Manufacturing Industry Research Articles

“It takes a lot of sisu to get through it”- managerial experiences of facing adversities during pandemic

ABSTRACTPurposeSisu refers to extraordinary internal strength in the face of adversities. Although originally a Finnish concept, it can be a universally useful concept for studying managerial well-being and performance during difficult or even emergency periods. Previous research on sisu categorizes it into beneficial and harmful sisu and suggests that these two dimensions of sisu are useful when studying determination and resoluteness in crisis situations. This study applies the concept of sisu to examine managers’ well-being in times of crisis.MethodologyInterviews were conducted with 15 managers in the hotel, manufacturing and retail industries in Finland.ResultsThe beneficial effects of sisu seem to dissolve when it is used too often. The study also highlighted the paradoxical tension involved in the decision to stop or continue actions in a difficult situation and contributes to sisu research by discussing collective sisu.ConclusionThe results of this study suggest that beneficial sisu helps to overcome adversity, while harmful sisu causes distortions in thinking that are detrimental to managers. Understanding one’s own tendency towards beneficial or harmful sisu in difficult situations can help in managerial work. Collective sisu is an excellent resource for overcoming difficult situations in workplaces.

Colour sorting ROS-based robot evaluation under different lights and camera angles

Automated colour sorting, aided by mobile robots, is widely prevalent in the current manufacturing industry. Obstacles, such as fluctuating light conditions and camera angles, frequently hinder this procedure. Creating a colour sorting robot is a complex and time-consuming task, especially due to the vulnerability of the RGB colour space to detection errors in extreme brightness or darkness. In response to these concerns, we introduce a mobile robot that operates on the robot operating system (ROS) platform and incorporates OpenCV. This robot employs the hue, saturation, and value (HSV) colour space model for its image processing capabilities in recognising the colours and Welzl’s algorithm for the ball’s diameter estimation. The robot’s performance was assessed across various luminous fluxes and camera tilt angles. It demonstrated exceptional performance at 64 lm and a tilt angle of 40 degrees, achieving an average accuracy of 87.5% for detecting the colour of the ball, and 81.25% for determining its location based on colour. For the ball’s diameter estimation, it was found that the best estimation was received at 64 lm and 30 degrees, with both 96.32%.

A modified Johnson-Cook constitutive model of Inconel 690 weld overlay taking into account the strain rate softening effect

Precise material constitutive models are essential for finite element (FE) cutting simulation and analytical calculations of cutting mechanics. In this work, a split Hopkinson pressure bar (SHPB) equipment was employed to investigate the dynamic mechanical behavior of Inconel 690 weld overlay with a wide range of temperatures (20℃, 200℃, 400℃, 600℃, and 800℃) and strain rates (5000s-1, 7500s-1, 10,000s-1, and 12,500s-1). Based on the material flow characteristics at different loading conditions, a modified Johnson-Cook (JC) constitutive model was established, which takes into account the strain rate softening effect at high strain rates and the softening behavior induced by the competition between work hardening and dynamic recovery at large strains. Compared with the original JC constitutive model, the modified constitutive model can provide a closer correspondence between the predicted flow stresses and experimental data. The modified material constitutive model was introduced into FE simulation for orthogonal cutting of Inconel 690 weld overlay. The results indicate that the average prediction errors for the main cutting force and thrust force are 6.38% and 11.38%, respectively, validating the applicability and reliability of the modified JC constitutive model for cutting simulation applications. This study can provide a mechanical foundation for FE analysis in high-speed cutting of Inconel 690 weld overlay, which is conducive to optimizing cutting parameters, reducing machining costs, and enhancing production efficiency in practical industrial manufacturing.

