A mixed-method study on the barriers of industry 4.0 adoption in the Indonesian SMEs manufacturing supply chains

PurposeThis paper aims to investigate the adoption barriers of Industry 4.0 in the Indonesian manufacturing supply chains.Design/methodology/approachThe mixed method was deployed to validate the findings. First, the qualitative study was conducted based on the interviews. Then, the companies were approached using filter questions on the involvement in adopting industry 4.0 and its impact on the supply chain.FindingsBased on the qualitative study, nine main barriers were found in the thematic analysis. Thus, to get a consensus on the barriers in the industry, the barrier indicators were tested using a structural equation model retrieved from 173 small and medium Indonesian manufacturing firms. Results indicate that five main barriers (e.g. unclear Industry 4.0 policy, higher-risk investment, insecure data sharing, lack of expertise and lack of incentive) are confirmed as the adoption barriers.Practical implicationsThe successful adoption of supply chain integration with Industry 4.0 technology can strengthen the manufacturing sector and competitiveness. Therefore, this study can be a complimentary assessment to evaluate the Indonesia Industry 4.0 Readiness Index (INDI 4.0) and the effectiveness of the government support program.Originality/valueThe results can be used as the framework to foresee the successful implementation of smart manufacturing supply chain management and its integration. Therefore, the authors proposed the framework to foresee the successful implementation of smart manufacturing, supply chain management and integration.

Shocks, resilience and regional industry policy: Brexit and the automotive sector in two Midlands regions

This article draws upon novel survey evidence to examine the possible regional impacts of Brexit as a ‘disruptive process' to manufacturing operations and logistics in the automotive industry, in the context of the regional resilience literature. The current Brexit (and Covid-19) context, along with the sector's need to re-orientate towards electrification, provides renewed urgency to reconsider industrial policy in spatial terms. The findings have salience not only in the context of anticipating and reacting to Brexit-induced economic shocks at a regional level, but also over the role of decentralized regional bodies. In this regard, the UK government's agenda of ‘levelling up' will be challenging, especially in the context of the place-based shocks likely to arise from Brexit as well as the impact of Covid-19. The article concludes that a more place-based regional industrial policy is required both to anticipate and to respond to shocks and also to reposition the sector in the region going forward.

VENDOR PERSPECTIVES

The people who work at companies that manufacture chromatography instruments and consumables are often well positioned to be aware of developments, needs, and trends that not everyone else sees, because they serve customers in a range of areas of focus and with diverse demands-such as academic researchers investigating fundamental aspects of separation science techniques, industrial analysts solving problems that they may not be allowed to talk about at conferences, or chemists working in government laboratories in areas like environmental research. Inaccurate data can be generated from variable, glass vial surface chemistries, which can lead to investigations or flawed decisions can be made from these results. The Reduced Surface Activity (RSA(tm)) Glass Technology was developed to address these issues, and to provide chemists with sample containers for LC-MS, MS, HPLC, GC, and CE that deliver reliable, consistent results by not adsorbing basic analytes, or adding metals to, or changing the pH of the diluent. The stringent RSA manufacturing processes are continued through to final contaminate-free packaging and quality control, where they are tested for adsorption, metal content, and residual materials.

Institutional Differences and the Choice of Outward Foreign Direct Investment Mode under the "Belt and Road” Initiative: Experience Analysis Based on China's Manufacturing Enterprises

Based on 1692 outward foreign direct investment (OFDI) events of 735 A-share listed companies in China's manufacturing industry from 2010 to 2019, this paper empirically examines the effect of investment motivation and the impact of institutional differences between China and the host country on the choice of OFDI entry mode;the paper also investigates the moderating effect of the "Belt and Road” Initiative (BRI) on Chinese manufacturing enterprises (CMEs) through use of the logit model. The empirical results show that, with greater institutional differences, CMEs become more inclined to choose cross-border mergers and acquisitions (M&A). Furthermore, a positive moderating effect of resource-seeking motivation on the choice of M&A OFDI by CMEs is observed. The signing of the "Belt and Road” cooperation document positively moderates institutional differences in promoting CMEs—especially state-owned CMEs—to choose the M&A mode. The "Belt and Road” Initiative provides an efficient supply system for OFDI by CMEs. This study enriches and extends existing institutional theories and provides suggestions for the promotion of the geopolitical pattern and international cooperation regarding the "Belt and Road” Initiative.

