Managing maturity in process-based improvement organizations: a perspective of the Romanian companies.
Paunescu, Carmen ; Acatrinei, Carmen
1. Introduction
Organizations today need performance measures to drive long-term
strategies and organizational change, to manage efficiently resources
and capabilities and to operate processes effectively and continuously
improve (Paunescu 2009a; Bieker 2004). It is no longer enough for
companies just to make profits for their stakeholders and to obey the
law. They are increasingly accountable to more environmentally and
socially aware shareholders, to civil society in general, to employees,
to customers, to partners and to a variety of other stakeholders (Retief
2007; Bovee et al. 2005). A successful company should have the ability
to continuously monitor and assess the external environment for
challenges, changes, trends and risks, as well as to analyze its
internal environment for opportunities of continuous improvement. It
should be able to identify, attract and allocate necessary resources to
achieve objectives. Furthermore, to achieve superior business
performance, companies should develop a systematic procedure for
continuous control performance monitoring and optimization in order for
them to determine their overall progress and process results. This is
done by combining different control performance metrics and assessment
methods, by performing continual assessment of their strategies,
functions and operations and by monitoring the maturity level (Jelali
2006; Strandskov 2006; Julien et al. 2004; Eickelmann 2004; Knox et al.
2003; Ravichandran, Rai 2000; Pfleeger 1995). At the same time, the
company needs to have the ability to continuously learn, change and
innovate to be competitive. The creation and sustainable development of
the companies is now central to our economic and social lives (Bieker
2004).
There are a growing number of evaluation models being provided to
organizations, either directly or indirectly, to assist with the
assessment of how mature an organization is (Fitterer, Rohner 2009; Kent
Crawford 2006; Cooke-Davies 2004, to name a few of them). Over the
years, maturity models have been used in many industries. At the same
time, there is an intense interest inside organizations in the topic of
how to best measure process performance and enhance customer
relationships (Cater-Steel et al. 2006; Lindgreena et al. 2006). The use
of such an assessment tool should enable the company to determine its
ability to maintain or develop process performance in the long term, as
well as ability to manage the development, acquisition, and maintenance
of its products or services. It also helps the company to appraise its
organizational maturity or process area capability, establish priorities
for improvement, and implement these improvements.
In this context, building upon various models provided in the
literature, the paper introduces a maturity model, which was designed to
help the Romanian companies to assess the current level of performance
and further improve. In the paper we analyze the relationships that
exist among the critical components of an organization's management
system at the strategic and operational level so that key drivers or
outcomes will become the heart of sustainable development. In
particular, the paper examines how the organizational system influences
process maturity profile of Romanian companies, and the degree to which
process maturity level plays a role in sustainability improvement. The
paper employs a factor analysis to explain the pattern of correlations
within a set of observed variables that determine the process maturity
profile of the Romanian companies. However, it is important to provide
advanced empirical evidence to substantiate our beliefs.
The remainder of the paper is organized as follows. Next part
offers a brief overview of the process maturity concept and the
requirements for those organizations that aim at being sustainable. The
coming section presents our arguments of conceptualizing the major
constructs (dimensions) that constitute a sustainability-oriented
organizational management system for the organizations. The section
suggests a model that establishes theoretical relationships between
these dimensions. The subsequent section interprets the results and
discusses the findings. The paper ends with a section of conclusions.
2. Organization process maturity: a review of the literature
Maturity assessment approaches originate mainly from the field of
quality management. The concept of maturity has been first introduced in
Crosby's quality management maturity grid (CQMM) (Crosby 1979).
Crosby (1979) defined five evolutionary stages of how an organization
adopts quality management practices. The concept of process maturity is
continually being used in many aspects of organizations as a means of
assessment or as a part of a framework for improvement. Maturity means
"the extent to which an organization has explicitly and
consistently deployed processes that are documented, managed, measured,
controlled, and continually improved". (CMMI Product Team 2002:
582). The concept of process maturity derives from the understanding
that processes--like products or organizations--have life cycles or
clearly defined stages that can be managed, measured, monitored and
controlled (Soderberg, Bengtsson 2010; Hermann et al. 2007).
The notion of measuring an organization's maturity has been
the subject of various academic papers (April, Abran 2009; Antonucci et
al. 2004; Ravichandran, Rai 2000; Harter et al. 2000; Rosenquist 1997;
Humphrey 1989; Scott 1974). International standards also provide
different models for assessing an organization's maturity level
(ISO 9004, ISO/IEC 15504). A maturity model can be used as a benchmark
for comparison and as an aid to understanding the business processes. By
understanding a maturity model, organizations can use this to help not
only assess their current maturity level, but also to help efficiently
advance them to a higher level of maturity (Meidani et al. 2010;
Veronesi, Visioli 2010; Rad, Levin 2006; Antonucci et al. 2004).