IoT- enabled crop waste mulching machine for sustainable farming: perspective of circular economy

Abstract A large amount of crop waste is generated after crop harvesting. A substantial quantity of waste on the farm is burnt to clear the field, contributing to environmental harm and global warming. The study aims to develop a technological solution for crop waste management (CWM) by designing and developing an IoT-enabled crop waste mulching machine. This initiative addresses the environmental and health issues caused by burning crop waste on farms. The research involves designing and developing an IoT-enabled mulching machine that can be attached to the back side of a tractor. The machine consists of power-transmitting elements, supports, and mechanisms to distribute mulching material evenly onto the plant bed. It incorporates advanced IoT load cell sensors, soil moisture sensors, and proximity sensors to optimize the mulching process based on soil moisture content and to prevent machinery blockages. The IoT-enabled mulching machine effectively manages crop waste by utilizing it in mulch, thus preventing the harmful practice of burning waste. This machine ensures a more efficient, safer, and environmentally friendly approach to mulching, offering significant improvements over traditional manual mulching methods. The novelty of this research lies in integrating IoT technologies within mulching machine. The mulching machine uses sensors to automate and optimize the mulching process, representing a significant technological advancement over conventional methods. The study offers practical benefits by reducing labour requirements, minimizing the risk of injuries, and ensuring a more uniform mulch application. This technology facilitates more sustainable farming practices, aligning with global sustainability goals. The study can potentially revolutionize the agricultural equipment manufacturing industry (AEMI) by shifting the focus toward environmental sustainability. It promotes replacing plastic film mulching machines with more eco-friendly crop waste mulching solutions. This technology has the potential to reduce air pollution, carbon emissions, and mitigate climate. It also enhances soil health through better mulching practices.

Detection of Defects in Polyethylene and Polyamide Flat Panels Using Airborne Ultrasound-Traditional and Machine Learning Approach

This paper presents the use of noncontact ultrasound for the nondestructive detection of defects in two plastic plates made of polyamide (PA6) and polyethylene (PE). The aim of the study was to: (1) assess the presence of defects as well as their size, type, and orientation based on the amplitudes of Lamb ultrasonic waves measured in plates made of polyamide (PA6) and polyethylene (PE) due to their homogeneous internal structure, which mainly determined the selection of such model materials for testing; and (2) verify the possibilities of building automatic quality control and defect detection systems based on ML based on the results of the above-mentioned studies within the Industry 4.0/5.0 paradigm. Tests were conducted on plates with generated synthetic defects resembling defects found in real materials such as delamination and cracking at the edge of the plate and a crack (discontinuity) in the center of the plate. Defect sizes ranged from 1 mm to 15 mm. Probes at 30 kHz were used to excite Lamb waves in the slab material. This method is sensitive to the slightest changes in material integrity. A significant decrease in signal amplitude was observed, even for defects of a few millimeters in length. In addition to traditional methods, machine learning (ML) was used for the analysis, allowing an initial assessment of the method’s potential for building cyber-physical systems and digital twins. By training ML models on ultrasonic data, algorithms can distinguish subtle differences between signals reflected from normal and defective areas of the material. Defect types such as voids, cracks, or weak bonds often produce distinct acoustic signatures, which ML models can learn to recognize with high accuracy. Using techniques like feature extraction, ML can process these high-dimensional ultrasonic datasets, identifying patterns that human inspectors might overlook. Furthermore, ML models are adaptable, allowing the same trained algorithms to work on various material batches or panel types with minimal retraining. This combination of automation and precision significantly enhances the reliability and efficiency of quality control in industrial manufacturing settings. The achieved accuracy results, 0.9431 in classification and 0.9721 in prediction, are comparable to or better than the AI-based quality control results in other noninvasive methods of flat surface defect detection, and in the presented ultrasonic method, they are the first described in this way. This approach demonstrates the novelty and contribution of artificial intelligence (AI) methods and tools, significantly extending and automating existing applications of traditional methods. The susceptibility to augmentation by AI/ML may represent an important new property of traditional methods crucial to assessing their suitability for future Industry 4.0/5.0 applications.

