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One Size Fits All? How Does Firm Heterogeneity Affect ERP Adaptation and Firm Performance?

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Open Journal of Business and Management, 2020, 8, 2597-2622
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DOI:
10.4236/ojbm.2020.86161 Nov. 20, 2020 2597 Open Journal of Business and Management
One Size Fits All? How Does Firm Heterogeneity
Affect ERP Adaptation and Firm Performance?
Pei-Fang Hsu
Institute of Service Science, National Tsing Hua University
Abstract
A cause of high ERP failure rate is the misfit between organization processes
and ERP systems. To solve the misfit problem, literature suggests two adapta-
tion approaches: conducting business process reengineering (BPR) to fit or-
ganization processes into ERP systems, or customizing ERP systems to fit or-
ganizational processes. However, extant
studies seldom explore how firms
should choose between the two adaptation approaches and adjustment level
based on their heterogeneous firm characteristics. Through the lens of con-
tingency based task-technology-fit (TTF) theory, this study collects data f
rom
150 U.S. manufacturing firms that use ERP systems, and empirically investi-
gates how firms could choose a suitable adaptation approach between BPR
and system customization while considering their heterogeneous characteris-
tics, such as firm size, industry, top management involvement, big-
bang or
phase-
in implementation speed. Consequently, this study further examines
how such choice of different adaptation strategies and adaptation level affect
firms final firm performance. Our results show that industry, top manage-
ment involvement, and implementation speed significantly affect firms
choice of adaptation approaches and adaptation level, while their choices also
significantly affect final firm performance.
Keywords
Enterprise Resource Planning, ERP, Business Process Reengineering, BPR,
Customization, Firm Performance
1. Introduction
Enterprise resource planning (ERP) is an enterprise system that helps integrate
business information and reengineer business processes (Tian & Xu 2015; Strong
& Volkoff, 2010). ERPs ability to collect complete, real-time, and integrated
business information enables firms to efficiently and effectively respond to a ra-
How to cite this paper:
Hsu, P.-F. (2020).
One Size Fits All? How Does Firm Heter
o-
geneity Affect ERP Adaptation and Firm
Performance?
Open Journal of Business and
Management
, 8,
2597-2622.
https://doi.org/10.4236/ojbm.2020.86161
Received:
October 19, 2020
Accepted:
November 17, 2020
Published:
November 20, 2020
Copyright © 20
20 by author(s) and
Scientific
Research Publishing Inc.
This work is licensed under the Creative
Commons
Attribution International
License (CC BY
4.0).
http://creativecommons.org/licenses/by/4.0/
Open Access
P.-F. Hsu
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10.4236/ojbm.2020.86161 2598 Open Journal of Business and Management
pidly changing environment and fulfill customersneeds. However, when im-
plementing ERP systems, many enterprises encounter a
misfit
problem between
existing business processes and ERP systems that, consequently, involves huge
costs to either adapt their ERP systems or organization processes (Strong &
Volkoff, 2010). A recent survey found that 66% of organizations experienced
operational disruption while implementing a new ERP system, while 81% failed
to realize their expected benefits after implementation due to such a misfit prob-
lem (Panorama Consulting Group, 2019).
The misfit between business processes and ERP systems is a critical challenge
for firms (Hong & Kim, 2002). ERP vendors design systems to comply with the
general mass market, rather than specific needs of individual companies (Strong
& Volkoff, 2010). That type of design leads to a problem in that ERP systems
cannot fully comply with firmsspecific and unique requirements (Seddon et al.,
2003; Strong & Volkoff, 2010). As Rajagopal (2002) points out, the high failure
rate of ERP implementation results from a gap between ERP-using firms and
ERP vendors: firms desire a customized ERP system, while ERP vendors develop
systems for generic mass markets to achieve economies of scale. It has been es-
timated that, on average, off-the-shelf ERP systems can only address about 70%
of the business needs of ordinary firms (Markus et al., 2000). The misalignment
is considered to be a gap between the “best practices” embedded in ERP systems
and organizations original business processes (Van Beijsterveld & Van Groe-
nendaal, 2016). In order to solve the misalignment problem, previous studies
propose two adaptation approaches: 1) Business Process Reengineering (BPR),
and 2) customizing ERP systems (Soh et al., 2003).
Business process reengineering (BPR) refers to adjusting a company’s internal
processes to fit its ERP system. In other words, organizations change their origi-
nal business processes to follow the best practices embedded in ERP systems in
order to improve operational efficiency, thereby enhancing their competitive-
ness in the market. IS scholars and practitioners suggest this approach involves
lower costs, smaller technical risks, and can incorporate learned experiences
from leading enterprises (Liang & Xue, 2004). Therefore, many firms, in partic-
ularly small and medium-sized firms (SMEs), often use or are strongly recom-
mended to use such an adaptation approach when implementing ERP systems.
However, previous studies also show that while BPR can mitigate the misfit
problem, it does not guarantee successful ERP implementation (Liang & Xue,
2004). Business process reengineering often fails due to difficulties in changing
employees’ habits (Yen et al., 2015), huge hidden costs in long transformation
processes, and risks of losing firmsoriginal competiveness and unique business
processes (Liang & Xue, 2004).
Customizing ERP systems to address existing organizational processes is
another approach to solve the misfit problem. Some firms choose this approach
due to their large size, complex business processes, and/or intention to maintain
their core and specific business processes (Mabert et al., 2003a, 2003b). The cus-
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tomized ERP system approach could better retain firmskey processes, maintain
original workflow, reduce employee resistance to change and, thereby, enhance
the likelihood of implementation success. Nevertheless, this approach also brings
concerns, such as the complex technical skills needed for customizing, upgrad-
ing, and maintaining ERP systems. The high technical risks increase the diffi-
culty of putting ERP customization into practice.
In summary, both BPR and customization have pros and cons (Law & Ngai,
2007). Most ERP scholars, vendors, and consultants, however, advocate that en-
terprises should adopt BPR to improve their business processes in order to
benchmark with the embedded ERP best practices (Tsai et al., 2010; Finney &
Corbett, 2007), implying that the full benefits of ERP systems can only be rea-
lized with a great deal of BPR. In contrast, other scholars, such as Rothenberger
and Srite (2009), argue that all ERP implementation projects inevitably need
some degree of customization, which is one of the important steps in ERP im-
plementation that cannot be avoided. Thus, existing studies propose two com-
peting opinions (i.e., “adjust systems” or “adjust organization processes”) and
have not reached a clear conclusion regarding how to solve the misfit problem.
