Managing customer experience dimensions in B2B express delivery services for better customer satisfaction: a PLS-SEM illustration

Abstract

Purpose

This article aims to open the black box of the relationship between customer experience and customer satisfaction. The authors also take a fine-grained approach to the concept of customer experience analysis in terms of four dimensions: basic service experience (BSE), moments of truth (MT), focus on results (FR) and peace of mind (PM).

Design/methodology/approach

A total sample of 185 industrial customers in Spain was collected via an online platform from March to April 2020. The data were analysed using partial least squares-structural equation modelling (PLS-SEM).

Findings

The results indicated that the four dimensions of customer experience are the foundation of commercial success (i.e. customer satisfaction) for express parcel companies in the business-to-business (B2B) environment. Therefore, the most innovative express parcel companies should not only pay attention to providing services in accordance with the customer agreement but also go beyond that; hence, these companies must understand customer needs to be able to offer a unique experience. Therefore, these companies must design experiences that go beyond pure technical delivery services.

Originality/value

Although previous work has linked customer experience to customer satisfaction, there is little work that does so specifically in an industry as in vogue as express parcels and less so in the B2B environment. In addition, this work analyses fine-grained customer experience in terms of grain's four dimensions, and therefore, the authors analyse how each dimension (e.g. more rational or more subjective dimensions) impacts customer satisfaction. Few studies have focussed on this type of analysis for express parcel companies in the B2B environment.

Citation

Cepeda-Carrión, I., Alarcon-Rubio, D., Correa-Rodriguez, C. and Cepeda-Carrion, G. (2023), "Managing customer experience dimensions in B2B express delivery services for better customer satisfaction: a PLS-SEM illustration", International Journal of Physical Distribution & Logistics Management, Vol. 53 No. 7/8, pp. 886-912. https://doi.org/10.1108/IJPDLM-04-2022-0127

1. Introduction

In this study, our aim is to analyse and open the black box of the relationship between customer experience and customer satisfaction in the express parcel industry in a business-to-business (B2B) context. Specifically, we analyse customer experience by breaking it down into four different dimensions and considering how each one influences customer satisfaction.

There is a global vision in the related doctrine that agrees that to achieve customer satisfaction, it is necessary to be able to develop a positive customer experience. This situation has been studied and demonstrated by many authors in several industries (Berry et al., 2002; Carbone and Haeckel, 1994; Gentile et al., 2007; Schmitt, 1999; Shaw and Ivens, 2002).

Customer experience plays an important role in strategic customer-supplier relationships (Pine and Gilmore, 1998). In the business world, a change in direction is emerging in terms of the way in which companies compete for market share and is influencing the competitive and economic scenario. This change arises from the need of customers to consume more than just products or services in a transactional and more rational way (Shaw and Ivens, 2002). Amongstst consumers, there is a desire, with more emotional weight, to not only consume these products or services but also enjoy experiences whilst buying products or services (Klaus and Maklan, 2012).

When focussing on the concept of customer experience, we must bear in mind that it includes multiple elements and dimensions, both rational and emotional; that it is not usually interpreted from a unique point of view; and that it includes different aspects (i.e. peace of mind (PM), moments of truth (MT), basic service experience (BSE) and quality), which lead the customer to achieve a high level of satisfaction through the integration of all of these aspects (Edvardsson et al., 2005; Klaus and Maklan, 2012; Karpen et al., 2015; Roy et al., 2019; Ruiz et al., 2008).

Customer experience has been extensively studied by the doctrine in the business-to-consumer (B2C) context; however, in the B2B context, research on customer experience remains underdeveloped and understudied (Biedenbach and Marell, 2010; Kushwaha et al., 2021). In the B2B context, research on the quality of service (Casidy and Nyadzayo, 2019; Moore and Schlegelmilch, 1994; Ng et al., 2016; Pomirleanu et al., 2016; Sarapaivanich and Patterson, 2015), which fundamentally involves rational and cognitive aspects and not the subjective, internal and emotional aspects that are included in the customer experience construct, has predominated the literature (Klaus and Maklan, 2012; McColl-Kennedy et al., 2019).

Therefore, this article, according to the view of Klaus and Maklan (2012) and Roy et al. (2019), opens the black box of customer experience and considers its four dimensions: BSE, MT, focus on results (FR) and PM.

Given this aim, we pose the following research questions:

RQ1.

How does each dimension of customer experience relate to the other dimensions in impacting customer satisfaction in the B2B context?

RQ2.

Which dimensions of customer experience have the greatest impact on customer satisfaction?

Logistics is a service industry in the world economy that has increasingly changed in the last few years (Winkelhaus and Grosse, 2020). The global coronavirus disease 2019 (COVID-19) pandemic has caused a major change in consumer habits, which has accelerated the process of using online procedures, compared to offline processes and has made the logistics industry face great growth and transformation, aiming to find ways to improve customer experience and customer satisfaction (Moon, 2020). Within the logistics industry, the express parcel subindustry is one of the most critical because express parcel companies are usually the last link in the supply chain before delivery to the final customer.

In recent years, an approach called service-dominant logic has emerged in the field of service sector research (Vargo and Lusch, 2008; Karpen et al., 2012; Alves et al., 2020). This approach recognises that all economies are service economies and, therefore, that all companies are focussed on the exchange of services to achieve superior value when being delivered to their customers. There are a large number of studies on the abovementioned approach in the service industry in B2C contexts, whilst in the B2B context, studies have focussed mainly on manufacturing or industrial industries and not so much on service industries (Vural, 2017; Maas et al., 2014; Kowalkowski, 2010). Thus, the present study is framed within this last theoretical approach.

Since these issues have been understudied in the literature and even less in the case of the B2B context of express parcel delivery, they are considered critical for parcel express companies because if these companies achieve high levels of satisfaction amongst their customers, then it is highly likely that their customers will continue to contract with and recommend them and thus, such companies will be able to achieve a sustainable competitive advantage (Verhoef et al., 2009; Gentile et al., 2007; Barbosa et al., 2021; Yuen and Van Thai, 2015; Witell et al., 2020). Therefore, the research gap that this study fills is in understanding and analysing how the four dimensions of customer experience are linked as drivers of customer satisfaction within the express parcel industry in a B2B context. In addition, we respond to the call for further research on customer experience (Grewal et al., 2009; Payne et al., 2008; Verhoef et al., 2009; Voss et al., 2008), specifically in the B2B context (Biedenbach and Marell, 2010), and for empirical studies in the service industry within the service-dominant logic approach (Karpen et al., 2015; Alves et al., 2020). For these reasons, in our work, we try to shed light on and fill this gap in the literature, and from a practical level, we try to help the managers of parcel express firms generate greater customer satisfaction in express parcel delivery through the proper management of each element (i.e. dimension) of the customer experience.

The remainder of this paper is structured as follows. Section 2 presents a theoretical review of the study, where the constructs used in the proposed research model, as well as the hypothesised relationships, are shown; Section 3 presents the methodology used, explaining the composition of the sample of companies, data collection, measurement scales used and data analysis carried out; Section 4 presents the results obtained for the data analysis (i.e. partial least squares-structural equation modelling (PLS-SEM)); Section 5 discusses the results achieved in the empirical study and previous studies and practical and theoretical implications; finally, Section 6 sets out the limitations of the study and future directions through which to extend this research.