Novel Insights into the Enrichment Pattern of Aroma and Taste in Cooked Marinated Meat Products of Black Pork via Typical Process Steps

This study aims to reveal the evolution mechanism of odour and taste active compounds in cooked marinated pork knuckles via typical process steps; among them, the brine soup stage was the most important part due to spices’ enriching flavours. These results revealed that the content and diversity of volatile compounds increased due to the addition of spices and heating temperature, imparting a unique aroma. Aldehydes played the main role in the overall odour. Benzaldehyde, hexanal, 1-octen-3-ol, levulinic acid, hydroxyacetone, ethyl octanoate, and 2-pentyl-furan were identified as the most important odour-active compounds. The key taste-active amino acids were glutamine, leucine, valine, and lysine. The IMP, AMP, and GMP provided a strong umami taste. Taste nucleotides and Val, Leu, Ile, and Phe were important precursor substances for aldehydes. The high responses of the electronic nose indicated that the gas component contained alkanes, alcohols, and aldehydes. The synergistic effects between umami-free amino acids and nucleotides correlated well with umami, as assessed by the electronic tongue. These results could be a starting point for the manufacturing industry, contributing to a better understanding of product performance.

The Lithuanian Soviet Nomenclature as an Object and Subject of Control

The topic of nomenclature in the context of research on the Soviet period is usually associated with the power and influence exerted by a single group in society. While assessing the evolution of the post-Stalin system, the role of a separate ruling stratum rather than that of an individual leader becomes more apparent. Many studies hesitate to call the post-Stalin system totalitarian and secretary-generals of the Communist Party as unilateral leaders. In individual studies, nomenclature, particularly top ranking, is clearly perceived as an acting and influencing subject, capable of subjecting system resources to its articulated aims. However, the discussion of the role of Lithuanian nomenclature as a certain peripheral elite and its relationship with the system shows that it is impossible to treat it only as an acting subject. The interests and management style of the central government determined greater or lesser control of the Soviet republics. So it is interesting to consider which situations would define the Lithuanian Soviet nomenclature itself as an object of control, and in which areas it could express its own interests and be activity. The Soviet institutional environment was saturated with control. Control measures were designed not only to control processes in society; they were also applied to the system of nomenclature itself by applying hierarchical control principles both to the governing political party and lower executive ranks. Information was collected about ideological compliance and participation in the execution of plans. Transgressions recognised to be ideological were assessed most strictly. The tools of control were diverse: in addition to control bodies (people’s control, GLAVLIT, KGB) Communist party structures and ministries had their own divisions responsible for control of the activities of a particular area and even the suitability of people. In addition to routine planned activity, individual influential officials carried out controlling functions and exposed ad hoc cases. It can be stated that the system was essentially saturated with control activities – the same area was controlled by at least 3-4 levels different levels of officials and this obviously stifled the dynamics of any activity. In the late Soviet period, locality as a factor of social environment can be clearly distinguished whereby local representatives of the nomenclature would often reduce the effect of control by replacing the object ‘what and how to control’ with the consensus of higher ranking officials of the nomenclature regarding the areas and leaders to be controlled and possible consequences of control (including penalties for members of the Communist Party). This model complemented institutional regulation and was applied not only to ensure control inside a republic but also in relation to the supervision carried out by the centre. The government of the republic frequently faced hostility from some leaders controlling the manufacturing industry, but except for one incident, it managed to ‘localise’ both local and arriving leaders, thereby creating a certain closed social network of members of the nomenclature of the republic and simultaneously opening more opportunities for higher ranking officials of Lithuanian nomenclature to express their personal interests, assess the situation subjectively, and act in the area of control as a subject rather than an object. The social network of members of the nomenclature of the republic rather actively and using various measures sought to isolate their own circle therefore ensuring not only the integrity of the circle, but also control of the space subordinate to it, while at the same time maintaining good relations with the centre. The top ranking officials of the republic were very keen to maintain the stability of this network and their primary goal was to preserve the routine of governing, given that this status quo was in line with Brezhnev’s governing style.

