Volume 13 - Issue 74
/ February 2024
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http:// www.amazoniainvestiga.info ISSN 2322- 6307
DOI: https://doi.org/10.34069/AI/2024.74.02.3
How to Cite:
Parveen, M., & Javaid, S. (2024). Saudi firms' performance dynamics: Organizational learning, innovation, and the dual roles of
firm size and type. Amazonia Investiga, 13(74), 35-50. https://doi.org/10.34069/AI/2024.74.02.3
Saudi firms' performance dynamics: Organizational learning, innovation,
and the dual roles of firm size and type
 : 
Received: January 5, 2024 Accepted: February 20, 2024
Written by:
Musrrat Parveen1
https://orcid.org/0000-0002-3796-8061
Saima Javaid2
https://orcid.org/0000-0001-7961-1629
Abstract
The objective of this research paper is to propose a
robust framework for understanding the correlation
between organizational learning, innovation, and the
performance of Saudi Arabian firms, encompassing
both financial and non-financial aspects. Additionally,
the study evaluates how factors such as “firm type”
and “firm size” influence organizational learning,
innovation, and overall firm performance. For this
study, we distributed a questionnaire to Jeddah, Saudi
Arabia's private firm employees for a year. Analysis
involved 815 complete sets, utilizing Structural
Equation Modeling (SEM) through Confirmatory
Factor Analysis (CFA) to explore relationships among
latent variables via path analysis. Organization
learning significantly enhances both financial and non-
financial performance. Additionally, innovation
positively influences firm performance. The combined
impact of organizational learning and innovation
strongly influences overall firm performance.
Introducing the mediating variable "type of firm"
enhances the relationship between organizational
learning, innovation, and firm performance, as
depicted in Model 2. The result of path analysis shows
that “firm size” as moderating variable is significantly
negatively related with innovation and firm
performance. This study contributes by exploring the
interplay of organizational learning, innovation, and
their impact on firm performance, particularly within
the emerging Saudi context, enhancing existing
knowledge.
Keywords: Organization learning, innovation, firm
financial and non-financial performance,
Confirmatory factor analysis, Structural equation
modeling.
1
Ph.D, Associate Professor, Faculty of Economics and Administration, Department of Human Resource Management, King
Abdulaziz University, Jeddah, Saudi Arabia. WoS Researcher ID: A-5238-2013
2
Ph.D, Assistant Professor, Department of Commerce, Zakir Husain New Delhi, India. WoS Researcher ID: B-2115-2013
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Introduction
In today's dynamic society, companies face
constant challenges derived from technological
advances and market changes. To survive and
thrive, organizations must adapt, foster
innovation, and embrace change. In this context,
organizational learning and innovation have
become fundamental pillars for business success.
Organizational learning is the continuous process
of acquiring, creating and applying knowledge
within an organization. According to
Kyoungshin & Zhenqiu (2019), this process
allows companies to adapt to changes in the
environment, improve their efficiency and
develop new ideas and products. Innovation, on
the other hand, is the implementation of new
ideas and methods to improve a company's
processes, products or services.
Saudi Arabia, historically reliant on oil exports,
has undergone a remarkable evolution into a
burgeoning economy ripe with diverse business
prospects. Embracing this transformation, the
Saudi government acknowledges the critical role
of organizational learning and innovation in
driving economic progress. In response, it has
instituted a range of policies aimed at fostering
these practices within companies operating
within its borders. This strategic approach not
only enhances the nation's competitiveness but
also propels it towards sustainable growth,
positioning Saudi Arabia as a dynamic player in
the global marketplace.
Studying organizational learning and innovation
dynamics within the country could provide
valuable insights into their impact on financial
and non-financial performance across sectors,
making it a compelling case study for
understanding evolving organizational dynamics
and innovation in a changing business landscape.
Our integrated framework, comprising three
pivotal pillars for business success, lays the
groundwork for this exploration. Through our
research, we aim to uncover the intricate
relationships between organizational learning,
innovation, and firm performance within the
Saudi context. By focusing on the mediating role
of company type and the moderating influence of
company size, we seek to provide insights into
the mechanisms shaping organizational
resilience and growth in the Saudi business
environment. The research questions addressed
in this study are:
• Is there a positive relationship between
organizational learning and innovation?
• Is innovation positively correlated with
company performance?
• Is there a positive relationship between
organizational learning and company
performance?
Additionally, the study investigates: the effects
of organizational learning on innovation and firm
performance, innovation on firm performance,
and organizational learning on firm performance.
It also analyzes how firm type and size influence
organizational learning, innovation, and firm
performance. The article encompasses a
literature review, hypotheses, methodology,
results, and a conclusion, offering managerial
insights.