More importantly, prior studies rarely investigated how firms with different
characteristics (e.g., firm size, industry, ERP implementation speed, and level of
top management involvement) should choose between the two adaptation strat-
egies. It is also not well understood what adaptation level of the two adaptation
strategies may affect final firm performance. Thus, the lack of a clear conclusion
motivates us to conduct a large-scale empirical study to further address the fol-
lowing research questions:
1) How does firms heterogeneity (e.g., firm size, industry, ERP implementa-
tion speed, level of top management involvement) affect their choices of adapta-
tion strategy (BPR or customization) and their adaptation levels to solve the
misfit problem?
2) What is the subsequent ERP performance after firms adopt different adap-
tation strategies (BPR or customization) and different adaptation levels?
2. Literature Review
Firmschoice between BPR and customization is considered a critical factor for
ERP success (Plant & Willcocks, 2007). In the following, we review literature to
understand the details of the two types of adaptation.
2.1. Business Process Reengineering (BPR)
BPR is defined as an organization redesigning its business processes in order to
improve competitiveness in the market (Hammer, 1990). More specifically, im-
proving customer services, shortening delivery time, lowering production costs,
and improving quality are considered important BPR benefits (Huq & Martin,
2006). BPR is also proposed as a critical success factor in many IT implementa-
tion projects. Since BPR aims to simplify and reengineer business processes, in
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theory, it should smoothly link the newly implemented IT systems with business
processes, and effectively complement the new IT system with business func-
tions (Law & Ngai, 2007; Tsai et al., 2010).
Most ERP vendors and consultants recommend companies carry out business
process reengineering (BPR) while implementing ERP systems (Kang et al.,
2008). In other words, adjusting internal workflows to fit the best practices em-
bedded in ERP systems (Esteves et al., 2002). Best practices aim to guide ordi-
nary companies toward following the processes and management practices of
industry leaders so they can improve their organizations and enhance firm
competitiveness. By conducting BPR to fit the best practices embedded in ERP
systems, companies could break boundaries of traditional departments and
functions, enable them to be more flexible, respond rapidly to market changes,
shorten transaction times, reduce human errors, and improve data accuracy,
thereby enhancing firm efficiency (Tsai et al., 2010).
When implementing ERP systems, organizations have to select an appropriate
BPR adaptation level, thus enabling ERP systems to connect smoothly with or-
ganizations so that firms can receive the full benefits of ERP. The magnitude of
the adaptation level can be as low as merely streamlining, to a very high reinven-
tion level (Figure 1). Streamlining refers to incremental changes in existing or-
ganization processes in order to enhance product quality, shorten cycle times,
and reduce costs. Reinvention refers to discarding existing processes and creat-
ing new processes that the organization really needs (Bancroft et al., 1997). Since
streamlining adopts a periodic approach to improving partial processes first and
then slowly extends to other processes, it is considered a low magnitude BPR
change. On the other hand, reinvention removes outdated processes and creates
new ones to improve the organization, and is viewed as high magnitude BPR.
Following the definition of (Bancroft et al., 1997), this study categorizes BPR
level into two groups: 1) high magnitude change and 2) low magnitude change.
However, a recent study shows that 53% of organizations consider BPR diffi-
cult (Panorama Consulting Group, 2019), which implies the dilemma of BPR
implementation in practice. Hong and Kim (2002) and Wei et al. (2005) further
indicate that when organizations adjust their processes to fit ERP, it affects not
only existing internal processes but also involves other organization difficulties,
in particular, personnel problems. Several prior studies identify these
BPR-related personnel problems, including:
1) Employee resistance: employees are not willing to change from their habits
of conducting work to the new ERP or BPR way of completing tasks (Yen et al.,
2015).
2) Barriers in cross-departmental communication: Due to business process
reengineering, firms have to abandon traditional department perspectives and
change to a process-oriented perspective, which may cause conflicts among de-
partments (Hodge, 2002).
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Figure 1. BPR level (adapted from Bancroft et al., 1997).
3) Unmotivated organizations: it usually takes a long time for firms to complete
BPR, which may result in demoralized organizations, and it also prolongs ERP im-
plementation. If organizations happen to lack strong leaders, the long implementa-
tion usually leads to decreased firm performance (Subramoniam et al., 2009).
4) Increased costs rather than cost savings: hidden costs happen in imple-
menting business process reengineering and redesign, so firms have to put in
more manpower, financing, and time (Subramoniam et al., 2009).
The greatest challenge of BPR is to solve problems related to resistance from
employees, role changes, training, and other personnel issues (Panorama Con-
sulting Group, 2019). However, many previous studies show that implementing
ERP alone without BPR does not improve organization efficiency. Nevertheless,
very few existing studies explore what magnitude of BPR adjustment (i.e.,
streamlining vs. reinvention) is appropriate for obtaining desired beneficial re-
sults from ERP implementation. Nah et al. (2001) claim that while implementing
BPR, a great deal of process reengineering should be adopted repeatedly in order
to get the benefits of ERP, but this strategy is criticized by increasing risks, com-
plexity, and costs (Davenport, 1998). Wei et al. (2005) report that BPR affects
employees’ work content, responsibilities, and performance. It can be seen that
while BPR may improve organization processes, it also has extensive impacts,
and sometimes negative impacts, on the whole organizations. Therefore, some
firms choose another adaptation strategycustomized ERP systemdue to
their large number of employees and complexity of organization processes.
2.2. ERP System Customization
Customization refers to meeting organizationsneeds by adjusting ERP systems
(Chou & Chang, 2008; Rajagopal, 2002). Rothenberger and Srite (2009) define
low level ERP customization as enterprises choosing a suitable ERP system and
setting parameters in the system, then they can have ERP functions needed un-
der an established scope of work. The drawback of conducting this kind of basic
adjustment is that it only meets organizationspartial needs in many cases, and
cannot fully fit firms existing processes. On the other hand, extensive ERP cus-
tomization refers to that organizations adopt third-party packages to supple-
ment existing ERP functions or, if firms need a very unique modification, they
may further construct their own ERP platforms (i.e., core customization). Light
(2001) categorizes the degree of customization into five levels (Figure 2), in-
cluding new report, amend existing reports/displays, process automation, add
functionality, and change functionality. The higher the degree of customization,
the greater the maintenance efforts needed.
Reinvention
Low HighPotential maintenance effort
Streamlining
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Figure 2. ERP system customization level (adapted from Light, 2001).
Customization usually requires high-level technical skills since organizations
may encounter many technical difficulties during the customization process.
Thus, prior studies suggest that customization is preferred only in the following
scenarios (Mabert et al., 2003b):
1) Industry: if an organization is in a distinctive industry with specific needs,
such as complex order management and short-term orders, and off-the-shelf
ERP solutions are not able to meet its needs, then customized ERP systems are
needed.
2) Core competence: when firms want to keep their core business processes,
they then need to significantly customize ERP systems to maintain their core
competency.
3) Multinational corporations and large companies: multi-plant and mul-
ti-national enterprises mostly choose customized ERP systems to meet their
complex processes.
