Industrial Management Research Articles

Evolving waste management: The impact of environmental technology, taxes, and carbon emissions on incineration in EU countries

Amid the urgent global imperatives concerning climate change and resource preservation, our research delves into the critical domains of waste management and environmental sustainability within the European Union (EU), collecting data from 1990 to 2022. The Method of Moments Quantile Regression (MMQR) results reveal a resounding commitment among EU member states to diminish their reliance on incineration, which is evident through adopting green technologies and environmentally conscious taxation policies, aligning with the European Union's sustainability objectives. However, this transition presents the intricate task of harmonizing industrial emissions management with efficient waste disposal. Tailoring waste management strategies to accommodate diverse consumption patterns and unique circumstances within individual member states becomes imperative. Cointegrating regressions highlighted the long-run relationship among the selected variables, while Feasible Generalized Least Squares (FGLS) and Panel-Corrected Standard Errors (PCSE) estimates roughly confirmed MMQR results. ML analyses, conducted through two ensemble methods (Gradient Boosting, GB, and Extreme Gradient Boosting, XGBoost) shed light on the relative importance of the predictors: in particular, environmental taxation, consumption-based emissions, and production-based emissions greatly contribute to determining the variation of combustible renewables and waste. This study recommends that EU countries establish monitoring mechanisms to advance waste management and environmental sustainability through green technology adoption, enhance environmental taxation policies, and accelerate the renewable energy transition.

Improving efficiency and quality of operational industrial production assets information management in customer–vendor interaction

Industrial assets management has a key role in ensuring continuity of industrial production via purchasing, warehousing and maintenance of devices and spare parts used in manufacturing, but it suffers from data and information accessibility, completeness, consistency, interoperability, and timeliness challenges in the customer - equipment vendors interaction and information sharing. Current practices in devices and spare parts purchasing, warehousing and maintenance include a lot of manual information search, validation and update work done at the manufacturing companies to maintain high quality master data records for the production assets management. This article addresses the challenge of improving efficiency and quality of operational production assets information management in industry customer company interaction with their production equipment vendors, with a design science research approach. The research was executed in collaboration between research organizations, forest industry companies, industrial production equipment vendors and information technology (IT) companies on two parallel and complementary research tracks, which are described with their key research results. The contributions are an ex-ante evaluated digital marketplace concept with preliminary requirements, and an ex-post evaluated semi-automated master data harmonization tool, as artefacts for improving efficiency and quality of production assets information management in industrial customer–vendors interaction. As theoretical contribution, the article provides a synthesis of the research results in the framework of information systems (IS) design theory as a nascent and novel design theory on designing IS artifacts for improving efficiency and quality of production assets information management, both in single industrial customer company and ecosystem settings. The contributions provide a starting point for further research and development of a digital marketplace for improving production assets information management in industrial customers – vendors interaction, potentially also applicable for similar ecosystems in other continuous production process -based manufacturing industries.

Change Assessment as a Key Factor in Delivering Strategic Agility in Business Transformation

Objective: This study aims to investigate business transformation and its relationship with organizational agility in the software industry sector. Theoretical Framework: The main concepts related to change management and organizational agility are presented. The importance of continuous adaptation of organizations in a dynamic business environment is highlighted. Method: A three-stage study design was employed that included literature analysis and interviews with managers in the software industry. Data collection was conducted through focus group interviews. Results and Discussion: The results reveal the need for effective change management to improve organizational agility. Implications of the findings in the theoretical and practical context, as well as possible limitations of the study, are discussed. Research Implications: The practical and theoretical implications of the results for the software industry sector are explored. Areas that could benefit from the study's findings are highlighted. Originality/Value: This study contributes to the field of change management and organizational agility by providing new insights into the relationship between business transformation and organizational adaptability in the software industry sector.

Potential water reuse pathways from a life cycle analysis perspective in the poultry industry

Achieving sustainable water use in the food industry requires a paradigm change and transforming it towards circularity by exploring opportunities for water reuse. Life Cycle Assessment can comprehensively evaluate the environmental impacts of the water management and reuse options throughout their life cycle to ensure improvement of environmental performance. Through the example of a poultry processing plant it was shown that improving wastewater treatment technology can reduce the metal depletion category by almost 30 % by cutting down chemical use by 90 %. Further mitigation of environmental damage could be achieved by either choosing technological or agricultural reuse options depending on the needs or local constraints. While the improvement is not massive since the implementation of additional technologies require further resources, the Reduce scenario (technological reuse with 50 % reduction of water consumption) compared to the direct discharge into a surface water body resulted a decrease of 11.0 % and 17.1 % in freshwater consumption and 7.6 % and 2.6 % in metal depletion units. On the contrary, irrigation is better than technological reuse with regard to human health and resource scarcity indicators. Legislative requirements and social acceptance issues might slow down the process of closing the loop in the water management of the food industry; nonetheless results show that opting for water reuse is an environmentally sound solution even in this sector.

