Industrial Management Research Articles

Sustainable management in the hospitality industry: the influence of green human resource management on employees’ pro-environmental behavior and environmental performance

PurposeThis study aims to evaluate the impact of green human resource management on cognitive factors, employees' pro-environmental behavior and the environmental performance of hotels in Vietnam.Design/methodology/approachDrawing upon primary data collected from a sample of 393 hotel employees, this study utilizes covariance-based structural equation modeling (CB-SEM) to test hypotheses and conduct path analysis.FindingsThe study’s findings reveal several key insights. Firstly, GHRM positively influences environmental knowledge (EK), environmental attitude (EA), attitude toward pro-environmental behavior (APB), pro-environmental behavior (PB) of employees and environmental performance (EP) of hotels. Additionally, the results also indicate that EK promotes EA, APB and PB, and both EA and EK can predict PB of employees. Surprisingly, the results from CB-SEM analysis reject the direct impact of APB on PB. Moreover, this study also confirmed the positive influence of PB on EP of hotels. Notably, the research delves into the moderating effect of work overload (WO) on the associations between GHRM and PB, as well as APB and PB.Practical implicationsThe findings offer valuable recommendations for hotel managers and policymakers, aiming for effective GHRM practices and improvement of employees’ PB and EP of hotels.Originality/valueThis study broadens the understanding of sustainable management practices, specifically in human resource management in the hospitality industry. Notably, it identifies significant avenues to address theoretical gaps left by previous studies.

Exploring industry perspectives and preferences about calf handling and restraint methods used during spring processing of calves in western Canada

Abstract Calf processing events have important animal health, management, and sociocultural roles in the beef cattle industry. In western Canada, the three most commonly used methods for spring processing are roping and wrestling (RW), roping and Nord fork (NF), and tilt table (TT). The objective of this study was to understand the preferences and perceptions of handling event participants about calf handling and restraint methods commonly used during western Canadian beef calf processing events during the spring season. Data were collected using a mixed-methods online questionnaire. Quantitative analysis was used to describe the study participants and determine preference rankings. Qualitative, thematic analysis was used to explore participants’ perceptions about the common handling and restraint methods and to identify values within and across participants. The majority of participants were farm hands or staff members (92.8%), followed by owners (4.9%), family members (1.4%), friends (0.5%), and others (0.4%). The most preferred method to use was RW, and TT was the least preferred (χ2 = 3239.1, df = 6, p < 0.001). Participants shared values regarding calf safety and stress, processing efficiency, convenience, human safety, and labour intensity when explaining their preference to use calf handling and restraint methods for spring processing. Responses highlighted the need for understanding and skill in low stress handling and processing tasks in order for any of the methods to be effective. These values identify aspects to address when developing best practice recommendations for calf handling and restraint. Furthermore, focusing communication through the lens of these shared values will likely positively engage participants in extension efforts and community discussions.

Fundus camera-based precision monitoring of blood vitamin A level for Wagyu cattle using deep learning

In the wagyu industry worldwide, high-quality marbling beef is produced by promoting intramuscular fat deposition during cattle fattening stage through dietary vitamin A control. Thus, however, cattle become susceptible to either vitamin A deficiency or excess state, not only influencing cattle performance and beef quality, but also causing health problems. Researchers have been exploring eye photography monitoring methods for cattle blood vitamin A levels based on the relation between vitamin A and retina colour changes. But previous endeavours cannot realise real-time monitoring and their prediction accuracy still need improvement in a practical sense. This study developed a handheld camera system capable of capturing cattle fundus images and predicting vitamin A levels in real time using deep learning. 4000 fundus images from 50 Japanese Black cattle were used to train and test the prediction algorithms, and the model achieved an average 87%, 83%, and 80% accuracy for three levels of vitamin A deficiency classification (particularly 87% for severe level), demonstrating the effectiveness of camera system in vitamin A deficiency prediction, especially for screening and early warning. More importantly, a new method was exemplified to utilise visualisation heatmap for colour-related DNNs tasks, and it was found that chromatic features extracted from LRP heatmap highlighted-ROI could account for 70% accuracy for the prediction of vitamin A deficiency. This system can assist farmers in blood vitamin A level monitoring and related disease prevention, contributing to precision livestock management and animal well-being in wagyu industry.