Theoretical framework and hypothesis
development
Teylor's 1900 discovery of knowledge transfer's
positive impact on industry marked the birth of
learning organizations. Cyert and March coined
"organizational learning" in 1978, introducing
single and double loop learning. The concept
gained prominence in the 1990s, emphasizing
that learning extends beyond individual skills to
group dynamics, thriving in a conducive work
environment (Nemeth, 1997). Since then,
organizational learning has become a focal point
for researchers and practitioners, reflecting its
profound influence on organizational distinctions
(Jyothibabu & Farooq, 2010). Organizations,
especially in high-tech industries, strive to adapt
and innovate to meet market demands, maintain
market share, and stay profitable in the dynamic
realm of technology. Understanding how
businesses can adjust and enhance
competitiveness amid environmental changes is
crucial. Scientists predominantly employ
organizational learning to explore strategies for
adaptation. Research affirms that learning is
integral to long-term performance improvement
and serves as the cornerstone for attaining
sustainable competitive advantages.
Calantone et al. (2002) and Jiménez-Jiménez &
Sanz-Valle (2011) highlight that learning-
oriented businesses respond to market changes,
with competition driven by the acquisition and
application of knowledge to provide added value
to customers. This concept forms the basis for
research in management and organizational
studies, emphasizing learning as a crucial
competitive advantage for firms.
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Bolaji Bello, & Adeoye (2018) found significant
correlations between organizational learning,
innovation, and organizational performance
(financial and non-financial). These variables
also exhibited positive relationships with each
other. However, limited research explores these
interconnections, particularly in Saudi Arabia.
Addressing this gap, our study aims to
comprehensively investigate the relationships
and impacts of organizational learning,
innovation, and performance. It will depict the
organizational learning process and assess
innovation (product, process, and culture) and
organizational performance (financial and non-
financial) within a comprehensive framework or
model.
Organizational learning
Giniuniene and Jurksiene (2015) define
Organizational Learning (OL) as the process of
collecting and transforming data into knowledge.
OL facilitates quick learning and application of
knowledge, allowing businesses to continually
improve processes. According to (DiBella,
Nevis, & Gould, 1996), organizational learning
comprises four forms: information acquisition
(Infacq), informational distribution (infdis),
informational interpretation (infant), and
behavioral and cognitive changes (BCC).
Organizational learning unfolds in four forms.
Firstly, information acquisition involves creating
and reinforcing knowledge as a precursor to
gathering information. Secondly, information
distribution sees the dissemination of acquired
information within the organization. The third
form is information interpretation, where
organizations emphasize understanding acquired
and distributed information through electronic,
formal, and informal channels. The fourth form,
behavioral and cognitive changes, represents
significant learning at the top level, causing
alterations in norms and rules, dynamically
impacting the business climate.
There were several modified models developed
by researchers (Hung et al., 2011; Sarros et al.,
2008; & Tamininau et al., 2009) pertaining to
organizational learning and innovativeness.
However, we have followed the pathway by
(Škerlavaj et al., 2010) and made an attempt to
study an empirical investigation of the
relationship between organizational learning and
innovation that leads to firm performance in both
financial and non-financial way pertaining to the
Saudi context. Based on the review literature, we
hypothesize that:
H1: Organizational learning (information
acquisition, information distribution,
information interpretation, and behavioral and
cognitive changes) has a significant and strong
impact on Saudi Arabian Firm Performance
(both Financial and Non-Financial).
H1a: Information Acquisition has a positive and
significant impact on Organisational learning in
context of Saudi Arabia.
H1b: Information Distribution has a positive and
significant impact on Organisational learning in
context of Saudi Arabia.
H1c: Information Interpretation has a positive
and significant impact on Organisational
learning in context of Saudi Arabia.
H1d: Behavioral and Cognitive behavior has a
positive and significant impact on
Organisational learning in context of Saudi
Arabia.
Innovations
The concept of innovation at the organizational
level we need to understand the amalgamation of
two constructs as by (Crossan & Apaydin, 2010):
First, Technical innovation (Product), Second,
Administrative innovation (Process) and (3)
Innovative culture. Innovative culture can be
defined as an organization means that all the
organization members are engaged actively in
generating new processes, product and services
(Sarros et al., 2008).
Impact of Innovation on Organization/firm
performance
Recent research consistently shows a positive
correlation between innovation and various
measures of firm performance (Ayinaddis, 2022;
Dessie et al., 2022; & Issau et al., 2021). This
highlights the crucial role of innovation in
sustaining and boosting revenues, contributing to
overall improved performance. Chen (2017)
emphasizes the necessity of innovation for firms
to enhance their performances. While innovation
is often associated with individual companies, it
has become a key driver for a country's economic
growth and social welfare. In the present
dynamic landscape, both developing and
developed nations focus on innovation to drive
growth and competitiveness, ensuring business
sustainability (Chen, Yin, & Mei, 2018). Yıldız
et al. (2014) confirm that innovation significantly
and positively impacts business performance.
