Logistics as a value in e-commerce and its influence on satisfaction in industries: a multilevel analysis

1. Introduction

Today, e-commerce is treated as one of the most dynamic and important sectors of the economy, as well as one of the main factors leading to greater competitiveness (Joong‐Kun Cho et al., 2018; Singh and Srivastava, 2019; Wakil et al., 2019). The dynamic development of e-commerce is driven not only by rapidly expanding internet access but also by growing mobility and popularity of portable devices.

Because of the internet, trade has become as easy and convenient as never before (Lin et al., 2016). Its beneficiaries are both companies and customers. Almost each firm has the potential to become a successful trader (WTO, 2016). Companies are able to save on both fixed and variable costs, such as rent, labor and other overheads associated with a physical presence in shopping centers and brick-and-mortar stores. It is particularly important in the case of cross-border trade because companies do not have to spend a lot of money on the international expansion (Giuffrida et al., 2017). In turn, customers can have access to information about companies and their products at any time (Leung et al., 2020). They can easily find offers, compare them and read other users’ opinions (Kumar et al., 2012). They may also purchase new products, which they did not previously use due to their unavailability in terms of location in distant places (e.g. goods from abroad), lack of time or a different lifestyle (Saridakis et al., 2018). Moreover, online shopping allows customers to save money because they are closer to the manufacturers and bypass many intermediaries, such as wholesalers, distributors. Therefore, because of e-commerce, an additional value is created for the customers which is associated with a competitive price of the product, convenience in the form of 24 h access to e-stores and various methods of payment (Chen and Zhang, 2015). The value is delivered not only by the online store but also by many other entities, such as sales platforms, financial institutions (banks, online payment service providers) and logistics companies.

Just a few years ago, the internet was mainly used for business-to-business (B2B) co-operation. At present, e-commerce is dominated by business-to-consumer (B2C) transactions, where the product is ordered by individual customers and sold by businesses (Yu et al., 2017). Serving such clients is much more difficult and requires special procedures. They often make one-off purchases, so their loyalty is relatively low and their expectations grow all the time.

If the product fails to arrive at the right time, is damaged or the driver’s service is inadequate, the customer may not re-purchase at the online store. Logistics, then, plays a crucial role in e-commerce (Moagar-Poladian et al., 2017; Vakulenko et al., 2019). Managers know well that logistics customer service is a very important issue. Therefore, they pay more and more attention to it. The delivery process to the end customer is relatively simple for them, as it is almost always outsourced to external companies (Delfmann et al., 2002). For this purpose, courier, express and parcel services operators (CEP) are engaged. Other logistics processes of the online retailer (e-trailer), such as warehousing, picking and packaging, are a greater challenge.

E-commerce is becoming more and more demanding in terms of both innovative solutions and consumers’ expectations. The logistics needs of this are varied due to growing diversity of products (e.g. clothes, consumer electronics, domestic appliances and building materials), due to the value, importance and size. Customers increasingly care about receiving information about the shipment in real-time, simplified and free returns of goods and flexible delivery options. Products meeting the same needs of the clients form an industry. Nature of the industry is one of the factors that can affect logistics expectations and requirements and the service level in the industry is related to customer satisfaction.

The literature draws attention to the industry and its characteristics in the context of e-commerce (Preissl, 2003). However, there have still been few studies that concern e-commerce in individual industries, with particular emphasis on logistics. It is an especially important issue because the nature of the industry is one of the factors that can potentially lead to differences and discrepancies in the use of e-commerce (Saridakis et al., 2018). It is even claimed that the effectiveness of e-commerce depends on the characteristics of the industry, and the collective behavior of all companies in a given industry can reflect the behavior of the majority, as each industry has its own specificity and logistics strategies (Hu and Quan, 2003).