OIL PRICES-GROWTH NEXUS IN ALGERIA USING THE MEDAS MODEL

Objective: This Article aims to investigate the impact of oil price fluctuations on economic growth in Algeria using the Mixed Data Sampling (MIDAS) model during the period 1990-2022 Theoretical Framework: The theoretical aspect addressed the different time-frequency models by defining the Midas models and addressing the weighting functions used in estimating these models. Method: Estimating the relationship between a high-frequency variable (oil price) and a low-frequency variable (economic growth rate GDP) using the MIDAS model using Almon's PDL Weighting Function. Results and Discussion: The results of the study concluded economic growth in Algeria is directly affected by oil prices, The study also found that MIDAS models have a great predictive ability, allowing researchers to utilize all available information for high-frequency variables, instead of using annual average values that lose a lot of information. avoid converting low-frequency variables to match high-frequency variables, reducing econometric issues in model diagnostics. Research Implications: Research Implications: It is suggested that rentier countries such as Algeria work to diversify their economy away from oil, and follow up the process of economic diversification and accelerate it during periods of high fuel prices by working to develop non-strategic sectors as agriculture, tourism and manufacturing industries. Originality/Value: These results are expected to benefit policymakers in government, by urging them to invest in renewable energies as a crucial strategy for achieving sustainable development and a key factor in mitigating oil price shocks, particularly during recent recurring global crises. Future research directions include exploring existing gaps and promoting standardized studies.

Hybrid-DC: A Hybrid Framework Using ResNet-50 and Vision Transformer for Steel Surface Defect Classification in the Rolling Process

This study introduces Hybrid-DC, a hybrid deep-learning model integrating ResNet-50 and Vision Transformer (ViT) for high-accuracy steel surface defect classification. Hybrid-DC leverages ResNet-50 for efficient feature extraction at both low and high levels and utilizes ViT’s global context learning to enhance classification precision. A unique hybrid attention layer and an attention fusion mechanism enable Hybrid-DC to adapt to the complex, variable patterns typical of steel surface defects. Experimental evaluations demonstrate that Hybrid-DC achieves substantial accuracy improvements and significantly reduced loss compared to traditional models like MobileNetV2 and ResNet, with a validation accuracy reaching 0.9944. The results suggest that this model, characterized by rapid convergence and stable learning, can be applied for real-time quality control in steel manufacturing and other high-precision industries, enhancing automated defect detection efficiency.

The Synergy Between Industry 5.0 and Circular Economy for Sustainable Performance in the Chinese Manufacturing Industry

The industrial shift from Industry 4.0 to Industry 5.0 has transformed organizational thinking, moving the focus from purely technological implementation to a more human-centered approach. The current study has focused on the Industry 5.0 technological capabilities to bring into circular economy practices aligned with sustainable development goals, aiming to enhance sustainable performance. Moreover, the resource-based theory has grounded the development of the comprehensive framework on Industry 5.0 technological capabilities (artificial intelligence capabilities, big data analytical capabilities, Internet of Things capabilities, machine learning capabilities, and blockchain technology capabilities) and circular economy practices (eco-design, management system, and investment recovery) to achieve sustainable performance (environmental performance, social performance, and economic performance). Data have been collected from 179 respondents from the Chinese manufacturing industry. Additionally, data have been analyzed using the structural equation modeling technique. The results showed that Industry 5.0 technological capabilities directly affect sustainable performance. Moreover, circular economy practices played a dual, moderating, and mediating role between Industry 5.0 technological capabilities and sustainable performance. The current study has contributed to filling a gap in the literature on Industry 5.0 capabilities, especially in the circular economy and sustainable performance perspective. The practical contribution recommended is that if organizations focused on their Industry 5.0 technological capabilities, it would boost circular economy practices and sustainable performance to achieve sustainable development goals.

