www.ijcrsee.com

Vasić, S. et al. (2026). Social and Technological Drivers of Sustainable Manufacturing Performance: A Cognitive-Organizational

Perspective, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE), 14(1), 1-10.

Original scientific paper

Received: February 11, 2025.

Revised: April 30, 2026.

Accepted: May 05, 2026.

UDC:

338:502.131.1(497.11)

10.23947/2334-8496-2026-14-1-001-010

Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

Corresponding author: vasic.stana@uns.ac.rs

Abstract: Sustainable manufacturing has become a strategic priority for organizations seeking to balance com

petitiveness with environmental and social responsibility. However, the successful implementation of sustainability initiatives

depends not only on technological and economic factors but also on workforce competencies, organizational learning, and

human-centered drivers. This study examines the cognitive and organizational determinants of sustainable manufacturing

performance through the analysis of social, economic, environmental, and quality drivers in manufacturing organizations in

the Republic of Serbia. Data were collected using a structured questionnaire administered to 153 employees in production-

related positions. The research model integrates human, technological, and managerial dimensions of sustainability and

evaluates their influence on six performance outcomes: environmental, social, economic, process, innovation, and stakeholder

performance. Multiple regression analyses were applied to test the proposed relationships. The findings indicate that environ-

mental and economic drivers exert the strongest positive effects on performance outcomes, while social drivers - reflecting

employee engagement, training, and organizational culture - demonstrate moderate but significant influence. Quality drivers

show mixed effects across performance domains. The results highlight the critical role of workforce involvement, knowledge

development, and sustainability-oriented competencies in supporting organizational transformation toward sustainable manu-

facturing. This study contributes to the emerging cognitive-organizational perspective on sustainability by integrating human

and technological drivers within a single empirical framework and providing evidence from a transition economy context.

Keywords: sustainable manufacturing, cognitive-organizational drivers, organizational performance, workforce

competencies, technological drivers, human factors.

Stana Vasić

, Kristina Ristić

, Nebojša Brkljač

, Daria Vidović

, Srđan Vulanović

Faculty of Technical Sciences, University of Novi Sad, Department of Industrial Engineering and Management, Novi Sad, Serbia,

e-mail: vasic.stana@uns.ac.rs, kristina.ristic@uns.ac.rs, n.brkljac@uns.ac.rs, daria.vidovic98@uns.ac.rs, srdjanv@uns.ac.rs

Social and Technological Drivers of Sustainable Manufacturing

Performance: A Cognitive-Organizational Perspective

Introduction

Sustainable manufacturing is evolving as organizations adopt strategies to reduce waste, enhance

efficiency, and align competitiveness with environmental and social responsibility (Karuppiah et al., 2024;

Scharmer et al., 2024). The concept of sustainable manufacturing encompasses the integration of en-

vironmental management, social responsibility, and operational excellence into production processes to

achieve long-term viability (Scharmer et al., 2024). However, despite theoretical advances, the empirical

evidence on which drivers are crucial in the process of implementing sustainable manufacturing, espe-

cially in transitional economies, remains limited. Companies are increasingly under pressure to align

competitiveness with responsibility toward the environment, employees, and society. Beyond technologi-

cal investments and environmental initiatives, sustainable manufacturing increasingly depends on human

and cognitive factors embedded within organizational systems. Employee competencies, sustainability

awareness, continuous learning, and knowledge sharing practices shape the extent to which sustainabil-

ity principles are adopted and operationalized in production environments. From this perspective, manu-

facturing transformation is not solely a technical process but also a cognitive and educational one, requir-

www.ijcrsee.com

Vasić, S. et al. (2026). Social and Technological Drivers of Sustainable Manufacturing Performance: A Cognitive-Organizational

Perspective, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE), 14(1), 1-10.

ing the development of workforce capabilities aligned with sustainable and digital industrial paradigms. In

transition economies, the pressure rises, as traditional industrial models must adapt to the requirements

of sustainability and digital transformation (Karuppiah et al., 2024). Manufacturing organizations are,

therefore, expected to integrate sustainability principles not only to meet regulatory demands but also to

improve resilience, innovation capacity, and market reputation (Scharmer et al., 2024). Identifying driv-

ers of sustainable manufacturing provides both theoretical and practical insights into how manufacturing

organizations can align economic efficiency with environmental and social objectives (Karuppiah et al.,

