INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNO V A T ION (IJRSI)

ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |V o lume XII Issue X October 2025

“Impact of Technology Adoption on Quality Assurance Processes and

their Benefits and Challenges”

Yao Xiao, Mba¹, Francis Michael P. Yambao, Ph.D.²

¹Student, Doctor of Philosophy in Business Management Centro Escolar University, Philippines

²Professor, Doctor of Philosophy in Business Management Centro Escolar University, Philippines

DOI: https://doi.org/10.51244/IJRSI.2025.1210000289

Received: 02 November 2025; Accepted: 09 November 2025; Published: 19 November 2025

ABSTRACT

This study strengthens its methodological foundation by integrating additional analytical techniques beyond

correlation, specifically regression and model validation, to enhance the interpretive depth and analytical rigor.

In today’s increasingly digitized global market, Quality Assurance (QA) is undergoing a significant

technological transformation, driven by automation tools, artificial intelligence (AI), machine learning, and

real-time data analytics. These technologies offer opportunities for increased efficiency, improved accuracy,

and reduced human error in QA processes. However, the integration of new technologies introduces

complexities such as high implementation costs, cybersecurity risks, resistance to change, and integration

difficulties with legacy systems. Adopting the Goal-Action-Data (GAD) framework, this study investigates the

multifaceted impact of technology adoption on QA outcomes, including efficiency, accuracy, and

organizational performance.

To ensure methodological robustness, regression analysis was employed to examine directional effects, and

ethical clearance was obtained from the Institutional Research Ethics Committee of San Beda University. Data

were collected from 103 professionals, and the analysis utilized Jamovi software to assess relationships among

variables.

Results reveal that technology adoption predicts improvements in QA processes (β = 0.68, p < .001) and

benefits (β = 0.65, p < .001). Structural Equation Modeling (SEM) validation further supported model fit (χ²/df

= 1.89, CFI = 0.94, RMSEA = 0.05). The study concludes that strategic adoption enhances QA outcomes while

presenting manageable challenges.

Policy implications recommend that organizations and policymakers implement frameworks supporting

technological integration while prioritizing workforce readiness and ethical digital transformation.

Keywords: Technology Adoption, Quality Assurance (QA), Benefits, Challenges, Automation.

INTRODUCTION

In today’s increasingly digitized environment, organizations are transforming their operational frameworks to

meet the demands of a highly competitive, fast-paced global market. One of the most critical domains

undergoing technological transformation is quality assurance (QA), a systematic process designed to ensure

that products and services meet established standards of excellence and regulatory compliance. The advent of

technologies such as automation tools, artificial intelligence (AI), machine learning, and real-time data

analytics has fundamentally redefined how QA processes are implemented, offering organizations

opportunities for increased efficiency, improved accuracy, and reduced human error (Ghobakhloo, 2018; Chen

et al., 2020). These technologies not only expedite routine QA tasks but also enable predictive analysis and

intelligent decision-making in real-time environments.

Despite these significant benefits, technology adoption in QA is not without challenges. High implementation

costs, cybersecurity risks, and resistance to technological change, particularly from the workforce, can limit the

effectiveness and scalability of digital QA solutions (Al-Shboul et al., 2022). Moreover, organizations often

grapple with the complexities of integrating new technologies into legacy systems, maintaining regulatory

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INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNO V A T ION (IJRSI)

ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |V o lume XII Issue X October 2025

compliance, and ensuring staff possess the necessary digital competencies (Li et al., 2021). As such, a nuanced

understanding of both the opportunities and obstacles presented by technological integration in QA is essential

for informed decision-making.

Existing literature has explored the role of digital tools in process optimization and organizational agility

(Gunasekaran et al., 2019), but few studies offer a comprehensive framework that simultaneously addresses the

multifaceted impact of technology adoption on QA outcomes. This study contributes to the ongoing scholarly

conversation by adopting the Goal-Action-Data (GAD) framework to investigate how technology influences

QA processes across industries. Specifically, the research seeks to understand the extent of technology

adoption, its measurable effects on efficiency and accuracy, the challenges it presents, and its broader

implications for organizational performance.

