other intangibles (dominant service logic, Vargo and Lusch, 2014) and therefore allows not only a brand experience through direct and continuous contact, but also provides other high-value elements such as effectiveness, efficiency, support and other useful services (Inbound Marketing, InboundCycle, 2020).

Despite the divergence between sectors, it is important to highlight that what has unanimously changed is the way in which consumers approach the sector and where the service is resolved in real time in a simple way (EIOPA, 2017). In the case of the insurance field, the client cannot compare the digital experience with the immediate competitors, but it will do so with the sectors with which it is digitally related (Amezua, 2019). Moreover, as the author says, social change is parallel to technology “so that adaptation to new technologies is essential to avoid the obsolescence of company services” (Amezua, 2019).

However, despite the social changes and the relevance of the digital customer experience on trust in the insurance brands (Han and Hyun, 2015) and loyalty in the consumer (Ali et al., 2018), in a sector that is 8.92 of the world GDP (OECD, 2020), the authors Pérez-Rave and Muñoz-Giraldo (2014) warn of insufficient research on the quality of service from an attitudinal perspective and studies of models of complex behaviors, where multiple variables interact.

But what is customer experience? The term “customer experience” was introduced by Holbrook and Hirschman (1982), but it was not until 1999 that it first appeared in academic literature (Pine and Gilmore, 1999). The ability to simultaneously meet functional and pleasurable needs (Tamborini et al., 2011), through surprising and memorable encounters (Ali et al., 2014), allows the creation of unique experiences (Blut et al., 2014) of a cognitive, social, affective, and physical nature between the client and the company (Vargo and Lusch, 2014).

The challenge for insurance companies is to turn digital interaction into an added value for the customer and not simply meet their needs in a way that allows them to increase their brand experience (Wanick et al., 2017), increase their reputation (De Quevedo et al., 2005), and enhance channel loyalty (Chen and Phou, 2013). New values are incorporated into the service (cite Anne’s book), beyond intrinsic functional value, such as (1) Emotional Value or capacity for well-being, emotion, and happiness in digital interaction (Lemon and Verhoef, 2016); (2) Social Value, where the client improves his position and his own identity by adopting technology (Bilgihan et al., 2016); and (3) Epistemic Value: it relates to the sense of adventure and the satisfaction of obtaining a knowledge that takes away his curiosity (related to flow perception, Nakamura and Csikszentmihalyi, 2014). Therefore, it is necessary to know the mechanism through which the digital service allows the simultaneous customer to (1) achieve the functional need that led them to use the service and (2) obtain a personal, unique, and an exclusive experience, in such a way as to produce a high degree of customer satisfaction. In this process, the quality of the web service is key to obtaining the desired result, in accordance with the initial expectations of the client (Michalco et al., 2015) and make evaluations accordingly (Kujala et al., 2017). Although the co-creation process is a common marketing practice (García Haro, 2018), in the virtual field the lack of possibility of customization of private web areas is notorious. In this process, it is not only necessary to listen to the needs of the client, but, as García Haro (2018) indicates, also necessary to incorporate the virtual consumer in the decision processes that improve the experience, such as the digital design of the services and what attributes they should possess, so that they respond to the expectations of the users of those private web areas. This requires innovation in knowledge, relationship models, new technologies, and services to provide new experiences (Marketing Science Institute, 2016).

