Ekuilibrium: Jurnal Ilmiah Bidang Ilmu Ekonomi Volume 20.
No.
: March, pp.
The Influence of Knowledge Management and Learning Organization on Competitive Advantage in Startups in Malang City with Organizational Creativity as a Mediating Variable (A Study on Startup Companies at Stasion Malan.
Enaldi a,1,*.
Hamidah Nayati Utami a,2.
Tri Wulida Afrianty a,3 a Department of Business Administration.
Faculty of Administrative Sciences.
Brawijaya University.
Malang.
Indonesia 1 enaldyenal27@gmail.
2 hamidahn@ub.
3 twulidafia@ub.
*corresponding author 24269/ekuilibrium.
ARTICLE INFO
Article history Received:
22-11-2024
Revised:
06-02-2025
Accepted:
24-02-2025
Keywords Competitive Advantage Knowledge Management Learning Organization Organizational Creativity ABSTRACT This study explores the impact of learning organization and knowledge management on competitive advantage, with organizational creativity as a mediating variable, within MalangAos startup ecosystem.
Using a quantitative approach and Partial Least Squares Structural Equation Modeling (PLS-SEM), data were collected from 104 startups.
Results reveal that Learning Organization (=0.
289, p<0.
and Knowledge Management (=0.
260, p<0.
significantly enhance Competitive Advantage.
Both Learning Organization (=0.
249, p<0.
and Knowledge Management (=0.
523, p<0.
positively influence Organizational Creativity, which subsequently strengthens Competitive Advantage (=0.
p<0.
Organizational Creativity partially mediates the impact of Learning Organization (=0.
093, p<0.
and Knowledge Management (=0.
196, p<0.
on Competitive Advantage.
The model shows that 50.
9% of what affects creativity in organizations and 5% of what affects their competitive advantage can be understood from the data.
This highlights how important creativity and knowledge are for staying competitive over time.
Findings emphasize the strategic value for startups in integrating learning and knowledge frameworks to maintain competitive positioning.
Findings emphasize the strategic value for startups in integrating learning and knowledge frameworks to maintain competitive positioning.
These insights apply not just to startups but also to industries like manufacturing, healthcare, and education.
By focusing on learning, managing knowledge well, and encouraging creativity, these sectors can boost innovation, work more efficiently, and become more adaptable.
This will help them stay competitive over time.
This is an open access article under the CCAeBY-SA license.
http://journal.
id/index.
php/ekuilibrium Introduction In the BANI (Brittle.
Anxious.
Non-linear.
Incomprehensibl.
era and the digital revolution, the digital industry has opened significant business opportunities, including the growth of startups in Indonesia.
According to the Minister of Communication and Informatics.
Indonesia's digital economy sector contributed 11% to the national GDP in 2019, largely driven by the development of startups.
The startup ecosystem in Indonesia has reached its peak with various digital innovations in e-commerce, fintech, edtech, and healthtech, driving economic growth, innovation, and technological advancement.
Data from Startup Ranking shows that Indonesia ranks sixth in the world with 2,502 startups as of March 2023, indicating significant global competitiveness, even surpassing Singapore, the Philippines, and Malaysia in Southeast Asia (Annur 2.
Startups in Indonesia are mainly concentrated in the Greater Jakarta area, with other regions, such as Malang.
Bandung.
Yogyakarta, and other cities in Java, also showing growth.
However, not all startups are thriving evenly across Indonesia.
The success of the startup sector in Indonesia, despite the global wave of layoffs, still shows growth with an increase of more than 60% in employee numbers between May 2022 and May 2023.
Nevertheless, a major challenge for startups in Indonesia is the difficulty in accessing funding due to unstable global economic conditions, forcing startups to survive with existing resources (Kompa.
According to the Indonesian Information and Communication Technology Creative Industry Society (MIKTI), other major issues faced by startups include limited capital, regulations, market access, business strategies, human resources (HR), and facilities.
These challenges demand competent HR support to help startups survive in uncertain economic situations (Sivitas 2.
The threat of an economic recession in 2023 presents a serious challenge for the sustainability of startups.
Startups that succeed in achieving competitive advantage are those that can combine innovation, adaptation, and networks to face challenges and seize opportunities (Hendi et al.
Research shows that the concepts of learning organizations and creativity are crucial for the success of startups, especially in facing intense competition in high-tech sectors (Mai and Nguyen 2.
(Huang and Yao 2.
In addition, knowledge management also plays a role in achieving competitive advantage, with research showing the positive impact of knowledge management on organizational success (Dalmarco et al.
(Bresciani et al.
(Rehman et al.
Creativity also plays an important role in the relationship between knowledge management and competitive advantage.
(Shateri et al.
found that creativity can be achieved through effective knowledge management, while (Mazhar and Akhtar 2.
emphasized the role of creativity in building competitive advantage.
Unfortunately, research on the relationship between knowledge management and organizational creativity is still limited to the education sector and has not been widely conducted in the startup sector, making it a novel contribution to startup research (Centobelli et al.