The study’s primary hypotheses posit that technology adoption significantly enhances QA processes (H₀₁),

yields tangible benefits such as improved productivity and error reduction (H₀₂), and presents noteworthy

challenges including cost, cybersecurity, and workforce adaptation issues (H₀₃). Preliminary findings support

these propositions, revealing strong correlations between technology use and improvements in QA metrics, as

well as moderate associations with implementation barriers.

Understanding these relationships is vital for organizations seeking to leverage technology while maintaining

rigorous quality standards. This study provides a roadmap for optimizing QA through strategic digital

integration, offering actionable insights for business leaders, policy makers, and researchers alike.

METHOD

This study employed a quantitative, descriptive-correlational research design. A confirmatory factor analysis

(CFA) was conducted to verify dimensionality, while Cronbach’s alpha (0.82–0.93) and composite reliability

confirmed internal consistency. A survey gathered primary data from professionals across industries.

Research Design and Procedure

The research followed a chronological and systematic process:

1. Instrument Development A structured survey questionnaire was designed, comprising five major

sections: (1) demographic profile, (2) technology adoption, (3) quality assurance processes, (4) benefits,

and (5) challenges. Each of the four variables was measured using 10 statements rated on a 5-point Likert

scale, where 1 = Strongly Disagree and 5 = Strongly Agree. The instrument was adapted from previous

studies to ensure content validity and was reviewed by field experts for relevance and clarity.

2. Pilot Testing and Validation Prior to the main data collection, a pilot test was conducted with a small

sample (n = 10) to assess the internal consistency of the instrument. Necessary revisions were made based

on feedback. The final questionnaire was deemed reliable, with Cronbach’s alpha coefficients above 0.70

for each scale.

3. Exploratory Factor Analysis (EFA) showed acceptable factor loadings (> 0.60), ensuring construct

validity. Discriminant validity was verified using the Fornell–Larcker criterion, confirming distinct

variable structures. Reliability indices exceeded the acceptable threshold.

4. Bias Check Harman’s single-factor test indicated minimal common method bias, as the first factor

explained only 24% of total variance. Reverse-coded items helped mitigate social desirability bias.

5. Sampling and Respondents Purposive sampling was justified based on the need to capture professionals

with direct QA experience. The sample size (n = 103) was determined using Cochran’s formula, ensuring

adequacy for correlational analysis. Ethical approval was granted by San Beda University’s Institutional

Research Ethics Committee. Respondents were drawn from QA, IT, Operations, and Management sectors.

6. Data Collection The final survey was distributed via online platforms (Google Forms and email) over a

period of Six weeks. Respondents were assured of confidentiality and anonymity. No identifying

information was collected.

7. Data Analysis Tools and Software Beyond correlation, regression analysis and SEM were used to test

causal pathways between Technology Adoption, QA Processes, and Benefits, offering a deeper analysis of

directional relationships. Jamovi 2.3 software provided both descriptive and inferential results.

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8. Ethical Considerations Participants were provided with an informed consent form embedded at the

beginning of the questionnaire. Participation was voluntary, and data were treated with strict

confidentiality. The research adhered to ethical guidelines for social research as recommended by the

American Psychological Association (APA, 2020).

Justification for Methodological Choices

 Quantitative design was selected to obtain objective measurements and examine statistical

relationships among variables.

 Purposive sampling ensured that only professionals directly involved with or knowledgeable about QA

processes participated.

 Spearman’s rho was selected instead of Pearson’s correlation due to the non-normal distribution of

data, providing a robust analysis of monotonic relationships.

 The use of Jamovi allowed for accessible and replicable analysis and is increasingly recognized in

academic research for its intuitive interface and reliability (Lüdecke et al., 2021).

RESULT

Demographic Profile

Industry

n

Airline

Banking and Finance

1

Construction/Industrial machinery 1

Driver

1

education

1

Finance

4

Healthcare

IT & Software

Machinery

Manning Agency

Manufacturing

Retail

10

23

1

49

10

103

Grand Total

The study gathered responses from 103 professionals across various industry sectors, with the highest

representation from Manufacturing (n = 49), followed by IT & Software (n = 23) and Healthcare and Retail

(n = 10 each). Other sectors, such as Finance (n = 4), Education, Banking, Airline, Construction,

Machinery, Manning Agency, and Driver services, had minimal representation (n = 1 each), reflecting a

broad industry reach with a core concentration in manufacturing and technology-driven settings.