Toxic elements identified in breast milk of mothers residing in water contaminated region of Sindh and their impact on infants’ growth patterns: A case-control study

Breast milk is a vital source of nutrition for breastfed infants, providing essential nutrients and elements but, in some cases, toxic ones. This is the first case-control study that investigated the elemental profile of breast milk samples collected from mothers residing in Matiari (Sindh), a region with insufficient industrial waste management, and its potential impact on infants' anthropometrics. Precisely, 62 milk samples, including 42 cases and 20 controls, were analyzed using the ICP-MS technique. Overall, six elements showed significance between the two groups, arsenic (As) was present at 0.68 μg/L in cases and absent in controls, while lead (Pb) exhibited elevated concentrations in the case group at 4.56 μg/L compared to 0.25 μg/L in controls, well-known for their toxicity. Barium (Ba) and manganese (Mn) levels were also higher in cases, associated with reported health effects on child well-being. Essential elements molybdenum (Mo) and selenium (Se) were higher in the controls. Furthermore, the association of these metals with the child growth standards as per WHO guidelines was calculated. Linear regression analysis revealed As negatively associated with WAZ and WHZ scores, while Mo was positively associated with WAZ, WHZ, and HAZ scores. These findings highlight serious health concerns in the region, where toxic elements pervade drinking water and food sources. Immediate actions are imperative to maintain the wellness of future generations.

Operational Risk Assessment of Engineering Vehicles Considering Driver Characteristics

As vehicles with high accident and casualty rates within the road transportation system, engineering vehicles have been receiving much attention and emphasis in terms of safety. Accurate analyses and evaluations of risk factors in vehicle operation are imperative for enhancing the management level of engineering vehicles. This study explores the differences between various types of drivers by analyzing the driving characteristics of professional drivers. The evaluation index system is developed and quantified by integrating factors related to engineering vehicle drivers, road environment, and industry management. Additionally, the risk assessment model is developed using the error backpropagation algorithm. The optimal model is determined by comparing the number of nodes in different hidden layers, the activation function, and regularization optimization. The prediction accuracy of this model’s coefficient of determination is 0.912, indicating that the model has validity. This study is conducive to improving the safety level of engineering vehicle operation in order to reduce the rate of vehicle traffic accidents, the severity of accidents, and the consequences of losses. It also has practical application value in safeguarding social security.

Protective efficacy and immune responses of largemouth bass (Micropterus salmoides) immunized with an inactivated vaccine against the viral hemorrhagic septicemia virus genotype IVa

Viral hemorrhagic septicemia virus (VHSV) poses a significant threat to the aquaculture industry, prompting the need for effective preventive measures. Here, we developed an inactivated VHSV and revealed the molecular mechanisms underlying the host's protective response against VHSV. The vaccine was created by treating VHSV with 0.05 % formalin at 16 °C for 48 h, which was determined to be the most effective inactivation method. Compared with nonvaccinated fish, vaccinated fish exhibited a remarkable increase in survival rate (99 %) and elevated levels of serum neutralizing antibodies, indicating strong immunization. To investigate the gene changes induced by vaccination, RNA sequencing was performed on spleen samples from control and vaccinated fish 14 days after vaccination. The analysis revealed 893 differentially expressed genes (DEGs), with notable up-regulation of immune-related genes such as annexin A1a, coxsackievirus and adenovirus receptor homolog, V-set domain-containing T-cell activation inhibitor 1-like, and heat shock protein 90 alpha class A member 1 tandem duplicate 2, indicating a vigorous innate immune response. Furthermore, KEGG enrichment analysis highlighted significant enrichment of DEGs in processes related to antigen processing and presentation, necroptosis, and viral carcinogenesis. GO enrichment analysis further revealed enrichment of DEGs related to the regulation of type I interferon (IFN) production, type I IFN production, and negative regulation of viral processes. Moreover, protein-protein interaction network analysis identified central hub genes, including IRF3 and HSP90AA1.2, suggesting their crucial roles in coordinating the immune response elicited by the vaccine. These findings not only confirm the effectiveness of our vaccine formulation but also offer valuable insights into the underlying immunological mechanisms, which can be valuable for future vaccine development and disease management in the aquaculture industry.

