Ardeshir
Bazrkar
North
Tehran Branch, Islamic Azad University, Iran
E-mail: Ardeshir.Bazrkar@gmail.com
Mohammad
Hajimohammadi
Islamic
Azad University, Iran
E-mail: mohammad.hmohammadi64@gmail.com
Submission: 2/7/2020
Revision: 3/19/2020
Accept: 3/29/2020
ABSTRACT
Knowledge management is essential for activities such as problem-solving, dynamic learning and decision making, and can improve organizational performance and in particular the financial performance of the organizations by enabling the organization to perform smarter. The main purpose of this study was to investigate the impact of knowledge management on the financial performance of the organizations considering the mediating role of innovation and organizational intelligence in the e-banking industry. The statistical population of the study consisted of senior executives of 31 public and private banks active in the e-banking industry of Iran (n=54). Library and field research (questionnaire) was used to collect research data. In this study, Kolmogorov-Smirnov and Spearman-Bartlett tests were used to analyze the data to determine the normality of the research data and to determine the adequacy of the sample size (using SPSS software). Then, structural equation modeling was used to analyze the conceptual model and to test the hypotheses (using Smart PLS software). The hypotheses test results showed that knowledge management has a positive and significant impact on the financial performance of the organizations active in the e-banking industry and the innovation and organizational intelligence influence the relationship between knowledge management and the financial performance of the organizations. Thus, the mediating role of these two variables (innovation and intelligence) was confirmed.
Keywords: Knowledge; Management; Organizations’; Financial; Performance; Innovation; Organizational; Intelligence; e-Banking
1.
INTRODUCTION
Nowadays, knowledge is considered as the most important
factor in competition. Along with knowledge, innovation is recognized as the
most important factor for the survival of companies (Hau et al., 2013; Wu &
Wang, 2016).
In the post-industrial societies, survival and progress
are based on knowledge and physical assets have been replaced by intangible
resources, namely knowledge, and information, as major sources of production,
improved business processes and increased user and customer satisfaction.
Knowledge management is now widely recognized as a competitive advantage
(Orenga-Roglá & Chalmeta, 2019).
Most organizations, whether for-profit or non-profit organizations,
plan and implement knowledge management strategies (Gordon & Ditomaso,
1992). Contemporary management scholars often emphasize the effectiveness of
coming from acquiring, developing, and applying knowledge. Organizational
intelligence is recognized as an important factor among organizational
development and organizational behavior experts (Ulrich & Brockbank, 2005).
Organizational intelligence ensures long-term excellence
for organizations and communities and improves their performance. Organizational
intelligence refers to a set of tools and techniques that help convert large
amounts of data from multiple sources into meaningful information to support
decision making and improve organizational performance. In the last decade,
organizational intelligence has emerged as a major driving force for
organizational efficiency and effectiveness (Ramakrishnan et al., 2016).
Competition between organizations is intensifying every
day and the rate of innovation is increasing. Competition between organizations
has led to reduced human resources to reduce costs, forcing organizations to
convert tacit knowledge among employees to explicit knowledge (Hercy &
Belanchard, 2001). More innovative organizations are also more successful in
responding to changing environments and creating new capabilities toward better
innovations (Sadeghi & Mohtashami, 2011). Innovation is a critical factor
in sustainably creating value and advantage (Yusuf, 2009).
Organizations need to have sustainable innovations that
are created through the continuous sharing of knowledge between employees and
customers (Gordon & Ditomaso, 1992). Knowledge management helps an
organization find, select, organize and disseminate important and useful
information (Shujahat et al., 2019). Knowledge management is essential for
activities such as problem-solving, dynamic learning and decision making,
Improved organizational performance and in particular the financial performance
of the organization by enabling the organization to perform smarter (Yolles,
2005).
Kianto et al. (2013) in their research, they found that
informed and systematic management of organizational knowledge had an impact on
corporate financial performance. Inkinen (2016) In their review of knowledge
management and financial performance of organizations, they found that
knowledge management practices can have a positive impact on the financial
performance of organizations.
Karabulut(2015) In a study aimed at examining the impact of different types of innovation on the performance of manufacturing organizations in Turkey, he found that the innovation capabilities of production organizations are highly influential on the quality of manufactured products.
Shashi et al. (2019) in a study that examined the impact of innovation management on the environmental and financial performance of small and medium-sized enterprises in India found that a variety of innovations could affect the environmental and financial performance of an organization. Soltani et al. (2019) In their research they found that e-learning systems positively affected organizational intelligence. Also, they show that the influence of knowledge management and organizational learning on organizational intelligence is important.
