ERP Usage and its Impact on Firm Performance A Quantitative Study of Swedish SMEs

Alejandra Chavez & Michael Duberg

Stockholm Business School

Master’s Degree Thesis 30 HE Credits

Subject: Business Administration

Spring semester 2021

Supervisor: Thomas Hartman

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1. INTRODUCTION .................................................................................................................................... 1 1.1 BACKGROUND ............................................................................................................................................... 1 1.2 PROBLEMATIZATION ..................................................................................................................................... 3 1.3 RESEARCH QUESTIONS ................................................................................................................................. 4 1.4 CONTRIBUTION ............................................................................................................................................. 4 1.5 OUTLINE OF THE THESIS ............................................................................................................................... 4

2. LITERATURE REVIEW AND THEORETICAL FRAMEWORK ........................................................ 5 2.1 ENTERPRISE RESOURCE PLANNING (ERP) .................................................................................................... 5 2.2 PHASES OF ERP ............................................................................................................................................ 6 2.3 PREVIOUS RESEARCH ON ERP USAGE .......................................................................................................... 6 2.4 THEORETICAL FRAMEWORK: ERP USAGE MODEL ....................................................................................... 8 2.5 HYPOTHESES ................................................................................................................................................. 9

2.5.1 Clear Vision and Planning ................................................................................................................... 9 2.5.2 Top Management Support ..................................................................................................................... 9 2.5.3 Effective Communication .................................................................................................................... 10 2.5.4 Effective Project Management ............................................................................................................ 11 2.5.5 Change Management .......................................................................................................................... 11 2.5.6 Teamwork and Composition ............................................................................................................... 12 2.5.7 Training .............................................................................................................................................. 12 2.5.8 Data Quality and Integrity .................................................................................................................. 13 2.5.9 IT-infrastructure ................................................................................................................................. 14 2.5.10 ERP Usage ........................................................................................................................................ 14

2.6 FIRM PERFORMANCE AND ERP ................................................................................................................... 15 3. METHOD ............................................................................................................................................... 16

3.1 EMPIRICAL SETTING ................................................................................................................................... 16 3.2 RESEARCH DESIGN ..................................................................................................................................... 16 3.3 PHILOSOPHY OF SOCIAL SCIENCE ............................................................................................................... 17 3.4 DATA GENERATION METHOD ..................................................................................................................... 18 3.5 DATA ANALYSIS METHOD .......................................................................................................................... 19

3.5.1 Partial Least Square (PLS) ................................................................................................................. 19 3.5.2 Indicator Reliability ............................................................................................................................ 20 3.5.3 Internal Consistency Reliability .......................................................................................................... 21 3.5.4 Convergent Validity ............................................................................................................................ 21 3.5.5 Discriminant Validity .......................................................................................................................... 22 3.5.6 Bootstrapping ..................................................................................................................................... 22 3.5.7 Blindfolding ........................................................................................................................................ 23 3.5.8 Control Variables ............................................................................................................................... 23

3.6 RESEARCH ETHICS ...................................................................................................................................... 23 4. FINDINGS AND ANALYSIS ................................................................................................................. 25

4.1 DESCRIPTIVE STATISTICS ............................................................................................................................ 25 4.1.1 Sample Characteristics ....................................................................................................................... 25 4.1.2 Indicator Characteristics .................................................................................................................... 28

4.2 ROBUSTNESS OF THE DATA ......................................................................................................................... 30 4.3 MAIN FINDINGS .......................................................................................................................................... 34

5. DISCUSSION ......................................................................................................................................... 39 6. CONCLUSION ....................................................................................................................................... 43 APPENDIX ................................................................................................................................................. 45 REFERENCE LIST ................................................................................................................................... 47

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Acknowledgements We wish to express the sincerest gratitude and appreciation towards our supervisor, professor

Thomas Hartman, whose feedback has been really helpful during the writing process. We

would also like to acknowledge the valuable inputs received from our peer reviewers, who did

the best to bring improvement through their suggestions. Further, we would like to thank the

staff of Stockholm University Statistical Institution for the enlightenment in regards to the PLS

research model. Finally, we extend our gratitude to the survey respondents, this research would

not be possible without their participation.

Alejandra Chavez

Michael Duberg

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Abstract Purpose: In literature, there is not sufficient research of the impact of Enterprise Resource

planning (ERP) usage in firm performance for small and medium sized Swedish companies.

Therefore, the purpose of this paper is to provide clarifications regarding this topic by

investigating two research questions: What factors drive ERP usage in small- and medium-

sized Swedish enterprises? Does ERP usage affect Firm Performance in small- and medium-

sized Swedish enterprises?

Design/Methodology/Approach: To approach our research questions 10 hypotheses are

constructed based on previous studies. Further, to gather the data required to test these

hypotheses, a survey was sent to 1000 Swedish small- and medium-size enterprises which

generated 100 responses. The data was later analyzed using a Partial Least Square Structural

Model.

Findings: The first outcome of this study is that the main drivers of ERP usage in Swedish

small- and medium-sized enterprises are Top Management Support and Effective Project

Management. The second outcome is that ERP usage has a significant positive impact on Firm

Performance.

Contribution/implication: The main practical contribution derived from our results is that

small- and medium-size firms should focus on Top management support and Effective project

management in order to increase their ERP usage, which in turn could lead to greater levels of

firm performance. In the theoretical spectrum, we contribute to the literature by enhancing the

importance of effective project management not previously tested in the ERP usage context

and by adding question marks regarding the effect of certain variables on ERP usage.

Key words: ERP usage, SME, Firm performance, PLS, Post-implementation.

Paper type: Research paper.

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1. Introduction

1.1 Background

Economic globalization along with the rising competitiveness in the global markets has under

the past decades been accompanied by changes in how management systems are being adopted.

Companies have in the past mainly emphasized two competitive factors; price and quality

(Yusuf et al., 2004), but over the past few years information systems have started to play a big

role in achieving competitive advantage (Budiman, 2021). Large companies worldwide have

opted for integrating their business processes and functions in one central database to allow

information to be accessed from many different organizational positions (Dechow &

Mouritsen, 2005) and enabling the possibility of faster decision making (Budiman, 2021).

A package of integrated systems is known as an Enterprise resource planning (ERP) system.

Such a system has been suggested to facilitate unprecedented levels of organizational

integration (Dechow & Mouritsen, 2005) by allowing the integration of different components

in a firm (such as production, logistics, distribution, inventory, transport, accounting, etc.) into

a common system. At the same time, this integrated package promotes the controlling and

automatization of business activities involving management of human resources, sales

deliveries, invoices, etc. (Bălună, 2008). Beyond the information processing facilities that

usually are related to the use of ERP systems, there are other advantages related to this

information software such as reduced costs, high functionality and competitive advantages in

supply chain for firms (Yusuf et al., 2004).

Some challenges following the implementation of ERP systems are the importance of

organizational fit and adaptation. For example, firms in which various departments either are

correctly organized or have their own agendas/objectives in conflict with each other, are most

likely to not fully achieve the benefits of ERP (Yusuf et al., 2004). The organization also faces

an extensive list of risks in the post-implementation stage which include: technical pitfalls,

emergent business needs, inadequate user behavior, and deficient system design (Peng and

Nunes, 2009). Since these challenges occur when implementing ERP, it is hard to execute

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everything according to plan. Panorama Consulting Solutions (2018) reports that 64% of ERP

projects exceed their budget and 28% of ERP projects fail.

Another challenge faced in the post-implementation phase is related to a decrease of

productivity during the first three to nine months of implementing a ERP system. (Caldwell,

1998). Some common reasons for the productivity to decrease are: the users are not trained and

supported adequately to use the new system, the system not being sufficiently tested yet, and

the objectives of the system have not been adequately communicated to the users (Nicolaou,

2004). Hence the nature and timing of the ERP post-implementation processes is stressed to

have a significant impact on a firm's financial performance (Nicolaou, 2004) where ERP

implementations are most likely to have positive financial and productivity effects only if

installed correctly (Dechow & Mouritsen, 2005) and possibly after going through a “learning

curve” to benefit from their investment (Dechow & Mouritsen, 2005).

Additionally, some scholars accentuate some differences in terms of risk depending on firm

size when adopting ERP systems (Laukkanen et al., 2005, A. Mabert et al., 2003). For example,

the adoption of ERP systems in small- and medium-sized enterprises (SMEs) are denoted to

represent a greater resource commitment; as SMEs are often characterized for having limited

resources in terms of time, skills, and money compared to larger companies (Mabert et al.,

2003). Moreover, smaller companies are typically seen to be spending a higher proportion of

their budget on software, while larger companies choose instead to invest their revenues on

ERP implementation teams (Mabert et al., 2003).

As the risks faced by SMEs were greater than the ones related to large companies; small- and

medium- sized companies were in the past reluctant to innovate in ERP systems (Deep et al.,

2008). However, within the past decades, there have been large advancements in information

technology and ERP vendors have been focusing more on creating systems that can be used

for SMEs. The adoption of ERP systems has therefore become more viable for SMEs, and

consequently there is now an increased need to study ERP in the context of SMEs

(AlMuhayfith & Shaiti, 2020).

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1.2 Problematization

Research on the implementation stage of the ERP process is extensive, however little research

has been done on the post-implementation stage. Regardless of whether the ERP

implementation was successful, for many experts and researchers, the real test is whether the

system is actually used or not. Thus, even if ERP is implemented successfully, if it is later not

used or accepted by the users, the ERP project is considered a failure (Haddara and Zach, 2011).

There is therefore a need to further investigate the post-implementation phase of the ERP

process to fully understand how ERPs should be used to be the most effective.

Two recent studies investigate ERP usage in the post-implementation stage, one examining

Portuguese SMEs written by Ruivo et al. (2014), the other one investigating Saudi Arabian

SMEs written by AlMuhayfith & Shaiti (2020). Ruivo et al. (2014) studied the determinants

that drive ERP use and value when firms are in the post-implementation stage. They found that

the main drivers of ERP use were compatibility, complexity, best-practices, efficiency,

training, and competitive pressure. They also found that system compatibility was the main

driver of ERP value. AlMuhayfith & Shaiti (2020), on the other hand, found that management

support, user satisfaction, and training have an impact on effective ERP usage and that ERP

systems improve SMEs business performance.

Since there are only a few studies that investigate ERP usage and these solely study SMEs in

specific countries, there is a need to research this further in different nationality contexts.