Utilizing Machine Learning Techniques for Power Estimation in Residential Electricity Consumption

Electricity consumption analysis and estimation play a vital role in various contexts, ranging from business planning to household financial management. In an era where electrical energy is a primary source of human activity, understanding power usage patterns and estimating them becomes crucial. This research enables an understanding of energy consumption from individual to national scales and supports efficient energy policy planning. Power estimation provides insights into the expenditures that consumers or companies will incur, enabling effective financial planning. Research and development of accurate predictive models using machine learning techniques are essential to providing useful information for the energy industry and the general public. This study uses machine learning to compare the performance of different power prediction models. The decision tree (DT) method, with an MAE of 0.3613, an MSE of 0.4184, and an RMSE of 0.6469, gives the best results. The results show that the model can provide accurate predictions of electricity consumption, potentially becoming a reliable tool in power management and financial management. Its contribution is crucial in the energy industry and daily life, providing insights needed to enhance energy efficiency and budget management. As a result, this research provides relevant and beneficial solutions to energy and financial management for society and industry. Keywords: Electricity consumption analysis, Estimation, Machine learning, Power management, financial management

Revolutionizing Land-Based Vehicle Parts Management: Basis for a Unified Digital Platform for Auto Parts Information Systems

The study provides the basis for a much-needed digital platform one that is consumer-friendly by examining current practices, pain points, and stakeholder requirements. This marketplace aims to aggregate the information of all of the autoparts dealers, suppliers, distributors, and manufacturers centrally for collaboration. Our goal is to reduce TCO and time associated with data access/wait times for real-time insights around inventory management, supply chain visibility, and actionable insights. This study is designed to save the face of the land and rarity-based car parts management at the end, foster efficiency in the automotive industry while taking on sustainability. To be a highly effective, secure and scalable "Autohub System" the design is implemented using several strategies and tactics in database domain.In order to reduce redundancy, this entails using a Relational Database Management System (RDBMS) such as MySQL and implementing strategies such table definition, relationship building, and normalization. Indexing helps enhance efficiency while looking for information and to model the autoparts ecosystem we use Entity-Relationship Diagrams (ERD). For stronger security against attacks and availability of data some methods used are concurrency control, adequate back-up and recovery procedures, and encryption of data. Performance tuning and considering scalability are other topics as they focus on the efficiency of work and further increase in the company’s possibilities, whereas data auditing gives them accountability. Altogether these strategies can accurately set the database to cater for the goals that the autoparts management system has to accomplish. It’s the exact same concept with the Autohub System which revolutionizes the conventional autoparts management sector by producing IoT and predictive maintenance, making operations smoother while eliminating waste. It is beneficial for many businesses as they experience reduced costs, less downtime, and an overall increase in productivity. This is true because the indicated system enhances earth-friendly causes since the automotive industry it fosters is more competitive because of worldwide cooperation. The newly introduced system, the Autohub System, has posed a major change in the ways businesses within the autoparts management industry operate today. This is achieved through offering real-time information, supplier control and a clear and user-friendly front-end in order to ensure that it facilitates the tends, procurement and cooperation. Through sustained supplier dynamics, buy order optimization, and analytical proficiency, decision making becomes enhanced tremendously. The long-term feasibility is guaranteed by the very fact of the opportunity to expand on it and adapt to new needs. This technology takes autoparts management to a new level because it has an overall positive impact on the functions of decreasing downtime, improving supplier interaction, and improving the supply chain. Summing up, The Model T exemplifies the milestone in progress toward sustainability and efficiency that shapes the future of the automobile industry.

Code Similarity Prediction Model for Industrial Management Features Based on Graph Neural Networks.

