Acceptance of Digital Sales and Marketing Tools: A Customer

Perspective on Intention to Use

Tommi Mahlamäki

, Kaj Storbacka

and Samuli Pylkkönen

Unit of Industrial Engineering and Management, Tampere University, Tampere, Finland

Department of Marketing, Hanken School of Economics, Helsinki, Finland

Keywords: Information System Acceptance, Sales Configurators, Customer Perspective.

Abstract: Digital sales and marketing tools are important for the success of modern business-to-business (B2B)

companies. While many of these information systems or tools can also be used by the customer, surprisingly

little is known about the customer’s perception of these tools and the acceptance process itself. This research

targets this research gap by developing and testing a structural equation model (PLS-SEM) to investigate

customer perceptions and intentions to use digital sales and marketing tools, namely sales configurators. An

online questionnaire was developed, and the responses of 113 B2B professionals were analyzed. The results

showed that customers’ intention to use digital sales and marketing tools was influenced by ease of use and

perceived usefulness. Ease of navigation and information quality also played a significant role in the

acceptance process of these tools.

1 INTRODUCTION

Digital sales and marketing tools are important for the

success of modern business-to-business companies

(Hunter & Panagopoulos, 2015; Mahlamäki,

Storbacka, Pylkkönen & Ojala, 2020; Maridoss,

Milewicz, Lee & Sahaym, 2014; Sheth & Sharma,

2008). One important digital sales and marketing tool

category is the sales configurator. These tools are

used in the process of providing relevant product

information to the buyer. Sales configurators’ main

purpose is to produce valid configurations of market

offerings that fulfill the customer requirements while

keeping in mind the interests of the selling company

(Trentin, Perin & Forza, 2013; Rogoll & Piller, 2004).

Sales configurators have been studied in terms of

their design (Salavador & Forza, 2007), capabilities

(Trentin, Perin & Forza, 2013), and the benefits they

generate (Trentin, Perin & Forza, 2014). While

research on sales configurators, and digital sales and

marketing tools in general, has been robust, only a

few studies have focused purely on customer

perspectives about using such tools. For example,

Boujena, Johnston & Merunka (2009) studied the

customer benefits of Sales Force automation, while

https://orcid.org/0000-0003-3329-4351

https://orcid.org/0000-0002-1360-4167

Mahlamäki et al. (2020) studied the perceived

usefulness of sales configurators from customer

perspective. The previous studies fail to investigate

the customer perspectives regarding behavior or

behavioral intent on the use of digital tools. The

present study, however, focuses on this important

research gap and examines customer attitudes

towards sales configurator use. More specifically, we

define our research question as: What are the

antecedents of intention to use regarding sales

configurators from the customer perspective.

From a theoretical perspective, this study builds

on research on sales configurators by Trentin et al.

(2014) and on the technology acceptance model

developed by Davis, Bagozzi & Warshaw (1989). To

address our research questions, we developed a

quantitative study, in which the empirical data consist

of online survey data, and tested the hypotheses with

a structural equation model developed based on the

technology acceptance literature.

The main contribution of this study is the

increased knowledge about intention to use and its

antecedents regarding sales configurators from the

customer perspective. The study also contributes to

the technology acceptance literature.

224

Mahlamäki, T., Storbacka, K. and Pylkkönen, S.

Acceptance of Digital Sales and Marketing Tools: A Customer Perspective on Intention to Use.

DOI: 10.5220/0012204800003598

In Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2023) - Volume 3: KMIS, pages 224-230

ISBN: 978-989-758-671-2; ISSN: 2184-3228

The paper proceeds as follows. The next section

provides a theoretical contextualization and builds

towards the research model. Section 3 describes the

hypotheses and the research model, followed by

explanations of the methods in section 4. The results

are presented in section 5. Conclusions are presented

in section 6.

2 LITERATURE REVIEW

2.1 Sales Configurators

A sales configurator is a digital tool that is responsible

for guiding the user through a service- or product-

configuration process (Rogoll & Piller, 2004). Sales

configurators may be stand-alone applications or

modules of other applications that support translation

of needs into sales specifications, as well as

translation of sales specifications into the product

data necessary to build the product variant requested

by the customer (Trentin, Perin & Forza, 2013;

Rogoll & Piller, 2004).

