Factor Analysis Techniques Research Articles

Acoustic Response Discrimination of Phulae Pineapple Maturity and Defects using Factor Analysis of Mixed Data and Machine Learning Algorithms

Acoustic response is non-destructive evaluation technique that replicates the conventional method for determining maturity by tapping the fruit. The physical (dimensions, color, firmness, and specific gravity) chemical (TSS, %TA, and TSS/TA), and acoustic properties of Phulae pineapple were determined and used to classify the maturity and defect, e.g. translucency flesh symptoms. Results showed that all physical parameters of the two maturity stages were not significantly different (p>0.05). Translucency flesh (TF) defects were observed in 23.5% and 27.3% of pineappls in the green and green-yellow stages, respectively. The dominant resonance frequency (fn) of Phulae pineapple ranged of 0.057 to 3.010 kHz. All the physical, chemical, and acoustic properties were used to classify for maturity and defects using the factor analysis (FA) technique and machine learning (ML). Results showed that maturity was correctly classified at 84.0% by all parameters, while elected non-destructive parameters (color, specific gravity, and stiffness coefficients) showed lower results for distinguishing pineapples. Random Forest (RF) provided a better classification than other MLs with 99.93% accuracy of maturity classification, while TF classification was 99.59%. Results showed acoustic method integrated with ML was a fast reliable, and cost effective technique for assessing Phulae pineapple quality.

The Forced‐To‐Penetrate Myth Acceptance Scale (FTP‐MAS): A New Attitudinal Tool for Assessing Myths That Surround Female Perpetrated Sexual Violence Against Men

ABSTRACTThe purpose of this study was to develop and validate a new measurement tool designed to capture endorsement of myths surrounding female perpetrated sexual violence against men, specifically in ‘forced‐to‐penetrate’ cases. Data were collected among a sample of 4152 UK adults aged 18–55+ (52% female). Dimensionality and construct validity of the Forced‐to‐Penetrate Myth Acceptance Scale (FTP‐MAS) was investigated using traditional Confirmatory Factor Analysis (CFA) techniques separately for the complete sample, males only, and females only. CFA results indicated that FTP‐MAS scores are best captured by a three‐factor model (1. Distorted Sex and Gender Roles; 2. Harm Minimisation; 3. Offence Denial) across all samples tested. Excellent composite reliability and differential predictive validity were observed for all three subscales. The validated 22‐item FTP‐MAS constitutes the first measurement tool which allows for the assessment and evaluation of public attitudes towards female perpetrators who force men to penetrate them without consent. As such, this tool enables researchers to better understand the multi‐faceted nature of these myths, assess prevalence in different contexts, and can also be used as an outcome measure in research seeking to evaluate the effectiveness of interventions that aim to debunk endorsement of such myths and stereotypes.

Mediating effect of perceived risk on familiarity and purchase intention: an empirical analysis for private label apparels

PurposeThe current study aims to analyze the mediating role of perceived risk from private label apparels in between the familiarity and consumer purchase intention (PI).Design/methodology/approachExploratory factor analysis and structural equation modeling techniques were utilized for evaluating the relationship between different constructs. Participants were given the opportunity for expressing their degree of approval or disapproval using a series of statements using a five-point Likert scale. For the purpose of the survey, a total of 400 participants were selected.FindingsThe finding highlights the mediating role of perceived risk between familiarity and PI of consumers toward private label apparels. When there is a mediator, the direct influence of familiarity on PI is absorbed.Originality/valueThis research aims to offer an understanding of buyer preferences in developing markets as well as providing suggestions for retailers and marketers on how to successfully promote and position private label apparel in such an environment through investigating the importance of familiarity with private label apparel. While doing so, it develops the premise that familiarity with private label apparel directly impacts perceived risk while indirectly impacting PI of consumers towards private label apparel.

