Innovative Construction and Petrochemical Technologies

Al-Shirqat – Iraq Reducing Energy Demand in Iraq Buildings Using Shallow Geothermal

Firas Ahmed Mohammed — 2026-07-10

This study presents an economic and environmental assessment of a proposed shallow geothermal heating system for building applications under local climatic conditions in Iraq. The analysis was carried out for a room with a total heating load of 905 W, with the performance of the geothermal system compared with two conventional alternatives, namely: a fossil-fuel-based heating system and a direct electric heating system. The total electrical input power of the geothermal system included both the heat pump power and the auxiliary fan power, in order to reach a more realistic economic evaluation. The assessment relied on economic and environmental indicators including annual energy consumption, annual operating cost, life cycle cost, annualized cost, unit thermal energy cost, annual savings, and direct carbon dioxide emissions. The results showed that the geothermal system required a total electrical power of 340.67 W, and an annual electrical energy consumption of 2486.87 kWh/year. By adopting an electricity price of 0.18 USD/kWh, the annual electricity cost reached 447.64 USD/year, while the total annual operating cost reached 496.54 USD/year. As for the conventional fuel-based system, its annual operating cost reached 645.79 USD/year, which led to achieving annual savings of 149.25 USD/year in favor of the geothermal system, with a reduction in operating cost of 23.11%. The annual energy cost difference between the geothermal system and the direct electric heating system also reached 741.53 USD/year. From the environmental point of view, the amount of direct emissions from the conventional system reached approximately 1.41 ton CO₂/year, whereas the geothermal system produced no direct combustion emissions at the point of use. The results confirm that the proposed geothermal system represents an economically and environmentally applicable alternative in building heating applications under local conditions.

The Use of Data in Improving The Customer Experience: A Field Study at K-Card Iraq

Rasha Salih Mosleh — 2026-07-24

The aim of this research was to determine the nature of the impact of big data on improving the customer experience at K-Card Digital Services. Using a descriptive-analytical approach, the research population comprised the general management of K-Card, with a sample of 80 employees whose views were surveyed using a questionnaire; the data were analysed using the SPSS V27 statistical package. The study concluded that there is a strong positive impact of big data on improving the customer experience at the company under study, Several recommendations were put forward, the most important of which was the need to provide organisational support to integrate the requirements of the transition towards utilising big data. This can be achieved by establishing departments and units specialising in its digital and technological aspects, which provide studies and information on the services offered by the company and the extent of customer satisfaction with them.

The Impact of Psychological Capital on Enhancing Proactive Behavior among Employees: An Analytical Study in Iraqi Commercial Banks

Ali Abdul Salam Abdul Daim — 2026-07-24

The present research aims to examine the impact of psychological capital on improving proactive behavior of employees working in Iraqi commercial banks. Psychological capital has been measured based on four factors which include self-efficacy, hope, optimism, and resilience whereas proactive behavior has been defined by four dimensions which consist of taking initiative, problem prevention, opportunity seeking, and constructive voice. Descriptive-analytical method has been adopted for collecting information by using a questionnaire. The sample included 300 employees from selected Iraqi commercial banks in Baghdad. Data analysis has been conducted by using SPSS including descriptive statistics, Cronbach's Alpha, Pearson Correlation, and regression analyses. It has been found that the participants had moderate scores regarding psychological capital and proactive behavior. In addition, positive and statistically significant correlation coefficients have been found between dimensions of psychological capital and those of proactive behavior. Furthermore, regression analysis showed that dimensions of psychological capital had a statistically significant effect on dimensions of proactive behavior. Resilience and hope had been the most influential predictors of proactive behavior while the effects of self-efficacy and optimism varied among proactive behavior dimensions. This research proves that psychological capital is an important psychological resource which can help in taking initiative, problem prevention, opportunity seeking, and constructive voice.

