Transekonomika: Akuntansi.
Bisnis dan Keuangan https://transpublika.
id/ojs/index.
php/Transekonomika Online ISSN 2963-7325 Examining the Impact of Miles & Snow's Business Strategy.
Operational Efficiency, and Logistics Efficiency on Company Performance Original Article https://doi.
org/10.
55047/transekonomika.
Taufiq Chandra Adimanta Habibie 1.
Hastin Umi Anisah2* 1,2Department of Management.
Faculty of Economics and Business.
Universitas Lambung Mangkurat.
Banjarmasin.
Indonesia Email: .
tchandraadimantah@gmail.
com, .
humianisah@ulm.
Received : 04 May - 2025 Accepted : 10 July - 2025 Published online : 07 August - 2025 Abstract The COVID-19 pandemic has caused major disruptions to the global economy, resulting in significant economic contraction and rising unemployment across many nations.
This study explores the influence of Miles & SnowAos business strategy typology, alongside operational and logistics efficiency, on the financial performance of This study employs a quantitative approach, with data collected from 45 respondents serving as managers and supervisors at Midea-Toshiba Electronics during the 2021-2024 period.
Purposive sampling method was used to ensure that only functionally relevant participants were involved.
The survey instrument was distributed online and analyzed using the Partial Least Squares (PLS) approach.
Hypothesis testing results show that all three independent variables significantly influence company performance.
Logistics efficiency demonstrates the strongest influence ( = 0.
fA = 0.
, followed by operational efficiency ( = 0.
fA = .
, and business strategy ( = 0.
fA = 0.
These findings emphasize that optimal company performance is determined not only by appropriate strategic direction, but also by operational effectiveness and supply chain This study provides contextual contribution in applying Miles & Snow's strategy typology in the regional industrial sector and serves as a foundation for formulating data-based company performance improvement Keywords: Business Strategy.
COVID-19.
Financial Performance.
Logistics Efficiency.
Operational Efficiency.
Introduction The global economy has been profoundly affected by the COVID-19 pandemic, resulting in significant economic declines in numerous nations, including Indonesia.
As reported by the Central Statistics Agency .
Indonesia's GDP shrank by 5.
05% during the second quarter of 2020, largely due to the impact of social distancing measures on consumer spending and Particularly hard-hit were the tourism and transportation sectors, which faced severe restrictions on movement.
In response to the crisis, the Indonesian government introduced several initiatives to stabilize the economy, such as fiscal and monetary stimulus packages and the launch of a national economic recovery program.
Despite these efforts, the recovery process remains challenging, with persistent uncertainty surrounding private consumption and investment, compounded by the continuing effects of emergency public activity restrictions .
lso known as PPKM) on overall economic performance (World Bank.
The World Bank advocates for continued structural reforms in Indonesia, focusing on enhancing economic competitiveness and productivity while strengthening public health systems and financial infrastructure (World Bank, 2021.
Copyright: A 2025 by the authors.
This is an open access article distributed under the terms and conditions of the CC BY 4.
Taufiq Chandra Adimanta Habibie et al.
| Volume 5 No.
Business is fundamentally an activity that involves individuals or groups engaged in the trade of goods and services with the primary goal of generating profit.
To fully grasp the essence of business, it is necessary to consider it from a philosophical perspective, encompassing areas such as epistemology, axiology, and ontology.
From an epistemological viewpoint, business knowledge is gathered and applied through a framework that guides action and understanding (Kurtz et al.
, 2.
In an ontological sense, businesses exist as independent entities within a larger social system, where different stakeholders interact and exert influence, ensuring the business's sustainability over time (John et al.
, 2.
Performance, refers to the ability of an individual or organization to achieve desired outcomes, with a deeper epistemological understanding of performance emerging through experience, observation, and research (Conte, 2.
Axiologically, the concept of performance reflects the values that an organization upholds, such as a commitment to maintaining high product quality (Gelle-Jimenez et al.
, 2023.
Waal, 2.
Performance can be quantitatively assessed using various tools, such as self-assessments or peer evaluations (Aguinis & Burgi-Tian, 2.
Strategy, as defined by David Hunger, is a comprehensive plan aimed at achieving longterm organizational objectives (Leiblein & Reuer, 2.
It is viewed as a continuous learning process, enabling organizations to adapt to external changes and understand both their internal capabilities and market conditions (Wheelen et al.
, 2.
Axiologically, strategy is deeply ingrained in the organization's core values, providing a clear direction that aligns with its overall vision (Emmanuel et al.
, 2.
The Blue Ocean Strategy, introduced by Kim and Mauborgne, emphasizes creating new, uncontested market spaces through innovation and creativity, challenging traditional competitive models.
This strategy focuses on reconstructing market boundaries to unlock previously untapped opportunities, ultimately creating a unique competitive advantage (Hokianto, 2.
The six-track framework provides a structured approach to this strategy, offering insights on how businesses can reshape their market environment to establish new and profitable areas.
Study by DeSarbo et al.
, shows that the Prospector.
Analyzer, and Defender strategies tend to produce better performance than the Reactor because they have a more stable strategic direction.
The importance of paying attention to internal capabilities and external factors in choosing the right strategy (Leemann & Kanbach, 2.
The Prospector strategy is suitable for fast-changing industries, with high innovation needs.
In contrast, the Defender strategy is more suitable for stable industries with a clear and limited market.
The Analyzer strategy is suitable for companies operating in moderately dynamic environments, which require a balance between stability and innovation.