In this socioeconomic and technological context described, in deep current change, this research sets the objective of investigating the antecedents and consequences of the digital customer experience in the insurance sector. It is intended to confirm whether the client’s behavior model presents differences compared to the reference framework consulted due to the different bias that differentiates it from other services. On the one hand, the one that provides the adoption of technology in a traditionally non-digital sector (Thakur and Srivastava, 2014; Minsait, Asociación para el Desarrollo de la Experiencia de Cliente (DEC), 2019; Capgemini, 2020). Second is the risk due to the very nature of its coverage (García, 2010). In this new business strategy focused on the customer experience (Comisión nacional de los Mercados y la Competencia [CNMC], 2019), where users expect quality services, congruent with the value of the brand (Rather and Camilleri, 2019), it seems necessary to delve into the sector and relate consumer expectations and quality of service as a background to the experience. Research that has not been identified in the reference literature on the digital attributes is considered. Although it would be necessary to reel how the customer experience is shaped from cognitive, affective, and behavioral dimensions (Rather, 2020), this research raises three objectives specific about the digital customer experience: (a) the objective of verifying whether the user’s attitude is relevant to user satisfaction from the perceived experience, that is, identify the true role of expectations and the perceived quality of the customer’s digital experience, (b) identify the relationship between customer experience and satisfaction gained in private insurance web areas, and (c) identify the most valued digital attributes by the user. For it, this article proposes an analysis of these private customer web areas, where it is possible to measure and analyze customer behavior through a relational model of customer experience. To do this, it begins by analyzing the relationship between the following four variables: expectations, quality, customer experience, and satisfaction.

The results statistically confirmed the robustness of the model, which allows the customer experience to be considered as predictive based on the proposed determinants and, therefore, to formulate implications/conclusions of great interest to the scientific community in the knowledge of the behavior of the digital consumer. However, the business implications are also noteworthy as they promote in the business community the consolidation of Customer Experience programs and offer immersive digital services, essential for the sustainability of companies given the influence that the customer experience has on satisfaction and this on trust (Han and Hyun, 2015) and loyalty to the company (Yoo et al., 2013) and the adoption of the new communication channels (Chen and Phou, 2013). Knowing the best communication strategy with one’s client by new channels can bring considerable savings in companies in their digital transformation (Comisión nacional de los Mercados y la Competencia [CNMC], 2019) in order to optimize million-dollar investments in advertising, technology, and better information systems (SI).

According to the iabSpain (2018), the new customer profile is that of the connected individual. Intensive use of digital channels enables this individual to maintain a continuous, direct, and interactive relationship with the company. This demands a highly personalized product and service offering as well as immediacy and agility in the delivery of value (Deloitte, 2017).

This new type of relationship enables customers to formulate expectations in relation to what they expect to receive from the digital service, on which they will evaluate the results obtained in terms of experience and satisfaction according to the quality presented by the web service.

To be sure to choose a robust digital sample, it was therefore necessary to select a relevant company in the insurance business whose digital presence is indisputable (El Independiente, 2018; La Vanguardia, 2019), with plenty of customer area activity (21.000 monthly accesses). Only two criteria were taken into account in extracting the representative sample: (1) contractual continuity of three months and (2) having performed at least one operation in the private customer web areas during the previous month, to ensure the customer’s familiarity with both the company and the channel. The requested online service covers any operation: contracting, claiming, or requesting insurance services. The scope includes auto, motorcycle, home, life, or savings insurance.

Once the state of the art had been reviewed, and the hypotheses and the model to be tested had been defined, in-depth interviews with experts were carried out to confirm the most appropriate indicators for the online channel and how the approach to the customer should take place.

To confirm the hypotheses formulated, 4 constructs were identified that would allow studying the underlying relationships questioned: digital quality, expectations, experience, and customer satisfaction. The corresponding reference framework was established on the proposed relationships and, in particular, in the digital context (see Table 1). Similarly, the most precise indicators that define them were defined: 4 items to measure expectations; 17 to assess the digital quality of the service; 2 for customer experience; and 5 to measure satisfaction. In total, 28 scales were incorporated into the definitive form, ratified again by experts, from the qualitative methodology proposed. The choice of an 11-point Likert scale ensured adequate variance in the responses (Bisquerra Alzina and Pérez Escoda, 2015) and enabled the subsequent treatment of the data according to groups of points obtained.

In June 2016, three mass mails were sent to the 23,223 sample candidates. The sample is indeed large to guarantee the required 95% confidence level and a sampling error of ± 1.5%. It also guarantees the behavior of more than 250,000 registered users, who carry out all kinds of operations. This is to avoid bias in the operation, age, and type of insurance that the client has. It should be added that the proposed sample size contemplated the possibility of studying segments of it. In this way, it is guaranteed that with a single fieldwork we could have enough population to study the influence of the client’s previous attitude toward the company (NPS factor). As a future line of research, also work on the experience bias is produced by the platform: url/App. As a result of this, 4,178 valid surveys were obtained online (see Table 2), in accordance with the required 95% confidence level. Participation was encouraged to increase response and interest in the research project.

