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This paper aims to identify all variables and parameters related to business and emission within the petrochemical industry. The variables and parameters specified will be modeled into a system dynamic model that will be a baseline for the proposed best scenario(s) to address the business issue related to emission reduction in the petrochemical industry.

Literature review and stakeholder interviews were conducted to define the key factors contributing to the emission reduction of the petrochemical industry. The key factors are then developed into a system dynamic model to measure the quantitative impact of changes in those variables on emission and industry profitability.

This paper provides an analysis of system dynamic model. It suggests that process optimization can lead to a slight amount reduction in emissions. In contrast, a significant reduction shows in the simulation result of bio-based feedstock utilization and implementation of advanced technology. To sustain the emission reduction, strong commitment from stakeholders and support from the government will play an important role.

This research is limited to problem analysis of the primary product (high-value chemical) of the petrochemical industry by only considering the changes in the key factors of emission reduction.

This paper includes implications for interventions that can be imposed to reduce emission while retaining the business profitability.

The contribution of this study is to find the best scenario that can boost emission reduction within Indonesia’s petrochemical industry.

This paper aims to propose a framework that combines the triple helix model with competitive strategies concepts to capture and guide any innovation-led national development strategies.

This paper adopted a methodological framework based on existing methods and guidelines, the most commonly reported approach for developing a methodological framework. The review of fundamental approaches to achieving fast and sustained economic development, triple helix model and competitive strategies helped develop the methodological framework. The framework was validated and tested using the case studies approach on Korea, Taiwan and Singapore.

Kuwait aims to create an innovative environment to benefit from the innovation strategies anchored by the East Asian miracle economies and how they used the triple helix actors at different developmental stages. First, Kuwait’s research institutes and universities need to design interactive programs and activities with industry and community to help innovate solutions to current and prospective challenges. Second, the government needs to provide a competitive business environment and effective policies. Thirdly, the Kuwait industry must be encouraged to innovate and infuse modern technology practices.

Developing countries are trying to use science, technology and innovation as an effective strategy for achieving sustained economic growth. However, since each country has its unique conditions, learning from other success stories proved difficult if not structured in a framework designed to serve a specific purpose such as the one the authors propose in this paper.

This study aims to assess the efficiency of managing working capital in 1,388 Indian manufacturing firms from 2008 to 2019 and investigate the effects of firm-specific and macro-level determinants on working capital management (WCM) efficiency.

The current study accommodates a slack-based measure (SBM) in data envelopment analysis (DEA) for computing WCM efficiency. Further, we implement a panel data fixed-effects model that controls for heterogeneity across firms in determining the relationships of selected variables with WCM efficiency.

The results highlight that manufacturing firms operate at around 50 percent efficiency, which is constant throughout the study period. Furthermore, among the selected variables, yield, earnings, age, size, ability to create internal resources, interest rate and gross domestic product (GDP) significantly affect WCM efficiency.

Instead of the traditional models used for assessing efficiency, the SBM-DEA model is unit-invariant and monotone for slacks, implying that it can handle zero and negative data, which overcomes the incapability of prior DEA models. Hence, this provides accurate efficiency scores for robust analysis. Additionally, this paper provides a holistic working capital model recognizing firm-specific and macro-level determinants for a more explicit estimation of the relationship between WCM efficiency and the selected determinants.

This paper aims to address two fundamental questions: (1) How has Bahrain's industrial policy evolved during the 21st century? and (2) what factors contribute to this evolution?

Utilizing secondary data, this paper identifies key decision-makers responsible for economic policy in Bahrain and delineates the evolution of Bahrain's industrial policy throughout the 21st century. Subsequently, it employs a series of interviews with elite civil servants engaged in the formulation and implementation of Bahrain's economic policies to understand the reasons behind the observed changes.

Since assuming the role of Crown Prince in 1999, Sh. Salman bin Hamad Al Khalifa has been the key economic decision-maker in Bahrain. During the 21st century, Bahrain has shifted away from decisions closely aligned with the Washington Consensus towards those more in line with classical industrial policy. Interviews reveal that the private sector's underperformance in job creation, coupled with fiscal pressures, has driven this departure from the Washington Consensus. Moreover, the early successes of the interventionist Saudi Vision 2030 and Bahrain's own success in technocratically managing the COVID-19 pandemic have accelerated this transition.

Insights into the determinants of Bahrain's industrial policy can guide policymakers in refining future strategies. Recognizing the positive role of intellectual developments in academic economics literature becomes crucial for informed decision-making.

This paper fills a gap in the existing literature by providing answers to its research questions, particularly considering the significant changes witnessed in Bahrain's industrial policy post-pandemic.

The purpose of this study is to enhance the functional properties of Hessian fabric through resin finishing. Hessian bags made of lignocellulosic jute fiber are commonly used to pack, store and transport agro-commodities, including horticultural crops such as rice, potato, onion and wheat. However, because of high water affinity, these bags undergo degradation in properties due to moisture release by the stored commodities themselves. Exposure to natural elements, e.g. rain and dew, also causes moisture absorption in hessian bags. Once the bag gets moistened, degradation of jute bags starts due to microbial attack, leading to loss in tensile strength and change in extensibility, leading to ultimate breakage in warp and weft directions of the fabric.

To overcome the degradation in the functional properties of hessian fabric due to exposure to moisture and microbial attack, the application of semi-synthetic polymeric materials was carried out.

Tenacity, bursting strength, puncture resistance, tear strength and breaking load, as well as life cycle of resin-treated jute fabric was found to be better than control jute.