While there have been many studies on business strategy, operational efficiency, and logistics efficiency separately, there is still a significant gap in the literature regarding the integration of these three variables in a unified analytical framework.
Previous research by Mangla et al.
emphasizes the importance of operational excellence in improving sustainable supply chain performance.
Similarly.
Lee .
in its systematic review of big data in supply chain management, highlights the role of logistics efficiency on company In addition, a meta-analysis by Alfalla-Luque et al.
shows that supply chain agility has a significant effect on company performance.
Sanusi et al.
has also studied the efficiency of logistics companies in Malaysia, but focused more on an operational Even in the context of construction projects.
Shahbaz et al.
revealed the importance of supply chain capabilities to logistics efficiency, but strategic approaches have not been the main focus.
However, there is still a gap in empirical research on how such strategies interact with operational and logistics efficiency, especially in the context of postpandemic economic disruptions.
In fact, operational and logistics systems play a crucial role Taufiq Chandra Adimanta Habibie et al.
| Volume 5 No.
in ensuring business continuity and cost efficiency, which directly affect a company's financial Therefore, this study addresses this gap by integrating Miles & Snow's strategy typology-Defender.
Prospector.
Analyzer, and Reactor-in a model that examines the simultaneous relationship between business strategy, operational efficiency, and logistics efficiency on firm performance.
Thus, this research not only makes a theoretical contribution by bringing together three important domains that were previously separate in academic studies, but also a practical contribution for managers in formulating efficiency-based strategies that are aligned with the internal and external conditions of the company.
This research seeks to address this gap by investigating the linkages between strategic typologies and operational/logistics capabilities in determining a firm's financial success.
This understanding is important so that companies can formulate adaptive and resilient strategies in the face of highly volatile economic dynamics.
Therefore, this study aims to analyze the influence of Miles & Snow's business strategy typology, operational efficiency, and logistics efficiency on the company's financial performance.
Literature Review Business Strategy Business strategy is an essential component in enhancing a companyAos competitive positioning within its industry or market segment (Martynez et al.
, 2.
Research indicates that the influence of business units on a companyAos overall performance is twice as significant as the effect of industry-specific or company-wide factors (Baye, 2.
Strategies can be competitive or cooperative, guiding how businesses or their divisions compete or collaborate within their respective industries.
According to the Miles and Snow typology, organizations fall into four strategic categories: prospector, defender, analyzer, and reactor (Akingbade.
Each category applies a unique approach to innovation, efficiency, and market Prospectors aggressively seek new market opportunities and prioritize innovation over efficiency, while defenders focus on maintaining stability and controlling costs.
Analyzers balance stability and innovation by observing competitors and adopting successful strategies, whereas reactors lack a clear strategy and respond only to competitive pressures.
Aligning operations, organizational structures, and strategic goals, such as differentiation and cost leadership, is critical for achieving superior profitability and sustaining a competitive advantage (Hill, 2.
yaAycycaycuycc yaycycycnycyc ycnycuyccyceycu .
= yayceyceyceycaycycnycyce ycAycaycycoyceyc O ycIyceycoycaycycnycyce ycEycycnycayce O yaycycycaycaycnycoycnycyca.
yaycuycyc ycycu ycIycaycoyceyc ycIycaycycnycu % = yaycuycyc ycuyce yaycuycuycc ycIycuycoycc .
aycCyaycI) ycNycuycycayco ycIycaycoyceyc ycOycuycnyc ycIycaycoyceyc ycAycaycycoyceyc ycIEaycaycyce % = ycNycuycycayco ycAycaycycoyceyc ycIycaycoyceyc.
Operational Efficiency Operational Efficiency is a critical factor in driving organizational performance and is typically evaluated through key metrics such as employee productivity and overall operational Achieving operational efficiency ensures that resources are utilized optimally, waste is minimized, and productivity is enhanced.
According to (Taylor, 2.
, task and workflow standardization plays a crucial role in improving efficiency by creating a more predictable and streamlined operation.
Employee motivation is a key driver of productivity, which in turn directly impacts operational efficiency (Alam et al.
, 2.
In addition, the principles of Lean Management according to Womack & Jones .
advocate for the reduction of waste and Taufiq Chandra Adimanta Habibie et al.
| Volume 5 No.
continuous improvement of processes, which are fundamental to achieving higher operational efficiency (Powell, 2.
The Resource-Based View highlights the importance of leveraging an organizationAos unique resources, including skilled employees and proprietary technologies, as a means of enhancing operational performance (Cobbinah et al.
, 2.
Moreover, the Balanced Scorecard framework offers a comprehensive approach by linking operational performance to strategic objectives, thereby ensuring that efficiency improvements are aligned with long-term organizational goals (Mtau & Rahul, 2.
Collectively, these theories emphasize the need for aligning operational processes with employee performance and organizational strategy to achieve sustainable efficiency and competitive advantage.
ycEycycuycycnyc .
ycIycCya ycCycyyc = ycCycyyceycycaycycnycuycuycayco yaycuycyceycycycoyceycuyc ycaycuycyc .
ycEycEyayc = ycNycuycycayco ycEycycaycoycnycyc ycEycuycnycuycyc yaycaycycuyceycc ycNycuycycayco ycEycEyayc Logistic Efficiency Logistics efficiency is a critical factor in the successful management of supply chain Essential indicators, such as the turnover rate of inventory and the ratio of logistics costs, assess a companyAos ability to effectively balance inventory management and logistics expenses, which in turn boosts both profitability and customer satisfaction (Christopher.