The maturity level of an organization provides a way to predict its
future performance within a given discipline or industry. Experience has
shown that companies do their best when they focus their
process-improvement efforts on a manageable number of process areas or
those business decisions that require increasingly sophisticated effort
as the organization improves (Ladley 2010; Yuan et al. 2009;
Zinkeviciute 2007; Humphrey 1989). A maturity level is a defined
evolutionary scale of process improvement. Each maturity level
stabilizes an important part of the organization's processes (CMMI
Product Team 2002). Any attempt to skip maturity levels is
counterproductive, since each level builds a foundation from which to
achieve the next level (Soderberg, Bengtsson 2010).
The companies have adopted various competition strategies to reduce
product development time and deliver higher quality products and
services to their respective customers at lower costs. Under time-based
competition, the companies strive to constantly improve the reliability
and capability of their manufacturing processes, but also improve the
after-sales services (Harter et al. 2000). A key premise underlying
process improvement in manufacturing is the elimination of waste and
rework in manufacturing activities by reducing product defects. These
improvements are thought to arise from reduced defects, scrap and rework
in a mature manufacturing process. Harter et al. (2000) found that
improvements in process maturity lead to higher quality. However, higher
quality in turn leads to reduced cycle time and development effort in
the products. Jiang et al. (2004) found that by examining performance of
projects in relation to the activities at various levels of maturity,
the activities associated with the managerial control of development
related positively to project performance measures. Even so, there are
many reasons which explain why companies do not adopt a capability
maturity model, such as: the size, costs involved, and time required
(Staples et al. 2007).
Literature provides different models for assessing an
organization's maturity level. A maturity model can be used as a
benchmark for comparison of different organizations where there is
something in common that can be used as a basis for comparison. In the
following paragraphs we address some of the most popular maturity models
which proved to be successful when applied by companies.
The ISO 9004:2009 international standard provides organizations
with guidelines and tools on the application of the eight quality
management principles to the purpose of achieving long term sustainable
success. Although, the organizations have at their hand, through this
standard, guidance on managing the movement of the organization as a
whole, rather than just some of its constituent parts, towards
increasing performance. According to ISO 9004, there are five levels of
maturity that organizations can attain, namely: (1) "beginner"
organization, (2) "proactive" organization, (3)
"flexible" organization, (4) "innovative"
organization and (5) "sustainable" organization. The maturity
levels are derived from the same eight principles of quality management
(ISO 9000:2005) and determine the level of maturity of an organization
in relation to six criteria: organization's environment;
strategies, policies and communication; resources; processes;
measurements and analysis, and learning, improvement and innovation. For
attaining the levels of "innovative" or
"sustainable", an organization must take into account
different mechanisms and instruments for results' evaluation, that
can be implemented both at strategic and operational level.
A second maturity model, also known as SPICE (Software Process
Improvement and Capability dEtermination), makes the purpose of another
international standard, ISO/ IEC 15504 (Joint Technical Subcommittee
between ISO--International Organization for Standardization and
IEC--International Electrotechnical Commission). The standard provides a
framework for the assessment of processes, which can be used in two
contexts: process improvement, and capability determination,
respectively evaluation of supplier's process capability. ISO/IEC
15504 is the reference model for maturity models (consisting of
capability levels which in turn consist of process attributes and
further consist of generic practices) against which the assessors can
place the evidence that they collect during their assessment, so that
the assessors can give an overall determination of the
organization's capabilities for delivering products. The SPICE
criteria levels can be applied to an organization in order to determine
its level of maturity in relation to people, process, technology, and
measurement. The levels 0 to 5 in the model are the following: (0)
incomplete, (1) performed, (2) managed, (3) established, (4)
predictable, and (5) optimizing.
Another maturity model suggested for our research was firstly
described by Watts Humphrey (1989) and is known as Capability Maturity
Model (CMM). The CMM is a process capability maturity model which aids
in the definition and understanding of an organization's processes.
The model was developed by the Software Engineering Institute (SEI) of
Carnegie Mellon University for the software engineering process. The CMM
is now popular and has been effective in emphasizing the importance of
process improvement. Anecdotal evidence suggests that organizations
implementing CMM-based software process improvement have incurred gains
in the development time cycle and programmer productivity (Xirogiannis,
Glykas 2007; Pooley, Wilcox 2004; Ravichandran, Rai 2000).