Preventing Contract Termination Pitfalls in Construction Projects: Insights from the Ghanaian Construction Industry

<i>Purpose</i>: Premature termination of construction contracts is a recurring problem that causes financial losses, delays, and strained stakeholder relationships. However, there is limited research on the specific causes of contract termination, particularly in Ghana, and practical solutions to prevent it are often overlooked. This study aims to investigate the pitfalls of contract termination in the construction industry and to propose effective prevention strategies based on empirical data. <i>Design/Methodology/Approach</i>: A quantitative research approach was adopted, employing a combination of purposive and random sampling techniques to gather data from 315 participants representing diverse roles within the construction sector. A structured questionnaire based on contract termination pitfalls was used, and data analysis involved descriptive statistics, factor analysis, and structural equation modeling (SEM) techniques. <i>Findings</i>: The study identified several critical factors influencing contract termination, including prompt payment by clients, adherence to contractual terms, competent supervision, and effective dispute resolution mechanisms. Through factor analysis and SEM, two main constructs emerged: Proactive Contract Management Strategies (PCMS) and Contract Termination Risk Mitigation Measures (CTRMM), each comprising specific indicators crucial for preventing terminations. <i>Originality/value</i>: This research contributes to the existing literature by providing empirical evidence of contract termination pitfalls and effective prevention strategies within the construction industry. The study’s multidimensional analysis approach, integrating quantitative techniques with Structural Equation Modelling (SEM), enhances the understanding of complex contractual dynamics and risk management in construction projects. <i>Implications</i>: The findings have practical implications for construction stakeholders, project managers, and policymakers. Implementing proactive contract management strategies such as clear payment protocols, rigorous quality control, and effective communication channels can significantly reduce contract termination risks. The study underscores the importance of integrating legal frameworks, innovative techniques, and managerial approaches in contract portfolios to enhance contract performance and project success in the construction sector.

Academic Entrepreneurial Orientation for Sustainable Development Goals in Higher Education in Indonesia: A Confirmatory Factor Analysis

Objective: This study aims to create a model for measuring the academic entrepreneurial orientation of higher education lecturers in Indonesia. Theoretical Framework: Academic entrepreneurship theory describes the perspective of commercialization of research in higher education to benefit society. This theoretical framework highlights the importance of individual entrepreneurial orientation in commercialization and academic entrepreneurship in higher education. Method: This research uses a quantitative survey. The target population was lecturers who have conducted research with commercial potential at eleven state universities in Indonesia. The research consisted of 330 samples, according to the ideal number of samples for structural equation modeling (SEM) analysis. Data analysis was conducted using confirmatory factor analysis and SEM techniques using the AMOS 21 program. Results and Discussion: The study shows that 15 factors contribute to lecturers' academic entrepreneurial orientation, proving to measure and explain their existence in the model and significantly supporting it. Research Implications: This study's findings can be a practical guide for universities to measure the entrepreneurial orientation of lecturers and develop it in the future. For the academic world, this research can inspire further research in the field of entrepreneurship, especially academic entrepreneurship. Originality/Value: This study presents a new model for building and measuring the academic entrepreneurial orientation of higher education lecturers in Indonesia in support of sustainable development goals.

Development of a Short-Form Hwa-Byung Symptom Scale Using Machine Learning Approaches.

Background/Objectives: Hwa-byung (HB), also known as "anger syndrome" or "fire illness", is a culture-bound syndrome primarily observed among Koreans. This study aims to develop a short-form version of the HB symptom scale using machine learning approaches. Methods: Utilizing exploratory factor analysis (EFA) and various machine learning techniques (i.e., XGBoost, Logistic Regression, Random Forest, Support Vector Machine, Decision Tree, and Multi-Layer Perceptron), we sought to create an efficient HB assessment tool. A survey was conducted on 500 Korean adults using the original 15-item HB symptom scale. Results: The EFA revealed two distinct factors: psychological symptoms and somatic manifestations of HB. Statistical testing showed no significant differences between using different numbers of items per factor (ANOVA: F = 0.8593, p = 0.5051), supporting a minimalist approach with one item per factor. The resulting two-item short-form scale (Q3 and Q10) demonstrated high predictive power for the presence of HB. Multiple machine learning models achieved a consistent accuracy (90.00% for most models) with high discriminative ability (AUC = 0.9436-0.9579), with the Multi-Layer Perceptron showing the highest performance (AUC = 0.9579). The models showed balanced performance in identifying both HB and non-HB cases, with precision and recall values consistently around 0.90. Conclusions: The findings of this study highlighted the effectiveness of integrating EFA and artificial intelligence via machine learning in developing practical assessment tools. This study contributes to advancing methodological approaches for scale development and offers a model for creating efficient assessments of Korean medicine.