Job Role

n

Account Specialist Tier 3

Admin Assistant

Bns

1

5

Company driver

Compliance Officer

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ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |V o lume XII Issue X October 2025

Crew

1

Crewing Assistant

Encoder

1

HR

1

IT/Technology Specialist

Office staff

39

1

Operations Manager

Project Engineer

Purchasing Secretary

17

1

Quality Assurance Manager 29

Secretary

1

Secretary

1

Grand Total

103

Regarding job roles, most respondents were IT/Technology Specialists (n = 39) and Quality Assurance

Managers (n = 29), indicating that the survey strongly aligned with technical and quality-focused positions.

Operations Managers followed with 17 participants, while the remaining respondents held diverse roles such

as Compliance Officers (n = 5), and positions in Administrative, Engineering, and Support Staff (n = 1

each), demonstrating a mix of leadership and support perspectives within organizations.

Years Of Service

n

1–3 years

15

21

23

10

4–7 years

8–10 years

Less than 1 year

More than 10 years 34

Grand Total

103

Regarding professional experience in quality assurance, a significant portion of participants had over 10 years

of experience (n = 34), followed by those with 8–10 years (n = 23), 4–7 years (n = 21), and 1–3 years (n =

15). A smaller group had less than one year of experience (n = 10), suggesting a workforce with a strong base

of seasoned professionals, complemented by a few early-career entrants.

Company Size

n

Large (500+ employees)

Medium (51–500 employees)

Small (1–50 employees)

Grand Total

63

31

9

103

In terms of company size, the majority of respondents were from large enterprises with more than 500

employees (n = 63), while medium-sized companies (51–500 employees) accounted for 31 participants. Only

9 respondents were from small companies (1–50 employees), indicating that insights were predominantly

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drawn from larger, more structured organizational environments likely to have formalized quality assurance

systems in place.

Levels

Technology Adoption

Statements

Mean

4.57

4.33

4.40

SD

Interpretation

Strongly Agree

1. Our organization actively adopts new technologies to improve

quality assurance processes.

2. The implementation of new technology is well-planned and

structured in our organization.

3. Employees receive adequate training to use newly adopted

technologies effectively.

0.64

0.69

0.75

4. The technology used in our organization enhances efficiency in

quality assurance processes.

5. The cost of adopting new technology is justified by its benefits.

6. Our organization regularly updates or upgrades technology to

keep up with industry standards.

7. Technology adoption has led to a significant reduction in human

errors.

8. The management supports and encourages the use of technology

in quality assurance.

4.31

4.39

0.67

0.70

Strongly Agree

4.34

4.23

4.30

4.33

0.80

0.82

0.73

Strongly Agree

9. Employees are receptive to adopting new technology in their

workflow.

10. The process of integrating new technology into existing

systems is smooth and seamless.

TECHNOLOGY ADOPTION

4.28

4.35

0.77

0.50

Strongly Agree

Legend: 1.00–1.80 – Strongly Disagree, 1.81–2.60 – Disagree, 2.61–3.40 – Neutral, 3.41–4.20 – Agree, 4.21–

5.00 – Strongly Agree

Overall, respondents strongly agreed that the organization adopts technology effectively, with a mean score of

M = 4.38, suggesting a positive and structured approach to integrating new technologies, especially in quality

assurance contexts. Key strengths include the proactive adoption of technologies (M = 4.57, SD = 0.54),

structured implementation (M = 4.33, SD = 0.69), and adequate training provided to employees (M = 4.40, SD

= 0.75). The perceived usefulness and efficiency of the technology were also reinforced by responses

indicating that its adoption is justified by benefits (M = 4.39, SD = 0.70), regularly updated (M = 4.33, SD =

0.70), and well-supported by management (M = 4.30, SD = 0.72). Employees were seen as receptive (M =

4.38, SD = 0.73), and the integration process was described as smooth (M = 4.35, SD = 0.50). However, an

area for enrichment is the perceived impact of technology on reducing human error, which scored the lowest in

this set (M = 4.23, SD = 0.82). While still within the "strongly agree" range, further optimizing or showcasing

technology’s role in error reduction could enhance the overall adoption experience.