EXAMINING THE IMPACT OF AGE ON ORGANIZATIONAL IDENTIFICATION AND TURNOVER INTENTION: A MULTIGROUP ANALYSIS

Employment in the manufacturing industry is critical, especially in developing countries, where job turnover significantly impacts employment sustainability. While numerous studies have explored antecedents of turnover, this study focuses on the impact of organizational identification—how strongly employees align with their organization's values and goals—on turnover intentions. We specifically examine the moderating role of age in this relationship. Data were collected from 291 manufacturing employees in Adana province, Türkiye. Our findings reveal that organizational identification significantly influences turnover intentions, with age moderating this effect. Using multigroup analysis, we provide detailed insights into these variations. These results offer a clearer understanding of age-related differences in organizational identification and turnover intentions. The insights may also assist decision-makers, policymakers, and human resource managers in developing targeted strategies that consider age-related differences to effectively manage and reduce employee turnover.

Barriers Hindering the Successful Deployment of GLSS in Organizations: Findings from an Empirical Study

Given the growing interest in manufacturing’s environmental effects, understanding the impact of improvement strategies on sustainability is critical. This study focuses on the implementation of Green Lean Six Sigma (GLSS) in the Mexican manufacturing sector, an area where research is limited, particularly concerning the barriers to successful outcomes. Through the development of a structural equation model using SPSS software,,version 25, 64-bit edition, key barriers such as lack of investment, insufficient participation, ineffective infrastructure, and lack of training were identified as the primary challenges to GLSS implementation. This study also highlights the interrelationships between these barriers and their influence on project success, emphasizing the role of insufficient participation as a critical mediating factor. This insight allows for a deeper understanding of how these obstacles affect the benefits of GLSS initiatives. The findings offer valuable guidance for researchers and practitioners, providing tools to enhance the effectiveness of GLSS implementation in the manufacturing industry. The study’s marginal contribution lies in its identification of key barriers and the introduction of insufficient participation as a mediating factor, offering a more comprehensive understanding of their impact on project success and contributing to improved GLSS adoption strategies.

The Development of MSMEs in India: a Bibliometric Analysis

This paper aims to evaluate the structure and evolutionary trend of research on development in MSMEs in India using a bibliometric examination. An analysis of academic research from 2008 to 2022 was conducted using bibliometric methods, searching for the terms used in emergent technologies. The research is intended to assess the development of micro, small, and medium companies and understand how far this topic has been achieved. Several research methods were applied, including the network method, thematic maps, and coefficients of approaching or intellectual tools. The result of this work can be helpful for new scholars and current trends, based on the study trends to achieve sustainable development goals against poverty, micro MSMEs (Micro Small Medium Enterprises), and unemployment. The highlight of the report stresses on the key issues where researchers are giving more focus which includes innovation and supply chain strategies. The findings from the study serve to enhance our understanding about areas within the extant research literature where less is known, particularly when it comes to advances and barriers that impact Micro, small-and medium enterprises (MSMEs). The present study will imbibe researchers and the experts from industry to have a profound insight into intellectual domains surrounding certain technologies that can facilitate growth of MSMEs in India, as well provide above detailed unattended research scope. The research concerning MSMEs has undoubtedly provided many facts about the growth and development of these enterprises, applicable particularly in India. To describe the research and intellectual progress on the Manufacturing industry, a bibliometric approach was used in this study. A comprehensive literature review and meta-analysis were employed to get trustworthy results.

Effects of regional network economies on industrial productivity in Japan: dynamic total factor productivity function approach

AbstractImproving productivity performance is essential for achieving regional economic sustainability. With the advent of the network society, regional economic agents have benefited not only from local within-region interactions but also from external out-of-region economies. However, it remains unclear whether localized external economies or spillovers of externalities through inter-regional networks are more significant in improving productivity performance. Therefore, this study examined the relationships between productivity performance and geographical externalities from an industry perspective using regional economic data for Japan and employed a dynamic total factor productivity function approach. It also assessed the impact of high-quality transportation infrastructure improvements on productivity catch-up. The results revealed the long-run effects of inter-regional networks on the productivity performance of industries. Furthermore, it revealed that, as a geographical externality, the “borrowed size” effect contributed significantly to productivity improvements in both manufacturing and non-manufacturing industries. These findings suggest that enhancing both high-quality transportation infrastructure and inter-regional transportation networks is likely to improve industry productivity performance.