The descriptive analysis indicated online users with an average age of 44; the majority (30%) aged between 36 and 45, followed by groups aged 26–35 and 46–55 years. The online purchase volume was once a month, the average being 3.9 purchases per month, far higher than the Spanish national average of 3.4 recorded in 2016 (iabSpain, 2016). The computer was the platform habitually used for the consumption of services (71.9%), though the use of smartphones was already identified as a growing trend, with 18.1% of users opting for m-commerce. A total of 78% of the users regularly used the digital medium for their transactions; 79% expressed a preference for it because of the convenience of the operating hours. A total of 88.4% of the respondents had not used the App.

The digital profile obtained was fully current in 2018 (iabSpain, 2018); hence, the results were valid, providing a sample of expert users able to objectively evaluate website quality without any issues of poor adjustment to technology, producing biases with respect to perceived satisfaction.

The proposed relational model was validated using partial least squares structural equation modeling (PLS/SEM) (statistical output of SmartPLS 3, confirmed in the Figure 1) and Smart PLS 3.0 software (Ringle et al., 2015). Using a bootstrap sampling technique, the 10.000 repetitions performed (Hair et al., 2017) ensured the individual reliability of the item, the scale, the convergent validity, and the discriminant validity obtained through Cronbach’s alpha statistics, composite reliability, % of accumulated variance, the determinant of the correlation matrix, the Barlett test of sphericity, and the KMO index, as well as other important reliability statistics for PLS, such as R² and the Stone-Geisser and Q2 index offered by the PLS predict. Thanks to verification, the reliability and validity of the items and constructs, as well as their predictive capacity, were confirmed, as will be seen in the results.

Partial least squares structural equation modelling uses a two-step estimation process: evaluation of the measurement model and the structural model. To analyze the measurement model, it is necessary to assess the reliability, convergent validity, and discriminant validity. As we can observe in Table 3, to ensure item validity, items with values less than 0.7 were removed (Bagozzi and Yi, 2012). The composite reliability (CR) and Cronbach’s alpha were calculated in the same way for values greater than 0.7 (Cronbach, 1951; Malhotra, 2008). The average variance extracted (AVE) was contrasted with the recommended value equal to or greater than 0.5, as indicated by Fornell and Larcker (1981). The rho_A coefficient (Dijkstra and Henseler, 2015) with a value close to 1.0 indicated the reliability of the model analyzed with PLS. The discriminant validity of the measurement model was also accepted where a given construct was different to the others (Authors’ note: It should be noted that this measurement is only applicable to constructs with reflective indicators, as is the case in this research project.) Fornell and Larcker (1981) established the need for the variance shared between a given variable and its attributes to be greater than that shared with the other variables of the proposed model, as we can observe in Table 4. The diagonal of Table 4 shows the value of the square root of the AVE for the corresponding construct, which met the criterion that the correlations between constructs must be less than the square root of the AVE.

As we can observe in Table 5, the predictive capacity of the constructs is also confirmed. Using the Stone–Geisser test, the values obtained for Q2 in customer experience of 0.585, Q² by predictive PLS of 0.665, exceed by far the theoretical threshold of 0.35 for highly predictive constructs (García Haro, 2018). The same happens with the satisfaction construct, with values of 0.282 and 0.522, respectively. Regarding the values of R² (maximizing the explained variance of the dependent variables, Chin and Newsted, 1999), The values obtained for customer experience (0.666) and satisfaction (0.533), higher than 0.5, confirm the robustness of both constructs.