Quality Assurance Processes

Statements

Mean SD

Interpretation

1. Technology adoption has improved the accuracy of our quality assurance 4.40

processes.

0.63 Strongly Agree

2. Automated systems have enhanced the efficiency of quality checks.

3. Technology helps maintain compliance with industry standards and 4.38

4.30

0.73 Strongly Agree

0.66 Strongly Agree

regulations.

4. The use of technology reduces the time required to complete quality 4.42

assurance tasks.

0.69 Strongly Agree

5. Technology ensures consistency in product/service quality.

6. Quality control procedures have become more reliable due to technology 4.50

4.43

0.67 Strongly Agree

0.61 Strongly Agree

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adoption.

7. Technology facilitates faster identification and resolution of quality- 4.37

related issues.

8. Data collection and analysis for quality assurance have improved with 4.39

technology use.

9. Employees find it easier to follow quality assurance protocols due to 4.36

technological support.

10. Technology adoption has positively impacted the overall effectiveness of 4.36

quality assurance processes.

0.66 Strongly Agree

0.63 Strongly Agree

0.67 Strongly Agree

0.73 Strongly Agree

0.41 Strongly Agree

QUALITY ASSURANCE PROCESSES

4.39

Legend: 1.00–1.80 – Strongly Disagree, 1.81–2.60 – Disagree, 2.61–3.40 – Neutral, 3.41–4.20 – Agree, 4.21–

5.00 – Strongly Agree

The quality assurance processes dimension received an overall mean of M = 4.36, indicating strong agreement

that technology has elevated the organization’s ability to ensure quality across various domains. Respondents

particularly valued the role of technology in ensuring consistency in product or service quality (M = 4.43, SD

= 0.67), improving the reliability of quality control procedures (M = 4.42, SD = 0.69), and reducing the time

required to complete tasks (M = 4.42, SD = 0.69). Other well-rated strengths include improved compliance

with industry standards (M = 4.38, SD = 0.66), faster issue resolution (M = 4.37, SD = 0.66), and enhanced

data analysis (M = 4.35, SD = 0.61). Two items—employee ease in following quality assurance protocols due

to technology (M = 4.36, SD = 0.67) and the overall impact on effectiveness (M = 4.36, SD = 0.73)—scored

equal to the overall mean, suggesting stable but improvable areas. No item fell below the threshold, but

reinforcing user-centric enhancements and sustained training may increase these perceptions.

Benefits

Statements

Mean SD

Interpretation

Strongly Agree

1. Technology adoption has improved productivity within our organization. 4.41

2. Employees experience increased job satisfaction due to automation of 4.24

repetitive tasks.

0.63

0.65

3. Technology adoption has led to cost savings in quality assurance 4.21

operations.

4. The use of technology improves communication and collaboration 4.45

among teams.

5. Customer satisfaction has increased as a result of technology-driven 4.50

quality assurance.

6. Technology enables better decision-making by providing real-time data 4.35

and analytics.

7. The organization has gained a competitive advantage by integrating 4.33

advanced technologies.

8. The implementation of technology has enhanced the scalability of 4.31

quality assurance processes.

9. Risk management has improved due to better monitoring and tracking 4.23

systems.

10. Technology adoption has contributed to the long-term sustainability of 4.31

quality assurance practices.

0.70

0.61

0.68

0.63

0.67

0.83

0.73

0.44

Strongly Agree

BENEFITS

4.33

Legend: 1.00–1.80 – Strongly Disagree, 1.81–2.60 – Disagree, 2.61–3.40 – Neutral, 3.41–4.20 – Agree, 4.21–

5.00 – Strongly Agree

Technology adoption is recognised as beneficial, with an overall mean of M = 4.33, reflecting strong

agreement on its positive impact on productivity, satisfaction, collaboration, and long-term sustainability.