Raj and Srivastava (2014) define innovation as a
firm's capacity to develop new products,
services, and processes. Crossan and Apaydin
(2010) further suggest that, at the organizational
level, innovation encompasses an innovative
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culture and technical innovations (products,
services), along with administrative innovations
(processes). Batmaz and Özcan (2008) define
product innovation as the transformation of an
idea into a marketable, new/improved product,
method, or service. Veugelers (2008) notes that
process innovation impacts output, production
growth, and cost-effectiveness. The introduction
of innovative products is expected to positively
influence employment, income growth, and
process innovation, with potential cost-cutting
benefits (Fagerberg et al., 2004). Additionally, an
innovative culture serves as a valuable resource,
distinguishing organizations from competitors
and significantly impacting both financial and
non-financial performance (Rehman et al.,
2019).
Mabrouk and Mamoghli (2010) highlight the
positive impact of product and process
innovation on productivity and profitability.
Githikawa (2011) argues that fostering an
organized innovative culture, along with process
and product innovation, enhances a firm's
flexibility, leading to improved products,
expanded networks, and heightened
technological competitiveness. Prior studies
(Reed et al., 2012; Yavarzadeh et al., 2015)
affirm a positive relationship between
organizational performance and innovation. The
study affirms that innovation, whether in
product, process, or organizational structure,
significantly and positively influences
organizational performance across growth,
finances, internal processes, and customer
satisfaction. Existing empirical studies from
various countries, including Ireland, the UK,
Finland, Sri Lanka, South Korea, and China,
consistently underscore the importance of
innovation in organizational performance (Ken
& Tsai, 2010; Saunila, Ukko, & Rantanen, 2014;
De Mel, McKenzie, & Woodruff, 2009; Han et
al., 2017; Wang & Lin, 2013).
Recent global studies highlight innovation's
positive impact, including product and process
types, on companies, improving performance and
financial value (Rajapathirana & Hui, 2018;
Spescha & Woerter, 2018). Zaefarian et al.,
(2017) research emphasizes the role of business
relationships with suppliers and customers in
fostering innovation and enhancing firm
performance. They emphasize that these
relationships are strengthened by an innovative
culture. Despite potential negatives and some
contradictory evidence, theories and empirical
studies consistently propose a positive and
significant relationship between innovative
activities and company performance. Hence,
following are the hypothesis that,
H2: Innovation has a significant and strong
impact on both financial and non-financial Saudi
Arabian firm performance.
H2a: Product/service (technical) innovation has
significantly and positively impacted on
Innovation in context of Saudi Arabia.
H2b: Process innovation (administrative) has
significantly and positively impacted on
Innovation in context of Saudi Arabia.
H2c: Innovative culture has significant and a
positive influence on Innovation in context of
Saudi Arabia.
Impact of organizational learning on
innovation
Studies by (de Pablo Gonzalez del Campo &
Skerlavaj, 2009; Škerlavaj et al., 2010) showcase
empirical and theoretical research on the pivotal
role of organizational learning in driving
innovation. Effective organizational learning is
deemed essential for fostering innovation within
firms (Park & Kim, 2006). Firms with diverse
resources, potential, skills, and competencies
facilitate a faster learning process, generating
internal and external opportunities.
Organizational learning, thus, enhances a firm's
innovation and creativity (Rodan & Galunic,
2004), cultivating an innovative culture through
knowledge development. With knowledge as a
crucial component, firms must innovate in
research and development to manage and utilize
it effectively (Liao, Fei, & Liu, 2008). The
foundation of innovation lies in organizational
learning, enriching firms' knowledge. A high
degree of knowledge sharing enhances firm
innovation. Fostering innovation demands
efforts like acknowledging innovative behavior,
dedicating resources, and cultivating a structure
and culture that promotes innovation
implementation and development (Senge et al.,
1994).
Kandemir and Hult (2005) posit that positive
changes in behavior and understanding the
environment are linked to an innovative culture
and administrative/technical innovations.
Encouraging cognitive map changes fosters
innovation acceptance and motivates
experimentation for creativity, essential for
improving organizational learning efficiency.
Prioritizing all four forms of organizational
learning—information acquisition, distribution,
interpretation, and behavioral/cognitive
changes—is vital.
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Hence, following are the hypothesis that,
H3: Organisational Learning has a significant
and strong impact on Innovation in context of
Saudi Arabia.