Process-maturity measured on the CMM maturity scale reflects the
company's level of investment to improve software process
capabilities. The CMM framework includes 18 key process areas such as
quality assurance, configuration management, defect prevention, peer
review, and training (Ravichandran, Rai 2000). The CMM model identifies
five levels of process maturity for an organization, namely: (1) initial
(ad hoc, chaotic), (2) repeatable (process discipline), (3) defined
(institutionalized), (4) managed (quantified, measured), and (5)
optimized (process improvement).
Building upon the CMM model, Niazi et al. (2005) design a maturity
model for implementation of software process improvement, which has
three dimensions -maturity stage dimension, critical success factor
dimension and assessment dimension. It provides a very practical
structure with which to assess and improve software implementation
processes. A similar model was developed for assessing the maturity of
requirements engineering process (Niazi et al. 2007). Rainer and Hall
(2002), using a maturity-based analysis, report that the key success
factors that impact majorly software process improvement are reviews,
standards and procedures, training and mentoring, and experienced staff,
internal leadership, inspections, executive support and internal process
ownership, that the more mature companies considered.
Andersen and Jessen (2003) propose a model to assess project
maturity in organizations along three dimensions: knowledge (capability
to carry out different tasks), attitudes (willingness to carry them
out), and actions (actually doing them). The different dimensions of
maturity are further divided into sub-concepts, which should provide a
good understanding of the project maturity of an organization. Demir and
Kocabas. (2010) demonstrate how a project management maturity model can
enhance the quality of education delivered. Implementation of such model
allows the educational organization to identify the steps needed to be
taken for accomplishing the expected results and in what sequence to
realize meaningful and measurable results. Kent Crawford (2006)
introduces a five-level maturity model to assess project management in
organizations against the following knowledge areas: integration, scope,
time, cost, quality, human resources, communications, risk, and
procurement. The five levels, similar to those of the CMM model are as
follows: (1) initial process, (2) structured process and standards, (3)
organizational standards and institutionalized process, (4) managed
process and (5) optimizing process.
Other researchers Cooke-Davies and Arzymanow (2003) investigate the
nature and extent of variations between project management practices in
different industries. They found that the most highly developed project
management models were found in the Petrochemical and Defense
industries, which on average scored highly on most dimensions considered
for assessment. This can be considered to equate a measure of project
management maturity in that particular industry.
Lee et al. (2010) introduce a maturity model based on communities
of practice evaluation framework which helps Korean companies to move
from immature, inconsistent activities to mature, disciplined approaches
aligned to strategic business imperatives.
Gottschalk (2008a) proposes a four-stage maturity model for
criminal organizations. The maturity levels are activity-based,
knowledge-based, strategy-based, and valuebased criminal organizations,
respectively. Also, Gottschalk (2008b) introduces a conceptual
four-stage maturity model for email communication in knowledge
organizations. The stages are labeled person-to-technology,
person-to-person, person-to-information and person-to-application,
respectively. A similar model was also developed for interoperability in
digital government (Gottschalk 2009). The maturity levels are: (1)
computer interoperability, (2) process interoperability, (3) knowledge
interoperability, (4) value interoperability, (5) goal interoperability.
Similarly, Andersen and Henriksen (2006) propose an e-government
maturity model by focusing IT applications to improve the core
activities and bring end-users as the key stakeholders for future
e-government investments.
Another stream of research regards Rosenthal and Vigeland's
work (1996) which proposes a maturity benchmarking method to assess
process performance for electronics organizations, as an indicator of
industry practices, such as: customer requirements gathering, customer
involvement in development, training and support of managers and
engineers, use of aids for electronic design and component data
transfer. Kruger (Neels) and Johnson (2010) demonstrate how information
and communications technology and information management enhance
knowledge management maturity of organizations.
Berg et al. (2002) present a method for assessing the quality and
maturity of R&D against six viewpoints: business strategy, product
and technology strategy, strategic implementation, R&D as a business
section, outputs, and implementation of R&Dprojects. Procedures in
each of the six viewpoints are assessed and scored by five maturity
levels.
Strutt et al. (2006) introduce a design safety capability maturity
model, outlining the key processes considered necessary to safety
achievement. The maturity levels defined and the scoring methods are
related to regulatory mechanisms and risk based decision making together
with the environmental risk management.
Other researchers Fitterer and Rohner (2009) propose a
networkability maturity model for health care providers. The components
of this assessment model are: IT management, process management,
organizational project management, cooperation management, and systems
architecture. The five maturity levels are defined as follows: initial,
managed, defined, quantitatively managed, and optimizing (CMMI Product
Team 2002).
