How do B2B and B2C customers vary in terms of their search
behaviour in a multichannel environment?
Huiberdina Anne Verburg 10000232
Master’s thesis
30-06-2014 Final version
MSc in Business Studies – Marketing Track
University of Amsterdam, Faculty of Economics and Business Primary supervisor: dhr. dr. U. Konuᶊ
Table of Contents
Abstract 4
Acknowledgements 5
1.0 Introduction 6
2.0 Literature review 10
2.1 Multichannel customer management 10
2.2 Searching behaviour 11
2.3 B2B and B2C customers 12
2.4 Search behaviour in different situations 14
2.5 Main drivers impacting searching behaviour 16
2.6 Main research question 18
3.0 Theoretical framework 19 3.1 Comparing information 21 3.2 Search convenience 22 3.3 Price promotion 23 3.4 Clientele 24 3.5 Enjoyment 25 4.0 Conceptual framework 28 5.0 Method 29 5.1 Sample description 29 5.2 Procedure 29 5.3 Description of measures 30 5.4 Control variables 34 5.5 Coding 34 5.6 Possible limitations 34 6.0 Results 35
6.1 Data cleaning and missing values 35
6.2 Recoding of the items 35
6.3 Reliability tests 35
6.3.1 Cronbach’s alpha 35
6.4 Computing scale means and correlation matrix 36
6.5 General information of the dataset 36
6.6 Hypotheses results 37
6.7 General findings for B2B customers 47
6.8 General findings for B2C customers 47
7.0 Discussion and conclusion 49
7.1 Managerial implications 56
7.2 Limitations and further research 57
7.3 General conclusion 58
Reference list 60
Appendix B: Letter of thanks 64
Appendix C: Survey 65
Appendix D: Table of Variables 71
Table of figures
TablesTable 1. B2B and B2C customers’ characteristics 14
Table 2. Variables of the study of Verhoef et al. (2007) used in this study 17
Table 3. Hypotheses and expectations 27 Table 4. Correlation matrix 36 Table 5. Education levels 37 Figures
Figure 1. Conceptual model 28 Figure 2. Differences on comparing products 39 Figure 3. Channel preferences in case of exclusive promotions 41
Abstract
This study investigates how B2B and B2C customers differ in their searching behaviours across multiple channels in two different sectors. Currently, there is no research that distinguishes between these two groups with respect to multichannel marketing. When more is known about this relationship, business managers or the marketing departments of firms can use the information to appropriately adapt their strategies. The technical retail sector and the banking sector are used in this study. The searching stage refers to the first stage of the buying process. This is the stage in which people orient themselves and search for specific products or information before they actually buy these products. The researcher proposed that B2B and B2C differ in their searching behaviours. Therefore, five variables were examined: comparing information, search convenience, prices and promotions, clientele, and enjoyment. Four channels were used to test these variables: the store, the web store, the mobile website, and the call centre. Data were collected from 511 B2B customers and 166 B2C customers in both sectors by means of a survey. The results showed that B2B and B2C customers do not differ significantly from each other with regard to their multichannel use. Both prefer to use multiple channels for their searching behaviour. Furthermore, some differences were found between B2B and B2C customers with regard to the five used variables.
Acknowledgements
I would like to thank my supervisor Dr Umut Konuᶊ for his good support and interesting advice. He was always critical and gave me good advice in order to achieve a good result. In addition, I would like to thank my second supervisor Dr F.B. Situmeang for his time to read my paper. I would also like to thank the marketing department and all the employees of Warmteservice for the opportunity to do my research and for the access to the many customers of the company. In addition, I would also like to thank all the participants of this study for their time and effort.
In addition, I would like to thank my family and friends for their great support. Special thanks goes to my boyfriend and my mother, who have always given me the support I needed, even when I did not have any motivation left. They inspired me to go on and to write a good thesis.
1.0 Introduction
Society has changed significantly over the past 50 years (Quix & van der Kind, 2012). Customers can now obtain products any time of the day, any day of the week, and through many different channels. In the past, people could only shop during certain hours. Today, people can search for and order products using their desktops, laptops, and tablets. Not only is it easier to use various channels, the type of customers who use the different channels has also changed over the years. There is, for example, the emergence of the Net Generation; these are young people born between 1977 and 1997. These people have been using information technology devices throughout the majority of their lives (Comegys, Hannula & Väisänen, 2006). For this generation, it is entirely natural to use multiple channels for shopping behaviour; this generation’s population does not see it as unusual. On the other hand, the baby boomers, born between 1945 and 1955, are far less adapted to mobile devices than the younger generations. Online sales, especially, are strongly influenced by these developments, and this is expected to increase over the next ten years. For example, the online sales in the Netherlands are estimated at 10.6 billion euros in 2013. This is a substantial increase compared to the online sales in 2005, which were only 2.8 billion euros (thuiswinkel.org).
When customers intend to buy a product, either online or offline, they go through different stages. The stages of this process are search, purchase, and after-sales (Gensler, Verhoef & Böhm, 2012). In the search stage, customers orientate, searching for specific products or information. In the purchase stage, they will actually buy products. After-sales concern services, such as warranties and after-care. Previous research showed that customers have a tendency to use different channels in different stages of the buying process (Bellenger & Kor-goanker, 1980; Bloch, Sherrell & Ridgway, 1986; Gensler et al., 2012; Gensler,
Verhoef & Böhm, 2012; Kumar, 2010; Neslin et al., 2006; Quix & Van der Kind, 2012; Stone, Hobbs & Khaheeli, 2002).
The search stage can be seen as the most important stage of the whole process. In this stage, people are searching for a firm that fits their demands. When firms make a good impression in the searching stage, people are more likely to buy products (purchase stage) from that firm as well (Quix et al., 2012). It is also likely that the channel used in the search stage affects the subsequent phases of the searching process (Gensler et al., 2012). It is, therefore, important for firms to know the preferences of both kinds of customers and then implement those characteristics in their channels to satisfy customer needs and allow them to remain with the company. That is why the search stage was selected as the source of this study. The search stage can be divided into product search and information search (Brucks, 1985). Product search is the search for products on a particular channel, including more specific products or products from particular categories, such as bathrooms. Information search is broader and focuses on general orienting information, like firm characteristics, hours of operation, and delivery times.