2. Theoretical background

In this section, we present the current state of the research on customer experience and customer satisfaction. In relation to customer experience, and according to Klaus and Maklan (2012) and Roy et al. (2019), the present research does not focus on the general concept of customer experience but opens the black box of customer experience and studies its different (four) components or dimensions. Subsequently, the dependent variable of the present study, customer satisfaction, is discussed and finally, the proposed research model is presented. As a summary, we include an Appendix with les containing the main definitions provided by relevant researchers in the literature regarding the main variables included in this study.

2.1 Customer experience

Customer experience is currently a very relevant competitive tool in the B2B environment. According to research conducted by the international consulting firm Gartner, 89% of companies believe that customer experience will be their main competitive foundation (Gartner Inc, 2016). The research on customer experience in the B2B environment is based on those industrial customer touchpoints that build customer experience. Touchpoints, especially critical touchpoints, also called MT, are the key stages through which to achieve memorable experiences that lead to repurchase and recommendation (Homburg et al., 2015; Puccinelli et al., 2009). Schmitt (1999) indicated that in the B2B environment, the customer experience is complicated, as different actors are involved in the different points of contact amongst customers, suppliers, distributors and end users. Some authors have defined customer experience as an experience that involves cognitive, emotional, affective, physical, sensory, spiritual and social aspects (De Keyser et al., 2015; Lemon and Verhoef, 2016; McColl-Kennedy et al., 2015). Therefore, as Witell et al. (2020) revealed in their research, customer experience management has become one of the top priorities of marketing managers and researchers. At present, the components of the customer experience and how they influence the business relationship in the B2B environment remain unexplored.

Traditionally, companies used the tangible attributes, products and intangible attributes of the service as a means through which to generate special moments for their customers (Chen and Lin, 2015; Tsaur et al., 2006). However, Pine and Gilmore (1998) indicate that the experience occurs when a brand strategically uses its products or services as an ecosystem to involve the customer and thus create a milestone that is stored in the memory both positively and pleasantly. At the same time, the above authors describe experiences as the result of the customer–company interaction and its mental, emotional, physical, intellectual and spiritual perception. From this perspective, Schmitt (1999) defines customer experience as that perception or recognition that continues with a stimulated motivation on the part of the customer, who observes or participates in an event that can enrich the value of the products or services offered by the company.

Customer experience is a multidimensional construct that must be understood from the customer perspective (Edvardsson et al., 2005; Klaus and Maklan, 2012; Roy et al., 2019; Verhoef et al., 2009). Klaus and Maklan (2012) and Roy et al. (2019) recognise that customer experience is composed of diftabferent elements, both cognitive and emotional, all of which must be taken into account by companies to achieve better outcomes. Moreover, Gentile et al. (2007) indicate that experiences are perceived individually by those customers who experience them. Arnould and Price (1993) state that a relevant aspect is that customers may select suppliers based on whether they want a simple material good or a complete buying experience. Carbone and Haeckel (1994) reveal that customer experience is the sum of the aggregated and accumulated perceptions of the customer part when they are related to the use of a product or service of a particular brand. More recently, Pareigis et al. (2012) describe the construct as the general evaluation of the formation of a three-dimensional interactive value: a cognitive evaluation and two affective dimensions—positive activation and positive deactivation. However, Woodward and Holbrook (2013) consider that “all experiences are consumption experiences” (p. 325); these experiences are largely considered an interactive phenomenon that results in a state of pleasure or displeasure.

Based on the above and other previous studies on the multidimensional vision of customer experience, in this work, we focus on the concept of customer experience proposed by Klaus and Maklan (2012) and Roy et al. (2019). This proposed customer experience includes both cognitive elements linked to more rational elements and subjective elements related to emotional or affective elements and is in line with the majority of the doctrine. The above authors point out that customer experience includes four dimensions: BSE, PM, MT and FR. It is through these dimensions that we aim to achieve the goals of our study.

In the literature, many studies relate customer experience to customer satisfaction in the B2C context (Lam et al., 2004; Nobar and Rostamzadeh, 2018). However, in the B2B context, the relationships between several customer experience elements and customer satisfaction remain understudied. Although few studies are available in the B2B context (Bardauskaite, 2014; Vakulenko et al., 2019), even fewer are available on how the different components of customer experience are related to customer satisfaction and how these components connect to achieve a higher level of customer satisfaction. According to Vakulenko et al. (2019), in the B2B context, the express parcel industry has experienced exponential growth due to e-commerce. This growth has driven companies to innovate, focussing on usability and the customer experience, with the latter becoming a key antecedent of customer value and satisfaction.

In our study, we try to go deeper, opening the black box of the customer experience and analysing, in a fine-grained way, how each dimension of customer experience is related to customer satisfaction, both directly and indirectly. Additionally, we analyse the relationships amongst the four different dimensions of customer experience as antecedents of customer satisfaction.

2.1.1 Basic service experience

Any business service provider must know the processes that comprise its value proposition. According to Eiglier and Langeard (1987), the BSE is the main reason that a customer chooses a particular supplier. Keller (1998) states that when we talk about a BSE, we are referring to the process by which the outcome is obtained. Grace and O'Cass (2004) state that the main service experience in the hotel industry is the main service offered by the hotel, which is sleeping in a room with certain characteristics. Therefore, according to Williams et al. (2019), in the B2B transportation industry for small businesses, the core experience of this service is the movement of goods. Tax and Stuart (1997) indicate that the BSE is more complex than is the production of a product since, in the former, there are various factors or actors who lead the service. To define the basic experience of an express B2B parcel service, aspects such as whether the supplier offers different delivery options, makes beneficial offers related to business needs, or has a contact person to assist the client during the service process should be taken into account (Roy et al., 2019). For example, in the express parcel delivery process, we do not refer to the outcome of the delivered package but rather to the delivery process and all points of contact and processes involved until the outcome is reached, such as problem handling, quality of response and empathy with the supplier or end customer.

Abhari et al. (2019) study B2B industrial services and conclude that for a successful customer experience, the BSE must be implemented with complementary services that increase the customer's perceived value and trust in the service and the touchpoints must be successful. To evolve this basic service and achieve greater differentiation, managers need to offer superior customer satisfaction and experience that is conducive to customer loyalty; therefore, investments must be made in complementary services that enrich these basic services (Moorman and Finch, 2017).

In conclusion, we can state that the BSE is the core of the business. In the case of a last-mile service company, this experience would be, for example, the transportation of a package from a factory to a customer. However, if companies want the basic service to become a satisfying experience, then this experience must have other attributes that are aligned with the tastes and desires of industrial customers, such as being able to choose from different service options, such as hourly ranges and special services. Offers must be beneficial to the customer, providing confidence and ensuring that they are designed to meet their needs, and there must also be a contact person available to attend to the customer if needed (Roy et al., 2019). These attributes, peripheral to basic services, contribute to increased satisfaction and achieve a better customer experience. However, the adequate fulfilment of a basic service is a necessary, although not sufficient, requirement for satisfaction in package delivery.