Besides the above mentioned assessment models, numerous other
performance assessment models exist in the literature with the purpose
of helping the companies to advance their business to an upper level of
performance. It is worth to mention here the well known models like
Total Quality Management (TQM), Six Sigma or Business Process
Management, which do not make the object of our direct investigation
because of the high complexity of this research and space constraints.
Additionally, the paper deals mainly with researching multiple maturity
models adopted by companies operating in different fields and
industries, with the purpose of designing a generic model for maturity
assessment in the Romanian organizations. However, various works were
considered to understand the links between the ISO 9000 requirements,
TQM model and Six Sigma practices (Yang, Hsieh 2009; Zu et al. 2008) and
to investigate the models' impact on innovation and firm
performance (Mellat-Parast 2011; Quist et al. 2007).
Therefore, when it comes to choosing an appropriate assessment
model for a particular type of business it is important to know that the
assessment criteria have a critical role in the success of assessments,
but the customers may have different purposes for assessments (Jokela
2004). Such as, for the purpose of this research, we focused mainly on
understanding how various maturity assessment models work for different
industries and what are the critical dimensions which describe a
successful business and drive its competitiveness on the market. Limited
attention has been devoted to define process management, identify and
define its constitutive dimensions, and develop reliable and valid
measurement instruments for each of these dimensions (Ravichandran, Rai
2000). Furthermore, the dearth of empirical research examining
organization process maturity in emerging countries constitutes a
critical gap in the business process management literature that needs
much more attention from scholars. Additionally, none of the maturity
models existing in the literature do not link the internal and external
results achieved by a company with sustainability improvement.
Integrative theory development is required to understand the
relationships between process management practices and other elements of
the organization management system, which enable or constrain effective
process management. Attempts to design a unified model for
implementation of a maturity model in organizations exist and are
valuable as they resolve various issues related to business process
performance (Mellat-Parast 2011; Zu et al. 2010; Yoo et al. 2006).
In this context, for the purpose of this paper, a maturity model
was proposed in an attempt to integrate the fundamental features of the
most commonly spread maturity assessment models mentioned above, to meet
particular needs and characteristics of the Romanian companies. The main
goal of such an assessment tool is not only to assist Romanian
organizations with the evaluation of their business performance, but to
help them to advance the maturity level of each one of their key
processes or other processes.
3. Conceptual model and research approach
3.1. Research model
The core model introduced in the paper builds, mainly, upon the
concept of managing process performance for sustainability improvement
developed by the international standard ISO 9004:2009, and tries to
integrate all components of an effective organizational management
system as they are described by various research models mentioned above.
Romanian companies have particular needs concerning the necessary
resources, operations and the target market; also, their business
performance assessments cannot be described as current practices
throughout the organizations. Therefore, a more simplistic, but
effective assessment model is required for the Romanian organizations,
to help them evolve faster and smarter in a more and more dynamic and
competitive world, and achieve sustainable success.
According to the ISO 9004:2009 the sustainability of the
organization is reliant on its ability to independently monitor the
external environment for opportunities, changes, trends and risks and
analyze its internal environment. At the same time, the organization
needs to have the ability to learn, change and innovate in response to
the results of monitoring, through cohesive, efficient and aligned
processes that are based on quality management principles. The sequence
of steps needed for the process of managing for sustainability follows
the well known "Plan-Do-Check-Act" (or P-D-C-A) cycle, which
seems to work properly in our case (considering the particular
characteristics of the Romanian organizations and
"easy-to-use" feature of this improvement tool).
Drawing from the above business process management literature and
focusing mainly on developing an organizational management system for
sustainability improvement, we introduce a conceptual framework which
integrates ten dimensions that inter-relate each other. Other researches
also pointed out the significance of most of these key business factors
in the overall business performance of an organization (Ahmed, Capretz
2010; Yang, Hsieh 2009; Zu et al. 2008; Yoo et al. 2006; CMMI Product
Team 2002). The ten dimensions of the maturity model proposed are the
following: (1) Organizational context; (2) Strategic planning; (3) Risk
management; (4) Process management; (5) Human resource management; (6)
Results analysis; (7) Performance indicators; (8) Learning; (9)
Improvement, and (10) Innovation. To discuss the organization process
maturity profile at operational level we developed a model that
inter-relates these variables with the organization's maturity
level and are specified as drivers of sustainability improvement (see
Fig. 1). The model is tested using data collected from 1302 companies
from Romania.