Organizational performance
Organizational performance, defined by
(Peterson, Gijsbers, & Wilks, 2003), involves
efficient resource use, producing consistent
outcomes aligned with goals. Antony and
Bhattacharyya (2010) consider it a measure of
success delivering value to customers, while De
Waal & Sultan (2012) define it as meeting
financial and non-financial criteria. Gentry and
Shen (2010) stress a comprehensive evaluation
considering both financial and non-financial
aspects as the key approach.
Organizational learning and its impact on Saudi
Arabian firm/organization performance
(Financial and Non-financial).
Organizational learning, as highlighted by
(Sanzo et al., 2012), is a dynamic process
involving creating, acquiring, and integrating
knowledge to enhance internal resources and
competencies, ultimately empowering the
organization for higher performance.
Megheirkouni (2017) emphasizes the significant
benefits of this type of organizational learning,
particularly for organizations in uncertain and
dynamic environments, enriching their
performance through a learning-oriented
workforce. Thus, organizational learning plays a
crucial role in shaping firm performance.
According to (Kim, Watkins & Lu, 2017),
organizational learning is a critical component
explaining organizational performance. Studies
by (Shurafa & Mohamed, 2016; Rehman, Bhatti
& Chaudhry, 2019) proclaimed, organizational
learning's pivotal role in shaping firm financial
and non-financial performance. This raises the
question: How is organizational performance
evaluated? In the modern business landscape, the
emphasis is on strengthening relationships
among employees, customers, and society In
addition, it demonstrates a significant and
positive impact of organizational learning on
both financial (Return on Assets and Value added
per employee) and non-financial firm
performance (in terms of suppliers, employees,
and customers).
H4: Organizational learning and Innovation has
a significant and strong impact on both Saudi
Arabian firms’ financial and non-financial
performance.
Impact of Firms’ Type and Firm’s Size on
organizational learning, innovation and
firm’s performance
The learning organization framework helps
businesses by fostering experimentation,
creativity, and brainstorming, which increases
total innovation. Giving employees the space
(and time) to learn new things, pursue interests,
and share their views enables them to excel in
their jobs. Innovative activities in large-sized
companies and SMEs differ even when they have
the same physical capital structure (Noori et al.,
2017). In contrast to SMEs, large sized
companies are more adept at securing external
finance for the advancement of research and
development (R&D) projects by (Noori et al.,
2017). The performance of the company may
benefit from this capability. SMEs and large
companies often engage in different kinds of
innovative activity. Externally-driven innovation
makes use of both internal and external
resources, as well as technological expertise.
These primarily include raising a company's
productivity levels. Internal innovation refers to
the assets and skills a business has available for
innovative R&D projects (Kim et al., 2016). The
analysis revealed that even though both external
and internal creative R&D activities have an
impact on the performance of large-sized firms,
only internal innovative R&D activities have an
impact on the performance of SMEs (Kim et al.,
2016). Mabenge et al. (2020) find larger and
younger enterprises are more influenced by
innovation. Studies establish a direct link
between innovation and company performance
(Mustafa & Yaakub, 2018; & Ullah, 2020).
Large companies leverage economies of scale,
gaining advantages in input cost negotiations and
output levels. Lee's (2009) study supports this by
revealing higher profitability with larger total
assets. Theoretically, larger organizations engage
in more operations, generating more sales and
products, leading to increased revenues. Higher
sales yield higher profits, translating to increased
income. More income or profit after taxes
enhances the return on assets, investments, and
equity, highlighting the benefits of size in
achieving financial success. Empirical evidence
indicates an association between firm size and
performance/profitability (Bolarinwa &
Obembe, 2019 & Dang et al., 2018). Companies
in different sectors engage in diverse primary
activities, leading to varied innovation
approaches. According to Abdu & Jibir (2018),
manufacturing companies, followed by service
and retail companies, show the highest
innovation levels. Across diverse industries,
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public and private entities pursue technological
and innovative endeavors, impacting the
profitability of their companies in various ways.
H5a: Type of Firms has strong and significant
effect on organizational learning, innovation and
firms’ performance.
H5b: Firm size has a significant and positive
relationship between organizational learning,
innovation and firms’ performance.
H6a: Firms type mediates the relationship
between innovation and firms’ performance
H6b: Firm size moderates the relationship
between innovation and firm’s performance.