The code of industrial management software typically features few system API calls and a high number of customized variables and structures. This makes the similarity of such codes difficult to compute using text features or traditional neural network methods. In this paper, we propose an FSPS-GNN model, which is based on graph neural networks (GNNs), to address this problem. The model categorizes code features into two types, outer graph and inner graph, and conducts training and prediction with four stages-feature embedding, feature enhancement, feature fusion, and similarity prediction. Moreover, differently structured GNNs were used in the embedding and enhancement stages, respectively, to increase the interaction of code features. Experiments with code from three open-source projects demonstrate that the model achieves an average precision of 87.57% and an F0.5 Score of 89.12%. Compared to existing similarity-computation models based on GNNs, this model exhibits a Mean Squared Error (MSE) that is approximately 0.0041 to 0.0266 lower and an F0.5 Score that is 3.3259% to 6.4392% higher. It broadens the application scope of GNNs and offers additional insights for the study of code-similarity issues.

Ml-driven approaches to enhance inventory planning: Inoculant weight application in casting processes

This study addresses the complexities of inventory management in the casting industry, focusing on a proprietary inoculant crucial for casting process. The unpredictable nature of inoculant usage, influenced by various casting variables, challenges conventional calculations. To overcome these, expert opinions were gathered to identify features for accurate prediction of inoculant requirements, and an annual dataset was created. This dataset underwent data preprocessing to introduce new features and mitigate outliers. Machine learning models, including decision tree, random forest, least absolute shrinkage and selection operator (LASSO), elastic net regression, ridge regression, multiple linear regression, gradient boosting, extreme gradient boosting, and voting regression, were trained and tested. The voting regression algorithm, which combines decision tree and LASSO through hard voting, demonstrates that combining high-variance low-bias and low-variance high-bias algorithms can achieve competitive performance with low errorwithout overfitting. Predictions obtained using the voting regression algorithm for the entire year’s data were analyzed using batch size determination algorithms, Wagner-Whitin and Silver-Meal, with Wagner-Whitin providing cost advantage. This study highlights the potential of predicting materials with uncertain usage through AI techniques and historical data, minimizing inventory cost. The findings offer practical insights for managers, demonstrating how advanced predictive models can enhance decision-making, improve inventory planning, and achieve cost savings. This approach is valuable without needing additional investment in material requirement planning, especially where bill of materials’ reliability is reduced by environmental factors.

Advancing Industrial Process Electrification and Heat Pump Integration with New Exergy Pinch Analysis Targeting Techniques

The process integration and electrification concept has significant potential to support the industrial transition to low- and net-zero-carbon process heating. This increasingly essential concept requires an expanded set of process analysis tools to fully comprehend the interplay of heat recovery and process electrification (e.g., heat pumping). In this paper, new Exergy Pinch Analysis tools and methods are proposed that can set lower bound work targets by acutely balancing process heat recovery and heat pumping. As part of the analysis, net energy and exergy load curves enable visualization of energy and exergy surpluses and deficits. As extensions to the grand composite curve in conventional Pinch Analysis, these curves enable examination of different pocket-cutting strategies, revealing their distinct impacts on heat, exergy, and work targets. Demonstrated via case studies on a spray dryer and an evaporator, the exergy analysis targets net shaft-work correctly. In the evaporator case study, the analysis points to the heat recovery pockets playing an essential role in reducing the work target by 25.7%. The findings offer substantial potential for improved industrial energy management, providing a robust framework for engineers to enhance industrial process and energy sustainability.

Fabrication of used-tea embedded alginate beads for cationic dye remediation: Synergistic effect of surface adsorption and intraparticle diffusion

Water pollution has become a major concern due to high industrialization and poor waste management policies. Recently, organic contaminants such as dyes are severely influencing environmental conditions and therefore, it must be employed to efficient treatment processes before discarding in waterbodies. In this work, used-tea embedded alginate beads were fabricated as a novel adsorbent for removing a cationic dye (methylene blue, MB). The physical properties of fabricated beads were investigated and also characterized using field emission scanning electron microscope (FESEM), energy dispersive X-ray spectroscopy (EDX), Fourier-transform infrared spectroscopy (FTIR), and X-ray diffraction (XRD). The highest MB removal efficiency of about 97.38 ± 1.84% was obtained at optimized pH 7 and 25 °C. The adsorption capacity (qe) increased with MB concentration and the pseudo-second-order model exhibited excellent fit. MB adsorption comprises rapid surface adsorption (initial 5 min) and slow inter-particle diffusion (after 5 min). Supporting the Langmuir isotherm indicated the formation of a monolayer of MB on the surface of the beads and the calculated maximum adsorption capacity (qm) was 421.94 mg/g. Negative ΔG (-7.25 kJ/mol) and ΔH (-52.67 kJ/mol) reveal MB removal as a spontaneous and exothermic process. Positive entropy change of ΔS (152.05 J/mol) states increased randomness, facilitating promoted affinity of beads for free MB molecules. A removal efficiency of about 90.40 ± 2.91% was obtained up to 3rd cycle of its reuse, which further decreased to 67.11 ± 1.72% during 5th cycle. The MB adsorption mechanism was investigated by comparing the physiochemical and morphological characteristics of beads before and after MB removal.