Customization reduces incompatibility between ERP systems and organiza-
tion processes, lowers employees resistance, and reduces the need for training
and organizational adjustment (Hong & Kim, 2002). Mabert et al. (2003b) indi-
cate that most organizations more or less adopt some degree of customization.
While customization has many advantages, it also involves risk. First, customi-
zation requires a great amount of IT manpower, resources, and time, followed by
difficult system maintenance and upgrades (Rothenberger & Srite, 2009). The
greatest challenge of customization is to understand the customized ERP system.
Mabert et al. (2003b) indicate that if a customized ERP system is too compli-
cated, then even an experienced ERP developer may find it difficult to maintain
and upgrade. Additionally, since the core value of an ERP system emphasizes
integration, IT staff in ERP-using firms may feel it is difficult to decide which
ERP functions are to be kept, and which can be modified or removed in the cus-
tomization process.
Customizing an ERP system has its pros and cons; it keeps firmscore processes,
but it also suffers from many drawbacks. Panorama Consulting Group (2018)
show that 89% of their surveyed enterprises customized some degree of their
ERP systems to fit existing processes. Among them, 10% adopted minor custo-
mization (1% - 10% of code modified), 70% chose some customization (11% -
25% of code modified), and 5% adopted extreme customization. While organi-
zations indeed adopt different levels of customization, it is still unclear what ex-
tent of customization enables firms to reach satisfactory performance. Brehm et
al. (2001) propose that the success of ERP implementation is highly related to
New report
Low Potential maintenance effort High
Amend existing
report/displays
Process
automation
Add
functionality
Change
functionality
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the degree of customization. Somers and Nelson (2001) propose that a low de-
gree of customization is more likely to result in successful ERP implementation
because heavy customization involves increased costs, prolonged implementa-
tion time, and difficult upgrade/maintenance problems. Organizations are not
recommended to adopt customization unless it is necessary or the organizations
competitive advantage is derived from a non-standardized process (Hong &
Kim, 2002). However, high degrees of customization still exist in reality; Subra-
moniam et al. (2009) indicate that most large organizations with abundant re-
sources and unique processes choose a high degree of customization because
these vast firms think it is easier to change ERP systems than to change a large
group of employees. Since prior studies show no conclusions on how firms
should choose between high and low degrees of customization (Rothenberger &
Srite, 2009), and diversified levels of customization do exist in industry, this
study aims to further investigate this issue.
2.3. Firm Performance
While most prior research evaluates ERP performance by measuring financial
indexes such as cost decreases, productivity improvement, and profit increases
(e.g., Hitt et al., 2002), many scholars suggest that intangible organizational im-
provements should also be considered when evaluating whether or not an im-
plementation of ERP is successful. For example, Mabert et al. (2003a, 2003b) re-
port that most improvements after using ERP are in intangible areas, such as in-
creased interaction across the enterprise, quicker response times for informa-
tion, integration of business processes, and availability and quality of informa-
tion. Gattiker and Goodhue (2005) show that ERP can deliver intangible benefits
to firms, including better information, more efficient internal business processes,
and better coordination between different units of a firm. Banker et al. (2006)
found that ERP systems have positive impacts on product quality, product time
to market, and plant efficiency, while Mabert et al. (2003a, 2003b) indicate there
are also operational improvements in order management, on-time deliveries,
and customer interactions. Cotteleer and Bendoly (2006) show that order ful-
fillment lead-time is significantly improved after ERP system deployment. Kari-
mi et al. (2007a, 2007b) found that ERP implementation is associated with
process efficiency, effectiveness, and flexibility. Ranganathan and Brown (2006)
report that ERP projects with greater functional scope or greater physical scope
result in higher shareholder returns. Following these previous studies, we cate-
gorize ERP-related firm performance improvement into three categories:
cost
decrease
,
productivity
&
efficiency
, and
higher
level
of
organizational
benefits
.
More importantly, our study aims to investigate how different ERP adaptation
approaches (BPR and customization) and adaptation level would result in dif-
ferent types of firm performance. The details of how we operationalize these va-
riables are in Section 4 and in Appendix A.
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2.4. Research Gaps
Conducting a literature review (Table 1) by using keywords including ERP and
misfit to search recent studies, we found existing efforts are greatly dominated
by conceptual or qualitative studies, while empirical investigation is relatively
limited. For instance, Seddon et al. (2010) use content analysis to qualitatively
show good fit leads to better firm performance, but without illuminating how to
choose adaptation approaches to achieve such good fit. Alsulami et al. (2014)
conduct a conceptual analysis to categorize different types of ERP implementa-
tion misfits, while Strong and Volkoff (2010), Peng and Nunes (2017), Hustad et
al. (2016), Van Beijsterveld and Van Groenendaal (2016), and Kouki et al. (2010)
use case studies in the United States, China, Norway, Netherlands, Canada and
Tunisia, respectively, to explore the ERP misfit phenomenon. A handful of stu-
dies examine the misfit issue empirically. Among them, Wang et al. (2006) ex-
amine how consultant quality, top management support, and country of origin
of ERP package may affect misfit through 85 ERP using firms in Taiwan. It is
noted that more than half of their surveyed firms use local ERP packages rather
than international brands; thus, their study can be further improved by expand-
ing the ERP surveyed packages to include more international brands. Hong and
Kim (2002) and Sun et al. (2016) survey ERP using firms and show that good fit
leads to ERP implementation success, while Shiang-Yen et al. (2013) using sur-
vey method to empirically examine how different adaptation approaches may be
used to different misfit scenarios by investigating 305 ERP users in Malaysia.
These existing studies can be strengthened by further investigating how firm
could choose different adaptation strategies and level, according to firm hetero-
geneity to achieve good fit and final firm performance.
In summary, the review indicates a research gap that much more empirical
studies are needed to validate the qualitative and conception findings on ERP
misfit problem, and our study aims to bridge this gap by conducting a large scale
empirical investigation in ERP using firms. In particular, our study empirically
examines firms in manufacturing industry in the United States, which is the
most mature industry on ERP using (Mabert et al., 2003a, 2003b), with a verity
of local and international ERP brands being used. More importantly, our study
is different from extant works by taking a deeper examination on how firms can
choose appropriate adaptation approaches and levels based on their different
heterogeneity.
3. Task-Technology Fit Theory and Research Model
Task-technology fit (TTF) theory is a compelling basis for a depiction of IS sys-
tem performance (Goodhue & Thompson, 1995). TTF theory defines fitas the
extent to which the technology functionalities match with organizationstask
requirements. TTF theory proposes that information systems will have positive
impact on firm performance only when there is a good fit between the system
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Table 1. Representative studies on ERP Misfit (Qualitative vs. Quantitative).