To analyze the relationships between variables from organization and industry levels we applied multi-level analysis method (Lee et al., 2017). There are relatively few articles, in which such method is used in the context of e-commerce (Mithas et al., 2006; Cho et al., 2014; Venkatesan et al., 2006; Wan et al., 2016). To our best knowledge, there are no studies that investigated the dependence between logistics value and customer satisfaction at the organization level and product and service characteristics at the industry level in e-commerce. The aim of this paper is to identify the components of logistics value and examine their influence on customer satisfaction in e-commerce. We investigate the moderation effect between those two variables using the overall service level in the different industries of e-commerce. 592 correctly filled questionnaires from telephone and web interviews (computer-assisted telephone interviews [CATI] and computer-assisted web interviews [CAWI]) were scrutinized. Hierarchical linear modeling (HLM) was used to verify the dependencies between variables from organization and industry levels. The geographical scope of our empirical research covered Poland.

2. Value

Value, as one of the philosophical categories, means something that is precious and desirable. It is the goal of human aspirations and refers both to ideas, persons, things, situations, phenomena and to their specific properties. The term of value in management studies was introduced by Drucker (1954). He claimed that it is essential for a business to meet the needs of the customer, who determines the value for which he/she is willing to pay. For this reason, value refers to the concept of “value for the customer” Zeithaml (1988), in turn, treats value as an evaluation of the usefulness of a product, resulting from the ratio of what was obtained to what was given.

The internet has become an important medium and tool in creating value. It has changed the catalog of value components, and their importance for the client. Because of the internet, as mentioned earlier, customers have more information about companies and their products. Non-economic benefits are of great importance. Customers look for products that are not available in brick-and-mortar stores. They accept certain non-financial costs, mainly related to the time of waiting for the product, the limited possibility of trying it out before the purchase, the risk of getting the wrong product, etc.

For this reason, logistics has become the basis for the activities of companies selling physical products via the internet. Without efficient logistics, in particular, without delivery of goods to the customer, implementation of the internet sales process would be very limited. At present, there is no doubt that logistics is a process, which affects value creation. According to Kilibarda et al. (2013), logistics is “the sphere of creating and increasing the value.”

The concept of logistics value has a slightly shorter history than value itself. In 1995 the Council of Logistics Management published a paper entitled “Creating Logistics Value: Themes for the Future,” whose authors, Novack et al. (1995), presented how logistics created or added value and how this value could be quantified.

Like value itself, logistics value also has many definitions. Most often, it boils down to a combination of quality, price and services provided to the customer, i.e. delivering what he or she wants and when he or she wants (Mentzer et al., 1997).

3. Components of logistics value and satisfaction

Logistics value is a construct which consists of variables related to convenience (Kaswengi and Lambey-Checchin, 2019), communication (Pal, 2017), experience (Yazdanparast et al., 2010) and time (Yu, 2017).

The value of convenience refers to both the lack of the need for the customer to move around to purchase products and the simple and quick chance to select products anywhere, anytime and with any electronic device (Yazdanparast et al., 2010; Kaswengi and Lambey-Checchin, 2019).

The value of communication is related to the way of contacting the customer. It is very important because, unlike traditional sales, e-commerce lacks direct contact between the seller and the buyer. Therefore, customers expect more frequent contact, which will provide them with information about their orders and quick answers to their questions (Lu, 2018).

The value of experience is, in turn, based on the customer's experience associated with the purchasing process carried out using the internet (Vakulenko et al., 2019). It is connected with the use of the customer's emotions and refers to the lifestyle, fashion, trends, social affiliation. Experience concerns the purchasing process itself but also what happened before and after the transaction, for example, after returning the product. It is important to create an integrated ecosystem that enables customers to move seamlessly from touchpoint to touchpoint, thereby creating smooth transitions. (Tax et al., 2013).

Due to the signaled lack of direct contact between the seller and the buyer, and the lack of immediate access to the purchased products, the time category is of great importance in shaping the customer value. Therefore, it is about the time spent finding the product and information about it, answering customers' questions and the time needed for the realization of the order, especially for the delivery of the product ordered (Joong‐Kun Cho et al., 2008).