Research methodology
Measurement instrument
We employed Škerlavaj et al. (2010) instrument
with three constructs and 42 items rated on a five-
point Likert scale. Innovativeness was measured
using a five-item scale for innovative culture and
a 13-item scale for innovations (Process and
Product). Organization/firm performance was
assessed with a 19-item bipolar scale, measuring
financial performance (Return on Assets, Value
added per employee) and non-financial
performance from suppliers (3 items), employees
(12 items), and customers (4 items) perspectives,
along with demographic details (7 items). The
questionnaire was translated into Arabic. Among
the 815 respondents, 63.2% were male, 36.8%
were female, 51.2% held bachelor's degrees,
78.5% were Saudi, and 40.4% had 6 to 10 years
of professional experience. IBM SPSS (version
24) and Amos (version 20) software were used
for data analysis, employing Structural Equation
Modelling (SEM) to test stated hypotheses.
Participants and sample size
In the latter part of 2022, 1000 questionnaires
were disseminated to employees in private and
public firms located in Jeddah, Saudi Arabia. A
total of 835 complete sets were gathered from
October 2023 to January 2024, and the size was
determined by calculating the natural logarithm
of total assets. After performing Cook and
Leverage's outlier test, 815 responses were
retained for further analysis. Demographic
details are presented in Table 1.
Table 1.
Demographic profile
Demographic variables
Responses
Frequency
Respondents
Gender
Male
515
63.2
815
Female
300
36.8
Age
20 – 30
214
26.2
31 – 40
375
46.01
815
41 – 50
126
15.4
51 and Above
100
12.3
Highest Educational level
Diploma
215
26.3
815
Bachelor’s degree
418
51.2
Master’s degree
182
22.3
PhD
40
4.9
Work Experience
0–5 years
205
25.1
815
6–10 years
330
40.4
11–15 years
165
20.2
More than 15 years
115
14.3
Nationality
Saudi
640
78.5
815
Non-Saudi
175
21.5
Firm Size
Total Assets
815
100
815
Firm Type
Public
484
59.4
815
Private
331
40.6
Survey Results
Preliminary analysis
Data set is analyzed to ensure instrument quality
by convergent and discriminant validity, which
leads to better constructs value and before testing
the hypothesis using SEM. In the words of
(Rehman et al., 2019) stated that convergent
validity refers to a situation where items of a
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variable reflect effectively to their associated
indicator. As per Hair et al. (2013) prescribed to
calculate three things to see convergent validity,
that is, Average Variance-Extracted (AVE),
factor loadings, and composite reliability. The
standardized values of AVE and factor loadings
should be at least 0.50 and CR value must be
higher than 0.70 (Hair et al., 2013; Rehman et al.,
2019). To get better results concerning CR and
AVE we have deleted all those items that have
factors loadings less than 0.50 to make a good
model as recommended by (Hayduk & Littvay,
2012 and Rehman et al., 2019). Non-financial
Performance from suppliers has been dropped
from the analysis at the preliminary stage to get
reliability and best fit. To ensure the Composite
Reliability, we have dropped few items from
Non-Financial Firm Performance variable, and
all reaches above 0.60 (Rehman et al. 2019).
Refer to Table-2 (Annexture I) AVE of all the
three constructs i.e. Organizational Learning,
Innovation and Firm Performance is 0.792, 0.885
and 0.766 respectively, all greater than 0.7,
suggesting the convergent validity of the
constructs. Also, the results presented in Table-2
confirm the discriminant validity as the AVE of
the underlying factors is higher than the squared
correlation between the factors and the ASV for
each factor is lower than the AVE value,
consistent with the previous studies like (Alarcon
& Sanchez; 2015; Parveen & Adeinat, 2019).
Table 2.
Assessment of Reliability, Convergent Validity and Discriminant Validity
Mean
SD
CR
AVE
ASV
OL
Inno
Firm Perf
OL
9.514
3.67
0.769
0.792
0.234
0.889
Inno
5.312
2.69
0.771
0.885
0.338
0.541
0.941
Firm Perf
13.65
5.38
0.818
0.766
0.321
0.455
0.372
0.875
Notes: CR, composite reliability; AVE, average variance extracted; ASV, average shared variance.
Values below the diagonal are correlation
estimates among factors, diagonal elements are
the squared root of AVE and values above the
diagonal are squared inter-factor correlations.
Based on Alarcon & Sanchez, 2015) threshold,
reliability = CR > 0.70; convergent validity =
AVE > 0.50; discriminant validity = ASV < AVE
or the squared root AVE >inter-factor
correlations.
Result analysis
The present study took special care in research
design, data collection and related factors
affecting missing values (Bagozzi & Yi, 2012).
Effective steps taken to address the conventional
considerations such as dealing with missing
values, identifying suspicious responses and
outliers etc. The present study used the full
information maximum likelihood (FIML)
method which is considered as more efficient
than list wise deletion, pairwise deletion and
similar response pattern imputation (Enders &
Bandalos, 2001; Xiong et al., 2015). In our case,
the maximum likelihood estimates are all
positive and significant at p<0.05. The SEM
model was employed to examine the relationship
between different latent variables using the path
analysis using Confirmatory Factor Analysis
(CFA) technique as depictedinModel-1.