The Knowledge-Based View (KBV) approach emphasizes that knowledge is the primary resource that can create long-term competitive advantage (Centobelli et al.
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In the context of KBV, a learning organization that continuously updates and develops its knowledge will have a stronger competitive advantage (Huang and Yao The integration of knowledge management, learning culture, and creativity adds sustainable value to organizations.
Research by (Djamaludin et al.
shows that learning organizations, creativity, and knowledge management all influence each other.
Recent studies by (Mai and Nguyen 2.
indicate that factors such as creativity, innovation, and supportive ecosystems are key for the success of startups.
They also highlight the importance of support from ecosystems, such as mentorship, incubation programs, funding access, and other forms of assistance that help startups grow.
This supportive ecosystem, through relevant education and training, helps enhance the skills of entrepreneurs, which in turn improves the chances of success for startups in a competitive market.
Previous studies show the relationship between learning organization, knowledge management, organizational creativity, and competitive advantage.
(Huang and Yao 2.
found that learning organizations positively influence communication and organizational creativity in the high-tech industry.
(Shehabat 2.
demonstrated that effective knowledge management can enhance organizational performance and establish a sustainable competitive advantage.
(Makabila et al.
found that learning culture, learning processes, and systems thinking positively contribute to the competitive advantage of state-owned companies in Kenya.
(Miri et al.
found a significant positive relationship between learning organization and creativity in general hospitals in Shiraz.
Iran.
(Sutanto 2.
identified that organizational learning ability and creativity positively affect organizational innovation in universities in East Java.
Indonesia.
However, most of these studies have limitations regarding the generalization of results and focus on a single location or type of organization.
This study makes a unique contribution by examining the burgeoning startup ecosystem in Malang.
Indonesia.
Unlike previous research focused on established industries, this study explores how startups leverage learning organization and knowledge management frameworks, with organizational creativity as a key link to enhanced competitive advantage.
The findings indicate that organizational creativity distinguishes successful startups from failures by fostering innovation, adaptability, and responsiveness to market changes crucial in the fast-paced startup world.
Creative startups can innovate, differentiate, and seize opportunities faster than competitors.
Conversely, a lack of creativity results in inflexible business models that struggle to adapt, hindering growth.
Thus, organizational creativity is essential for startup success in the volatile and competitive modern business environment.
This study aims to examine the effect of learning organization and knowledge management on competitive advantage with organizational creativity as a mediating variable, focusing on startups under the auspices of Station Malang, a startup community that supports digital entrepreneurs in Malang City.
Station Malang has developed into a startup accelerator for the Malang Raya area.
However, in 2023, the growth of this community has stagnated, making it a relevant case to study how internal organizational Copyright A 2025.
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2528-7672 factors contribute to competitive advantage.
The combination of learning organization, knowledge management, and organizational creativity offers new insights into how startups can build sustainable competitiveness in a dynamic market.
This study highlights that organizational creativity plays a critical role in translating organizational learning and knowledge management into competitive advantage.
These findings provide actionable strategies to foster creativity and innovation in their organizations.
Implementing a learning-oriented culture and effective knowledge management practices can increase adaptability, accelerate product innovation, and improve business performance.
In addition, startup ecosystems such as Station Malang can leverage these insights to design more effective incubation and training programs that empower startups to grow sustainably.
Literature Review Resource-Based View (RBV) The Resource-Based View (RBV) focuses on internal resources and capabilities to explain organizational profit and value (Mahmood and Mubarik 2.
(Budiarto et al.
It highlights differences in firm performance within industries emphasizing that not all resources are equal.
To achieve competitive advantage, firms must identify, develop, and leverage rare, valuable, inimitable, and non-substitutable resources.
Ricardian logic attributes performance heterogeneity to varying resource Firms must possess superior capabilities to acquire valuable resources, enabling accurate future resource valuation (Barney et al.
This heterogeneity sustains performance differences, as certain resources remain difficult to replicate (Furr and Eisenhardt 2.
RBV underscores intangible assets, particularly knowledge, as vital for competitive advantage due to their uniqueness and sustainability (Chahal et al.
The Knowledge-Based View (KBV), an extension of RBV, positions firms as knowledge-driven entities (Grant and Phene 2.
KnowledgeAispanning technical, market, and customer insightsAicreates value and fosters adaptability in dynamic environments Effective knowledge management and continuous organizational learning are essential for maintaining competitiveness.
Knowledge-Based View The Knowledge-Based View (KBV), is a strategic management approach emphasizing knowledge as a critical resource for achieving competitive advantage.
KBV
focuses on how organizations effectively manage and utilize knowledge to create value and enhance performance (Grant and Phene 2.
KBV underscores the importance of linking knowledge management with organizational learning to sustain competitiveness.
It argues that enhancing knowledge and learning capabilities fosters organizational creativity, serving as a mediator between knowledge management, learning organizations, and competitive advantage.