Convenience in logistics value of e-commerce is related to the place of delivery and return of products. Time and flexibility are connected with delivery, too. In turn, communication in e-commerce logistics refers to delivery monitoring. These constructs are conceptualized later in this section.

4. Industry differentiation

At the beginning of e-commerce most products bought online were relatively easy to handle in logistics (e.g. packed in cardboard or foil), resistant to transport conditions, and were not needed by the customer immediately. Today, customers can buy online a majority of products which are available in traditional stores. These products meet the different needs of customers. These are both small and large items whose logistical handling can be either simple or really complex. To facilitate the analysis, products that are substitutable for each other, but made using different technologies and materials, have been grouped. If they meet the same needs of the clients, then the companies that offer them form an industry.

Statista (online portal for statistics) distinguishes 5 main industries in e-commerce (fashion; electronics and media; food and personal care; furniture and appliances; toys, hobby and DIY), which are further divided into 13 branches (apparel, footwear, bags and accessories, consumer electronics, books, movies, music and games, food and beverages, personal care, furniture and homeware, household appliances, toys and baby, sports and outdoor, hobby and stationery, DIY; garden and pet). The most popular products bought online are: clothing, shoes and accessories (34.2% turnover); consumer electronics, books, music and games (24.6%); toys, hobby and DIY (20.8%) (Statista, 2020) (Table 1).

The characteristics and specifications of these products have an impact on the e-commerce supply chain. The diversity of products, their different value, size, weight, storage and transport conditions, etc. lead to varied requirements, needs and conditions. Additionally, these are influenced by different payment services, cross-border selling, the number of products in the order and returns. In turn, products that are not available or more expensive in the local market are purchased abroad. Frequently used products, for example, food, are bought more often. On the other hand, clothes and shoes are usually returned. Returns have been explained in Section 3, that is why they will be omitted here and we will focus on describing other characteristics, i.e. payment services, cross-border selling, the number of products in an order.

5. Hierarchical linear modeling in management studies

Research in management studies often involves hierarchical or nested data structures with strong group effects. In such situations, the researcher has to deal with a lack of independence of observation, required in traditional regression analyses, leading to wrong conclusions from the research analysis. The solution to such situations is to use multi-level analysis methods, of which HLM can be considered most popular (Raudenbush and Bryk, 2002). This method, although known in social sciences for at least thirty years, is relatively rarely used in management research (Meschnig et al., 2018).

With the use of HLM, one can simultaneously study relations within a given level of hierarchy and the relations between them (Meschnig et al., 2018). This methodological approach allows researchers to analyze the relationships between variables on at least two different levels of analysis (Lee et al., 2017). For example, it is possible to analyze the importance of several factors influencing customer satisfaction, taking into account not only the variables at the company level but also those at other levels of analysis (e.g. strategic groups, industries) (Dang and Lin, 2017). In many cases, a grouped data structure is in itself a source of interesting research questions.

Due to the aforementioned rarity of studies with the use of HLM in management studies, there are relatively few articles, in which the HLM method is used to investigate relationships between variables. For example, a search using the keywords “HLM” and “management” in the Emerald database reveals 940 articles, Sciencedirect (Elsevier) – 2051, Taylor and Francis – 1593.

There are even fewer articles on the use of HLM in e-commerce research, and those related to e-commerce and logistics are extremely scarce. Moreover, in the context of e-commerce, these are the customers who are studied – their behavior, feelings, while the sellers themselves are examined relatively rarely. For example, Mithas et al. (2006) researched the effect of website design on customer loyalty. In turn, Cho et al. (2014) studied the influence of wine attributes on the perceived risk and online wine repurchase intention. Information and service quality are important here. Another example is the investigation aiming at examining the interactions among market characteristics and online pricing strategies (Venkatesan et al., 2006; Antipov, 2014). HLM was applied to analyze the data structure and find the dependencies between logistics and loyalty in e-commerce using the positions of the value chain members (Kawa and Światowiec Szczepańska, 2020).