Role of Artificial Intelligence Technologies in Enhancing Management Accounting Functions: An Applied Study at the University of Tikrit

Hamad Abed Mustafa — 2026-07-31

This study aims to examine the role of artificial intelligence technologies (cloud computing, big data, blockchain) in enhancing management accounting functions. The study was conducted at the University of Tikrit using a random sample of (82) employees in the finance departments of various colleges. The researchers employed a range of statistical methods to test the causal relationships and correlations among the study variables. They adopted a descriptive-analytical approach in their study. The study reached a set of findings, the most important of which is that there is a statistically significant correlation between the study variables-that is, the more artificial intelligence (AI) technologies are employed, the more effective management accounting functions become and that the AI technology with the greatest impact on management accounting functions is cloud computing. The study recommended organizing training courses on the use of artificial intelligence technologies in performing business operations and tasks to ensure quality and effectiveness, as well as the need to provide the necessary infrastructure for these technologies. It also presented a set of recommendations to the university under study that should be adopted in its accounting and administrative practices.

The Impact of Negative Gossip in Educational Institutions on the Job Performance of School Teachers: The Mediating Role of Organizational Trust

Amar Essmat Mohammed — 2026-08-05

The current study was designed to evaluate the effects of negative gossips in school environments on teachers' job performance and explore the mediating role of organizational trust in the relationship between negative gossips and job performance in educational organizations. The study used a descriptive-analytical research. A questionnaire was prepared to record the study variables, and was applied on a sample of teachers at school in the Open Educational College – Tuz Branch in the Republic of Iraq. These data were then statistically analyzed using SPSS version 27 using correlation coefficient, regression analysis and mediation analysis using Bootstrap method. The results indicated that the intensity of negative gossips in the workplace was moderate, while the trust within the organization was high and the job performance was very high. The findings also showed that the direct effect of negative workplace gossip on the job performance was not statistically significant. The negative effect of negative workplace gossip, on the other hand, was significant on the organizational trust, and organizational trust was significant on job performance. In addition, the study showed that organizational trust completely mediated the relationship between negative workplace gossip and job performance, implying that the negative workplace gossip has an indirect influence on job performance through reducing organizational trust in the educational institution. The study found that creating a school culture that is built on trust and cooperation helps to mitigate the harmful impacts of school gossip and support teachers' effectiveness. It also suggested creating positive organizational culture, enhancing positive communication among employees and further training and guidance programs based on the principles of organizational trust and elimination of negative behaviors in education work environment.

The Role of Financial Digitalization in Developing Accounting Reporting Systems and Enhancing the Transparency of Financial Statements

Firas Mohammed Daham — 2026-08-15

The rapid evolution of finance into digital form has greatly influenced accounting systems and financial reporting practices for all businesses today, through the enhancement of the efficiency, accuracy and transparency of accounting reporting systems with the use of digital advances like cloud computing, artificial intelligence, blockchain and big data analytics. This research will discuss how financial digitization will support the transformation of accounting reporting systems and to provide more transparency into financial statements. It will also show how digital financial technology affects the quality of accounting information, reduces the risk of financial manipulation, increases the speed of reporting processes, and increases stakeholders' confidence in financial disclosure. The research methodology will use an analytical descriptive approach that includes reviewing current academic research literature and empirical studies on digital accounting transformation. Overall, the research indicates that the financial digitalization will lead to improvements in the quality of the results being reported from the accounting departments, as it will improve the accuracy of the data, enhance the internal control processes, and improve the quality of disclosures by supporting transparent reporting of financial information. Based on the results of this study, companies that utilize financial digitalization will achieve improved efficiency in their operations and greater levels of financial transparency, thus increasing corporate governance and investor confidence.

CDDO-Leo-Based Feature Selection for IoT Intrusion Detection

Mustafa Ahmed Abdulwahhab — 2026-08-20

An Internet of Things (IoT) intrusion detection system (IDS) can be defined as one of the security systems designed for monitoring, analyzing, and detecting malicious cyberthreats or activity on IoT network. It helps to protect IoT devices as well as infrastructure from malware, unauthorized access, denial-of-service (DoS) attacks, and other security breaches. IoT networks produce high-dimensional, redundant and noisy data and thus intrusion detection is a difficult task. The current techniques like HHO-SSA, GWO, and WOA are limited in terms of balancing exploitation and exploration, which results in sub optimal feature selection and low classification performance. To enhance classification accuracy and computational efficiency, the proposed CDDO-LEO hybrid algorithm enhances the feature selection through integrating Child Drawing Development Optimization algorithm (CDDO) with Lagrange Elementary Optimization algorithm (LEO) for dataset refinement. It was used to test an IoT intrusion detection dataset where 17 optimal features were selected and Gradient Boosting got 98.60% accuracy.