An organization that seeks to implement knowledge
management and improve its performance in various aspects learns to manage
knowledge intelligently through innovation in its current processes. The main
purpose of this study was to investigate the impact of knowledge management on
the financial performance of the organizations considering the mediating role
of innovation and organizational intelligence in the e-banking industry.
There have been numerous research on the impact of KM[1]
implementation on improving organizational performance, the impact of
innovation on improving organizational performance, as well as the impact of
innovation on the relationship between organizational intelligence and organizational
performance. But, so far no research has been conducted to simultaneously
examine the impact of innovation and organizational intelligence on the
relationship between organizational knowledge management and financial
performance in the e-banking industry.
This research is dedicated to filling this research gap. Research gap related to knowledge management, a financial performance
about variables of organizational intelligence and organizational innovation of
banks active in the Iranian banking industry. Addressing this issue can provide
ways for the banks in question to take appropriate measures to improve their
financial performance.
2.
LITERATURE REVIEW
2.1.
Knowledge Management
Today, knowledge management is recognized as an important
factor for maintaining competitive advantage and improving the financial and
non-financial performance of the organizations. The results of numerous
researches around the world indicate the direct impact of knowledge management
on performance, so if the quality of organizational knowledge is desired, it
can be expected that the performance of the organization in different aspects
can be significantly improved (Zaied et al., 2012).
Researchers have found that knowledge management is not
fleeting, but it has lasting effects. The survival of the organization and
maintaining the competitive advantage and with the help of knowledge management
is possible if the organization can continually create new knowledge (Yang,
2010). Finding a universally agreed definition for knowledge management is
difficult. This has led to various definitions of knowledge management.
Yang et al. (2010) define knowledge management as a set
of processes that use knowledge as a key factor for value creation (including
the most important components of knowledge: creation, acquisition,
registration, transfer, and application).
Rouse (2014) considers knowledge management as
consciously and comprehensively collecting, organizing, sharing and analyzing
the resources, documents, and skills of individuals by the organization. In
fact, it can be said that knowledge management is an approach to achieve
organizational goals through better application of knowledge (Fadia &
Kamel, 2014). Knowledge management involves the process of optimally combining
knowledge and information in an organization (Hajimohammadi et al., 2019).
The knowledge management system is a system for applying
the principles of knowledge management through the process of creation, transfer,
and application of knowledge in an organization (El Said, 2015). There is no
consensus on knowledge management processes. Different researchers have
identified different processes for knowledge management.
For example, Spender (1996) suggested knowledge
management processes as: knowledge creation, knowledge transfer, and knowledge
application (Trivella & Nasiopoulos, 2015). Probst et al. (2000) introduced
identification, acquisition, development, sharing/dissemination, transfer and
application of knowledge as knowledge management processes.
Alawi and Leidner (2001) identified four processes for
knowledge: creation, storage/retrieval, transfer, and application. In this
study, knowledge management processes were: knowledge creation, knowledge acquisition,
knowledge organization, knowledge dissemination, and knowledge application.
The following describes each component: Knowledge
creation: Knowledge creation is the first step in knowledge management.
Knowledge in the organization is created in two distinct cycles: personal and
group cycles. Personal Knowledge When used in the organizational context leads
to new knowledge which is called organizational knowledge (Bose, 2004).
Knowledge Acquisition: knowledge acquisition involves
acquiring external knowledge to be used within the organization (Gabriel &
Navarro, 2015). Knowledge acquisition includes explicit knowledge as well as
tacit knowledge (Fadia & Kamal, 2014). Knowledge organization: Knowledge
created must be stored in its original form in databases.
Many organizations use different types of resources to
acquire and maintain knowledge (Lin, 2007). Knowledge storage: Maintaining the
knowledge assets of an organization in an effective database (Lawson, 2003).
Dissemination of knowledge: Dissemination of knowledge requires two steps:
sending or delivering knowledge to a potential recipient and being absorbed by
the individual.
Knowledge dissemination is about increasing the ability
of the organization to do things and ultimately enhancing its value (Obeidat et
al., 2016). Knowledge Application: Knowledge application ensures that the
organization correctly utilizes the acquired knowledge. On the other hand,
knowledge application represents the collection of problem-solving instructions
and processes used in decision making (Gabriel & Navarro, 2015; Shujahat et
al., 2018).
By using knowledge management practices, organizations can maintain
their long-term competitive edge and enhance their performance in a variety of
financial and non-financial areas. Banks are no exception (Yun,2013). Studies show that organizations in different
industries have been able to improve their financial performance by
implementing and implementing knowledge management processes (Xu & Quaddus, 2012). In addition to the
performance of organizations, knowledge management practices can affect
different categories such as innovation and organizational intelligence. Accordingly, the first to third hypotheses of this study are formulated.
2.2.