2024). Thus, this research aims to identify and analyze the key drivers that influence the implementa-

tion of sustainable manufacturing practices in production-oriented organizations, with a specific focus

on the Republic of Serbia. Based on a comprehensive literature review, a list of factors that stimulate

the adoption of sustainable practices in industrial systems has been developed. The research provides

insights into the current state of sustainable manufacturing implementation within the Serbian manufactur-

ing sector. Drivers influence the degree to which sustainable manufacturing practices are adopted, which

in turn affects performance. Higher levels of drivers are expected to lead to stronger implementation

and thus better outcomes across performance dimensions. The model allows for cross-domain effects,

anticipating positive relationships across driver-performance pairs. While many explore drivers of sustain-

able practices in developed economies, fewer empirical investigations focus on transition economies or

on multiple driver types simultaneously. This research examines social, economic, environmental, and

quality drivers simultaneously in relation to multiple dimensions of organizational performance (social,

economic, environmental, process, innovation, and stakeholder). This research addresses the gap by

surveying manufacturing organizations in Serbia, encompassing operational, managerial, and supportive

roles. This approach provides novel empirical evidence from a transitional economy. Thus, this research

contributes by: (a) empirically validating a framework of sustainable manufacturing adoption; (b) testing

the relative influence of distinct drivers on multiple performance outcomes; and (c) providing evidence

from a country with limited prior research in this domain. Considering the research subject, objectives,

and existing literature, the following research question has been formulated: “How do social, economic,

environmental, and quality (technological) drivers influence sustainable manufacturing performance, from

a cognitive-organizational perspective?”

Literature review

Achieving sustainability-oriented performance improvement requires a multidimensional approach

that integrates organizational, technological, and human factors (Rosen and Kishawy, 2012). The idea

has evolved in recent years from being solely ecological to encompassing environmental management in

relation to innovation and operational excellence (Andronie et al., 2021). Manufacturing businesses are

increasingly recognizing that sustainability is not just a moral or environmental requirement; it is also a

business imperative. Resilience and long-term competitiveness are fueled by sustainability (Andronie et

al., 2021). The internal and external factors that are essential for organizations to implement socially and

environmentally responsible practices are represented by drivers in this framework. These drivers are

typically divided into four groups based on a summary of earlier research: social, economic, environmen-

tal, and quality drivers. Each of these groups influences sustainability outcomes through specific mecha-

nisms. These factors frequently work together to produce synergies that increase the overall impact on

the organization. Social drivers underscore the importance of organizational culture, employee well-being,

and human capital as the cornerstones of long-term success. Strong ergonomic work practices and a

safety culture increase dependability, lower accident rates, and promote long-term results (Małysa and

Gajdzik, 2020). Furthermore, ongoing human resource development and employee involvement improve

environmental commitment and accountability (Lee et al., 2019; Madero-Gómez et al., 2023). Recent

research emphasizes that sustainability implementation is strongly influenced by workforce knowledge

structures, pro-environmental attitudes, and training systems that foster sustainability-oriented behavior.

Learning organizations are more capable of integrating environmental practices into operational routines,

as employees develop cognitive frameworks that support long-term ecological decision-making. Conse-

quently, human capital development and sustainability education emerge as critical enablers of manufac-

turing transformation (Garavan et al., 2012; Wiek et al., 2011). Stakeholder engagement and corporate

social responsibility (CSR) programs are two strong external drivers that promote environmentally friendly

www.ijcrsee.com

Vasić, S. et al. (2026). Social and Technological Drivers of Sustainable Manufacturing Performance: A Cognitive-Organizational

Perspective, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE), 14(1), 1-10.

behavior (Afsar and Umrani, 2020). Organizations notice a greater alignment between social responsibil-

ity and organizational outcomes when employees actively participate in sustainability projects, particularly

in emerging markets (Baykal and Divrik, 2023). Cost-cutting, energy efficiency, productivity gains, and in-

novation are all examples of economic drivers that enhance competitive advantage. According to empiri-

cal research, data-driven decision-making and predictive maintenance dramatically reduce waste, opera-

tional expenses, and downtime, enhancing sustainability and profitability (Polese et al., 2021; Mahfoud et

al., 2024). Since businesses that utilize structured energy management techniques achieve quantifiable

savings and a reduction in their environmental impact, energy efficiency remains a significant economic

driver (Solnørdal and Foss, 2018). These factors also apply to supply chains: effective inventory control

and logistics reduce waste and improve responsiveness (Panigrahi et al., 2024; Gibson, 2023). Due to

their concrete and quantifiable nature, economic and environmental drivers typically have stronger and

more consistent effects on performance outcomes, according to the literature (Madero-Gómez et al.,