2.1.2 Moments of truth

MT comprise an extremely relevant dimension in the topic of customer experience since they refer to those moments when the customer interacts with the supplier, whose brand image is certainly at stake. According to Normann (1983), the first authors to describe the MT construct, the quality experienced by the customer is created when he or she meets face to face with the service provider. Moreover, Grönroos (1994) and Edvardsson et al. (2000) specified that these are situations where the customer and supplier interact and the quality of the service is defined by the customer. Along the same lines, De Keyser et al. (2015) defined MT as those specific instants in which the customer relates to the supplier. MT imply a broader meaning when referring to a customer's relationship with the product or service on any channel, either physical or digital (Pantano and Viassone, 2015; Zomerdijk and Voss, 2010). Therefore, customer experience management must take into account all possible channels of customer relationships with the supplier's brand to define the MT.

For a customer experience to be enjoyable, each MT must be classified and weighted to have an accurate understanding of the customer's subjective view (Lemke et al., 2011). In the financial services industry, Raina et al. (2018) show that investigating the MT experienced by the customer is key to understanding the overall customer experience and customer satisfaction when interacting with their financial services provider.

Therefore, MT are the key indicators that define experience and its assessment by the customer (Jüttner et al., 2013). MT are all those moments in which the company interacts with the customer and that involve cognitive, behavioural, emotional and social aspects (Lemon and Verhoef, 2016; Verhoef et al., 2009). Choi and Lee (2018) indicate that in the distribution of the telecommunications technology industry, the most relevant MT are the initial moments in the customer-supplier relationship, where trust and sympathy with the brand are transmitted, achieving a more satisfactory experience.

MT or touchpoints are important to build a customer experience that generates PM and builds confidence that the customer has made the right choice in supplier (Raina et al., 2018). In the B2B environment, suppliers must offer a flexible and up-to-date core service with customer-centric staff and skills to generate excellent MT (Mačiulis, 2020).

2.1.3 Focus on results

According to Klaus and Maklan (2012), FR reflects the importance of experiences that are guided by a specific objective that is associated with the reduction in customer transaction costs, such as the search for and qualification of new suppliers. In any industry, when selecting a supplier, a consumer must consider the supplier's predisposition to make it easy for it to market one customer's products or services in both the physical and digital channels (Cook, 2014; Roy et al., 2019). Furthermore, once the relationship is established through early experiences, these goal-oriented experiences (Roy et al., 2019) are seen as a solid foundation on which to build a new behaviour, despite the knowledge offered from others and the competitiveness of the existing provider, which is key in today's competitive environment.

Codina Barragán et al. (2017) indicate that for an industrial customer to enjoy a satisfactory purchasing experience, the experience must be easy, focussed on offering customers what is most interesting to them, be transparent with the customer without holding anything back or using fine print that goes against their interests and be an integrated commercial brand by comprehensively showing its promise and doing what is best for the customer. Berry et al. (2002) reveal that a customer wants mainly to have a supplier for the long term; such a supplier has to focus on facilitating the purchase and the pre- and post-purchase processes are essential to achieving a superior BSE. Williams et al. (2020) give examples of companies that strategically make life easier for the customer: Amazon, Costco, Uber and Netflix. Within these companies, the abovementioned types of facilitating strategies have been demonstrated as good strategies through which to increase customer satisfaction, helping them achieve the desired results for their organisations. Given these companies, the customer can make the following comment: “I know that there may be better options, but it is not worth the bother of switching providers. Here, I know what I am going to get, and it is easy”.

In particular, factors such as first-pass strategies, technology and design, frictionless entry, transparent information and ease of use lead to certain competitive advantages amongst digital platforms. When B2B customers make purchases on their suppliers' digital platforms, it is key that they receive a user-friendly and frictionless purchase experience. This frictionless digital experience contributes to customer satisfaction (Haase et al., 2020).

Likewise, the supplier's staff must be aligned with a service-oriented strategy to be able to offer what the customer needs in a timely manner. This is even more relevant with regard to the express parcel industry, where delivery times are increasingly tight and customers are becoming more demanding. In the same vein, many companies offer express parcel services, and thus, the supplier's involvement with the customer must be high to avoid the customer switching suppliers and to achieve a high level of customer satisfaction and loyalty. Demangeot and Broderick (2006) conclude that the customer experience achieves positive emotional and attitudinal results in terms of the behaviour of the customer who enjoys the experience. The key to this is to facilitate the customer's professional day-to-day life by being attentive to his or her needs and wishes and by having customer-centric staff taking a service-oriented approach.

2.1.4 Peace of mind

Lee et al. (2013) define PM from a psychological perspective that emphasises the categories of hedonic pleasure and affective well-being. The definition highlights an internal state of calm and harmony. Maklan and Klaus (2012) indicate that PM in a provider is a relevant aspect to ensure service quality. Bendapudi and Berry (1997) state that tranquillity is a dimension that has a direct impact on the customer's assessment when interacting with the supplier. This interaction occurs before purchase, during purchase and after purchase. PM is a manifestation with a representative emotional weight in the purchase experience.

A report by Klynveld Peat Marwick Goerdeler (KPMG) (Anderson and Simester, 2014), which studies the barometer with which to measure customer experience, states that PM is a fundamental attribute for achieving an excellent customer experience. We must differentiate that depending on the industry, PM is a more or less relevant attribute. Otto and Ritchie (1996) indicate that PM is the most important attribute for customer attraction. Subsequently, Sinčić Ćorić and Jélic (2015) research the B2B sector and state that B2B (service or product) providers should focus on conveying PM and trust to their customers that their brand image is associated with the desired reputation level. Brand image is a key emotional aspect that directly influences purchase decisions and customer satisfaction. In the logistics industry, PM becomes a higher-order benefit pursued by companies in their B2B relationships and influences purchase decisions (Mentzer et al., 1997). Peace of mind shows the emotional benefits that customers can experience based on the perceived experience of their service provider (Krishna and Deshwal, 2016). Since the COVID-19 health crisis, this attribute has been particularly influential amongst customers. Delivery at the customer's home generates greater PM and has become a key aspect of decision-making (Ahmad Nizar and Zainal Abidin, 2021). Therefore, PM plays a key role in logistics in general and last-mile delivery in particular in both B2B and B2C contexts. In addition, to retain customers and establish long-lasting relationships, PM helps strengthen the customer-supplier relationship through increased trust and satisfaction (Johnston and Clark, 2008; Sinčić Ćorić and Jélic, 2015).

2.2 Customer satisfaction

Although satisfaction is recognised as a key indicator of industrial company success, its influencing factors remain unclear (Chang and Thai, 2016). Most likely, customer satisfaction is the product of the customer's experience of using or consuming the service (Bardauskaite, 2014). Furthermore, the consequences of customer satisfaction for the customer's relationship with the company and future consumption of the service or product remain unclarified. High levels of satisfaction are usually associated with greater trust in the supplier and, at the same time, with an increase in switching costs, which, together, would lead to an increase in repurchase and recommendation intentions (Barbosa et al., 2021).

According to Geyskens et al. (1999), industrial customer satisfaction is defined as an emotional or affective state resulting from the evaluation and comparison with expectations of all relevant aspects of a business relationship.

Having obtained this product or service can be considered an effective experience, where the customer's feelings are attended to by the processes that the company has established before the use of this product (Churchill and Surprenant, 1982).