Innovation
The need for innovation in service organizations is an
important factor for competitive advantage in different areas (Bazrkar et al,
2017). Innovation is one of the most important factors for the company's
success, survival and competitive advantage (Jimenez & Sanz, 2011). Innovation
is defined by Schumpeter as a driving force for development: the willingness of
the company to adopt new ideas that lead to the development of new products
(Rubera & Kirca, 2012).
Innovation has a significant impact on organizational
outcomes and can encourage the organization to present new ideas, inventions,
and discoveries that lead to the production of new products and services
(Chahal & Bakshi, 2015). According to the findings of Ali Hakim and Hassan
(2016), innovation may be divided into radical and incremental, technological
and administrative innovations.
Researchers now know that the speed of innovation and the
quality of innovation for organizations in a complex and evolving business
environment is very important. The speed of innovation reflects the ability of
the organization to rapidly achieve processes that lead to the production of
new products and services compared to competitors so that it takes very little
time from idea generation to final commercialization (Wang et al., 2016).
The quality of innovation is related to the process and
the end result of innovation (Haner, 2002). The quality of innovation can be
measured through customer added value, features, cost, reliability and
flexibility of products and services and process effectiveness. (Wang &
Wang, 2012).
Innovation is defined as the application of knowledge to
deliver a new product or service demanded by customers. Here, knowledge refers
to technology or market knowledge. Technological knowledge is the knowledge of
the components; the link between the components; and methods, processes, and
techniques used for delivering a product or service. Market knowledge means
understanding the distribution channels, product applications, and needs and
demands of customers.
Innovation performance refers to a presented innovation
with regard to market knowledge and technological knowledge (Li et al., 2009).
Accordingly, in the present study, the innovation variable is measured on the
basis of two criteria (quality and speed) to study the relationship between
knowledge management, organizational innovation, and organizational financial
performance.
Rajapathirana and
Hui (2018) examined the relationship between innovation capability, type of
innovation, and corporate performance, including innovation, market, and
financial performance, in a study entitled Relationship between Innovation
Capability, Type of Innovation, and Corporate Performance. The results showed
that there is a positive and significant relationship between innovation capabilities,
innovation efforts and performance of the companies studied.
Innovation demonstrates the desire to support creativity in introducing
new products and services, technological leadership, and research and
development into new processes, and this demonstrates the need for R&D that
enables organizations to adapt to their innovation capabilities. Provide
conditions for performance improvement (Shan & Song, 2016).
Achieving organizational innovation by enhancing
innovation capabilities can empower an organization to utilize innovation
production and technological processes to better respond (Cozzarin, 2016).
Innovation can bring benefits to the organization. Processes related to product
innovation and new product development, on the other hand, increase flexibility
and, on the other hand, reduce the time required to market the product to a
significant extent, thereby enhancing organizational performance.
The fourth hypothesis of the present study is formulated on this basis
and seeks to investigate how the impact of innovation on the financial
performance of banks operating in the e-banking industry.
2.3.
Organizational Intelligence
Organizations need smart staff to survive and function so
they can perform their tasks more effectively (Mousavi et al., 2016). According
to Jang (2014), organizational intelligence is a combination of knowledge and
skills (both about tangible and intangible assets) that organizations can
develop to achieve their goals. Organizational intelligence is one of the
social consequences of teamwork (Ahadinezhad et al., 2012).
The importance of organizational intelligence in business
stems from the fact that individual intelligence alone is not capable of
overcoming the issues ahead; so it is important to build collective intelligence
within the organization as a necessity to overcome problems. (Maries &
Scarlat, 2012).
Promoting organizational intelligence is one of the
undeniable requirements for most organizations to enhance their capabilities
through acquisition and analysis of information, as well as increased knowledge
and awareness (Dealtry, 2005). Increased organizational intelligence enables
organizations to quickly and accurately analyze their surrounding information
and to make it available to decision-makers when needed (Travica, 2015).
Today, the advent of technology, innovative adaptive
methods, virtual reality, improved markets, and alternative business models
have accelerated changes. For success in business, according to Albrecht,
having intelligent human agents, smart teams, and intelligent organizations are
needed (Alberecht, 2003).
In 1995, Glynn published three models of organizational
intelligence: Aggregation or accumulation model, confluence model, and
distributive or dispersive model. Glynn also classified organizational
intelligence into three categories: 1) the accumulation of personal
intelligence 2) the interconnection and intertwining of personal intelligence
3) organizational intelligence as a larger system (Polat, 2009).
In this study, the organizational intelligence was
evaluated based on seven components proposed by Albrecht (2003) and Travica
(2015): strategic vision, shared destiny, desire for change, morale, unity and
agreement, knowledge utilization and performance pressure. Organizational intelligence can be considered as one of the types of
cognitive intelligence that is related to emotional intelligence (Law et al.,2014).