Jute mills as restorative landscapes: perceptions of environmental aesthetics in living industrial heritage

ABSTRACT This study explores the potential of industrial heritage sites to contribute to a restorative landscape by analysing the perceptions of the industrial workers towards the environmental aesthetics components. The conglomeration of operational and abandoned jute mills forms a distinctive continuous industrial landscape along both banks of the Hooghly River in West Bengal. Apart from creating a historic industrial landscape, the elements within each of the heritage jute mills contain aesthetically significant built and natural elements. Urban regeneration proposals for these sites are receiving attention from the stakeholders, where the concept of restorative landscapes may be incorporated. The study employs an empirical approach, to assess the aesthetic preferences and perceived environmental quality of jute mill environments, by the first-hand users of the site. Data was collected from 273 workers of six heritage jute mills in West Bengal, India. A binomial logistic regression analysis was conducted on the seven identified components of environmental aesthetics. The results indicate the potential and challenges of industrial heritage sites in proposing restorative landscapes. The findings highlight the contribution of these industrial heritage sites to the regional landscape, demonstrating the potential to enhance not only local worker well-being but also the overall urban environment. By integrating workers’ views, this study contributes to a more inclusive approach to industrial heritage management, offering a framework for applying restorative landscape theory to large-scale industrial sites.

STL-DCSInformer-ETS: A Hybrid Model for Medium- and Long-Term Sales Forecasting of Fast-Moving Consumer Goods

Accurately forecasting sales for fast-moving consumer goods (FMCG) remains a significant challenge due to the volatile and multi-faceted nature of sales data. Existing methods often struggle to capture intricate patterns driven by seasonal trends, external factors, and consumer behavior, hindering effective inventory management and strategic decision-making. To overcome these challenges, we propose STL-DCSInformer-ETS, a hybrid model that integrates three complementary components: STL decomposition, an enhanced DCSInformer model, and the ETS model. The model uses monthly sales data from a FMCG company, with key features including sales volume, product prices, promotional activities, and regulatory factors such as holidays, geographical information, consumer behavior, product factors, etc. STL decomposition partitions time-series data into trend, seasonal, and residual components, reducing data complexity and enabling more targeted forecasting. The enhanced DCSInformer employs dilated causal convolution and a multi-scale feature extraction mechanism to capture long-term dependencies and short-term variations effectively. Meanwhile, the ETS model specializes in modeling seasonal patterns, further refining forecasting precision. To further improve predictive performance, the Random Forest-based Recursive Feature Elimination (RF-RFE) method is applied to optimize feature selection. RF-RFE identifies key predictive factors from multiple dimensions, such as time, geography, and economy, which significantly influence forecasting accuracy. Through numerical experiments, the method demonstrates excellent performance by achieving a 35.9% reduction in Mean Squared Error and a 21.4% decrease in Mean Absolute Percentage Error, significantly outperforming traditional methods. Furthermore, the model effectively captures both medium- and long-term sales trends while addressing short-term fluctuations, leading to more accurate forecasting and improved decision-making for fast-moving consumer goods. This research provides new theoretical insights into hybrid forecasting models and practical solutions for optimizing inventory management and strategic planning in the FMCG industry.

A Current Review and Global Benchmark on the Challenges and Prospect of Air Pollution Mitigation in Malaysia

The paper delves into the global air pollution issue, with a particular focus on Malaysia as a member of the ASEAN community. It underscores that air pollution is not merely a statistical concern but a daily reality impacting public health and the environment. Over time, there has been a significant increase in mortality and hospitalization rates attributed to air pollution. Human activities, including industry, transportation, and waste management, are identified as major contributors to air pollution. The paper discusses Malaysia's governmental efforts to address air pollution, encompassing air quality monitoring and control systems, environmental protection acts, promotion of alternative green energy sources, and carbon emission control measures. Despite the potential of renewable energy sources such as wind and hydropower, their limitations in fully replacing traditional energy sources are highlighted. Furthermore, the paper identifies methods to control urban air pollution by reviewing successful strategies implemented in other developed countries, such as advanced public transport systems and vehicle registration and age limitations. In conclusion, this paper provides valuable insights into the air pollution issue in Malaysia and offers suggestions for controlling and mitigating its impact, thus, contributing towards cleaner air and healthier environments.