The most thematically close to our research is the article by Wan et al. (2016), in which the authors investigated the dependence between the product categories at the retail store level and the customers’ satisfaction at the individual transaction level. The product categories, however, only concern their number and do not cover industry aspects. Moreover, satisfaction is a moderating factor between the product categories and the repurchase intention.

6. Methodology

7. Hierarchical linear modeling analysis and results

Table 6 shows the means, standard deviations and Pearson correlations of all variables in this study. The procedures of HLM method can be performed through five-step models, including unconstrained (null) model, random intercepts model, means as outcomes model, intercepted-as-outcomes and random intercepts and slopes model (Hofmann, 1997). Table 7 shows the successive phases of hierarchical modeling.

The first step of HLM refers to unconstrained model (null model), which does not contain any predictor. It decomposes the variance of the dependent variable into intra- and intergroup variance to test whether there are any differences at the group level on the dependent variable. Regression equations in particular groups are devoid of slope coefficients and have only a fixed value. This means that in the null model the result of the examined sellers can be predicted only on the basis of the average value of the dependent variable in the group (industry) to which the person belongs. The equations for unconstrained model are as follows:

The results indicate that there is a variability in satisfaction at the industry level (( χ2) = 35.07, df = 12, p < 0.001), representing the necessity for using the HLM analysis. The decomposition of variance allows estimating the percentages of variance in satisfaction that resides between industries (Hoffman, 1997). To calculate it, the intra-class correlation (ICC) is computed. The values in Table 7 indicate that the ICC was 0.058 (based on the formula τ00/(τ00 + σ2) = 0.02916/(0.02916 + 0.47825), which shows that 5.8% of satisfaction variance is generated only by differences related to the seller's industry.

The next stage of hierarchical data analysis is random intercepts model. It presents changes in the components of variance after the introduction of level-1 predictors – these are: delivery monitoring (DM), convenience of return (CR), time and flexibility of delivery (TD) and convenient place of delivery (CP). The equations for random intercepts model are as follows:

The results of the random intercepts model in Table 7 show that satisfaction is positively influenced by all predictors. The greatest influence on satisfaction have DM ( γ = 0.25, p < 0.001) and TFD ( γ = 0.23, p < 0.001). Slightly less impact, but still statistically significant have Convenience of return ( γ = 0.13, p < 0.05) and CP ( γ = 0.12, p < 0.05). This means that, logistics value, consisting of delivery monitoring, time and flexibility of delivery, a convenient place of delivery, convenience of return, has a positive impact on customer satisfaction. H1 was therefore supported. Furthermore, the inclusion of the predictors of the dependent variable results in the reduction of intragroup variance – the comparison of the value σ² indicates its noticeable decrease (0.393 < 0.478). On the basis of differences between estimators σ² in the unconstrained model and random intercepts model, it can be concluded that predictors from level-1 explain 17.8% of satisfaction variance [R²_{level-1 model} = (1 – (0.393/0.478)) × 100%]. HLM estimation of variance components is shown in Table 8. It also turns out that Level-1 predictors have some influence on the variance of group constants ( τ00). It fell from 0.02961 to 0.02796, so it can be concluded that the predictors from Level-1 explain 6.8% of the intergroup variance of satisfaction. The surveyed industries differ in the average intensity of satisfaction, and these differences are systematically related to the level of predictors from the individual level [(1 – (0.02761/0.02961)) × 100% = 6.8%].

Random intercepts model concludes the stage of explaining the variance on an individual level (σ²). The next step of HLM is to test the relationship between the independent variables at group level and the dependent variable. The subject of interest are components of the existing variance at the group level − τ₀₀ and τ₁₁. In Means-as-outcomes model Foreign sales (FS), Cash on delivery (COD), Returns (R) and Number of products in order (NPO) were included in the analysis of predictors from Level-2. The equations for means-as-outcomes model are as follows:

According to the procedure of hierarchical models, the variables from the macro-level explain the intergroup variance (constant values) τ₀₀, which in this case is the result of differences in average satisfaction in particular industries. Means-as-outcomes model shows that the predictors from Level-2 affect the individual's perceived satisfaction, except Foreign sales (constructs, items and scales of logistics value and satisfaction). On the basis of differences between estimators τ00 in the unconstrained model and means-as-outcomes model, it can be concluded that predictors from Level-2 explain 26.3% of satisfaction variance [R²_{level-2 intercept model} = ((0.02916−0.0215)/0.02916) × 100%] (Table 8).