Leader Emotional Capital Matters in The Automotive Manufacturing Industry

The ability of an organisation to adapt to constant change determines its long-term viability. Organisations that understand the value of excellent leadership in organisational performance will see growth and sustainable profitability. This research focused on the automotive industry in Isipingo, Durban, KwaZulu-Natal, South Africa. To thrive and remain competitive, the automotive sector must adapt to changing conditions, be efficient, and respond rapidly to external forces. Managers are frequently challenged with balancing the frustrations of different interest groups within the sector; becoming skilled and successful leaders; and steering the organisation in the appropriate direction. The emotional competency of employees is a valuable resource designated as emotional capital. The purpose of this study was to investigate whether the emotions of leadership influence employee performance in this Industry Therefore, the research question entailed, what is the role of leaders' emotions? Analysis of extant literature was related to aspects that impact leaders’ emotional capability. The main variables included emotional capital, leadership, emotions, and employee performance. This study was based on a quantitative approach. Primary data was collected through a survey which was analysed using Spearman’s rho correlation. The results highlighted how a leader's emotions affect employees and demonstrated a substantial association between employee performance and the leader's overall emotional state. It was determined that a leader's emotions can have both beneficial and negative effects on employee performance. The recommendations offered useful advice on how to choose tactics that would yield appropriate results for the organisation to be sustainable.

Analysis of Industrial Performance Indicators Effects On Enhance The Competitiveness of The Food Sector and The Possibility Of Support in The Iraqi Economy (2003-2020)

This study was aimed on analyze the competitiveness of the food industry sector, and showed the level of industrial and competitive performance of these industries, and the possibility of supporting and enhancing their competitiveness in the Iraqi economy for the period 2003-2020. Competitiveness is considered one of the most important indicators of the efficiency of the food industry tool in local and foreign markets, as competitiveness represents the ability of productive sectors of different types to distribute their products and increase their sales level. Therefore, the food industry in Iraq is considered one of the most important sectors of the manufacturing industries that suffer from problems and obstacles including a decrease in the level of competitive and industrial performance. It is believed that the problem of the research is the weak competitiveness of the food industry in Iraq due to the decline in the quality of manufactured products and the weak level of competitiveness of these products compared to imported products. The results of the study showed a decrease and fluctuation in the contribution of the manufacturing sector to the gross domestic product (GDP) in Iraq. The economic policy was not providing the possibility of an investment climate to support and enhance the private sector in industrial investment. The indicators of production requirements such as labor productivity, worker productivity, and the value added index of the food industries sector began to fluctuate between decrease and increase, and to measure the extent of the reciprocal effects between all parts of the standard model and the extent of its contribution to developing the competitive capacity of this sector.

Validation of computer vision-based ergonomic risk assessment tools for real manufacturing environments.

This study contributes to understanding semi-automated ergonomic risk assessments in industrial manufacturing environments, proposing a practical tool for enhancing worker safety and operational efficiency. In the Industry 5.0 era, the human-centric approach in manufacturing is crucial, especially considering the aging workforce and the dynamic nature of the entire modern industrial sector, today integrating digital technology, automation, and sustainable practices to enhance productivity and environmental responsibility. This approach aims to adapt work conditions to individual capabilities, addressing the high incidence of work-related musculoskeletal disorders (MSDs). The traditional, subjective methods of ergonomic assessment are inadequate for dynamic settings, highlighting the need for affordable, automatic tools for continuous monitoring of workers' postures to evaluate ergonomic risks effectively during tasks. To enable this perspective, 2D RGB Motion Capture (MoCap) systems based on computer vision currently seem the technologies of choice, given their low intrusiveness, cost, and implementation effort. However, the reliability and applicability of these systems in the dynamic and varied manufacturing environment remain uncertain. This research benchmarks various literature proposed MoCap tools and examines the viability of MoCap systems for ergonomic risk assessments in Industry 5.0 by exploiting one of the benchmarked semi-automated, low-cost and non-intrusive 2D RGB MoCap system, capable of continuously monitoring and analysing workers' postures. By conducting experiments across varied manufacturing environments, this research evaluates the system's effectiveness in assessing ergonomic risks and its adaptability to different production lines. Results reveal that the accuracy of risk assessments varies by specific environmental conditions and workstation setups. Although these systems are not yet optimized for expert-level risk certification, they offer significant potential for enhancing workplace safety and efficiency by providing continuous posture monitoring. Future improvements could explore advanced computational techniques like machine learning to refine ergonomic assessments further.