Global perspectives on environmental policy innovations: Driving green tourism and consumer behavior in circular economy.

The tourism industry plays a pivotal role in both economic growth and environmental stewardship, making it essential to adopt practices that ensure long-term sustainability. This research aims at relating to the global concern of sustainable management in the tourism industry which is significant in eradicating the effects of environmental degradation as well as promoting economic development. The research problem focuses on exploring sustainable solutions for encouraging green tourism and implementing circular economy concepts. To address this, this study uses the Pythagorean Fuzzy Step-wise Weight Assessment Ratio Analysis (PF-SWARA) and Pythagorean Fuzzy Complex Proportional Assessment (PF-COPRAS) method to assess and rank the appropriate factors and strategies. From literature research and a consultation with the expert's panel, six factors and twenty-four significant factors have been uncovered. The PF-SWARA outcomes reveal that the most important factors are sustainable resource use (A2), ecosystem restoration (A4), and environmental regulations (A5), signifying that they play a critical role in the attainment of environmental sustainability. Subsequently, the PF-COPRAS analysis was conducted for seven strategic solutions to determine the most important strategy for furthering green tourism. Technological innovations promotion (S3) is the most crucial strategic approach for implementing circular economy concept in the tourism industry. While sustainable infrastructures (S5) and promoting green financing and investments (S2) are next key solutions for sustainable development of the tourism sector. The findings help policymakers and industry players to promote sustainable tourism in changing socio-environmental conditions.

Global perspectives on industrial wastewater management: A bibliometric analysis of research output

Global perspectives on industrial wastewater management: A bibliometric analysis of research output

Implementation of Circular Economy in Regional Public Service Agencies in Waste Management as a Waste Reduction Strategy in Indonesia

This study explores the implementation of the circular economy in waste management at UPTD Pelayanan Persampahan Kota Cimahi, which operates as a Regional Public Service Agency (BLUD) and functions as a quasi public entity. As a BLUD, UPTD has the flexibility to manage waste processes independently and efficiently, converting waste into Refuse Derived Fuel (RDF), which is then sold to PT Indocement as an alternative fuel in cement production. The research uses a qualitative case study approach, gathering data through in-depth interviews, observations, and documentation to examine the systematic stages of waste processing. Waste collected from various sources, such as offices, hospitals, markets, and residential areas, is processed into RDF. This circular economy cycle enables the cement produced with RDF to be reused by the community, including the original waste generating consumers. Findings reveal that this model not only reduces environmental impact by diverting waste from landfills but also generates economic value, highlighting the potential for sustainable partnerships between public waste management and private industry. The study suggests that optimizing BLUD’s quasi-public entity status and applying a circular economy model could enhance the effectiveness of responsible, sustainable waste management practices.

Machine learning-based automated waste sorting in the construction industry: A comparative competitiveness case study.

This article presents a comparative analysis of the circularity and cost-efficiency of two distinct construction material recycling processes: ML-based automated sorting (MLAS) and conventional sorting technologies. Empirical data was collected from two Finnish companies, providing a robust foundation for this comparison. Our study examines the operational specifics, economic implications, and environmental impacts of each method, highlighting the advantages and drawbacks. By leveraging data-driven insights, we aim to illustrate how MLAS can enhance recycling efficiency and sustainability compared to traditional methods. In our cost modeling over a seven-year period, MLAS achieved a cumulative cost of €12.76 million, significantly lower than CS, which incurred €21.47 million, underscoring the long-term cost efficiency of MLAS. The findings underscore the potential for advanced AI technologies to revolutionize waste management practices, offering significant improvements in sorting accuracy, material recovery rates, and overall cost-effectiveness. This analysis provides valuable perspectives for stakeholders in the construction and waste management industries, emphasizing the importance of integrating innovative technologies to achieve higher circularity and sustainability goals.