Author(s)
Theoretical Foundation
Main Findings
Qualitative studies
Seddon et al. (2010)
Organizational benefits from
enterprise systems model
Content analysis
Good fit leads to better
firm performance.
Alsulami et al. (2014)
Dialectic model of
organizational change process
Conceptual analysis
Categorizing ERP implementation conflicts (misfits).
Strong and Volkoff (2010)
Critical realism.
Grounded theory.
Case study
Six misfit domains (functionality, data, usability, role,
control and organizational culture)
Peng and Nunes (201
7)
Updated IS success model
Case study
A 9D (nine dimensions) ERP evaluation framework is
pro
posed to explore ERP misfit.
Hustad et al. (2016)
Conceptual framework for
ERP customization
Case study
Connecting different types of customization and different
categories of misfits.
Van
Beijsterveld and
Van
Groenendaal (2016)
Misfit analytical framework.
Case study
The proposed framework can distinguish actual from
perceived misfits.
Kouki et al. (2010)
Diffusion of innovation theory
Case study
Alignment gap between ERP systems and business is a key
factor to receive ERP benefits.
Robey et al. (2002)
Dialectic motor of change
Case study
Core teams and consulting relationships address
configuration knowledge barriers. User training and
phased implementation overcome assimilation knowledge
barriers.
Luo and Strong (2004)
Conceptual framework
Conceptual analysis
Proposing a framework for choosing BPR or
customization.
Wei et al. (2005)
A stage view of ERP
implementation
Case
study
Different types of misfits are categorized into different
stage of ERP implementation.
Rothenberger and Srite (2009)
N.A.
Case study
Exploring the reasons of high customization
Quantitative studies
Wang
et al. (2006)
Social Shaping of Technology
Perspective
Survey of 85 ERP
using firms in Taiwan
Country of origin of ERP package, consultant quality, and
top management support could alleviate misfit problems.
Hong and Kim (2002)
Literature related to fit
Survey of 34 ERP
using firms
Good fit may lead to ERP implementation success.
Sun et al. (2016)
ERP implementation lifecycle
models
Survey of 144 ERP
users
Fit between ERP (packages vendors, implementation
partners) and organization needs affects time and budget
of ERP implementation.
Shiang
-Yen et al. (2013)
Task
-Technology Fit theory
Survey of 305 ERP
users in Malaysia
System modification could more effectively solve deeper
layer of ERP misfits. Surface layer misfits are more
appropriate to be resolved through changes in business
practices.
functionality and the task requirements of firms (Goodhue & Thompson, 1995).
In contrast, poor fit between task requirements and system functionalities is ex-
pected to have negative impact on firm performance. TTF theory provides a sol-
id theoretical foundation and logical lens to investigate how ERP fit (or misfit)
affects firm performance. Recently, some scholars further propose that the rela-
tionship between task-technology fit and firm performance is not static, but is
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influenced by contingencies (Morton & Hu, 2008; Ifinedo, 2011). Such contin-
gencies may include firm heterogeneity and other surrounding environmental
factors. This contingency perspective claims that there is no single best way (best
practices) to design organizational structures (Morton & Hu, 2008). Instead, the
best way is contingent upon the internal and external situations of the company.
Therefore, the
contingency
based
task-technology-fit
(TTF) perspective pro-
vides a solid rationale to our study, which proposes to examine how firm hete-
rogeneities may serve as
contingency
factors
affect ERP adaptation choices and
final firm performance. In line with the contingency based TTF theory, firms
have to conduct adaptation by choosing either BPR or customizing ERP systems
based on their heterogeneity to solve the misfit problem. Thus, we propose a re-
search model (Figure 3) to understand how firms should choose a suitable
adaptation approach considering their heterogeneity (firm size, industry, top
management involvement, and implementation speed). Consequently, we ex-
amine how these adaptation choices affect firmsfinal performance.
3.1. Firm Size
Prior IT studies investigate how firm size may affect IT adoption, implementa-
tion, and performance, while firm size is usually measured by employee numbers
or revenue (Mabert et al., 2003a, 2003b). In the past, ERP systems were usually
adopted by large firms due to huge investments in capital and IT capability. Re-
cently, ERP is adapted by small and medium-sized enterprises (SMEs) to im-
prove business processes and gain firm competitiveness (Mabert et al., 2003a).
However, compared to large firms, SMEs usually have limited resources and ca-
pabilities, which could lead them to choose different IT adoption and adaptation
approaches than large firms, and may result in different benefits (Achanga et al.,
2006). For example, with limited budgets and resources, small firms are more
likely to choose a BPR approach (Laukkanen et al., 2007). Small firms have few
IT employees and limited IT capability to customize ERP systems, so they would
prefer to change their business processes and train their employees to fit with the
ERP systems (Deep et al., 2008). On the other hand, Mabert et al. (2003a, 2003b)
found that more than 50% of large firms conducted significant amounts custo-
mization while small firms usually adopted minor customization; thus, large
firms usually customize ERP systems more than small firms, because the former
usually run their businesses all over the world with complicated business
processes and structure, while resources and IT manpower are more sufficient.
The degree of customization varies significantly across firm size, with larger
companies typically customizing more to preserve their competitive advantages,
while smaller companies tend to leverage best practices. Accordingly, we hypo-
thesize that:
H1a: Small firms are more likely to conduct high level BPR.
H1b: Large firms are more likely to conduct high level customization.
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Figure 3. Research Model.
3.2. Industry
Prior studies indicate that a firms industry environment also affects how it
chooses its ERP adaptation approaches (Mabert et al., 2003b). The manufactur-
ing industry is the most matured ERP-using industry. However, even within the
manufacturing sector, traditional manufacturing and high-tech manufacturing
firms choose different ERP adaptation strategies. For example, many traditional
textile and clothing firms indicate their industry has unique characteristics (e.g.,
seasonal issues) that should be carefully considered when adopting ERP systems.
The seasonal issues require firms in this industry to quickly adjust inventory, ef-
ficiently monitor supply chains to swiftly locate production-to-sales in different
countries, and rapidly notice changes in fashion trends, etc. Due to these indus-
try-specific characteristics, off-the-shelf ERP systems usually cannot fully fulfill
their business needs, and many firms in this industry customize their ERP systems.
On the other hand, high-tech industries, such as electronics and computers,
are in a high-speed and constantly-changing environment. The characteristics of
this industry result in short product lifecycles, and potential backlogs of out-
dated products force the industry to maintain low inventories. Firms in this in-
dustry may have greater requirements of their ERP system functions customized
to support optimal transaction efficiency. ERP vendors sometimes label a man-
ufacturer as a “dumbbell” type or an “olive” type according to where the com-
panys competency is located in its operation (Liang & Xue, 2004). Dumbbell
type companies, such as pharmaceutical and high-tech industries, compete on
R&D and marketing, whereas olive type companies, such as textile and furniture
industries, compete on manufacturing efficiency (Liang & Xue, 2004). Choosing
a flexible, industry-specific ERP system to meet firms needs and processes is a
critical decision. In response, this study analyzes 11 sub-industries in manufac-
turing, and divides them into traditional manufacturing industries and high-tech
manufacturing industries to understand their ERP adaptation approaches.