Model-1. Relationship between Organizational Learning, Innovation and Saudi Arabian firm performance.
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The Path analysis using Confirmatory Factor
Analysis (CFA) in the above Model-1 comprises
of three exogenous latent factor variables i.e.,
Organizational Learning, Innovation and their
impact on Firm Performance covering both
financial and non-financial aspects without the
mediating and moderating variables.
Organizational Learning factor is measured by
four observed variables viz. Information
Acquisition, Information Distribution,
Information Interpretation and Cognitive
behavior, whereas the Innovation is measured by
three observed variables viz. Product innovation,
Process Innovation and Innovation Culture, and
Firm Performance by Financial and Non-
Financial Variables, the reliability of which is
influenced by random measurement error as
indicated by associated error term. Each of these
observed variables is regressed onto its
respective factor. Finally, the above three factors
are shown to be inter-correlated.
The Chi-square (χ2) test predicts overall model fit
by analyzing the discrepancy between the sample
model and the proposed model (Hu & Bentler,
1999). We found the normed chi-squared value is
1.67. Also, the comparative χ2 of the χ2 to degrees
of freedom ratio can be used to minimize the
effect of sample size (Hooper et al., 2008). We
have got the values of this ratio less than 2 i.e.,
1.221 that indicates a good fit consistent with the
previous studies (Marsh & Hou, 1996; Reisinger
& Turner, 1999; Xiong et al., 2015). As per the
Baseline comparisons, we found CFI
(Comparative Fit Statistic) as 1.997 greater than
0.9, which is considered as the model is fitted
good. (See Annexture I, table-3)
Table 3.
Model Fit
Goodness of Fit Indices
Construct
/ degree of freedom
2
χ
1.221
CFI (Comparative Fit Index)
1.997
TLT (Tusker-Lewis fit Test)
0.997
RMSEA (Root Mean Square Error)
0.042
GFI (Goodness Fit Index)
0.938
Further, the absolute indices are the most vital
signal of how well the proposed theory fits the
real world (Hooper et al., 2008; Xiong et al.,
2015). In addition to the χ2 test, the absolute
indices include the root mean square error of
approximation (RMSEA), goodness-of-fit index
(GFI), adjusted goodness-of-fit index (AGFI),
root mean square residual (RMR) and
standardized root mean square residual (SRMR).
RMSEA, as a very informative statistic,
measures how well the parameter estimates
generated in the proposed model fit the
population matrix (Byrne, 2001; Xiong et al.,
2015). The RMSEA considers the error of
approximation in the population and asks the
question “How well would the model, with
unknown but optimally chosen parameter values,
fit the population covariance matrix if it were
available?” (Browne & Cudeck, 1992; Byrne,
2010). This discrepancy, as measured by the
RMSEA, is expressed per degree of freedom,
thus making it sensitive to the number of
estimated parameters in the model (i.e., the
complexity of the model); values less than .05
indicate good fit (Xiong et al., 2015), which is in
our case is found out to be 0.042. (see Annexure
I, table-3)
Figure-1 illustrates that Organizational Learning
accounts for 62% of Information Acquisition,
91% of Information Distribution, 71% of
Information Interpretation, and 87% of Cognitive
Behavior. Product innovation, Process
Innovation, and Innovation Culture contribute
69%, 65%, and 98% to Innovation, respectively.
Regarding Firm Performance, 96% is clarified by
Financial Variable, and 66% by Non-Financial
Variable. Particularly, 67% of Organizational
Learning and 53% of Innovation impact Firm
Performance, confirming the significance of the
stated hypotheses.
Refer to Table 4, Model-1 shows that
Information Acquisition Information distribution
Information Interpretation and Cognitive
Behavior have significant positive influence on
organizational learning significant at p value ⩽
0.05 and p ⩽ 0.001 level supporting H1a, H1b,
H1c and H1d respectively. Further,
Organizational Learning have significant
positive impact on Firms financial and non-
financial performance, have positive coefficients
and t-value significant at p ⩽ 0.001 level
supporting H1. Also, Product Innovation,
Process innovation and Innovation culture has
positive and significant impact on Innovation at
p value ⩽ 0.10 p value ⩽ 0.05 and p ⩽ 0.001 level
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supporting H2a, H2b, and H2c respectively.