Influencer Marketing, EWOM, E-Brand Experience, and Retail E-Brand Loyalty: Moderating Influence of E-Brand Love

This article, through developing a conceptual model, attempts to examine how influencer marketing impacts electronic word-of-mouth (EWOM), consumer E-brand experience, and E-brand loyalty, while considering the moderating influence of E-brand love. The study involved the collection and analysis of 310 questionnaires collected from online retail consumers in Iran. In this study, we employed exploratory and confirmatory factor analysis and path analysis techniques using a structural equation modeling approach. The research uniquely emphasizes the customer perspective, focusing not only on the outcomes but also on the processes and antecedents that drive E-brand loyalty. The results indicate that influencer marketing has positive influences on EWOM, E-brand experience, and E-brand loyalty. Furthermore, EWOM and E-brand experience exhibit positive effects on E-brand loyalty. Notably, E-brand love plays a moderating role in the relationships between influencer marketing and E-brand loyalty, as well as the relationships between E-brand experience and E-brand loyalty. It is worth mentioning that E-brand love does not moderate the relationship between EWOM and E-brand loyalty. The results help to further comprehend how influencer marketing can successfully improve loyalty in the online realm. However, it is crucial to consider ethical implications in fostering emotional bonds, as unethical practices in this regard can trigger negative outcomes, including "hate" and distrust, particularly across different cultures and contexts. This insight emphasizes the significance of emotional bonds in fostering brand loyalty and proposes that nurturing the link between influencer marketing and E-brand loyalty through brand love, favorable E-brand experiences, and EWOM can result in more robust and lasting connections with consumers. To cultivate E-brand loyalty and love, it’s vital to build trust with influencers, as ethical missteps can have severe repercussions. Hence, it’s important to approach this relationship with careful consideration and awareness.

Examining the AISAS Model and Tourist Citizenship Behaviour in context of Mining Heritage Tourism

Therefore, the objective of the research is to test the effectiveness of the Attention-Interest-Search-Action-Share model in mining heritage tourism but replacing the Action component with tourist citizenship behaviour. This will therefore yield new and key insights because there are few studies conducted in this area. The current study selected 220 tourists through a convenience sampling technique. The gathered data was treated with exploratory factor analysis and structural equation modelling techniques. Confirmatory results found on the acceptance of the first hypothesis, which is Attention Influences Interest, attention becomes very important in ensuring riveting tactics are put in place when promoting mining heritage tourism. The second hypothesis, Curiosity influences Search, is also supported and confirms that only in the condition of a deep level of curiosity, tourists search for information. The third hypothesis is stating that interest influences TCB. The fourth hypothesis postulates that interest influences Share. It supported the sixth hypothesis stating that Search influences TCB. The rejection was of the sixth hypothesis that stated Search influences Share. The seventh theory stated that TCB influences Share, thereby is accepted. These findings bear some significant implications for attaining the sustainable development goals. Precisely, it means that the promotion of mining heritage tourism advances SDG 11—Sustainable Cities and Communities—since this ensures cultural heritage is preserved and contributes to inclusive and sustainable urban development. Likewise, with increased engagement of tourists during citizenship behaviours, this particular act contributes to SDG 12 through the aspect of sustainable tourism and raising awareness. Similarly, the positive experiences that are increasingly wide follow up essentially give rise to SDG 8 through the promotion of tourism as one of the prime movers for improving the economy.

Pollution of organophosphorus pesticides in the Dongting Lake, China and its relationship with dissolved organic matter: Occurrence, source identification and risk assessment

The escalating global demand for food and industrialization has placed significant pressure on the integrity and management of inland lake ecosystems. Herein, the organophosphorus pesticides (OPPs) pollution status and their relationship with dissolved organic matter (DOM) in Dongting Lake were investigated to identify the ecological risks and potential sources of OPPs. The total concentrations of 18 detected OPPs were in the range of 13.49–375.24 ng/L, with higher concentration observed in east and west lake regions. Among these, fenthion was the dominant contributor, accounting for 64% of total OPPs, posing significant ecological risk to aquatic organisms. Nearly all of sites showed high combined risk of total OPPs. Parallel factor analysis (PARAFAC) and fluorescence regional integration (FRI) technique showed that DOM was mainly composed of terrestrial humic-like and tryptophan-like substances. Moreover, correlation analysis revealed a close association between DOM optical parameters and OPP concentrations. Specifically, OPPs exhibited a significantly positive correlation with tyrosine-like substances, while displaying a negative correlation with fulvic acid-like substances. These results indicated that OPP concentrations may decrease with increasing humification levels and declining tyrosine-like substance contents. This study underscores the critical role of DOM in assessing the occurrence and sources of OPPs in aquatic environments, providing valuable insights for effective environmental management strategies.