H2a: Industry type is associated with firmsBPR adaptation strategies and
BPR level.
H2b: Industry type is associated with firmscustomization strategies and cus-
tomization level.
ERP Adaptation Choice
Firm Performance
Cost decrease
Productivity &
efficiency
Organizational
benefits
Firm size
Firm Characteristics
Industry
Customization Level
High customization
Low customization
BPR Level
High BPR
Low BPR
H5
H6
Top management
involvement
H1-4
Implementation
speed
P.-F. Hsu
DOI:
10.4236/ojbm.2020.86161 2608 Open Journal of Business and Management
3.3. Top Management Involvement
Top management involvement refers to top managements willingness to cham-
pion ERP within the organization and allocate the resources required for suc-
cessful ERP infusion (Stratman & Roth, 2002). Top management commitment is
a recurring factor in studies examining large-scale implementation of new busi-
ness processes and information systems. For example, Chou and Chang (2008)
and Gattiker and Goodhue (2005) indicate that top management involvement
improves ERP performance, and is a critical factor for ERP success. Higher levels
of top management involvement can increase firmsacceptance of and commit-
ment to ERP systems, and can facilitate ERP implementation (Kamhawi & Gu-
nasekaran, 2009; Reimers, 2003). Top management announces the new goals and
objectives, communicates with employees, establishes a shared vision and a new
organizational structure, defines staff roles and responsibilities, and establishes
policies for the new ERP system (Jafari et al., 2009). The higher the level of top
management involvement in the ERP implementation, the more ERP is regarded
as a high-level, strategic project rather than an IT project (Savage et al., 2010).
On the other hand, case studies from Robey et al. (2002) and Rothenberger and
Strite (2009) found that when top management involvement is low, ERP projects
are treated as an IT project and, consequently, customization level is usually
high in such cases. These companies usually aim to replace their legacy IT sys-
tems with ERP systems, they prefer keeping their original business processes,
and tend to extensively customize their systems. Accordingly, we hypothesize:
H3a: High level top management involvement in an ERP project is associated
with high BPR level.
H3b: Low level top management involvement in an ERP project is associated
with high customization level.
3.4. Implementation Speed (Big-Bang versus Phase-In)
Existing studies indicate that implementation speed could affect BPR and system
customization level, and eventually, ERP system performance (Tsai et al., 2010).
However, how different IT implementation speeds (big bang vs. phase-in) result
in different levels of effectiveness for firms that have different organizational
needs and operate in different environments is not clearly understood. A big
bang approach refers to implementing all ERP modules and functions at once
and the implementation time is usually short. In contrast, a phase-in approach
means implementing ERP modules gradually (phase-in by module) or imple-
menting ERP functions in some sites only (phase-in by site), which usually takes
a longer time to implement. While some firms focus more on receiving the
first-mover advantage from a big bang IT implementation, others consider this
approach as dangerous and risky. On the other hand, phase-in strategies entail
lower risks, but might require more resources and longer implementation time,
which might prevent a firm from developing specific competencies (OLeary, 2000).
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ERP implementation speed seems to be associated with degree of BPR, but
mixed results are reported. For instance, Tsai et al. (2010) found that fast-speed
implementation has a positive relationship with a high degree of BPR, but a
phase-in approach shows an insignificant relationship. Some scholars propose
that, since a big-bang approach involves implementing multiple modules at one
time, implying firmsintentions to transform organizations on a large scale at a
fast speed, limited customization and BPR levels are usually preferred in order to
increase the success rate of the new ERP implementation (Mabert et al., 2003a,
2003b). Big-bang adopted firms seem less likely to accept the idea of extensively
reorganizing business processes, redesigning workflow, or extensively customiz-
ing ERP systems that might delay their implementation project, and prefer to
quickly implement the new systems so all firm data can be transferred to the new
system during the cutover, and the new ERP system can function cohesively.
Therefore, we hypothesize:
H4a: A big-bang implementation approach is associated with a lower level of
BPR and customization.
On the other hand, a phase-in approach allows organizations more time to
rethink and revise business processes and structures, as well as customization of
ERP systems in order to achieve a better competitive position in a market
through the ERP implementation project (Anderson et al., 2011). Phasing an
ERP implementation by modules or by sites is beneficial as it gives firms more
time to learn ERP systems to customize the functions they need, or to adjust the
business processes they desire. It is often observed that firms using phase-in im-
plementation usually have a clearer business vision of how the ERP implementa-
tion could enable their corporate strategies (Panorama Consulting Group, 2018).
Therefore, we hypothesize:
H4b: A phase-in implementation approach is associated with higher level of
BPR and customization.
3.5. Implementation Approaches and Performance
As discussed above, it is debatable and inconclusive whether choosing customi-
zation or BPR can result in better firm performance. Many ERP-adopting firms
are reluctant to incur technical problems that may delay their ERP implementa-
tion projects and increase the risks of technical failures (Chen et al., 2009). A
high degree of customization is difficult not only in the adoption stage, but also
very challenging when upgrading and maintaining ERP systems (Mabert et al.,
2003b). Firms must strictly control the degree of customization (Chen et al.,
2009). Thus, although it is possible to customize ERP systems to better fit their
business needs (Davenport, 1998), many firms prefer to follow a standard con-
figuration scheme (e.g., no or minor customization) suggested by ERP vendors,
and choose to change their existing processes (BPR) to minimize the number of
interventions in changing ERP systems (Chen et al., 2009). Rothenberger and
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10.4236/ojbm.2020.86161 2610 Open Journal of Business and Management
Srite (2009) suggest that a low level of customization may have a greater chance
to result in better firm performance. Therefore, we hypothesize:
H5a: A low level of customization is more likely to be associated with better
firm performance than a high level of customization.
On the other hand, BPR is a typical and highly recommended implementation
approach by many ERP vendors and IS scholars. However, conducting BPR to fit
ERP systems also has its inherent dangers, since it may force firms to abandon
some of its valuable profit-generating processes. Forced selection among a li-
mited number of built-in alternatives in ERP systems also constrains a firms
ability to generate competitive advantages. Furthermore, since the built-in alter-
natives are also available to a firms competitors, the focal firm that opts for a
standard configuration provided by ERP vendors may have difficulties differen-
tiating itself from others. Therefore, while we hypothesize that a high level of
BPR may result in better performance according to the literature, we also use our
data to examine whether a high level of BPR has negative impacts.