The variables to be studied--against which organization process
maturity at strategic and operational level can be described and
assessed--are defined as folows: (1) Organization context (OrgContext)
assesses the extent to which the organization monitors and analyzes its
external and internal environment, and collects ongoing data and
information about it; (2) Strategic planning (StrategPlan) assesses the
degree to which the organization develops strategic orientations and
policies based on the risks and opportunities identified and determines
its current and future capabilities needed for sustainability; (3) Risk
management (RiskManag) measures the degree to which the organization
identifies and analyses the risks in cost, time, quality, technology,
resources, etc.; (4) Process management (ProcessManag) assesses the
degree to which the organization uses a 'process approach' to
identify its processes and their interactions, and appoints
'process owners' to ensure process responsibility and
authority; (5) Resource management (ResManag) measures the extent to
which the organization identifies the resources that are critical to its
development and achievement of performance, develops a plan for
providing, controlling, monitoring, protecting and developing its
resources, assesses its resources needs and establishes priorities for
the allocation of resources; (6) Results measurement and analysis
(ResultEval) assesses the extent to which the organization monitors and
measures systematically the performance of all its relevant processes;
(7) Performance indicators (Indicators) identifies the
organization's key performance indicators, monitors the degree and
speed at which it achieves its objectives, and takes corrective action
when objectives are not met; (8) Learning (Learning) assesses the extent
to which the organization detects changes and trends in its business
environment and establishes the culture of a learning organization; (9)
Improvement (Improv) measures the extent to which the organization
defines objectives for improvement and seeks to achieve these
objectives, and (10) Innovation (Innovation) assesses the degree to
which the organization innovates in its capabilities and organizational
constitution as necessary to ensure future success.
[FIGURE 1 OMITTED]
The aim of the paper is to identify the degree to which the
Romanian organizations assess process maturity at both strategic and
operational level and offer continued satisfaction to their
stakeholders, and to help organizations identify areas in which they can
improve their performances. As it was mentioned before, the paper
examines how the organizational system of the companies participating in
the survey influences process maturity profile, and the degree to which
process maturity levels play a part in their sustainability improvement.
3.2. Sample and data collection
For the collection of the data, respondents were asked to fill in a
statistical research instrument--namely Maturity Assessment Survey
(MAS), which collected information on the strategic and operational
maturity level of the organization (Paunescu 2009b). Multiple managers,
quality managers and other executives from Romanian organizations filled
in the questionnaire in their organizations. The MAS was designed to
identify the degree to which organizations are sustainable and offer
continuous satisfaction to their stakeholders, and to help organizations
identify areas in which they can improve their performances. The
questionnaire behind the model was designed to answer how the companies
work with their processes, how they define and document them, and how
they measure and link them vertically and horizontally. The questions
can apply to small companies (SMEs) as well as large organizations
(public and private).
The sample consisted of 1302 Romanian organizations that met the
following sampling criteria: (1) respondents have been working with the
company as quality manager/ responsible or other executive position for
more than six months, (2) company has been in operation at least three
years, and (3) company has at least five employees. The reporting
companies represented a range of industries, including commerce and
sales (46%), real estate (15%), consulting (10%), distribution and
transportation (7%), banking and insurance (6%), IT (6%),
telecommunications (3%), advertising (2%), and a mix of other industries
(5%). The companies had been present on the market for a significant
number of years (average = 8.5). As regards the organization size, 26%
of companies have less than 10 employees (n = 339), 34% of them employ
between 10 and 49 employees (n = 443), 23% of organizations employ
between 50 and 249 employees (n = 299), and 17% of organizations employ
more than 250 employees (n = 221). Of the 1302 responding organizations,
820 (63%) achieved profitability in the last three consecutive years of
operation or more. The sample consisted of 664 men (51%) and 638 women
(49%), while 31% were general managers (n = 404), 22% quality managers
(n = 286) and 47% were from various executive positions (n = 612: sales
and marketing managers, financial managers, operations managers, HR
managers, product managers, account managers, etc.). The average age of
respondents was 38 years. Data were collected by students and graduates
who completed quality management courses at the Faculty of Business
Administration from the Bucharest University of Economic Studies. The
responses were gathered during October 2007 and January 2009, through
face-to-face interviews (100%), using a structured questionnaire. Out of
more than 1500 Romanian organizations which were asked to participate in
the study, 1420 agreed to fill in the survey for a 90% of response rate.
Through interviews we could ensure that all data needed were in
place. With interviewing we could be sure that the respondents
understood the questions, so the data based on wrong assumptions could
be kept to a minimum. Our approach also allowed us to explain the
unclear concepts, making them easier for the respondents to understand
the criteria for evaluating the company's maturity.