The use of different channels by firms is called multichannel marketing (MCM). This thesis defines MCM as the design, deployment, coordination, and evaluation of channels through which firms and customers interact, with the goal of enhancing customers’ value through effective customer acquisition, retention, and development, following Neslin et al. (2006). MCM enables firms to build long-lasting relationships with their customers by offering information, products, services, and support through two or more channels (Rangaswamy & Van Bruggen, 2005).
This research draws a distinction between business-to-business (B2B) customers and business-to-consumer (B2C) customers. B2B means that business A is doing business with business B. B2C means that a firm does business with an individual consumer. Because the
B2B customer is doing business with a firm for his or her job, different motivations are expected as opposed to when someone is doing business with a firm for his or her own consumption, which is the case for B2C customers. The foundational prediction of this paper is that B2B and B2C customers will differ in their channel-choice behaviour because they seek to satisfy different needs. Much research been done on MCM, but a comparison between B2B and B2C customers regarding their shopping behaviour is lacking.
This gap in the literature indicates that little is known about the differences and comparisons between B2B and B2C customers, while there are still many companies that are in contact with both types of clients. Therefore, it would be beneficial if there was more known about these types of customers. Hence, this research will analyse the main drivers for both B2B and B2C customers, as well as why these customers prefer one channel above another. The focus will be on the search stage of the buying process. This is the first, and probably the most important, stage of the buying process. This study explores the differences between B2B and B2C customers in their intentions and preferences regarding four specific channels (physical stores, web stores, mobile websites, and call centres). The study is conducted in two sectors: the technical retail sector and the banking sector. Using these two different sectors ensures generalization. This study uses a survey to interview both categories of customers. The total sample consists of 511 B2B and 166 B2C clients. The data will be processed quantitatively.
This research aims to significantly contribute to both managerial and scientific knowledge in the field of marketing by establishing the differences between B2B and B2C customers in their search behaviour. Practically, when firms have more knowledge about customers’ channel choices, they can use that knowledge to adjust their channels to meet the expectations of customers, thereby increasing their sales. This can be done, for example, by means of special landing pages for certain kinds of customers when a firm’s customer base
appears to include both B2B and B2C customers. Firms can then make a distinction between B2B and B2C customers and focus on the most attractive target group or develop new strategies for both groups. Furthermore, firms can use the information to learn how customers may be segmented with respect to their information-searching behaviours (Konus, Verhoef & Neslin, 2008), and firms can use this kind of information to increase customer loyalty (Hsieh et al., 2012). This research’s scientific contribution will fill a gap in the existing literature by establishing the distinction between B2B and B2C customers in terms of their searching behaviours. This research will use a customer-centric approach because it is more effective in this context than a product-centred approach (Shah, Rust, Parasuraman, Staelin & Day, 2006). Therefore, the focus will be on the customers instead of the products.
This thesis will begin with a literature review; this section gives an overview of known information in the field. That is followed by theoretical and conceptual frameworks, which explain the relationships between the variables. Then, a methodology and results section will be presented; this section will investigate the method used and the results of the study. Lastly, there will be a conclusion and discussion section. In this section, implications, further research, and limitations will be discussed.
2.0 Literature review
2.1 Multichannel customer management
As mentioned earlier, MCM is the design, deployment, coordination, and evaluation of channels through which firms and customers interact with the goal of enhancing customers’ value through effective customer acquisition, retention, and development (Neslin et al., 2006). Therefore, it is the integration of traditional channels with new effective online marketing (Duffy, 2004). MCM is growing quickly; more companies are expanding into different channels. This can happen in different orders; firms can start with a web store and later open a physical store, or vice versa (Schoenbachler & Gordon, 2002). Multichannel marketing does not always happen in the same manner. A retailer can, for example, start with one channel and move to other channels later or remain with only two channels (Zhang, et al., 2010). In this sense, multichannel marketing does not have to be a major change to a company.
Schoenbachler et al. (2002) stated that multiple channels extend customer choice in terms of information and products: information because customers know more about what they are doing and products because they have more options. This statement refers to the first stage of the buying process, the search stage, but different channels can be used as well for all stages of the buying process (search, purchase, and after-sales). The channel a customer chooses depends on the customer’s references. Multiple studies investigated the relationship between customers’ preferences and channel choice. Thus, Frambach, Roest, and Krishnan (2007) found that channel choice depends on internet experience, especially in the search (pre-purchase) stage and after-sales (post-purchase) stage. A study by Gensler, Verhoef and Böhm (2012) showed that channel attributes, customer experience, and spill over effects can influence customer channel choice. Channel attributes are the perceived risk or convenience of a channel, so when someone perceive a certain channel as pleasant or convenient, the
likelihood of using that channel again in the future increases. The second variable is customer experience; this happens when someone becomes familiar with a channel. People have a tendency to perceive familiarity as pleasant because they have experience navigating that particular channel (Schoebachler et al., 2002). Thus, it is likely that they will use a familiar channel again in the future. The last variable, the spill over effect, is the likelihood of using a channel for more than one stage of the buying process, as in using a channel for both searching and buying products in the same purchase process. The channel-choice decision depends on different variables. Certain characteristics, such as experience and spill over effects, might influence the perception of the customer with respect to the usefulness of the channel. When people experience a channel as useful, they will use it again and even recommend it to other customers. Currently, there are many studies that indicate certain important characteristics influencing these experiences, but these studies are mainly focused on one or two characteristics. Additionally, no study focused on the differences between B2B and B2C customers regarding their motivations and preferences in the search phase. In this thesis, the focus is, therefore, much broader (five variables) and focuses on both B2B and B2C customers.