Finally, the measurement model is analyzed. As we can observe from Table 6, all the proposed hypotheses were accepted. Coefficients with a 99% confidence level were accepted, using Student’s t-test values greater (in absolute value) than 2.58, and where a p-value of less than 0.01 was satisfied. The path coefficient values (β), or standardized regression coefficients, indicated a strong relationship between both digital customer quality and experience (β = 0.856; p < 0.01; H3) and customer experience and satisfaction (β = 0.675; p < 0.01; H4). The hypothesis regarding the influence of expectations on customer experience was accepted despite the negative value of the path coefficient, which indicated a negative correlation between two variables (β = −0.087; p < 0.01; H1). Given the robustness obtained from the p-value and bootstrap-t statistics for both hypotheses (Table 6), the relationship between expectations and satisfaction (β = 0,059; p < 0.01; H2) was also accepted, despite the low value of the path coefficient (β). Moreover, H1 and H2 were admitted on the basis of the conclusions obtained from studying the measurement of customer experience according to expectations and satisfaction (statistical output of SmartPLS 3, confirmed in Figure 2), whereby the path coefficient (β) values for H1 (β = 0.336; p < 0.01; H1) and H2 (β = 0,346; p < 0,01; H2) are highly significant, as we will argue below.

The proposed client–company relationship model investigates the underlying relationship between user expectations in the face of virtual interaction, digital quality, and experience and perceived satisfaction in private insurance areas. As can be seen in the results, the model is robust and highly conclusive to predict customer experience in private insurance web areas. Whereas the usual theoretical framework places the primary focus of interest on attracting new users, this analysis enables a different perspective which stresses the importance of research into the omni-channel experience of registered users.

There are extensive and continual examples in the literature which directly link quality to customer satisfaction (Hoekstra et al., 2015; Héctor San Martín et al., 2019) and which even include service experience attributes among their value scales (Al Sokkar and Law, 2013; Bilgihan et al., 2015). However, it has become necessary to include the perspective of the event or service that produces it (Walls et al., 2011; Lourido Gómez and Otero Neira, 2016; Olarte-Pascual et al., 2016; Pelegrín-Borondo et al., 2017), applying a behavioral model to independently study how quality is experienced differently by the customer at different stages during the process: what they expected before experiencing the service (Basfirinci and Mitra, 2015; Kujala et al., 2017) and whether their expectations were fulfilled (Hall, 2012; Oliver, 2014), or even surpassed, in absolute terms (Michalco et al., 2015; Schmitt et al., 2015). The impact of the “wow” effect on customer satisfaction is amply demonstrated in the fourth hypothesis, H4. This conclusion is important for business growth management strategy in terms of generating both customer loyalty (Yoo et al., 2013; Lin and Lekhawipat, 2014; Ali et al., 2018) and consolidation of the use of the digital channel used (Brown et al., 2014).

With respect to quality, and as we were able to observe by comparing Figures 1, 2, a comprehensive study of the model enables us to draw five important conclusions:

Key economic observers (Berger, 2017; Instituto Nacional de Estadística [INE], 2018a,b; iabSpain, 2018) augur a significant impact of digital transformation on countries’ economic growth in an imminent timeframe. As indicated by the DESI Index (Digital Economy and Society Index), the level of competitiveness of companies in the European Union evolves according to expectations and the digitization of data management (storage and sharing of open data, (Comisión Nacional del Mercado de Valores [CNMV], 2019), although it is true that it is very disparate according to economic sectors and countries (Paradigma Digital, Rodríguez Calvente, 2019).

However, there are serious indications that warn of the danger of turning digital transformation into a technological renewal rather than a new approach to customer encounter in market developments (It User, 2018). A redesign of the services according to the expectations of the clients is necessary (Congress DEC 2019).

The insurance field is a traditional sector that provides so-called experience products. The perception of the risk of this type of services means that the adoption of the channel was not relevant until the very recent past (Inese, 2018). Customers prefer face-to-face channels to ensure information and personal treatment by agents from whom they obtain security (Minsait, Asociación para el Desarrollo de la Experiencia de Cliente (DEC), 2019 report on digitization in Spain). The idea that “customers don’t just buy products or services; they also buy experiences and relationships” (Liferay Inc, 2019) is evidence, and competitive pressure and centricity vision (Kohli et al., 2019) impose a roadmap of no return in the digital transformation of the insurance sector. That is why it is considered relevant to present a contrasted behavior model that facilitates the implementation of the digital channel by the insurance sector, within the investigative prudence on the bias that the sample may present at the international level.