Awareness and Practices Regarding 'Long COVID' among Employees of Selected Smart Phone Manufacturing Industries in South India

Introduction: Long COVID is a term coined for long term post COVID-19 disease complications. Touted as the 'pandemic after the pandemic' it has significant implications for employment especially on productivity and quality of worker output. Objective(s): 1. To assess the baseline knowledge among employees working in selected smart phone manufacturing companies regarding COVID-19 disease, COVID vaccination and long COVID complications. 2. To assess the prevalence of long COVID complications among the study subjects. Methodology: We followed a quantitative cross-sectional study design between May-Jun 2022 in 6 factories across South India. A semi-structured, face-validated interview schedule was administered to the employees via Google Forms. Data was analyzed using SPSS v.21. Result(s): A total of 118 employees were included in the study. Most employees were male (89.2%), between 25-30 years of age (46.3%) and had completed their Bachelor's degree (71.29%). Most had at least 1-5 years of current work experience (80.5%). Almost 55.1% of the employees had suffered from COVID-19 in the past of which 33.8% had been hospitalised. Only 42.8% of employees knew about long COVID complications and 33.1% knew of only one symptom. None of the employees had taken the booster dose of the vaccine despite 67.8% knowing that the vaccine protected against severe disease. Almost 75% of employees reported to suffer from one or more post COVID complications. Long standing fatigue (16.9%), cough and breathing difficulty (6.1%) were the most common complaints. Conclusion and recommendations: Low awareness regarding long COVID will impact health seeking behavior and increase presenteeism at the workplace. Increasing awareness regarding COVID-19 disease, vaccinations and the post COVID complications through training programmes and health education sessions will bridge the key knowledge gaps identified. Promotion of booster dose vaccination against COVID-19 for all employees will help in reducing the burden of long COVID at the workplace.

Validity and reliability of sustainable supply chain management frameworks in Indian smart manufacturing industries

PurposeSustainable supply chain management (SSCM) ensures integration of socially, environmentally and economically feasible practices in entire supply chain. SSCM principles can be implemented to improve efficiency and productivity of a system by different attributes of the system. The purpose of this article is to identify the most appropriate existing (SSCM) framework that can be implemented suitably in Indian smart manufacturing industries.Design/methodology/approachValidity and reliability analysis on the existing SSCM frameworks was carried out with the help of empirical data collected using questionnaire survey methodology from various Indian smart manufacturing organizations. The empirical data were gathered from various experts from top- and middle-level management in different smart manufacturing organizations across the country. Further, factor analysis was carried on the collected data to estimate the unidimensionality of each SSCM frameworks. Cronbach's alpha value was used to assess reliability of each framework. Subsequently, the frequency distribution analysis was done to obtain familiar elements in the segregated frameworks based on validity and reliability analysis.FindingsThe work observed that only five SSCM frameworks have shown unidimensionality in terms of the elements or constructs. The work further found that these segregated frameworks have not shown sufficiently high level of reliability. Additionally, this work attempted frequency distribution analysis and observed that there were very few elements which were being repeatedly used in numerous frameworks proposed by researchers. Based on the findings of this work, the work concluded that there is acute need of a new SSCM framework for Indian smart manufacturing industries.Research limitations/implicationsThis study gathered empirical data from 388 Indian smart manufacturing organizations. Thus, before generalizing the findings of the study across the sectors, there is a possibility of some more explication.Originality/valueThe main purpose of this article is to explore the feasibility of the existing SSCM frameworks in Indian smart manufacturing sector. The study also assumes that the manufacturing managers and executives may have the complete understanding on the existing sustainable manufacturing frameworks and a chance to executing proper suitable framework in the respective manufacturing organization.