2.2 Searching behaviour
There is no question that the internet has, and will continue to have, a major impact on consumer information searching behaviour, as well as the other stages of the buying process (Peterson & Merino, 2003). When people want to purchase a product, they go through different stages. The stages of buying behaviour, as indicated by Gensler, Verhoef and Böhm (2012), include search, purchase, and after-sales. This research focuses on the first stage of the process, the search stage. In the search stage, customers search for information about the products/company or just for orientation. Information search, or the motivated activation of knowledge stored in memory or acquisition of information from external sources, is required
prior to a purchase decision (Blackwell, Miniard & Engel, 2001). When knowledge stored in memory does not provide adequate or sufficient information to make purchase decisions, consumers tend to engage in collecting relevant additional information for product purchase using various channels. External information acquisition can take place when someone actually has the intention to buy something, is engaged in pre-purchase search, or holds more general searching intentions; this is called on-going search (Bloch, Sherrell & Ridgway, 2001). Several studies have examined the impact of prior knowledge on searching behaviour. There are many studies that showed that prior knowledge (familiarity and product experience) is an explanatory variable related to searching behaviour (Brucks, 1958). Therefore, people with prior knowledge search differently than people without prior knowledge. Since this is plausible it will be taken into account in the study.
2.3 B2B and B2C customers
B2B customers are companies or sole proprietorships that do business with other companies. B2C customers are retail customers who do business with a company for their own consumption. Currently, not much is known about the differences between B2B and B2C customers related to their channel-choice preferences and multiple versus single channel shopping preferences. There is one scientific study that claims that there are some similarities and differences between B2B and B2C customers, but these claims are not scientifically tested (Kumar, 2010). However, there are some interesting findings derived from market research. Thus, it is clear that the decision-making process differs between B2B and B2C customers. For B2B customers, a decision is often made by the decision-making unit (DMU). This is a group of people making a purchasing decision together. The composition of that group varies depending on the characteristics of the target, such as organization size, location, and industry (b2bmarketeers.nl). However, B2B customers are not exclusive in their
dependence on a DMU; a family unit or a group of friends in the B2C customer context can also be seen as a DMU. Although the family may influence the decision, it is ultimately one specific person who makes the final decision.
What is generally known is that B2B customers already have, on average, more knowledge about the products and more transaction history with the supplying business than B2C customers. Thus, it is likely that B2B customers need less information about the products than B2C customers before making a purchase. It is likely that B2C customers are more dependent on various information-rich channels or the opinion of others to gather information. However, it is not true that only B2C customers find information on different channels important. A study by Chakraborty, Lala, and Warren (2003) showed that B2B customers perceive the organization of a website as the most important factor for that channel. In this context, organization means the ability of a website to arrange content, information, images, graphics, etc. in a manner that increases clarity of presentation and makes it easy for a visitor to find needed information. This indicates that B2B customers do find it important to receive good information. It is also likely that B2C customers rely more on the opinion of others and do not have much information in advance.
It is also known that B2B customers are more loyal to a company (De Pelsmacker, Geuens & Van den Bergh, 2008). This is plausible because these customers also tend to have better contact with the employees of a certain supplying company. In some cases, the relationship between the employees and the B2B customer is even stronger than the relationship between the customer and the company (Bendapudi & Leone, 2002). This can be explained by the fact that B2B customers often have regular interaction with the employees of a certain company, while B2C customers may visit a company once in a while. Therefore, it can be concluded that a good relationship with a firm’s employees is important, especially for B2B customers.
This is the extent of the current information regarding the differences between B2B and B2C customers. The aim of this research is to test existing relationships and to develop some new findings regarding these different customer groups.
Table 1. B2B and B2C customers’ characteristics
B2B customers B2C customers
Decision making - Depending on the DMU
- Collective decision making
- Decision is usually taken by one person
- Individual decision making - Decision may be partly
influenced by family or friends
Knowledge - More specific knowledge
about products - More experience
- Less knowledge about products
- Rely on the opinion of others
Accessible information - Like to have full
information available
- Like to have full information available
Loyalty - More loyal, because of the
regular contact
- Appreciate a good relationship with the employees.
- Less loyal, because they may visit a supplier just once in a while.
2.4 Search behaviour in different situations
Customers use different channels in different situations. There are many studies that confirm this assumption (Bellenger & Kor-goanker, 1980; Bloch, Sherrell & Ridgway, 1986; Quix & van der Kind, 2012). Hutchinson and Eisenstein (2008) found that customers use and choose a certain channel for specific goals at a particular point in time, based on their expertise and experiences. Additionally, Quix et al. (2012) claimed that there are differences in motivations for choosing a certain channel. They showed that online shopping intentions are mostly based on convenience and price and that offline shopping motivations are often based on wanting to touch the products, avoiding shopping costs, and reducing delivery time,
as well as customer experience. Some research suggested that customers can also search purely for entertainment (Bellenger & Kor-goankar, 1980). Another study showed that in many situations there is no purchase intention at all (Bloch et al., 1986). In such situations, people are just orientating or looking at the website/store because of curiosity or for entertainment. Which shopping behaviours people exhibit and which channels they prefer thus depend on their motivations and the products for which they are searching. For example, when customers search for specific products, they prefer convenience and complete information about the products, but when they are only searching for entertainment, they prefer more variety and an attractive presentations of the products (Quix et al., 2012). In this case, the internet is more applicable for orientation searching because of the many websites available. People can then use these websites to compare the price and product functions of items. On the other hand, offline shops are more useful for specific questions because of employee knowledge. It is likely that a customer would choose a channel that meets his or her intentions. A study by Schröder and Zaharia (2008) showed that most customers use only one channel within the buying process. Customers select a channel that they believe best satisfies their shopping motives in each situation. The results of several other studies, however, contradict this outcome (Bellenger & Kor-goanker, 1980; Gensler et al., 2012; Gensler, Verhoef & Böhm, 2012; Kumar, 2010; Quix & Van der Kind, 2012; Stone, Hobbs & Khaheeli, 2002). This could be explained by the fact that people change over time. Fifty years ago people shopped in a totally different manner than they do today. In the past, people tried to remain with one channel if it satisfied. Now new generations, such as the Net Generation, are accustomed to using different channels for shopping in all the stages of the process.