H5b: A high level of BPR is more likely to be associated with better firm per-
formance.
4. Methodology
To test our research model, a questionnaire was designed to collect data on each
of the variables in the model. Each of the measurement items on the question-
naire were adapted from previously validated measures, and the details of the
prior reference support are shown in Appendix A. Additionally, these measure-
ment items were reviewed for content validity by an expert panel comprised of
faculty whose work focuses on ERP systems, as well as some practitioners and
consultants from industry. The initial questionnaires were pilot tested on twenty
firms randomly selected from the sampling frame and, based on their responses,
some items were revised for clarity.
The sample was randomly selected from within U.S. manufacturing industries
since these industries use ERP systems most frequently (Mabert et al., 2003a,
2003b). Our survey was administered only to individuals from companies that
make use of ERP in conducting their business. Eligible respondents for the sur-
vey were individuals considered to be the most knowledgeable about ERP use in
their companies. For large sites, respondents were CIOs or IS managers. For
smaller sites, respondents were business owners or senior IS mangers. Table 2
shows the sample characteristics. In total, we successfully collected 150 com-
pleted questionnaires, and the response rate is comparable to reports of previous
large-scale ERP surveys1. Furthermore, we compare the profiles of the respond-
ing firms with non-responding firms on demographic variables such as firm size
and revenue using Chi-square analysis. The results indicate no significant re-
sponse bias. We also examine common method bias that can potentially occur in
1These studies include Mabert et al. (2003a, 2003b), Stratman and Roth (2002), and
Gattiker and
Goodhue (2005).
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Table 2. Sample characteristics (N = 150).
Industry
#
%
Employee no.
#
%
Computer and Electronic Products
Transportation Equipment
Measuring & Controlling Instruments
Miscellaneous Manufacturing
Food
Apparel and Fabric
Furniture and Fixtures
Chemicals and Allied Products
Rubber, Plastics, and Leather
Metal and Fabricate Metal
61
5
35
15
5
3
4
9
9
4
40.7%
3.3%
13.3%
10%
3.3%
2.0%
2.7%
6.0%
6.0%
2.7%
<199
200 - 499
500 - 999
>1000
58
40
14
38
38.6%
26.7%
9.3%
25.4%
Respondents
#
%
Years of ERP Use
#
%
CIO
IS Manager
Others (Vice President, General Manager,
CFO, COO, etc.)
74
58
18
49.3%
38.7%
12.0%
<1 Year
1 - 5 Years
6 - 10 Years
11 - 15 Years
>15 Years
3
22
70
28
24
2.0%
14.7%
46.7%
18.7%
16.0%
survey data with two different approaches. First, following Malhotra et al.
(2006), we perform a common method factor test. We found that each indica-
tors variance explained by its substantive construct is much greater than that
explained by the common method factor, which suggests that common method
bias is unlikely to be serious. Second, a Harmans single-factor test (Podsakoff et
al., 2003) is conducted. In this test, all items were entered into an un-rotated
factor analysis to determine whether a single factor accounts for the majority of
the variance. In our test, the factor explained 19.28 percent of the variance, and
the result provides further evidence that common methods bias is not a serious
issue in this study.
Table 3 shows descriptive statistics of the key variables used in our model.
Regarding
firm
size
, we separate our sample into large and small firms using 500
hundred employees as a cutoff, according to the definition published by U.S.
Small Business Administration. In our sample, around two thirds (65.3%) of our
respondents are from small firms, while the rest belong to large firms. In terms
of
industry
, our sample includes 10 industries, and we separate them into two
groups: around one-third belong to traditional manufacturing (e.g., food, appa-
rel, furniture, chemicals, rubber, etc.) and the rest belong to high-tech manufac-
turing (computer, electronic products, transportation equipment, etc.). Using an
average as a cutoff, we separate our sample firms into two groups in terms of
their
top
management
involvement
level in ERP implementation: high-level top
management involvement, and low-level top management involvement. In terms
of
Implementation
speed
: 36% of our surveyed respondents indicated their firm
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used a big-bang approach, while 55.3% of our sampled firms used phase in (by
site or by phase).
Regarding ERP firm performance, we use twelve items which are categorized
into three groups:
cost
decrease
,
productivity
&
efficiency
,
and
organizational
benefits
(Table 4) following previous studies that claim ERPs main benefits are
related to the three categories (Mabert et al., 2003a, 2003b; Karimi et al., 2007a,
2007b). Using seven point Likert scales, where one means a large decrease, four
means no change, and seven means a large increase, we found that order man-
agement, information quality, and decision making are those areas where firms
felt they improved the most (mean value 5.69, 5.89, 5.50, respectively). On the
other hand, cost-related items that firms usually care about the most do not de-
crease significantly.
5. Data Analysis and Results
We first conduct a cross-examination and chi-square test on how each of our
models important variables are related to BPR and customization level, respec-
tively (Table 5). In terms of firm size and BPR level, small firms are more likely
to choose a low BPR level (56.7%), while fewer small firms choose a high BPR
level (43.3%). For large firms, a similar pattern shows that more firms prefer a
low level of BPR (57.4%), compared to high BPR (42.6%). The Chi-square result
shows no significance (
p
= 0.88). As for customization, many more small firms
(71.1%) choose low customization, indicating that small firms have fewer re-
sources and IT human resources to customize their ERP systems. Additionally,
contrary to conventional believe, large firms also prefer low-level customization
(65.3%) versus a high level of customization (34.7%). Therefore, firm size shows
no significant effect on customization (
p
= 0.46).
To further explore how firm size affects firmschoice of BPR level and custo-
mization level, we conduct a
t
-test. As shown in Table 6, small firmsaverage
BPR level equals 3.26, while large firmsaverage BPR level equals 3.12, and a
t
-test shows there is no significant difference between the two groups (
p
= 0.184).
Table 3. Descriptive statistics.
Variables Categories Count Percentage
Firm size
Employees < 500 (small firm) 98 65.3
Employees 500 (large firm) 52 34.7
Industry
Traditional manufacturing 49 32.6
High tech manufacturing 101 67.3
Top management
involvement
Low involvement 24 16.0
High involvement 126 84.0
Implementation
speed
Big bang 54 36.0
Phased in 83 55.3
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Table 4. ERP firm performance.
Category Item Mean Mode
Cost Decrease
Operational costs 3.38 4
Procurement costs 3.15 4
Inventory costs 2.82 4
Productivity
and Efficiency
On time delivery 5.31 4
Productivity 5.40 6
Product quality 4.82 4
Order management 5.69 7
Organizational
benefits
Customer service 5.39 4
Information quality 5.89 7
Decision making 5.50 7
Market share 4.69 4
Profitability 5.18 4
Table 5. Frequency of BPR and customization.