It performed better than the conventional feature selection methods like HHO-SSA, GWO and WOA. The comparative analysis establishes the fact that CDDO-LEO has better and more consistent classification performance especially when using ensemble classifiers. The proposed method also consumes less processing time and is also has high accuracy thus useful in high dimensional datasets.

Dynamic Capabilities and Climate Resilience in Nigeria's Energy and Extractive Industries: Testing Sensing, Seizing, and Transforming Capabilities

Oludolapo Oriyomi Akinola — 2026-08-20

Firms in Nigeria's energy and extractive industries face intensifying climate-related transition and physical risk, yet management research has rarely tested how the microfoundations of dynamic capabilities, sensing, seizing, and transforming, translate into climate-resilient organizational performance in this setting. Building on Teece's dynamic capabilities framework, this paper develops and specifies an empirically testable model in which sensing capability (the identification and interpretation of climate-related threats and opportunities), seizing capability (the mobilization of resources and business-model responses to those threats and opportunities), and transforming capability (the reconfiguration of organizational assets, routines, and structures) are hypothesized as sequential yet interacting drivers of climate resilience, with institutional and regulatory pressure specified as a boundary condition. The paper situates this model within the empirical tradition established by dynamic-capabilities studies of oil and gas, coal, and shipping incumbents confronting decarbonization and climate transition pressure, and extends that tradition to a Nigerian context marked by a distinctive combination of climate exposure, regulatory volatility, and community-level accountability contestation. A mixed-methods research design is specified, combining a firm-level quantitative survey of Nigerian oil, gas, and solid-minerals companies, analyzed using partial least squares structural equation modeling, with semi-structured interviews to validate and contextualize the quantitative findings. Four hypotheses and a moderation proposition are advanced for empirical testing. The paper is positioned as a research design contribution consistent with Business Strategy and the Environment's focus on firm-level strategic responses to environmental and climate pressure, and it lays the empirical groundwork for the data-collection phase of this research program.

The Impact of the Dimensions of Electronic Phone Service Quality on Enhancing Customer Loyalty through the Mediating Role of Positive Word-of-Mouth and Customer Satisfaction: An Analytical Study of the Opinions of a Sample of Customers of Asiacell Telecom

Saad Salim Ghanim — 2026-08-26

This study is designed to explore how the dimensions of electronic phone service quality (reliability, coverage, assurance, accessibility, and tangibility) affect customer loyalty in the telecommunications service and what role customer satisfaction and positive word-of-mouth play in this process. The research method and technique used in this study was descriptive analytical. The study population was all customers at Asiacell Telecommunications Company Limited and a sample of 385 respondents were randomly selected. The data collection was done using verified and validated electronic questionnaire. Data were analysed statistically in the form of data analysis in (SPSS.26) and JASP (0.96.0.0). The data were analysed using several statistical techniques, such as One-Sample t-test to compare the gaps with the accepted benchmark, simple linear regression analysis, and multiple linear regression analysis, the latter of which was used to check the significance of the mediating paths – the Sobel Test. The study was able to draw several conclusions, one of the leading ones being that Asiacell Telecommunications Company Limited managed to exceed the accepted benchmark of the measurement instrument on all aspects related to electronic phone service quality. Tangibility was the strongest dimension in terms of awareness and perception of customers followed by the coverage dimension. Additionally, positive word of mouth was discovered to be an important and mediating role in enhancing the relationship between the dimensions of electronic phone service quality and customer loyalty. The study suggests that in the light of these findings, the company should focus on improving the quality of its digital services, which should be regarded as the underpinning of the improvement and maintenance of customer loyalty in the long term.