An important advantage of the use of MCM is that firms can reach many customers with different preferences in all stages of the buying process. Earlier research showed that customers use channels to satisfy five goals: economic, self-affirmation, symbolic meaning,
socialization and experiential, and routine or script maintenance (Balasubramanian, Raghunathan & Mahajan, 2005). The economic goals are met by obtaining good deals, and the self-affirmation goals, by showing expertise in selecting particular channels. The symbolic meaning goals are met by being thoughtful and thorough during the shopping process; the socialization and experiential goals, by being part of a social milieu and a stimulating environment. The last goals, the routine or script-maintenance goals, are met by achieving regularity and familiarity in the shopping process (Dholakia et al., 2010). People will choose a channel that meets their goals in a specific stage of the buying process and for specific intentions. It is expected that B2C customers will use channels that satisfy their economic goals and symbolic meaning goals because it is presumed that B2C customers will pay as little as possible for their product and want to be seen as thoughtful customers. On the other hand, it is expected that B2B customers will choose channels that satisfy their self-affirmation goals, socialization and experiential goals, and routine or script goals because it is presumed that B2B customers like to show their knowledge, especially to their colleagues. Thereby, these B2B customers are expected to choose a channel that is familiar, so that they can complete their shopping as quickly as possible. However, these variables will not be tested directly in this study.
2.5 Main drivers impacting searching behaviour
As previously mentioned, there is more known about the main drivers of searching behaviour related to B2C customers than to B2B customers. Nevertheless, some of these B2C customer findings can also be applied to B2B customers. One such driver is quality. Research shows that perceived quality of a channel influences the channel-choice decision for most customers (Verhoef, Neslin & Vroomen, 2007). For example, when customers visit a poorly working website and perceive it as annoying, it is unlikely that they will visit that channel again. Another study showed that the characteristics that influence channel choice are mostly
psychographic or demographic, or are based on costs and benefits (Konus, Verhoef & Neslin, 2008). Examples of these characteristics are price-consciousness, shopping enjoyment, motivation to conform, and education. Price-consciousness means that people use multiple channels to search for the best product price. Customers like to compare products and search for the best offers. Shopping enjoyment refers to the pleasure one experiences while shopping; this can be on one channel or across multiple channels. The motivation to conform refers to the possibility that customers use multiple channels because they want to conform to other customers. They want to show that they are also able to use multiple channels and to use the channels that someone else used. Education refers to the fact that customers with higher education often have higher incomes and, therefore, have more resources at their disposal, for example, tablets and smartphones, to search for products (Konus et al., 2008).
The variables derived from the study of Verhoef et al. (2007), comparing information, search convenience, price promotion, clientele, and enjoyment, are more summarizing and will, therefore, be the main variables used in this study. The table below shows the variables and their explanations. The next section, the theoretical framework, will further elaborate on these variables.
Table 2. Variables of the study of Verhoef et al. (2007) used in this study
Variable Explanation
Comparing information
Relates to how easy it is to make a comparison of products and prices on one specific channel
Search convenience Relates to how easy and convenient it is to collect information from a particular channel
Price promotion Is the availability of attractive offers on a particular channel
Clientele Indicates the likelihood that someone will use a channel because a friend or colleague uses that channel.
These variables are used because they are more general and complete than the other mentioned variables in the literature review. Additionally, the items related to these categories have exemplary validity and reliability and are, therefore, appropriate to use for this study.
At this time, there is no research available that compares B2B and B2C customers in the search stage, or in any stage, of the buying process. As the literature review also showed, there is still very little known about the similarities and differences between B2B and B2C customers in general. This can be seen as a gap in the current literature. In order to fill this gap, this study will compare the searching behaviour of B2B and B2C customers with each other. The focus of this study is, therefore, on the differences and similarities of B2B and B2C clients regarding their searching behaviours across different channels and should provide useful information for both business and science. When the differences between B2B and B2C customers in their searching behaviour are known, managers can use that information to improve their MCM, in order to satisfy the needs of both B2B and B2C customers. MCM has many advantages for the results of a company. Kumar and Venkatesan (2005) found that customers who shop across multiple transaction channels provide higher revenues and higher shares of wallet, and have higher past-customer value and a higher likelihood of being active than single-channel customers. This implies that MCM can lead to better business results.
2.6 Main research question
In light of these drivers and their implications, the central research question of this study has been developed as follows:
How do B2B and B2C customers vary in terms of their searching
behaviours in a multichannel environment? This thesis reports on a study that investigated
this question by means of five variables: comparing information, search convenience, price promotion, clientele, and enjoyment. These variables are based on the study of Verhoef et al. (2007) and summarize the previously stated variables from the literature review into measurable variables. Appendix D contains a table that shows the variables of the study.
3.0 Theoretical framework
The research question will be answered by using two sectors: the technical retail sector and the banking sector. These two extremely different sectors were chosen in order to increase the external validity (generalizability) of the research. As previously mentioned, the main contribution of this research will be to distinguish between B2B and B2C customers. Therefore, the predictions are tested with both B2B and B2C customers. Customers from both groups were asked to answer multiple questions regarding their searching behaviours in both the banking and the technical sectors.
First, a number of questions were asked about MCM in general. It was expected that B2C customers would use multiple channels more often than B2B customers for their searching behaviour because B2C customers, in general, have less knowledge about products than B2B customers. B2B customers buy products for their profession, so it is likely that they already have some experience with the products. De Pelsmacker, Geuens, and Van den Bergh (2008) found that B2B customers’ buy motives are more rational, more objective, and more focused on efficiency than the motives of B2C customers. Additionally, using one channel is generally the most efficient way for B2B customers to buy products. Hsieh, et al. (2012) found that perceived difficulty in channel switching negatively related to satisfaction. This could mean that if customers perceived the multichannel strategy of a company as difficult, their satisfaction would likely be negatively influenced. Furthermore, using more than one channel often takes considerable time. This requires starting up devices, logging in or, in the worst case, even travelling to visit the store. In addition, it is likely that B2C customers would better orientate themselves before buying a product. They would want the best choice and the best price for their products. Orientation is best achieved through different channels because different channels have different advantages. Thereby, B2C customers shop during their leisure time, so they can choose the channel that fits their needs best at a particular moment.
When they are already in a shopping mall, they can visit a store, but if they prefer to stay inside, they can search on a tablet or laptop for the same products. B2B customers are paid for store visits during working hours, so they would not be expected to have motivation to shop online. They can go to a store and directly receive the necessary information. This applies primarily to the technical retail sector. Although it is somewhat different for the banking sector, B2B customers are paid for visiting a bank as well, so they have less motivation to search online when they can make an appointment and receive an instant reply. Therefore, hypothesis 1 is expected:
H1: Multichannel use in the search stage of the buying process is higher for B2C customers than for B2B customer.
