Importance of the perceived quality of touchpoints for customer journey analysis – evidence from the B2B sector

Koch, Christian; Hartmann, Michael Article — Published Version Importance of the perceived quality of touchpoints for customer journey analysis – evidence from the B2B sector Electronic Commerce Research Provided in Cooperation with: Springer Nature Suggested Citation: Koch, Christian; Hartmann, Michael (2022) : Importance of the perceived quality of touchpoints for customer journey analysis – evidence from the B2B sector, Electronic Commerce Research, ISSN 1572-9362, Springer US, New York, NY, Vol. 23, Iss. 3, pp. 1515-1538, https://doi.org/10.1007/s10660-022-09654-5 This Version is available at: https://hdl.handle.net/10419/311066 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Electronic Commerce Research (2023) 23:1515–1538 https://doi.org/10.1007/s10660-022-09654-5 Abstract We investigate how the perceived quality influences touchpoint performance to provide a more nuanced understanding for analysing customer journeys. To answer the research questions, a survey in a real life online setting was carried out. The setting contained complex service solutions that were offered in a business-to-business context. The quantitative study shows that the perceived quality of a website has an influence on consumers’ buying intention. This correlation increases as the customer journey progresses. The perceived quality influences the website’s impact on visitors’ buying intention with a medium to strong effect size and the influence of a website’s quality on the impact on visitors’ buying intention varies significantly at different customer journey phases. While extant research focusses either on customer experience at touchpoints or touchpoints’ effects on buying behavior, we combine insights from both streams of research to highlight the role of website quality in determining touchpoint performance along the customer journey. Practitioners can use these insights to allocate resources in marketing and sales more efficiently. Keywords Customer journey analysis · Website quality · Online buying intention · Customer journey Accepted: 5 December 2022 / Published online: 26 December 2022 © The Author(s) 2022 Importance of the perceived quality of touchpoints for customer journey analysis – evidence from the B2B sector ChristianKoch1· MichaelHartmann2 Christian Koch [email protected] Michael Hartmann [email protected] 1 University of Hagen, Hagen, Germany 2 Coburg University of Applied Sciences and Arts, Coburg, Germany 1 3 C. Koch, M. Hartmann 1 Introduction Technological advances are radically changing how consumers search for information, evaluate products and services, make buying decisions, and share their experiences [55]. Therefore, it is becoming increasingly important to know consumers’ preferred channels, how channels link to each other, and how channels are related to consumers’ buying intentions to allocate resources efficiently [6]. In this vein, customer journeys describe the various phases of a buying decision and paths consumers take before making a buying decision. Along these paths, communication and interaction between companies and consumers takes place at various points of contact, socalled touchpoints. [18, 29, 40]. The customer journey analysis then provides insights on the buying behavior of consumers when interacting with companies at manifold touchpoints in various buying phases [24]. It provides a starting point to optimize the journey a customer takes from becoming aware of a brand until the usage of products and services offered by a brand to create and improve customer loyalty. Studies on customer journey analysis can essentially be divided into two different research strands [40]. First, several papers address the experience customers make at a certain touchpoint, i.e., how they perceive and react to touchpoint elements [30, 31]. These studies provide insights on how a touchpoint shapes beliefs about the attractiveness and performance of a brand [61] thus, on its ability to influence buying behavior and buying decisions. Second, studies focus on the effects touchpoints have on buying behavior along the journey the customer takes to illuminate differences in the significance different touchpoints have on buying behavior. These studies identify critical touchpoints, i.e., “moments of truth” [40], that have a high impact on a buying decision [6, 13], using so-called attribution models. Attribution models explain the performance of single touchpoints as well as effects of their combined usage in multitouchpoint environments [4]. Performance measurement is based on data indicating whether a touchpoint was used, at what position in the customer journey it was used, and whether its usage led to a purchase [i.e., 32, 5, 14]. By doing this, attribution models measure the influence of touchpoints on buying behaviour but do not count for the perceived quality of the elements determining the touchpoints’ ability in shaping beliefs about the attractiveness and performance of a brand. Given that the customer experience at a certain touchpoint influences whether and how the journey continues, it is likely that touchpoint performance, i.e. the result an attribution model produces, is moderated by its perceived quality. Consequently, research on the antecedents of attribution results seems warranted in order to better explain differences in touchpoint performance and to identify possibilities for improving the performance of a customer journey. To address this research gap, we integrate insights from both streams of research to account for the influence of the perceived quality of touchpoints regarding the touchpoints’ significance for buying behaviour along the journey a customer takes. The corresponding research questions of our study are as follows: Research question 1 When (in which phases of the customer journey) does the company website have the greatest impact on the buying intention of its visitors? 1 3 1516 Importance of the perceived quality of touchpoints for customer–… Research question 2 How is the impact of a company website on the visitor’s buying intention conditioned by the perceived quality of the website? Research question 3 What differences exist in the influence of the perceived quality of the company website on the visitor’s buying intention between customer journey phases? The three research questions are summarized in Fig. 1. To answer the research questions, we carried out a survey in an online real life setting of business-to-business (B2B) buying processes for complex logistic services at the touchpoint company website. The informed, complex, and intentional character of B2B buying processes [11] as well as the use of ‘high-involvement products’ [66] allow to distinguish more specifically between buying intentions and decisions at different phases of a customer journey. Furthermore, B2B websites typically do not offer the possibility of e-shopping as it is often the case in business-to-consumer (B2C) websites. This setting contributes to a more realistic scenery of the research since it does not require participants to imagine doing a sale as it would be the case in a B2C setting. The touchpoint company website was selected for