Variables Category
BPR (%) Customization (%)
Low High Low High
Firm size
Small 56.7% 43.3% 71.1% 28.9%
Large 57.4% 42.6% 65.3% 34.7%
Chi-square test Sig = 0.88 Chi-square test Sig = 0.46
Industry
Traditional 58.8% 41.2% 52.9% 47.1%
High tech 58.6% 41.4% 76.2% 23.8%
Chi-square test Sig = 0.44 Chi-square test
Sig = 0.050**
Top management
involvement
Low 82.6% 17.3% 75.0% 25.0%
High 52.4% 47.6% 68.0% 32.0%
Chi-square test
Sig = 0.001***
Chi-square test sig = 0.49
Implementation
speed
Big bang 61.1% 39.9% 77.4% 24.6%
Phased in 52.4% 47.6% 60.2% 39.8%
Chi-square test
Sig = 0.078*
Chi-square test
Sig = 0.039**
Note: ***
p
< 0.01, **
p
< 0.05 *
p
< 0.01.
Table 6. Firm size vs. BPR and customization level.
BPR level Customization level
Firm size Count Mean SD
p
-value Mean SD
p
-value
Small 97 3.26 1.063
0.184
2.64 1.348
0.235
Large 52 3.12 1.043 2.65 1.399
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Similarly, we found that the customization level between the two groups shows
no significant difference (mean 2.64 vs. 2.65,
p
= 0.235), thus, H1a and H1b are
not supported. Our data provides updated empirical evidence different from ex-
isting studies. Nowadays, firm size is no longer a critical factor to determine how
firms choose between BPR or customization approaches, nor their BPR or cus-
tomization level. Other firm characteristics determine ERP adaptation strategies
and adaptation level.
Further examining
industry
effect (Table 5), we found that traditional and
high-tech manufacturing firms show a similar distribution on BPR level.
Roughly 60% of firms choose a low level of BPR, while the remaining 40%
choose a high level of BPR, suggesting that industry type does not affect BPR
level (H2a). However, in terms of
customization
, a much larger portion of
high-tech firms (76.2%) choose low customization, compared to traditional
manufacturing firms (52.9%). Chi-square results also show there is a significant
difference (
p
= 0.05**) between these two industries on customization. We fur-
ther conduct a
t
-test and the results in Table 7 show that while firms in tradi-
tional industries or high-tech industries choose similar BPR levels (mean 3.18 vs.
3.20, respectively,
p
= 0.902), firms in traditional industries choose a much
higher level of customization than firms in high-tech industries (mean 3.24 vs.
2.42,
p
= 0.002***). Thus, in our case, industry type is a critical factor affecting
the level at which firms customize their ERP systems, supporting H2b.
When examining
top
management
involvement
(Table 5), we found that
firms with a low level of top management involvement mostly choose low BPR
(82.6%) and low customization (75%); a low-low situation. On the other hand,
firms with a high level of top management involvement seem to have higher in-
tention to choose high BPR (47.6%), but low customization (32%) is still ob-
served; (a high-low scenario). The chi-square test result also confirms that there
is a significant difference between high and low levels of top management in-
volvement in terms of firmsBPR levels (
p
= 0.001***, Table 5). A
t
-test result in
Table 8 further shows that firms with a high level of top management involve-
ment choose high level BPR (mean = 3.30) vs. those firms with a low level of top
management involvement (mean = 2.74), and the difference is significant (
p
=
0.019**), supporting H3a. On the other hand, level of top management involve-
ment does not affect firmsERP customization choices or adaptation levels (2.25
vs. 2.72,
p
= 0.122), and H3b is not supported.
Table 7. Industry vs. BPR and customization level.
BPR level Customization level
Industry Count Mean SD
p
-value Mean SD
p
-value
Traditional 34 3.18 1.114
0.902
3.24 1.415
0.002***
High tech 101 3.20 1.020 2.42 1.227
Note: ***
p
< 0.01, **
p
< 0.05 *
p
< 0.01.
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Table 8. Top management involvement vs. BPR and customization level.
Top management
involvement
BPR level Customization level
Count Mean SD
p
-value Mean SD
p
-value
Low 24 2.74 1.214
0.019**
2.25 1.359 0.122
High 125 3.30 1.004 2.72 1.354
Note: ***
p
< 0.01, **
p
< 0.05 *
p
< 0.01.
In Table 5, we found that when firms choose a big-bang approach, many
more firms (61.1%) chose a low level of BPR and, similarly, a greater portion of
firms (77.4%) choose a low level of customization. This low-low (BPR and cus-
tomization) phenomenon shows that when firms use a big-bang approach, the
speedy ERP implementation does not allow firms to conduct a high level of BPR
or customization. On the other hand, firms choosing a phased-in approach are
more likely to conduct a high level of BPR (47.6%) than firms choosing a
big-bang approach (39.9%). Similarly, more firms choosing phase-in ERP
(39.8%) conduct a high level of customization than firms using a big-bang ap-
proach (24.6%). The significant chi-square results (
p
= 0.078* and
p
= 0.039**,
respectively) support H4a and H4b. A
t
-test result in Table 9 further reveals that
firms choosing a phased-in approach has a significantly higher BPR level than
firms choosing big-bang (3.30 vs. 2.59,
p
= 0.048**). In terms of customization
level, firms choosing a phased-in approach also show higher level of customiza-
tion than firms choosing a big-bang approach (2.88 vs. 2.40,
p
= 0.046**), pro-
viding more empirical evidence to support H4a and H4b.
In terms of firm performance, Table 10 shows that firms choosing a high level
of BPR have better firm performance in all three of categories. Specifically, in the
cost
decrease
category, firms choosing a high BPR level have better performance
than firms choosing a low BPR (3.00 vs. 3.34,
p
= 0.085*)2. Furthermore, firms
with high level of BPR also show better
operation
efficiency
than firms with low
BPR (5.50 vs. 5.24,
p
= 0.09*). Lastly, firms choosing high BPR also show better
organizational
benefits
than firms with low BPR (5.70 vs. 5.54,
p
= 0.038*).
These results provide strong support of H5b that predicts firms with high levels
of BPR have better firm performance in terms of all three firm performance di-
mensions. In terms of customization level (Table 11), we found that firms with a
low customization level indeed have better firm performance, but only in the
cost
decrease
category, than firms choosing a high customization level (3.07 vs.
3.49,
p
= 0.049**). In the
operation
efficiency
and
organizational
benefits
dimen-
sions, firms choosing high or low customization levels show no significant dif-
ference, partially supporting H5a.
6. Discussion
To successfully implement ERP, solving the misfit between ERP systems and or-
ganization processes is critical. While literature has suggested that misfit can be
2For the
cost decrease variable, we use reverse coding; the smaller the number is, the better the firm
performance.