Fractals as Pre-Training Datasets for Anomaly Detection and Localization

Anomaly detection is crucial in large-scale industrial manufacturing as it helps to detect and localize defective parts. Pre-training feature extractors on large-scale datasets is a popular approach for this task. Stringent data security, privacy regulations, high costs, and long acquisition time hinder the development of large-scale datasets for training and benchmarking. Despite recent work focusing primarily on the development of new anomaly detection methods based on such extractors, not much attention has been paid to the importance of the data used for pre-training. This study compares representative models pre-trained with fractal images against those pre-trained with ImageNet, without subsequent task-specific fine-tuning. We evaluated the performance of eleven state-of-the-art methods on MVTecAD, MVTec LOCO AD, and VisA, well-known benchmark datasets inspired by real-world industrial inspection scenarios. Further, we propose a novel method to create a dataset by combining the dynamically generated fractal images creating a “Multi-Formula” dataset. Even though pre-training with ImageNet leads to better results, fractals can achieve close performance to ImageNet under proper parametrization. This opens up the possibility for a new research direction where feature extractors could be trained on synthetically generated abstract datasets mitigating the ever-increasing demand for data in machine learning while circumventing privacy and security concerns.

Can the Synergy of Digitalization and Greening Boost Manufacturing Industry Chain Resilience? Evidence from China’s Provincial Panel Data

The synergy of digitalization and greening (DGS) enhances the resilience of the industrial chain (ICR), a critical aspect for the sustainable growth of the national economy. This symbiotic relationship not only facilitates the enhancement of quality and efficiency within the manufacturing sector but also propels forward sustainable economic development. Therefore, how DGS can improve ICR deserves in-depth study. Drawing on the panel data encompassing manufacturing industries across 30 provincial-level administrative regions in China from 2013 to 2022, this study employs a regression model to delve into the mechanisms through which DGS within manufacturing industries impacts ICR. The main results are as follows: (1) DGS in manufacturing makes a positive contribution to ICR; (2) The impact of DGS on ICR varies across regions, with a diminishing effect from the eastern to the western region. (3) Technological innovation reinforces the influence of DGS on ICR. (4) Spatial measures show that the development of DGS in one location has a negative spatial spillover effect on ICR in neighboring areas. Based on the above conclusions, this paper puts forward a series of recommendations aimed at promoting industrial chain resilience.

When Guests Become Hosts: The Boomerang Effect of Tourists’ Negative Behaviors on Their Country of Origin

Tourists are commonly regarded as ambassadors shaping their country’s image in international tourism. However, the role of the boomerang effect for outbound tourists on their country of origin is often overlooked in theory and practice. Through four studies, this groundbreaking research explores the boomerang effect of tourists’ negative behaviors on their home countries, encompassing the impact on tourism imports and subsequent spillover effects on manufacturing product exports. The findings reveal that tourists’ negative behaviors not only diminish the willingness of destination residents to visit the tourist-originating country by influencing perceived warmth and psychological distance but also decrease their intention to purchase manufactured products from the country of origin by amplifying psychological distance. Importantly, the moderating effect of product added value in spillover effects is highlighted. These conclusions expand the influence of tourists’ negative behaviors and innovatively bridge theoretical perspectives between the tourism and manufacturing industries on the reverse country-of-origin effects.