All this being said, it must be underlined that the respondents
were not selected at random and therefore generalization is an important
limitation of the study. Nevertheless, the present paper could prove a
solid basis for further research in the fields it addresses.
3.3. Factor analysis
Factor analysis is used to identify underlying variables, or
factors, that explain the pattern of correlations within a set of
observed variables. The method of factor extraction used in this paper
is Principal components.
We took into account the ten categories of variables described
above, for which factor analysis was employed to explain the pattern of
correlations within variables that determine the process maturity
profile of the Romanian companies. The descriptive statistics for this
group of variables is presented in Table 1. The Cronbach's Alpha
score for these variables was 0.945, over 0.7, which highly meets the
reliability requirements of the analysis.
We observe that the greatest scores in our sample were obtained by
Strategic planning and deployment and Results analysis, while the
smallest scores were obtained by Performance indicators and Learning.
This may suggest that the use of a systematic process approach to manage
process performance and interactions between them, as well as the
development of inter-relating strategic and operational processes are
key drivers for achieving performance and sustainability improvement.
Table 2 shows that any of the ten items is relevant for analysis and
increases its reliability. Nevertheless, the deletion of any of the ten
items would lead to a decrease in reliability, which means that keeping
them all is desirable.
We notice that all items are significantly correlated with the
scale. The weakest correlation with the rest of the scale is for the
ninth item, Improvement, while the most consistent (the strongest
correlation) with the rest of the scale is item seven, Performance
Indicators, followed by item eight, Learning and item one, Organization
context. The results of the correlation analysis performed on the sample
are presented in the following section.
4. Factor analysis results and findings
The main outputs obtained by employing a factor analysis are
presented below.
The first output, the correlation matrix, helps identifying the
patterns of relationships between the variables examined.
The Pearson correlation analysis (see Table 3) revealed that there
are strong positive relationships between the variables examined, which
proves that each one accounts for advancing the maturity level of
organization's processes and its sustainability development.
Thus, there is a strong positive correlation between Innovation and
Learning (0.787), and a great consistency with Performance indicators
(0.72). Also, there is a significant positive correlation between
Performance indicators and Learning (0.773). This may suggest that
identifying key performance indicators for the organization's
relevant processes, monitoring and measuring systematically the
performance of its processes, and taking corrective actions when
objectives are not met, together with building a continuously
improvement and learning environment, are key factors for driving
organization development and achieving a higher level of process
maturity (and performance). The planned outcomes are monitored and
measured and the measures developed provide useful and efficient
information concerning the working of the core activities. Furthermore,
there is a strong positive correlation between Process management and
Results' measurement and analysis. Therefore, cross-functional
coordination among business departments, use of a systematic process
view of the organization and appointment of process owners responsible
for monitoring the processes and their improvement, together with
allocation of necessary resources to achieve objectives, shape the
consistency of business performance and results.
The Pearson correlation analysis revealed also a lower positive
connection between Risk management and Improvement (0.522). Also, there
are low correlations between Results evaluation and Improvement (0.536)
and Learning (0.554) and Innovation (0.564). Consequently, the analysis
of the core activities contribute to the sustainability of the
organization, regardless the identification of the improvement and
learning needs about processes, products, structures and systems or
determination of the innovations and necessary changes needed to achieve
the organization's articulated mission, vision and objectives. Low
correlations are also found between Process management and Improvement
(0.559), meaning that it could be sufficient for organizations to set up
a strategic aim for its core activities that reflects the needs of all
its stakeholders in a sustainable way, and define appropriately
processes to address the aim for the core activities being considered.
Communalities, the second output, indicate the amount of variance
in each variable that is accounted for, before and after extraction.
Initial communalities are estimates of the variance in each variable
accounted for by all components or factors. Extraction communalities are
estimates of the variance in each variable accounted for by the factors
(or components) in the factor solution. Principal component analysis
works on the initial assumption that all variance is common; therefore,
before extraction the communalities are all 1. The communalities in the
column labeled Extraction reflect the common variance in the data
structure. So, for example we can say that 71.3% of the variance
associated with component 1--Organization context--is common, or shared,
variance. Another way to look at these communalities is in terms of the
proportion of variance explained by the underlying factors. Small values
indicate variables that do not fit well with the factor solution, and
should possibly be dropped from the analysis (see Table 4). In our study
such variables refer to Improvement.