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2528-7672 Learning Organizational A Learning Organization is a concept introduced as a framework where members of an organization continuously develop the capacity to achieve desired results through an open mindset, group aspirations, and shared learning (Haider et al.
(Purnomo et al.
Senge identified five key components for creating a learning organization:
systems thinking, which helps understand the interactions of elements within the personal mastery for self-development.
mental models that shape thinking and problem-solving approaches.
shared vision that aligns individual and organizational and team learning, which allows groups to think collectively and innovatively.
Knowledge Management According to Nonaka & Takeuchi, knowledge management connects data, information, and knowledge, where data is raw information, information is data that holds meaning, and knowledge is the understanding of information to support specific goals, such as decision-making (Latifah et al.
(Martins et al.
Human knowledge is divided into explicit knowledgeAidocumented and easily sharedAiand tacit knowledgeAiresiding in individual experience and difficult to document.
Although tacit knowledge is hard to codify, it holds high value because 5 tis based on skills and when shared through social interaction, it can generate new knowledge (Olaisen and Revang 2.
Competitive Advantage According to Porter, competitive advantage is a companyAos ability to achieve superior performance through its characteristics and resources, surpassing competitors in the same industry (Al-Khawaldah et al.
This advantage is rooted in the value offered to buyers, either through lower prices or unique benefits.
Porter developed the value chain concept as a framework for analyzing business activities that support competitive advantage.
PorterAos five competitive forces, which include the threat of new entrants, the threat of substitute products, the bargaining power of suppliers, the bargaining power of buyers, and industry rivalry, provide the basis for identifying and managing factors that influence competitive advantage.
Organizational Creativity Creativity, often regarded positively, is defined as the process of using imagination and skills to generate unique ideas or products and as the ability to create (Alnor et al.
Organizational creativity heavily depends on collective knowledge, research activities, design and development efforts, and interactions with the external Wallas in The Art of Thought, identified four stages of the creative process:
preparation, incubation, illumination, and verification.
The preparation stage involves gathering information and experiences, including past failures, to build a foundation for creative thinking (Botella and Lubart 2.
During the incubation stage, collected ideas are processed subconsciously, allowing for intuitive reflection and maturation.
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2528-7672 Research Method Research Type This research uses a quantitative approach with an explanatory research type to examine the relationships and effects between variables, including Learning Organization.
Learning Management, and Competitive Advantage, with Organizational Creativity as a mediator variable.
Through this method, the researcher collects numerical data and analyzes it using statistics to test hypotheses objectively.
This study focuses on Start Up organizations in Station Malang, where company leaders are selected as respondents because they have in-depth insights into the business being conducted.
This approach aims to provide empirical evidence on the factors influencing competitive advantage among startups.
Population and Sample The population of this study consists of all startup companies affiliated with Stasion Malang, totaling 139 startups that meet specific characteristics for this research, namely based in Malang, part of Station Malang, and operating for at least 3 years.
Sampling was conducted using a probability sampling method, where every unit in the population has an equal chance of being selected, thus ensuring proportional representation of the population and reducing bias.
The sample size was calculated using the Slovin formula, resulting in 104 startup companies as the sample.
The selection of managers or CEOs of startups as respondents is considered ideal because they have an in-depth understanding of business strategies, operations, and internal dynamics of the organization, as well as access to extensive information, allowing them to provide comprehensive insights into business practices to achieve competitive advantage.
Research Variables This study uses four main variables: Learning Organization (X.
and Knowledge Management (X.
as independent variables.
Organizational Creativity (Y.
as the mediating variable, and Competitive Advantage (Y.
as the dependent variable.
Each variable is measured using specific indicators and items to ensure the validity of the Indicators serve as a guide to illustrate the concept of the variable, while items are questions that measure those indicators.
In this quantitative approach, the items are measured using a specific scale to obtain representative results that can be This study examines the interplay of Learning Organization.
Knowledge Management, and Organizational Creativity in achieving Competitive Advantage within the startup ecosystem of Malang.
Indonesia.
Learning Organization is assessed through continuous learning, dialogue and inquiry, team learning and collaboration, integrated systems, empowerment, systems relationships, and strategic leadership.
Knowledge Management is evaluated based on knowledge acquisition, conversion, and application.
Organizational Creativity is measured through individual creativity, group creativity, internal organizational environment, and knowledge creation.
Finally.
Competitive Advantage is measured by brand image, product quality, cost, production system, and economic scale.
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2528-7672 Data Analysis The data analysis method in this study employs Partial Least Squares (PLS), a variant-based Structural Equation Modeling (SEM) technique, to test hypotheses and confirm theories.
SEM PLS was chosen as the analytical tool for this research due to its excellent capabilities in processing data based on Partial Least Squares Structural Equation Modeling.
The PLS-SEM method is highly suitable for exploratory research, complex models with numerous latent variables, and data that does not meet normality Inferential analysis uses SEM based on PLS to explore relationships between variables (Creswell and Creswell 2.