Sweden has a very different culture and business environment compared to Portugal and Saudi

Arabia. Therefore, we believe that studying SMEs ERP usage in Sweden can create a more

profound understanding of this phenomenon and help us evaluate if the results from previous

studies are applicable to another context, thus, filling a research gap. This study also aims at

investigating factors not thoroughly tested in the ERP usage context, such as Clear vision and

planning, Effective communication, Effective project management, and Data quality and

integrity.

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1.3 Research Questions

As mentioned above the lack of studies investigating ERP usage and its impact on firm

performance of Swedish small and medium sized for companies have granted incentive of

developing the following research questions:

Research question 1: What factors drive ERP usage in Small- and medium-sized Swedish

enterprises?

Research question 2: Does ERP usage affect Firm Performance in Small- and medium-sized

Swedish enterprises?

1.4 Contribution

The purpose of this study is to examine the factors that affect ERP systems and its impact on

firm performance for Small- and medium- sized enterprises (SME). In this research paper the

theoretical basis of ERP usage and implications related to firm performance is discussed. An

empirical test of a novel conceptual model is conducted in which technological, organizational,

and processing factors are evaluated to test our hypotheses. Henceforth, the findings of this

research paper intend to contribute both empirically and theoretically to the field of information

systems and the field of management control.

1.5 Outline of the Thesis

The research paper is divided into 6 chapters. In the first chapter we introduce our research

topic and review the problematization existent in the current research linked to ERP-usage and

firm performance. The second chapter contains the literature review, previous research on the

topic and the conceptual framework in which we ground the hypotheses that are tested.

Furthermore, chapter three describes the empirical methodology used to approach our research

questions along with the research design. Thereupon, the findings and outcomes of our research

are displayed in Chapter 4 and later discussed in Chapter 5. Lastly, in Chapter 6 we conclude

the result of the study and provide implications for future research.

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2. Literature Review and Theoretical

Framework

2.1 Enterprise Resource Planning (ERP)

ERP is a computer software which can be seen as a developed object for mapping all processes

and data into an integrated package that delivers solutions for businesses from a single

information and IT structure. This package is often tailored to the specific requirement of the

firm. Although many may relate this customization as negative, it is just the unique design and

configuration that ERP acquires which distinguish this software from other packages in the

market (Klaus et al., 2000). The ERP software possesses an underlying database which

integrates master and transactional data, eliminating the need for multiple entries of the same

data, besides it allows all users within a firm to share and transfer information freely

(Muscatello et al., 2003). Further, the ERP-system supports the core processes of the business

and administrative functionality (Klaus et al., 2000) and it is an exceptional tool for reducing

inventory cost, improving efficiency, increasing profitability, and most importantly ERP-

systems are found to be a key factor for improving customer satisfaction (Muscatello et al.,

2003).

Even though there are several advantages that can be encountered when implementing ERP-

systems correctly, it should be noted that not all companies are able to experience them, since

28% of implementations end in failure (Panorama Consulting Solutions, 2018). This is due to

the success of implementation not merely depending on purchasing and installing the

technology; but also depending on planning, reengineering, and developing extensive training

programs that will make the user more proficient at operating the proposed system at the post-

implementation stage. Such activities are usually very expensive for SMEs and, if not carried

out accordingly, could lead to total abandonment and losses for the project. SMEs are therefore

more susceptible to these failures and less likely to survive an unsuccessful implementation of

ERP-packages (Muscatello et al., 2003).

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2.2 Phases of ERP

ERP projects are a process that can be conceptualized into different phases. There are several

alternative ways that researchers have defined the phases, although they usually consist of 3-6

separate phases. There is, for instance, a three-phase model suggested by Parr and Shanks

(2000), a five-phase model proposed by Ross and Vitale (2000), and a six-phase model

suggested by Cooper & Zmud (1990). This study will apply the six-phase model (Cooper &

Zmud, 1990) due to its sophisticated categorization of the phases after ERP implementation.

Even though this categorization model is dated from the 1990s, it is still being cited by current

ERP studies, for example, Maas et al., (2018). As the first three phases of the model regard the

pre-implementation stages not handled in this study, the focus will mainly reside on the last

three phases; Acceptance, Routinization, and Infusion.

The acceptance is the first post-implementation phase and it occurs when the system is first

ready to use. During this phase the users are gaining knowledge about the system and observing

its benefits to gain acceptance. The system is continually being modified to solve the users’

problems and to make it easy to use. The next phase is routinization, in this phase the users

have accepted the system and are starting to standardize their processes using the system.

Following this is the last phase called the infusion phase. During this phase the ERP system

becomes a deeply integral part of the organization’s processes. This is the phase in which the

system enhances the organization’s performance the most (Cooper & Zmud, 1990).

2.3 Previous Research on ERP Usage

ERP use is a critical part in making the system successful (Nwankpa, 2015). To ensure that an

organization uses the ERP system adequately it is important that they have the acceptance of

the system users. The acceptance of the users and level of ERP use have been researched by

several scholars. This research mainly uses the technology acceptance model (TAM) which

focuses on the factors perceived usefulness (PU) and the perceived ease of use (PEOU) and

their effect on usage from the perspective of the user. PU and PEOU have been shown to have

an effect on the users’ intentions towards the ERP system (Youngberg et al., 2009, Hsieh &

Wang, 2007, Nah et al., 2004), therefore when the ERP system is perceived to be easy to use

the users will be more likely to extend their usage beyond what is required by management

(Hsieh and Wang, 2007). Ease of use requires that the users are educated on the system's

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capabilities and trained to handle it effectively. When the users possess PU and PEOU they are

more likely to accept the system which is when the firm can benefit the most from the system.

(Nah et al., 2004)

There have also been studies that investigate how various firm characteristics have an effect on

ERP usage. Top management support is perceived as a crucial factor in determining the ERP

usage and therefore also the system's success. (Somers and Nelson, 2001, Akkermans and van

Helden, 2002). The factor has been tested in studies by Almuhayfith and Shaiti (2020) and Lin

(2010), which have confirmed its effect on ERP usage. Zhang et al., (2013) provide the

explanation that managers have a high hierarchical position and therefore their acceptance of

the system will influence the employees attitude towards the system which then affects the

level of usage. To be able to use the ERP system, it can be required that the employees are

trained (Nah et al., 2004), this has been tested by Ruivo et al. (2014) and Almuhayfith and

Shaiti (2020) who found training to significantly affect ERP usage, in which their sample

consisted mainly of SMEs that had implemented ERP during the last 5 years. Training is the

most crucial during the routinization period of the ERP process. Most firms implementing ERP

experience a 6-month learning curve in which training is needed to familiarize the employees

with the new system (Somers and Nelson, 2001). Another factor that has been demonstrated to

have an effect on ERP usage is technical resources. This refers to the firm's technical

capabilities, such as competence, hardware, software, and network application. (Nwankpa,

2015)

ERP usage has also been tested by some researchers in regards to how it affects firm

performance. Ruivo et al. (2014) and Almuhayfith and Shaiti (2020) found that there is a

positive relationship between ERP usage and firm performance. Kallunki (2011) found that the

ERP system does not directly have a positive effect on a firm's non-financial performance;

however, with the mediated effect of formal control system this relationship is significant.

Formal control is a part of the management control system in combination with informal control

and Ruivo et al. (2014) found management control to be the most important factor in producing

firm performance.

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2.4 Theoretical Framework: ERP Usage Model Previous studies on ERP usage have used frameworks such as, Technology acceptance model,

TAM, (Youngberg et al., 2009, Hsieh and Wang, 2007, Nah et al., 2004), Technology–

organization–environment framework (Ruivo et al., 2014), or a self-developed framework

using contingency factors (Almuhayfith and Shaiti, 2020). This study however uses a relatively

novel ERP usage model that combines Critical Success Factors theory, Contingency theory and

Expectancy theory.

This model was developed by Nofal & Yusof (2016) and consists of Organizational, Process,

and Technological characteristics that influence ERP usage and thereby are proposed to have

an impact on the overall firm performance. The characteristics in the organizational category

are: Clear vision and planning, Top management support, and Effective communication. The

process category consists of: Effective Project management, Change management, Teamwork

and composition, and Training. The Technological category consists of: Data quality and

integrity, and IT-infrastructure. To summarize the model focuses on 9 firm characteristics and

investigates their relationship with ERP usage which in turn have a proposed relationship with

firm performance. The model is visualized in Figure 1.

Figure 1: Visualization of the ERP usage framework.

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2.5 Hypotheses

2.5.1 Clear Vision and Planning

Having a clear business plan and vision during the ERP life cycle is needed for an ERP project

to be successful, since this is used as a guide to give the project a direction. The organization

should also have a clear business model which instructs the organization’s members of the

strategy which will be used after implementing the ERP system. (Loh & Koh, 2004, Ngai et

al., 2008, Nah & Delgado, 2006). The business plan should provide an outline for strategic and

tangible benefits, and plans regarding resources, cost, risks and timeline which is crucial for

the organization to maintain their focus on business benefits (Nah et al., 2001).

An organization implementing ERP should have a clear business model which states how the

organization is going to operate posterior to the implementation. There has to exist justification

for investing in the ERP system and this should be grounded in something the organization had

trouble with, and the change should be in line with the direction the firm is heading towards

(Nah et al., 2001). The mission of the ERP project should also be clearly specified, and it should

be connected to the business needs. Firms also need to pinpoint their goals and benefits, which

consistently need to be followed up on. Having a clear business plan makes operating easier

thus it has an impact on the way the members of organizations do their work (Nah et al., 2001).

Hence, the following hypothesis is proposed:

Hypothesis 1: Firms that have a higher level of clear vision and planning are more likely to

use ERP.

2.5.2 Top Management Support

Top management support has been shown to play a crucial role in successfully implementing

ERP projects (Fawaz et al., 2008, Croteau & Li, 2003). Top management support means that

the top managers or leaders of an organization prioritize the project and thus engage in actions

that aid the process of achieving the goals of the project (Martin, 1982). One way of engaging

top management in a project is by linking the project's success to management's bonuses (Nah

et al., 2001).

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The two primary aspects of top management support to consider when implementing ERP, is

firstly, the extent in which top managers offer the proper leadership to conduct the project, and

secondly, the extent in which the necessary resources related to ERP are provided (Zhang et

al., 2002). Therefore, it is critical for top managers to publicly and openly establish the project

as a main priority for the organization (Nah et al., 2001). Also, they must be prepared to allocate

resources such as proper human capital and a suitable amount of time to conduct the project.