An accurate and accurate understanding of employee emotions
facilitates interpersonal coordination and action, which in turn improves
organizational performance in various areas including financial performance. Accordingly, the fifth research hypothesis has been formulated to
investigate the impact of organizational intelligence on the financial
performance of the e-banking industry.
2.4.
Financial Performance of the
Organization
Oxford dictionary defines performance as "the act or process of performing a task, an action,
etc". This definition, in addition to being related to inputs and
outputs, indicates that performance is closely related to performance and
outcomes. Therefore, performance can be regarded as behavior (Busi, 2006).
Organizational performance shows how an organization
achieves its mission and goal; organizational performance refers to starting
from a given situation and achieving a specific goal, which may include several
objectives such as market share, sales volume, Employee motivation, customer
satisfaction, quality level, etc. (Liu & Wu, 2010).
Organizational performance is a complex phenomenon that
may be the simplest phrase to describe as a set of activities aimed at
achieving the organization's goal. Like function, the targets should be studied
from different perspectives (Rus et al, 2012). Organizational performance
refers to how the tasks, works, and activities are accomplished and the results
of their accomplishment (Busi, 2006).
Organizational performance is a very important measure in
evaluating the success of an organization and is always one of the most
important variables in performance management research (Iqba, et al., 2018).
According to Kuhang et al., optimal Organizational performance indicates the
growth and development of an organization. Organizational performance has many
dimensions. Many researchers focus more on the financial aspect of evaluating
and measuring the performance of organizations.
This study also emphasizes on the financial performance
of the organization. Based on researches of by Zhao et al. (2018) and Gandhi et
al. (2011) three Indicators were selected: profitability, market share and
sales volume and sales growth (all of them compared to competitors).
2.5.
Conceptual Research Model
According to the purpose of the research and the
variables studied in this study, the conceptual model of this research is
presented in figure 1.
Figure 1:
Conceptual model of research
Given the conceptual model and purpose of the research,
the research hypotheses are as follows:
·
H1:
Knowledge management has a positive impact on the financial performance of the
organizations active in the e-banking industry.
·
H2: Knowledge management has a positive impact on
the innovation of the organizations active in the e-banking industry.
·
H3:
Knowledge management has a positive impact on organizational intelligence in
the e-banking industry.
·
H4: Innovation has a positive impact on the
financial performance of the organizations active in the e-banking industry.
·
H5:
Organizational intelligence has a positive impact on the financial performance
of the organizations active in the e-banking industry.
·
H6:
Knowledge management has a positive impact on the financial performance of the
organizations active in the e-banking industry due to the mediating role of
innovation.
·
H7:
Knowledge management has a positive impact on the financial performance of the
organizations active in the e-banking industry due to the mediating role of
organizational intelligence.
3.
RESEARCH METHODOLOGY
Since the results of this study will be used by managers and
decision-makers in the e-banking industry to improve the financial performance
and solve existing organizational problems, therefore, this study is applied
research in terms of its purpose. In addition, it is survey research in terms
of data collection and in terms of the method is descriptive-correlational
research. The statistical population of this study was senior executives of 31
public and private banks active in the e-banking industry of Iran (n=54). Due
to the small statistical population, the statistical sample size was considered
equal to the statistical population. Library and field research methods
(questionnaires) were used to collect research data.
Questionnaires used were based on Lawson (2003), Obaidat
et al. (2016) and Shajat et al. (2018) researches on knowledge management, Wang
and Wang (2012) and Wang et al. (2016) researches on Innovation, Albrecht
(2003).) and Travika (2015) researches on organizational intelligence, Zhao et
al. (2018) and Gandhi et al. (2011) researches on financial performance
management. The answers were based on a Likert scale ranging from 1 (very weak)
to 5 (very strong). The number of questionnaire questions based on the
variables studied and the components of each variable along with the sources
are presented in Table 1.