In Table 5 we have packaged that common variance into two factors,
both before and after a varimax rotation:
The eigenvalues associated with each linear component (variable)
represent the variance explained by that particular factor and SPSS also
displays the eigenvalues in terms of the percentage of variance
explained (so, factor 1 explains 67.373% of total variance). It is clear
that the first factor--Organization context--explains a relatively large
amount of variance whereas the subsequent factors explain only a small
amount of variance.
5. Conclusions
This study is one of the first in the Romanian context with such a
large pool of respondents. It contributes to understanding maturity of
the organization's management system. Various researches were
integrated to identify the critical dimensions of an organization
management system that shape its process maturity profile and drive its
sustainable development in the long term. These dimensions were
synthesized into higher level constructs that together define an
organizational system. The ten constructs are: organizational context;
strategic planning; risk management; process management; human resource
management; results analysis; performance indicators; learning;
improvement, and innovation. None of the maturity models existing in the
literature do not link the internal and external results achieved by a
company with sustainability improvement. The model introduced in this
paper integrates components, which need to be monitored in the external
environment, with components from the internal environment, and are set
as key drivers for sustainability development.
The research results show that there are strong positive
correlations between the dimensions examined that account for a higher
level of maturity and performance of organization's processes. This
means that if organizations use the maturity dimensions referred to in
this paper, they will most likely achieve a positive effect on their
overall performance.
The results of the factor analysis revealed that all variables
observed and analyzed account for building process maturity of Romanian
companies and increasing their maturity level, and play a role in
sustainability improvement in these organizations. This is consistent
with the results obtained by Soderberg and Bengtsson (2010), which show
that there is a positive and strong correlation between supply chain
management maturity in SMEs and quality variables such as delivery
performance and productivity, which includes order fulfillment capacity
and information system support. This is also consistent with
Ravichandran and Rai's (2000) findings, which show that software
quality performance is impacted, for example, by process management
efficacy, integrated strategy or management infrastructure practices.
The results tell us that the organizations surveyed are more mature
in their organizational management system than we expected, with none of
the respondents at the lowest maturity level. This could be an
indication that even smaller companies or public organizations have
begun to fully realize the potential of business process management for
performance improvement.
Such as, the Romanian organizations surveyed report that they are
aware of and understand their core competences and competitive
priorities on the market, consider the needs and interests of various
stakeholders of their business offerings, are improvement-oriented, plan
to insure predictability of the results, focus on innovation and invest
in their capabilities as necessary to ensure future success.
In terms of managerial implications of our study, one implication
is that it is effective for a company to focus on its own processes,
define and document them, set up process goals, monitor and measure
them, and manage them for higher maturity. Furthermore, understanding
continuous improvements is vital to making companies advance to a higher
maturity level.
It must be underlined that the respondents (organizations) were not
selected at random and therefore, generalization is an important
limitation of the study. Furthermore, due to the large differences in
the size of the samples and to the complexity of the questionnaire, the
error estimated for data collection and processing is of maximum 5%.
Nevertheless, the present paper could prove a solid basis for further
research in the fields it addresses. Further empirical evidence to
substantiate our research findings is required.
doi:10.3846/16111699.2011.620149
Acknowledgements
The contribution was prepared under the support of the
UEFISCDI-CNCSIS PN II research project, code ID_828/ 2007, run by the
Bucharest University of Economic Studies in Romania. We are grateful for
the financial support received.
Received 04 February 2011; accepted 11 May 2011
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Carmen PAUNESCU is a Full Professor at the Faculty of Business
Administration from the Bucharest University of Economic Studies (ASE),
Romania. Prof. Paunescu holds a Ph.D. in Economics from ASE. Her
qualification raise includes: University of Rome "La
Sapienza", Italy--2004; Harvard Business School--EECPCL 2007, USA;
IMTA 2008, Bled, Slovenia and EEC 2009, Maastricht, Holland. Her
research interests lie in the areas of organization's maturity
assessment, organizational entrepreneurial culture, innovation and
corporate entrepreneurship, entrepreneurship education, and
cross-cultural management.
Carmen ACATRINEI (PANTEA) is an Assistant Professor at the Faculty
of Business Administration from the Bucharest University of Economic
Studies (ASE), Romania. Her research and teaching interests lie in the
areas of customer relationship management, direct marketing, project
management, negotiation techniques and other business-related topics.
Carmen holds a Ph.D. in Marketing from ASE. Her area of interest for the
doctoral research is related to the online marketing tools used for
managing customer relationships.