Thus, the following hypothesis is developed:

Hypothesis 2: Firms that have a higher level of Top management support are more likely to

use ERP.

2.5.3 Effective Communication

Implementing ERP successfully requires clear and effective communication (Nah et al., 2003).

To have effective communication, it is necessary that the expectations at all various stages of

the organization are communicated (Nah et al., 2001). Communication also refers to formally

developing teams that handle the ERP project and provide the rest of the members in the

organization with progress and status updates to achieve great levels of sync (Nah et al., 2003).

When the project team and top management have constant communication, it facilitates quick

decision making and helps deter the project from being delayed when it is implemented (Umble

et al., 2003). It is also important that the user's input is managed so that the user's requirements,

comments, reactions and approval are communicated and taken into consideration (Rosario,

2000).

It has been shown by Esteves and Pastor (2001) that effective communication is an important

characteristic because it impacts the success and acceptance of technology in the context of

implementing ERP. Garg and Garg (2014) found effective communication to be one of the

most influential factors for the success of ERP in the retail industry. They establish that having

open and honest communication throughout the organization provides the system users with

the information they need and prevents the presence of unfounded rumors that could deter the

progress of ERP in the organization. Therefore, the following proposition has been made:

Hypothesis 3: Firms that have a higher level of effective communication are more likely to use

ERP.

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2.5.4 Effective Project Management

Project management regards how knowledge, techniques, tools, and skills can be applied to

project activities to fulfill the project's requirements from the stakeholder. In a firm there can

be opposing demands in regards to scope, time, cost, and quality. There can also be competing

views among the stakeholders' needs and expectations. Therefore, to ensure that the project is

successful while also meeting the various demands, it is critical that an organization’s structure

and method of managing projects is aligned with the objectives of the ERP project (Carton et

al., 2008). To be able to sufficiently manage a project, the manager needs to use both strategic

and tactical project management processes to guide the direction of system use (Clark et al.,

2007).

Project management activities are relevant in all phases of the ERP life cycle. The ERP project

team should consist of the organization’s best people, there should also exist a mix of

consultants and internal staff, since this helps the internal staff develop the required skills to

handle the ERP system (Sumner, 1999). Planning and controlling of an ERP project is a

function of the characteristics the project has, for example project size, structure, and

experience. Because ERP projects involve issues such as organizational, human, political, as

well as hardware and software, it often becomes a huge project that is very complex and risky.

Effective project management is therefore vital for the success of the project and is a big part

in securing initial acceptance in the organization (Somers and Nelson, 2004). Hence, we create

the following hypothesis:

Hypothesis 4: Firms that have a higher level of effective project management are more likely

to use ERP.

2.5.5 Change Management

With regard to ERP, change management is one of the most widely recognized Critical success

factors in ERP implementation. This is because a firm that starts utilizing an ERP system will

be met with a lot of changes. It is therefore important that the firm is good at change

management in order to handle these changes. Change management can be seen as a technique

or strategy that helps a firm manage their change from old systems to new ones in a sustainable

way. Regarding the point of ERP usage, change management plays a role in making sure the

organization and in particular the employees that will use the ERP system are prepared for the

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several changes that come with it. To prepare for the changes, the organization should create a

formal change management program which will guide the organization in the case of problems

such as employee resistance, confusion, redundancies, and errors that happen because the users

are not accustomed to the new system (Nah and Delgado, 2006). Hence, the following

hypothesis is formed:

Hypothesis 5: Firms that have a higher level of change management are more likely to use

ERP.

2.5.6 Teamwork and Composition

Teamwork is conceptualized as a group of organized systems involving cooperation actors that

aim to achieve a specific goal. Within the team, individuals are assigned a set of activities/roles

where the most important aspect, beyond solving problems in the firm, is to identify the

individual's own tasks relative to the goal and to the other members. This role expectation is

part of the organization culture and therefore agreeing on what the group expects from each of

its members is crucial for effective teamwork (Verville & Halingten, 2003). Although task

determination is necessary to facilitate coordination among project members, the

implementation of Enterprise systems is characterized for being goal-interdependent (Jiang et

al., 2019). This means that the success of the ERP system is achieved through a collective

pursuit of the team’s common goal. Factors that affect teamwork have been the subject of

extensive research (Hwang, 2018). The most commonly predominant characteristics of

teamwork performance in the literature are: team composition, good collaboration and

communication, task characteristics and team members behavior (Hwang, 2018). Hence, a

careful selection of each member of the team along with a clear definition of their task and

responsibilities post ERP is necessary to achieve an acceptable level of performance (Verville

& Halingten, 2003). Thus, the following hypothesis is constructed:

Hypothesis 6: Firms that have a higher level of teamwork and composition are more likely to

use ERP.

2.5.7 Training

Training programs play a considerable role in the correct usage of ERP since these enterprise

systems require massive re-engineering of the organization. Several studies found that the

initial user experience towards ERP is often the most important and that employees reported to

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feel more comfortable when they receive appropriate comprehensive training. A

comprehensive training is one that does not merely focus on the technological aspect of the

system usage, but also involves the work processes related to it. Still, negative user attitudes

are found when firms opt for “commercial” ERP packages rather than customized ones in order

to reduce training costs. However, training the employees is assumed to be beneficial since the

operational and cultural issues encountered under the ERP usage are reduced along with

productivity dips and mishandled in customers’ orders (Bradley & Lee, 2007).

Hypothesis 7: Firms that have a higher level of training are more likely to use ERP.

2.5.8 Data Quality and Integrity

Data quality ensures the usefulness of the business data in a company. The term data quality is

defined as “the measure of the agreement between the data views presented by ERP and that

same data in the real world” (Park & Kusiak, 2005, p.3962). By this, the data inserted to ERP

systems need to fulfill a purpose to be considered as quality data. Poor data quality on the

contrary, could be associated with less usage of ERP systems since the data inserted in the

system is automatically linked to each of the ERP modules, which could negatively affect the

function and performance of the firm (Haug et al., 2009). Therefore, there is a need for

evaluating the ERP data and ensuring it is inserted correctly. Data usefulness and usability are

categorized (Strong et al.,1997) by (1) intrinsic data quality which covers the accuracy,

objectivity, reputation, etc., (2) contextual data quality that enhances relevance, timeliness, and

amount of data, (3) accessible data accuracy which cover the accessibility and access security,

(4) representational data which cover the interpretability, ease of understanding, etc. In case

one of the dimensions that covers data quality is disrupted it will most likely lead to problems

in its usefulness and usability. Intrinsic aspects in data quality are considered as the most

important followed by contextual, accessible and representational (Strong et al., 1997); while

other studies rather enhance the intrinsic and accessibility aspects as the most relevant data

quality dimensions (Haug et al., 2009). Thereby, the following proposition is made:

Hypothesis 8: Firms that have a higher level of data quality and integrity are more likely to

use ERP.

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2.5.9 IT-infrastructure

The success of a business project is suggested to be significantly impacted by the IT

infrastructure (Woo, 2007). The IT infrastructure must have sufficient hardware for the project,

this implies that there is enough storage space that can handle the new large volumes of data

that comes with the system. The hardware also has to be able to handle the processing power

to run the project's new system. There is also a necessity for the organization's networks to be

sufficient, this entails that they are reliable, fast, and secure. If an organization ensures that

these needs are fulfilled, they are more likely to experience success in their implementation

stage along with their usage and maintenance stage (Nguyen, 2011).

An organization that implements ERP systems will have to transform a lot of their business

processes and information systems previously separated into a united IT infrastructure. Since

this often causes big transformations, it is important that the ERP software system that the

organization chooses is matched with the proper hardware. The hardware that is required is

validated by the vendor of the ERP software system (Bhatti, 2005). Results from Al-Mashari

(2003) and Jarrar et al. (2000) have indicated that the success of an ERP system is highly

dependent on the organization's IT infrastructure, networks, and hardware. Hence, the

following hypothesis is proposed:

Hypothesis 9: Firms that have a higher level of IT infrastructure are more likely to use ERP.

2.5.10 ERP Usage

ERP usage considers how much the information system is being used by the organization. This

is usually measured by the number of frequent users the system has, the time per day spent

using the system, or the number of reports that are generated per day by using the system

(Ruivo, 2014). The objective of implementing ERP systems is often to improve the

organizational performance by enhancing the quality of analysis and reporting (Elbashir et al.,

2008). The more the ERP system is used, the more likely it is to create capabilities that are

valuable, thus improving firm performance (Ruivo, 2014). Zhu and Kraemer (2005) suggested

that there is a connection between how much the system is used and how much the system

impacts the firm, therefore an ERP system will only improve the firm's performance if it is

actually used. Hence, the following hypothesis is proposed:

15

Hypothesis 10: Firms that have a higher level of ERP usage are more likely to have high firm

performance.

2.6 Firm Performance and ERP

Company managers are increasingly under pressure to improve firm performance (Hooshang & Cyrus,

2010). Several measurements have been developed in order to estimate business performance and

profitability. Most observed proxies in firm performance are the ones that successfully combine a

balanced amount of operational and financial measurements (Kaplan & Norton, 1992). Operational

measurements refer to the drivers of performance which includes customer satisfaction, internal

processes, and organizational innovation & improvement activities (Kaplan & Norton 1992); while

financial measurements are the ones that reflect the outcomes of the operational drivers (Kaplan &

Norton 1992). Within the organizational innovation and improvement activities most recent studies

have put emphasis on technological innovations to integrate business activities with ERP systems, as a

driver in ensuring market leadership due to cost reduction and increased customer satisfaction and

productivity (Hooshang & Cyrus, 2010). Therefore, the performance of the firm could be measured by

using some operational indicators for productivity, use/customer satisfaction, intern control along with

financial indicators for cost reduction in linkage with ERP usage.

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3. Method

3.1 Empirical Setting

The empirical setting of this study is that it investigates Swedish SMEs. This means that firms

investigated in this study can have any geographical location within the Swedish borders. To

define SME, we have used the European Commission's (2020) recommended definition, which

considers (1) staff headcount and (2) either turnover or balance sheet totals. A firm's size is

hence classified in the following way:

Table 1: Firm size classification

Firm category Staff headcount Turnover OR Balance sheet total Medium <250 ≤ € 50 m ≤ € 43 m

Small <50 ≤ € 10 m ≤ € 10 m Micro <10 ≤ € 2 m ≤ € 2 m

From these size classifications we have chosen to only investigate medium and small firms.