Table 1: Questionnaire components
and questions
Abbreviation |
Number of questions |
Source |
Components |
Variable |
KNC |
1 |
LAWSON (2003) OBEIDAT et al. (2016) SHUJAHAT et al. (2018) |
knowledge
creation |
Knowledge management |
KND |
1 |
knowledge acquisition |
||
KNO |
1 |
knowledge
organization |
||
KNS |
1 |
knowledge storage |
||
KNP |
1 |
Knowledge
dissemination |
||
KNA |
1 |
knowledge
application |
||
INS |
1 |
WANG & WANG (2012) WANG et al. ( 2016) |
Speed of
innovation |
Innovation |
INQ |
1 |
Quality of
Innovation |
||
STV |
1 |
ALBERECHT (2003) TRAVICA (2015) |
Strategic vision |
Organizational intelligence |
COD |
1 |
Common destiny |
||
DTC |
1 |
The desire to
change |
||
MOO |
1 |
Mood |
||
UAG |
1 |
Unity and
agreement |
||
APK |
1 |
Applying
knowledge |
||
PEP |
1 |
Performance
pressure |
||
PRO |
1 |
ZHOU et al. (2018) GÜNDAY et al. (2011) |
Profitability |
Financial performance |
MAS |
1 |
Market share |
||
SAV |
1 |
Sales volume |
||
SAG |
1 |
Sales growth |
3.1.
Defining research variables
The components of each of the main variables of the study
are described:
3.1.1. Knowledge Management
·
Knowledge Creation: The process of creating new knowledge is about changing and transforming
skills and knowledge People are fortified with knowledge. In this study, the amount of knowledge creation over a one-year period
was considered.
·
Knowledge
Acquisition: The extent to which information and knowledge are acquired from
interactive networks within and outside the organization over a period of one
year.
·
Knowledge
Organization: Through this action, the necessary knowledge is easily organized
for everyone to use. In this study, the amount of knowledge organized over a
one-year period was considered.
·
Knowledge Storage:
Through this action, the necessary knowledge is easily stored for use at the
right time. In this study, the amount of knowledge storage over a one-year
period is considered.
·
Knowledge
Dissemination: In doing so, the organization disseminates
information to its members, thereby enhancing learning and creating new
knowledge. In this study, the amount of knowledge distributed over a one-year
period was considered.
·
Knowledge
Application: Knowledge sources in organizational operations and
decisions define the application of knowledge. This study considers the
utilization of knowledge produced over a one-year period.
3.1.2. Innovation
·
Speed of
Innovation: The speed of innovation refers to the extent to which process and
product innovation are used. In this study, the rate of application of
innovation in the implementation of processes and production of new products
over a one-year period was considered.
·
Quality of
Innovation: The quality of innovation refers to how the process and product
innovation is applied. This study focused on how to effectively apply
innovation to successfully execute processes and produce new quality products
over a one-year period.
3.1.3. Organizational intelligence
·
Strategic Vision:
It refers to the extent to which employees perceive the organization's
strategic vision. In this study, people's awareness of the strategic plans of
the organization was considered.
·
Common Destiny: To
the extent that the employees of the organization are aware of their
effectiveness in achieving the goals of the organization.
·
The Desire to
Change: Indicates the extent to which employees accept organizational change.
·
Mood: Indicates the
extent to which employees understand organizational goals. In fact, it refers
to the degree to which the spirit of employee engagement in the organization is
high.
·
Unity and
Agreement: Employee perception refers to organization collaboration and
alliance. In fact, the degree of unity and empathy of employees for researching
the goals of the organization.
·
Applying Knowledge:
The extent to which the knowledge produced in the organization is used by the
staff. In this study, the rate of staff utilization over a one-year period was
considered.
·
Performance
Pressure: Indicates the amount of stress that employees experience when
performing organizational processes.
3.1.4. Financial Performance
·
Profitability: The
profitability index is the rate obtained by dividing the present value of the
cash inflows by the present value of the costs in this study, net profit margin
was considered as the main basis of profitability index over a period of one
year.
·
Market Share: It is
the ratio of the number of sales of a brand in a product to the total sales of
that product in seasonal and annual time periods. In this study, this index was
also based on the measurement of banking services over a period of one year,
which resulted in attracting customers.
·
Sales Volume: This
refers to the extent to which the goals of selling products and services have
been met and in this study refers to the extent of banking services offered
over a one-year period.
·
Sales Growth: The
rate of sales of products and services over the previous period is the basis
for measuring this indicator.
3.2.
Data Analysis Methods
In order to determine the normality of the research data
and to determine the adequacy of the sample size, Kolmogorov-Smirnoff and
Spearman Bartlett tests were used (using SPSS software). Structural equation
modeling was used for analyzing data and conceptual model of research and
testing research hypotheses (using Smart PLS software, the reason for using
this software is that there is no need for normal distribution and also its
ability to solve models with fewer items and fewer samples than other available
software (Hair, Ringle & Sarstedt, 2011).
The structural equation modeling method is not solely a
statistical method; it refers to a family of related processes and different
equivalents have been used in the literature for it: covariance structure
analysis, covariance structure modeling and causal modeling (Klein, 2011).