2023; Hariyani et al., 2023). Environmental drivers encourage businesses to adopt green innovation, eco-

efficiency, and cleaner production practices to minimize their environmental impact. They frequently result

from proactive environmental management, consumer expectations, or regulatory frameworks (Rosen

and Kishawy, 2012; Andronie et al., 2021). With their clear routes to resource-efficient and low-carbon

production, eco-innovation and circular economy concepts have taken center stage in contemporary

manufacturing strategies (Chaurasiya and Singh, 2024). Sustainability initiatives are further aligned with

market demand by the incorporation of green product design and rising consumer awareness (Bravo et

al., 2022). Environmental performance is now recognized as being greatly enhanced by technological

innovation, energy-efficient production systems, and the effective management of renewable resources

(Fatma and Haleem, 2023; Panagiotopoulou et al., 2022). Developing internal green competencies also

promotes continuous improvement and long-term capability building (Fatma and Haleem, 2023). Sustain-

ability is closely linked to innovation and ongoing improvement driven by quality. Achieving environmental

and social objectives without sacrificing process or product standards is made possible by combining

quality management concepts with sustainable manufacturing (Abubakr et al., 2020). By coordinating

sustainability objectives with competitiveness and customer value creation, innovation-oriented quality

systems promote change (Cordova and Celone, 2019). Moreover, sustainable relationship management

and customer-centric strategies that strengthen stakeholder trust and company reputation are examples

of quality drivers that go beyond production (Ferrer-Estévez and Chalmeta, 2023). Together, these factors

show that manufacturing sustainability is not only ecologically conscious but also operationally depend-

able, creative, and value-driven.

Materials and Methods

To investigate the impact of various factors on sustainable manufacturing performance in produc-

tion-oriented organizations in Serbia, this study employed a quantitative, cross-sectional research design.

This method was considered suitable for evaluating multiple relationships simultaneously and identifying

trends across a wide range of industrial settings.

Data collection

To ensure widespread participation, a structured questionnaire was disseminated both electronical-

ly and in print to gather data. Employees from manufacturing companies across various industrial sectors,

including food production, machinery, metal processing, and automotive components, provided a total

of 153 valid responses. Because the respondents held a variety of professional roles, from managerial

and administrative to technical and operational, the sample was representative of those directly involved

in or supporting production processes. The survey was anonymous and voluntary to complete. Prior to

their involvement, all participants were informed about the purpose of the study and the confidentiality of

their responses, and they provided their informed consent. The study was conducted in accordance with

ethical standards governing research involving human participants. In line with institutional and national

guidelines, formal ethical approval was not required because the research did not involve interventions,

experimental procedures, or the collection of sensitive personal data.

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Vasić, S. et al. (2026). Social and Technological Drivers of Sustainable Manufacturing Performance: A Cognitive-Organizational

Perspective, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE), 14(1), 1-10.

Sample characteristics

The final sample consisted of 153 respondents employed in manufacturing and production-related

organizations in the Republic of Serbia. With respect to gender, 139 respondents were male (90.8%)

and 14 were female (9.2%), reflecting the gender structure commonly observed in manufacturing and

industrial sectors. The demographic characteristics of the respondents are summarised in Table 1. The

sample is predominantly male (90.8%), with most respondents holding a university degree (75.8%). The

average age of respondents was 41.19 years (SD = 8.74), ranging from 20 to 66 years. Regarding educa-

tional attainment, the majority of respondents held higher education degrees. More than three quarters of

the participants had completed university-level education, while a smaller proportion possessed doctoral

qualifications. The remaining respondents had completed secondary or college-level education. Partici-

pants were employed across a wide range of manufacturing sectors, including food and beverage pro-

duction, metal processing, automotive industry, energy and gas, chemical and pharmaceutical industries,

as well as other manufacturing activities. In terms of organizational roles, the respondents represented

various hierarchical levels, including top and middle management (e.g., directors, owners, and depart-

ment managers), engineers and technical specialists (e.g., production, quality, and process engineers),

and operational and technical staff. This diversity enhances the relevance and credibility of the collected

data for analyzing sustainable manufacturing practices.