The fourth step of HLM is to test whether variance in the intercept term is significantly related to Level-2 predictors. For this purpose, the intercepts-as-outcomes model is used which is also a direct test for H2. The equations for intercepts-as-outcomes model are as follows:

The results in Table 7 indicate that all predictors, except Foreign sales, have a significant impact on the Level 1 output variable (i.e. satisfaction). It may therefore be concluded that H2 is partially supported. The slope coefficient variance τ11  estimated in Model 4 is 0.01806.

Models 4 and 5 test the research hypothesis that the positive relationship between the elements of logistics value and satisfaction will be strongest in industries with high overall service levels. Therefore, the next stage of modeling concerns the variance of coefficients β, representing interactive effects. To test the interaction hypothesis, in Model 5 (intercepts-and-slopes-as-outcomes model) random predictive effects from Level-1 were included in the hierarchical analysis, which allowed estimating the slope parameters of the regression line ( β-factor) separately for each industry and estimating group variance of β coefficients (τ₁₁). As we search for factors that can explain the variance of regression slope parameters estimated separately for each industry, they must be an intrinsic property of the industries and not of the respondents. In this situation, according to the H3, we check whether these moderators can be the service characteristics of each industry (Level-2 predictors). It is worth noting that this scheme is very similar to testing the interaction effect in classical regression analysis. The only difference is that in hierarchical analysis we are dealing with inter-level interaction (variables from the group level influence variables measured at individual level). Therefore, a final step of HLM is to test for cross-level interactions between the independent variables at an individual level and a group level. The equations for random intercepts and slopes model are as follows:

Table 7 shows the results of random intercepts and slopes model. This model provides a direct test of H3. The results indicate that the most interaction effects included in Model 5 are statistically significant. The non-statistical significance is shown by the interactions of one of the Level-2 variables, namely, Foreign sales, which indicates that this variable does not play a mediating role in the relationship between perceived logistics value and customer satisfaction. However, apart from this exception, it can be stated that the impact of the general service level explains the variance of β-factors. Using the value of τ11 from the Intercepts-as-outcomes model (i.e. the total between-group variance in β1j) and the value of τ11 from the intercepts-and-slopes-as-outcomes model, one can obtain R² for Level-2 slope model (interaction term between logistics value and satisfaction). It has explained 36.5% of total variance [R²_{level-2 slope model} = ((0.01806–0.01147)/0.01806) × 100%] (Table 7). Thus, the H3 was partially confirmed. Most of the elements characterizing the general level of service in the industry showed a significant moderating effect on the components of perceived logistics values and satisfaction.

8. Discussion

In accordance with H1, at an individual level, all elements of logistics value showed a significantly positive effect on customer satisfaction. Given that, the respondents were e-commerce sellers, this indicates their consistent assessment of the importance of particular elements of logistics value. It is very important because e-commerce research often emphasizes that delivering the right value to the customer increases his/her satisfaction, which, in turn, can translate into customer loyalty and, further, into repurchasing (Chiou and Pan, 2009; Chiu et al., 2009). A good understanding of the expected value for the customer by e-commerce stores is the basis for an appropriate development of this sector.

In our study, logistics value consists of a convenient place of delivery, time and flexibility of delivery, delivery monitoring and convenience of return. The possibility to choose the place of delivery or pickup of the shipments makes the customer influence the configuration of his/her value chain and thus his/her satisfaction. Our research shows that if an e-trailer wants to be successful, customers have to be offered various forms of delivery and pickup of the products ordered. More and more often, customers are interested in a kind of delivery different from the courier because it is the most expensive of all forms of delivery and requires waiting for the courier in a specific place. Customers seem to expect to be able to receive and send shipments at the PUDO points or parcel lockers.