Preface

The 2nd Agrifood System International Conference (ASIC)Professor Jurnalis Kamil Convention Hall, Padang, West Sumatra, Indonesia, 8-9 November 2022"Research advancement and innovations in agroecology and smart agrifood systems.”The 2nd Agrifood System International Conference (ASIC 2022) was successfully held on 8-9 November 2022. Due to the covid-19 pandemic, this event was held virtually via the zoom platform, directly from Professor Jurnalis Kamil Convention Hall, Padang, West Sumatra, Indonesia. This event was organized by the Faculty of Agriculture, Universitas Andalas, Indonesia, and became a part of the event to commemorate the 68th anniversary of the faculty. The theme of the ASIC 2022 was: "Research advancement and innovations in agroecology and smart agrifood systems.”There have been numerous revolutions in agriculture, which have improved competency and led to record-breaking yields and gains. The latest process is "smart farming,” contributing to humanity's survival and future prosperity. Smart farming presents numerous prospects for pervasive interconnection and database computer technology as part of Industry 4.0. Smart farming is the idea of agricultural practice in a creative manner while utilizing cutting-edge technology to improve the quantity and quality of agricultural goods. New methods to assure global food safety are part of the future of the food manufacturing industry. It enables farmers to boost yields more effectively and efficiently. Fertilizers, labor, seeds, and water are just a few resources that can be saved. Smart farming has supporting applications, including land management, selection of varieties, minimizing synthetic fertilizers and pesticide inputs, and replacing them with environmentally friendly inputs. Research and related technological innovations have been carried out but have yet to be adopted and properly integrated.The main objective of this conference was to provide a venue for exchanging knowledge, scientific advancement, and innovative ideas among researchers, academicians, governments, and organizations. The scope includes plant breeding and crop production, soil management, plant protection and food safety, the socio-economic of agriculture and natural resources, and all topics related to agriculture. The committee received more than two hundred paper s coming from 46 institutions, national and international. We encourage student presenters from undergraduate to doctoral programs to present their papers;hence, around 25% of s come from them.The conference program was divided into two main segments: plenary and parallel. The plenary session invited 13 speakers from within and outside the country and was attended by 610 participants during the two days' activities. On behalf of the committee, we greatly appreciate the seven speakers contributing and sharing their knowledge at this event: Dr. Silvain R Perret, Scientific Director of CIRAD, France;Mr. Pierre Ferrand from FAO, Regional Office for Asia and the Pacific;Prof. Norman Uphoff, SRI Scientist from Cornell University, USA;Dr. Jauhar Ali, Rice hybrid breeder from IRRI, Philippines;Dr. Trevor A. Jackson, Plant protection scientist from IAPPS/ Coordinator Region XII;Prof. Shamshuddin Jusop, Soil Science Scientist from UPM, Malaysia;and Dr. Wahono: Drone creator from UMM, Indonesia. We also introduced five invited speakers from the Faculty of Agriculture: Dr. Irawati Chaniago - Crop Production;Dr. Dini Hervani - Plant Breeding;Dr. Eka Candra Lina - Plant Protection;Dr. Yuerlita - Socio-economics of Agriculture;Dr. Hery Bachrizal Tanjung - Agricultural Extension. In addition, we have provided an online workshop conducted as a side event on successfully publishing an article in IOP-EES Proceeding.Finally, let me express my sincere gratitude to all presenters, participants, and committee members who contributed significantly to this event's success. Special thanks go to the Rector of Universitas Andalas and the head of the research institute and community service of Universi as Andalas for all the support during the event. We hope to deliver the 3rd ASIC in 2024.Warmest regards,Dr. My SyahrawatiChairperson of the Organizing CommitteeList of Documentations, Conference Committee, Conference Schedule, Parallel Schedule, List of Presenters are available in this Pdf.