investigation since it allows the combined collection of data on experience and buying intention. It is one of the most frequently used touchpoints in a customer journey and has a great importance for marketing and sales purposes [20, 42]. Company websites represent companies in the World Wide Web containing relevant content for visitors, such as information on an organization’s profile, portfolio of offerings, references, press releases, videos, podcasts, and live chats [21, 45, 64]. With its ability to attract visitors, convey information, engage in dialogues with customers, increase brand awareness, and gain competitive advantage, the corporate website was well suited to study the importance of touchpoint quality and buying intention [8, 19, 53, 64]. We Fig. 1 Summary of research questions 1 3 1517 C. Koch, M. Hartmann took the perceived quality of a touchpoint as a measure for the customer experience provided at a touchpoint. The results of our study show a multifarious relationship between the perceived quality of the touchpoint website and consumers’ buying intention along the customer journey. By combining insights from customer journey design, user experience design and attributional modeling, this research contributes to the debate on measuring touchpoint performance in customer journey analysis by providing a more nuanced understanding of how a touchpoint’s quality impacts buying intention in different customer journey phases. Practitioners can use these insights to improve customer experience management and to allocate resources in marketing and sales more efficiently. Marketing managers should incorporate the quality of a touchpoint when evaluating a touchpoint’s relevance in the customer journey and they can analyze in which phases of the customer journey their touchpoints, such as the company website, deliver the greatest value. These insights can help tailoring the design of touchpoints to the specific needs of consumers and thus address them more effectively. The paper is organized as follows. First, we show the relevance of customer journey analyses and the importance of a corporate website as a central touchpoint. Next, factors influencing a corporate websites quality are examined to lay out how a valuable customer experience at the touchpoint website can be created. Grounded in literature of customer journey analysis and buying behavior, we develop and test our hypotheses. After presenting the results, a discussion section follows including theoretical and practical implications. The final part of the paper is devoted to the limitations of our work and derives possibilities for further research on customer journey analyses. 2 Theoretical background 2.1 Customer journey analysis The noun customer journey describes various phases and touchpoints consumers pass through before making buying-decisions or consuming products [18, 40]. Here, phases organize the process a customer completes to form a buying decision and executing it. The formation of a buying decision is based on the experience a customer makes throughout the different phases. Typically, models describing different phases start with the recognition of a certain need and end with a sales or after-sales phase [40]. Divergent number of phases and phase classifications of the customer journey can be found in the literature, although they are close in terms of content [25]. Recent contributions describe the customer journey as a nonlinear process [15, 40]. This description is particularly useful for illustrating consumers’ ongoing relationships with brands and retailers that lead to repeat buying thus, capturing customer loyalty [40]. It also points to the fact that customers may reach out for a certain point of contact, e.g., a website, at different phases during the customer journey so that the point of contact may have different effects on the buying behavior depending on the phase in which it is contacted. 1 3 1518 Importance of the perceived quality of touchpoints for customer–… The points of contact are called touchpoints and describe the communication and interaction between an organization and an individual consumer [29]. The interaction at touchpoints can be manifold including different levels of involvement such as rational, emotional, or physical involvement [23]. The experience at a touchpoint denotes a touchpoint’s ability to shape consumer preferences and buying behavior. On the one hand, customer experience is influenced by the perceived quality of a respective touchpoint. In this vein, user experience design is concerned with designing individual touchpoints using design elements to promote an intuitive and natural experience [28, 46]. On the other hand, customer experience is influenced by the relationship and connection of different touchpoints along the buying process. In this context, Kuehnl, Jozic, and Homburg present, among others, the thematic coherence, consistency, and context sensitivity of the touchpoints as important elements of an effective customer journey design [36]. When interacting with an organization, numerous touchpoints exist along the customer journey allowing companies to connect with their customer so that a brand experience can be built [36, 65]. The touchpoints affect each other and have a direct or indirect impact on buying decisions. The preference for certain kinds of touchpoints, e.g., touchpoints in the online world, vary by consumer and phase of the customer journey [40]. By examining whether touchpoints provide the best possible customer experience, customer journey analysis helps companies to capture, understand and improve their consumers’ buying processes [59, 60]. Consequently, a better understanding of consumers’ activities and cognitive as well as emotional perceptions in the different phases of the customer journey and at the different touchpoints helps targeting resources, e.g., advertising budgets, to improve conversion predictions of touchpoints [67]. Consequently, technological advances in tracking touchpoints are increasing the interest in so called attribution models. Attribution captures and evaluates the contribution of one or more touchpoints to a buying event [12]. These models can be divided in heuristic and analytical models [5, 47]. Heuristic models use predefined rule sets to determine channel impacts. Analytical models use multivariate analyses to determine the probability of an event based on the movement patterns of consumers at different touchpoints [54]. While these models provide insights on the importance of a touchpoint within a network of possible touchpoints customers can choose from and interact with, their analytical procedure does not explicitly account for the customer experience at a touchpoint. Given that the customer experience shapes the evaluation of a brand and services offered, it can be supposed an important factor in determining a touchpoints importance. Thus, the perceived quality that determines how a touchpoint shapes customer experience may have a significant influence on the importance a touchpoint has in the customer journey. Given that a coherent and targeted management of touchpoints can influence brand perception and help to build and secure profitable relationships with customers [44], research on the importance of the perceived quality of a touchpoint regarding a touchpoints position along the customer journey seems warranted. 