Table 1. Demographic characteristics of respondents (N=153).

Variable Category n %

Gender

Male 139 90.8

Female 14 9.2

Education level

Secondary school 10 6.5

College 19 12.4

University degree 116 75.8

Doctoral degree 8 5.2

Age (years)

Mean (SD) 41.19 (8.74)

Range 20–66

Note. Percentages may not sum to exactly 100 due to rounding

Instrument development and data analysis

Prior empirical studies on organizational performance and sustainable manufacturing provided the

foundation for developing the questionnaire. It contained measurement items intended to evaluate four

types of drivers: quality drivers (QM), environmental protection drivers (EPM), economic drivers (EM),

and social drivers (SM). In this study, the term “drivers” is used consistently to refer to the social, eco-

nomic, environmental, and quality-related factors that support sustainable manufacturing implementa-

tion. Environmental performance (ENP), social performance (SP), economic performance (EP), process

performance (PP), innovation and development performance (IDP), and stakeholder performance (STP)

were the six performance outcomes that were measured accordingly. A five-point Likert scale (1 = strongly

disagree; 5 = strongly agree) was used to rate each item. Items were modified to represent the unique

traits of Serbian manufacturing companies, ensuring clarity and contextual relevance. The appropriate-

ness of the wording, structure, and response options for the target population was confirmed by a pilot test

conducted with a small subset of respondents (n = 15). Before the full-scale survey, minor formatting and

language changes were made. IBM SPSS Statistics (version 26) was used to analyze the data. To give a

general picture of respondent characteristics and variable distributions, descriptive statistics were calcu-

lated. Bivariate relationships between drivers and performance metrics were investigated using Pearson’s

correlation coefficients. Several multiple linear regression analyses were conducted to test the proposed

relationships. Six categories of performance outcomes - environmental, social, economic, process, inno-

vation and development, and stakeholder - were designated as dependent variables, and four categories

of drivers - social, economic, environmental, and quality - were designated as predictor variables. To eval-

uate each driver’s distinct contribution while accounting for shared variance among predictors, separate

regression models were estimated for each outcome dimension, with all drivers entered simultaneously.

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Vasić, S. et al. (2026). Social and Technological Drivers of Sustainable Manufacturing Performance: A Cognitive-Organizational

Perspective, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE), 14(1), 1-10.

Unstandardized coefficients (B), standard errors (SE), standardized coefficients (β), p-values, and R2 and

adjusted R2 values were used to report the results as measures of explanatory power. Model diagnostics

validated the assumptions of linearity and homoscedasticity. Multicollinearity diagnostics were examined

prior to regression analyses, and variance inflation factor (VIF) values were below commonly accepted

thresholds, indicating that multicollinearity did not pose a concern for the estimated models.

Results

Descriptive statistics and correlations between study variables

Descriptive statistics, reliability coefficients, and correlations between study variables are present-

ed in Table 2. All scales demonstrated acceptable internal consistency, with Cronbach’s alpha values

ranging from .69 to .87. The distributions of the variables were considered acceptable for the planned

parametric analyses. Skewness values were within ±2, while kurtosis values were mostly close to con-

ventional thresholds, with several values slightly exceeding ±2. Given the sample size and the absence

of extreme deviations, the variables were treated as sufficiently approximately normally distributed for

correlation and regression analyses. The four types of drivers were positively and strongly correlated

with each other (rs = .57–.74, ps < .001). They also showed positive associations with all performance

outcomes, with correlations ranging from moderate to strong (rs = .41–.75, ps < .001). The pattern of cor-

relations suggests that stronger sustainability drivers are consistently associated with higher performance

across environmental, social, economic, process, innovation and development, and stakeholder domains.

Among the performance indicators, intercorrelations were consistently strong (rs = .51–.76, ps < .001),

indicating that higher levels of one type of performance were generally accompanied by higher levels of

other performance dimensions.