The Resource-Based View asserts that leveraging sophisticated logistics systems can offer a strategic advantage, while Lean Management advocates for the ongoing refinement of logistics processes to maximize efficiency.
The coordination of various logistics functions, including transportation, warehousing, and inventory control, is vital for reducing operational costs and enhancing service quality (Hasim & Holiawati, 2.
Well-optimized logistics contribute to a firmAos growth in market share, as the efficiency of logistics operations has a direct influence on customer satisfaction and revenue generation (F.
Liu et al.
, 2.
Christopher .
emphasizes how logistics efficiency is essential in streamlining supply chain processes, leading to reduced costs, higher production capacity, and greater customer satisfaction, all of which significantly influence a companyAos overall performance.
ycEycycuycycnyc .
ycIycCya yaycuyci = yaycuyciycnycycycnyca yaycuycyceycycycoyceycuyc ycaycuycyc .
yaycuycyceycuycycuycyc ycNycycycuycuycyceyc = yaycuycyc ycuyce yaycuycuyccyc ycIycuycoycc .
aycCyaycI) yaycyceycycayciyce yaycuycyceycuycycuycyc Company Performance Return on Assets (ROA) and Return on Sales (ROS) are important metrics for evaluating a company's financial performance and operational efficiency.
ROA measures how effectively a company utilizes its total assets to generate net profit, making it particularly relevant for asset-dense industries (Brigham & Houston, 2.
On the other hand.
ROS assesses the percentage of sales revenue converted into operating profit, which highlights cost efficiency and operational effectiveness.
While ROA is calculated by dividing net profit by total assets.
ROS is obtained by dividing net profit by sales revenue, which offers complementary insights into profitability and efficiency.
Together, these metrics provide stakeholders with valuable tools to assess a company's financial health and strategic performance.
ycEycycuyceycnyc .
ycIyceycycycycu ycuycu yaycycyceycyc = ycNycuycycayco yaycycyceyc .
ycEycycuyceycnyc .
ycIyceycycycycu ycuycu ycIycaycoyceyc = ycNycuycycayco ycIycaycoyceyc.
Taufiq Chandra Adimanta Habibie et al.
| Volume 5 No.
Hypothesis Development The Effect of Business Strategy on Company Performance Business strategy is a crucial determinant in shaping a companyAos competitive position and driving its long-term success.
It involves the strategic decisions that a company makes to gain an edge over its competitors, improve its market standing, and achieve sustainable These strategies may include approaches like cost leadership .
here the company aims to be the low-cost producer in its industr.
, differentiation .
here the company seeks to offer unique products or service.
, or focusing on specific niche markets to cater to customer needs.
The effectiveness of a business strategy depends on how well it aligns with current market conditions, as this alignment influences the company's ability to allocate resources efficiently and respond to external challenges.
Porter .
suggests that a welldefined strategy positions companies to leverage their strengths, exploit market opportunities, and outperform competitors (Kakeesh et al.
, 2.
Aji et al.
emphasizes the importance of integrating operational, marketing, and financial strategies in driving optimal performance.
Meanwhile.
Huda & Lingga .
also found that product and market development strategies can increase firm value through Next.
Faraiddin et al.
emphasized that the integration of internal and external processes drives efficiency and strengthens the company's competitiveness.
These findings consistently support the positive relationship between business strategy and firm The Effect of Operational Efficiency on Company Performance Operational efficiency plays an essential role in enhancing a companyAos performance by optimizing how resources such as labor, capital, and technology are utilized to produce goods and services.
Efficient operations involve minimizing waste, improving productivity, and reducing costs without compromising quality, leading to better profitability.
For instance, companies that achieve high operational efficiency can lower operational expenses, increase the output produced from the same amount of input, and ultimately boost financial results.
Key metrics used to assess operational efficiency include the operating cost ratio, which evaluates how effectively a company turns its expenses into revenue.
Companies that excel in operational efficiency are better positioned to reduce costs, improve productivity, and maintain competitive quality standards.
Several studies show that operational efficiency has a significant role in improving company performance, both in the context of profitability, management, and cost efficiency.
Wiryawan et al.
emphasizes that operational efficiency directly affects a bank's financial performance, whereas Annisa .
found a positive relationship between operational efficiency and profitability of Islamic banks.
Similar findings were also revealed by Tania & Abdi .
, which shows that the higher the operational efficiency, the more optimal the financial performance of coal companies.
Febriyanti & Citradewi .
even placing operational efficiency as an important mediator in the relationship between liquidity and profitability in the property sector.
Overall, operational efficiency proves to be a key factor supporting improved corporate performance across sectors.
The Effect of Logistics Efficiency on Company Performance Logistics efficiency refers to the optimization of supply chain processes, including transportation, warehousing, inventory management, and distribution, to ensure timely deliveries at the lowest possible cost.
Effective logistics operations are critical for reducing operational costs and enhancing service quality, which leads to higher customer satisfaction Taufiq Chandra Adimanta Habibie et al.
| Volume 5 No.
and improved profitability.
Key indicators of logistics efficiency include inventory turnover .
hich measures the rate at which inventory is sold and replace.
and logistics cost ratios .
hich compare logistics costs to total revenu.
Efficient management of these logistics functions helps companies streamline their operations, reduce waste, and deliver products more quickly and cost-effectively.