1 3 1519 C. Koch, M. Hartmann 2.2 Company website as a digital touchpoint of the customer journey One of the most common touchpoints that is under the direct control of the company, a so-called owned touchpoint, is the company website [42, 64]. Companies use websites to attract visitors, convey information, establish a dialog with customers, increase brand awareness, and to gain competitive advantage [8, 19, 53, 64]. Given that the company website serves different functions, it may be of different importance at different phases along the customer journey. This is because customers pursue different goals when reaching out for the touchpoint company website at different phases of the customer journey. The company website consists of various elements. The entry point to the website is the home page, which acts as a table of content for the website and directs visitors to further pages and specific information [26]. These pages usually include information on an organization’s profile, portfolio of offerings, references, press releases, videos, podcasts, and live chats [21, 45, 64]. In addition, the website serves to communicate news, attitudes, and a company’s expertise, which are often presented in combination with a web blog [48]. Visitors expect dynamic contact options, such as a form to fill out or chat functionalities, which enable efficient communication with the company and quick answers to simple questions [38]. Therefore, marketers should carefully design digital content with relevant information, keywords, and meta tags (information about the structure of the website) so that buyers’ search queries lead to the company’s website [64]. In contrast to informational websites that contain only information about a topic, a company, or a product or service respectively, transactional websites also offer buying functionalities [49]. Notably, users form a first impression of a website within a few seconds [41]. The result determines whether the website succeeds in generating attention and in promoting the establishment of a relationship between consumers and the company [21]. Whether a company website fulfills the purpose for which it was developed can be measured by its perceived quality [31]. Given that customer journey analysis is designed to identify critical touchpoints for the customer experience and buying behavior [34, 52], we propose to include a website’s perceived quality when investigating its importance along the customer journey. 2.3 Quality factors of a website The perception of the quality of a website is always subjective as the fulfillment of purpose is assessed differently depending on the user [27]. However, various studies have identified consistent factors of a website influencing quality perception. Based on a meta-analysis of these studies, website quality is operationalized using five individual factors. These can be grouped into the main factors of content, design, and overall impression. Content is the most original and widely used quality criterion for websites. A website’s content should provide information and answers to visitors’ questions to generate value [3, 31, 64]. The second main factor, design, is made up of three individual factors. The first factor is layout and represents the appearance of the website which stimulates the visitor’s emotions with visual impressions [1, 41]. Navigation 1 3 1520 Importance of the perceived quality of touchpoints for customer–… is the second factor of the design. It includes the structuring of the website and influences the intuitive use of the website as well as the findability of information [1, 58]. Interactivity of the website is the third criterion and stands for the possibility to act flexibly on the platform and to enable website visitors contacting the company in a convenient way. This can be accomplished for example by implementing live chats [39, 45]. Finally, the overall impression of a website stands for the coherence of the individual elements in the overall picture [31]. It denotes how well the various factors interact and therefore represents more than the sum of the single quality factors. E.g., if the content and the layout of a website are each rated well, but the design has no thematic reference to the content of the website, the rating of the overall impression is likely deviate from the rating of the other quality factors. Therefore, the overall impression is treated as an individual variable. The presented quality factors are applicable in both B2C and B2B contexts. However, when creating a website, it must be considered that there are differences in how a high perceived quality can be reached at each factor. This is because B2C customers have different requirements when visiting a website than B2B customers. The differences are summarized in Table 1 [57]: The differences in Table 1 indicate that B2B customers have different needs when visiting a website, particularly in terms of content, layout, and overall impression. This is due to the higher complexity of products and services they typically search for and the complexity of the buying process they are involved in. Consequently, the design of the touchpoint website must be adjusted to the field of application (B2C or B2B). The findings of the literature review are summarized in Table 2. They present a basis for deriving the research hypotheses. Table 1 Differences of B2C and B2B websites Quality factor B2C B2B Content - High resolution, expressive images - Discounts and special offers - Social proof in the form of customer reviews and rating videos - Product features and highlights - Comprehensive buying guides - In-depth product videos, articles, and blog posts - Easy access to sales reps - Case studies with customer success stories Layout - A lively, eye-catching homepage - Catchy and creative headlines - Creative call-to-actions - Highlighted sales, discounts, and special offers - Highlighted images of the main products - Simple, straightforward design - To-the-point call-to-actions - Fast ordering systems (e.g., via product name, SKU) Overall impression - Cohesive website elements creating an attention-grabbing website that focuses on sales through discounts and special offers to encourage customers to buy - Coherence of website elements that promote the perception of a highly informative website and allow buyers to independently learn about the company’s offer 1 3 1521 C. Koch, M. Hartmann 3 Hypotheses development 3.1 Research question 1 The first research question is about identifying the phases of the customer journey in which the company website fulfills the greatest benefit as a touchpoint. In each phase of the customer journey, there are different requirements for the interaction between the customer and the company. In the phase “awareness” consciousness of an offer is created. In the “favorability” phase, a concrete interest in an offer is strengthened. The “consideration” phase is characterized by considering buying an offer and selecting a possible product or service alternative. In the “intent to buy” phase, the buying intention is concretized, heading to a final “conversion” phase