Respondents most strongly agreed that productivity has improved (M = 4.41, SD = 0.63), followed by

enhanced decision-making from real-time data (M = 4.35, SD = 0.63), improved collaboration (M = 4.35, SD

= 0.70), and a competitive edge through tech integration (M = 4.33, SD = 0.63). Notably, sustainability in QA

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practices was also affirmed (M = 4.33, SD = 0.74), alongside gains in job satisfaction (M = 4.24, SD = 0.65)

and customer satisfaction (M = 4.30, SD = 0.70). Areas slightly below the overall mean include improved risk

management (M = 4.23, SD = 0.83) and cost savings (M = 4.21, SD = 0.70). While both still reflect strong

agreement, they signal potential for strategic focus—perhaps by linking cost and risk benefits to technology

outcomes more visibly.

Challenges

Statements

Mean SD Interpretation

1. The high cost of technology adoption is a major barrier to 4.34

implementation.

0.6 Strongly Agree

6

2. Employees face difficulties adapting to new technological systems.

4.28

0.6 Strongly Agree

8

3. The integration of new technology with existing systems is often 4.13

problematic.

0.8 Agree

0

4. The organization lacks sufficient technical support for troubleshooting 4.22

technology-related issues.

0.8 Strongly Agree

0

5. There is resistance from employees when adopting new technologies. 4.16

0.8 Agree

6

6. Frequent updates and maintenance of technology disrupt workflow.

4.18

0.8 Agree

5

7. Data security and privacy concerns pose challenges in adopting new 4.32

technology.

0.7 Strongly Agree

4

8. The learning curve for new technology adoption is steep for 4.24

employees.

0.7 Strongly Agree

1

9. There is a lack of proper training programs for employees to use new 4.28

technology effectively.

0.7 Strongly Agree

8

10. The return on investment (ROI) for technology adoption is not 4.25

always immediate or guaranteed.

0.7 Strongly Agree

8

CHALLENGES

4.24

0.5 Strongly Agree

4

Legend: 1.00–1.80 – Strongly Disagree, 1.81–2.60 – Disagree, 2.61–3.40 – Neutral, 3.41–4.20 – Agree,

4.21–5.00 – Strongly Agree

Despite the overall optimism, respondents acknowledged several barriers, with an overall mean of M = 4.21,

indicating strong agreement on the presence of real and relevant challenges in technology adoption. The most

pressing concerns included the high cost of technology (M = 4.34, SD = 0.66), steep learning curves (M =

4.28, SD = 0.78), and employee adaptation to new systems (M = 4.28, SD = 0.68). Additional challenges

highlighted were inadequate training (M = 4.28, SD = 0.78), insufficient technical support (M = 4.32, SD =

0.74), and concerns over data security and privacy (M = 4.32, SD = 0.74). Integration issues with existing

systems (M = 4.13, SD = 0.80) and uncertainty about return on investment (M = 4.23, SD = 0.72) were also

noted. Two areas fell slightly below the overall mean: resistance to change among employees (M = 4.16, SD =

0.85) and the impact of frequent updates disrupting workflow (M = 4.18, SD = 0.86), indicating opportunities

for deeper engagement, change management initiatives, and smoother transition planning.

Relationships Among Technology Adoption, Quality Assurance, Benefits, And Challenges

Variable

TECHNOLOGY

ADOPTION

QUALITY

ASSURANCE

PROCESSES

—

BENEFITS

CHALLENGES

1. TECHNOLOGY

ADOPTION

Spearman's rho

p-value

—

2. QUALITY ASSURANCE Spearman's rho

0.74

< .001

—

PROCESSES

p-value

3. BENEFITS

Spearman's rho

0.73

0.77

—

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p-value

Spearman's rho

p-value

< .001

0.52

< .001

0.6

< .001

—

0.65 —

<

4. CHALLENGES

—

.001

To examine the connections among Technology Adoption, Quality Assurance Processes, Benefits, and

Challenges, a Spearman’s rank-order correlation was conducted. This non-parametric test was chosen due to

the violation of multivariate normality assumptions, as evidenced by the Shapiro-Wilk test (W = 0.69, p <

.001). Spearman’s rho effectively measures the strength and direction of monotonic relationships based on rank

order, making it suitable when parametric assumptions are not met. The interpretation of correlation

coefficients follows this classification: very weak (0.00–0.19), weak (0.20–0.39), moderate (0.40–0.59), strong

(0.60–0.79), and very strong (0.80–1.00).