In the service marketing literature, satisfaction is recognised as the best indicator of future business benefits (Yuen and Van Thai, 2015; Parasuraman et al., 1994). Customer satisfaction is a key factor in understanding the reasons why, in the B2B market, companies decide to use and recommend their main express delivery provider (Roy et al., 2019). Teixeira et al. (2020), investigating buyers of B2B telecommunication services, indicate that the level of satisfaction of these buyers is strongly influenced by ease or low complexity at the time of contracting. Oliver (1993) establishes, in the B2B context, a duality in terms of the concept of satisfaction, as he conceives it as both an evaluation that is made about a defined transaction with a given company and an evaluation as a whole of several processes that satisfy the customer's feelings about the offer that has been made by the company. In B2B technology services, Huang et al. (2019) show that customers who experience satisfaction through the services of these technology companies are inclined to be more loyal to suppliers. For these reasons, customer satisfaction is the outcome variable in our study.

Currently, there are trends on the part of logistics companies to carry out actions and strategies to increase customer satisfaction (Daugherty, 2011; Lima et al., 2017). However, Gil Saura et al. (2015) indicate that no conclusive results on satisfaction in the transportation industry have been obtained in previous studies. In general, there are discrepancies between the perception of satisfaction between the shipper or customer and that between the supplier or carrier. The present study analyses the relationship between customer experience dimensions and customer satisfaction in B2B express parcel services.

2.3 Proposed research model

After reviewing the dimensions of customer experience that we analyse in our study, we make a statement about the different relationships amongst them and with customer satisfaction. All these statements (i.e. hypotheses) form the model proposed in our investigation.

First, we propose that basic service is the main reason that a customer hires a supplier (Abhari et al., 2019; Eiglier and Langeard, 1987; Moorman and Finch, 2017):

H1.

BSE is positively associated with MT.

Both rational and emotional aspects play a role in customer satisfaction and, thus, in the decision to hire a supplier. Therefore, in our work, we suggest that basic service positively influences both the FR (rational aspect) and PM (emotional aspect). These relationships are mediated by how the interaction between the customer and supplier transpires (i.e. touchpoints or MT); hence, we declare that these relationships are mediated by MT. Therefore, we propose the following hypotheses:

H2.

BSE has an indirect and positive influence on the FR, mediated by MT.

H3.

BSE has an indirect and positive influence on PM, mediated by MT.

When a provider has performed a satisfactory core service, achieving customer satisfaction requires that the moment when the customer and provider interact (both online and offline), their outcome be positive so that the customer's trust and PM in their provider is increased (Roy et al., 2019). In this way, MT and PM mediate the relationship between basic service and customer satisfaction. Furthermore, we also suggest that the relationship between basic service and customer satisfaction might also be sequentially mediated by MT and FR. Thus, in the customer-supplier interaction, the customer should perceive that the supplier will do everything possible to solve his or her problems and make his or her life easier and that the customer is therefore worth the costs and efforts that would be involved in contracting with that supplier (Roy et al., 2019). This description of the process helps open the black box relating to the relationships amongst customer experience, its dimensions and customer satisfaction.

Then, we suggest that MT and PM sequentially mediate the relationship between BSE and customer satisfaction and that MT and FR sequentially mediate the relationship between BSE and customer satisfaction. Therefore, we propose the following hypotheses:

H4.

BSE has an indirect and positive influence on satisfaction, mediated sequentially by MT and a FR.

H5.

BSE has an indirect and positive influence on satisfaction, sequentially mediated by MT and PM.

Based on the theoretical review of the different constructs seen in the previous sections and the hypotheses, we propose the following research model, which is represented graphically in Figure 1.

3. Methodology

3.1 Sample

Spain is a country with a service-based economy (67% of the Spanish gross domestic product (GDP) in 2020), and the logistics sector in this economy has experienced significant growth in recent years (Bank of Spain, 2021). Therefore, we consider it appropriate to focus on Spain, which has a strategic global geographic location, being the gateway to Europe from the Atlantic Ocean and Africa.

For our study, we needed companies that regularly operated as express parcel delivery providers. The company population was obtained from the SABI database, a database of 2.5 million companies in Spain. Considering firms within this industry with at least ten employees and of at least five years of age, we identified a population of 3,987 firms. Then, we used a random stratified sampling procedure to generate a representative sample of companies in terms of dimensional parameters and industry. We also performed an analysis of the power statistics necessary to estimate our model. In PLS-SEM, one of the more recent methods to analyse the minimum sample size has been the inverse square root method of Kock and Hadaya (2018). Our sample size was 185 firms, representing 4.64% of the population. If we apply the inverse square method, then the results indicate a minimum sample size of 132, with p < 0.001.

We contacted by email the operations managers, logistics managers, or general managers of the companies. The questionnaires were completed online using the Typeform platform between March 3 and April 8, 2020.

A total of 185 customer industrial companies participated in this study. Table 1 shows the characteristics of the companies in the sample.

3.2 Measurement scales

Roy et al.’s (2019) scale based on Klaus and Maklan (2012) was used to measure the dimensions of customer experience. The scale is composed of four dimensions: BSE (two items), MT (four items), FR (three items) and PM (five items) with 14 items. It has been used in industries such as manufacturing, banking services, real estate and healthcare services (Gao et al., 2022; Roy et al., 2019).

Customer satisfaction was measured using a scale adapted from Ruiz et al. (2008) and based on the studies of Taylor and Baker (1994) and Oliver (1980). The scale is made up of 6 items. This scale has been used in previous studies to compare customer satisfaction with service providers in the US and Spain (Ruiz et al., 2008).

A response model was used for all scales using a 7-point Likert-type scale to measure each statement in each item. Specifically, the response options ranged from 1 (strongly disagree) to 7 (strongly agree).

All items were translated into Spanish using the usual method of translation and back translation (Brislin, 1970), and inconsistencies were resolved by discussion within the research team. As a result, our questionnaire was administered previously to a small set of academics and practitioners (i.e. ten people), with no issues concerning readability and comprehension being identified.

3.3 Data analysis

To analyse the data derived from the questionnaires, the PLS-SEM technique was used. This technique is composite based and part of the multivariate analysis family to analyse the relationships between unobservable variables measured with indicators. In our model, all measures were operationalised as composites (Rigdon, 2016; Henseler, 2017); therefore, we decided to use PLS-SEM to test our proposed hypotheses in the research model (Cepeda et al., 2019). The main reasons for using PLS-SEM were as follows: (1) we used composites estimated in Modes A and B (Rigdon et al., 2017; Hair et al., 2019a, b), and (2) we adopted an explanatory and predictive approach following the methods of Henseler (2018) and Hair et al. (2019a, b).

Thus, a composite was estimated in Mode A (i.e. some authors use the term reflective measures) when its indicators (i.e. components) were highly correlated, whilst a composite was estimated in Mode B when its indicators were just combined to create the construct (i.e. some authors use the term formative measures) and were not necessarily correlated (Cepeda et al., 2019).

Amongst the different approaches adopted in PLS-SEM (see Henseler, 2018; Cepeda et al., 2019; Hair et al., 2019a, b), the explanatory approach looks to test hypotheses through the parameter significance (i.e. bootstrapping) of the inner model and by minimising the residual variance in the dependent construct (Chin, 1998). Otherwise, the predictive purposes are to determine whether the estimation of parameters is stable across different times and contexts.