Another assumption is that B2B customers attach more value to good relationships with the employees of a certain firm than B2C customers do, due to the regular contact they have with the employees (Bendapudi et al., 2002). It is expected that this applies more to the technical retail sector than to the banking sector. This is because an installer will visit the technical sector almost every day, but a business relation of the bank (B2B customer) will generally visit infrequently. As a result, B2C customers will likely visit the bank more often than B2B customers, so the expectation holds mostly for the technical sector. Therefore, hypothesis 2 is expected:
H2: Relationships with employees are perceived as more important by B2B than B2C customers, primarily in the technical sector.
The goal of this paper is to identify the characteristics of the searching behaviour of B2B and B2C customers both at the technical retailer (Warmteservice) and at their chosen banks. Warmteservice is a Dutch technical retailer specializing in central heating and plumbing. They have a web store, 52 physical stores throughout the country, a mobile website, and a call centre. The chosen banks varied based on the respondents.
The variables, comparing information, search convenience, search effort, prices and promotion, clientele, and enjoyment, were derived from a factor analysis of Verhoef et al. (2007). These variables were chosen because of the exemplary validity and reliability of the items. The questionnaire included questions about these variables with respect to all four channels: web store, physical stores, mobile website, and call centre. First, each variable will be briefly discussed.
3.1 Comparing information
Comparing information relates to the ease in which a customer can compare products
and prices on a specific channel. Previous research has shown that online channels are often used for the first stage of the buying process, the search stage (Verhoef et al., 2007). Another study showed that price-conscious customers will use MCM more often because they expect to save money by comparing various channels (Konus et al., 2008). Comparing information to save money could be seen as an economic goal (Balasubramanian et al., 2005). The expectations are that B2C customers will use MCM for information comparison more often than B2B customers because B2B customers already know about the products. Thus, B2B customers do not expect to find new information on channels other than the channel they generally use. It is expected that this holds true both for the banking and the technical sector and that B2B customers also expect some discount when they frequent a physical store and have personal relationships with the employees (Bendapudi, 2002). Using the physical stores
exclusively is seen as the most attractive option for the B2B customer. Therefore, the expectation is that B2B customers will use a single channel more frequently because they do not have to compare as much information as B2C customers do. These expectations result in hypotheses 3 and 4:
H3: B2B customers would use a single channel to compare information more often than multiple channels for their searching behaviours.
H4: B2C customers would rather use multiple channels to compare information than focus on one specific channel for their searching behaviours.
3.2 Search convenience
Search convenience relates to how easy and convenient it is to collect information
from a particular channel. It is also related to the speed at which customers can gather information from that channel (Childer, Carr & Peck, 2001, in Verhoef et al., 2007). As mentioned earlier, Quix et al. (2012) showed that online shopping intentions are mostly based on convenience and price and that offline shopping motivations are often based on wanting to touch the products, avoiding shopping costs, and reducing delivery time, as well as customer experience. It is, therefore, expected that B2B customers would see physical stores as the easiest way to gather their information. Especially in the technical sector, they can check their ideas with the employees, and they can ask specific questions about the products. In the banking sector, this holds true as well: B2B customers can make an appointment and do not have to wait in line or wait for an email reply. It is expected that, in general, B2B and B2C customers differ significantly regarding their questions. B2C customers usually have more general questions, while B2B customers have more specific questions. It is, therefore, likely that B2C customers will orientate and buy using different channels. This is called
cross-channel synergy (Verhoef et al., 2007). B2B customers often already know what they need and only seek extra information. For them, there is often too much uninteresting general information on these different channels. Therefore, B2B customers need less general information and can often find the information needed on a single channel. On the other hand, the B2C customer can save time by using multiple channels for their searching behaviours. They can orientate on different channels, search for general information, and ask specific questions in physical stores or when they contact a call centre. Thus, it is expected that B2B customers will see one channel with much knowledge and expertise as more efficient and that B2C customers will experience multiple channels with different advantages as more convenient for their searching behaviours. Hypotheses 5 and 6 are posed based on this assumption:
H5: B2B customers will perceive one channel with extensive information and expertise to be more convenient for their searching behaviours than multiple channels.
H6: B2C customers will experience more channels with different information and advantages as more convenient for their searching behaviours than one channel.
3.3 Price promotion
Price promotion is the availability of attractive offers on a particular channel (Verhoef
et al., 2007). Channels can distinguish themselves by offering attractive deals or prices that are not available on other channels. It is expected that both B2B and B2C customers would be influenced by certain offers and that these offers would make the use of that particular channel more attractive. Previous research shows that people often have economic goals; this means that they are very price sensitive and search for attractive promotions (Balasubramanian et al., 2005). This holds true for both B2B and B2C customers. People like
to save money, even when they are working for someone else. When they are working for someone else, they can show how committed they are to the company by utilising attractive offers that save the company money. Therefore, it is expected that attractive offers, divided over different channels, can increase multichannel use. People appreciate offers and discounts in all shapes and sizes. It is expected that retail sectors can offer more promotions than the banking sector, but the banking sector can offer attractive promotions related to high interest rates. Hypothesis 7 is thus as follows:
H7: Price promotions, which are offered on exclusive channels, increase the probability that both B2B and B2C customers will use multiple channels for their searching behaviours in both sectors.
3.4 Clientele
Clientele indicates the likelihood that someone will use a channel because a friend or
colleague used that channel as well. It is expected that B2C customers are more influenced by friends than B2B customers are because they do not have as much knowledge about the products themselves. B2C customers like to hear the opinions of others before they choose a company. They can read reviews on websites or contact friends and colleagues to ask their experiences with a certain firm (Quix et al., 2012). This is expected because B2B customers do business more often with a given company and have a great deal of knowledge about that company’s products. They often know more than their friends do, and probably as much as colleagues, about products and services.