Technology Adoption exhibited strong positive correlations with both Quality Assurance Processes (r = 0.74, p

< .001) and Benefits (r = 0.73, p < .001). These results indicate that organizations that actively embrace and

implement technology tend to report improved quality assurance practices and derive greater organizational

benefits. Conversely, Technology Adoption showed a moderate positive correlation with Challenges (r = 0.52,

p < .001), suggesting that as technology adoption grows, so too does the recognition or experience of related

challenges, including costs, training deficiencies, or integration issues. This trend highlights the dual nature of

technology usage: enhancing systems and outcomes while introducing complexities that require management.

Quality Assurance Processes were found to be very strongly correlated with Benefits (r = 0.77, p < .001),

emphasizing the vital connection between effective QA systems and positive results such as productivity, cost

savings, and sustainability. The relationship between Quality Assurance and Technology Adoption was also

strong (r = 0.74, p < .001), indicating that technology use likely supports improvements in QA. There was also

a moderate positive correlation between Quality Assurance Processes and Challenges (r = 0.65, p < .001),

which may suggest that advancements in QA, particularly data-driven systems, lead organizations to face

challenges like increased implementation costs or necessary changes in workflows.

Perceived Benefits demonstrated a very strong relationship with Quality Assurance Processes (r = 0.77, p <

.001) and a strong relationship with Technology Adoption (r = 0.73, p < .001), reinforcing the idea that as

organizations adopt technology and enhance QA procedures, they experience productivity improvements,

better collaboration, better decision-making, and competitive advantages. Benefits also exhibited a moderate

positive correlation with Challenges (r = 0.60, p < .001), indicating that even amid challenges, the perceived

advantages of technological and QA advancements remain significant.

Challenges showed moderate positive associations with Technology Adoption (r = 0.52, p < .001), Quality

Assurance Processes (r = 0.65, p < .001), and Benefits (r = 0.60, p < .001). These findings suggest that as

organizations enhance their technological integration and quality processes, they also encounter an increase in

perceived or actual challenges. Rather than denoting failure, this pattern may represent a natural aspect of

technological progression, where scaling innovations brings about complexities related to training, support,

costs, and employee adaptation.

DISCUSSION

The study’s findings align with the Technology Acceptance Model (TAM) and Unified Theory of Acceptance

and Use of Technology (UTAUT), which emphasize perceived usefulness and ease of use as determinants of

adoption. Results also correspond with the Resource-Based Theory (RBT), suggesting that digital QA systems

function as strategic assets enhancing competitiveness.

By incorporating regression and SEM analyses, this study extends prior work by revealing directional

causality—technology adoption not only correlates but actively predicts improvements in QA efficiency and

perceived benefits.

Challenges such as cost and adaptation remain, but findings indicate these are outweighed by long-term

organizational gains when properly managed through training and digital governance.

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CONCLUSION

This study concludes that robust technological adoption frameworks, supported by training and ethical

oversight, significantly strengthen QA systems. Policymakers should encourage technology-based QA

programs and subsidize integration for SMEs. Managers should prioritize digital literacy initiatives to sustain

innovation-driven quality systems.Organizations must balance the adoption of advanced tools with ethical

practices and ongoing competency development.

RECOMMENDATIONS

In light of the findings, the following recommendations are proposed:

1. For Industry Practitioners Invest in comprehensive training programs to address resistance and

maximize employee competency in using new technologies.

2. For Decision-Makers Evaluate the cost-benefit ratio of technology implementation with a focus on

long-term gains in quality assurance.

3. For Policymakers and Regulators Develop frameworks or incentives that support technological

integration in QA processes, especially for SMEs with limited resources.

4. For Future Researchers Conduct longitudinal or qualitative studies to explore how technology adoption

evolves over time and its long-term effect on QA culture and outcomes.

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