where a customer either buys a product or service, or at least engages in a specific action directed at buying an offer subsequently [7]. The different interactions in customer journey phases show that consumers’ need for information is still comparatively low at the beginning of the customer journey, while more intensive exchanges and more specific information are required as the customer journey progresses [66]. For example, hearing about a product or service for the first time is usually enough to attract attention, whereas the actual purchase requires prior intensive information research, e.g., on prices, product features or scope of services. This is especially true for complex solutions that drive cognitive and emotional involvement of customers as well as the intensity of processing information before a buying [35]. Due to their specific characteristics, customer touchpoints fulfill different benefits for each individual phase and are therefore differently suited to fulfill the requirements within the different buying phases [40, 62]. In this vein, company websites are particularly suitable for Table 2 Summary key findings of literature review Topic Finding Customer journey analysis - Customer journeys describe the various phases that consumers go through before making buying decisions or consuming products. - Within these phases, communication, and interaction between a company and an individual consumer takes place at various touchpoints. - Attribution models measure the impact of touchpoints but neglect how the perceived touchpoint quality influences the touchpoints’ performance and thus the attribution results. Company website as a digital touchpoint - Company websites are one of the most common touchpoints and typically include elements such as a home page, information pages, web blogs, and chats. - Company websites are used to attract visitors, convey information, establish a dialog with customers, increase brand awareness, and gain competitive advantage. - Whether a company website fulfills the purpose for which it was developed can be measured by its perceived quality. Website quality factors - Perceived website quality can be operationalized using five quality factors: (1) content = website information that provide answers to visitors’ questions (2) layout = website appearance that stimulates visitor’s emotions with visual impressions (3) navigation = website structure that enables an intuitive use and information findability (4) interactivity = possibility to act flexible on the website to create efficient interaction (5) overall impression = coherence of the individual website elements in the overall picture 1 3 1522 Importance of the perceived quality of touchpoints for customer–… A pairwise comparison was used to determine the weighting of the quality factors by comparing the individual factors in pairs to derive a ranking using a dominance score. The pairwise comparison illustrates that the quality perception is determined by the general impression with a weighting of 31.25%, navigation with 21.00%, content with 19,00%, layout with 16.50% and interactivity with 12.25%. The results were multiplied by the associated quality factor scores to form the overall quality score per participant. In conclusion, the result for the weighted overall quality of the website is 4.40 points (Table 6). To further specify the relationship between the influence of the quality of a website on its impact on the buying intention, a simple regression analysis was conducted. First, buying intention was considered aggregated across all phases. The simple linear regression with website quality as the independent and the impact of the website on the buying intention as the dependent variable indicates a significant correlation (F(1,38) = 18.51, p < .01). 32.8% of the variance from the websites impact on the buying intention can be explained by the website quality. Thus, website quality is a significant predictor of the websites impact on the buying intention. The estimated increase in the websites impact on the buying intention is 0.61, per level of website quality (ß = 0.61; t (38) = 4.30; p < .01). Overall, website quality has a moderate effect (f = 0.35) on the websites impact on the buying intention. Considering the average buying intention across all buying phases, an influence of the quality of a website on its impact on the buying intention is concluded. Thus, hypothesis 2 is supported. Hypothesis 3 Building on the second study, the final task was to analyze the extent to which the influence of the perceived quality of the website on its impact on consumers’ buying intention differs between the different customer journey phases. The influence of the quality of a website on its impact on the buying intention was analyzed for each customer journey phase. The consideration phase (F(1,38) = 183.40, p < .01) has the highest effect size with a value of f = 0.60, followed by the intent to buy phase (F(1,38) = 8.90, p = .01) with a value of f = 0.48. This is followed by the conversion phase (F(1,38) = 7.32, p =. 01) with a value of f = 0.44 and the awareness phase (F(1,38) = 6.53, p = .02) with an effect size of f = 0.42. Only buying intention in the favorability phase (F(1,38) = 5.89, p = .02), with an effect size of f = 0.39, minimally fails to be classified as a strong effect. Consequently, differences in the influence of the quality of a website on its impact on the buying intention can be identified along the customer journey. To determine the extent to which the differences between the customer journey phases are significant, a stepwise regression is conducted according to the procedure Quality Factor ˉx σ CV Weighting Content Layout Navigation Interactivity Overall Impression Weighted overall website quality 4.18 4.53 4.23 4.53 4.53 4.40 0.67 0.84 0.85 0.55 0.67 - 15.97% 18.48% 20.14% 12.09% 14.81 - 19.00% 16.50% 21.00% 12.25% 31.25% - Table 6 Descriptive statistics website quality 1 3 1529 C. Koch, M. Hartmann proposed by Kühnel (1996) [37]. The group differences are determined based on the influence of the quality of a website on its impact on the buying intention between two of the five customer journey phases. This results in a total of ten multi-group comparisons. To perform the stepwise regression, three variables are considered in the regression analysis. The first variable represents the influence of the quality of a website on its impact on the buying intention (output variable). Second, a dichotomous dummy variable is added to the dataset that assigns the values of the dependent and independent variables to one of the two compared customer journey phases based on the assigned number ‘0’ or ‘1’ (0/1 coded dummy variable). The third variable represents the product of the output variable and the 0/1 coded dummy variable (product variable). Since the 0/1-coded dummy variable for one of the two compared customer journey phases has the value ‘0’, all values of the corresponding product variables also need be zeroed. The product variables calculated based on the dummy value ‘1’ are again identical to the initial variable. Based on the determined variables, we calculated two regression analyses to test the group differences. The first regression equation determines the