Ideally, B2B customers do business with a firm when they like the company and the associated service; if they do not like the company, they will not do business with it. In many cases, however, the B2B customers have no choice at all due to the DMU. They have to do
business with a firm because it is a directive to do business with that company. B2C customers, however, have no obligation to a company, but they also have no experience. This kind of customer has to trust the opinions of others about services and quality of the products. This is called worth-of-mouth (WOM) (Berger & Schwartz, 2011). People like to share their opinions, and new customers trust these opinions when deciding to visit a store or website. It is expected that B2C customers rely more on the opinion of others regarding their channel choices than B2B customers do. Therefore, hypothesis 8 is as follows:
H8: B2C customers are more influenced by the opinion of others with regard to their searching behaviour than B2B customers.
3.5 Enjoyment
Enjoyment is the fun that someone experiences from shopping on a particular channel.
A study conducted by Ha and Stoel (2009) showed that enjoyment plays a significant role in the adoption of e-commerce. It is thus likely that customers would use MCM more often if they perceive it as pleasurable.
The pleasure derived from shopping is assumed to be higher for B2C customers than for B2B customers in all channels. B2C customers shop for their own consumption, while B2B customers shop for their jobs. Thus, it is plausible that B2C customers will experience more fun from shopping than B2B customers. It is expected that B2B customers experience the most pleasure when they visit a firm. They can see the company and create a relationship with the employees of a firm. Thus, it is expected that B2B customers experience more pleasure from shopping on a single channel. On the other hand, B2C customers can search on different channels for new products. It is expected that they will perceive this as entertaining because they are looking for new products for their own consumption. This holds true more
for the technical sector than for the banking sector. It is posited that banking customers do not experience any pleasure while shopping at a bank. When customers are shopping at a retail shop (technical sector), they can search for nice items and accessories, but this is not the case for the banking sector. The only pleasure one can attain at a bank is when he or she receives more interest by opening a new banking account. Thus, this effect should be larger for the technical sector than for the banking sector. Moreover, it is expected that the pleasure derived from visiting a firm is the same for both sectors. Therefore, hypotheses 9 and 10 are as follows:
H9: B2C customers will experience more pleasure than B2B customers when searching for products on multiple channels, especially in the technical sector.
H10: B2B customers will experience more pleasure than B2C customers when searching for products on a single channel in both sectors.
This study has ten hypotheses. The hypotheses are based on previous research and the study of Verhoef et al. (2007). It is expected that these assumptions provide an accurate framework of the differences and similarities between B2B and B2C customers. On the following page is a table listing the hypotheses of this study. In the next section, a conceptual model is presented. It clarifies the relationships between the variables. Also shown are variables, which probably influence customer behaviour but are not investigated in this study. These variables are included in order to place the relationships in a better context.
Table 3. Hypotheses and expectations
Variable Hypotheses Expectation
MCM H1: Multi-channel use in the searching stage of the buying process is higher for B2C customers than for B2B customer
More use of MCM for B2C than for B2B customers due to more interest because it is for the customer’s own consumption. Employee
relationship
H2: Relationships with employees are perceived as more important by B2B than B2C customers, primarily in the technical sector.
Employee relationships more important for the B2B customer than the B2C customer, especially in the technical sector, due to the regular contact.
Comparing information
H3: B2B customers would use a single channel to compare information more often than multiple channels for their searching behaviour.
H4: B2C customers would rather use multi-channels to compare information than focus on one specific channel for their searching behaviours.
B2B customers prefer to use one single channel due to ease and job performance. B2C customers prefer to use multiple channels because they can use all the different advantages of the different channels.
Search convenience
H5: B2B customers will perceive one channel with extensive information and expertise to be more convenient for their searching behaviour than multiple channels. H6: B2C customers will perceive more channels with different information and advantages as more convenient for their searching behaviour than one channel
B2B customers will perceive one channel with extensive information and expertise to be more convenient for their searching behaviour because they more often have complicated questions.
B2C customers like to use more channels because of the different information and advantages provided on the multiple channels.
Price promotions
H7: Price promotions, which are offered on exclusive channels, increase the probability that both B2B and B2C customers will use multiple channels for their searching behaviours; this holds true for both sectors.
Both types of customers like to receive attractive promotions; therefore, they will search different channels if they expect to find promotions on those channels.
Clientele H8: B2C customers are more influenced by the opinions of others with regard to their searching behaviours than B2B customers.
B2C customers are more influenced by the opinion of others than B2B customers because they have less knowledge about the products and have to trust the opinions of others.
Enjoyment H9: B2C customers will experience more pleasure than B2B customers when searching for products on multiple channels, especially in the technical sector.
H10: B2B customers will experience more pleasure than B2B customers when searching for products on a single channel; this holds true for both sectors.
B2C customers like to search on multiple channels because they can do it wherever they like. Additionally, it is pleasurable to shop for one’s own consumption, especially in the technical sector.
B2B customers will experience more pleasure when searching on a single channel, especially physical stores, because they can build good contacts with the employees of a firm.
4.0 Conceptual framework
5.0 Method
5.1 Sample description
For this paper, both B2B and B2C customers were used. Participants were selected on the basis of the email addresses provided by Warmteservice. Warmteservice is a technical retailer. The company sells to both B2B and B2C customers and can, therefore, provide email addresses for both categories. The ideal sample was 217 participants from both categories. This sample size was calculated by means of the sample size calculator using a population of 500 participants, a confidence interval of 5, and a confidence level of 95%. In order to succeed, questionnaires were sent to 5000 B2B customers and to 1500 B2C customers. The final sample consisted of 307 B2C customers and 765 B2B customers.
Quota sampling was used because the intention of the research was to search for specific characteristics among the participants, namely their relationship, business-to-business or business-to-consumer, to the company.The same participants were asked about their searching behaviours in both categories. Because the same participants were used, the responses can be compared with each other. This will positively affect the validity and reliability of this research. A survey method was chosen for this study because it offered the opportunity to gain considerable information from many different participants without having to manipulate situations. Since the research focuses on in the opinions of a large number of customers regarding specific subjects, a survey, in place of all other research methods, was deemed most appropriate.
5.2 Procedure
The study was pre-tested with a pilot test of 10 participants. The pilot participants were well-known customers of the firm. They checked for misunderstandings in the
introductions and questions and for technical problems. Afterward, the main study began. Participants received an invitation for the study by email (link) or when they visited one of Warmteservice’s physical stores, by means of a flyer with a link. The invitation included an introduction, description of the study, and an informed consent form. The participants had to sign the informed consent form in order to participate. This implies that the customer knew his or her rights and was aware that it was possible to leave the study at any time. The participant was also told that information would be processed anonymously.