The reason for this is that micro firms have a low staff count, which makes them less likely to

be able to answer some of our questions, in particular regarding the factor teamwork and

composition. In Sweden there are over one million firms, 6 295 of those are medium-sized and

37 324 are small-sized (Holmström, 2020). This means that this study's sample will be gathered

from a population that consists of 43 169 firms.

3.2 Research Design

This study uses a cross-sectional research design, this means that a sample from the population

was drawn once at a specific time (one month) and then studied (Bryman & Bell, 2011). This

study is therefore only focusing on investigating the impact of independent variables on the

dependent variables at a certain point in time to provide a snapshot of the outcome and the

characteristics associated with it (Kate Ann Levin, 2006). The time aspect is thereby not a point

of interest. This is used because it is much less time consuming than for example longitudinal

study, and given that the study uses multiple different variables and seeks to gather as big a

sample as possible, a cross-sectional study is most fitting. (Bryman & Bell, 2011). However, it

is necessary to clarify that, cross-sectional studies are also limited in the sense that it does not

give indication of the sequence of the events which makes it difficult to assess causal inference

17

and at the same time could differ in result if the sample collected was drawn in another time-

frame (Kate Ann Levin, 2006).

3.3 Philosophy of Social Science

The ontological position adopted in this study is based on Internal Realism. This viewpoint

assumes that there is a single reality that exists independent of the researcher and the

opportunity to directly obtain evidence of this reality is not possible. The evidence is instead

gathered indirectly (Easterby-Smith et al., 2015). This is fitting to this study's methodology

since it uses surveys to gather data. In the survey, different firm characteristics are measured,

Top management support, for example, is a characteristic of a real phenomenon that is

independent of the researcher and regards the amount of support exerted by top management.

This characteristic is not possible to measure directly, instead, proxies are used to indirectly

capture the reality of this characteristic.

The epistemological view used in this study is grounded on positivism. This is the most

commonly used philosophical standpoint when conducting large scale-survey research

(Easterby-Smith et al., 2015). Positivism is a frame of the philosophical methodology that aims

to develop generalized findings from field observations and experimentation. Within the

context of methodological science, positivists are assumed to gather data objectively and by

remaining external and independent of the phenomena being researched (Bryman and Bell,

2011). Moreover, the outcomes should be replicable factual generalizations of the social

phenomena, hence it is important that the validity and reliability of the data is adequately tested

(Easterby-Smith et al., 2015). Because of this, a large section of this study's analysis will be

focusing on the validity and reliability of the data to ensure that the survey instruments are

stable and measure what it is intended to. Knowledge through the positivism standpoint is

usually gained from a deductive approach which involves the formation of hypotheses to form

a starting point of the phenomenon, these are then sought to be confirmed or disconfirmed

(Easterby-Smith et al., 2015). This study is therefore using 10 hypotheses based on previous

research as the starting point for the study's analysis.

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3.4 Data Generation Method

To generate data, a quantitative method has been applied in this study. This is fitting

because the study seeks to investigate how some already theorized factors impact the

dependent variable, therefore a big data set is needed to confirm/reject the impact of these

factors. Collecting sufficient data will make it possible to generalize the outcomes and also

provide us with more precise results to test the accuracy of our hypothesis (Bryman &

Bell, 2011).

To determine which companies can participate in the study, a random selection of 1000

Swedish small- and medium- sized companies that fit the descriptive criteria mentioned earlier

were selected from the database Bisnode Infotorg. Random selection is used to avoid subjective

criteria from occurring and to ensure that all members of the population have an equal chance

of participating in the sample (Bryman & Bell, 2011).

The data was later generated by conducting a survey containing 35 statements in a Likert scale

format (1=strongly disagree, 2=disagree, 3=neutral, 4=agree 5=strongly agree). This survey

was sent out to the 1000 selected companies from mid-March, 2021, until mid-April, 2021,

with a response rate of 10%, resulting in a sample of 100 companies. Surveys are instruments

used to quantitatively evaluate subject data (Eysenbach & Wyatt, 2002). Some of the

statements used in the surveys were previously tested by other scholars. However, as the

theoretical framework used to approach our research question is quite new, some of the

variables have not been tested before in the usage context. Therefore, there was a need to

construct new measurements for those variables. This was performed by analyzing what factors

previous research deemed important for the variables and then formulate survey statements

based on this.

Before the actual survey was sent out, a pilot study was performed. The reason behind this was

mainly due to the survey being based on self-completion, which means that the respondents

will not be able to, without delay, ask questions about the survey to clear up any confusions

(Bryman & Bell, 2011). The pilot survey therefore facilitates acquiring feedback on the survey

statements before initiating the data gathering. Three persons were contacted to partake in the

pilot study. The participants were instructed to answer the survey and afterwards provide

feedback regarding any confusion and/or recommendations for changes they had. This was an

19

important step in this research since some of the feedback really helped improve the survey in

elements such as wording, structure, and content.

To conduct the survey, an online method was chosen, this was deemed the most fitting due to

the low cost and great administrative benefits it brings. It also ensured that the survey

statements could be made compulsory which is necessary for conducting the analysis. The

survey was sent by email, which meant that the email addresses of all firms needed to be

gathered beforehand. These were collected through the firm's website. The email addresses of

CEOs were prioritized because the survey questions required the respondent to have general

knowledge of the different processes in the firm and also be able to answer questions regarding

top management support. However, since the email addresses of all CEOs could not be found,

the survey was then either sent to the next best fitting person or the firm's general email.

3.5 Data Analysis Method

3.5.1 Partial Least Square (PLS)

The statistical model applied in this research is known as Partial Least Square Structural

Equation modelling (PLS-SEM) most commonly known as PLS. PLS models are widely used

in the fields of strategic management, business research, information systems, (Nitz & Chin

2017) due to the high degree of statistical power in research. Some of the advantages in using

Structural equations models is that this method enables the estimation of complex models with

many constructs without imposing distributional assumptions of the data (Hair et al., 2019) by

computing principal component analysis with ordinary least square regressions (Aparicio,

2011). Hence, PLS offers the possibility to place multiple predictors and criteria to construct a

set of latent variables to model measurement errors for observed variables and identify the

relationships between them, all in one single model (Nitz & Chin 2017).

To determine the sample size required to run a PLS model and prevent statistical errors there

are some guidelines to comply with (Wolf et al., 2013): (a) a minimum sample size of 100 or

200 observations. (b) 5 or 10 observations per indicator. (c) 10 cases per variable. Since our

research contains 9 latent variables, we will adhere to a sample size of 100 observations (as

stated in guideline c).

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When performing a PLS-SME analysis, there are two different measurement models that can

be used, either a formative or a reflective one. This study uses reflective indicators and will

hence use the reflective measurement model. In this model, there is a direct relationship from

the constructs (latent variables) to the indicators. The indicators are here regarded as error-

prone demonstrations of the latent variable (Sarstedt & Ringle, 2017). The equation of the

relationship can be illustrated as the following:

x = lY + e

In this equation, x is the indicator which we have observed from our survey questions, l is the

loading which is a regression coefficient that determines how strong the relationship between

x and Y is, Y symbolizes the latent variable, and e is the random measurement error (Sarstedt

& Ringle, 2017). Reflective indicators should all represent the latent variable’s conceptual

domain, therefore, the different indicators should be highly correlated which can be an indicator

of internal consistency (Christophersen & Konradt, 2011).

Besides internal consistency, we will also examine reliability, construct validity, convergent

validity, discriminant validity to check the robustness of the data. The software used to run our

statistical model is SmartPLS which was specially developed by Ringle et al. (2005) to run

structural equation models.

3.5.2 Indicator Reliability

Loadings

Loadings are used in a reflective measurement model to measure how much the indicators

contribute to the latent variable (Hair, et a 2017). The indicator loadings should preferably be

over 0.7 however 0.6 is also acceptable (Chin, 1998, Yana et al., 2015). This is because a

loading of more than 0.7 implies that 50 percent of the variance in the indicator is a

consequence of variance in the latent variable. If a loading value is low, it could be due to; the

indicator being insufficiently formulated, the indicator being inappropriate, or shifting the

context of the indicator in an inappropriate way (Hulland, 1999).

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3.5.3 Internal Consistency Reliability

Composite Reliability and Cronbach alpha

Reliability measures are needed to estimate the statistical consistency of the test. One of the

most commonly used measures to test reliability is by estimating the Cronbach coefficient. This

measurement is calculated on the basis of the number of contributory variables and the extent

of correlation between them (Berthoud, 2010). Cronbach alpha and composite reliability values

that are equal or higher than 0.7 are mostly acceptable in research, but even values of 0.6

upwards could be considered as sufficient (Berthoud, 2010, Hair et al., 2017). A Composite

reliability value of above 0.95 is considered too high since this indicates that the indicators

measure the same phenomenon which raises doubts regarding the validity (Hair et al., 2017).

The Cronbach coefficient however, has been extensively evaluated and highly criticized due to

their lower bound and therefore underestimation of true reliability (Peterson & Kim, Y. 2013).

An alternative estimate for Cronbach alpha is Composite Reliability (CR). One of principal

differences between Cronbach alpha and Composite Reliability is that Cronbach produces

lower values since the loading or weights for alpha are constrained to be equal; while

Composite Reliability (CR) allows weights to vary, enabling the possibility to overcome some

of the limiting assumption of coefficient alpha (Peterson & Kim, Y. 2013). Cronbach alpha is

sensitive to the number of indicators that the variable has, much more so than composite

reliability (Hair et al., 2017). Still, some scholars may consider Composite Reliability as too

liberal while others may consider its equivalent, Cronbach, as too conservative (Hair et al.

2019). True reliability however, is assumed to place between these two estimators and therefore

we have chosen to take into account both measures to estimate reliability.

3.5.4 Convergent Validity

The purpose of convergent validity testing is to estimate the level of correlation existing

between the multiple indicators of the same construct. The metric chosen to assess convergent

validity in our study is Average Variance Extracted (AVE).