Innovations in Green Technologies: Analyzing their Contribution to Job Creation and Sustainable Economic Transitions

Transition into green technological innovation has become one of the ways that be more crucial for sustainable economic growth, job creation, and environmental responsibility. This study here seeks to outline ways through which adopting green technology influences employment generation and economic transformation through insights from companies in the sectors of renewable energy, electric vehicles, waste management, green manufacturing, and smart infrastructure. The study relied on primary data from 384 respondents in developing economies and performed quantitative analysis in order to identify the relationship of green investments to workforce expansion and industrial growth. Generally, implementation of green technology benefit the economy by a large margin through job creation and growth in economic activities. For firms that invest in green innovations, improved investment and increase in employment result with strengthened revenue growth and increased marketplace competitiveness. In addition, automobile and waste management industries have been identified as having the potential to offer high job creation, whereas green manufacturing is associated with problems such as automation and capital-intensive processes. Government incentive policies are also crucial in fast-tracking industrial transformation, hence the policy support and regulatory frameworks imperative. However, maintaining skill shortages and expensive cost of training at hiring remains to be the barrier to increasing the workforce in the green industry. The study emphasizes the pressing need for workforce development programs, R&D investment, and specific policy interventions directed at maximizing the overall benefits of adopting green technology. Tracing the best fit of the Green Growth Model with Schumpeter's Innovation Theory align its findings and insights that can channel practice into policy, business, and industry practice. It brings the conclusion of green technologies providing a key entry into an economic future, whereby success depends upon investment in human capital, favorable policies, and sector-specific strategies for growth. Long-term effects, cross-country comparisons, and trends in new market developments form areas of ongoing research in this area.

Enhanced Sustainability Assessment Framework for Plywood Manufacturing: A Multi-Method Approach Using Delphi Technique, BWM, and S-VSM

Sustainable manufacturing has emerged as a critical priority in addressing the complex environmental, social, and economic challenges of modern industry. This study focuses on the plywood sector, a significant contributor to manufacturing, which faces distinct sustainability issues such as high energy consumption, material inefficiencies, and hazardous working conditions. To address these challenges, the research introduces workload and noise level as critical indicators for assessing sustainability, broadening the scope of traditional evaluation methods. A multi-method framework was employed, integrating the Delphi technique to identify key sustainability indicators, the Best Worst Method (BWM) to assign weights to these indicators, and Sustainable Value Stream Mapping (S-VSM) paired with a Traffic Light System (TLS) to evaluate and visualize the Manufacturing Sustainability Score (MSS). Applied to a plywood manufacturing case study, the framework highlighted areas requiring improvement, particularly in worker well-being and operational safety, while demonstrating the industry's moderate overall efficiency. By offering actionable insights for improving resource use, operational processes, and employee conditions, this framework provides a practical tool for industry managers aiming to enhance sustainability. Furthermore, its adaptability makes it a valuable reference for other manufacturing sectors seeking to implement resource-efficient and sustainable practices. This research not only fills critical gaps in sustainability assessment but also contributes to advancing industry practices by emphasizing holistic and innovative approaches to manufacturing efficiency.

Advanced methanotrophic bioreactor design for efficient bioremediation of hydrogen sulfide (H₂S) And Volatile Organic Compounds (Vocs): Integrating genetic engineering and industrial scalability

The convergence of advanced microbial biotechnology and metabolic engineering has facilitated groundbreaking advancements in bioremediation. This study presents the engineered Methylomicrobium buryatense strain 5GB1C-RO1, optimized for the simultaneous removal of hydrogen sulfide (H₂S) and volatile organic compounds (VOCs) within a two-stage methanotrophic bioreactor system. Through precise CRISPR/Cas9-mediated genome editing, critical metabolic pathways for sulfide oxidation (SQR, FCCAB, SOXABXYZ) and VOC degradation (alkB, adhP, todC1C2BA) were integrated, achieving catalytic efficiencies exceeding 3.2 × 10⁷ M⁻¹s⁻¹ and substrate conversion rates above 450 nmol min⁻¹ mg⁻¹ protein. The strain demonstrates exceptional robustness under industrial conditions, maintaining 95% pollutant removal efficiency at H₂S concentrations up to 1000 ppm and VOC concentrations exceeding 500 ppm. The innovative bioreactor system incorporates enhanced gas-liquid mass transfer mechanisms, achieving mass transfer coefficients (kLa) exceeding 300 h⁻¹ and enabling stable operation for over 1000 continuous hours. Experimental results confirm the system's capacity for pollutant mineralization, generating methane-rich biogas (>95% CH₄) and high-protein microbial biomass (>85%), which are valuable for energy and agricultural applications. This integrated bioremediation approach not only reduces reliance on chemical scrubbing and flaring but also supports circular economy principles by transforming waste gases into renewable resources. The technology provides a scalable, sustainable, and cost-effective solution to mitigate industrial emissions while addressing environmental and regulatory challenges. The findings highlight the potential of combining advanced genetic engineering with innovative bioreactor design to redefine industrial pollutant management and resource recovery.