Table 2. Descriptive statistics, reliability coefficients, and correlations between study variables

1 2 3 4 5 6 7 8 9 10

1. Social drivers

2. Economic drivers

0.622 ** 1

3. Environmental drivers

0.624 ** 0.734 ** 1

4. Quality drivers

0.628 ** 0.643 ** 0.571 ** 1

5. Environmental performance

0.524 ** 0.611 ** 0.704 ** 0.445 ** 1

6. Social performance

0.559 ** 0.645 ** 0.604 ** 0.579 ** 0.619 ** 1

7. Economic performance

0.592 ** 0.633 ** 0.629 ** 0.586 ** 0.704 ** 0.701 ** 1

8. Process performance

0.557 ** 0.624 ** 0.660 ** 0.558 ** 0.655 ** 0.668 ** 0.746 ** 1

9. Innovation and development

performance

0.474 ** 0.638 ** 0.665 ** 0.417 ** 0.751 ** 0.647 ** 0.764 ** 0.732 ** 1

10. Stakeholder performance

0.494 ** 0.552 ** 0.500 ** 0.528 ** 0.508 ** 0.630 ** 0.687 ** 0.701 ** 0.608 ** 1

Min–max

6–25 4–19 4–20 4–20 5–24 4–20 4–20 4–20 4–20 4–20

18.58 14.77 14.75 15.05 17.98 15.21 14.13 14.95 14.98 14.86

3.36 2.79 3.29 3.01 3.99 2.94 3.19 2.79 3.34 3.08

Skewness

−0.85 −1.34 −1.27 −1.06 −1.10 −1.23 −0.82 −1.39 −1.21 −1.18

Kurtosis

0.86 2.55 1.87 1.12 0.69 1.91 0.88 3.13 1.30 2.06

0.69 0.71 0.74 0.75 0.79 0.76 0.76 0.74 0.87 0.81

Note: M = mean; SD = standard deviation. ** p < 0.01

Multiple regression analyses of drivers predicting performance outcomes

Results of six multiple regression analyses are presented in Table 3. Together, the four types of

drivers explained between 36% and 51% of the variance in performance outcomes, with adjusted R² val-

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Vasić, S. et al. (2026). Social and Technological Drivers of Sustainable Manufacturing Performance: A Cognitive-Organizational

Perspective, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE), 14(1), 1-10.

ues ranging from .36 (stakeholder performance) to .51 (environmental performance). For environmental

performance, environmental drivers were the strongest positive predictor (β = .53, p < .01), and economic

drivers also showed a significant positive effect (β = .19, p < .05), whereas social and quality drivers were

nonsignificant. In the case of social performance, three drivers, economic (β = .30, p < .01), environmental

(β = .19, p < .05), and quality (β = .20, p < .05), emerged as significant positive predictors, while social

drivers did not contribute significantly. For economic performance, all four drivers showed significant posi-

tive associations: social (β = .18, p < .05), economic (β = .21, p < .05), environmental (β = .25, p < .01),

and quality (β = .19, p < .05). In contrast, for process performance, only environmental drivers significantly

predicted outcomes (β = .36, p < .01), while the remaining predictors were nonsignificant. Regarding in-

novation and development performance, economic (β = .35, p < .01) and environmental drivers (β = .43,

p < .01) both emerged as strong predictors, whereas social and quality drivers did not reach significance.

Finally, for stakeholder performance, economic (β = .25, p < .05) and quality drivers (β = .23, p < .05) were

significant positive predictors, while social and environmental drivers were nonsignificant. Taken together,

the results indicate that environmental and economic drivers most consistently predicted performance

across domains, with environmental drivers in particular showing the strongest effects for environmental

and innovation and development outcomes.

Table 3. Multiple regression analyses predicting performance outcomes from sustainability drivers

Performance

Environmental Social Economic Process IDP Stakeholder

Drivers

B (SE) β B (SE) β B (SE) β B (SE) β B (SE) β B (SE) β

Social

0.13 (0.09) 0.11 0.12 (0.07) 0.13 0.18 (0.08) 0.18 * 0.10 (0.07) 0.16 0.04 (0.08) 0.04 0.12 (0.08) 0.13

Economic

0.27 (0.13) 0.19 * 0.31 (0.10) 0.30 ** 0.24 (0.11) 0.21 * 0.18 (0.09) 0.18 0.42 (0.11) 0.35 ** 0.27 (0.12) 0.25 *

Environmental

0.64 (0.11) 0.53 ** 0.17 (0.08) 0.19 * 0.24 (0.09) 0.25 ** 0.31 (0.08) 0.36 ** 0.44 (0.09) 0.43 ** 0.10 (0.09) 0.11