Customer Satisfaction Analysis On Tokopedia Marketplace Services In Jakarta Area

This research aims to determine the satisfaction factors of Tokopedia marketplace users in Jakarta. This research uses a quantitative approach with descriptive analysis methods. The population in this study includes individuals who currently or have used the Tokopedia marketplace. In this study, samples were taken using no-probability purposive sampling and distributing questionnaires online via Google From. The sample used in this research was 240 respondents. The sample criteria in this research consist of individuals who live in the Jakarta area, aged 17-45 years and users of the Tokopedia marketplace. This research uses validity and reliability tests as research instrument tests. This research only uses two analytical techniques, namely descriptive analysis with the aim of describing or illustrating Tokopedia marketplace user data and then classifying it so that later it can be formulated to get a clear picture of the problems that cause customers to feel satisfied. Then there is the factor analysis technique or Expolatory Factor Analysis which aims to form new factors to support customer satisfaction. This research uses the e-service quality dimension as a measurement of customer satisfaction. There are seven indicators in this research, namely, Efficiency, Fulfillment, Reliability, Privacy, Responsiveness, Compensation, Contact. The results of this research say that by using the EFA analysis technique, three factors have been formed to support customer satisfaction, the names of these three factors are: service reliability and security, customer service quality, customer comfort and efficiency.

Development and testing of the capacity of organisations for system practices scale.

Systems change approaches are increasingly adopted in public health to address complex problems. It is important that measures of systems change be developed so that the effects of systems change on health outcomes can be evaluated. Organisational practices are potential levers for systems change. However, robust measures of organisational capacity to engage in these practices are lacking. Informed by the Theory of Systems Change, we developed and tested the Capacity of Organisations for System Practices (COSP) scale. The COSP scale comprises four inter-related system practices within organisations-adaptation, alignment, collaboration and evidence-driven action and learning. We applied a three-stage process: (1) Item generation; (2) Scale pre-testing; and (3) Structural analyses. Item response theory tests and semantic review, together with factor analytic techniques, were applied to refine the item set and determine the scale structure. An initial pool of 97 items was generated and pre-tested with six content experts and four target audience representatives. Modifications resulted in 60 items. In total, 126 participants provided data for the structural analysis. A second-order hierarchical four-factor model fit the data better than the more basic correlated factor model (Δχ2 = 1.758, p = .415). The fit indices for the final 31-item model were acceptable (RMSEA = .084, TLI = .819). The COSP scale is ready for further testing to ensure construct validity, stability and utility. SO WHAT?: Once validated, the Capacity of Organisations for System Practices (COSP) scale has the potential to advance the theory and practice of systems change approaches.

Analysis of Factors Influencing Customers' Decision to Choose Wadiah Savings Products

This study aims to analyze product knowledge, benefit perception, promotion, and motivation factors as factors in customers' decision to choose wadiah savings products at Sharia Banks. The background of this research is the growth of the Islamic banking industry in Indonesia, which is predicted to be higher than conventional banks, driven by product and service innovation in accordance with Islamic law. This study uses a quantitative approach with confirmatory factor analysis (CFA) techniques. Research data was collected through a questionnaire with a probability sampling method using a simple random sampling technique. The results of the study show that promotional factors, product knowledge, and benefit perception are factors in customer decisions in choosing wadiah savings products. The motivation factor is not a factor in the customer's decision to choose a wadiah savings product. This research provides insight into the importance of proper promotion, in-depth product knowledge, and clear benefit perception in attracting customers to choose wadiah savings products at Sharia Banks

Revolutionizing construction: A cutting-edge decision-making model for artificial intelligence implementation in sustainable building projects

This study examines how certain artificial intelligence (AI) drivers affect the industry's adoption of this technology in the construction industry. The research methods comprised a comprehensive analysis of previous studies to pinpoint the primary factors influencing AI adoption in the construction industry. Data collection was carried out through a well-structured survey involving relevant stakeholders in the building construction sector. The three main constructs of technological devices, advancement, and knowledge were found from the set of drivers with the technique of exploratory factor analysis. The deployment of AI in construction has the potential to improve health and safety and expedite project completion, as this research has evaluated. To figure out how these factors relate to the adoption of AI in the construction industry, partial least squares structural equation modeling was used. The study's conclusions showed that the influence of AI installation in the construction industry is reasonably significant thanks to the technology, advancement, and knowledge, contributing around 15 % of the effects that have been directly witnessed. The practical implications of AI for policy makers, engineers, and construction stakeholders are extensive and provide valuable insights for customized strategies aimed at using AI's potential to improve projects, promote sustainability, and elevate health and safety standards.