A two-step process has been indicated to evaluate our proposed research model using PLS-SEM (Hair et al., 2019a, b): (1) the evaluation of the measurement model and (2) the evaluation of the structural model.

We used a bootstrap procedure (Chin, 1998) to find the significance of the parameter estimation. With this bootstrapping method, which is a resampling procedure, we could determine the significance of the parameters such as path coefficients, weights and indicator loadings for each composite (i.e. nonobservable variable). To perform our data analysis, we used SmartPLS 4 (Ringle et al., 2022), and to test the mediating effects, we followed the procedure described by Nitzl et al. (2016) and Carrión et al. (2017).

Given the explanatory nature of our study and following Hair et al.’s (2019a, b) suggestions, we ran an endogeneity test based on the Gaussian copula already implemented in a new version of SmartPLS 4 (Ringle et al., 2022). Finally, to take advantage of the predictive nature of the algorithm behind PLS-SEM (i.e. ordinary least squares (OLS)), we executed an out-of-sample prediction analysis named PLSpredict.

4. Results

4.1 Evaluation of the measurement model

The study adopts a composite structure for all the model constructs. The composite of BSE is estimated in Mode B and all other composites are estimated in Mode A. This composite structure is chosen because all composites are considered human-designed tools to measure the latent variables (i.e. artefacts), which are not easy to measure directly in nature (Henseler, 2017). The indicators of the constructs estimated as Mode B imply that they are not necessarily correlated; consequently, traditional reliability and validity assessments are inappropriate and illogical for a composite estimated as Mode B (Hair et al., 2019a, b). In this case, we test the collinearity of the indicators and the significance of their associated OLS weights.

However, a good measurement model for the composites estimated in Mode A must demonstrate adequate reliability and validity. The results show that the measurement model meets all the commonly stipulated requirements. Table 2 shows the factor loading matrix of the composites estimated as Mode A; therefore, all elements have a standardised loading greater than 0.7 on the composites, which is the appropriate level at which 50% of the variance in the indicators can be explained (Hair et al., 2016).

For composites estimated in Mode A, the most appropriate consistent measures of internal consistency reliability are composite reliability, Dijkstra and Henseler's rho A and Cronbach's alpha (Hair et al., 2022). The model satisfies the prerequisite of construct reliability and all indices are greater than 0.8 (see Table 3).

The average variance extracted (AVE) serves as a measure of unidimensionality (Fornell and Larcker, 1981) and convergent validity (Hair et al., 2022). The AVE value for all constructs is greater than 0.5 (see Table 3), showing the adequate unidimensionality and convergent validity of the measures. Finally, the heterotrait--monotrait criterion (HTMT) provides evidence of discriminant validity (Hair et al., 2019a, b). All variables obtain adequate discriminant validity, as indicated by values below 0.9 for the HTMT ratio (see Table 4).

Given that the composites estimated in Mode B (i.e. sometimes termed formative measures) can have multicollinearity problems (i.e. bias estimation) (Hair et al., 2022), we consider a measure of multicollinearity as the variance inflation factor (VIF) (Hair et al., 2022). The composite indicators estimated in Mode B obtain a VIF value of 1.083 and are consistently below the threshold value of 3.3 (Diamantopoulos and Siguaw, 2006; Hair et al., 2022). Therefore, it can be concluded that collinearity does not reach critical levels and is not an issue in our external model. Then, the significance and relevance of the external weights of the composites estimated in Mode B are examined (see Table 5). The results show that the weights of all indicators are significant, with p < 0.05.

4.2 Evaluation of the structural model

After verifying that our model meets all requirements for an adequate assessment of the measurement model, we perform this assessment by testing the sign, size (relevance) and significance of the path coefficients as proxies of the different hypotheses proposed (see Table 6).

H1, BSE, is positively associated with MT (β = 0.347, p < 0.001).

Table 6 shows the indirect effects of BSE on FR mediated by MT (H2, β = 0.290, p < 0.001) and on PM mediated by MT (H3, β = 0.259, p < 0.001). The indirect effects of MT on satisfaction mediated by MT and FR (H4, β = 0.112, p < 0.01) and on satisfaction mediated by MT and PM are also shown (H5, β = 0.136, p < 0.01).

The results (see Figure 2) show that BSE explains 12% of the variance in MT (R² = 0.120) and that MT explain more than 70% of the variance in both FR (R² = 0.701) and PM (R² = 0.561). In general, the model explains more than 70% of the variance in satisfaction (R² = 0.770).

The dotted lines represent the relationships that make up the indirect effects proposed and tested in the research model.

4.3 Endogeneity analysis

As previously mentioned, it is necessary to consider endogeneity in explanatory studies using OLS algorithms because it is necessary to demonstrate that PLS-SEM estimations are not biased by endogeneity issues. Endogeneity is an issue in which some important variables are omitted in the model (Antonakis et al., 2010); then, the parameter estimations generated by PLS-SEM may be incorrect, and therefore, the estimation of the results could be questioned. All potential variables have been fixed and/or considered in the proposed model; these variables would truly be the estimated parameters. If this is not the case, then endogeneity could be a serious problem.

Our endogeneity test is based on Hult et al. (2018): we include several instrumental variables (e.g. control variables) in our model (i.e. age, gender, firm size and education level), pointing to the dependent variable of satisfaction. We also include Gaussian copulas estimated by Park and Gupta (2012) and described by Hult et al. (2018) to address endogeneity in the PLS-SEM context.

We follow two procedures to analyse endogeneity. First, as mentioned above, four control variables, which are related to the dependent variable (i.e. satisfaction), are used. After running a 5,000-bootstrapping routine, we find all the links for the control variables to be nonsignificant.

Second, we run the Gaussian copula procedure, following the illustration described by Isabel et al. (2021). We are able to run this procedure after reviewing the new and last recommendations provided by Becker et al. (2021), where the Gaussian copula procedure is revisited to consider differences if the intercept exists in the regression implied by this procedure. In our case, the intercept is zero, and therefore, we do not need all cautions provided by these authors. The goal of this procedure (Gaussian copula) is to demonstrate that the Gaussian copulas added to our model do not affect our model parameters (i.e. all Gaussian copulas are nonsignificant).

The procedure for PLS-SEM is described by Hult et al. (2018). Following its different stages, we first check whether the variables, which potentially have endogeneity, are distributed in a nonnormal way. We do this by running the Cramer-van Mises test on the standardised composite scores of BSE, satisfaction, PM and FR (Becker et al., 2021), which provides the PLS-SEM estimation. If the p value is less than 0.05, then the variable does not follow a normal distribution. The results indicate (p = 0.001, 0.002 and 0.020) that none of the constructs have normally distributed scores, which allows us to analyse endogeneity through Gaussian copula analysis.

Second, we run the Gaussian copula analysis by adding a copula for each independent variable of each dependent variable. There are two independent variables (i.e. PM and FR) and a dependent variable (satisfaction); therefore, we add two Gaussian copulas. Neither of the two copulas introduced in our model is significant. Therefore, endogeneity is not an issue for the estimation of the relationships in our proposed model.