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Table 9. Implementation speed vs. BPR and customization level.
Implementation
speed
BPR level Customization level
Count Mean SD
p
-value Mean SD
p
-value
Big-bang 54 3.30 1.021
0.048**
2.40 1.335
0.046**
Phased-in 83 2.59 1.064 2.88 1.383
Note: ***
p
< 0.01, **
p
< 0.05 *
p
< 0.01.
Table 10. BPR level vs. firm performance.
BPR
Level
Cost decrease Operation efficiency Organizational benefits
Mean SD
p
-value Mean SD
p
-value Mean SD p-value
Low 3.34 1.137
0.085*
5.24 0.888
0.09*
5.54 0.887
0.038**
High 3.00 1.155 5.50 0.923 5.70 0.854
Note: ***
p
< 0.01, **
p
< 0.05 *
p
< 0.01.
Table 11. Customization level vs. firm performance.
Custom.
Level
Cost decrease Operation efficiency Organizational benefits
Mean SD
p
-value Mean SD
p
-value Mean SD p-value
Low 3.07 1.071
0.049**
5.31 0.881
0.279
5.52 0.869
0.822
High 3.49 1.330 5.50 1.031 5.56 0.969
Note: For the cost decrease variable, the smaller the number is, the better the firm performance. ***
p
< 0.01,
**
p
< 0.05 *
p
< 0.01.
reduced via either BPR or customization, extant studies do not clearly explain
how firms should choose their adaptation strategies and levels based on their
own heterogeneous characteristics, such as firm size, industry, implementation
speed, and top management involvement. Additionally, prior studies have sel-
dom assessed subsequent ERP implementation performance after choosing dif-
ferent adaptation strategies (BPR or customization) and different adaptation le-
vels. Lastly, most existing studies assume that firms should choose from one of
the two adaptation strategies (BPR or customization), but overlook the possibil-
ity of simultaneously adopting both strategies. This study aims to bridge the
three research gaps by proposing and empirically examining an ERP implemen-
tation framework to further understand how firms can choose appropriate
adaptation strategies and adaptation levels based on their heterogeneous charac-
teristics, while further exploring how these different choices influence subse-
quent firm performance. Our study contributes to extant ERP literature in the
following ways.
First, unlike most extant studies that assume large companies are more likely
to choose a customization strategy, our findings update our understanding of
firm size on ERP implementation. Our results show that
firm
size
is not a main
factor affecting level of BPR and customization. Rather,
industry
type
is the key
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differentiator. A small firm belonging to a special industry still needs customiza-
tion to fully receive the benefits of using ERP systems. Large companies, in con-
trast, even if they have sufficient resources and IT manpower, may still choose a
low level of customization since they do not see the need for customization while
undertaking the unnecessary risks and difficulties related to a highly customized
ERP system. Indeed, when we interviewed our surveyed companies in the
high-tech industry, many mentioned that the off-the-shelf ERP systems were
sufficient enough to meet most of their business needs, so they did not choose a
high level of customization, although some of the firm sizes were vast and re-
sources were sufficient.
Second, this study provides empirical evidence that when the level of
top
management
involvement
is high, a high BPR level can be achieved, echoing ear-
lier studiespropositions (e.g., Savage et al., 2010). A high level of top manage-
ment involvement signifies the ERP project is not merely an IT project, but a
strategic firm-level priority that enables firm competitiveness. However, a high
level of top management involvement does not affect customization level. We
also found that firms choose a big-bang implementation approach involving a
low level of BPR and customization (low-low scenario), while firms choosing a
phase-in approach could allow them more time to conduct high level BPR and
customization (high-high scenario). These findings provide more empirical evi-
dence to validate earlier studiespropositions and findings (e.g. Anderson et al.,
2011).
Third, we found that high levels of BPR have a significant impact on all three
firm
performance
categories (cost efficiency, productivity, and organizational
effectiveness). On the other hand, our study also shows that low level customiza-
tion can improve organizational cost efficiency (primarily in reducing operation,
procurement, and inventory costs), providing empirical evidence to support ear-
lier studies (e.g., Finney & Corbett, 2007; Mabert et al., 2003a). However, high
level customization involves risks, and our analysis shows that high customiza-
tion is not significantly associated with the other two firm performance aspects
(operation efficiency and organizational benefits); therefore, suggesting firms
should carefully choose their customization level.
Our results also provide some managerial insights. For example, we suggest
the two adaptation methods (BPR and customization) are not exclusive, but
could be adopted simultaneously according to firmsheterogeneous characteris-
tics and needs, echoing earlier studies propositions (e.g., Luo & Strong, 2004;
Wei et al., 2005). Additionally, contrary to a conventional belief, we found there
may not be a “best practice” for ERP implementation but, rather, a “best-fit” strat-
egy. We provide empirical evidence to echo the statement in Swan et al. (1999):
the
illusion
of
best
practice
in
information
systems
for
operations
manage-
ment
. Which implementation approach works best for firms to achieve the de-
sired ERP outcomes depends on firms organizational characteristics, capabili-
ties, constraints, trade-offs, and risks that they are willing to take. Our results
P.-F. Hsu
DOI:
10.4236/ojbm.2020.86161 2618 Open Journal of Business and Management
show that when firms choose a best-fit implementation approach based on firm
heterogeneity, they can significantly improve performance. In contrast, firms
choosing limited adaptation (i.e., LBLC fine-tuners) may implement ERP system
with less efforts, yet suffer from the misfit problem, and lose the opportunity to
gain competitive advantages from their ERP implementation (OLeary, 2000).
Further research might extend our study in several directions. For example, in
our key informant approach, we asked the most knowledgeable and senior level
IS managers to provide evaluative responses to the construct measures. Al-
though we checked for common method and nonresponding biases, further re-
search could benefit from using multiple methods and data sources to achieve
triangulation. Furthermore, accounting based firm performance data could also
be collected to more objectively evaluate the ERP outcomes.
7. Concluding Marks
Our study contributes to the ERP literature by providing a robust taxonomy that
sheds light on what drives firms to make their adaptation decisions, and what
adaptation approaches and levels might lead firms to better firm performance
from the ERP implementation. While firms may intuitively favor customizing
their ERP systems because few firms want to change the way they have been working
for decades, our analysis shows that it is not beneficial if firms over-customize their
systems. On the other hand, recognizing where to preserve firms original
processes is also critical during ERP implementation. It is a balancing act be-
tween adapting ERP systems and changing firms processes to achieve the best
firm performance. Our study illuminates guidelines for firms on these critical
decision points based on their firm-specific characteristics and heterogeneity.
Conflicts of Interest
The author declares no conflicts of interest regarding the publication of this paper.
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