A Review on the Recycling Technologies of Fibre-Reinforced Plastic (FRP) Materials Used in Industrial Fields

Fibre-reinforced plastic (FRP) materials are attracting growing interest because of their high specific mechanical properties. These characteristics, in addition to a high level of tailorability and design of freedom, make them attractive for marine, aerospace, automotive, sports and energy applications. However, the large use of this class of material dramatically increases the amount of waste that derives from end-of-life products and offcuts generated during the manufacturing processes. In this context, especially when thermosetting matrices are considered, the need to deeply study the recycling process of FRPs is an open topic both in academic and industrial research. This review aims to present the current state of the art of the most affirmed recycling technologies used for polymeric composites commonly used in industrial applications, such as carbon and glass FRPs. Each recycling method (i.e., chemical, thermal and mechanical) was analysed in terms of technological solutions and process parameters required for matrix dissolution and fibre recovery, showing their advantages, drawbacks, applications and properties of the recycled composites. Therefore, the aim of this review is to offer an extensive overview of the recycling process of polymeric composite materials, which is useful to academic and industrial researchers that work on this topic.

Administrative concerns in healthcare during COVID 19 pandemic in cement industry in India

IntroductionPurpose of this research is to describe the general administrative concerns and specific financial concerns in Occupational Health Centre in Cement manufacturing Industry in India during COVID19 and to compare the Administrative concerns in Healthcare in cement Industry.MethodsA descriptive qualitative study with ethnography approach to understand and determine administrative concerns faced by healthcare professionals in cement Industry was conducted using a motivational interview technique by Miller and Rollnick.ResultsIt was observed that financial concerns of purchase of new equipment, liaison and budget approval for health activities from senior management, promotions or designation were at par with other technical counterparts of the industry, minimizing the difference and managing the hospital budget and Non-financial concerns of staffing and manpower, Infrastructure development, Health surveillance and awareness, School health and CSR, Motivation of paramedical staff regarding promotion, Security on social and personal perspective, Optimum usage of available resources, Fitness of employees' vs sickness absenteeism – all had increased during the COVID19 pandemic.ConclusionsEmployee expectations were very high with demand for privileged services. Appropriate usage of services for smooth administration and prevention of wastage, strict waste control measures, 100% legal compliance, liaison with external hospitals including local government for appropriate help to be provided for the management within the legal boundaries, proactive health awareness programs on a time-weighted scale with result oriented mindset for the benefit of employees. Similar health awareness programs for dependents and society with assessment of exposure risk under CSR can be achieved by collecting the required data, analysing employee's health status outcomes, setting of goals, awareness of roles and responsibilities, rewards and recognition, proactive approach towards Preventive medicine, collaboration with other organizations or higher centres, setting of clear guidelines, working with HRD, Focus on Telehealth and Virtual Healthcare training.

Few-Shot Semantic Relation Prediction Across Heterogeneous Graphs

Semantic relation prediction aims to mine the implicit relationships between objects in heterogeneous graphs, which consist of different types of objects and different types of links. In real-world scenarios, new semantic relations constantly emerge and they typically appear with only a few labeled data. Since a variety of semantic relations exist in multiple heterogeneous graphs, the transferable knowledge can be mined from some existing semantic relations to help predict the new semantic relations with few labeled data. This inspires a novel problem of few-shot semantic relation prediction across heterogeneous graphs. However, the existing methods cannot solve this problem because they not only require a large number of labeled samples as input, but also focus on a single graph with a fixed heterogeneity. Targeting this novel and challenging problem, in this paper, we propose a Meta-learning based Graph neural network for Semantic relation prediction, named MetaGS. Firstly, MetaGS decomposes the graph structure between objects into multiple normalized subgraphs, then adopts a two-view graph neural network to capture local heterogeneous information and global structure information of these subgraphs. Secondly, MetaGS aggregates the information of these subgraphs with a hyper-prototypical network, which can learn from existing semantic relations and adapt to new semantic relations. Thirdly, using the well-initialized two-view graph neural network and hyper-prototypical network, MetaGS can effectively learn new semantic relations from different graphs while overcoming the limitation of few labeled data. Extensive experiments on three real-world datasets have demonstrated the superior performance of MetaGS over the state-of-the-art methods. IEEE