Model and Implementation of a Novel Heat-Powered Battery-Less IIoT Architecture for Predictive Industrial Maintenance

The research and management of Industry 4.0 increasingly relies on accurate real-time quality data to apply efficient algorithms for predictive maintenance. Currently, Low-Power Wide-Area Networks (LPWANs) offer potential advantages in monitoring tasks for predictive maintenance. However, their applicability requires improvements in aspects such as energy consumption, transmission range, data rate and constant quality of service. Commonly used battery-operated IIoT devices have several limitations in their adoption in large facilities or heat-intensive industries (iron and steel, cement, etc.). In these cases, the self-heating nodes together with the appropriate low-power processing platform and industrial sensors are aligned with the requirements and real-time criteria required for industrial monitoring. From an environmental point of view, the carbon footprint associated with human activity leads to a steady rise in global average temperature. Most of the gases emitted into the atmosphere are due to these heat-intensive industries. In fact, much of the energy consumed by industries is dissipated in the form of waste heat. With this scenario, it makes sense to build heat transformation collection systems as guarantors of battery-free self-powered IIoT devices. Thermal energy harvesters work on the physical basis of the Seebeck effect. In this way, this paper gathers the methodology that standardizes the modelling and simulation of waste heat recovery systems for IoT nodes, gathering energy from any hot surface, such as a pipe or chimney. The statistical analysis is carried out with the data obtained from two different IoT architectures showing a good correlation between model simulation and prototype behaviour. Additionally, the selected model will be coupled to a low-power processing platform with LoRaWAN connectivity to demonstrate its effectiveness and self-powering ability in a real industrial environment.

A REVIEW OF BLOCKCHAIN TECHNOLOGY'S IMPACT ON MODERN SUPPLY CHAIN MANAGEMENT IN THE AUTOMOTIVE INDUSTRY

Blockchain technology has emerged as a transformative force in various industries, including supply chain management within the automotive sector. This review examines the impact of blockchain on the automotive supply chain by analyzing 183 articles, focusing on its ability to enhance transparency, traceability, and efficiency. By providing a decentralized and immutable ledger, blockchain ensures real-time tracking of parts and components, thereby reducing the risk of counterfeit products and ensuring compliance with regulatory standards. The automation of transactions through smart contracts streamlines processes, reduces the need for intermediaries, and leads to substantial cost savings and faster delivery times. However, the implementation of blockchain also presents challenges such as scalability, interoperability with existing systems, high costs, and regulatory concerns. Addressing these challenges through future research and pilot projects is essential for unlocking the full potential of blockchain technology in revolutionizing supply chain management in the automotive industry. This review synthesizes current literature to provide a comprehensive understanding of both the benefits and challenges associated with blockchain implementation, highlighting its transformative potential and the necessary steps for successful adoption.

Scope Management in Industry 4.0 Projects

This study investigates the implementation of marketing innovations within the framework of Industry 4.0, focusing on Asian corporate culture. Companies must innovate in the rapid globalization and digital transformation era to stay competitive and meet evolving client demands. The research identifies 15 critical attributes essential for marketing innovation in Industry 4.0, including augmented reality, virtual cryptocurrencies, and the Internet of Things (IoT). These innovations represent a blend of technological and non-technological advancements companies use to enhance their marketing strategies. The study examines the impacts of these innovations through a survey of 50 companies utilizing Industry 4.0 technologies. Key findings indicate significant impacts such as increased competitiveness, improved customer communication, and enhanced work efficiency. Larger companies and those in the automotive sector rate these impacts more highly, reflecting their greater reliance on technological advancements. Additionally, the study reveals that cultural factors influence the perception of these impacts, with companies in Asian cultures exhibiting varied responses compared to those with a global business culture. The pilot study utilized qualitative and quantitative methods, including structured surveys and descriptive statistical analysis. The results underscore the importance of effective scope management in successfully implementing Industry 4.0 projects, emphasizing the need for clear goal-setting, resource allocation, and change control. The research highlights the necessity for organizations to adapt their corporate culture and develop innovative marketing strategies tailored to their specific contexts. These insights offer practical recommendations for optimizing project outcomes and maintaining competitiveness in the digital age.