influence of the quality of the website on its impact on buying intention (simple regression model). The second regression analysis includes the 0/1 coded dummy variable and the product variable as an independent variables in addition to the initial variable (multiple regression model). Based on the results of the two regression analyses, an F-test is performed, and the F-statistic is determined using the following formula: F= ( SS ( E 0) −SS ( E 1)) / ( df 0 −df 1) SS (E1)/df1 SS ( E 0) Square sum of the residuals from the simple regression model SS (E1) Square sum of the residuals from the multiple regression model df0 Degrees of freedom of the simple regression model df1 Degrees of freedom of the multiple regression model The F-statistic uses the sums of squares of the residuals and freedom variables of the simple and multiple regression models to test whether the group means of the two models are equal. Table 7 shows the relevant results for hypothesis 3 for each pair of customer journey phases. There are differences between the customer journey phases regarding the influence of the quality of a website on its impact on the buying intention. In customer journey phases that are close to each other, such as the awareness and favorability phase (F(2,76) = 0.19, p = .83), no differences in influence can be identified. In customer journey phases that are further apart, however, the influence of the quality of a website on its effect on buying intention differs. Thus, the multi-group comparison revealed significant differences for the awareness and intent to buy phase (F(2,76) = 4.00, p = .02), awareness and conversion phase (F(2,76) = 6.26, p < .01), favorability and intent to buy phase (F(2,76) = 5.54, p < .01), as well as favorability and conversion phase (F(2,76) = 8.18, p < .01). Considering the determined effect 1 3 1530 Importance of the perceived quality of touchpoints for customer–… sizes, the influence in the intent to buy (f = 0.48) and conversion (f = 0.44) phases is more pronounced than in the awareness (f = 0.42) and favorability (f = 0.39) phases. Even if the differences in the multi-group comparison are not significant for each pair, differences in the influence of the quality of a website on its impact on buying intention can be identified when all customer journey phases are considered. These significant differences in the influence of website quality are particularly significant for customer journey phases that are distant from one another. It becomes clear that the influence of the quality of a website on its effect on buying intention increases in the later phases of the customer journey. Thus, hypothesis 3 is supported. 5 Results and implications The findings of our empirical analysis provide a basis to derive implications for research and practice regarding customer journey analyses and touchpoint management. Companies use customer journey analyses to examine in different buying phases which touchpoints are available to consumers, which ones they select, and how they interact with them [40, 63]. The results of our study extend this perspective on customer journey analysis by highlighting the importance of the qualitative design of touchpoints. Following, we present our three key findings that are of particular importance for future studies on customer journey analyses and practitioners. First, our findings illustrate how the quality of touchpoints affects consumer behavior in the buying process. The regression analysis indicates that website quality has a medium to high influence on the effect of the website in influencing visitors’ buying intention. Previous studies measuring the effectiveness of touchpoints focus on whether a specific touchpoint was used as part of the customer journey and to what extent its use affected the likelihood of buying [6, 32]. How the consumer qualitatively evaluates the touchpoint is not considered in determining the effectiveness of the touchpoint. Thus, our study demonstrates the influence of the perceived quality on the effectiveness of a touchpoint in shaping buying intention. This finding points to the need to extend attribution modeling by integrating the quality perception of touchpoints to measure touchpoint performance more precisely. To better explain differences in touchpoint performance, there is need for cross-disTable 7 Results of multi group comparison Compared groups F-value Degree of freedom counter Degrees of freedom denominator P-value awareness – favorability awareness – consideration awareness – intent to buy awareness – conversion favorability – consideration favorability – intent to buy favorability – conversion consideration – intent to buy consideration – conversion intent to buy – conversion 0.19 1.36 4.00 6.26 2.39 5.54 8.18 1.19 2.77 0.30 2 76 0.83 0.26 0.02* > 0.01* 0.09 > 0.01* > 0.01* 0.31 0.07 0.74 *Significant differences 1 3 1531 C. Koch, M. Hartmann ciplinary-research including studies that combine attribution modeling with research on customer journey and user experience design to better understand touchpoints’ impact on buying behaviour. For example, surveys on the perception of touchpoint quality can integrate quantitative indicators like the duration of website usage that provide information about the perceived quality of the touchpoint. Integrating qualitative aspects in customer journey analysis also allows practitioners to improve the effectiveness of how they allocate marketing resources, i.e., advertising budgets. Instead of investing the advertising budget primarily in those touchpoints that lead to the most sales in present, companies should also invest in touchpoints’ potential and determine whether investing in the perceived quality of an underperforming touchpoint would be more effective than just promoting well performing touchpoints. Thus, considering the perceived quality of touchpoints in customer journey analysis can help companies explain differences in performance and re-consider the advertising potential of their touchpoints to improve the allocation of their advertising budget. Second, our study shows how the quality of a touchpoint affects buying behavior in different phases of a customer journey, leading to new insights about the effective design of touchpoints. Research describes the customer journey as a nonlinear process with ongoing relationships between brands and consumers [15, 40] and that touchpoints can be visited multiple times during different phases in the buying procedure. The results of our study show that also the quality of a website has a different impact on buying intention depending on the phase of the customer journey the consumer is in. Consistent with findings that consumers form beliefs and expectations about the quality and suitability of an offer that need to be met to create customer satisfaction [2], consumers also seem to form expectations about the quality of a touchpoint along the customer journey. To create satisfaction, touchpoints must fit customers’ expectations derived from their individual experience and usage intentions which differ depending on the customer journey phase consumers are in when visiting a touchpoint. To exceed a moderate satisfaction level, the expectations must be topped. For example, in the initial phases