The Achilles heel of the U.S. food industries: Exposure to labor and upstream industries in the supply chain

The modern‐day food industries are part of a complex agri‐food supply chain, where food production has become efficient yet potentially vulnerable to supply chain risks. The COVID‐19 pandemic is a testament to that end. This article measures and identifies the U.S. food manufacturing industries' vulnerability to upstream industries and labor occupations by (a) calculating a food industry's diversification of intermediate input purchases across upstream industries, (b) quantifying the relative exposure of food manufacturing in a given industry and location to upstream input suppliers and labor occupations, and (c) estimating each food industry's gross output elasticity of inputs. This article also explores geographic heterogeneity in food industries' vulnerability. Among our results, we find evidence that the animal processing industry's output is relatively vulnerable to production labor, consistent with the observed disruptions to the meatpacking sector during COVID‐19, which were largely caused by labor issues. Our results may help academics and practitioners to understand food industries' vulnerabilities to upstream industries and labor occupations.

Spatial Concentration of Health Industry under COVID-19.

The COVID-19 pandemic has impacted the industry immensely and, in some cases, irreversibly. This research pioneers in studying how the pandemic have influenced the survival and spatial distribution of the health-related manufacturing industry (HRMI) in Taiwan. Eight categories of HRMI are examined, with their change in survival performances and spatial concentration between 2018 and 2020. Average Nearest Neighbour and Local Indicators of Spatial Association are conducted, to visualise the distribution of industrial clusters. We found the pandemic did not shock the HRMI in Taiwan, but actually induced its growth and spatial concentration to a certain extent. Additionally, due to it being a knowledge-intensive industry, the HRMI mainly concentrate in metropolitan areas with which universities and science parks may have largely supported. However, the spatial concentration and cluster scope growth do not necessarily accompany the improvement of spatial survival, which may be resulted from the different life cycle stages an industry category is in. This research fills in the gap of medical studies with literatures and data from the field of spatial studies. It provides interdisciplinary insights under the condition of pandemic.

Employee associations with R&D investment, firm performance, disruption risk, and supply chain performance during the COVID-19 pandemic: A multiple mediational model

Purpose: Scholars have concentrated their efforts on COVID-19's impact on industries worldwide in order to manage timely supply chain disruptions. Epidemic outbursts are a unique supply chain risk that is distinguished by prolonged disruption propagation, disruption existence, and high uncertainty. The purpose of this study was to investigate the role of R&D investment and firm performance in mediating the relationship between disruption risk and supply chain performance in Pakistani manufacturing industries and supply chain employees during the recovery phase of the COVID-19 pandemic via application of dynamic capability theory. Methodology: From July 21 to August 23, 2020, three hundred and eighteen employees from supply chains of manufacturing industries in Rawalpindi and Islamabad, Pakistan, participated in this cross-sectional online web-based survey. The four standard research scales were used to examine the research and development, disruption risk, firm, and supply chain performance. The response link was distributed to respondents via Facebook, WhatsApp, and email. The data was analyzed using structural equation modelling and a partial least squares technique in the study. Results: The study's findings suggest that disruption risk, research and development investment, and firm performance all improve supply chain performance, but the mediation effect is unsupported by the data. These measures help to plan a better supply chain in the face of disruption risk, and they provide one of the timely empirical conclusions on the role of R&D investment in mitigating risk disruptions and improving supply chain performance

Industry 5.0: From Manufacturing Industry to Sustainable Society

Industry 4.0 brought a new revolution in industries by making them fully automated via innovative technologies, without considering human-power. Industry 4.0 aims to establish 'smart manufacturing industry' by emphasizing on Information Technology (IT), Internet of Things (IOT), Cyber Physical System (CPS), Industrial Internet of Things (IIOT), Artificial Intelligence (AI), Big Data, and Robotics. This highly automated industry neglected human's intellectual and cognitive skills, causing an increase in unemployment rate and devastation of ecosystem. In this paper, we proposed a framework of emerging technologies of Industry 5.0. Here, we examined how Industry 5.0 will further extend the development of Industry 4.0 and how humans can contribute to its manufacturing process. In addition, prestigious and significant skills for workforce in manufacturing industry are also explored. We also investigated how the Covid-19 epidemic was associated to Industry 5.0 and the idea of sustainable development goals (SDGs). Finally, we highlighted some of the challenges facing the industrial sector as research direction of Industry 5.0. © 2022 IEEE.