The inner model tests the relationships between latent variables and measures hypotheses, with R-Square (RA) analysis used to evaluate model quality, where values of 0.
75, 0.
50, and 0.
25 indicate strong, moderate, and weak models, respectively.
Q2 analysis is used to evaluate predictive relevance, with values greater than zero indicating significant relevance.
The outer model measures validity and reliability, with convergent validity .
alues >0.
, discriminant validity (AVE >0.
, and Composite Reliability (CR) .
alues >0.
Hypothesis and mediation testing are performed based on the following criteria: .
if a, b, and c are significant but c < b, partial mediation occurs.
if a and b are significant but c is not, perfect mediation occurs.
if a, b, and c are significant with c = b, it is not mediation.
if a or b is not significant, it is not mediation.
Figure 1.
Model Hypothesis Source: Processed data, 2024 Results and Discussion Results Second-Order Structural Equation Modeling Analysis Measurement Model Analysis for Lower Order Constructs (LOC) There are three key criteria for data analysis using SmartPLS 4 to evaluate the outer model.
The first is convergent validity, which assesses the validity of each relationship between indicators and their corresponding constructs or latent variables.
The second is discriminant validity, which ensures that each latent construct is distinct Copyright A 2025.
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2528-7672 from other variables in the model.
Lastly, composite reliability measures the actual reliability of a construct within each variable, ensuring consistency and accuracy.
Convergent Validity Convergent validity assesses the strength of the correlation between constructs and their latent variables.
It is evaluated through the loading factor of each construct An ideal loading factor value is >0.
7, indicating that the indicator is valid in measuring the construct.
In empirical research, a loading factor value >0.
5 is still acceptable (Hair et al.
, and some experts even consider 0.
4 as acceptable.
This value reflects the percentage of variance in the indicators explained by the construct:
Table 1.
Convergent Validity Variabel Item Learning Organization (X.
X1.
X1.
X1.
X1.
X1.
X1.
X1.
X1.
X1.
X1.
X1.
X1.
X1.
X1.
X1.
X1.
X1.
X1.
X1.
X1.
Convergent Validity Loading Factor
Information 0,933 Valid
0,919
Valid
0,933
Valid
0,913
Valid
0,881
Valid
0,928
Valid
0,898
Valid
0,894
Valid
0,911
Valid
0,941
Valid
0,950
Valid
0,947
Valid
0,927
Valid
0,917
Valid
0,895
Valid
0,984
Valid
0,984
Valid
0,935
Valid
0,938
Valid
0,881
Valid X2.
X2.
X2.
X2.
X2.
X2.
X2.
X2.
X2.
X2.
X2.
X2.
X2.
0,848
0,933
0,854
0,929
0,873
0,873
0,865
0,884
0,836
0,850
0,906
0,883
0,922
Valid Valid Valid Valid Valid Valid Valid Valid Valid Valid Valid Valid Valid Y1.
Y1.
Y1.
0,787
0,879
0,883
Valid Valid Valid Knowledge Management (X.
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2528-7672 Variabel Item Organizational Creativity (Y.
Y1.
Y1.
Y1.
Y1.
Y1.
Y1.
Y1.
Y1.
Y1.
Y1.
Y1.
Y1.
Y1.
Y1.
Y1.
Y1.
Y1.
Y1.
Y1.
Y1.
Y1.
Y1.
Y1.
Y1.
Y1.
Y2.
Y2.
Y2.
Y2.
Y2.
Y2.
Y2.
Y2.
Y2.
Y2.
Y2.
Y2.
Y2.
Y2.
Y2.
Y2.
Y2.
Source : Processed Data .
Competitive Advantage (Y.
Convergent Validity Loading Factor
Information 0,898 Valid
0,861
Valid
0,854
Valid
0,868
Valid
0,888
Valid
0,899
Valid
0,859
Valid
0,821
Valid
0,894
Valid
0,865
Valid
0,911
Valid
0,877
Valid
0,866
Valid
0,805
Valid
0,860
Valid
0,858
Valid
0,821
Valid
0,860
Valid
0,859
Valid
0,797
Valid
0,864
Valid
0,860
Valid
0,855
Valid
0,895
Valid
0,924
Valid
0,916
0,934
0,954
0,893
0,876
0,887
0,882
0,921
0,942
0,956
0,841
0,890
0,879
0,908
0,884
0,880
0,875
Valid Valid Valid Valid Valid Valid Valid Valid Valid Valid Valid Valid Valid Valid Valid Valid Valid Table 1 shows that the loading factor values .
onvergent validit.
for each construct item are generally valid, as they meet the ideal threshold of >0.
However, one item has a loading factor <0.
According to (Hair et al.
, loading factor values between 0.
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2528-7672 the criteria for validity and reliability.
Therefore, the loading factor results in this study still satisfy the basic requirements for convergent validity.
Table 2.