By improving logistics efficiency, companies can strengthen their competitive advantage in the market.
Several studies show that logistics efficiency plays an important role in improving company performance.
Prakoso et al.
found that supply chain efficiency has a positive impact on achieving production targets and business performance.
Adawiyah & Takaya .
emphasized that Just-In-Time strategies are capable of improving logistics efficiency and inventory control.
These findings support that logistics efficiency is one of the key factors affecting overall company performance, making it worth formulating the research hypothesis.
Figure 1.
Conceptual Framework Source: Researcher .
From the framework figure 1 above, we can highlight the following hypotheses:
H1: Business strategy has a positive and significant effect on company performance.
H2: Operational efficiency has a positive and significant effect on company performance.
H3: Logistics efficiency has a positive and significant effect on company performance.
H4: Business strategy, operational efficiency, and logistics efficiency have a positive and significant effect on company performance.
Methods Type of Research Lorem This study adopts a quantitative research approach, focusing on the statistical analysis of data collected through surveys.
The data was processed numerically and analyzed using advanced statistical techniques including panel data regression analysis, to assess the direct effects of independent variables such as business strategy, operational efficiency, and logistics efficiency on the dependent variable, company performance.
The research leveraged by time-series data, which allow the trends and patterns over the specified time period.
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Research Characteristics This research is replicative in nature, focusing on examining the differences across various business strategies.
The uniqueness of this study lies in its application of Miles & Snow's business strategy typology to assess the effects of business strategy, operational efficiency, and logistics efficiency on company performance in the electronics industry in Kalimantan.
The study differs from previous research in terms of location, industry focus, and the specific time frame .
, offering fresh insights into the effects of strategic models in this particular context.
Population and Sample The population for this research consists of employees at an electronics company in Kalimantan, specifically focusing on 45 participants who hold managerial and supervisory positions responsible for measuring Quality Performance Indicators (QPI.
within the Data was collected over a period from 2021 to June 2024, providing a comprehensive timeframe to assess the long-term impact of these independent variables on company performance.
By evaluating these key variables, the study aims to identify areas for improvement and offer actionable insights to enhance overall performance.
The sample will allow for exploring how business strategy, operational efficiency, and logistics efficiency interact in real-world company settings, contributing valuable findings to the electronics Sampling Technique This study employs a non-probability sampling method, specifically purposive sampling technique, to select participants who meet certain criteria directly relevant to the research The sample consists of 45 employees at Midea-Toshiba Electronics company in Kalimantan.
These participants hold managerial and supervisory positions and have responsibilities in measuring Quality Performance Indicators (QPI) within the company.
The inclusion criteria include: .
having worked for at least one year at the company, .
serving as managers or supervisors in operational and logistics fields, and .
being directly involved in evaluation or decision-making related to quality performance indicators.
Meanwhile, the exclusion criteria encompass: .
employees who only work in administrative fields without operational responsibilities, and .
contract or temporary workers who do not have authority in evaluation.
This purposive sampling technique ensures that only individuals who have direct involvement in operational and logistics decision-making serve as respondents, so that the data obtained is relevant to the research focus.
Data Sources Data was gathered from primary and secondary sources:
Primary data was obtained directly from participants through surveys, providing firsthand insights into business strategy, operational efficiency, and logistics efficiency.
Secondary data was drawn from existing literature, including books, articles, and journals, to support the research framework and offer background on the relevant Data Collection Methods Data was collected through online questionnaires distributed via WhatsApp and Instagram using Google Forms links.
The survey employed a Likert scale to measure respondentsAo attitudes, behaviors, and perceptions related to the independent variables.
This scale which help quantify responses and offer valuable insights into how business strategy, operational efficiency, and logistics efficiency impact company performance.
The use of online Taufiq Chandra Adimanta Habibie et al.
| Volume 5 No.
platforms ensures accessibility and efficiency in reaching the target population, providing convenience for respondents to participate in the study.
Data Analysis Method The data in this study were analyzed using Partial Least Squares (PLS), a variance-based structural equation modeling method suitable for complex models with many constructs and Theoretically.
PLS was chosen because it is able to predict the relationship between latent variables and does not require the assumption of normal distribution.
Practically.
PLS is suitable for research with a small sample size like this .
, and can handle potential multicollinearity between variables.
The analysis was conducted through three main Construct validity to ensure that the measurement items accurately reflect the theoretical constructs.
Reliability to confirm that the constructs consistently measure the intended variables.
Hypothesis testing to validate the proposed relationships between the variables.
Validity and Reliability The validity and reliability of the data collected was thoroughly evaluated to ensure the accuracy and consistency of the findings.
Construct validity was assessed to ensure that the measurement items accurately reflect the theoretical constructs they are intended to measure.
Construct validity was tested through convergent and discriminant validity analysis.
Average Variance Extracted (AVE) was used to measure convergent validity, while Fornell-Larcker criteria and cross-loading were used to ensure that each construct is unique.
Indicators with loadings below 0.
70 were evaluated theoretically and statistically before deciding to retain or eliminate them.
Reliability testing was conducted using Cronbach's Alpha and Composite Reliability (CR) values.
Results show that Cronbach's Alpha for business strategy is 0.
85, operational 80, and logistics efficiency 0.
78, all exceeding the 0.
70 threshold indicating good internal consistency.
CR values for each construct also exceed 0.
70, confirming that the instrument has strong reliability.
Overall, these validity and reliability tests ensure that the instrument used in the study is capable of measuring constructs accurately and consistently, and supports empirical strength in hypothesis testing.