Average Variance Extracted (AVE)

The extent in which each construct measure converges to explain the variance of its indicators

is measured by using AVE. An acceptable AVE value should be 0.5 or higher, which means

that the construct explains at least 50 percent of the variance of its items. If the AVE is lower

22

than 0.5 the variance is explained more by the measurement error than the construct, hence the

validity of the indicators and construct is unreliable (Fornell, & Larcker, 1981).

3.5.5 Discriminant Validity

To evaluate the extent to which the constructs actually differ from one another empirically as

well as the degree of difference between overlapping constructs, we will conduct a discriminant

validity test by using the Fornell-Lacker criterion and cross-loadings factors.

Fornell-Lacker Criterion

According to the Fornell-Lacker criterion, the AVE metric could be also used to assess

discriminant validity by comparing the square root of every AVE value belonging to each latent

construct with the correlation of latent constructs. Followed by this, each construct's AVE

should be larger than any correlation coefficient, if not, the items with smaller AVE than

correlation coefficient will not measure well separated latent concepts (Kim & Park 2016). This

estimation makes it possible to observe if the items of the construct could explain more variance

than the items for the other constructs (Zait & Bertea, 2011).

Cross-loadings

A cross-loading was also performed to test the discriminant validity of the data. This is done

by first determining the correlation of the loadings between the latent variables and the

indicators. The values should then be monitored to ensure that none of the indicators have a

higher loading than with the one it was planned to measure. If this happens the concern is that

the indicator may not measure its intended construct, hence the validity of the construct can be

questioned (Chin, 1998).

3.5.6 Bootstrapping

To test if the construct reliability is significantly higher than the recommended minimum

threshold Bootstrapping is additionally recommended (Hair et al., 2019). Since PLS models do

not make any assumption of the distribution of the data, parametric significance test cannot be

used, instead, PLS relies on nonparametric bootstrap procedures (Hair et al., 2017). When

conducting a bootstrapping test, subsamples are drawn from the original sample with

replacement. Further, each subsample is used to estimate the path model. Then, the estimate of

the coefficients forms a bootstrap distribution which grants us with an approximation of the

sampling distribution (Hair et al., 2017). This allows the possibility to determine the standard

23

error of the coefficient and their statistical significance. The recommended number of

bootstrapping is 5000 (Hair et al., 2017).

3.5.7 Blindfolding

Blindfolding procedures are used to evaluate the inner model parameters for a specified

omission distance D with values between 5 and 10 (Hair et al., 2011). This procedure calculates

the Stone-Guesser (Q-square) values to measure the quality of the PLS path model (Hussain et

al., 2018) which by excluding selected inner model relations and computing changes in the

criterion estimates (Hair et al., 2011). The resulting estimate then allows us to predict the

omitted data point and the difference between the true data points and the predicted ones is

used as an input for estimating Q-square (Hair et al., 2017).

3.5.8 Control Variables

To ensure that the tested relationships are not caused by an underlying variable that is not

included in the framework, this study uses the control variables; size and industry type when

performing the PLS analysis. Size is used because it has been indicated by many scholars

(Sedera et al., 2003, Bohórquez & Esteves, 2008, Mabert et al., 2003) that it can affect the

success and way the firm handles the ERP system. Industry type has also been described to be

affecting how firms handle the ERP system, since the type of information a firm requires in

their ERP system and to what extent this needs to be utilized differ between industry types

(Dwivedi et al., 2009, Raymond & Uwizeyemungu, 2007).

3.6 Research Ethics

In order to carry out, analyze, and document our research in a well-considered manner we

followed essential ethical criteria during the research period in accordance with the European

Code of conduct for research integrity (2017) and the Good Research practices (2017)

suggested by the Swedish Research Council. When conducting surveys to collect data, for

example, there are some specific ethical considerations to take into account. The first

consideration was anonymity, each participant of the survey was informed of the anonymity of

their response meaning that their response would not reveal their identity. A second

consideration we factored in was the informed consent, each participant was aware of the aim

of the survey and that their answers will be used entirely for research purposes.

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Additionally, we clarified that participation was completely voluntary, so no persuasive

actions/follows up have been taken. Moreover, to increase the transparency of our research we

also answered questions the respondent had regarding our survey to encourage their

participation. The participants were contacted via their business email publicly disclosed on

the firm’s website. In cases where the email was not available on the firm’s website, we did

not further attempt to find their email through other channels.

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4. Findings and Analysis

4.1 Descriptive Statistics

4.1.1 Sample Characteristics

To gain an overview of the sample, this section provides characteristics data of the 100

observations obtained. In Figure 2 the respondents' positions are displayed, the CEO position

is the most prevalent which is reasonable as the survey was mainly sent out to people in this

position. When the CEOs email address was not found the survey was sent either to the person

expected to have the most knowledge in the firm's ERP system or the firm's general email

(requesting that they forward it to a relevant person). Therefore, the second and third most

common respondents were the CFOs and the CTOs, since they are usually involved in the ERP

process. The “Other” category consists of members in the financial, sales, and engineering

departments.

Figure 2: Respondents’ position.

Total participants=100

Figure 3 shows that the most frequently used ERP system in the sample is Monitor, this is a

common system for manufacturing firms to use. Most of the participants are from the

manufacturing industry, hence the prevalence of Monitor is reasonable. Visma is the second

26

most common ERP system in the data set, this is a very frequently used system for Swedish

small firms (Nathan, 2014) which most of the sample consists of (71% of respondents measured

by number of employees, shown in Figure 5). Overall, there is a wide variety of ERP systems

used by the companies in the sample, in total 24 different systems were observed.

Figure 3: ERP systems used by the sample.

Total participants=100

Figure 4 shows that the two most frequently occurring industries found in the study are

Manufacturing and Wholesale and retail trade. According to Eurostat (2019) these industries

rank second and third in using an ERP software package in Europe. However, it is unexpected

that Construction is the third most frequent in the sample because they rank 11th in Eurostat’s

(2019) rankings. Since there are a wide variety of industries in the sample it is important that

this is controlled for when testing the hypotheses as this factor can affect ERP usage (Dwivedi

et al., 2009, Raymond & Uwizeyemungu, 2007).

27

Figure 4: Industry overview of the sample.

Total participants=100

Figure 5 shows that 71% of the companies chosen to respond to the surveys have less than 50

employees while 29% have between 50 to 250 employees. Hence, most of the sample consists

of small firms. Lastly, Figure 5 also displays that companies using the ERP systems for more

than 5 years represent 86% of the observations, while companies which have used their ERP

system for less than five years correspond to 14% of the observations. This means that most of

the sample firms are in the later phases of the ERP process and the system is therefore likely

deeply integrated in these firms' processes.

Figure 5: Number of employees per company. Figure 6: Years of ERP use per company.

Total participants=100 Total participants=100

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4.1.2 Indicator Characteristics

Table 2 reveals the mean and standard deviation of the indicators, each indicator represents a

statement used in the survey and is designed to measure the latent variable, the statements can

be found in Appendix 1. The variable that has the highest mean is top management support,

with the value 4.233. This indicates that the managers of the companies in general support the

use of ERP systems to a high degree. Since most of the sample consist of firms that have used

an ERP system for over five years (shown in Figure 5) the system has probably been deeply

integrated into the firm's processes and started to enhance their performance thus resulting in

the managers having a positive attitude towards the system. The majority of the firms using the

system for a long time could also lead to the managers getting used to the system and becoming

comfortable with it, resulting in a positive opinion regarding the system. The indicator with the

lowest mean (2.590) is CM2, this indicator measures the extent in which the firms have

constructed an action plan in order to handle potential problems with ERP use. This low mean

value denotes that in general these firms do not have a comprehensive action plan to follow in

the event of an ERP problem.

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Table 2: Descriptive statistics of the variables and indicators Variable Indicator Variable Mean Indicator Mean SD

Clear vision & planning VP1

3.707 3.750 0.876

VP2 3.960 0.871 VP3 3.410 1.030

Top management support TMS1

4.233 4.350 0.973

TMS2 4.360 0.922 TMS3 3.990 1.082

Effective communication EC1

3.953 4.110 0.882

EC2 3.720 1.059 EC3 4.030 0.074

Effective project management

EPM1 3.843

3.990 0.933 EPM2 3.860 1.175 EPM3 3.680 1.094

Change management CM1 3.025 3.460 0.984 CM2 2.590 1.158

Teamwork & composition TW1

3.870 3.960 1.122

TW2 4.150 0.841 TW3 3.500 1.091

Training TR1

3.803 3.730 1.028

TR2 3.460 0.853 TR3 4.220 0.867

IT infrastructure

IT1

4.065

4.200 0.775 IT2 4.100 0.768 IT3 3.840 0.997 IT4 4.120 1.098

Data quality & integrity DQ1 3.820 3.450 1.143 DQ2 4.190 0.924

Usage USE11

3.050 2.910 1.504

USE21 2.730 1.182 USE31 3.510 1.533

Firm performance

FP1

3.753

3.640 0.944 FP2 3.610 1.009 FP3 4.030 0.974 FP4 3.580 1.022 FP5 3.880 0.983 FP6 3.780 1.082

1 These indicators are measured differently compared to the rest indicators, 1=0-19%, 2=20-39%, 3=40-59%, 4=60-79%, 5=80-100% Note: The survey statements for each indicator can be found in Appendix 1

The ERP usage mean and standard deviation is displayed in Table 2, this illustrates that the

indicators connected to ERP usage have the highest standard deviation (1.504, 1.182, 1.533).

This suggests that the amount of ERP use differs considerably between the various companies.

Table 2 also displays that the IT-infrastructure indicators have a very similar mean value except

for IT3, which has the value 3.840. This indicator measures whether the firm's networks are

fast. Thus, in general the sample experiences more problems with network speed than they do

with issues regarding security and reliability. It is also displayed that FP3 has the highest mean

30

(4.030) out of the Firm performance indicators, this indicator measures the improvement the

ERP system has on management control (shown in Appendix 1). This suggests that out of the

measured benefits that the ERP system can deliver, the management control aspect gains the

most value.