Intelligent manufacturing eco-system: A post COVID-19 recovery and growth opportunity for manufacturing industry in Sub-Saharan countries.

The lagging behind intelligent technologies and the COVID-19 pandemic together have impacted the emerging economy particularly the manufacturing sector in sub-Saharan countries. This paper systematically discusses intelligent manufacturing technologies with an aim to map out their importance and industrial applicability and to show their significance to contain COVID-19 pandemic. Intelligent Manufacturing Systems (IMS) is then adapted as a post COVID-19 recovery and growth opportunity to ensemble to production processes of manufacturing industry in the sub-Saharan countries. Proposition of a Triple Helix Collaboration Eco-system that delineate a recursive contribution of Government(s), academia, and industry accompanies the IMS adoption. The intention is to shape the existing industrial challenges through networking in the area of intelligence technologies. While proposing the Eco-system, a post COVID-19 recovery and growth opportunity and intra-Africa scientific collaborations are taken into account.

Research on Credit Assessment and Prediction Based on Deep Learning

The huge pressure of market demand and competitive environment makes supply chain finance the choice of most enterprises. The emergence of public health emergencies such as the COVID-19 epidemic has made it particularly urgent to improve the risk management capabilities of the pharmaceutical industry's supply chain in a transitional period. In-depth exploration of the key factors affecting the financial credit risk of pharmaceutical companies' supply chain, and the construction of a high-accuracy forecast model is of great significance to the stability of the macroeconomy. Combining the characteristics of the pharmaceutical manufacturing industry, this paper builds a financial credit risk assessment system for the pharmaceutical supply chain. On the basis of Factor Analysis and Random Forest variable screening, the AdaBoost algorithm is used to build the prediction model. By comparing basic machine learning models such as SVM model, decision tree, logistic regression, Bayesian classifier, BP neural network, and integrated learning models such as Random Forest, Bagging meta-estimator, GBM, and XGBoost, the study found that the AdaBoost model has higher accuracy. And through the data forecast in 2020, the superiority and effectiveness of the model for credit risk assessment in the pharmaceutical industry are further verified. According to the prediction results, this paper finds that the epidemic has no obvious negative impact on pharmaceutical manufacturing enterprises and proposes suggestions from the perspectives of the government and enterprises for reference. © 2022 SPIE.

Excess Mortality in the Siberian Regions in the Context of the COVID-19 Pandemic: Dynamics and Affecting Factors

Introduction. The COVID-19 pandemic has had a strong negative impact on demographic processes in Russian regions. Mortality has increased significantly, the life expectancy has declined, and the natural decline in the population over the two pandemic years has reached ultra-high indicators. The article aims to analyze the dynamics of the mortality growth in the regions of Siberia for 2020-2021 and assess the impact of demographic, socio-economic, medical and infrastructure factors on it. Materials and Methods. The study is based on official statistical data for individual constituent entities of Russia published by Rosstat (Russian Statistics Agency). The regression and correlation analysis was used to identify the most significant factors that determined the increase and interregional differences in mortality displacement rates. Results. For the Siberian regions the significant factors that determine the increase and interregional differences in mortality displacement include the median age of the population;proportion of children in the age distribution structure;population of the regional capital;as well as the share of extractive and manufacturing industries in regional employment pattern. The median age of 39 and above, the low proportion of minors, and the specialization of a particular region in the manufacturing industry (implying more intensive contacts of employees) contribute to a significant increase in mortality during the pandemic. Regions of Siberia with a relatively low rate of mortality displacement have a younger age distribution structure and economic specialization in the extraction of minerals (contacts of the labor force are rather less frequent). The factors of urbanization level, average per capita income of the population, poverty incidence, general unemployment rate, number of hospital beds, and the number of doctors do not show a significant relationship with the increase in mortality. Discussion and Conclusion. The analysis established the causes of a significant increase in mortality in the Siberian regions. The leading role in it was played by demographic factors and economic specialization of the region. The results obtained can be used in the implementation of social and demographic policy aimed at maintaining the population health in regions with different demographic and socio-economic situations.