Hypothesis Testing Hypothesis testing was conducted to evaluate the relationships between the independent variables .
usiness strategy, operational efficiency, and logistics efficienc.
and the dependent variable .
ompany performanc.
The primary method for hypothesis testing will be significance testing, using path coefficients and p-values.
A p-value less than 0.
05 will indicate statistical significance, confirming that the observed relationships are unlikely to have occurred by chance.
In addition to significance testing, confidence intervals will be calculated for the path coefficients.
These intervals will provide insight into the range of values within which the true population parameters are expected to fall, offering a level of certainty about the direction and strength of the relationships.
To measure the effect size, f-square values will also be calculated.
These values indicate the strength of the impact that each independent variable has on the dependent variable.
The values are categorized into low .
, medium .
, and high .
levels of influence, which will help assess the relative importance of each independent variable in explaining company performance.
These tests will provide a robust framework for validating the hypotheses and assessing the significance of the relationships within the model.
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Model Fit Testing To ensure the quality and predictive ability of the research model, model fit testing will be performed using several key criteria.
First, the RA values will be evaluated to measure how much variance in the dependent variable is explained by the independent variables.
Higher RA values indicate a better model fit, with values of 0.
66 representing a good model, 0.
indicating a moderate fit, and 0.
19 representing a weak model.
Additionally, the QA values will be calculated to measure the predictive relevance of the model.
These values range from 0.
igh predictive relevanc.
to 0 .
ow predictive relevanc.
, with values above 0 indicating that the model has meaningful predictive power (J.
Hair Jr et al.
, 2.
Another important criterion is the SRMR (Standardized Root Mean Square Residua.
, where values below 0.
indicate a good model fit.
Additionally, the Goodness-of-Fit (GoF) index will be used to assess the overall fit of the model, with high GoF .
, medium GoF .
, and low GoF .
providing an indication of the modelAos overall explanatory power.
Finally, a robustness check will be conducted, ensuring that the model is stable and reliable, with a p-value greater than 05 indicating the model's robustness.
By assessing these criteria, the study will confirm the reliability and validity of the research model and its ability to explain and predict company performance accurately (J.
Hair Jr et al.
, 2.
Results and Discussion Research Results Description of Respondent Characteristics Results The respondents in this study come from different departments within MideaToshiba Electronics Company.
Below is a breakdown of the respondents by department, showcasing their contribution to the overall sample:
Table 1.
Respondent Distribution by Department Department Quantity (Peopl.
Contribution (%) Top Management Finance Department Product Department Sales Department Trade Marketing Dept Logistics Department Service Department Total Source: Processed Data .
As shown in table 1, the majority of respondents come from the Trade Marketing Department, which represents 51.
11% of the sample, followed by respondents from the Logistics Department .
33%), and Service Department .
11%).
This diversity ensures a broad perspective on how business strategy, operational efficiency, and logistics efficiency are applied across different functional areas within the company.
The inclusion of various departments enhances the robustness of the study by incorporating a range of viewpoints and experiences relevant to the research questions.
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Descriptive Analysis of Variables Table 2.
Research Data of X1 Indicators Every Year Indicator Innovation (Brand Equity Inde.
Efficiency (Cost to Sales Rati.
Market Share Expansion Source: Processed Data .
As seen in table 2, the company's strategic trajectory from 2021 to 2024 follows the Miles and Snow typology, which captures the shift between the Defender.
Analyzer, and Prospector Initially, in 2021, the company embraced a Defender strategy, prioritizing operational efficiency with a Cost to Sales Ratio of 0.
964 while limiting its focus on innovation (-0.
and market expansion (-0.
This approach reflects the Martins et al.
theory, which suggests that Defender strategies concentrate on maintaining stability and cost control over market expansion.
By 2022, the company transitioned to an Analyzer strategy, seeking a balance between innovation .
and efficiency .
, while closely observing competitors.
This aligns with Martins et al.
, who emphasize incremental innovation and operational From 2023 to 2024, the company adopted the Prospector strategy, pushing for significant innovation .
and market expansion .
, though efficiency dropped to This shift to Prospector highlights a clear focus on growth and market share, even at the expense of operational efficiency.
These evolving strategic trends emphasize the critical role of aligning a companyAos strategic direction with operational efficiency to gain a competitive edge and ensure long-term growth.
Outer Model Test (Validity and Reliability Test Result.
To ensure the reliability and accuracy of the measurement model in this study, both validity and reliability were thoroughly assessed for the three key latent variables which are Business Strategy (X.
Operational Efficiency (X.
, and Logistics Efficiency (X.
The validity of these variables was confirmed through various indicators such as Outer Loadings and Average Variance Extracted (AVE).
Table 3.
Reflective Measurement Test Results Data (Outer Model Te.
Outer Cronbach's Variable Indicator AVE Loadings Alpha Innovation Business Strategy (X.
Efficiency Market Share ROI Ops Operational Efficiency (X.
QPIs ROI Log Efficiency Logistics (X.
Inventory Turnovers Source: Processed Data .
To ensure the reliability of the constructs.
Cronbach's Alpha and Composite Reliability tests were conducted.
As seen in table 3, the CronbachAos Alpha for business strategy was 0.
for operational efficiency it was 0.
80, and for logistics efficiency, it was 0.
78, all exceeding the 70 threshold, which is considered acceptable for internal consistency.