Nilai Average Variance Extracted (AVE) Variable Brand Image Competitive Advantage Cost Dialogue and Inquiry Economic Scale Group Creativity System Relationships Individual Creativity Internal Organizational Environment Strategic Leadership Knowledge Acquisition Knowledge Application Knowledge Conversion Knowledge Creation Knowledge Management Learning Organization Organizational Creativity Continuous Learning Team Learning and Collaboration Empowerment Product Quality Production System Integrated System Source : Processed Data .
Nilai Average Variance Extracted (AVE) 0,874 0,631 0,838 0,824 0,774 0,767 0,968 0,749 0,707 Composite Reliability 0,844 0,795 0,774 0,764 0,774 0,632 0,531 0,586 0,862 0,812 0,942 0,939 0,945 0,928 0,945 0,960 0,957 0,975 0,949 0,928 0,834 0,783 0,796 0,894 0,938 0,935 0,921 0,962 0,954 0,967
0,954
0,933
0,911
0,958
0,984
0,960
0,951
Convergent Validity refers to the extent to which different measures of the same concept demonstrate consistency or agreement.
When the Average Variance Extracted (AVE) value meets or exceeds the recommended threshold of 0.
50, the items converge to measure the underlying construct, establishing convergent validity (Hair et al.
this study, the AVE results indicate that all constructs have values greater than 0.
confirming that the study meets the criteria for convergent validity.
Measurement Model Analysis for Higher Order Construct (HOC) Once all measurement model criteria for the Lower Order Construct (LOC) are met, the next step is to evaluate the measurement model at the Higher Order Construct (HOC) This evaluation follows the same criteria as applied to the LOC.
The results of the 2nd-order outer model evaluation are presented in the figure below.
Outer Loading Second Order This test examines the loading factor values, which represent the correlation between each item's outcome and its latent variable score.
The loading factor values are obtained from the outer loadings output.
According to (Hair et al.
reflective items are considered valid and effective for measuring variables if their outer loadings exceed Copyright A 2025.
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2528-7672 The results of the outer loadings measurement using the PLS-Algorithm are presented below.
Table 3.
Outer Loading Competitive Advantage 0,877 Brand Image Competitive Advantage Cost Dialogue and Inquiry Economic Scale Group Creativity System Relationships Individual Creativity Internal Organizational Environment Strategic Leadership Knowledge Acquisition Knowledge Application Knowledge Conversion Knowledge Creation Knowledge Management Learning Organization Organizational Creativity Continuous Learning Team Learning and Collaboration Source : Processed Data .
Knowledge Management Learning Organization Organizational Creativity 0,928 0,755 0,843 0,874 0,748 0,874 0,905 0,824 0,902 0,904 0,895 0,878 0,821 0,734 0,772 0,901 0,824 0,829 Table 3 illustrates the loading factor values .
onvergent validit.
for each indicator.
A loading factor value > 0.
7 is considered valid.
The table reveals that all loading factor values for the indicators of Learning Organization (X.
Knowledge Management (X.
Organizational Creativity (Y.
, and Competitive Advantage (Y.
7, indicating that these indicators are valid.
Composite Reliability A questionnaire is considered reliable if respondents' answers to the questions are consistent and stable.
In PLS, reliability can be tested using two parameters: Cronbach's alpha and composite reliability for each variable.
An instrument is deemed reliable if the Cronbach's alpha value exceeds 0.
6 and the composite reliability valueexceeds 0.
7 (Hair Copyright A 2025.
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Below are the Cronbach's alpha and composite reliability values obtained from the data analysis.
Table 4.
Composite Reliability dan Cronbach Alpha Cronbach's alpha Competitive Advantage 0,923 Knowledge Management 0,883 Learning Organization 0,895 Organizational Creativity 0,906 Source : Processed Data .
Composite reliability 0,943 0,928 0,918 0,934 Information Reliabel Reliabel Reliabel Reliabel Based on the data analysis results in Table 3, all latent variables used in this study have Cronbach's alpha and composite reliability values above 0.
7, indicating that the variables meet the required criteria.
Therefore, it can be concluded that each variable demonstrates good reliability and is suitable for proceeding to the next testing stage.
Average Variance Extracted The average variance extracted (AVE) values for each variable are used to conduct the convergent validity test.
The AVE value describes the extent of variance or diversity of the manifest variables .
that a latent variable can possess.
An instrument is considered to have passed the convergent validity test if the AVE value exceeds 0.
5 (Hair et al.
The results of the calculations are shown in the table below.
Table 5.
Average Variance Extracted (AVE) Competitive Advantage Knowledge Management Learning Organization Organizational Creativity Source : Processed Data .
Average variance extracted (AVE) 0,767 0,810 0,615 0,780 Based on the calculations in Table 5, all the variables used in this study have values Therefore, it can be concluded that the variables in this study have met the criteria for convergent validity testing and can proceed to the next stage of testing.
Discriminant Validity Discriminant validity is conducted to ensure that each concept of the latent models is distinct from other variables.
Therefore, this study uses the Heterotrait-Monotrait ratio of correlations (HTMT) to assess discriminant validity.