External Audit and Financial Characteristics in Predicting Economic-Sector Membership Using Machine Learning: Evidence from Iraqi Listed Companies

Nadia Talib Salman — 2026-08-30

This study investigates the predictive role of external-audit and financial characteristics in identifying economic-sector membership among Iraqi listed companies using machine-learning techniques. The analysis is based on 263 firm-year observations from 33 companies distributed across six verified economic sectors during 2015–2024. Three predictive specifications are evaluated: financial characteristics only, external-audit characteristics only, and a combined model incorporating both information sets. Logistic regression is adopted as the primary classifier and assessed using leakage-free leave-one-company-out cross-validation with class-preserving nested hyperparameter selection. Additional robustness procedures include alternative machine-learning algorithms, company-level aggregation, permutation testing, stratified company-block bootstrap inference, drop-column analysis, and sample-sensitivity tests. The combined model achieved the highest firm-year macro F1-score of 0.321, compared with 0.266 for the financial-only model and 0.242 for the audit-only model. However, the incremental improvement of 0.055 over the financial-only specification was not statistically conclusive, as the 95% bootstrap confidence interval ranged from −0.024 to 0.129. Firm size emerged as the strongest overall predictor, while modified audit opinion and auditor change were the most influential audit-related characteristics. The combined-model advantage was not consistent at the company level or across all alternative classifiers. The findings indicate that both financial and external-audit characteristics contain sector-related predictive information, while highlighting the need for cautious interpretation in small and imbalanced emerging-market samples.

The Impact of Big Data Strategic Proactiveness on Accelerating the Product Life Cycle in the E-Retail Sector

Qahtan Adnan Rouhi Hassan — 2026-09-04

The purpose of this work was to explore the effects of strategic proactivity of big data for product life cycle acceleration in e-retail. A sample of workers in e-retail companies and platforms in Iraq was selected. A descriptive-analytical approach was taken and a questionnaire was the main instrument for collecting data. The study sample comprised 108 respondents with a background in digital marketing, product management, data analysis, e-sales, customer service delivery and supply chain-related job roles. Strategic proactivity of big data was measured using five dimensions: customer data analysis, demand forecasting, monitoring market trends, competitor data analysis, and real-time analytics and decision support. The dependent variable, acceleration of the product life cycle, was quantified in terms of five dimensions: increase in the rate of product idea generation, increase in product development, increase in product launch, increase in product modification and improvement, and increase in product decision making (including continuation or withdrawal of products). The results showed that the level of strategic proactivity of big data in e-retail companies was high. They further revealed that the product life cycle acceleration level was high. In addition, the study results showed that the size of strategic proactivity of big data has a significant impact on the acceleration of product life cycle. The results also revealed a high positive statistically significant correlation between strategic proactivity of big data with product life cycle acceleration. Furthermore, the total of the strategic proactivity of big data accounted for a considerable amount of variance in product life cycle acceleration. The study found that big data is not a mere technical gadget, but a strategic asset that can shorten the time taken to make decisions, enhance product management and react quickly to the changes in customers, markets and competitors of e-retail companies. The study suggested enhancements to the analytical and digital infrastructure, and skills of employees to analyse data, activation of early warning systems for monitoring product demand and competition, and integration of product development, launch, modification and withdrawal decisions with real-time analytics.

Adaptive Life Cycle Costing as an Approach to Achieving Environmental Sustainability: A Study of the Iraqi Soft Drinks Company

Ali Jasim Obaid — 2026-09-07

The purpose of this study is to create an adaptive framework for Life Cycle Costing (LCC) analysis which can be utilized to improve environmental and economic sustainability in the soft drinks industry by including recycling practices of packaging materials and recycled raw materials in the scope of cost analysis. To meet the aim of the study, a multi-stage exploratory and explanatory approach was used based on the Abductive Methodology with ISO 15686-5:2017 as a key basic for the development of Adaptive Life Cycle Costing framework. An Environmental and Circular Life Cycle Costing (EC-LCC) model was designed and then tested for its applicability by applying it to a case study of Baghdad Soft Drinks Company with available financial information. Analysis results indicate that the benefits of circular economy practices result in lower life cycle costs than in the conventional practice. The study suggests to apply ‘Adaptive Life Cycle Costing' in industrial companies for economic and environmental decisions, with the highest priority being the investment in the recovery and recycling of PET bottles and packaging materials, the progressive adoption of recovered materials and enhancement of energy and water use efficiency. It also calls for creating more precise waste collection and monitoring systems, to verify in the field the expected results of the model and to refine the results of investments in circular economy practices.