Quality

−0.06 (0.11) −0.04 0.19 (0.08) 0.20* 0.21 (0.09) 0.19 * 0.15 (0.08) 0.16 0.08 (0.09) 0.08 0.23 (0.09) 0.23 *

0.521 0.494 0.512 0.507 0.494 0.374

adj. R

0.508 0.480 0.499 0.494 0.480 0.357

Note. B = unstandardized regression coefficient (standard errors in parentheses); β = standardized regression coefficient; R2

= coefficient of determination; adj. R2 = adjusted R2. * p < 0.05; ** p < 0.01

Discussion

The study’s findings offer new empirical insights into how social, economic, environmental, and

quality drivers impact long-term organizational performance in Serbia’s manufacturing industry. The re-

sults indicate that social drivers have a moderate but positive influence, while quality drivers exhibit mixed

effects. In contrast, economic and environmental drivers have the most substantial impact on performance

outcomes, aligning with global research trends. The significant influence of economic drivers underscores

the ongoing importance of cost optimization, efficiency, and innovation in fostering sustainable manufac-

turing. Technologies such as automation, artificial intelligence, and predictive maintenance can increase

productivity while reducing waste production and operating expenses (Polese et al., 2021; Mahfoud et

al., 2024; Agrawal et al., 2023). In addition to increasing production efficiency, these technological ad-

vancements make organizational systems more robust and flexible. Additionally, one of the main factors

facilitating sustainable industrial transformation is technological and economic preparedness (Narkhede

et al., 2025; Renna and Materi, 2021). Environmental factors are also important, as evidenced by the

increased consciousness of ecological responsibility in contemporary manufacturing. The importance of

environmental considerations in this study aligns with earlier research that emphasizes energy efficiency,

the circular economy, and green innovation as crucial components of sustainable operations (Rosen and

Kishawy, 2012; Panagiotopoulou et al., 2022; Shaikh et al., 2024). This research indicates that manu-

facturing companies in transition economies, such as Serbia, are starting to recognize environmental

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Vasić, S. et al. (2026). Social and Technological Drivers of Sustainable Manufacturing Performance: A Cognitive-Organizational

Perspective, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE), 14(1), 1-10.

performance as both a source of competitive advantage and a compliance requirement. Furthermore, it

has been demonstrated that combining environmental management systems with green product design

improves an organization’s long-term resilience and reputation (Bravo et al., 2022; Ghazali et al., 2023).

Employee engagement, safety, and corporate social responsibility are valued, but they are still evolving

as strategic pillars in the region’s manufacturing culture, influenced by moderate social drivers. From a

cognitive-organizational perspective, the findings suggest that sustainability performance is not driven

exclusively by technological readiness or financial investment, but also by the extent to which employees

internalize sustainability principles. Workforce training, participatory decision-making, and organizational

learning mechanisms contribute to the translation of sustainability strategies into operational practices.

This reinforces the view that sustainable manufacturing transformation requires alignment between tech-

nological systems and human cognitive capacities (Norton et al., 2015). This partially supports earlier

research, which shows that when social initiatives - such as training, well-being, and stakeholder involve-

ment - are fully incorporated into management practices, they improve performance focused on sustain-

ability (Madero-Gómez et al., 2023; Baykal and Divrik, 2023; Lee, 2018). The statistically minor impact,

compared to economic and environmental drivers, however, may be the result of organizational priorities

that prioritize operational and financial stability over more extensive social change, in the context of devel-

oping or transition economies such as Serbia. Challenges such as limited financial resources, regulatory

gaps, and lower awareness present significant barriers to adoption. For example, studies on the long-term

growth of the Serbian manufacturing sector highlight the need for innovative industrial policy, structural

limitations, and a lack of specialization in high-value goods (Mićić and Savić, 2018). Additionally, sustain-

ability advancement has been linked to Serbia’s readiness for digital transformation, indicating that tech-

nological capabilities may serve as a facilitator of sustainable practices in manufacturing organizations