4.2 Robustness of the Data

To confirm the acceptability of the measurement model, it is necessary to assess the reliability

of each individual item, the internal consistency between them and analyse the models

convergent and discriminant validity. This is estimated by using the PLS-algorithm procedure

in the smartPLS software. The first step in the PLS algorithm consists of iteratively estimating

the latent variable scores repeatedly, until convergence is achieved. The second step consists

of estimating the loading and path coefficient for the indicators. Finally, the third and final step

is to estimate the location parameters. The values obtained from the PLS algorithm procedure

are shown in Table 3. A brief search into the loadings of each variable reveals that all indicators,

except for CM1, have a loading value over 0.7. What this indicates is that more than a half of

the variance in the indicator is a consequence of the variance in the latent variable meaning that

the indicators are considered appropriate and sufficiently formulated and therefore the criteria

for convergent validity can be confirmed. Even though one of the indicators for Change

Management (CM1) obtained a loading of 0.662, this is also acceptable since it is over the 0.6

benchmark (Chin, 1998, Yana et al., 2015). However, the reason the loading value is lower for

this indicator than for the rest of the indicators could be due to the fact that it deals with the

implementation process (shown in the Appendix 1) of the of the ERP system, and since most

of the firms in the study as shown in figure 6 have used ERP for more than 5 years this could

affect their ability to answer this question because they either were not present during the

implementation process or their recollection of it is compromised.

Table 3 shows the values obtained for internal consistency. The column for Cronbach alpha

coefficient has an acceptable consistency for most variables (values are above or very close to

0.7) except for the variable change management which possesses an alpha value of 0.3 meaning

that items within this variable probably are not measuring the same underlying construct.

However, a brief view on the composite reliability measure provides us with a value of 0.75,

which is sufficient. The big difference could be due to the low number of indicators this

variable has; this usually affects Cronbach alpha more than the composite reliability since it is

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a more conservative measure. Furthermore, all the values for composite reliability are

acceptable since they fall in the desirable range of 0.7 to 0.95.

The latent variables AVE values are also displayed in Table 3. These values should be higher

than 0.5 to assess for convergent validity. For all variables in the model, it is observed that

average variance extracted have values in the range 0.610 and 0.782. These outcomes indicate

that the measurement used reflects the characteristics of each research variable, thereby

confirming the quality of the measure and the convergent validity.

Table 3: Indicator loadings and measures of reliability (CA, CR) and validity (AVE)

Variable Indicator Loading t-stat CA CR AVE

Vision & planning VP1 0.797 11.384

0.691 0.829 0.619 VP2 0.841 20.573 VP3 0.717 7.852

Top management support

TMS1 0.892 28.201 0.850 0.909 0.770 TMS2 0.813 13.124

TMS3 0.924 53.590

Effective communication

EC1 0.804 13.977 0.800 0.811 0.711 EC2 0.883 32.934

EC3 0.841 18.814

Effective project management

EPM1 0.779 9.988 0.832 0.893 0.735 EPM2 0.877 20.278

EPM3 0.911 40.249 Change

management CM1 0.662 2.946 0.381 0.754 0.610 CM2 0.885 7.586

Teamwork & composition

TW1 0.867 11.439 0.715 0.839 0.636 TW2 0.764 7.118

TW3 0.757 7.452

Training TR1 0.890 20.362

0.793 0.871 0.694 TR2 0.740 6.822 TR3 0.861 22.098

IT infrastructure

IT1 0.829 8.728

0.803 0.870 0.626 IT2 0.838 10.373 IT3 0.775 8.703 IT4 0.717 8.641

Data quality & integrity

DQ1 0.843 9.033 0.728 0.877 0.782 DQ2 0.923 21.064

Usage USE1 0.789 17.623

0.701 0.835 0.629 USE2 0.716 10.354 USE3 0.866 29.729

Firm performance

FP1 0.783 12.048

0.881 0.910 0.627

FP2 0.772 12.295 FP3 0.753 13.736 FP4 0.826 22.927 FP5 0.854 27.788 FP6 0.756 12.255

Note: The survey statements for each indicator can be found in Appendix 1

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To assess the degree of shared variance between the latent variables of the model, a test of

discriminant validity using the Fornell-Lacker criterion was conducted, shown in Table 4. This

test uses the latent variables square root of AVE and compares it to its squared correlations

with the other constructs in the model. To meet the criterion, the diagonal scores should be

higher than the rest of the cross-correlations. As shown in Table 4, the bold values are all

greater than their cross-correlated counterparts, indicating that each of the constructs explain

better the variance of its own indicator rather than the other latent constructs in the model.

Thus, the Fornell-Lacker criterion is satisfied.

Table 4: Fornell-Lacker criteria

VP TMS EC EPM CM TW TR IT DQ USE FP VP 0.787 TMS 0.642 0.877 EC 0.642 0.702 0.843 EPM 0.621 0.504 0.658 0.858 CM 0.474 0.462 0.346 0.521 0.781 TW 0.580 0.533 0.681 0.726 0.482 0.798 TR 0.677 0.685 0.605 0.614 0.448 0.662 0.833 IT 0.459 0.565 0.538 0.566 0.317 0.579 0.564 0.791 DQ 0.573 0.512 0.505 0.455 0.333 0.324 0.518 0.480 0.884 USE 0.580 0.533 0.480 0.442 0.294 0.309 0.417 0.418 0.306 0.793 FP 0.641 0.612 0.658 0.615 0.436 0.635 0.667 0.658 0.514 0.435 0.792 VP=Clear vision & planning, TMS=Top management support, EC=Effective communication, EPM=Effective project management, CM=Change management, TW=Teamwork & composition, TR=Training, IT=IT infrastructure, DQ=Data quality & integrity, USE=Usage, FP=Firm performance

Another popular approach for assessing discriminant validity is by analyzing the cross-loadings

of the data. In the cross-loading test, the latent variables should have the highest correlation

loadings with the indicators used to measure the construct. The bolded values in Table 5 should

therefore be higher than their vertical and horizontal counterparts. As observed in the table, all

indicators with their respective variables rated higher than the other variables. For instance,

Clear vision and planning and the indicators used to measure this variable (VP1, VP2, VP3)

obtained higher values (0.797, 0.841, and 0.717) compared to the other values in the same

column and row. Therefore, it is demonstrated that this measure is not a reflection of the other

constructs. Same explanation applies for the rest of variables.

33

Table 5: Cross-loadings estimators

VP TMS EC EPM CM TW TR IT DQ USE FP VP1 0.797 0.488 0.411 0.392 0.350 0.407 0.497 0.388 0.279 0.364 0.487 VP2 0.841 0.539 0.394 0.546 0.333 0.578 0.559 0.363 0.477 0.314 0.574 VP3 0.717 0.499 0.459 0.544 0.441 0.390 0.546 0.326 0.625 0.309 0.452 TMS1 0.498 0.892 0.619 0.362 0.341 0.418 0.593 0.447 0.395 0.440 0.453 TMS2 0.551 0.813 0.517 0.446 0.395 0.468 0.586 0.543 0.461 0.414 0.563 TMS3 0.632 0.924 0.696 0.510 0.471 0.512 0.628 0.503 0.489 0.536 0.591 EC1 0.487 0.587 0.804 0.437 0.324 0.507 0.449 0.440 0.324 0.325 0.508 EC2 0.595 0.636 0.883 0.554 0.438 0.608 0.582 0.501 0.438 0.486 0.589 EC3 0.529 0.551 0.841 0.669 0.322 0.600 0.480 0.413 0.351 0.375 0.549 EPM1 0.566 0.402 0.607 0.779 0.489 0.670 0.518 0.431 0.388 0.228 0.515 EPM2 0.591 0.463 0.644 0.877 0.414 0.709 0587 0.509 0.440 0.317 0.526 EPM3 0.500 0.443 0.514 0.911 0.463 0.568 0.511 0.513 0.354 0.500 0.553 CM1 0.345 0.398 0.300 0.326 0.662 0.492 0.405 0.343 0.128 0.171 0.420 CM2 0.401 0.352 0.379 0.472 0.885 0.318 0.329 0.197 0.352 0.275 0.304 TW1 0.601 0.552 0.643 0.744 0.453 0.867 0.663 0.502 0.360 0.292 0.550 TW2 0.373 0.379 0.512 0.451 0.237 0.764 0.475 0.461 0.139 0.212 0.447 TW3 0.379 0.313 0.456 0.498 0.443 0.757 0.416 0.421 0.245 0.225 0.516 TR1 0.639 0.621 0.583 0.509 0.402 0.586 0.890 0.532 0.478 0.377 0.631 TR2 0.592 0.482 0.391 0.480 0.509 0.551 0.740 0.367 0.390 0.169 0.491 TR3 0.512 0.592 0.503 0.553 0.310 0.548 0.861 0.478 0.426 0.411 0.542 IT1 0.290 0.295 0.334 0.433 0.234 0.416 0.336 0.829 0.310 0.227 0.536 IT2 0.349 0.374 0.371 0.387 0.131 0.394 0.326 0.838 0.288 0.354 0.478 IT3 0.407 0.409 0.363 0.490 0.273 0.425 0.398 0.775 0.438 0.292 0.495 IT4 0.375 0.617 0.564 0.467 0.346 0.553 0.643 0.717 0.450 0.390 0.554 DQ1 0.513 0.447 0.386 0.362 0.348 0.221 0.448 0.373 0.843 0.221 0.433 DQ2 0.508 0.462 0.495 0.420 0.260 0.336 0.470 0.476 0.923 0.309 0.476 USE1 0.305 0.474 0.342 0.376 0.276 0.303 0.402 0.344 0.234 0.789 0.370 USE2 0.295 0.288 0.411 0.354 0.188 0.240 0.186 0.350 0.191 0.716 0.352 USE3 0.395 0.490 0.394 0.325 0.232 0.196 0.387 0.306 0.296 0.866 0.317 FP1 0.452 0.376 0.448 0.524 0.410 0.589 0.506 0.514 0.343 0.242 0.783 FP2 0.512 0.385 0.400 0.556 0.404 0.494 0.564 0.513 0.303 0.365 0.772 FP3 0.407 0.448 0.464 0.372 0.258 0.359 0.487 0.488 0.418 0.331 0.753 FP4 0.560 0.549 0.611 0.493 0.366 0.506 0.526 0.510 0.520 0.311 0.826 FP5 0.575 0.618 0.637 0.480 0.308 0.503 0.552 0.559 0.463 0.398 0.854 FP6 0.516 0.488 0.514 0.508 0.351 0.579 0.528 0.531 0.380 0.378 0.756 VP=Clear vision & planning, TMS=Top management support, EC=Effective communication, EPM=Effective project management, CM=Change management, TW=Teamwork & composition, TR=Training, IT=IT-infrastructure, DQ=Data quality & integrity, USE=Usage, FP=Firm performance. Note: The survey statements for each indicator can be found in appendix 1

34

4.3 Main Findings After confirming the great levels of validity and reliability obtained from the analysis of the

measurement model, we proceed to analyze the estimation of the structural model. In order to

investigate the variables, impact on the ERP usage and thereby its influence on firm

performance, the bootstrapping function is used in SmartPLS. This enables the possibility to

test the study’s hypotheses, estimate the t-statistics and P-values of the path model. A visual

illustration of the relationship model with t-statistics and P-values is presented in Figure 7 along

with a table summarizing the main results. This illustrates that there are two dependent

variables: ERP usage and Firm Performance. Although ERP usage (USE) is the dependent

variable in which hypotheses 1 to 9 are tested, this variable also acts as independent when

linked to the variable of Firm Performance (FP).