To the best of the authors’ knowledge, no recent reports of resin finishing on jute (hessian) fabric with semi-synthetic resins are presently available, other than coating with rubber.

After reading and discussing the case study, students should be able to identify the reasons why the company needed to digitise and how this links to the company’s strategy around technology and innovation, analyse the digitalisation implementation process followed in the case study by using an organisational change management model and make recommendations and propose a solution for the protagonist to consider for the successful roll-out of the digitalisation project.

DRA Projects is part of the DRA Global business based in South Africa. The company is known locally in the mining and engineering industry for its project development, delivery, execution and operations capabilities. Digital transformation is a key strategic focus in the industry, as clients seek digitised integrated systems. For this client offering, J.C. Heslinga, managing director of DRA Projects, was tasked with leading the digitalisation of the project delivery system. From July 2020 until July 2022, Heslinga led the implementation team through different organisational change stages. As the next phase included rolling out digitalisation to pilot projects and engaging employees and clients in the new process, Heslinga wondered if enough was done to ready the business for this change. The end users would be executing the changes, so their adoption will be imperative for successfully rolling out digitalisation. The case study concludes with Heslinga pondering the approach needed for the next phase. The case study focuses on the digitalisation implementation process through the lens of organisational change. The case study presents an opportunity to analyse and identify the theories and models used in organisational change within a real-life business context. The organisational change learnings can be adapted to help students with any transformation changes in similar business scenarios.

Postgraduate- and master’s-level students and business executives attending short courses will benefit from the learnings. The learnings can be applied to improve decision-making, organisational behaviour and strategic implementation using the fundamental principles of organisational change.

Teaching notes are available for educators only.

CSS 6: Human resource management

The crude oil supply chain (COSC) is one of the most complex and largest supply chains in the world. It is easily vulnerable to extreme events. Recently, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (often known as COVID-19) pandemic created a massive imbalance between supply and demand which caused significant price fluctuations. The purpose of this study is to explore the influential factors affecting the international COSC in terms of consumption, production and price. Furthermore, it develops a model to predict the international crude oil price during disease outbreaks using Random Forest (RF) regression.

This study uses both qualitative and quantitative approaches. A qualitative study is conducted using a literature review to explore the influential factors on COSC. All the data are extracted from Web sources. In addition to COVID-19, four other diseases are considered to optimize the accuracy of predictive results. A principal component analysis is deployed to reduce the number of variables. A forecasting model is developed using RF regression.

The findings of the qualitative analysis characterize the factors that influence international COSC. The findings of quantitative analysis emphasize that production and consumption have a higher contribution to the variance of the data set. Also, this study found that the impact caused to crude oil price varies with the region. Most importantly, the model introduced using the RF technique provides a high predictive ability in short horizons such as infectious diseases. This study delivers future directions and insights to researchers and practitioners to expand the study further.

This is one of the few available pieces of research which uses the RF method in the context of crude oil price forecasting. Additionally, this study examines international COSC in the events of emergencies, specifically disease outbreaks using machine learning techniques.

Coal is a critical global energy source, and fluctuations in its price significantly impact related enterprises' profitability. This study aims to develop a robust model for predicting the coal price index to enhance coal purchase strategies for coal-consuming enterprises and provide crucial information for global carbon emission reduction.

The proposed coal price forecasting system combines data decomposition, semi-supervised feature engineering, ensemble learning and deep learning. It addresses the challenge of merging low-resolution and high-resolution data by adaptively combining both types of data and filling in missing gaps through interpolation for internal missing data and self-supervision for initiate/terminal missing data. The system employs self-supervised learning to complete the filling of complex missing data.

The ensemble model, which combines long short-term memory, XGBoost and support vector regression, demonstrated the best prediction performance among the tested models. It exhibited superior accuracy and stability across multiple indices in two datasets, namely the Bohai-Rim steam-coal price index and coal daily settlement price.

The proposed coal price forecasting system stands out as it integrates data decomposition, semi-supervised feature engineering, ensemble learning and deep learning. Moreover, the system pioneers the use of self-supervised learning for filling in complex missing data, contributing to its originality and effectiveness.

This paper aims to provide a comprehensive bibliometric study of housing prices according to the articles collected by the Web of Science (WOS).

This paper studies 4,125 research papers on housing prices in the core collection database of WOS. Using VOSviewer, this paper makes a bibliometric and visual analysis of the housing prices research from 1960 to 2020 and probes into the housing prices research from five aspects: time, international cooperation, institutions author cooperation and research focuses.

Keywords such as influencing factors of housing prices, analysis of supply and demand, policy and housing prices and regional cities appear frequently, which indicates the main direction of housing price research literature. Recent common keywords include regression analysis and house price forecast. Countries, like the USA started early in the study of housing prices, and the means and methods in the field of housing price research are mature, leading the forefront of housing price research. Compared with the USA and other Western developed countries, the housing price research in developing countries needs to use innovative research methods and put more effort on sustainability. Research shows that housing price is closely related to economy, and keyword cluster analysis shows that gross domestic product, interest rate, currency and other keywords related to economy are of high-frequency.

This paper only uses articles from one database (WOS), which does not represent all research papers published worldwide. Some studies have been published for a long time, and the reference value to the research focuses and future research might be limited. There are many kinds of journals included in the study with different publishing frequencies, time ranges and numbers of papers. These may have some influence on the research results.

The main theoretical contribution of this paper is to supplement the current academic research on housing prices. This paper reveals the key points of housing prices research and possible research problems that need attention. We can know from the future research direction and practice which can offer insights for future innovative direction.