The fundamental idea behind the configurator is

that it makes the customization of complex products

and services as easy for the user as possible. The user

should not be able to make invalid configurations, and

the system should guide the configuration process so

that the end result is a valid product or service that

can be delivered by the supplier and the distributor.

The configuration rules can be implemented in many

ways, but some of the most common methods include

the following logic-systems (Felfernig, Hotz, Bagley

& Tiihonen, 2014; Sabin & Weigel, 1998):

1. Rule-based systems. In these systems, the

system rules have the formation “if condition then

consequence.” These systems derive solutions in a

forward-chaining manner: at each step, the system

examines the entire set of rules and considers only the

rules it can execute next. The system then selects and

executes one of the rules under consideration by

performing its action part. As the system rules do not

separate directed relationships from actions,

knowledge maintenance may become difficult, due to

the knowledge of a single entity being spread across

multiple rules.

2. Constraint-based systems. In these systems,

each component is defined by a set of properties and

a set of ports for connecting to other components.

Constraints among components restrict the ways in

which various components can be combined to form

a valid configuration. As opposed to rules, the order

in which constraints are invoked does not matter: one

option restricts another, regardless of which one is

chosen first.

3. Resource-based systems. The goal of a

resource-based system is to find a set of components

that bring the overall set of resources to a balanced

state, one in which all demands are satisfied. A

configuration is acceptable only if the resources that

the environment and different components demand

are all balanced by the resources that the environment

and components can maximally supply.

2.2 Technology Acceptance Model

According to Davis et al. (1989), the technology

acceptance model (TAM) is an adaptation of Fishbein

and Ajzen’s (1975) theory of reasoned action (TRA)

model, specifically tailored for modelling user

acceptance of information systems. The goal of TAM

is “to provide an explanation of the determinants of

computer acceptance that is general, capable of

explaining user behavior across a broad range of end-

user computing technologies and user populations,

while at the same time being both parsimonious and

theoretically justified” (Davis, Bagozzi & Warshaw,

1989).

TAM is designed to be parsimonious so that it can

be readily adapted to various information system

contexts: perceived usefulness and perceived ease of

use are postulated a priori and are meant to be fairly

general determinants of user acceptance (Davis,

Bagozzi & Warshaw, 1989).

Although rooted in TRA, TAM closely resembles

aspects of social cognitive theory. This is also noted

by Davis (1989), according to whom the perceived

ease of use construct is similar to Bandura’s (1977)

concept of self-efficacy, while perceived usefulness

is similar Bandura’s outcome expectation.

Perceived usefulness items are measurements of

behavioral beliefs, which are indirect measures of

attitude, according to TRA. Although originally

included in the model, the attitude construct did little

to help explain the linkages between beliefs and

intentions in Davis (1989) and Davis et al. (1989),

however, and was dropped from the model. Similar

results have been reported by Taylor and Todd

(1995), who suggested that the non-significant,

indirect link from attitude to behavior may have been

due to the fact that TAM allows a direct link from

perceived usefulness to intention, which seemed to

capture the effect of attitude as well.

TAM has also been further developed since its

introduction: Venkatesh and Davis (2000) introduced

TAM2, which adds several antecedents to perceived

usefulness, while Venkatesh and Bala (2008)

Acceptance of Digital Sales and Marketing Tools: A Customer Perspective on Intention to Use

225

introduced TAM3, which adds antecedents to

perceived ease of use. Davis et al. (1992), Venkatesh

and Davis (2000), and Venkatesh and Bala (2008)

(Venkatesh & Bala, 2008) all demonstrated that the

perceived quality of a system’s output had a

significant effect on its perceived usefulness. Output

quality is judged by observing the quality of the

intermediate or end products of the system, as defined

by Davis, Bagozzi & Warshaw (1992). Therefore, in

order for a system to provide work performance

benefits for the user, the output of the system should

be of high quality.

3 HYPOTHESES AND

RESEARCH MODEL

This section presents our research model and

hypotheses development. Figure 1 illustrates the

overall model.

Figure 1: Research constructs and hypotheses.

3.1 Antecedents to Perceived

Usefulness

Following Goodhue and Thompson’s (1995)

conceptualization, information quality and system

adaptability constructs relate to the fit between the

technology and the task requirements: should the

information or functionalities provided by the system

be insufficient to the task requirements as judged by

the respondent, they should attribute the cause of their

own inefficacy to the system’s poor fit to the

requirements of the task. For example, a sales

configurator could be attributed a low task-

technology fit when it offers incorrect product

information to the user. Specifically, in a sales

configurator context, users may find it difficult to

trust the information provided by an automated expert

system (Tiihonen, Soininen, Männistö & Sulonen,

1996). Moreover, the sales configurator should

provide the user with information that is relevant and

on the right level of abstraction (Salavador & Forza

(2007).