Figure 7: Visualization of variable relationships. *Significant at 5%, **Significant at 1%

Note: Size and industry are used as control variables

Values displayed are t-statistics (without parentheses) and P-values (with parentheses)

35

Table 6: Hypotheses testing

Hypotheses T-Statistic P-Value Path coefficient Results H1 VP-->USE 0.050 0.960 -0.008 Not supported H2 TMS-->USE 2.180 0.029* 0.292 Supported H3 EC-->USE 1.216 0.224 0.169 Not supported H4 EPM-->USE 2.195 0.028* 0.325 Supported H5 CM-->USE 0.534 0.594 0.058 Not supported H6 TW-->USE 2.413 0.016* -0.360 Not supported H7 TR-->USE 0.625 0.532 0.079 Not supported H8 DQ-->USE 0.405 0.686 -0.049 Not supported H9 IT-->USE 1.214 0.225 0.143 Not supported H10 USE-->FP 4.805 0.000** 0.451 Supported

*Significant at 5%, **Significant at 1% Note: Size and industry are used as control variables

When testing the hypotheses, we first evaluate the P-value of the relationships, to achieve a

significant result these values should be below 0.05 (5% significance). Figure 7 shows that

there are three variables that have a significant relationship with our first dependent variable

(ERP usage), these variables are: Top management support (0.029), Effective Project

Management (0.028), and Teamwork & composition (0.016). To understand the direction of

the relationship the path coefficient is considered in Table 6. It is concluded that Top

management support and Effective project management have a positive relationship with ERP

usage since the values of their path coefficients are positive (0.292 and 0.325 respectively).

This implies that Top management support and Effective project management have significant

positive effect on ERP usage, thus hypotheses 2 and 4 are confirmed. Teamwork and

composition (TW) displayed in Table 6 have a negative path coefficient, thereby illustrating a

negative significant impact on ERP usage, since hypothesis 6 proposes that a positive

relationship should be found, this hypothesis cannot be supported, and is therefore rejected.

The tests for H1, H3, H5, H7, H8, and H9 are shown in Figure 7, these respectively handle

Clear vision and planning, Effective communication, Change management, Training, Data

quality and integrity, and IT-infrastructure. These hypotheses investigate if the variables have

a significant positive effect on ERP usage. The P-values of these variables are all higher than

0.05, this entails that the variables do not have a significant effect on ERP usage, Hypotheses

1,3,5,7,8, and 9 are therefore rejected.

36

Hypothesis 10, examines if ERP usage has a positive effect on Firm performance, as shown in

Figure 7, the relationship obtains a P-value of 0.000 and is thus significant at a 1% level. The

relationship is also positive because the path coefficient, displayed in Table 6, has a positive

value of 0.451. As a result of this hypothesis 10 can be confirmed meaning that the more the

ERP system is used the better the firm performs. Thereby, 3 of the total 10 hypotheses are

confirmed.

The path coefficients of the relationships are presented in Table 6. These values represent

standardized regression weights, which is similar in interpretation to beta weights in a multiple

regression. A general rule of thumb when analyzing path coefficients is that a value of less than

0.1, around 0.3, and over 0.5 indicates a small, medium and large impact respectively (Kline,

2005). Table 6 shows that the path coefficient for the relationship between the dependent

variable (ERP usage) and Top management support, Effective project management, Teamwork

and composition, 0.292, 0.325, and -0.360. These values suggest that Top management support

and Effective project management have a similar positive impact on ERP usage. Teamwork on

the other hand has a bigger impact although a negative one. ERP usage impact on Firm

performance is the largest out of all relationships in the study, with a path coefficient of 0.451

indicating a medium/almost large.

When evaluating how well the independent variables explain the dependent variables R-

squared was used. As revealed in Figure 7, the two dependent variables ERP usage and Firm

performance had an R-square of 0.450 and 0.221 respectively. This means that the nine

measured firm characteristics can explain 45% of the variance in ERP usage, and ERP usage

can explain 22.1% of the variance in Firm performance. R-square values of 0.67, 0.33, and

0.19 are respectively considered substantial, moderate, and weak (Chin, 1998). The R-square

for ERP usage is therefore considered moderate, and for Firm performance it is weak.

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Table 7: Effect size (F-squared) and Predictive relevance (Q-squared)

Hypotheses F2 Q2

H1 VP-->USE 0.000

0.2201

H2 TMS-->USE 0.052 H3 EC-->USE 0.017 H4 EPM-->USE 0.062 H5 CM-->USE 0.004 H6 TW-->USE 0.073 H7 TR-->USE 0.004 H8 DQ-->USE 0.002 H9 IT-->USE 0.019 H10 USE-->FP 0.234 0.123

1This value is for H1-H9 Control variables: Size & Industry

In addition to examining the R-squared values, the extent to which R-squared values change

when an exogenous construct is removed could be used to examine if the removed variable has

a substantive influence on the endogenous construct (Hair, 2017). This is estimated by the

effect size (F-squared). F-squared values less than 0.02 indicate that the effects of omitting

these variables are non-substantive while values of 0.02, 0.15, and 0.35 are considered to have

a small, medium, and large effect respectively at the structural level (Henseler et al., 2009).

Hence, as shown in Table 7 the effect size of removing variables such as Vision and planning,

Effective communication, change management, training, data quality and integrity, or IT-

infrastructure would not have a substantial impact in the model, since these variables all have

F-square values below 0.02. The impact of omitting Top management support, Effective

project management, or Teamwork and composition from the model are considered to be small,

since these variables have obtained the F-square values between 0.02-0.15. In contrast, the

effect size of omitting the usage (USE) measurement will lead to a medium impact on Firm

performance since the F-squared is 0.234 (F-square>0.15).

Predictive relevance is also important to ensure that the model provides a prediction of the

latent variable indicators. This can be measured by the Stone-Guesser (Q-squared) which we

obtained by performing a blindfolding procedure. While estimating the parameters, the

blindfolding technique omits data for a given block of indicators and then predicts the omitted

parts based on the calculated parameters. Hence, Q-squared values that are larger than zero are

considered indicative of predictive relevance (Hair 2017). As the Q-squared value estimated in

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Table 7 is larger than zero (0.123) we can ensure the predictive relevance of our model which

indicates that it accurately predicts data not used in the estimation of the model.

39

5. Discussion

The aim of this study is to identify the main drivers that contribute to ERP usage and if this

consequently affects firm performance in Swedish SMEs. For this, we constructed 10

hypotheses grounded in previous literature to explain ERP usage and its impact on Firm

performance. The variables investigated were proposed by the ERP Usage framework adherent

to the organizational, technological and process contexts of ERP usage. Among those variables,

only the ones belonging to the organizational and processing categories, such as: Teamwork

and composition (H6), Effective Project Management (H4), and Top management support (H2)

are significant at a 5% significance level, which confirms their impact on ERP usage.

Out of the significant results, the most surprising outcome was the negative impact Teamwork

and composition has on ERP usage, meaning that higher levels of teamwork in a company

correspond to lower usage of ERP-system and vice versa. This result differs from hypothesis 6

since we expected a positive relationship in accordance with Hwang (2018). Some explanation

behind this could be that the respondents may perceive teamwork as the extent to which the

firm's members have direct contact with each other, not considering the information sharing

abilities of the ERP system as a characteristic of teamwork. Another explanation could be that,

when teamwork is high, there is less need to use information systems due to the employees

being able to directly access the source of the information (colleagues) and therefore there is

less need to use the ERP system. Contrary to our framework, teamwork might be better suited

to have direct linkage to Firm performance, as is suggested by the model Ruivo et al. (2014)

applied, instead of being linked to ERP usage.

Effective project management is a variable derived from the theoretical framework; this

variable has barely been researched empirically in the ERP usage context. Here we also

discovered a significant variable. As expected from hypothesis 4, we encountered a positive

relationship between effective project management and ERP-usage as suggested in the

literature (Carton et al., 2008, Clark et al., 2007). The path coefficient of 0.325 also provides

us with the information about a medium intense relationship between these variables. We

assume that one explanation behind the significant result and the medium intensity in the

relationship, beyond project management being relevant in all phases of the ERP life cycle

40

(Fawaz et al., 2008), is due to the skills and qualification of the personnel being in charge of

the ERP-project which positively affect the overall system usage in the firm.

Regarding the organizational context, we observe that Top Management support has a

significant impact on ERP-usage (H2). Zhang et al. (2002) endorsed in their theory the

importance of top management support to drive ERP usage in the right direction. Because this

variable has previously been empirically tested, we can confirm its positive effect on ERP

usage in affinity with previous studies (Almuhayfith & Shaiti, 2020, & Lin, 2010). The top

management result path coefficient in our result was 0.29, which does not very much in

comparison to studies by Lin (2010) and Almuhayfith and Shaiti (2020) which obtained the

path coefficient of 0.32 and 0.25 respectively. The impact of this variable on ERP usage is

therefore proven to be of similar strength across multiple studies, this further confirms the

results. Thus, motivation, interests and good leadership exerted by the members of the top

management is corroborated in our study to have a significant positive influence on the degree

of ERP usage.

An unexpected result was found when examining the training variables' effect on ERP usage.

Ruivo et al. (2014) and Almuhayfith and Shaiti (2020) found significant positive results (at a

5% level), while our results showed no significance at all. These studies however had a sample

consisting mainly of SMEs that had implemented ERP for a few years (around 70% of both

studies samples had been using ERP for less than 5 years). As presented in the descriptive

statistics (shown in Table 1), our study's sample comprises primarily of firms (86%) that have

used ERP for more than 5 years. According to Somers and Nelson (2001), training is most

important during the initial phases of the ERP process since everything is new to the employees.