of the customer journey, consumers’ expectations are not yet solidified due to lower levels of experience. As the customer journey progresses, each contact with the company impacts the customer experience, thereby solidifying the expectations of the interaction [23]. To exceed an established satisfaction level and to improve a touchpoints ability to shape buying intention in later phases of the customer journey, there is need to exceed the perceived quality of a website. Consumers in the later phases are already better informed and search more specifically for answers to their questions. In order to be able to influence the already established buying intention in these phases, the rating of the website quality from the consumer’s point of view must be comparatively high. These results suggest that the contributions to the qualitative design of touchpoints must be more differentiated. For example, the design of the website differs depending on whether the goal is to make the website visitors aware of the service offering at the beginning of the customer journey or to motivate them to make a buying at the end of the customer journey. Studies must therefore clarify how the design of individual touchpoint elements (e.g., content, layout) can affect consumers’ buying behavior at different stages of the customer journey. 1 3 1532 Importance of the perceived quality of touchpoints for customer–… Practitioners can take the results to rethink possibilities for improving customer experience. In particular, our research suggests that companies may not only align their website regarding different customer segments that have different needs and wants, but also to align the design of their touchpoints dynamically regarding the different stages of the customer journey in which a touchpoint may be contacted by a customer. To improve customer experience at a specific touchpoint, companies must design their touchpoints sensitively regarding the phase in which it is contacted. This requires that they group touchpoint visitors according to pre-defined customer segments and according to the buying phase in which they are. For example, at the beginning of their journey, websites should be designed that they display general company information, visually supported by appealing graphic elements to foster awareness. Customers who are further along in their customer journey, on the other hand, should be offered additional landing pages that are easy to find and offer in-depth content on their specific search queries to promote a higher level of perceived quality. Third, our findings provide insights on the role websites play in the B2B context. While B2B settings are typically associated with more rational decision processes, the role of aesthetics often is downplayed considering the importance of informativeness and usability of a website [10]. The pairwise comparison in our study shows that the overall impression of the web page has the strongest influence on the perception of website quality with a weighting of 31.25%. In this vein, our study highlights the importance of aesthetic aspects in designing B2B web pages to support the creation of a desired image and the buying intention. Consequently, further studies should be conducted that deal with the perception of the design of B2B websites. Studies should shed light on how companies succeed in designing the various elements of a website to create a seamless overall picture. In this context, the interaction of content, navigation and layout of the website is of particular importance. From a practitioner’s point of view, the result suggests that marketing managers in B2B settings should adhere to insights from user experience design to promote customer experience and take care when designing the website to create a coherent overall image. B2B marketers can refer to user experience design approaches and use A/B tests to check the effectiveness of their measures. Furthermore, our research results confirm the importance of a company website in B2B settings for shaping the buying intention beyond the creation of awareness. In particular, the website’s impact on the buying intention demonstrates its special importance as a touchpoint in the first two phases of the customer journey, i.e., awareness and favorability. This finding highlights the importance and potential of websites to form buying decisions even in complex B2B settings. A practical implication is that B2B companies offering customized solutions should position the corporate website as an important touchpoint containing information that go beyond the mere presentation of a companies’ offerings to create awareness. The website should also allow customers to evaluate the company’s competences to foster favorability. This finding may encourage companies to integrate more functionalities in their website that previously have been fulfilled by sales agents, e.g., provision of information about possible technical solutions offered. 1 3 1533 C. Koch, M. Hartmann 6 Limitations of the study and future research directions In our study, we quantified the influence of a touchpoint’s perceived quality on consumer buying intention with special regard to different customer journey phases in which a touchpoint may be contacted. To conduct the study, data was collected in the B2B context to be able to differentiate more precisely between different phases of the customer journey a customer is in. The research design required a selective sampling of decision makers in a specialized field to foster external validity. However, the strict sampling process resulted in a small number of participants, thus limiting internal validity. Furthermore, to simulate conversion, participants were asked about their intention to buy the service offered but not to undertake a transaction. This procedure represents a realistic B2B scenario since a detailed buying contract is typically negotiated with sales representatives. Nevertheless, our results only show the intention to behave but not behaviour. Thus, it would be useful to initiate a replication study with creating a setting attracting more participants and testing for conversion rates by integrating e-shopping options to validate results. By creating an artificial environment, surveys do not necessarily correspond to a truthful answer, or they are distorted by a false self-perception of the participants. It would be useful to validate the research results by tracking unmodified transaction data of customers in an unmodified environment and different industries. Tracking real customer journeys is complicated given increasing data protection regulations. However, newer technologies in marketing automation and web analytics can pinpoint each touchpoint in the customer journey and provide important information about the buying behavior of potential customers. In addition to conducting the study as a survey in an online real life setting with a single test group, it would be fruitful to conduct an experiment with two groups, qualitatively distinct websites, and by performing A/B testing. The resulting divergence of scores could highlight the findings of this study. Furthermore, physiological response measuring techniques from the field of neuromarketing such as eye tracking