Innovation has positive impact on Firm's
Financial and Non-Financial Performance at p
value ⩽ 0.05 supporting H2. Then,
Organizational Learning has strong and positive
impact on Innovation at p ⩽ 0.001 supporting
H3. Lastly, Organizational Learning and
Innovation has significant strong impact on
Firm’s Financial and Non-Financial Performance
at p value ⩽ 0.05 supporting H4. The result is
consistent and supportive with the previous
literature.
Further, we have introduced “type of firm” as
mediating variable and “size” as moderating
variable in Model 2 to see the overall effect on
the firm’s financial and non-financial
performance. We have found a strong and
significant direct effect of introducing the
mediating variable “type of firm” to the
relationship of organizational learning,
innovation, and firm performance (see Model 2).
Then, we excluded the type of firm from the path
analysis and perform the bootstrap. The result
shows standardized path coefficients of indirect
effect as 0.53 and 0.771, t-statistic of 11.363 and
7.325, and co- efficiency of total effect as 0.881,
with t-statistics 17.651. Table 4 shows that the
total effect is statistically significantly stronger
than indirect effects, indicating that type of firm
is a mediator affecting the relationship between
organizational learning, innovation, and firm
performance. This shows that H5a and H6a is
supported. Later, we add the construct firm size
(Size) to see the moderating effect of its impact
on the relationship between the organizational
learning, innovation and firm performance.
Model-2. Effect of Moderating Variable and Mediating Variable as Size and Type of Firm on Firms’
Performance.
The result of path analysis shows that firm size
as moderating variable is significantly negatively
related with innovation and firm performance
(path coefficient of -0.213, t-statistics of 8.773)
at p⩽0.10 confidence level and significantly
positively related with organizational learning
and firm performance (path coefficient=0.173, t-
statistics of 9.728) at p⩽0.05 confidence level.
This shows that hypotheses 5b and 6b are
supported and consistent with previous studies
like Wolff and Pett (2006); Leal-Rodríguez et al.
(2015) and Kijkasiwat and Phuensane (2020).
Therefore, by adding type of firm as the
mediator, and firm size as the moderator in
Model 2, gives the R-square of 0.483 implying
organizational learning, innovation, type of firm,
and firm size explains the variance of firm
performance to 48.3 percent.
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Table 4.
Estimates of Parameters (Model 1 and Model 2)
Hypothesis
Pathways
Standardized
Pathway’s Coefficient
t-value
Model 1
H1
OL → Firm Per
*
0.389
5.982
a
Inf acq → OL
*
0.832
3.590
b
Inf distr → OL
***
0.451
14.213
c
Inf Int → OL
*
0.094
1.985
d
Cog_Beh → OL
***
0.253
4.494
H2
Inno → Firm Per
**
0.710
9.515
a
Prod inno → Inno
*
0.572
6.122
b
Proc inno → Inno
****
0.693
1.711
c
Inno_Cul → Inno
***
0.591
5.531
H3
OL → Inno
***
0.583
3.133
H4
OL → Inno → Firm Per
0.831*
11.329
Model 2
H5
a
OL → Type → Firm Per
0.053**
11.363
b
OL → Size → Firm Per
0.173**
9.728
H6
a
Inno → Type → Firm Per
0.771**
7.325
b
Inno → Size → Firm Per
-0.213*
8.773
*p ⩽ 0.05; **p ⩽ 0.01; ***p ⩽ 0.001
Note: Firm Performance includes both Financial and Non-Financial Variables.
Discussion
The relationship between innovation, financial
performance, and non-financial performance has
been extensively studied in the literature. The
analysis presented in this study indicates a dual
relationship between innovation and firm
performance, where innovation positively
influences both financial and non-financial
performance, while enhanced financial
performance facilitates increased funds for
innovation (Petare et al., 2023). Innovation has
been found to have a positive impact on both
financial and non-financial performance,
benefiting stakeholders such as employees,
stockholders, customers, and management.
Improved non-financial performance, such as
market share, customer satisfaction, and
employee engagement, can motivate further
innovation, leading to a virtuous cycle of
innovation and performance improvement; hence
this is aligned with the prior research by (Chen,
2017).
Moreover, the study reveals that public firms,
with greater access to financial resources,
allocate more to innovation, resulting in
heightened financial and non-financial
performance (Gurel, 2017). This finding is
consistent with the literature, which suggests that
public firms have more resources to invest in
innovation, leading to better performance
outcomes (Baumol, 2002).
Furthermore, firm size affects innovation and
performance, as larger total assets correlate with
increased innovation but lower financial and
non-financial performance (Hu & Wang, 2010).