Firms that have used ERP for more than 5 years have passed the initial ERP phases, hence the

non-significant result could be due to training not being as important in the latter stages of ERP

use. By this stage, the employees are familiar with the system and new employees are

presumably joining a firm with a system that has established routines. This could make the

system easier to comprehend, thus less need for training. The results of this study therefore

reject hypothesis seven.

Change management is another factor that this study's analysis proved to not be significant.

This is a factor that has been researched extensively in the implementation phase and proven

to be a crucial aspect of the success of the implementation (Nah & Delgado, 2006). The factors

41

have however not been well researched in the post-implementation stage of ERP. The main

reasoning Nah and Delgado (2006) provide for its importance in the implementation stage is

that firms experience a great deal of change in their processes when changing to a new system.

This change needs to be managed properly through change management. Hence, the same

reasoning as for training can be applied for this variable; since most of the sample consist of

firms that have used ERP for a longer time, and changes in the organization due to the ERP

system are not as common in the stages following the implementation stage, thus the need for

change management loses its importance. It is therefore concluded that hypothesis 5 is rejected.

The hypothesis testing for Data quality and integrity also resulted in non-significant results.

Despite the postulate that poor data quality is associated with less ERP usage (Haug et al.,

2009), we cannot sustain hypothesis 8 based on our empirical results. To some extent, this

could be because of the approach in which the variable Data quality and integrity was

constructed, only measuring the accuracy and relevance of the data1 (Strong, 1997) rather than

its accessibility. This somehow diverges from Haug et al. (2009) approach. Therefore, we

assume this variable is not fully covering the aspect of data quality since the focus has solely

been located on the intrinsic and contextual dimensions of data quality, which may have led to

an insignificant relationship towards ERP usage.

Regarding IT-infrastructure Al-Mashari (2003) and Jarrar et al. (2000) suggested that

networking, IT-infrastructure and hardware are crucial for the success of ERP systems.

Nwankpa (2015) researched technical resources, which is similar to IT infrastructure, in

regards to ERP usage and found this to have a significant relationship, this study however

provides contradictory results since no significant relationship was found. Our results therefore

imply that IT-infrastructure does not have an effect on ERP usage. A reason for the

contradictory results might be that the measurements used for the IT-infrastructure variable

were too focused on the network aspect of IT-infrastructure, only one of the four measurements

manages the hardware and software elements.

Effective communication is in this study found to be non-significant. Before rejecting the

hypothesis, we suggest that an underlying explanation for the non-significant results may be

that most of the respondents are the firm's CEOs. Since they are on top of the firm's hierarchy,

1 see appendix for more info about the construction of DQ indicators.

42

they can be biased when answering questions regarding the employee's possibilities to

communicate their dissatisfaction and their own ability to communicate necessary information

to the employees. Thus, we suggest that this factor requires further research with different

respondents, not merely composed by CEOs. For Clear vision and planning (H1) we encourage

the same approach. Though, the highest rating within this category was the vision about what

they want to achieve with the ERP system (VP2). We assume that the lack of control for the

number of years the companies had the ERP systems (due to the sample not being diverse

enough in this regard), could also lead to misleading results in the significance level for this

variable. For instance, a respondent in a firm using the system for a long time might not have

been around in the implementation stages and are therefore not adequate to answer this survey

statement.

The second research question of our study deals with the impact of ERP usage on Firm

performance, this is tested through hypothesis 10 and proven statistically significant. This

confirms results from previous studies (Ruivo et al., 2014 & Almuhayfith, & Shaiti, 2020), but

now applied to the Swedish context. Our results also showed that ERP usage impact on Firm

performance was the highest out of all the relationships tested in the study. This implies that

an increase in ERP usage leads to an increase in firm performance and that this increase is close

to a large magnitude. Out of the firm performance indicators used in this study, the one

measuring management control had the highest average value. This entails that the primary

importance of the ERP system is that it provides benefits for the firm's management control,

which aligns with the results from Kallunki (2011) and Ruivo et al. (2014) studies.

43

6. Conclusion The main interest of this study was to explore what the drivers of ERP usage are in SMEs and

to what extent this relates to firm performance. The main findings of the study suggest that

factors in the organizational and process categories have an impact on ERP usage while the

technological category does not prove to be relevant. Regarding the first research question, Top

management support and Effective project management are proven to be drivers of ERP usage,

thus conforming with previous studies (Carton et al., 2008, Clark et al., 2007, Almuhayfith &

Shaiti, 2020, & Lin, 2010). The study's second research question deals with the effect of ERP

usage on Firm performance. The findings suggest that there is a positive effect. These results

therefore align with the studies done by Ruivo et al. (2014) and Almuhayfith and Shaiti (2020).

This study also generated some unexpected results, particularly regarding the Teamwork and

composition variable, which displayed a negative relationship with ERP usage. This is

suspected to be either due to the respondents not considering the information sharing ability of

the ERP system as a teamwork characteristic, or due to that high teamwork leads to less need

for the ERP system since they can instead contact the colleagues with the information directly.

Other factors not found to be drivers of ERP usage are; Clear vision and planning, Effective

communication, Change management, Training, Data quality and integrity, and IT

infrastructure.

This research paper contributes to the ERP research area since the results for the variables Top

management support and Firm performance are in unity with previous studies results, thus

providing more certainty around the results. There is also a lack of research regarding Effective

project management's relationship with ERP usage, thus the significant results of this study

prove the importance of this variable and therefore strengthen the theoretical foundation of

ERP research. Because several of the factors investigated were found non-significant, this

study additionally offers some question marks regarding the inclusion of these variables in the

theoretical framework that has been used.

Moreover, since this study is specifically targeted to Small- and medium-sized companies who

already completed the process of adopting Enterprise Resource Systems, it will have a practical

contribution to the Swedish Business environment. As this research proves that ERP usage is

an important factor in achieving greater levels of firm performance in Swedish SMEs, this can

44

encourage firms to increase their degree of system use. Besides, this study provides empirical

evidence of what to focus on in the post implementation stage, such as, Top Management

support and Effective project management as these factors are proven to be main drivers of

ERP usage and will therefore bring benefits to these firms.

Suggestions for future research is to empirically assess if the importance of ERP usage of the

investigated factors differ depending on which phase of the ERP process the firm is in. Several

of the study's hypotheses are encountered to have a not-significant impact on usage which,

could be assumed, is due to the sample mainly consisting of firms that have used ERP for more

than 5 years. Therefore, these factors may not be considered as important in the latter phases

of the ERP process. Another suggestion for future research is to use a sample in which the

respondents have different positions in the firms. Most of the sample consists of CEOs and

CFOs, thus another sample (e.g., IT-responsible or sales responsible) may present different

findings. Lastly, we suggest that future studies can investigate different critical success factors

in the ERP usage context, since there are a large number of critical success factors that have

not yet been well researched in this context.

45

Appendix

Appendix 1: Survey statements

Category Variable Survey statement Source

Organizational

Clear vision and planning

-The company was prepared for the risks associated with the implementation of ERP (VP1) -The company has a clear vision of what they want to achieve with the ERP system (VP2) -The company has come up with a plan that drives the ERP project in the desired direction (VP3)

Nah et al., (2001), Loh & Koh (2004), Ngai et al., (2008), Nah &Delgado, 2006

Top management support

-The top management of the company is interested in using ERP (TMS1) -The top management of the company believes that the cost of ERP is a long-term investment (TMS2) -The top management of the company is aware of the commitment and leadership exercise required to achieve success with the ERP system (TMS3)

Almuhayfith & Shaiti (2020) Zhang et al., (2002)

Effective communication

-In the company, it is easy to communicate their acceptance / dissatisfaction with ERP (EC1) -In the company, the employees were informed about the structural changes followed by ERP implementation (EC2) -The company answers employees' questions regarding the ERP system (EC3)

Garg and Garg (2014), Rosario (2000), Wee (2000)

Process

Effective project management

-The team that manages the ERP project consists of competent staff from relevant departments in the organization. (EPM1) -The ERP project was assigned to a competent project manager (EPM2) -The development of the ERP project is regularly reviewed and improved (EPM3)

Sumner (1999),Wee (2000),Somers and Nelson (2004)

Change management

-The company has easy to adapt in connection with the structural / organizational changes followed by ERP implementation (CM1) -The company has created an action plan to deal with problems that may arise in connection with the use of ERP (e.g., dissatisfaction from employees, confusion, mismanagement). (CM2)

Nah and Delgado, (2006)

Teamwork and composition

-The team that handles the ERP project has both technical and business knowledge (TW1) -It is easy to collaborate between different departments within the company (TW2) -It is easy to collaborate with external ERP consultants (TW3)

Jacques Verville and Alannah Halingten (2003), Hwang (2018)

46

Training

-The company has trained its employees on the ERP system (TR1) -ERP training has helped users to use the ERP system effectively (TR2) -The company is willing to provide its employees with additional ERP training in the event of system changes (TR3)

Nah et al., (2003)

Technological

Data quality and integrity

-The data / information in the company's ERP system is examined to ensure that the data quality is high (DQ1) -The data / information in the company's ERP system is useful (DQ2)

Haug et al., (2009), Strong (1997)

IT infrastructure

-The company's existing network is reliable (IT1) -The company's existing network is secure (IT2) -The company's existing network is fast (IT3) -The company has prioritized investing in the infrastructure (hardware and software) needed for the chosen ERP system. (IT4)

Al-Mashari (2003), Jarrar et al. (2000)

ERP-usage

-How many employees use the ERP system daily? (%) (USE1) -How much time per day do employees work with the ERP system? (%) (USE2) -How many of the company's reports are generated per day using the ERP system? (sales order, new customers, complaints, etc.) (%) (USE3)

Ruivo (2014)

Firm performance

-The ERP system improves user satisfaction. (FP1) -The ERP system improves customer satisfaction. (FP2) -The ERP system improves the firm's management control (FP3) -The ERP system improves individual productivity. (FP4) -The ERP system improves overall productivity. (FP5) -The ERP system reduces operational and administrative costs. (FP6)

Ruivo (2014), Hooshang M & Cyrus M. Beheshti (2010), Kaplan & Norton (1992)

47

Reference list

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