These results are more relevant than ever in the pandemic stage experienced in 2020, where society has faced unprecedented digitization and where only platforms where the customer has been taken into account and from impeccably implemented services have triumphed. According to the Capgemini Global Report (2020), customers are adopting a “millennial” mindset and increasingly relying on their own judgment based on information on the Internet and purchasing insurance products themselves. Thus, bigtech and alerted providers offer innovative and personalized products that offer a better customer experience (CX), within a competitive strategy, also unprecedented. That is why all new research is relevant as it provides knowledge to companies in their career for digital transformation and digital customer orientation.

Therefore, this article aims to highlight the relevance of the digital service as an added value for the customer. To do this, the article proposes to the scientific and insurance business community the model of customer experience, related to satisfaction and expectations and considers the following recommendations based on the results obtained. At the theoretical level, this article reinforces the theoretical framework on the customer experience, scarce in the reference literature that has usually focused on perceived satisfaction. The statistical robustness of the construct allows to consider its determinants as predictive of the customer experience. Although the variables of the model are a well-known benchmark in the field of multisectoral and omni-channel customer behavior research, the proposed model questions the relevance of expectations in the digital field of insurance and raises a new vision where it is established that it is the quality that determines a true customer experience in this context. Thus, the theory initiated by Davis (1989) and fully current to this day is questionable in the field of private insurance areas. That is, the idea that the user’s attitude toward technology conditions their use of it and that this attitude becomes an evaluative judgment on the digital channel (Worchel et al., 2003) can be solved by brands from an excellent digital quality and from co-creation with the user.

Therefore, it can be concluded that the model determines the true role of expectations, which, while important in the pre-digital phase, is the perceived quality that determines the true customer experience. On the other hand, surprising the client digitally is revealed as a guarantor of their satisfaction, as the predictive nature of both constructs is statistically confirmed. This is a remarkable discovery compared to the competition and the benefits it entails since it allows the “industrialization” of the customer experience, absolutely current in the new customer centricity vision.

Finally, and in the context of co-creation, the study provides an exhaustive study of quality based on the grouping of attributes based on its objective: platform, service performed, and information. This has made it possible to highlight the relevance of the information in the digital service as well as the perceived usefulness of the channel with respect to the service achieved. Knowing the digital attributes most valued by users allows the implementation of more ergonomic platforms open to joint innovation. That is, by creating an authentic digital experience, the adoption of the channel by customers is guaranteed, in which companies can establish a continuous and open dialog with them, which favors retention and recommendation to their influence groups.

On a practical level, insurance companies should monitor the implementation of their website, where it should encourage the use of the web channel to ensure training in the website, achieve an adequate web atmosphere that replaces the agent, look for the wow factor in the user that impact on recommendation, strengthen digital trust in channel usage, coordinate brand communities to achieve fidelity and recommendation, and invest in customer knowledge through “Digital Customer Voice” programs and know their needs.

Translated into concrete recommendations, insurance companies are advised to monitor and incentivize the following points in their digital strategy:

The main limitation of the present study lies in the selected sample, i.e., insurance company customers. Subsequent research should seek to test the model in another industry with several different products and/or services. For the same reason, the selection of a single company may produce potential bias in the response, since the customer’s relationship with the company may distort the reality they perceive, as explained by cognitive dissonance theory. To overcome this limitation, we propose a study across many companies in different sectors.

Future studies should also include additional client profile variables, such as frequency of use (indicator of previous experience). This will enable a company to anticipate and manage the “wow” effect on an informed user, avoid any potential distortion of expectations and its corresponding impact on the customer’s experience, and shrewdly ration the (highly valued) information required throughout the process.

Finally, we wish to add that the digital customer experience continues to be analyzed in ideological or statistic–descriptive terms in companies, which makes it necessary to contribute to the creation of a new model. Moreover, the creation of new relationships is being driven by new digital quality characteristics oriented toward m-commerce, as well as by customers’ emotions (Boston Consulting Group, 2018) and how these affect their connection to a brand.

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. The patients/participants provided their written informed consent to participate in this study.

All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.