PROBABILITY OF CORPORATE BANKRUPTCY: APPLICATION TO PORTUGUESE MANUFACTURING INDUSTRY SMES

This paper aims to develop a model for predicting corporate bankruptcy for SMEs in the Portuguese manufacturing industry where this question remains rather unaddressed. Using profitability, activity, liquidity, leverage, and solvency ratios, it was added the size and age variables, for a group of 208 firms, including 49 bankrupt firms and 159 active firms, during the years 2011 to 2015. The logit model allowed us to estimate a model with 82.3% of predictive capacity. The most important variables identified were profitability, solvency, and size. Estimations only with the data closest to the bankruptcy date improved predictive capacity. It is evidenced that financial and non-financial variables can predict bankruptcy probability. A possible future approach would be to analyze a larger sample. Also, a larger period could be considered, allowing to test either the effects of the 2007/8 crisis or the effects of the recent economic turmoil related to Covid-19. Important for both corporate managers and investors. Conclusions may be disclosed regarding the influence that economic turmoil certainly has on corporate defaults and bankruptcies allowing its extension to other countries. The contribution of this paper is to find the best specification for a bankruptcy prediction model applied to the Portuguese manufacturing industry SMEs. This paper also contributes to the existing literature by using non-financial variables and analyzing a sector still unexplored in Portugal, albeit its conclusions can be extended to other countries. © 2022, Universiti Malaysia Sarawak. All rights reserved.

Intellectual Capital and Financial Performance of Chinese Manufacturing SMEs: An Analysis from the Perspective of Different Industry Types

In the knowledge era, intellectual capital (IC) has been recognized as the determinant of firm performance. The main goal of the current study is to analyze the relationship between IC and its elements and financial performance of Chinese manufacturing small and medium-sized enterprises (SMEs). We also examine whether industry type has an impact on this relationship. This study uses the data of 588 Chinese listed SMEs in the manufacturing industry between 2015 and 2020 and employs the modified value-added intellectual coefficient (MVAIC) model to assess IC. The results show that IC improves SMEs’ financial performance, and physical and human capitals are the main contributor. In addition, the impact of IC and its elements on the financial performance of Chinese manufacturing SMEs is different in different types of industries. Specifically, capital-intensive SMEs have a greater impact of IC on financial performance than labor- and technology-intensive SMEs;labor-intensive SMEs have a higher efficiency of physical capital, while technology-intensive SMEs have higher human capital efficiency. The findings could help SMEs’ managers improve corporate performance by the effective utilization of their IC.

Green Intellectual Capital and Green Supply Chain Performance: Does Big Data Analytics Capabilities Matter?

In light of global environmental concerns growing, environmental awareness within firms has become more important than before, and many scholars and researchers have argued the importance of environmental management in promoting sustainable organizational performance, especially in the context of supply chains. Thus, the current study aimed at identifying the impact of the components of green intellectual capital (green human capital, green structural capital, green relational capital) on green supply chain performance in the manufacturing sector in Jordan, as well as identifying the moderating role of big data analytics capabilities. To achieve this aim, we developed a conceptual model of Structural Equation Modelling-Partial Least squares and tested through the Smart-PLS software on a sample of 438 respondents. Empirical results showed that each of the components of green intellectual capital and big data analytics explains 71.1% of the variance in green supply chain performance and that all components of green intellectual capital have a statistically significant impact on green supply chain performance. The results also revealed that the relationship between green relational capital and green supply chain performance is moderated through big data analytics capabilities. Finally, this study made a theoretical and managerial implications to the supply chain literature and industry.