Carmen Paunescu [1], Carmen Acatrinei (Pantea) [2]
[1,2] Bucharest University of Economic Studies, 2-2A Calea Grivitei
street, District 1, 010701 Bucharest, Romania
E-mails: [1]
[email protected] (corresponding author);
[2]
[email protected]
Table 1. Descriptive statistics
N Minimum Maximum Mean
OrgContext 1302 1.00 5.00 4.1013
StrategPlan 1302 0.73 7.87 4.3126
RiskManag 1302 0.00 5.00 3.8709
ProcessManag 1302 0.64 5.21 3.8948
ResManag 1302 0.82 5.24 3.9018
ResultsEval 1302 0.33 5.11 4.1834
Indicators 1302 1.04 5.08 3.8302
Learning 1302 0.00 6.20 3.8092
Improv 1302 0.00 5.00 4.1336
Innovation 1302 0.00 5.00 4.0033
Valid N (listwise) 1302
Std. Deviation
OrgContext 0.64390
StrategPlan 0.64241
RiskManag 0.83338
ProcessManag 0.68336
ResManag 0.72492
ResultsEval 0.70511
Indicators 0.74784
Learning 0.75764
Improv 0.81643
Innovation 0.76178
Valid N (listwise)
Table 2. Item-total statistics
Scale Mean Scale Corrected Cronbach's
if Item Variance Item- Alph if
Deleted if Item Total Item
Deleted Correlation Deleted
OrgContext 35.939 29.937 0.800 0.938
StrategPlan 35.728 30.120 0.774 0.939
RiskManag 36.170 28.511 0.762 0.940
ProcessManag 36.146 29.910 0.751 0.940
ResManag 36.139 29.259 0.791 0.938
ResultsEval 35.857 29.784 0.742 0.940
Indicators 36.210 28.901 0.812 0.937
Learning 36.231 28.844 0.808 0.937
Improv 35.907 29.106 0.706 0.942
Innovation 36.037 28.945 0.789 0.938
Table 3. Inter-item correlation matrix
OrgContext StrategPlan RiskManag Process Manag
OrgContext 1.000 0.714 0.696 0.637
StrategPlan 0.714 1.000 0.649 0.627
RiskManag 0.696 0.649 1.000 0.595
ProcessManag 0.637 0.627 0.595 1.000
ResManag 0.675 0.614 0.666 0.605
ResultsEval 0.684 0.647 0.630 0.694
Indicators 0.666 0.620 0.685 0.633
Learning 0.641 0.648 0.623 0.626
Improv 0.569 0.567 0.522 0.559
Innovation 0.636 0.659 0.607 0.613
ResManag ResultsEval Indicators Learning
OrgContext 0.675 0.684 0.666 0.641
StrategPlan 0.614 0.647 0.620 0.648
RiskManag 0.666 0.630 0.685 0.623
ProcessManag 0.605 0.694 0.633 0.626
ResManag 1.000 0.640 0.681 0.670
ResultsEval 0.640 1.000 0.593 0.554
Indicators 0.681 0.593 1.000 0.773
Learning 0.670 0.554 0.773 1.000
Improv 0.671 0.536 0.600 0.623
Innovation 0.614 0.564 0.720 0.787
Improvement Innovation
OrgContext 0.569 0.636
StrategPlan 0.567 0.659
RiskManag 0.522 0.607
ProcessManag 0.559 0.613
ResManag 0.671 0.614
ResultsEval 0.536 0.564
Indicators 0.600 0.720
Learning 0.623 0.787
Improv 1.000 0.623
Innovation 0.623 1.000
Notes: N = 1302; Correlation is significant at the 0.01 level
(2-tailed)
Table 4. Communalities
Initial Extraction
OrgContext 1.000 0.713
StrategyPlanning 1.000 0.676
RiskManag 1.000 0.662
ProcessManag 1.000 0.643
HRManag 1.000 0.694
ResultsAnalysis 1.000 0.632
Indicators 1.000 0.726
Learning 1.000 0.720
Improv 1.000 0.578
Innovation 1.000 0.694
Extraction Method: Principal Component Analysis
Table 5. Total variance explained
Component Initial Eigenvalues Extraction Sums of
Total % of Cumulative % Squared Loadings
Variance Total % of
Variance
1 6.737 67.373 67.373 6.737 67.373
2 0.651 6.507 73.880
3 0.505 5.049 78.929
4 0.453 4.529 83.458
5 0.399 3.992 87.450
6 0.292 2.921 90.371
7 0.272 2.723 93.094
8 0.265 2.646 95.740
9 0.237 2.372 98.112
10 0.189 1.888 100.000
Component Extraction Sums of Squared Loadings
Cumulative
%
1 67.373
2
3
4
5
6
7
8
9
10
Extraction Method: Principal Component Analysis