Similarly, the Composite Reliability for each variable exceeded the 0.
70 threshold, further confirming the reliability of the measurement items.
These results suggest that the items used to measure Taufiq Chandra Adimanta Habibie et al.
| Volume 5 No.
business strategy, operational efficiency, and logistics efficiency are internally consistent and provide reliable measurements, which strengthens the overall reliability of the study.
Inner Model Test (RA & QA) The inner model test is an essential component of structural equation modeling (SEM) that evaluates how well the latent variables .
ndependent and dependen.
in the model explain the variation in the dependent variable and how well the model predicts changes in the dependent variable.
The RA and QA tests are used to assess explanatory power and predictive relevance of the inner model.
Table 4.
Inner Model Test Results (RA & QA) Variable RA RA Adjusted QA Standard Company Performance 999 Ou 0.
66 (RA).
Ou 0.
5 (QA)
Source: Processed Data .
Table 4 shows the RA value for Company Performance is 0.
966, indicating that 96.
6% of the variance in company performance is explained by the model.
This high RA value suggests that the model provides a strong explanation for changes in company performance, with the independent variables in the model .
usiness strategy, operational efficiency, and logistics efficienc.
accounting for a large portion of the variation in performance.
According to Hair Jr et al.
, an RA value greater than 0.
66 is considered to demonstrate a substantial fit, which aligns with the results of this study.
The adjusted RA value of 0.
963 reflects the small adjustment made to account for the number of predictors and sample size.
This indicates that the model remains robust and remains a good fit even after considering these factors.
The QA value for company performance 999, which is extremely high and indicates exceptional predictive relevance.
A QA value close to 1 suggests that the model has a strong ability to predict future variations in company Specifically, the QA value of 0.
999 means that the model can predict 99.
9% of the changes in company performance, confirming its excellent predictive capability.
In conclusion, the RA and QA results demonstrate that the inner model has both strong explanatory power and predictive relevance.
The high RA value indicates that the model does an excellent job of explaining the variance in company performance, while the impressive QA value underscores the model's ability to predict future changes in performance with high These results suggest that the model is robust and reliable in capturing the relationships between the variables and predicting company performance.
Model Fit Test.
GoF, and Linearity Test Results This section presents the results from the Model Fit Test.
Goodness of Fit (GoF) evaluation, and Linearity Test for the structural equation model (SEM).
These tests provide valuable insights into how well the model fits the observed data, how well it predicts future outcomes, and whether the assumed linear relationships between variables hold.
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| Volume 5 No.
Table 5.
Model Fit Test.
GoF, and Linearity Test Results Saturated Estimated Test
Metric
Interpretation Model
Model
SRMR
(Standardized Root Mean Square Residua.
d_ULS (Squared Euclidean Distanc.
d_G (Geodesic Distanc.
Chi-Square Fit Indicator SRMR < 0.
08: Good model fit (SchermellehEngel et al.
, 2.
Fit Indicator Indicates an acceptable model fit.
Adequate model fit.
Fit Indicator Fit Indicator Significant due to large sample size.
Below threshold of 0.
some room for model Excellent fit.
GoF > 0.
indicates strong model predictive relevance (Henseler et al.
, 2.
NFI (Normed Fit Inde.
Fit Indicator GoF (Goodness of Fi.
Overall Fit Path Coefficient & p-Value Linearity Met .
ut not statistically significan.
Path Coefficient & p-Value Linearity Met and statistically significant.
Path Coefficient & p-Value Linearity Met .
ot statistically significan.
Linearity Test (Business Strategy > Company Performanc.
Linearity Test (Operational Efficiency > Business Strateg.
Linearity Test (Logistics Efficiency > Company Performanc.
Source: Processed Data .
The table 5 shows the results of the Model Fit.
GoF, and Linearity Tests which suggest that the model fits the data reasonably well, with an excellent GoF score of 0.
97 indicating strong predictive relevance.
However, the NFI value and some of the linearity tests suggest that there is room for improvement in the model, particularly with regards to the relationship between logistics efficiency and company performance, which was not statistically significant.
Overall, the model provides a good foundation for understanding the relationships between the variables, but further refinement may be necessary to optimize its predictive capabilities.
Hypothesis Test Results The following table presents the results of the hypothesis tests, which examine the direct effects of business strategy, operational efficiency, and logistics efficiency on company The path coefficients, p-values, 95% confidence intervals, and f-square values are provided to assess the strength and significance of these relationships.
The hypothesis test results reveal that business strategy, operational efficiency, and logistics efficiency each have a significant and strong influence on company performance.
Specifically, business strategy shows a positive and substantial impact with a path coefficient Taufiq Chandra Adimanta Habibie et al.
| Volume 5 No.
253 and a very large effect size .
-square = 0.
, indicating that a well-formed and executed business strategy is crucial for better performance outcomes.
Hypothesis Table 6.
Hypothesis Test Results Path pfCoefficient Value Square H1.
Business Strategy Ie Company Performance H2.
Operational Efficiency Ie Company Performance H3.
Logistics Efficiency Ie Company Performance Interpretation Strong Influence Strong Influence Strong Influence Source: Processed Data .
Table 6 shows that operational efficiency also plays an important role with a path coefficient of 0.
296 and a medium to strong effect size .
-square = 0.
, suggesting that enhancing operational processes contributes to improved company performance, albeit to a lesser degree than business strategy.
The strongest influence is seen in logistics efficiency, with a path coefficient of 0.
481 and a strong effect size .