This finding indicates that larger firms may face
challenges in managing innovation and
performance, as they may have more complex
organizational structures and processes
(Burgelman, 2002). Recent studies have further
explored this relationship, finding that firm size
and innovation performance are positively
correlated, but moderated by factors such as
technology category, innovation strategy, and
organizational structure. For example, high-
technology firms are better able to leverage
innovation to improve performance compared to
low-technology firms (Agustia et al., 2022), and
firms with a more proactive innovation strategy
or a decentralized organizational structure are
better able to leverage their size to achieve higher
innovation performance (Kijkasiwat &
Phuensane, 2020; Song et al., 2015). These
findings highlight the importance of considering
multiple factors when examining the relationship
between firm size and innovation performance.
In addition, the study finds that organizational
learning leads to increased financial and non-
financial performance. Specifically, when profits
increase, there are more funds available for
training and development programs and R&D,
leading to the accomplishment of both individual
and organizational goals and enhancing more
effective and efficient organizational learning.
Kim (2016) suggests that a learning organization
influences knowledge performance, adaptive
performance, and financial performance, with
both knowledge performance and adaptive
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performance positively affecting financial
performance. However, a study by (Obadeyi,
2019) found no meaningful relationship between
organizational learning and financial
performance of start-up companies. The study
suggests that the relationship between
organizational learning and financial
performance may be more complex in start-up
companies compared to established firms.
Moreover, when there is an enhancement in non-
financial firm performance, it also helps in
gaining more effective organizational learning,
as it facilitates the overall growth and
development of its human resources, giving the
Saudi company a competitive edge in the global
world (Azizi, 2017). This finding is consistent
with the literature, which suggests that non-
financial performance, such as employee
satisfaction and customer loyalty, is critical for
organizational learning and competitiveness
(Easterby-Smith, Crossan, & Nicolini, 2002). A
recent study by (Jamai et al., 2021) also found
that non-financial performance significantly
impacts organizational learning, which
subsequently enhances firm performance.
Conclusion
In summary, this study contributes to the
literature by examining all variables collectively
in a single model, establishing a clear link
between organizational learning, innovation, and
firm performance (financial and non-financial) in
the Saudi context. Additionally, it introduces a
mediating variable (type of firm) and a
moderating variable (firm size) for a more robust
analysis, yielding interesting results in Model-2.
Lastly, the research utilizes a sample of Saudi
Arabian firms, addressing the scarcity of
empirical research in the Saudi context. The
outcome of this research paper have raised to
major recommendations to the top managerial
level, Human resource specialist and
practitioners, Finance managers and policy
makers for improvising the financial and non-
financial performances of Saudi Arabian firms.
Managerial implications
1. Saudi Arabian firms can opt for performance
linked learning, and there should be
increment in knowledge availability and
accessibility to knowledge sources. Firstly,
acquisition of new knowledge should be
promoted by preparing employees attending
numerous conferences/seminars/workshops
regularly, amalgamating their R&D policy
and raising the enhancement of novel
philosophies, ideas and experimentation
within the firm. Secondly, the firms should
encourage knowledge distribution and
interpretation within the firm by applying
various approaches and techniques to
promote coordination, preparing employees
accountable for accumulating, assembling
and sharing employee’s recommendations
within the firm. Thirdly, Saudi firms should
made efforts to retain the knowledge by
creating the databases and facilitating access
to these databases through divergent
networks. Also reflective culture should be
enhanced in order to enrich the learning
culture in the organization.
2. The findings also provide insight that
organizational learning expedites
innovation. Hence, a firm expecting to
develop and enhance corporate performance
through innovation should develop its
organizational learning processes and
practices.
3. This research also confirms that
organization learning has positive
association with firm financial and non-
financial performance. This provides an
implication for managers, practitioners, in
go-getting for an improved performance of
the firm. They should also utilize
organizational learning dimensions
effectively to achieve their performance
objectives.
4. The analysis provides a clear indication that
Saudi Arabian firms should pay more
attention to innovation in product
improvisation, process up gradation and
enhancement of innovative culture, and
inclined towards adoption of new
technologies and procedures for firm’s
sustainability in this current dynamic
environment.
5. Finally, this research study also shows
positive and significant relationship between
innovation and firm performance (financial
and non-financial). Since firm performance
is a major concern to all firms, it’s very
pertinent to understand the association
between innovation and firms’ financial and
non-financial performance will help the
Saudi Arabian firms to develop better
competitive strategies. The greater the
understanding of the significance of
innovation, the better would be the
comprehension into how firms can
accomplish improved competitive strategies
and firms’ financial and non-financial
performance.
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Limitation of the Study
The study only considers the impact of
organizational learning on firm performance in
the context of SMEs in Saudi Arabia. The
findings may not be applicable to SMEs in other
countries, where the organizational and
institutional contexts may be different. Future
studies should consider a more diverse sample of
countries in order to increase the generalizability
of the findings.
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