The order fulfilment time is one of the most important factors encouraging to make purchases over the internet. This time runs from the moment the seller confirms the order until the moment the goods are received by the customer. It is influenced by several processes –picking, packing, shipping and delivering. The last one is the longest and largely independent of the seller, as it is mostly performed by external logistics companies. Thus, the customer relies on the seller's promise of the order fulfilment time, and the seller, in turn, on his/her subcontractor. If this time is extended, it may reduce the subjectively perceived benefits of online shopping. The customer expects an increasingly faster delivery of products on the same working day or even within 2 h. Moreover, because of the option of delivery on non-working days and the opportunity to choose delivery times customer satisfaction increases.

The flow of things and information about them is the basis of e-commerce logistics. Up-to-date and accurate information is a very important value factor for customers in e-commerce. Passing information on is related to the availability of goods, time of order fulfilment and place of delivery or pick-up. Our research shows that delivery monitoring has the greatest influence on customer satisfaction. Therefore, sellers who want to be successful in e-commerce must provide information about the progress of the order and the place of delivery or pickup.

Returns in e-commerce are not usually pleasant for customers. They take time and can be stressful for some people. For these reasons, consumer return should be made easier for the customer. Our research indicates that convenience of return influences customer satisfaction, free return of products, easy return procedure and returning products over 14 days leave the customer with a positive experience that will make them want to return to the same seller (XiaoYan, 2012).

The results of the analysis supported our other research hypotheses. Although H2 and H3, assuming the impact of the overall level of service in the industries on customer satisfaction and the relationship between perceived logistics value and satisfaction, are only partially supported. The reason for this is the insignificant statistical impact of one of the variables characterizing the industries, which is foreign sales in e-commerce. This situation shows the need to look for better objective measures of customer service. Our aim was to use relatively hard data concerning the level of service in e-commerce in particular industries. The other variables concerning the industries are statistically significant correlated with customer satisfaction. So, payments, returns, number of products in the order influences customer satisfaction.

An important result of our research is to confirm the importance of industries as a basis for differentiating perceived logistics value and satisfaction. This means that the type of product and the need to be met have an impact on the logistics value achieved. In general, the higher the level of service in the industry, the higher the impact of logistics value on customer satisfaction can be expected.

9. Implications

10. Limitations and further research

We distinguished two kinds of research limitations in our study – methodological and substantive. The methodological limitations the very essence of the model which simplifies the economic reality and thus reduces the complex factual situation. In the developed model, certain elements, which, in the authors' opinion, were most important, were identified. The relationships between these elements expressed in the form of hypotheses were presented. The aim was to understand and explain the mechanisms and structure of the logistics value. However, the examined object consists of variables that are not directly observable. These variables are reflected in the literature, but their measurement is not clear. This was connected with the necessity to develop indicators measuring specific variables. However, there is a risk that some of them may have been blurred. Another methodological limitation is that we studied the customer satisfaction from the seller perspective. The simultaneous examination of two groups may be the subject of further research, but this requires access to the specific customers of the seller in question, which may be very difficult to implement on a larger scale.

The substantive limitation is that the developed model is aimed at identifying universal relationships which create the customer satisfaction mechanism for the logistics value. However, this may result in other aspects of customer satisfaction being neglected. We are aware that the creation of value by a company in e-commerce must be approached in a systematic manner. On the one hand, without marketing and sales support, it is difficult to identify the customers’ needs and prepare an offer for them, and on the other hand, negligence in the field of logistics eliminates the efforts made in these areas of business. Moreover, value is created and delivered not only by online stores but by many other entities, such as marketplaces, financial institutions, logistics companies, warehouse service providers, companies dealing with website positioning. In the process of value creation, all entities involved in creating value in e-commerce should be considered. Taking these observations into account may be the subject of future research, which would increase the substantive value of the model.