-square = 0.
, highlighting the critical role logistics plays in boosting company performance.
This emphasizes the importance of optimizing logistics processes, such as supply chain management, inventory handling, and timely deliveries.
Overall, the results confirm that strategic planning, operational improvements, and efficient logistics management are essential factors in achieving optimal company performance.
Discussion The Effect of Business Strategy on the Company's Performance The first hypothesis (H.
examines the relationship between business strategy and company performance.
Research results show a statistically significant positive influence, with a path coefficient value of 0.
253 and p-value of 0.
The 95% confidence interval ranging 126 to 0.
404 and the large effect size .
A = 0.
strengthen that business strategy has a substantial influence on performance.
Contextually, this influence can be explained through the alignment between the company's strategic orientation and utilization of internal Companies that implement prospector strategies with focus on innovation, agility, and exploration of new markets are better able to respond to environmental changes and evolving consumer needs.
This adaptability capability impacts improved operational responsiveness and customer orientation, which ultimately enhances performance indicators such as revenue growth and customer satisfaction.
These findings align with competitive strategy theory that emphasizes the importance of strategic position .
ost leadership, differentiation, and focu.
in gaining competitive advantage (Shah et al.
, 2.
In practice, companies that implement this strategy through internal processes such as continuous product development, customer feedback integration, and lean management systems tend to be better able to translate strategy into tangible performance results.
Additionally, based on the resource-based approach, business strategy effectiveness lies in the organization's ability to utilize unique resources and capabilities, such as human resource expertise, technology infrastructure, and brand strength, to create sustainable value (Sijabat, 2.
Periodic strategy evaluation through key performance indicators such as market share, customer retention, and operational efficiency helps companies adjust strategies in line with Taufiq Chandra Adimanta Habibie et al.
| Volume 5 No.
changing market conditions (Jonsdottir et al.
, 2.
This process provides a reasonable explanation for the relationship found in this research, where internal mechanisms in strategy implementation are proven to be closely connected with external performance achievement.
In this case, the importance of dynamic strategic approaches for long-term success (Vrontis et , 2.
, while findings from Handoyo et al.
show that market uncertainty and competition intensity act as moderating variables that strengthen the influence of business strategy on company performance.
The Effect of Operational Efficiency on the Company's Performance The second hypothesis (H.
tests the influence of operational efficiency on company Analysis results show a significant positive relationship, with a path coefficient 296 and p-value of 0.
The confidence interval between 0.
064 to 0.
536 further strengthens the validity of these findings.
The effect size value fA = 0.
433 shows moderate to strong influence, indicating the importance of operational efficiency as a strategic factor in performance improvement.
Contextually, this relationship can be explained through organizational mechanisms that occur in electronics companies in Kalimantan that are the object of this research.
Based on interviews and data from 45 participants holding managerial and supervisory positions, operational efficiency improvement is directly related to production process improvement efforts, work cycle time reduction, and optimization of logistics flow and quality control.
practice, efficiency is achieved through implementation of strict work standards, digitalization of product quality monitoring, and continuous training for production line staff.
This process enables companies to produce more units at lower costs and with minimal product defect rates, which directly impacts improvement of Quality Performance Indicators (QPI), such as inspection pass rates, production response times, and customer satisfaction.
These findings align with the view of Handoyo et al.
which emphasizes the importance of operational efficiency for companies in efforts to maximize output while minimizing resource use.
Additionally, the impact of operational efficiency can be strengthened or weakened by market uncertainty and competition intensity, which requires flexible managerial responses.
In this context, managers at Kalimantan electronics companies have implemented responsive strategies to fluctuating market demand, including production capacity adjustments and use of real-time data in decision-making.
Furthermore, study by Schyfers et al.
underline the importance of strategic revenue management, such as customer segmentation and differential pricing, which supports sales productivity optimization.
Implementation of Lean Six Sigma principles has proven to boost performance through process variation reduction and quality control improvement (Shah et al.
, 2.
This is reinforced by Liu et al.
, who found that operational leanness significantly impacts company financial performance, particularly in industrial sectors undergoing digital transformation.
Overall, operational efficiency not only plays a role in reducing costs, but also as a strategic capability that supports continuous improvement of overall company performance through measurable process management based on quality performance indicators (QPI).
The Effect of Logistics Efficiency on the Company's Performance The third hypothesis (H.
tests the influence of logistics efficiency on company Analysis results show a significant positive relationship, with a path coefficient 481 and p-value of 0.
The confidence interval ranging from 0.
179 to 0.
746, and the effect size .
-square.
value of 0.
524, indicate that logistics efficiency has a large influence on company performance.
Taufiq Chandra Adimanta Habibie et al.
| Volume 5 No.
In the context of electronics companies in Kalimantan, logistics efficiency is reflected in coordinated inventory management processes, delivery speed between production divisions and to customers, and the ability to reduce distribution costs.
Based on data from managers and supervisors involved in QPI measurement, companies implement integrated logistics systems that utilize real-time goods tracking technology, reorder point recalculation, and delivery route optimization to improve distribution speed and accuracy.
Improvements in ontime delivery indicators and reduced customer waiting times become concrete evidence that logistics efficiency provides direct contribution to improved customer satisfaction and market This aligns with the view of Govindan et al.
, which emphasizes the importance of logistics efficiency in improving supply chain performance.
Effective logistics management is not only capable of reducing operational costs, but also improving customer service levels which are crucial in the electronics industry, where supply chain complexity and demand pressure are very high.