Iivari and Koskela (1987) defined system

adaptability as the degree to which the system adapts

to changes in task requirements. The better the

functionalities of the sales configurator can adapt to

the different steps of the configuration task – that is,

selecting components, determining parameter values

for the components, designing the layout,

determining component connections, checking for

completeness and consistency of the configuration,

etc. – in different conditions and situations, the more

useful the configuration task is. Thus, we present the

following hypotheses:

H1: A positive relationship exists between

system adaptability and perceived

usefulness

Venkatesh and Davis (2000) argue that, given a

choice set containing multiple systems, one would be

inclined to choose the system that delivers the highest

output quality. Indeed Calisir et al., (2014), Davis et

al. (1992), Seddon and Kiew (1996), Venkatesh and

Davis (2000), and Venkatesh and Bala (2008) all

found a statistically significant relationship between

output or information quality and perceived

usefulness. Hence, we posit:

H2: A positive relationship exists between

information quality and perceived

usefulness

3.2 Antecedents to Perceived Ease of

Use

Similarly to Mahlamäki et al. (2020), we identified

format quality and ease of navigation as antecedents

to perceived ease of use. Format quality refers to the

user-interface design, which determines how easy or

difficult the system is to interact with. Interaction is

easy when the information provided by the system is

structured in visual hierarchies, information is

consistent, use of the system does not require

memorization but is based on recognition, and so on.

(Johnson, 2010) Thus, the degree of format quality

provided by the system affects how easy the system

is to use (Tiihonen et al., 1996; Bailey & Pearson,

1983; Wixom & Todd, 2005).

H3: A positive relationship exists between

format quality and perceived ease of use

Similarly, the degree to which the system’s user

interface is easy to navigate should affect how easy it

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is to interact with (Aladwani & Palvia, 2002; Palmer,

2002). Thus, ease of navigation is hypothesized to

affect perceived ease of use.

H4: A positive relationship exists between ease

of navigation and perceived ease of use

3.3 Relationships Between Intention to

Use, Perceived Usefulness, and

Perceived Ease of Use

Following the Venkatesh and Bala (2008) TAM3

model, we hypothesize that intention to use is affected

by both perceived ease of use and perceived

usefulness. In addition, we hypothesize that perceived

ease of use has an effect on perceived usefulness.

H5: A positive relationship exists between

perceived ease of use and perceived

usefulness

H6: A positive relationship exists between

perceived usefulness and intention to use

H7: A positive relationship exists between

perceived ease of use and intention to use

4 RESEARCH METHODOLOGY

To explore customer perceptions of sales

configurators, we developed a model based on

existing literature and designed an online

questionnaire that we administered to Finnish

industrial distributor companies.

4.1 Online Questionnaire Context and

Sample

To empirically validate the model, an online

questionnaire was created that targeted Finnish B2B

distributors. First, the names and contact information

of Finnish industrial distributor companies were

obtained from Statistics Finland, a governmental

statistical agency. We randomly phoned 630 of those

companies to identify the most relevant responder.

We then contacted the identified responders and

determined their willingness to participate in our

research. Of the 342 representatives who indicated

their willingness to respond, 152 eventually

responded. Of the 152 responses, 39 were removed

from the final data set because they either provided

answers that were too brief, did now answer the

relevant questions, or used repetitive answer patterns.

The data-cleaning process resulted in 113 responses

that could be used in the analysis.

4.2 Measures

The information quality scale was adapted from

Seddon and Kiew (1996) and Kankanhalli, Tan and

Wei (2005). Format quality and system adaptability

scales were both developed based on Bailey and

Pearson (1983), Wixom and Todd (2005), and Iivari

and Koskela (1987). The ease of navigation scale was

developed based on Aladwani and Palvia (2002),

Palmer (2002), and Yang, Zhou and Zhou, (2005).

The perceived usefulness scale was adapted from

Davis (1989), which was shortened from six to four

items. The intention to use scale was adapted from

Ajzen (2002). All the measurement items in the above

scales utilized a seven-point Likert scale.