can help improve the collection of data on the perceived quality of touchpoints [17, 51]. Finally, the study examines buying intention up to the conversion phase. To advance the results of this study, it would be useful to also gain insights into the relevance of the touchpoint website in the post-buying phase to consider the entire customer journey. This research provides a starting point for linking the different research strands of touchpoint effectiveness measurement with customer journey design and user experience design to evaluate the performance of touchpoints and channels more validly. Our findings suggest that further studies on touchpoint effectiveness measurement and the use of attribution models should also consider the perceived quality of touchpoints. Supplementary Information The online version contains supplementary material available at https://doi. org/10.1007/s10660-022-09654-5. Funding Open Access funding enabled and organized by Projekt DEAL. 1 3 1534 Importance of the perceived quality of touchpoints for customer–… Declarations Conflict of interest All authors declare that they have no conflict of interest. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/ licenses/by/4.0/. References 1. Ageeva, E., Melewar, T. C., Foroudi, P., Dennis, C., & Jin, Z. (2018). Examining the influence of corporate website favorability on corporate image and corporate reputation: findings from fsQCA. Journal of Business Research, 89, 287–304. 2. Akbari, K., & Wagner, U. (2021). Playing when paying and what happens next: customer satisfaction and word-of-mouth intention in gambled price promotions. Schmalenbach Journal of Business Research, 73, 243–271. 3. Aladwani, A. M., & Palvia, P. C. (2002). Developing and validating an instrument for measuring user-perceived web quality. Information & Management, 39(6), 467–476. 4. Anderl, E., Becker, I., von Wangenheim, F., & Schumann, J. H. (2016). Mapping the customer journey: Lessons learned from graph-based online attribution modelling. International Journal of Research in Marketing, 33(3), 457–474. 5. Anderl, E., Schumann, J., & Kunz, W. (2015). Helping firms reduce complexity in multichannel online data: a new taxonomy-based approach for customer journeys. Journal of Retailing, 92(2), 185–203. 6. Becker, I., Linzmajer, M., & von Wangenheim, F. (2017). Cross-industrial user channel preferences on the path to online purchase: homogenous, heterogenous, or mixed? Journal of Advertising, 46(2), 248–269. 7. Beilharz, F., Kattau, N., Kratz, K., Kopp, O., & Probst, A. (2017). Der Online Marketing Manager: Handbuch für die Praxis. d Punkt. 8. Berthon, P., Pitt, L., & Watson, R. T. (1996). The world wide web as an advertising medium: toward an understanding of conversion efficiency. Journal of Advertising Research, 36(1), 43–54. 9. Bitner, M. J. (1992). Servicescapes: the impact of physical surroundings on customers and employees. Journal of Marketing, 56(2), 57–71. 10. Chakraborty, G., Srivastava, P., & Warren, D. (2005). Understanding corporate B2B web sites’ effectiveness from north american and european perspective. Industrial Marketing Management, 34(5), 420–429. 11. Cortez, R. M., & Johnston, W. J. (2017). The future of B2B marketing theory: a historical and prospective analysis. Industrial Marketing Management, 66, 90–102. 12. Danaher, P. J., & van Heerde, H. J. (2018). Delusion in attribution: caveats in using attribution for multimedia budget allocation. Journal of Marketing Research, 55(5), 667–685. 13. De Haan, E., Wiesel, T., & Pauwels, K. (2016). The effectiveness of different forms of online advertising for purchase conversion in a multiple-channel attribution framework. International Journal of Research in Marketing, 33(3), 491–507. 14. Dinner, I., van Heerde, H., & Neslin, S. A. (2014). Driving online and Offline sales: the CrossChannel Effects of Traditional, Online Display, and Paid Search Advertising. Journal of Marketing Research, 51(5), 527–545. 15. Edelman, D. C., & Singer, M. (2015). Competing on customer journeys. Harvard Business Review, 93(11), 88–100. 1 3 1535 C. Koch, M. Hartmann 16. Elling, S., Lentz, L., de Jong, M., & van den Bergh, H. (2012). Measuring the quality of governmental websites in a controlled versus an online setting with the ‘Website evaluation questionnaire’. Government Information Quarterly, 29(3), 383–393. 17. Etzold, V., Braun, A., & Wanner, T. (2019). Eye tracking as a method of neuromarketing for attention research—An empirical analysis using the online appointment booking platform from MercedesBenz. In I. Czarnowski, R. Howlett, & L. Jain (Eds.), Intelligent decision Technologies 2019: Smart Innovation, Systems and Technologies (pp. 167–182). Springer. 18. Følstad, A., & Kvale, K. (2018). Customer journeys: a systematic literature review. Journal of Service Theory and Practice, 28(2), 196–227. 19. Foroudi, P., Yu, Q., Gupta, S., & Foroudi, M. M. (2019). Enhancing university brand image and reputation through customer value co-creation behaviour. Technological Forecasting and Social Change, 138, 218–227. 20. Gavin, R., Plotkin, C. L., Stanley, J., Harrison, L., & Spillecke, D. (2020). The B2B digital inflection point: How sales have changed during COVID-19. McKinsey Quarterly (April 2020), 1–8. 21. Geissler, G. L., Zinkhan, G. M., & Watson, R. T. (2006). The influence of home page complexity on consumer attention, attitudes, and purchase intent. Journal of Advertising, 35(2), 69–80. 22. Grewal, R., Lilien, G. L., Bharadwaj, S., Jindal, P., Kayande, U., Lusch, R. F., Mantrala, M., Palmatier, R. W., Rindfleisch, A., Scheer, L. K., Spekman, R., & Sridhar, S. (2015). Business-to-Business Buying: Challenges and Opportunities. Customer Needs and Solution, 2, 193–208. 23. Gunawardane, G. (2022). Enhancing customer satisfaction and experience in financial services: a survey of recent research in financial services journals.Journal of Financial Services Marketing, online publication. 24. Halb, F., & Seebacher, U. G. (2021). User experience and touchpoint management: a touchpoint performance management toolkit for the buyer journey. In U. G. Seebacher (Ed.), B2B-Marketing (pp. 285–316). Springer. 25. Hamilton, R., Ferraro, R., Haws, K. L., & Mukhopadhyay, A. (2021). Traveling with companions: the social customer journey. Journal of Marketing, 85(1), 68–92. 26. Hewitt, J. R., & Carlson, J. B. (2019). Securities practice and electronic technology. ALM Media Properties. 27. Herrera-Viedma, E., Pasi, G., Lopez-Herrera, A. G., & Porcel, C. (2006). Evaluating the information quality of web sites: a methodology based on fuzzy computing with words. Journal of the American Society for Information Science & Technology, 57(4), 538–549. 28. Hillmann, C. (2021). UX for XR. Apress. 29. Jenkinson, A. (2007). Evolutionary implications for touchpoint planning as a result of neuroscience: a practical fusion of database marketing and advertising. Database Marketing & Customer Strategy Management, 14(3), 164–185. 30. Kaur, S., & Gupta, S. K. (2021). A fuzzy-based framework for evaluation of website design quality index. International Journal on Digital Libraries, 22(4), 15–47. 31. Kincl, T., & Strach, P. (2012). Measuring website quality: asymmetric effect of user satisfaction. Behaviour & Information Technology, 31(7), 647–657. 