Heterotrait-Monotrait ratio of correlations (HTMT) HTMT is a new approach to assess discriminant validity in Partial-Least Square (PLS-SEM) variance-based models, as recommended by Henseler et al.
in (Rasoolimanesh 2.
In HTMT measurement, there is a threshold for meeting the discriminant validity criteria, where the HTMT value must be less than 0.
Table 6.
Heterotrait-Monotrait ratio of correlations (HTMT) Copyright A 2025.
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2528-7672 Competitive Advantage Competitive Advantage Knowledge Management 0,784 Learning Organization 0,745 Organizational Creativity 0,784 Source : Processed Data .
Knowledge Management Learning Organization Organizational Creativity 0,751 0,763 0,651 As shown in Table 6, the square root of the AVE for each latent variable is greater than its correlations with other latent variables, satisfying the Fornell-Larcker criterion.
Therefore, the variables in this study meet the necessary conditions, and further analysis can proceed.
Full Colinearity Assesment This test addresses collinearity issues by calculating the Variance Inflation Factor (VIF) before evaluating the structural model.
High collinearity between constructs can distort the accuracy of path coefficient estimates, potentially leading to biased and ineffective results.
VIF value above 3.
3 indicates high multicollinearity and requires attention to reduce correlations among variables in the model.
Table 7.
Full Colinearity Assessment Variable Learning Organization Knowledge Management Organizational Creativity Competitive Advantage Source : Processed Data .
Full VIF
1,926
2,357
2,038
1,800
Based on Table 7, the test results show that all VIF values are below 3.
3, indicating no data bias and no collinearity between constructs in this study's model.
Collinearity testing was done by comparing the VIF values to 3.
If the VIF exceeds 3.
multicollinearity is present.
Therefore, the results conclude that there is no multicollinearity among the independent variables since all VIF values are below 3.
R-Square The determination coefficient in this study is used to indicate the extent of the influence of exogenous variables on endogenous variables.
Therefore, as shown in Table 8 below, the R-Square values are presented.
Table 8.
R Square Variable R-Square Organizational Creativity 0,509 Competitive Advantage 0,655 Source : Processed Data .
The R-square value for Organizational Creativity is 0.
This indicates that Learning Organization and Knowledge Management explain 50.
9% of the variance in Organizational Creativity, while the remaining 49.
1% is attributed to factors outside the scope of this research.
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655, meaning that Learning Organization.
Knowledge Management, and Organizational Creativity account for 65.
5% of its variance.
The 5% is explained by variables not included in this model.
PLS Predict Predictive power demonstrates a model's ability to predict new or future research This step is carried out using the PLSpredict procedure, which compares the root mean squared error (RMSE) or mean absolute error (MAE) values from the PLS path model for each item with those generated by the linear regression (LM) model.
The results of the PLS Predict test are as follows.
Table 9.
PLS Predict
Indikator QApredi
0,375
0,555
0,375
0,431
0,411
0,273
0,396
PLSSEM_RMSE
0,801
0,676
0,801
0,764
0,777
0,863
0,786
PLSSEM_MAE
0,653
0,569
0,650
0,628
0,635
0,701
0,628
LM_RM
0,822
0,663
0,868
0,783
0,815
0,853
0,790
LM_RM
Brand Image
Cost
Economic Scale
Product Quality Production System
Group Creativity Individual Creativity Internal Organizational Environment 0,362 0,807 0,625 0,822 Knowledge Creation
0,466
0,740
0,588
0,771
Source : Processed Data .
Table 9 reveals that all items analyzed using PLS-SEM exhibit lower values compared to the linear regression model.
This is evident in the smaller RMSE values observed in PLS compared to the LM benchmark.
Consequently, the research model demonstrates a high predictive accuracy in representing the real-world phenomenon under investigation.
Hypothesis Testing Hypothesis testing in this study aims to determine the impact of all hypotheses, both direct and indirect.
In research, testing follows criteria that can be evaluated directly or indirectly by examining significance using p-values.
A hypothesis is accepted if the pvalue is less than 0.
05, indicating a significant effect.
Statistical testing is performed using the bootstrapping method via SmartPLS software, as shown in Table 10 below.
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Ekuilibrium: Jurnal Ilmiah Bidang Ilmu Ekonomi, 20.
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2528-7672 Figure 2.
Hypothesis Testing Source : Processed Data .
Table 11.
Direct Effect Hipotesis Learning Organization Ie Competitive Advantage Knowledge Management -> Competitive Advantage Learning Organization -> Organizational Creativity Knowledge Management -> Organizational Creativity Organizational Creativity -> Competitive Advantage Source : Processed Data .
Original Sample Statistic PValue Information 0,289 3,105 0,002 0,125 Signifikan 0,260 2,214 0,027 0,083 Signifikan 0,249 2,515 0,012 0,070 Signifikan 0,523 5,302 0,000 0,310 Signifikan 0,374 4,238 0,000 0,199 Signifikan Based on the results of the direct effect testing presented in Table 11, the path coefficients for each variable influence are as follows:
H1: Learning Organization (X.
has a positive and significant effect on Competitive Advantage (Y.
with a path coefficient of 0.