How Artificial Intelligence Can be Managed Cultural Industry

Artificial Intelligence (AI), a multifaceted and dynamic phenomenon, has significantly impacted various aspects of human life, particularly in the realm of business and technology. In the context of the cultural industry and International Human Resource Management (IHRM), AI has ushered in transformative changes that have redefined how organizations manage their creative and human capital across borders (Caldas, M. P., Tonelli, M. J., & Lacombe, B. M. B., 2011). This assessment delves into the intricate relationship between AI, the cultural industry, and IHRM, examining the challenges and opportunities that arise from this interplay. Also, the advent of AI has facilitated the seamless integration of advanced technologies into the cultural industry, creating a highly interconnected and innovative landscape. Consequently, organizations within this industry are compelled to adopt cut-ting-edge AI-driven practices to effectively manage a diverse range of cultural products and services (Arslan, A., Cooper, C., Khan, Z., Golgeci, I., & Ali, I., 2022). Artificial intelligence and human workers interaction at team level: a conceptual assessment of the challenges and potential HRM strategies. This analysis aims to critically evaluate the impact of AI on key cultural industry and IHRM functions, including recruitment and selection, training and development, performance management, and employee relations (Chew, J., 2004). By exploring the complexities and nuances of AI's influence on the cultural industry and IHRM, this assessment seeks to provide a comprehensive understanding of how AI trends shape industry and HR strategies and practices (Gordhan, P., 2007). Further-more, it will highlight the critical competencies required for HR professionals to navigate the challenges posed by AI, ultimately contributing to the success of cultural organizations in a rapidly evolving technological landscape. Finally, a particular focus will be placed on Greece as a case study to illustrate the broader impacts of AI on the cultural industry and IHRM (Stavroulakis, D., 2009). Greece, with its strategic geographical location and rich cultural heritage, presents a unique context for examining how global AI trends influence local cultural and HR practices.

Transfer Pricing and Permanent Establishment Implications under the New Italian Investment Management Exemption Regime

This article examines the new Italian rules on permanent establishments which, under certain conditions, constitute a safe harbour preventing foreign investment vehicles from being deemed to have a permanent establishment in Italy. More precisely, it will provide an overview of the asset management and private equity industries in Europe and in Italy, along with some comments on the new investment management exemption regime and transfer pricing methods that shall be applied in cross-border internal dealings according to the Italian Revenue Agency.

A Multidimensional Perspective of Chefs on the Role of Technology in Restaurants

ABSTRACT This research evaluates chefs’ perceptions of the role of technology in restaurant kitchens. It adopts a comprehensive approach that integrates the relevant factors identified in previous research to provide a multidimensional approach. Following a qualitative research design, semi-structured interviews were conducted with 27 restaurant chefs. The perceptions of chefs on the role of technology in restaurant kitchens consist of eight main themes, which include mostly positive aspects: food and service quality, human resources, financial, competitiveness, image, customer experience, food waste and job safety. This research contributes to a theoretical and practical understanding of the relationship between restaurant management and technology in service industries, specifically the restaurant industry, and offers a conceptual model of the role of technology in restaurant kitchens.

Factors Influencing Occupational Stress; with Special Reference to the Staff of the Public Sector Banks in Hambanthota District

The Sri Lankan banking sector after liberalization has operated in a relatively stable environment. The banking system has proven beneficial to both the world and Sri Lankan economies. This research investigates the influencing factors on occupational stress of staff of the public banks in the Hambantota District. The research was conducted through Physical environment, Workload, Role ambiguity and interpersonal Strain that mainly affect stress. This study adopted a quantitative research approach. Using survey questionnaire responses, a sample of 216 managerial and non-managerial employees in the Hambantota District selected using a convenience sampling technique. To test the hypothesized impact on variables, the multiple regression analysis was performed using the SPSS 23.0 version. According to the regression analysis, Workload and Role ambiguity have a significantly positive impact on occupational stress. Physical Environment significantly negatively impact occupational stress and interpersonal Strain do not impact occupational stress. The study's findings will be useful for industrial experts, Managers and Policymakers to identify Stress levels and improve strategies to reduce stress and Improve employee performance. Also, these research findings would develop new knowledge about suggestions for reducing Occupational Stress.