The reason for the widespread use and popularity of this
technique among researchers is its ability for providing a quantitative method
for testing the theory and overcoming the difficulty of analyzing the
relationships between variables in human research (Harrington, 2009). This method is
the best tool for analyzing research involving the relationships between
complex variables, small sample sizes, and abnormal data distributions.In this study, structural equation modeling with
partial least squares approach was used to analyze the model.
This algorithm consists of two main steps, namely: 1. Model fitting study; 2. Testing research hypotheses. The first part, namely model fitting, is done in three parts: fitting measurement models, structural mesh fitting and general model fitting. It should be noted that the research uses Sobel and VAF tests to investigate the sixth and seventh hypotheses. The Sobel test of the multiplicative approach is also called the Delta method or the normal theory approach.
The
Sobel test is used to deduce the indirect effect coefficient of the mediating
variable on the relationship between the independent and dependent variables.
In general, the Sobel test can be used to estimate the normality of the
relationship. By estimating the standard error of the indirect effect, one can
test the null hypothesis against the opposite hypothesis. In addition to the
Sobel test, a VAF statistic with a value between 0 and 1 is used to determine
the intensity and indirect effect of the mediator variable. In fact, this value
measures the ratio of the indirect effect on the total effect calculated by the
VAF formula.
4.
RESEARCH RESULTS
In this research, prior to testing
the research hypotheses, we examined the distribution normality (normal or
abnormal) of the research data using the Kolmogorov-Smirnov test to ensure the
correct application of the tests appropriate to the hypotheses and the data.
4.1.
Kolmogorov-Smirnov Test
The confirmation of the null hypothesis indicates the
normal distribution of the research variables, and confirmation of the alternative hypothesis
indicates the non-normality of distribution (Table 2).
Table 2: Kolmogorov-Smirnov test results
|
KM |
IN |
FP |
OI |
|
N |
54 |
54 |
54 |
54 |
|
Normal Parameters |
Mean |
3.0103 |
2.9055 |
2.9649 |
2.9690 |
Std. Deviation |
.20774 |
.35410 |
.37579 |
.17307 |
|
Asymp. Sig. (2-tailed) |
.038 |
.046 |
.011 |
.035 |
The
results of this test show that the significance level of the variables of this
study is less than 0.05. So with the probability of 0.95%, we can confirm the
non-normality of the distribution of the research variables.
4.2.
Sample Size Adequacy Test (KMO):
The sample size adequacy test and the Bartlett Spearman
test determine that factor analysis is applicable to the data collected or no.
The value of the KMO statistics is in the range of zero to one and the majority
of experts consider the minimum acceptable value to be 0.6 (Table 3).
Table 3: KMO statistics and Bartlett's Spearman
test results
0.775 |
Kaiser-Meyer-Olkin measure of sampling adequacy |
|
8921.198 |
Approx..chi-square |
Bartlett's test of sphericity |
171 |
df |
|
0.002 |
sig |
Based
on the results regarding the Kmo values and the significance
level of Bartlett test, it can be said that factor analysis can be done on the
research data.
4.3.
Structural Equation Modeling (SEM)
In this study, a three-stage approach (fitting
measurement models, fitting structural model, and fitting overall model) was
used to investigate the research model and test the hypotheses.
4.3.1. Fitting measurement models
The reliability and validity of measurement models are
used to evaluate the measurement models. Reliability was assessed using three
items: factor loadings coefficients, Cronbach's alpha, and composite
reliability. On the other hand, convergent validity and divergent validity were
used to assess validity.
Factor loadings coefficients: Factor loads are calculated
by calculating the correlation value of the indices of a structure with that
structure. The criterion for the suitability of factor loadings is at least 0.4
(Hulland, 1999). Given that these values
are equal to or greater than 0.4, this indicates that the variance between the
constructs and their indices is greater than the variance of the measurement
error of the construct and the reliability of that measurement model is
acceptable. The results are shown in Figure 2 and Table 4.
·
Cronbach's alpha: The
criterion for the suitability of alpha is at least 0.7. (Nunally, 1978).
However, some sources consider 0.6 as sufficient. Table 4 shows the results of
this analysis.
·
Composite
reliability: This criterion was introduced by Wertz, Lin and Jorscog (1974).
Values higher than 0.7 indicate the structural reliability of the construct.
Table 4 shows the results of this analysis.
·
Convergent
validity: Fournell and Larker (1981) introduced the mean-variance extracted for
measuring convergent validity and stated that its critical value is 0.5. Table
4 shows the results of the evaluation of convergent validity.
Divergent validity: One of the methods for calculating it
is the cross-factor load's method. This method compares the correlation of the
indices of one construct with that of other constructs. A result of
less than .85 tells us that discriminant validity likely exists between the two
scales. A result greater than .85, however, tells us that the two constructs
overlap greatly and they are likely measuring the same thing. Therefore, we
cannot claim discriminant validity between them. As shown in Table 5,
divergent validity is confirmed.