The survey consisted of two parts: one concerning the technical sector and the other concerning the banking sector. Both parts consisted of five categories: comparing information, clientele, enjoyment, price and promotions, and search convenience. The participants completed the survey online. The study began with the technical sector. The participants answered the questions in this section with respect to the company Warmteservice, an engineering sector retailer that sells plumbing and heating. In the second part of the study, the participants answered the same questions but with their banks in mind. The questions asked for the two sectors were identical.
Participation in the study, regardless of their answers, made the participants eligible to receive one of three raffle prizes: ICY smart thermostats offered by Warmteservice.
5.3 Description of measures
As explained, the relationship between B2B and B2C customers and their channel-choice preferences was measured by means of the five main variables. The survey began with demographic variables. Participants were asked to fill in their age, gender, education, occupation, family composition, and customer details. Subsequently, questions were presented regarding the five main variables. The questions were all based on a factor analysis
reliability of the items are high because the items are based on a well-known article of Verhoef et al. (2007). The reliability of the items in all categories in this paper ranged from 0.68 to 0.88, suggesting sufficient reliability (Verhoef et al., 2007).
The presented examples indicate that a question is applicable to all four categories; of course, the questions are presented in the survey in such a way that it is clear that the question concerns one specific category. A table of all variables is provided in Appendix 2.
Variable 1: Comparing information
To measure this variable, five questions were asked for the four categories (websites, stores, mobile websites, and call centres) in both sectors (technical and banking). The questions are based on the research of Alba et al.(1997) and Ratchford, Lee, and Talukdar, (2003), both cited in Verhoef et al., (2007). The questions were slightly modified in order to better align them with the study. Four questions were answered by means of a five point Likert scale and one question was multiple-choice. An example of a question from this category is as follows: “I can get a lot of information on product X on the website/in the stores/on the mobile website/when I call the call centre.”
This question, and the three other Likert scale questions, used a rating scale ranging from 1-5: strongly agree to strongly disagree. §The midpoint (3) meant neither agree nor disagree. The Likert scale was chosen because the 5-point scale allows the participants to express themselves fully, meaning there are not too many and not too few response options (Robson, 2011). Participants also had the option of abstaining if they had no experience with a certain channel. This measurement scale held true for all the opinion questions. This category has four polar questions and one nonpolar question.
To measure this variable, six questions were asked from the four categories in both sectors. The questions are based on research of Childers, Carr, Peck, and Carson (2001) and Hoque and Lohse (1999), both cited in Verhoef et al., (2007). The questions were slightly modified in order to better align them with the study. Five questions were answered by means of the five-point Likert scale and one question was multiple-choice. An example of a question from this category is as follows: “I can quickly get information on product X on the website/in the stores/on the mobile website/when I call the call centre.”
The measurement scale is the same for all opinion questions in the entire survey as explained during variable 1’s discussion. This category has five polar questions and one nonpolar question.
Variable 3: Price and promotions
To measure this variable, eight questions were asked for the four categories in both sectors. The questions are based on Dickson and Albaum (1977), Gijsbrechts, Campo, and Goosens (2003), and Kunkel and Berry (1969), all cited in Verhoef et al., (2007). The questions were slightly modified in order to better align them with the study. Four questions were answered by means of the five-point Likert scale and four questions were multiple-choice. An example of a question from this category is as follows: “There are attractive offers of product X on the website/in the stores/on the mobile website/when I call the call centre.”
The measurement scale was the same for all opinion questions in the entire survey as explained during variable 1’s discussion. This category has seven polar items and one nonpolar item.
To measure this variable, six questions were asked for the four categories in both sectors. The questions are based on Park and Parker Lessing (1977), cited in Verhoef et al., (2007). The questions were slightly modified in order to better align them with the study. All questions were answered by means of the five-point Likert scale. An example of a question from this category is as follows: “My friends and acquaintances seek for information on product X on the website/in the stores/on the mobile website/via the call centre.”
The measurement scale is the same for all opinion questions in the entire survey as explained during variable 1’s discussion. This category has four polar items and two nonpolar items.
Variable 5: Enjoyment
To measure this variable, four questions were asked for the four categories in both sectors. The questions are based on Mathwick, Malhotra, and Rigdon, (2001), cited in Verhoef et al., (2007), and the article of Verhoef et al., (2007). The questions were slightly modified in order to better align them with the study. One question was answered by means of the five-point Likert scale, two questions were multiple-choice, and one was open-ended. An example of a question from this category is as follows: “It is fun to search and buy product X on the website/in the stores/on the mobile website/when I call the call centre.”
The measurement scale is the same for all opinion questions in the entire survey as explained during variable 1’s discussion. This category has four polar items, including two items asking participants for possible improvements for one of the four channels.
5.4 Control variables
In this study, the researcher controlled for gender, age, education, family composition, and customer details. These variables were expected to have a possible impact on the results because they could influence the decision making process.
5.5 Coding
The polar items were positively coded. This means a high score is a large degree. When the items were nonpolar, they were recoded when running the analysis.
5.6 Possible limitations
By making a comparison between B2B and B2C customers in their channel choice behaviours, this thesis should make a major contribution to science. However, no research is without limitations. A possible limitation might be insufficient sectors for this study. This could affect the generalization of the study. The researcher tried to solve this by choosing two very different sectors, but this could be improved upon. Further research could repeat this study with more sectors and, thereby, contribute to the validity and reliability of these findings. Another limitation could be that the participants should have been asked more questions to get a better understanding about their shopping behaviours. The researcher chose not to ask additional questions because of potential time constraints of the participants, since questions were asked twice, both for the banking sector and the technical retail sector. Additional research could explore this further by asking more detailed questions about the customer’s intentions.
6.0 Results
6.1 Data cleaning and missing values
Before the dataset could be used the missing values had to be removed. Due to the high number of respondents, we removed all the participants who did not complete the entire survey. A total of 765 B2B customers and 307 B2C customers answered the survey, but some of the respondents only answered the questions at the beginning and therefore did not complete the survey. After the dataset was checked for missing data, 511 B2B customers (Nb2b = 511) and 166 B2C customers (NB2C = 166) remained.