Digital transformation and business model innovation: the relevance of strategic orientations under varying conditions of competitive intensity

PurposeThe COVID-19 pandemic disrupted existing business models, forcing managers in the manufacturing industry to look for new strategies that could help their firms bounce forward. The situation calls for a rethink, redesign, and development of new business models (BMs) using digital capabilities. Drawing from the dynamic capability theory, this paper investigates how digital transformation (DT) influences business model innovation (BMI) through technology orientation (TO). The paper further examined the moderating role of competitive intensity (CI) in the DT-TO link.Design/methodology/approachThe model was tested using survey data from 208 senior managers in manufacturing firms in Ghana. SPSS 23 and structural equation modeling (SEM) were used for the analyses.FindingsThe results revealed that both DT and technology orientation directly influence all four dimensions of BMI. The result further showed that technology orientation indirectly mediates the relationship between DT and all four dimensions of BMI. The findings further showed that the DT-BMI link is amplified at varying levels of competitive intensity.Originality/valueAlthough recent research has highlighted the pertinence of embracing DT to foster innovation, to the best of the authors’ knowledge, this study among the first few attempts to shed light on the role of DT for sensing, seizing, and (re) configuring firms’ resources to renovate manufacturing business models to stay competitive. In addition, to date, to the best of the authors’ knowledge, no study exists that has examined the conditions and mechanism through which optimal BMI can be achieved through DT. The paper offer practical guidance to managers of manufacturing firms by developing an actionable framework to effectively leverage digital transformation for business model innovation through enhanced technology orientation, offering clear guidelines for assessing and aligning organizational capabilities with digital strategies.

ERM value for community banks: a natural experiment

PurposeWe explore the apparent value of ERM within the CB landscape in the absence of endogeneity concerns.Design/methodology/approachWe explore the observed market value of enterprise risk management (ERM) in a specific industry, community banks (CBs). To do this we employ standard event study methodology. We use the surprise failures of Silicon Valley Bank (SVB) and Signature Bank (SBNY) as a natural experiment to investigate this phenomenon.FindingsWe observe several consistent results. CBs with high institutional ownership and high insider ownership exhibited a negative relationship with abnormal returns. Univariate results indicate that there is a negative relationship between ERM and CB. However, multivariate results controlling for other known factors which impact returns indicate no relationship between ERM implementation and value for CBs. Finally, we find evidence the market considers CBs to have less risk of failure or exposure to regional banking contagion, as CARs are positive when using a regional bank index as the market model benchmark.Research limitations/implicationsThese results call into question the value of ERM for CBs.Originality/valueThese results call into question the value of ERM for CBs. This is the first paper to explore ERM value within CBs using a natural experiment approach.

Metaverse in industrial contexts - a comprehensive review

This paper explores the potential of Metaverse technology in industrial Asset Management (AM). By integrating AI and digital technologies, the Metaverse can enhance Human-System-Interaction (HSI) and optimise AM processes. However, implementing a Metaverse in industrial contexts faces challenges, particularly in visualising physical and virtual assets. This paper conducts a systematic review to address these challenges and identify potential solutions. The findings reveal that while the necessary technologies are available, their widespread adoption in industrial AM is limited. The paper presents a comprehensive overview of research themes related to Metaverse applications in industrial contexts, highlighting the evolving landscape and potential benefits. Ultimately, this research aims to contribute to the advancement of Metaverse technology in industrial AM by providing insights into its development, implementation, and challenges along with an Industrial Metaverse Framework. An example of applying the Metaverse concept in the railway sector has been presented and validated using railway digital assets available within the eMaintenance LAB. The practical implications of this work are expected to result in increased efficiency and effectiveness in the operation and maintenance procedures across various industrial sectors.