Table 4: Validation characteristics
of research constructs
Factor load |
average variance extracted (AVE) |
composite reliability (CR) |
Cronbach's alpha |
Communality values |
Redundancy values |
Component |
constructs |
0.562 |
0.510 |
0.825 |
0.765 |
0.448 |
0.000 |
KNC |
Knowledge management |
0.487 |
KND |
||||||
0.927 |
KNO |
||||||
0.483 |
KNS |
||||||
0.693 |
KNP |
||||||
0.797 |
KNA |
||||||
0.857 |
0.623 |
0.771 |
0.750 |
0.525 |
0.202 |
INS |
Innovation |
0.565 |
INQ |
||||||
0.627 |
0.552 |
0.712 |
0.803 |
0.719 |
0.369 |
STV |
Organizational intelligence |
0.562 |
COD |
||||||
0.407 |
DTC |
||||||
0.723 |
MOO |
||||||
0.871 |
UAG |
||||||
0.807 |
APK |
||||||
0.546 |
PEP |
||||||
0.608 |
0.499 |
0.829 |
0.829 |
0.545 |
0.325 |
PRO |
Financial performance |
0.595 |
MAS |
||||||
0.743 |
SAV |
||||||
0.428 |
SAG |
Table 5: Structural Correlation Matrix and Divergent
Validity Results
|
Knowledge
management |
Innovation |
organizational
intelligence |
Financial performance
|
Knowledge
management |
0.714 |
|
||
Innovation |
0.519 |
0.789 |
|
|
organizational
intelligence |
0.598 |
0.567 |
0.743 |
|
Financial
performance |
0.603 |
0.473 |
0.612 |
0.706 |
4.3.2. the Fitness of the structural model
Significance level, R2, t-values and
redundancy criterion was used to evaluate the structural model.
·
R2: To
check the relationships between structures, the R2 criterion should
be used, which is applicable only for dependent structures. CHIN (2010)
identified three values of 0.19, 0.33, and 0.67 as weak,
moderate, and strong values (figure 2).
Figure 2: R2 values,
factor loadings, and path coefficients
t-values: the t-values greater than 1.96
with a 95% confidence level, indicates the validity of the relationships between
the model constructs and the confirmation of the related hypotheses (Figure 3).
Figure 3: T-values
·
Redundancy
criterion: This criterion is calculated for all dependent structures and
denotes the multiplication of the shared values of the structures
by their corresponding R2 values and the higher the
criterion, the better the structural model fit. The value of this criterion for
Innovation equal to 0.220, Organizational Intelligence equals 0.369 and for Organizational
Financial performance equals to 0.325 indicating a high fit of the structural
model.
4.3.3. Overall Model Fit and Test of
Hypotheses
In examining the fit of the overall model, the goodness
of fit criterion introduced by Tennenhaus et al. (2004) was used. Three values of
0.1, 0.25 and 0.36 were regarded as weak, moderate and strong values (Weitzel
et al. (2009)). This criterion is obtained by multiplying the shared values of
the dependent structures by the mean of R2.
GOF= = 0.527
Given the value obtained for this criterion, the fit of
the overall model can be considered as quite appropriate.
After examining the fitness of the overall research
model, the hypotheses were then examined. The results of the first-to-fifth
hypotheses test based on the results of t-values and path
coefficients (Figures 2 and 3) are presented in Table 6. It should be noted
that because of the mediating role of innovation and organizational
intelligence variables in the sixth and seventh hypotheses, the Sobel and Waff
tests were used to test these hypotheses.
Table 6: Results of the first to fifth research
hypotheses tests
Test result |
T-value |
path coefficient |
Hypothesis |
Confirmed |
3.435 |
0.645 |
The Positive Impact of
Knowledge Management on Financial Performance of the Organization |
Confirmed |
2.887 |
0.593 |
The Positive Impact of
Knowledge Management on Innovation |
Confirmed |
2.451 |
0.448 |
The Positive Impact of
Knowledge Management on Organizational Intelligence |
Confirmed |
3.713 |
0.718 |
The positive impact of innovation
on the financial performance of the organization |
Confirmed |
4.835 |
0.475 |
The Positive Impact of
Organizational Intelligence on Financial Performance of the Organization |
Results
from Table 6. It shows that KM directly predicts 0.645 changes related to the
financial performance of the organization, 0.593 changes related to innovation
and 0.448 changes related to organizational intelligence. Also, the innovation
variable predicts 0.718 and the organizational intelligence variable 0.475
predicts changes in organizational performance directly.