5 RESULTS

The model’s predictive ability was estimated with the

partial least squares (PLS) method. The reason for

choosing PLS-based structural equation method

(SEM) over the more conventional covariance-based

SEM was the relatively low sample size at hand and

the robustness of the PLS-SEM method (Hair,

Sarstedt, Ringle, and Mena, 2012).

We follow the typical two-stage approach, where

the measurement model is first assessed for reliability

and validity and then the structural model is assessed

(Hulland, 1999).

5.1 Measurement Model

The reliability of the measurement model was

assessed with Cronbach’s alpha and composite

reliability. All Cronbach alphas were above .90.

Similarly, the composite reliability levels were above

.90, while the recommended level is .70 (Hulland,

1999). To evaluate convergent validity, the average

variance extracted (AVE) was analyzed. The lowest

AVE value was .69, which is well above the

suggested .50 level (Henseler, Ringle, and Sinkovics,

2009). Table 1 presents the correlation analysis of the

constructs.

Table 2 shows the Cronbach alphas, composite

reliabilities, AVEs, and numbers of items used. In

addition, the discriminant validity was assessed with

the Fornell-Larcker criterion (Fornell & Larcker,

1981). Finally, the level of multi-collinearity was

assessed with the variance inflation factor (VIF). The

Acceptance of Digital Sales and Marketing Tools: A Customer Perspective on Intention to Use

227

largest VIF value was 2.18, which is well below the

suggested level of 5 (Hair, Risher, Sarstedt and

Ringle, 2018).

Table 1: Correlation analysis.

Table 2: Reliability, Average Variance Extracted, and the

number of items.

5.2 Structural Model

The path model was assessed with R-square statistics,

path coefficients, p-values, variance explained, and

SRMR. Figure 2 presents the path coefficients,

variances explained, and the significance levels based

on p-values. The SRMR for the model was .066 and

the R-squares ranged from 1.71 to 4.55.

5.3 Hypotheses

Next, we tested the hypotheses based on the results of

the analysis. Hypothesis 1 postulated that system

adaptability has a positive effect on perceived

usefulness. We did not find evidence supporting the

hypothesis (β = .106, p = .451). This result contradicts

previous research (Venkatesh & Bala, 2008). By

contrast, hypothesis 2 was supported by the current

research (β = .299, p = .012) and the previous

literature (Venkatesh & Bala, 2008).

Hypotheses 3 and 4 were supported. Format

quality was found to have a positive relationship with

perceived ease of use (β = .243, p = .030) and ease of

navigation with perceived ease of use (β = .476, p =

.000). These results indicate that ease of navigation

had a more significant relationship than format

quality. The results were in line with previous

research (Mahlamäki et al, 2020).

Surprisingly, hypothesis 5 was rejected, with no

evidence found on the relationship between perceived

ease of use and perceived usefulness (β = .076, p =

.503). This result contradicts the TAM3 model

(Venkatesh & Bala, 2008).

Hypotheses 6 and 7, which were also based on the

TAM3 (Venkatesh & Bala, 2008), were supported.

Perceived usefulness had a significant relationship

with intention to use (β = .545, p = .000). Another

significant relationship was found between perceived

ease of use and intention to use (β = .233, p = .003).

Table 3 summarizes the hypotheses, relationships,

path coefficients, t-values, and p-values.

Table 3: Hypotheses.

Figure 2: Results.

6 CONCLUSIONS

Many industrial companies are developing marketing

and sales tools that can be utilized on several levels

of their value chain; therefore, it is increasingly

important to understand the adoption process of these

tools from the perspectives of the different parties in

the value chain (e.g., supplier or customer).

This position paper presents the preliminary

results regarding intention to use and its antecedents

regarding sales configurators from a customer

perspective. The results aligned with the TAM3

model (Venkatesh & Davis, 2000; Venkatesh & Bala,

2008) regarding the relationship between perceived

usefulness and intention to use and between ease of

use and intention to use. Unlike the TAM3, we did

not find a relationship between ease of use and

perceived usefulness. Information quality was found

to have a strong relationship with perceived

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usefulness, while system adaptability played no

significant role in determining perceived usefulness.

As in Mahlamäki et al.’s (2020) model, format quality

and ease of navigation were statistically linked with

perceived ease of use.

In the following steps this position paper is

developed especially regarding literature review and

discussion of the findings. Future research agenda

and limitations are also to be introduced.

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