289 and a p-value < 0.
Therefore, the first hypothesis is accepted.
H2: Knowledge Management (X.
has a positive and significant effect on Competitive Advantage (Y.
with a path coefficient of 260 and a p-value < 0.
Therefore, the second hypothesis is accepted.
H3: Learning Organization (X.
has a positive and significant effect on Organizational Creativity (Y.
with a path coefficient of 0.
249 and a p-value < 0.
Therefore, the third hypothesis is accepted.
H4: Knowledge Management (X.
has a positive and significant effect on Organizational Creativity (Y.
with a path coefficient of 0.
523 and a p-value < 0.
Therefore, the fourth hypothesis is accepted.
H5: Organizational Creativity (Y.
has a positive and significant effect on Competitive Advantage (Y.
with a path coefficient of 0.
374 and a p-value < 0.
Therefore, the fifth hypothesis is accepted.
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2528-7672 Table 12.
Indirect Effect Hipotesis Original Sample T Statistic P-Value Information Learning Organization -> Organizational Creativity -> Competitive Advantage 0,093 2,105 0,035 Signifikan Knowledge Management Ie Organizational Creativity Ie Competitive Advantage 0,196 3,362 0,001 Signifikan Source : Processed Data .
Based on the results of the mediation effect testing presented in Table 12, the path coefficients for each variable influence are as follows:
H6: Organizational Creativity (Y.
mediates the relationship between Learning Organization (X.
and Competitive Advantage (Y.
positively and significantly with a path coefficient of 0.
093 and a p-value < 0.
Therefore, the sixth hypothesis is accepted.
H7: Organizational Creativity (Y.
mediates the relationship between Knowledge Management (X.
and Competitive Advantage (Y.
positively and significantly with a path coefficient of 0.
196 and a p-value < 0.
Therefore, the seventh hypothesis is accepted.
DISCUSSION
The Effect of Learning Organization on Competitive Advantage The results of this study show that a learning organization positively influences competitive advantage.
The original sample value is 0.
289, the T-statistic is 3.
105, and the P-value is 0.
002, which means the findings are significant.
This finding supports earlier research showing that learning within organizations can improve a company's competitive edge, especially for startups in fast-changing and competitive markets (Hosseini et al.
Organizations that can quickly adapt to market changes are more successful in gaining sustainable competitive advantages (Makabila et al.
H1: Learning Organization (X.
has a positive and significant effect on Competitive Advantage (Y.
The Effect of Knowledge Management on Competitive Advantage This study reveals that Knowledge Management (KM) has a positive and significant effect on Competitive Advantage (H2 is accepte.
, with an original sample value of 0.
a T-Statistic of 2.
214, and a P-Value of 0.
This finding supports the Knowledge-Based View (KBV), which asserts that knowledge is a crucial resource for achieving competitive advantage (Novianti 2.
This study aligns with research by Dalmarco et al.
who showed that KM is a critical strategy for gaining and sustaining competitive advantage, even in Brazilian startups.
H2: Knowledge Management (X.
has a positive and significant effect on Competitive Advantage (Y.
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2528-7672 The results show that learning organizations have a big impact on creativity within the organization.
This was supported by an original sample value of 0.
249, a T-statistic of 515, and a P-value of 0.
This emphasizes the importance of organizational learning in enhancing creativity, particularly in startups that tend to be more innovative and creative (Tajpour et al.
The knowledge-based concept, combined with organizational learning, can promote creativity, expand the knowledge base, and generate creative ideas and innovative solutions (Mai and Nguyen 2.
This is consistent with previous research by (Huang and Yao 2.
(Sutanto 2.
and (Tajpour et al.
, which found that organizational learning has a positive impact on creativity.
Therefore, startups that adopt a learning culture can enhance employee creativity and create the solutions needed to compete in dynamic markets.
H3: Learning Organization (X.
has a positive and significant effect on Organizational Creativity (Y.
The Effect of Knowledge Management on Organizational Creativity This study shows a significant influence between Knowledge Management (KM) and Organizational Creativity, with an original sample value of 0.
523, a T-Statistic of 302, and a P-Value of 0.
This validates the hypothesis (H.
that KM significantly boosts organizational creativity, particularly in startups.
(Andaleeb and Almuraqab 2.
suggest that managing knowledgeAilike creating, sharing, and using informationAi helps boost creativity in teams.
They say that having a good system for knowledge leads to more creative ideas.
Using knowledge-based principles makes knowledge an important resource that encourages innovation and creative problem-solving.
A study by (Khattak et al.
shows that knowledge management practices like training, meetings, seminars, and emails help share ideas, which boosts creativity in organizations.
H4: Knowledge Management (X.
has a positive and significant effect on Organizational Creativity (Y.
The Effect of Organizational Creativity on Competitive Advantage The study found a strong link between organizational creativity and competitive advantage, shown by a sample value of 0.