The role of digital transformation in achieving sustainable supply chain management in Industry 4.0: an editorial review perspective

ABSTRACT Before and aftermath of COVID-19 has created multiple waves of digital transformation adoption in supply chain management. The power of potential benefits is realised as a multitude however the transformation phases are not sustainable. This editorial special issue has edited papers of scholarly research contributions to the theory and practice of digital transformation aspects in the field of logistics and supply chain management. Based on the summary of each contribution and an extended literature review on the theme, this editorial paper proposes a novel framework that could facilitate the logistics industry at times of disruptive technologies when firms are struggling to cope with the challenges created due to the COVID-19 pandemic and the recovery.

Relationship Marketing and Customer Loyalty: The Moderating Effect of Relationship Duration, Strength, and Intensity

ABSTRACT Research shows that relationship marketing and customer loyalty are significantly correlated without considering the time or intensity dimension of the relationship. So, this study investigates whether relationship duration, strength, and intensity moderate the association between relationship marketing and customer loyalty in an emerging market. We adopt a quantitative research approach and a cross-sectional survey design to collect the data from 500 customers in the fund management industry of Ghana. We analyze the data using descriptive statistics, Pearson correlation and hierarchical regression model. The results reveal that the four relationship marketing dimensions (i.e. trust, commitment, communication, and conflict handling) have a significant positive association with customer loyalty. Furthermore, relationship strength and intensity significantly augment and moderate the association between relationship marketing dimensions and customer loyalty. However, relationship duration could neither augment nor moderate the association between the relationship marketing dimensions and customer loyalty. We have made a significant contribution to literature by establishing the effectiveness of relationship strength and intensity as moderators on the association between relationship marketing dimensions and customer loyalty in the fund management industry of an emerging market.

Development and Promotion Countermeasures for Export Growth of Pakistan Textile Products

Pakistan's textile industry is vital to GDP, employment, and exports. Pakistan is strategically located in the global textiles and apparel market due to its large production and export capabilities. This sector struggles with inconsistent product quality, market barriers, high global standards, and protectionist trade policies in wealthy nations. Addressing these issues is essential for industry growth and global competitiveness. This study identifies these challenges and proposes strategic solutions to improve export performance and global competitiveness to boost Pakistan's textile sector's economic stability and growth. A thorough mixed-methods strategy integrating qualitative and quantitative research methodologies was employed in the study. By conducting surveys by websites and literature reviews related to the industry, we were able to gather primary data. We also used secondary sources such as government reports, trade journals, and trade organizations to compile our data. We used quantitative analyses in our study to determine how spending on R&D, technology upgrades, and quality control affected export performance. We looked at energy consumption, production, waste management, and export amounts in various industries of Pakistan. Improving export performance and meeting international standards requires investments in quality control, technology upgrades, and research and development, according to the study. It also lists the obstacles to entering certain markets and suggests ways to overcome them, such as through government assistance, sustainable practices, and vocational education and training. If Pakistan's textile industry follows these suggestions, it will become more competitive internationally.

Study on effect of anti-bribery management system to the quality of authority organization operational management in construction industry

Abstract Unethical behaviours are quite common in the construction industries. This study aims to assess the impact of unethical behaviours within organization, and the effectiveness of implementing Anti-Bribery Management System (ABMS) within organization. Among many of the government organizations, the main target respondents for this study were workers such as consultants and contractors who had worked with organization ’B’. The organization is called as Organization ’B’ as this topic research might be sensitive. In this study, distribution of questionnaires via online platform was used as a quantitative method where a total of 54 respondents out of 146 expected sample sizes were obtained. Majority of the respondents are agreeable towards the unethical behaviour will contribute to lower the quality of their works as the mean value is greater than 4.0. Plus, the implementing of ABMS are also shows positive impact in the quality of their works as the mean value is also greater than 4.0. From overall respondents, the top 3 variables for unethical behaviour are ’Hard Control Measures’, ’Conflict of Interest’, and ’Lack Supervision’. As for the implementation of ABMS, the top 3 variables are ’ABMS Qualification’, ’Leadership’, and ’Policies and Procedures’.