Therefore, logistics efficiency is not merely cost-saving action, but a key component in driving company performance through improved delivery reliability and customer satisfaction.
Liu et al.
also noted that optimized logistics performance has a positive correlation with overall company success.
Furthermore.
Mahpour et al.
show that logistics performance is not only influenced by internal factors, but also by the company's approach to cross-border trade and its ability to manage global supply chains.
In this context, electronics companies in Kalimantan demonstrate resilience and adaptability through cooperation with regional distribution partners, and implementation of risk-based logistics protocols in facing supply chain disruptions.
These findings confirm the strategic role of logistics efficiency in maintaining competitiveness and business continuity, especially amid ever-changing market uncertainty.
Integration of Findings The integration of the findings from these three hypotheses demonstrates the interconnected nature of business strategy, operational efficiency, and logistics efficiency in driving company performance.
These elements do not function in isolation.
rather, they work together to optimize profitability and competitiveness.
Companies with a well-defined business strategy, efficient operations, and effective logistics systems are better equipped to navigate market challenges and achieve superior performance.
The results suggest that electronics companies, particularly in the post-pandemic era, should focus on aligning these three factors to ensure sustainable growth and long-term success.
The implications of this research emphasize the importance of integration between business strategy, operational efficiency, and logistics efficiency in improving company performance, particularly in the electronics industry sector.
Previous studies by Handoyo et .
Liu et al.
and Liu et al.
support the view that an integrated approach to these three aspects is not only capable of meeting short-term performance targets, but also building company resilience in facing long-term challenges.
For managers, these results provide practical guidance that business strategy must be aligned with company internal capabilities and external market dynamics.
Companies implementing prospector strategies, for example, are required to promote innovation culture, make quick decisions, and have flexible organizational structures to respond to environmental changes with agility.
Additionally, operational efficiency needs to be prioritized through implementation of lean management practices, investment in production technology, and strengthening human resource training.
Gittins & McElwee .
state that operational efficiency, characterized by maximum output from minimum input, has direct contribution to company profitability and Taufiq Chandra Adimanta Habibie et al.
| Volume 5 No.
Effective logistics management also has significant implications, as it affects customer satisfaction, delivery speed, and distribution costs.
In line with Yao & Wang .
, good logistics efficiency can improve supply chain reliability and reduce operational costs, which ultimately strengthens company competitiveness, especially in industries with high distribution complexity such as electronics.
The theoretical implications of these findings enrich strategic management and operations literature by confirming that company performance is not only determined by the strength of a single factor, but by synergy between long-term strategic planning, utilization of internal capabilities, and optimization of logistics processes and systems.
This research also supports the importance of holistic and dynamic approaches in viewing business strategy, where strategy must not only be efficient, but also responsive to uncertain market changes.
From a managerial perspective, company leaders need to play key roles in strategic decision-making processes, by building adaptive and data-based leadership systems.
Marketing strategy is equally important as a driver of sales performance.
this strategy plays a role in building brand image, increasing product visibility, and attracting consumer loyalty.
Strengthening sales teams through continuous training will improve communication skills, product understanding, and ability to adapt to market dynamics, which ultimately contributes to overall company growth and success.
Based on these findings, it is recommended that companies consistently strengthen implementation of business strategies that are adaptive to market changes, supported by routine evaluation and utilization of data in strategic decision-making.
Operational efficiency needs to be continuously improved through implementation of digital technology, process automation, and human resource competency development, so that performance measurement and improvement can be carried out continuously.
On the other hand, logistics system optimization must be a key management focus, through integration of tracking technology, intelligent inventory management, and distribution partnerships that are flexible and responsive to risks.
Additionally, companies need to build resilient and adaptive organizational capabilities, to be able to respond to market uncertainty quickly while maintaining operational stability.
Cross-functional collaboration-based approaches and process innovation will be key to performance sustainability amid increasingly complex Conclusion This research concludes that business strategy, operational efficiency, and logistics efficiency significantly influence the performance of electronics companies in Kalimantan.
These three variables are proven to provide strong contribution to improving company performance indicator achievements, particularly Quality Performance Indicators (QPI).
Business strategy that is well-designed and implemented plays an important role in determining the direction and competitive position of the company in the market.
Operational efficiency, realized through production process improvement, quality control, and workflow digitalization, is able to reduce costs while increasing productivity.
Meanwhile, logistics efficiency shows the most dominant influence, reflecting the crucial role of supply chain management, distribution reliability, and information system integration in maintaining customer satisfaction and market competitiveness.
These results confirm that company success in achieving optimal performance depends on synergy between strategic planning, internal process efficiency, and logistics management effectiveness.
Research conducted in Kalimantan.
Indonesia concluded that business strategy, operational efficiency, and logistics efficiency have a positive impact on company Taufiq Chandra Adimanta Habibie et al.
| Volume 5 No.
performance, especially in the post-pandemic period.
Factors such as market conditions, competitive dynamics, and internal capabilities significantly influence this relationship, highlighting the need for further exploration of external factors such as market uncertainty and competition intensity.
Recommendations for future research include longitudinal studies, industry-specific analysis, and the role of technological advancements, while managerial advice emphasizes radical redesign of business processes, shifting managerial roles, prioritizing customer-centric values, fostering innovation, and adopting efficient organizational structures.
These strategies aim to improve agility, employee empowerment, and overall business resilience in a competitive and ever-evolving market landscape.
References