Confirmatory Factor Analysis Job Relevant Information, Partisipasi Anggaran dan Kinerja Aparatur Pemerintah Daerah Kabupaten Malang

The purpose of this study was to analyze the confirmatory factors of job relevant information, participation in budgeting and the performance of local government apparatus in the Regional Government of Malang Regency. Analysis of the data used in the study using confirmatory factor analysis (CFA) technique, the results of the study showed that active indicators contributed to the strongest budgeting as a variable forming Budget Participation, the indicator gained the strongest strategy as a variable Job relevant information and indicators set budget targets with the strongest resources and time determined as a variable for apparatus performance

Sedimentological, Geochemical, and Environmental Assessment in an Eastern Mediterranean, Stressed Coastal Setting: The Gialova Lagoon, SW Peloponnese, Greece

This study describes the prevalent sedimentological and geochemical patterns and investigates the environmental status of the bottom of Gialova lagoon, a highly vulnerable coastal site of the EU’s Natura 2000 network. For this task, lithological, geochemical, and microfaunal analyses of sediment samples were combined with a high-resolution bathymetric survey. Potential pollution was determined using geochemical-based (EF, I-geo, and PLI) and faunal (Foram-AMBI) indices. We find that sedimentation is mainly controlled by the bottom morphology, hydrodynamic variations, and biogenic productivity of the lagoon. The application of the multivariate factor analysis technique revealed four dominant factors explaining the geochemical processes occurring in the lagoon. The first factor, namely “terrigenous aluminosilicates associated with Corg vs. autochthonous biogenic carbonates”, discriminates the deposition of detrital sediments, related to the high adsorption of heavy metals—versus bioclastic sediments. The “sulfides” factor represents an anoxic phase of the lagoon floor, whereas the “Mn-oxyhydroxides” factor indicates increased manganese content with several compounded trace elements. The “phosphate” factor reveals multiple sources of phosphorus in the lagoon. The lagoon bottom shows negligible to minor contamination in heavy metals, except Mo and Pb, which induce moderate pollution levels. The maximum contamination and environmental stress concern two small-sized, shallow basins within the lagoon.

The construction of urban cultural and creative industries using deep learning and information management

The ongoing transition within the Chinese economy assumes a pivotal role in shaping the landscape of the cultural and creative industries (CCI). Renowned for its environmentally sustainable attributes, coupled with high productivity, CCI has garnered considerable attention across diverse societal strata. This study endeavors to delineate the determinants influencing the developmental trajectory of CCI, with a focal point on City A as the primary subject of investigation, juxtaposed against Cities G, D, B, H, and X for comparative analysis, leveraging developmental data from the year 2021. Initially, the study elucidates the conceptual framework underpinning CCI and its intrinsic significance in facilitating urban metamorphosis. Subsequently, the positive impact of CCI through deep learning and information management technology is emphasized, and a cultural and creative recommendation model based on LSTM algorithm is constructed. Through performance evaluation, the recommendation accuracy for cultural and creative projects reaches 94.74 %. A robust developmental assessment model for CCI is then constructed via meticulous factor analysis of pertinent influencers. Employing factor analysis techniques, the study identifies two primary determinants exerting sway over CCI development: sustainable profitability factors and cultural influence factors. Noteworthy among the factors influencing CCI development within City A are fixed asset investment, cultural industry financing, the proliferation of university-based research institutions, and per capita cultural expenditure by residents. Of these, fixed asset investment, cultural industry financing, and the density of university research institutions prominently impinge upon sustainable profitability, with a discernible impact weight of 0.738 in the evaluative framework of CCI development, thus significantly shaping its trajectory. Moreover, consumer psychological factors, particularly market consumption patterns, are observed to exert a discernible influence on CCI evolution. This study augurs fresh insights into the realm of CCI development, infusing it with renewed vigor and vitality. Moreover, it underscores the inherent interdependence and positive correlation among the various research factors, offering novel perspectives and methodologies germane to the advancement of urban CCI.

Exploring the Potential of Variational Autoencoders for Modeling Nonlinear Relationships in Psychological Data.