4.3.4. Testing the impact of mediating
variables
The Sobel test was used to examine the significance of
the mediating role of a variable on the relationship between two variables and
values greater than 1.96 at 95% confidence level, indicate that the mediating
role of the related variable is significant (Preacher & Leonardelli, 2003).
In addition to the Sobel test, which is used to examine the significance of the
mediating role of a variable, the VAF statistic can be used to determine the
mediating intensity. This value is between 0 and 1 and the greater the value,
the greater the intensity of the mediating effect (Lacobucci & Duhachelc,
2003).
Sobel test: In the Sobel test, a Z value is obtained by
the following relation:
Which:
·
a: path coefficient between the independent
variable and the mediator.
·
b: Path coefficient between the mediator and
dependent variable.
·
Sa: path standard error of independent variable
and mediator.
·
Sb: path Standard error of mediator and dependent
variable.
Z – Value = = = 3.0989
Z – Value = = = 3.0460
According to the results of Sobel test, it can be
concluded that at 95% confidence level, the effect of mediating variables
(organizational innovation and organizational intelligence) on the relationship
between knowledge management and financial performance of organization is
significant. Thus, the sixth and seventh hypotheses with test values of
3.989 and 3.0460 are confirmed.
·
VAF Test: The value
of the VAF test can be obtained by:
which a is the value of the path coefficient between the
independent variable and mediator, b is the value of the path coefficient
between the mediator and dependent variable, and c is the value of the path
coefficient between the independent and dependent variables. Due to the
presence of two mediating variables, this relationship was calculated twice and
the values were: 0.39 and 0.25, respectively.
A value of 0.39 means that more than one-third of the
impact of knowledge management on the financial performance of an organization
is indirectly explained by the innovation variable and a value of 0.25
indicates that a quarter of the effect of knowledge management on financial
performance is indirectly explained by the organizational intelligence
variable.
VAF = = 0.39
VAF = = 0.25
5.
DISCUSSION AND CONCLUSION
The purpose of this study was to investigate the
mediating role of innovation and organizational intelligence variables in the relationship
between knowledge management and financial performance of organizations active
in the e-banking industry. Accordingly, structural equation modeling and
partial least squares algorithms were used. The findings showed that knowledge
management has a significant impact (3.435) on the financial performance of the
organizations active in the e-banking industry.
This finding is in line with the results of Zaied et al.
(2012). The hypotheses test results showed that knowledge management has
positive effects on innovation and organizational intelligence. This result is
in line with the results of OBEIDAT et al. (2016). Innovation also has a
positive and acceptable impact (3.713) on the financial performance of the
organizations. This finding is in line with the results of Wang et al. (2016)
and Li et al. (2009). The results of hypotheses tests showed that
organizational intelligence also had a significant positive effect (4.835) on
the financial performance of the organizations.
This finding is in line with the findings of Travica
(2015). Concerning the mediating role of innovation and organizational
intelligence variables on the relationship between knowledge management and
organizational financial performance, the results showed that knowledge management
through organizational innovation and organizational intelligence variables
affect the financial performance of the organization, thus the mediating role
of these variables (Innovation and organizational Intelligence) was confirmed.
The results of the research hypotheses showed that
knowledge management in general and its components including knowledge
creation, knowledge acquisition, knowledge organization, knowledge storage,
knowledge dissemination, and knowledge application have a positive and significant
impact on the financial performance of organizations active in the e-banking
industry. So that the better knowledge management, the better financial
performance.
Research findings show that knowledge management can be
used to manage and improve an organization's financial performance through
innovation and organizational intelligence. In general, it can be said that
organizational actions in the field of innovation with an emphasis on two areas
of speed and the quality of innovation during e-banking services and the
development of organizational intelligence related to the implementation of
e-banking service processes positively influence knowledge management practices
and components and have a positive impact on the financial performance of the organization.
Based on the results of the present study, regarding the
crucial role of knowledge management on the organizations operating in the
field of e-banking, it is recommended for Prioritizing knowledge management implementation
with regard to its two components (the speed and quality of innovations) to
increase the efficiency of the financial performance of the organization and
improve the services provided by banks.
The results also show that there is a positive and
significant relationship between organizational intelligence/knowledge
management with the financial performance of the organizations active in the
e-banking industry. Therefore, in order to enhance organizational intelligence
and knowledge management of employees and managers in order to improve
performance, it is suggested for organizations to create hardware and software
infrastructures for changes in order to improve the financial performance of
the organization.
In the meanwhile, to enhance the sense of common destiny
between managers and employees, organization are recommended to formulate
organizational policies so that managers share employees with important plans
and issues and their results. These actions prepare the conditions for achieving
continuous improvement in the performance of the organization and in particular
the financial performance of the organization in different areas more than
ever.
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