(Rakic et al., 2021). Understanding what drives businesses to adopt sustainable manufacturing practices

is crucial in light of these opportunities and challenges. The findings collectively demonstrate that a bal-

anced set of drivers is necessary for sustainable performance in manufacturing organizations. While

social and quality dimensions enhance organizational adaptability and long-term resilience, economic and

environmental factors offer the structural underpinnings for sustainable transformation. Understanding

these driver mechanisms is particularly crucial for guiding managerial strategy and industrial policy in tran-

sition economies like Serbia, where businesses frequently face financial constraints, technological gaps,

and evolving regulatory environments. Adopting green technologies or efficiency improvements alone will

not be enough to achieve sustainability in manufacturing; human-centric innovation, environmental re-

sponsibility, and economic rationality must all be integrated systemically. Sustainable manufacturing can

be accelerated by implementing circular economy practices, and enhancing digital transformation readi-

ness, according to emerging research (Lim et al., 2023; Dacre, 2024). This is especially true in develop-

ing nations where workforce engagement and resource optimization continue to be significant obstacles.

To achieve synergistic effects on sustainability outcomes, recent studies emphasize the importance of

multi-level driver frameworks that integrate social participation, technological capability, and quality man-

agement (Scharmer et al., 2024; Hariyani et al., 2023; Abubakr et al., 2020; Gholami et al., 2021). Future

studies should, therefore, examine how organizational culture, digital technologies, and policy tools in-

teract to strengthen these forces and bridge current implementation gaps. This kind of data can help the

transition economies develop targeted programmes that enhance competitiveness, foster innovation, and

ensure alignment with international sustainability goals. Although the sample included respondents from

diverse manufacturing sectors, future studies could further examine sector-specific differences in sustain-

ability drivers and performance relationships.

Conclusions

This research examined how quality, social, economic, and environmental drivers influence long-

term organizational performance in Serbian manufacturing firms. The findings underscore the importance

of creativity, effectiveness, and ecological responsibility in promoting sustainability, demonstrating that

both economic and environmental factors have a significant positive impact. By emphasizing the growing

importance of employee engagement, safety, and well-being in creating resilient organizations, social

drivers also play a significant role. Quality drivers, on the other hand, exhibit conflicting results, indicat-

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Vasić, S. et al. (2026). Social and Technological Drivers of Sustainable Manufacturing Performance: A Cognitive-Organizational

Perspective, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE), 14(1), 1-10.

ing that their role in sustainability is still developing in this regard. Overall, the results highlight the need

for a well-rounded strategy that balances economic and environmental priorities with more robust social

and quality-oriented practices. In addition to investing in people, organizational culture, and systems for

continuous improvement, managers should concentrate on increasing technological innovation and re-

source efficiency. Despite offering insightful empirical data from a transition economy, this study remains

limited to a single country and sector. Future studies should investigate the interactions between these

drivers over time and across different geographical and industrial contexts. A deeper understanding of the

dynamic mechanisms underlying sustainability in global manufacturing systems may be possible through

comparative, longitudinal, and multi-level analyses.

Acknowledgements

We would like to express our gratitude to everyone who has contributed to this research, including

those who have made contributions in aspects such as data collection and manuscript review.

Funding

This research has been supported by the Ministry of Science, Technological Development and In-

novation (Contract No. 451-03-137/2025-03/200156) and the Faculty of Technical Sciences, University of

Novi Sad through project “Scientific and Artistic Research Work of Researchers in Teaching and Associate

Positions at the Faculty of Technical Sciences, University of Novi Sad 2025” (No. 01-50/295).

Conflict of interests

The authors declare no conflict of interest.

Data availability statement

The original contributions presented in the study are included in the article. Further inquiries can be

directed to the corresponding author.

Institutional Review Board Statement

All ethical considerations were observed, including the confidentiality of personal information, vol-

untary participation, and data protection in accordance with applicable legal and institutional guidelines.

Author Contributions

Conceptualization, S.Vasić; methodology, S.Vasić; software, S.Vasić. and K.R.; validation, S.Vasić

and N.B.; formal analysis, S.Vasić; investigation, S.Vasić and N.B.; resources, S.Vulanović and N.B.;

data curation, S.Vasić., K.R., D.V. and N.B.; writing-original draft preparation, S.Vasić; writing-review and

editing, S.Vasić.; visualization, S.Vulanović; supervision, N.B.; project administration, S.Vasić and K.R.;

funding acquisition, S.Vasić, K.R., N.B., and D.V.;

All authors have read and agreed to the published version of the manuscript.

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