374, a T-statistic of 4.
238, and a P-value of 0.
The hypothesis (H.
, which emphasizes the role of organizational creativity in enhancing competitive advantage, especially in startup companies, receives this support.
Wellmanaged creativity enables firms to adapt to market changes and offer innovative solutions that differentiate them from competitors (Potjanajaruwit 2.
(Elidemir et al.
highlight that accumulated knowledge in startups becomes a critical asset that drives creativity and innovation, which in turn contributes to competitive advantage.
This study also emphasizes the importance of fostering a work culture that supports creativity to maximize innovation and management strategies that focus on developing employees' creative capabilities.
H5: Organizational Creativity (Y.
has a positive and significant effect on Competitive Advantage (Y.
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2528-7672 The Effect of Learning Organization on Competitive Advantage Through Organizational Creativity This study found a strong indirect link between learning organizations and competitive advantage through organizational creativity.
The original sample value was 093, with a T-statistic of 2.
105 and a P-value of 0.
035, which supports the hypothesis (H6 is accepte.
Organizational creativity acts as a mediator that links the learning process within the organization to competitive advantage, especially in the context of startups(Guntoro et al.
(Mai and Nguyen 2.
The application of learning organization principles allows companies to acquire new knowledge, foster creativity, and cultivate an innovation culture that contributes to competitive advantage (Baia et (Sarstedt et al.
This study emphasizes the importance of continuous learning programs to support creativity and innovation in facing market challenges.
H6: Organizational Creativity (Y.
mediates the relationship between Learning Organization (X.
and Competitive Advantage (Y.
positively and significantly The Effect of Knowledge Management on Competitive Advantage Through Organizational Creativity This study finds a strong indirect relationship between Knowledge Management and Competitive Advantage through Organizational Creativity.
The original sample value 196, with a T-Statistic of 3.
362 and a P-Value of 0.
001, which confirms the hypothesis (H7 is accepte.
Organizational creativity serves as a mediator linking knowledge management to competitive advantage.
Knowledge management acts as a driver of creativity in startup companies, which in turn enhances innovation and competitive positioning (Dalmarco et al.
The effective application of knowledge management enables companies to create innovative products and services that differentiate them in the market.
Therefore, startup companies should develop programs that support continuous learning and innovation to maximize the benefits of creativity and strengthen their competitive advantage.
H7: Organizational Creativity (Y.
mediates the relationship between Knowledge Management (X.
and Competitive Advantage (Y.
positively and significantly.
Conclusion Startup companies gain a competitive advantage when they prioritize learning and knowledge management.
This research confirms that both learning organizations and effective knowledge management significantly and positively impact a startup's competitive edge .
ith path coefficients of 0.
289 and 0.
260, respectively, and p-values < 05 for bot.
Furthermore, both factors boost organizational creativity .
ath coefficients of 0.
249 and 0.
523, respectively, and p-values < 0.
, which, in turn, strengthens competitive advantage .
ath coefficient 0.
374, p-value < 0.
Organizational creativity acts as a partial mediator between learning/knowledge management and competitive advantage .
ath coefficients of 0.
093 and 0.
respectively, and p-values < 0.
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2528-7672 For startups, this means prioritizing knowledge management strategies like knowledge sharing and continuous learning to foster creativity and innovation.
Investing in digital tools for collaboration and data-driven decisions, such as AI and big data analytics, can further enhance adaptability and innovation.
Policymakers should integrate knowledge management and organizational learning into incubation programs.
Facilitating access to training in digital tools and networking opportunities can also boost knowledge sharing and growth.
Supporting research collaborations between startups, universities, and accelerators can bridge the gap between theory and practice.
Future research should explore diverse sectors and geographical regions to understand these relationships in different markets and cultures.
Examining the role of emerging technologies like AI and big data analytics is also promising.
A mixed-methods approach, incorporating qualitative insights, would provide a richer understanding.
Finally, a more specific theoretical model based on the Knowledge-Based View could offer new insights into how organizational knowledge contributes to sustainable competitive advantage.
Based on the findings, startups, especially those within community-driven ecosystems like Station Malang, are recommended to adopt comprehensive strategies in learning organization and knowledge management to enhance their competitive To foster organizational creativity, which has been shown to mediate and strengthen competitive outcomes, startup leaders should implement structured knowledge-sharing systems and continuous learning programs that promote innovative Establishing a culture that encourages creativity as a core value can further optimize the benefits of knowledge management practices.
Additionally, collaboration with external knowledge resources, such as universities and technology incubators, can enrich internal learning processes, providing startups with a broader base of insights and innovative approaches.
Future studies could explore similar frameworks across diverse regions or sectors to validate these strategies' broader applicability and refine models that support startup competitiveness in various market conditions.
Acknowledgment This research was supported by the Education Fund Management Institute (LPDP) of the Ministry of Finance of the Republic of Indonesia.
The researcher would like to thank the support provided.
References