4.4 Prediction analysis with PLSpredict

Finally, we assess the predictive nature of our model following an out-of-sample procedure (Carrión et al., 2016) developed by Shmueli et al. (2019) and implemented in SmartPLS 4. This method needs to make two decisions before being executed. (1) The first decision concerns the number of folds or k subsets of data points of identical size. A subset with a minimum size of 30 is recommended to obtain the minimum statistical power, and since our sample size includes 185 data points, the number of folds is fixed at 6 (i.e. every fold has at least 30 sample elements). These folds are part of a cross-validation process repeated k times and each fold is used once as a test sample. (2) The second decision concerns the number of repetitions, which Shmueli et al. (2019) suggest be 10 repetitions.

After these decisions, the PLSpredict algorithm is executed and the prediction index (i.e. Q² predict) is calculated for all indicators of our dependent variable (i.e. satisfaction). The method suggests that Q² must be positive. Next, a measure of skewness is obtained for all prediction errors of all indicators calculated by the algorithm. If this skewness measure is greater than 1, then the root mean squared error (RMSE) should be used, and if it is less than 1, then the mean absolute error (MAE) is the recommended option. The last step of prediction analysis using PLSpredict is to check if the prediction error of PLS-SEM is lower than the linear regression error (i.e. LM); in that case, we can state that our proposed model has predictive power (Shmueli et al., 2019).

The results of the prediction analysis are shown in Table 7, and it can be seen that all Q² predicted values are positive for all satisfaction indicators and that all prediction errors for each satisfaction indicator (i.e. RMSE and MAE) are lower for PLS than for LM. According to the level of skewness, RMSE is the prediction error that should be considered. From these results, we can say that our model has high predictive power (Shmueli et al., 2019).

5. Discussion and theoretical and practical implications

5.1 Discussion of the findings

This study opens the black box of customer experience and links these dimensions to improve the level of customer satisfaction. We study and discuss the next dimensions of the customer experience – BSE, PM, MT and FR – which are related, to achieve optimal customer satisfaction.

In our model, BSE has a positive impact on the MT experienced by the customer when interacting with the provider. Conversely, MT, i.e. those key moments of the interaction between the express parcel company and the customer, directly influence the variable focus on the results. This variable is expressed when customers have a proactive predisposition on the part of their express package provider to facilitate the marketing of the customer's products or services in both the physical and digital channels (Cook, 2014; Roy et al., 2019).

Similarly, the results of the model of this study confirm that the FR directly influences the satisfaction perceived by the customer when the service is produced, which is higher if its perception is that the express delivery provider is proactive in facilitating the marketing of its products. For example, in an online store for industrial machinery spare parts, the easier the express delivery provider makes it to deliver his or her order, the more satisfied the customer. This result of our study is in line with that in Codina Barragán et al. (2017), who state that for an industrial customer to enjoy a satisfactory shopping experience, it must be easy, focussed on offering customers what is most interesting to them, transparent with the customer without holding anything back or using a fine print that goes against their interests and an integrated commercial brand by largely fulfilling its promise and doing what is best for the customer.

However, the results of the model in this study also confirm that MT influence the customer's PM when contracting with a particular supplier. This variable is critical when an express parcel customer entrusts the final logistics of its product to a specific parcel company. This result is aligned with the work of Maklan and Klaus (2012), who indicate that PM in a supplier is a relevant aspect to ensure service quality. In the same vein, Lee et al. (2013) also take a position along these lines, indicating that PM positively influences customer satisfaction. Furthermore, Ahmad Nizar and Zainal Abidin (2021) and Daugherty (2011) position themselves in line with these ideas.

From these findings, we can answer our first research question, in which we ask how the different dimensions of customer experience can influence customer satisfaction in the B2B context.

In relation to the second research question in this study, and according to Eiglier and Langeard (1987), the results of the current study show that BSE (our independent variable in the research model) is the main reason why a customer chooses a given provider.

According to the results of this study, the four dimensions of customer experience each individually influence customer satisfaction. This result is in line with that of Edvardsson et al. (2005) and Verhoef et al. (2009), who argued that we must consider which dimension is most valued by each customer. To do this, we must weigh the BSE dimension as the first link in the experiential chain in which the emphasis is placed since it is the main reason why the customer with a more rational profile uses the service (Keller, 1998). This first dimension gives way to the different interactions between the customer and provider, which are called key moments or MT, that, as indicated, agglutinate a relationship with the product or service in both channels, physical and digital (Pantano and Viassone, 2015; Zomerdijk and Voss, 2010). These MT generate an outcome in which customers perceive whether their supplier makes it easy for them to run their business and smooths the commercial path for them to easily get their products to their customers through the express parcel service (Cook, 2014; Roy et al., 2019). At the same time, an opinion regarding the PM that the supplier generates is formed, as this attribute is the key to achieving a satisfactory customer experience (Anderson and Simester, 2014).

The model also confirms that BSE is positively related to customer satisfaction through the mediation of FR and PM. Therefore, companies can help increase customer satisfaction by improving the experience provided by the supplier. Properly combining these variables is the key for express parcel companies that wish to achieve high levels of satisfaction amongst their industrial customers (B2B), thus achieving a relevant competitive advantage (Nobar and Rostamzadeh, 2018).

Therefore, to achieve customer satisfaction and superior advantage over competition in the express parcel industry, as noted by McColl-Kennedy et al. (2015), De Keyser et al. (2015) and Lemon and Verhoef (2016), parcel delivery must provide an experience that involves cognitive, emotional, affective, physical, sensory and social aspects. The results of our research are in line with those of the above authors, with the four dimensions included in the study reflecting these aspects. In this sense, BSE and FR are more rational and cognitive aspects, whilst PM is a more emotional and subjective aspect and MT are a more social and subjective aspect.

5.2 Theoretical implications

Based on the results obtained from the literature review and our empirical efforts and taking into account the objectives set out in this paper, we draw the below implications.

First, the present study is a new study in the context of B2B within the service sector using the dominant service logic approach. In this approach, studies in the B2B context are rather scarce, and thus, this work makes a relevant contribution.

Second, studies in the B2B context usually focus on the most rational and cognitive aspects in relation to the level of service experience perceived by customers. However, our study incorporates emotional and more subjective aspects to provide a better understanding of the real process that determines the decision-making of the industrial customers of express parcel companies. Normally, studies on customer experience and customer satisfaction in B2B contexts tend to be focussed on more rational and cognitive aspects (i.e. quality of service and basic service) and do not study the impact of elements such as PM or MT, which refer to more subjective and emotional aspects. Additionally, when the generic concept of customer experience is used, the impact that the different dimensions have on the dependent variable of the model is not known.

Third, this work provides a new empirical study for the literature in the B2B context in the express parcel sector. As discussed in the introduction section, there are few empirical studies in the B2B context in the express parcel sector, and therefore, our work contributes to the literature in the service sector (particularly express parcels) and in the B2B environment.

Fourth, this study opens the black box of customer experience and presents a model with which to analyse the direct and indirect effects (mediators) of the different dimensions of customer experience as antecedents of empirically tested industrial customer satisfaction.

Fifth, the basic service of the express delivery firm is presented as the key element from which the rest of the variables make sense in the proposed and tested model. BSE is the primary variable in achieving satisfaction.