32. Klapdor, S., Anderl, E., Schumann, J. H., & von Wangenheim, F. (2015). How to use multichannel behavior to predict online conversions: behavior patterns across online channels inform strategies for turning users into paying customers. Journal of Advertising Research, 55(4), 433–442. 33. Koch, C., & Hartmann, M. (2022). The impact of a company website and its perceived quality on the buying intention in B2B-settings. In F. J. Martínez-López, & L. F. Martinez (Eds.), Advances in Digital Marketing and eCommerce (pp. 9–18). Springer. 34. Kranzbühler, A. M., Kleijnen, M. H. P., & Verlegh, P. W. J. (2019). Outsourcing the pain, keeping the pleasure: effects of outsourced touchpoints in the customer journey. Journal of the Academic Marketing Science, 47, 308–327. 35. Kroeber-Riel, W., & Gröppel-Klein, A. (2013). Konsumentenverhalten (10th ed.). Vahlen. 36. Kuehnl, C., Jozic, D., & Homburg, C. (2019). Effective customer journey design: consumers’ conception, measurement, and consequences. Journal of the Academic Marketing Science, 47(3), 551–568. 37. Kühnel, S. M. (1996). Gruppenvergleiche in linearen und logistischen Regressionsmodellen. ZAInformation/Zentralarchiv für Empirische Sozialforschung, 39, 130–160. 38. Laudon, K. C., Laudon, J. P., & Schoder, D. (2015). Wirtschaftsinformatik – Eine Einführung (3rd ed.). Pearson. 39. Lee, Y., & Kozar, K. A. (2006). Investigating the effect of website quality on e-business success: an analytic hierarchy process (AHP) approach. Decision Support Systems, 42(3), 1381–1401. 1 3 1536 Importance of the perceived quality of touchpoints for customer–… 40. Lemon, K., & Verhoef, P. (2016). Understanding customer experience throughout the customer journey. Journal of Marketing, 80(6), 69–96. 41. Lindgaard, G., Fernandes, G., Dudek, C., & Brown, J. M. (2006). Attention web designers: you have 50 milliseconds to make a good first impression!. Behaviour & Information Technology, 25(2), 115–126. 42. Lo, J. J., & Wang, Y. J. (2012). Development of an adaptive EC website with online identified cognitive styles of anonymous customers. International Journal of Human-Computer Interaction, 28(8), 560–575. 43. Mehrabian, A., & Russell, J. A. (1974). An approach to environmental psychology. Cambridge, MA: MIT Press. 44. Melero, I., Sese, J. F., & Verhoef, P. C. (2016). Recasting the customer experience in today’s omni channel environment. Universia Business Review, 50(13), 18–37. 45. Moffett, J. W., Folse, J. A. G., & Palmatier, R. W. (2021). A theory of multiformat communication: mechanisms, dynamics, and strategies. Journal of the Academic Marketing Science, 49, 441–461. 46. Moore, R. J., & Arar, R. (2018). Conversational UX design: an introduction. In R. J. Moore, M. H. Szymanski, R. Arar, & G. J. Ren (Eds.), Studies in conversational UX Design, human–computer Interaction Series (pp. 1–18). Springer. 47. Nass, O., Garrigos, J. A., Gomez, H. G., & Schoeneberg, K. P. (2020). Attribution modelling in an omni-channel environment – new requirements and specifications from a practical perspective. International Journal of Electronic Marketing and Retailing, 11(1), 81–111. 48. Orzan, G., Octav-Ionut, M., Orzan, M., & Iconaru, C. (2013). The impact of blogs over corporate marketing communication: an empirical model. Economic Computation & Economic Cybernetics Studies & Research, 47(1), 79–96. 49. Pauwels, K., Leeflang, P., Teerling, M., & Huzingh, K. (2011). Does online information drive offline revenues?: only for specific products and consumer segments!. Journal of Retailing, 87(1), 1–17. 50. Pradhan, D., Malhotra, R., & Moharana, T. R. (2020). When fan engagement with sports club brands matters in sponsorship: influence of fan–brand personality congruence. Journal of Brand Management, 27(1), 77–92. 51. Rawnaque, F. S., Rahman, K. M., Anwar, S. F., Vaidyanathan, R., Chau, T., Sarker, F., & Mamun, A. (2020). K. Technological advancements and opportunities in Neuromarketing: a systematic review. Brain Informatics, 7(10), online publication. 52. Rawson, A., Duncan, E., & Jones, C. (2013). The truth about customer experience. Harvard Business Review, 91(9), 90–98. 53. Schach, A. (2015). Advertorial, Blogbeitrag, Contentstrategie & Co.: Neue Texte der Unternehmenskommunikation. Wiesbaden: Springer. 54. Schultz, C. D., & Dellnitz, A. S. (2017). Attribution modeling in online advertising. In K. C. C. Yang (Ed.), Multi-platform advertising strategies in the global marketplace (pp. 226–249). IGI Global. 55. Schweidel, D. A., Bart, Y., Inman, J. J., Stephan, A. T., Libai, B., Andrews, M., Rosario, A. B., Chae, I., Chen, Z., Kupor, D., Longoni, C., & Thomaz, F. (2022). How consumer digital signals are reshaping the customer journey. Journal of the Academic Marketing Science, online publication. 56. Sommer, R. (2014). Unternehmen im medialen Wettbewerb versus Messeund Eventplattformen, events und Messen: stand und Perspektiven der Eventforschung. Springer. 57. Shukla, S. (2021, October 27). 9 Differences between B2B and B2C eCommerce Websites, net solutions. Retrieved November 21, 2022, from https://www.netsolutions.com/insights/ difference-between-b2b-b2c-websites/. 58. Sutcliffe, A. G. (2002). Assessing the reliability of heuristic evaluation for website attractiveness and usability. Hawaii International Conference on System Sciences, 1838–1847. 59. Terra, L., & Casais, B. (2021). Moments of truth in social commerce customer journey: a literature review. In F. J. Martínez-López, & D. López López (Eds.), Advances in Digital Marketing and eCommerce (pp. 236–242). Springer. 60. Terragni, A., & Hassani, M. (2019). Optimizing customer journey using process mining and sequence-aware recommendation. Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing, 8.-12.04.2019, New York, 57–65. 61. Tueanrat, Y., Papgiannidis, S., & Alamanos, E. (2021). Going on a journey: a review of the customer journey literature. Journal of Business Research, 125, 336–353. 62. van der Veen, G., & van Ossenbruggen, R. (2015). Mapping out the customer’s journey: customer search strategy as a basis for channel management. Journal of Marketing, 22(3), 202–213. 1 3 1537 C. Koch, M. Hartmann 63. Verhoef, P. C., Kooge, E., & Walk, N. (2016). Creating value with big data analytics: making smarter marketing decisions. Routledge. 64. Vieira, V. A., de Almeida, M. I. S., Agnihotri, R., de Arruda, N. S., & Arunachalam, S. (2019). In pursuit of an effective B2B digital marketing strategy in an emerging market. Journal of the Academic Marketing Science, 47, 1085–1108. 65. Voorhees, C. M., Fombelle, P. W., Gregoire, Y., Bone, S., Gustafsson, A., Sousa, R., & Walkowiak, T. (2017). Service encounters, experiences and the customer journey: defining the field and a call to expand our lens. Journal of Business Research, 79, 269–280. 66. Wolny, J., & Charoensuksai, N. (2014). Mapping customer journeys in multichannel decision-making. Journal of Direct Data Digital Marketing Practice, 15(4), 317–326. 67. Zhou, Y., Mishra, S., Gligorijevic, J., Bhatia, T., & Bhamidipati, N. (2019). Deep neural net with attention for multi-channel multi-touch attribution. 25th ACM SIGKDD International Conference, 04.-08.08.2019, Anchorage, 3102–3111. Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. 1 3 1538