The Impact of the Hybrid Work System on Employees’ Organizational Commitment: An Analytical Study of the Opinions of a Sample of Employees at Asiacell Telecommunications Company

Omar Wasfi Mukhlif — 2026-09-07

This research was intended to study and analyze the effect of hybrid working models on organizational commitment of a sample of employees of Asiacell Telecommunications Company. Specifically, the study aimed to gain insight into the meaning of the dimensions of spatial and temporal flexibility and how they can influence the affective attachment and ethical commitment of employees.

A descriptive-analytical method was used in the study. Data collection was purposive sampling with number of respondents (100) employees of the company. The data were statistically analyzed through the statistical programs (SPSS, JASP, and SmartPLS) to check the direct relationship between the independent variable (hybrid work) and the dependent variable (organizational commitment).

The findings indicated that hybrid work positively and directly influenced work organizational commitment and statistically significantly. The study also revealed that affective commitment was greater among employees working in the hybrid model than among their counterparts, suggesting that increasing employees' autonomy in their work increases employee value of organizational identity.

The study suggests that evaluating employees with solely work-based criteria rather than tracking time in the office. This would boost the self-confidence of the employees and have an impact on their commitment to the organization that appreciates their real contribution.

Improving Arabic Text Classification Accuracy Using Lightweight NLP Techniques

Rasha Majid Hassoon — 2026-09-05

Arabic text classification is a challenging task because of the complex morphology of the language, the existence of different writing forms and a multitude of dialects, which can result in sparser common text representations. While transformer models such as AraBERT have obtained superior results on many Arabic NLP tasks, their high computational requirements make them difficult to deploy in environments with limited hardware resources. In some cases this can also make the model less practical for researchers working with basic computer systems.

This study focuses on a more practical issue: how much accuracy a simple classifier may lose when the amount of required computation is reduced. We use a combined TF-IDF representation based on both words and characters, then reduce the number of features using Chi-Square selection. The selected features are finally used with a linear SVM to create a model that is faster and more efficient, while still keeping good predictive performance. However, the method do not always provide the same level of accuracy as more complex models, especially with difficult text.

The proposed lightweight method is tested against several other approaches, including word-based TF-IDF, character-based TF-IDF, Naive Bayes, Logistic Regression, BiLSTM, and AraBERT. All methods are tested using the same data distribution to make the comparison fair. The dataset used is the arbml/arabic_100k_reviews corpus, which originally contains 99,999 balanced reviews from three sentiment categories. After cleaning the data and removing very short reviews, the dataset was reduced to 99,759 samples. The training, validation, and testing sets includes 69,831, 9,976, and 19,952 reviews, respectively. This setup allows the different models to be compared under similar conditions and with the same evaluation process.

After applying Chi-Square feature selection, the hybrid feature set was reduced from 250,000 to 50,000 dimensions, which represents an 80% reduction. That proposed method achieved 67.90% accuracy and a Macro-F1 score of 67.75%. Its training time was around 41 seconds, while the complete test set was processed in about 0.03 seconds. Among all the evaluated models, AraBERT achieved the best overall performance, reaching 74.08% accuracy and 74.25% Macro-F1. However, its computational requirements were much higher, with nearly 1,760 seconds needed for training and about 47 seconds for inference on the same test set. Depending on the processing stage, this makes AraBERT approximately 40 to 1,500 times slower than the proposed lightweight approach. The proposed method is therefore not presented as a replacement for AraBERT in terms of accuracy, since its accuracy is lower. Instead, its main advantage is the considerable and measurable reduction in computational cost. This trade-off can be useful in situations where GPU availability, memory capacity, or response time are limited.