Finally, the other three components of the customer experience (MT, FR and PM) contribute critically to achieving customer satisfaction. To build a memorable and differentiated customer experience for companies in this industry, it is not enough to comply with what has been agreed upon, that is, the delivery of goods in an agreed-upon time slot.

5.3 Practical implications

From a practical point of view, this study can help company managers make key decisions to achieve B2B customer satisfaction.

First, the managers of express parcel delivery companies should not stop at but should go beyond, achieving a correct technical level in the provision of services. The most innovative express parcel firms know that to have a memorable experience for the customer, they must design experiences that go beyond the technical delivery service. That is, we cannot have satisfied customers if we fail in terms of business basics, which is the delivery of a package coming (for example) from a manufacturer to an industrial component firm. Therefore, the entire organisation must align to work strategically and focus on the four dimensions of customer experience, starting with BSE and continuing with MT, FR and PM. From here, express parcel companies can achieve satisfied customers who exhibit positive behaviours and attitudes towards the company and, therefore, allow companies to achieve better results.

Second, to be successful in achieving customer satisfaction, managers must work strategically on these variables in terms of the impact that each dimension has on satisfaction. Customer satisfaction has an impact on company revenue, and thus, it can be considered a competitive advantage achieved through customer experience.

Finally, another key element that express parcel delivery companies must address is to achieve PM for their industrial customers. This PM has a substantial impact on the level of satisfaction of the industrial customer, which is why the managers of express parcel companies could work on actions to convey this PM. For example, augmented reality (AR) technology can be used to enhance the delivery experience; customers can use their smartphones to scan delivery order numbers, monitor routes in real time and see a 3D model of their parcel, giving them greater PM that their package will arrive intact. With a very small, inexpensive and fast detail, high levels of PM and confidence amongst industrial customers can be achieved.

6. Limitations and future research directions

This study has a series of limitations. First, because the study focusses on the express parcel industry from the customer's point of view, the results and conclusions cannot be generalised to other logistics businesses. We do not know whether by focussing this study on a different sector of logistics of the customer company, the results would be the same. Secondly, it should be noted that the data collected in this paper focus on companies in the Spanish business context. Therefore, we cannot be sure that the results can be generalised to other business contexts. For this reason, future studies may focus on comparative studies in different contexts to better understand how to achieve customer satisfaction in the B2B express parcel sector. Finally, in order to make our results more useful, the development of longitudinal studies will provide further insights by analysing the evolution of results over time.

In addition to the above paragraph, future studies could focus on the following. First, studies should be carried out in other logistics businesses that are different from those in the express parcel industry. Second, given that the present study collects only the voice of the industrial customer, a study could be carried out that collects the voice of the supplier to compare and analyse both sides of the business relationship. In addition to conducting a study from the perspective of the supplier company, it may be interesting, within the service-dominant logic approach, to conduct research that includes the different strategic capabilities (called service-dominant orientation by Karpen et al. (2015)) that companies must develop to deliver superior value to their customers and to determine how these capabilities might influence customer satisfaction.

Finally, we can include moderators in our mediation analysis, which permits the analysis of potential condition mediators (Cheah et al., 2021). Some potential variables could be volatility, uncertainty, complexity and ambiguity (VUCA) context variables, the number of providers, etc.

Figures

Figure 1

Proposed research model

Figure 2

Estimated research model

Table 1

Characteristics of the companies in the sample

Characteristics	Companies
Size (number of employees)
10–50	72
51–100	57
101–300	35
301–500	16
+500	5
Age (years since foundation)
5–10	65
11–25	78
26–50	33
+50	9
Total sample	185

Source(s): Table by authors

Table 2

External loadings of the scale items used in the composites estimated in Mode A

Scale items1	Loadings
PM1. I have confidence in the expertise of my PE supplier	0.902
PM2. For me, dealing with my PE provider is easy	0.839
PM3. I know that my PE provider has been taking care of my needs for a long time	0.807
PM4. I have stayed with my PE provider because of my years of experience with it	0.773
PM5. I have dealt with my PE supplier before, so getting what I need is truly easy	0.785
MT1. My PE provider is flexible with me and is attentive to my needs	0.845
MT2. My PE provider keeps me up to date on services and possible incidents	0.759
MT3. The employees of my PE provider have good social skills	0.845
MT4. My PE provider treats me appropriately when things go wrong	0.905
FR1. Staying with my PE provider makes my business process easier	0.877
FR2. My PE supplier gives me what I need quickly	0.886
FR3. The employees at my PE provider understand my situation	0.885
SAT1. I am happy with the service of my PE provider	0.874
SAT2. In general, I am satisfied with the services of my PE provider	0.923
SAT3. Using the services of this PE provider has been a satisfactory experience	0.911
SAT4. The choice to use my PE provider has been correct	0.940
SAT5. In general, I am satisfied with my PE provider	0.916
SAT6. I think that I did the right thing when I decided to use my PE provider for my service needs	0.985

Note(s): ¹PM: peace of mind, MT: moments of truth, FR: focus on results, and SAT: customer satisfaction and PE: parcel express

Source(s): Table by authors

Table 3

Descriptive statistics, reliability, and average variance extracted from composites estimated in Mode A

Items1	Mean	SD	CA	Rho_A	CF	AVE
PM	4.700	1.381	0.888	0.890	0.918	0.691
MT	4.700	1.381	0.862	0.867	0.907	0.709
FR	4.944	1.242	0.859	0.859	0.914	0.780
SAT	5.198	1.106	0.958	0.959	0.967	0.828

Note(s): ¹Mean = average score of all items included in this measure, SD = standard deviation, CA = Cronbach's alpha, CF = composite reliability, AVE = average variance extracted, PM: peace of mind, MT: moments of truth, FR: focus on results, and SAT: customer satisfaction

Source(s): Table by authors

Table 4

Discriminant validity: Heterotrait-monotrait ratio (HTMT) of the model

Scales1	PM	MT	FR	SAT
PM	–
MT	0.878	–
FR	0.793	0.848	–
SAT	0.831	0.741	0.818	–

Note(s): ¹PM: peace of mind, MT: moments of truth, FR: focus on results, and SAT: customer satisfaction

Source(s): Table by authors

Table 5

External weights and scale items used in the composites estimated in Mode B

Scale items1	Weight	t value	p	VIF
BSE1. I can choose different options for my PE provider	0.718	3.748	0.000	1.083
BSE2. I have a designated contact person at my PE provider	0.525	2.328	0.020	1.083

Note(s): ¹BSE: basic service experience

Source(s): Table by authors

Table 6

Direct and indirect effects on endogenous variables (including higher and lower bounds of the 90% percentile confidence interval of the model)

Effects on endogenous variables1	Path and indirect effects	Confidence interval (90%)		Significance of the effect (p)
		5%CIlo	95%CIhi
BSE→ MT (H1)	0.347	0.202	0.486	Yes (0.000)
BSE→ MT→ FR (H2)	0.290	0.162	0.410	Yes (0.000)
BSE→ MT→ PM (H3)	0.259	0.140	0.371	Yes (0.000)
BSE→ MT→ FR→ SAT (H4)	0.112	0.047	0.184	Yes (0.01)
BSE→ MT → PM →SAT (H5)	0.136	0.052	0.213	Yes (0.01)