6.2 Recoding of the items
The counter indicative items were recoded, so they could be compared to the indicative items of the dataset. A total of 10 counter indicative questions was used, including 4 sub questions for the four categories of channels (website, stores, mobile website and call centre).
6.3 Reliability tests
The number of items is the total number of items used for both sectors (technical sector and banking sector). The items of the four categories for each sector were the same.
6.3.1 Cronbach’s alpha
In order to measure the reliability of the items, a Cronbach’s Alpha test was conducted in SPSS. The results were as follows. The multi-channel uses subscales consisting of nine items (α=0.678). The comparing information scale consists of 20 items (α= 0.736), the search convenience subscale of 16 items (α= 0.704) and the price and promotions subscale (α=
0.742), the clientele subscale (
α=
0.762) and the enjoyment subscale of 12 items (α=
0.787). The reliability of the items is sufficient; all the items reach the minimum of 0.7 or higher.6.4 Computing scale means and correlation matrix
Table 4. Correlation matrix
Variables M SD 1 2 3 4 5 6 7 8 9 10 1. Age 2.59 0.706 - 2. Gender 1.03 0.166 0.088* - 3. Marital status 1.28 0.448 0.388** 0.075 - 4. Customer type 1.25 0.431 0.221** - 0.089* 0.047 - - 5. MC usage 2.456 0.395 -0.430 -0.250 0.039 -0.028 - 6. Comparing information 2.695 0.496 -0.060 0.012 0.006 0.059 0.101* - 7. Search convenience 3.347 0.503 0.058 0.065 0.047 -0.017 0.008 0.246** - 8. Price and promotion 2.440 0.602 -0.136** 0.041 -0.064 0.068 0.021 0.255** 0.146** - 9. Clientele 3.049 0.690 -0.054 -0.013 -0.033 0.052 0.011 0.181** 0.114** 0.324** - 10. Enjoyment 3.257 0.532 -0.066 0.042 0.006 0.035 0.147** 0.276** 0.142** 0.264** 0.241** -
The 5 variables used (comparing information, search convenience, price and promotions, clientele and enjoyment) were all significantly positively related. This means that when one variable increases, the other variables will increase as well. The value of the Pearson correlation can range from 0-1. The measured correlations were all below 5; this means that they are not strong related.
6.5 General information of the dataset
The total number of participants was 677 (Ntotal = 677). Most of the B2B participants were born between 1961 and 1980 (49,6 %) and most of the B2C participants were born between
1941-1960 (67,5%). The participants were mostly male (95,7%) and the majority of the participants was married (72,3%). Finally, we also looked at education. The results are shown below. On average, the customers have done further education.
Table 5. Education levels
6.6 Hypotheses results
First, the general hypotheses were tested. Then, the hypotheses were tested with respect to the five variables (comparing information, search effort, prices and promotions, clientele, and enjoyment). To test the hypotheses, an independent sample t-test was often used. With the independent t-test, the means of two independent groups were compared. Once, a chi-square test and a one-sample t-test were used. A chi-squared test is a statistical test to determine whether two or more partitions (populations) differ from each other and a one-sample t-test compares the means of a group.
Now, each hypothesis will be discussed individually. In the end, all the results will be presented and summarized in a table.
Multichannel use
The first hypothesis states that multichannel use in the search stage of the buying process is higher for B2C customers than for B2B customers. An independent-sample t-test
was conducted to compare multichannel use in the B2B group and B2C group for both the technical retail sector and the banking sector. An independent t-test was selected because the means of both groups (B2B and B2C) had to be compared. There was no significant difference in the scores of B2B customers (for the technical sector M = 3.67, SD = 0.797 and for banking sector M = 3.04, SD = 1.128) and B2C customers (for the technical sector M = 3.59, SD = 0.86 and for the banking sector M = 2.96, SD = 1.076) in both sectors: t(673) = 1.065, p = 0.287 for the technical sector and t(668) = 0.760, p = 0.448 for the banking sector. These results suggest that the multichannel use is the same for B2B customers and B2C customers. The first hypothesis is, therefore, not confirmed.
Employee relationship
The second hypothesis states that B2B customers find a good relationship with the employees of a company more important than B2C customers do, primarily in the technical sector. For this hypothesis, an independent-sample t-test has been conducted in order to compare the B2B and B2C customers for both sectors. Again, the means of both categories had to be compared to each other and the independent t-test was the best option. There was no significant difference between the scores of B2B customers (M = 4.46, SD = 0.609) and B2C customers (M = 4.38, SD = 0.638) in the technical sector: t(673) = 1.456, p = 0.146. However, the banking sector had a significant difference in the scores of the B2B customers (M = 3.43, SD = 1.130) and B2C customers (M = 3.65, SD = 1.086): t(668) = -2.220, p = 0.027. This means that B2C customers thought a good relationship with their bank employees was more important than B2B customers. Thus, hypothesis 2 is also disputed.
The next seven hypotheses concern the five possible moderators: comparing information, search convenience, prices and promotion, clientele, and enjoyment.
Comparing information
The third hypothesis states that B2B customers more often use a single channel than multiple channels for their searching behaviour when comparing information. The fourth hypothesis assumes that B2C customers would use multiple channels more often for their searching behaviour to compare information instead of focusing on one specific channel. The results showed that both customer groups preferred using more than one channel for their searching behaviour. The numbers are shown in the graphics below.
Figure 2. Differences on comparing products.
Clearly, the two figures appear nearly identical; this suggests that there was not a significant difference between both groups. Therefore, the focus shifted to the banking sector. To test the significance of the relationship between multiple channel use and the type of customer (B2B or B2C) a chi-square test was conducted. The chi-square test is used when two categorical variables have to be compared, in this case, the customer category (B2B or B2C) and MCM.
The chi-square test showed no significant differences between the two types of customers. The results for the technical sector were χ2 (4, N = 675) = 2.395, p < 0.664 and the
I like to use multiple channels to compare products in the
technical sector (B2B) Strongly disagree Disagree Neither agree, nor disagree Agree Strongly agree
I like to use multiple channels to compare products in the
technical sector (B2C) Strongly disagree Disagree Neither agree, nor diagree Agree Strongly agree