Latent variables analysis is an important part of psychometric research. In this context, factor analysis and other related techniques have been widely applied for the investigation of the internal structure of psychometric tests. However, these methods perform a linear dimensionality reduction under a series of assumptions that could not always be verified in psychological data. Predictive techniques, such as artificial neural networks, could complement and improve the exploration of latent space, overcoming the limits of traditional methods. In this study, we explore the latent space generated by a particular artificial neural network: the variational autoencoder. This autoencoder could perform a nonlinear dimensionality reduction and encourage the latent features to follow a predefined distribution (usually a normal distribution) by learning the most important relationships hidden in data. In this study, we investigate the capacity of autoencoders to model item-factor relationships in simulated data, which encompasses linear and nonlinear associations. We also extend our investigation to a real dataset. Results on simulated data show that the variational autoencoder performs similarly to factor analysis when the relationships among observed and latent variables are linear, and it is able to reproduce the factor scores. Moreover, results on nonlinear data show that, differently than factor analysis, it can also learn to reproduce nonlinear relationships among observed variables and factors. The factor score estimates are also more accurate with respect to factor analysis. The real case results confirm the potential of the autoencoder in reducing dimensionality with mild assumptions on input data and in recognizing the function that links observed and latent variables.

Some psychometric properties of the school refusal assessment scale–revised: Parent and child version in Tanzanian context

Considering the increase in primary and secondary school dropout rates worldwide, it is necessary to identify the underlying causes, particularly in places with high school refusal rates, such as Tanzania. Today, only a limited number of studies can explain, through valid and reliable measurements, why students in Tanzania refuse to attend school. The current study used confirmatory factor analysis techniques to investigate some psychometric properties of the child’s and parent’s versions of the School Refusal Assessment Scale–Revised (SRAS–R) in the Tanzanian population. Since previous studies found significant associations between the parent–child relationship and the risk of school refusal, we included the associations between the SRAS–R scales and measures of psychological control (parent ratings) and parent–child communication (child ratings) as external validity criteria. We collected data from five public primary schools, including 502 grades three to seven children with their parents (N = 465) in Ruvuma, Tanzania. Confirmatory factor analyses revealed that the four–factor model (avoidance of stimuli provoking negative affectivity, escape from aversive social situations, attention seeking, and tangible rewards) showed a good fit for both the parent and child versions of the SRAS–R. In line with expectations, we found a negative association between parent–child communication and school refusal and positive associations between the parent version of the SRAS–R subscales and the psychological control scale. The findings of this study support the use of this measurement in an African context and Tanzania specifically. The discussion stresses the appropriateness of Western cultural instruments in the African context.

MUNICIPAL STATE CAPACITIES

This research aims to analyse the relationship between the state capacities of the municipalities of the Legal Amazon region and their municipal development. It used the factorial analysis technique, which indicated the intensity of the relationship between the variables and their factors, attributing weight to the analysed variables, and through the values assigned by Stata, we created a Municipal State Capacity Index (ICEM) and then a municipal ranking of state capacities. Further, using a correlation matrix, the ICEM was correlated with the Municipal Human Development Index (HDI). When testing the model, ten variables proved adequate and could explain about 64% of the variation in municipal state capacities. We found that the municipalities of the Legal Amazon have deficit capacity indices since the municipalities with low, very low, or very low ICEM represented 62% of the analysed municipalities. In the analysis of the intensity between the ICEM and the IDHM, the correlation matrix showed positive results, meaning that when the value of a variable grows, the other also grows.

Nexus among the perceived infrastructural, social, economic, and environmental impact of CPEC: A case of Pakistan

Despite its ambitious “economic sustainability” objectives, the China-Pakistan Economic Corridor (CPEC) has been the subject of growing environmental anxiety. Considering the CPEC developments, it is clear that Pakistan is ready to fully embrace this new industrial chapter and take advantage of its major benefits to solve social, energy, infrastructure, and economic problems. However, it should also seriously commit to undertaking proper environmental impact assessments and upgrading system resilience. Data was collected from 400 respondents from Pakistan, and structural equation modeling was applied with the help of AMOS. Factor analysis and structural equation modeling techniques were used to estimate the results and test the study's hypothesis. The results indicate a strong socio-economic impact across perceived economic, infrastructure, social, and total impacts, but they identify a negative association between infrastructure innovation and environmental sustainability. Moreover, results revealed that infrastructure supports social and economic growth, but it might have a substantial negative impact on biodiversity. According to findings, Pakistan may be more vulnerable to climate change due to three potential environmental issues: coal-fired power plants, CO2 concentration along the CPEC route, and increased traffic on the Karakorum Highway. Furthermore, future international trade will be significantly impacted by the corridor. It may, however, also accelerate the destruction of the ecosystem over time due to the industrial revolution.