Note(s): ¹PM: peace of mind, MT: moments of truth, FR: focus on results, BSE: basic service experience, and SAT: customer satisfaction. CIlo: low confidence interval, and CIhi: high confidence interval using one tail

Source(s): Table by authors

Table 7

Results of prediction analysis

	Q2predict	PLS-SEM_RMSE	PLS-SEM_MAE	LM_RMSE	LM_MAE	Skewness	PLS-LM RMSE	PLS-LM MAE
SAT1	0.11	1.06	0.821	1.067	0.843	−0.689	−0.007	−0.022
SAT2	0.122	1.03	0.8	1.039	0.824	−0.456	−0.009	−0.024
SAT3	0.117	1.068	0.823	1.078	0.843	−0.453	−0.01	−0.02
SAT4	0.148	0.981	0.763	1.002	0.799	−0.443	−0.021	−0.036
SAT5	0.124	1.057	0.815	1.067	0.845	−0.531	−0.01	−0.03
SAT6	0.126	0.977	0.793	0.988	0.806	−0.333	−0.011	−0.013

Note(s): SAT: Customer satisfaction items. According to the level of skewness, RMSE is the prediction error that should be considered, and therefore the data to be considered are those indicated in the column with values in italics

Source(s): Table by authors

Table A1

Review of concepts in previous literature on the variables used in the study

Variable	Authors	Context	Concept
Customer experience	Carbone and Haeckel (1994)	B2B	The sum of aggregated and cumulative customer perceptions, both emotional and rational, in their relationship to the use of a specific brand
	Schmitt (1999)	B2B-B2C	That perception or recognition that continues with a stimulated motivation on the part of the customer
	Edvardsson et al. (2005) Verhoef et al. (2009)	B2B	Multidimensional construct that increases the loyalty of industrial customers by understanding their needs and must be understood from the customer's perspective
	Pareigis et al. (2012)	B2B	It is the global evaluation of interactive value formation three-dimensional: cognitive evaluation and two affective dimensions, positive activation, and positive deactivation
	De Keyser et al. (2015) Lemon and Verhoef (2016) McColl-Kennedy et al. (2015)	B2B-B2C	An experience that involves cognitive, emotional, affective, physical, sensory, spiritual, and social aspects
	Klaus and Maklan (2012) Roy et al. (2019)	B2B	It is composed of different elements, both cognitive and emotional, all of which must be taken into account by companies to achieve better outcomes
	Vakulenko et al. (2019)	B2B	Key antecedent of customer value and satisfaction
	Witell et al. (2020)	B2B-B2C	It becomes one of the top priorities of marketing managers and researchers
Basic Service experience	Eiglier and Langeard (1987)	B2B	The main reason why a customer chooses a particular supplier
	Keller (1998)	B2B	The process by which the result is obtained
	Grace and O ‘Cass (2004)	B2C	The main service experience of the hotel sector would be the main service offered by the hotel, i.e. sleeping in a room with certain characteristics
	Tax and Stuart (1997)	B2B	It is the core of the service that a provider offers to the market. They indicate that the basic service experience is more complex than the production of a product
	Abhari et al. (2019)	B2B	For a successful customer experience, the basic service experience must be implemented with complementary services that increase the customer's perceived value and trust in the service
	Moorman and Finch (2017)	B2B	To evolve this basic service and achieve greater differentiation, investments must be made in complementary services that enrich the basic services
	Roy et al. (2019)	B2B	Aspects such as whether the supplier offers different delivery options, makes beneficial offers related to business needs, or has a contact person to assist the client during the service process should be taken into account
Moments of truth	Grönroos (1994) Edvardsson et al. (2000)	B2B	When the customer and the supplier interact and the quality of service is defined by the customer
	De Keyser et al. (2015)	B2B	Those specific instants in which the customer relates to the supplier
	Pantano and Viassone (2015) Zomerdijk and Voss (2010)	B2B	Imply a broader meaning when referring to a customer's relationship with the product or service on any channel, both physical and digital
	Lemke et al. (2011)	B2B	For a customer experience to be enjoyable, each moment of truth must be classified and weighted to have an accurate understanding of the customer's subjective view
	Jüttner et al. (2013)	B2B-B2C	Key indicators that define experience and its assessment by the customer
	Raina et al. (2018)	B2B	Key to understanding the overall customer experience and customer satisfaction
Focus on results	Berry et al. (2002)	B2B	For a customer to want a long-term supplier, a focus on facilitating the purchase and the entire pre- and post-purchase process is essential
	Williams et al. (2020)	B2C	Variable that increases customer experience through ease of use
	Demangeot and Broderick (2006)	B2B	Part of the customer experience management that amplifies the experience that the customer feels when interacting with a company by feeling that the supplier is making their day-to-day life easier
	Gardner et al. (1994)	B2B	This dimension is concerned with partnership behavior to proactively contribute to facilitating business objectives in the B2B sector
	Codina Barragán et al. (2017)	B2B	The experience must be easy, focused on offering customers what is most interesting to them, being transparent with the customer without holding anything back or using fine print that goes against their interests, and being an integrated commercial brand by comprehensively showing its promise and doing what is best for the customer
	Maklan and Klaus (2012)	B2B	Reflects the importance of experiences that are guided by a specific objective which is associated with the reduction in customer transaction costs, such as the search for and qualification of new suppliers
Peace of mind	Otto and Ritchie (1996)	B2C	The most important attribute in attracting customers
	Bendapudi and Berry (1997)	B2B	A dimension that has a direct impact on the customer's assessment when interacting with the supplier
	Maklan and Klaus (2012)	B2B	A relevant aspect to ensure service quality
	Lee et al. (2013)	B2B-B2C	Peace of mind from a psychological perspective that emphasises the category of hedonic pleasure and affective well-being
	Anderson and Simester (2014)	B2B	Fundamental attribute of an excellent customer experience
	Johnston and Clark (2008) Sinčić Ćorić and Jélic (2015)	B2B	It helps strengthen the customer–supplier relationship through increased trust and satisfaction
	Krishna and Deshwal (2016)	B2B	It shows the emotional benefits that customers can experience based on the perceived experience of their service provider
	Ahmad Nizar and Zainal Abidin (2021)	B2B	Key aspect of decision-making
Customer satisfaction	Churchill and Surprenant (1982)	B2B	A customer's response to the consumption of a product or service. This response can be the result of a consumption experience in which the customer constantly evaluates all the variables of the experience
	Oliver (1993)	B2B	An evaluation that is made about a defined transaction with a given company
	Geyskens et al. (1999)	B2B	An emotional or affective state resulting from the evaluation and comparison with expectations of all relevant aspects of a business relationship
	Bardauskaite (2014)	B2B-B2C	The product of the customer's experience of using or consuming the service
	Chang and Thai (2016)	B2B	Key indicator of industrial company success
	Roy et al. (2019)	B2B	A key factor in understanding the reasons why companies decide to use and recommend their main express delivery provider
	Barbosa et al. (2021)	B2B	High levels of satisfaction are usually associated with greater trust in the supplier and, at the same time, with an increase in switching costs, which, together, would lead to an increase in repurchase and recommendation intentions

Source(s): Table by authors