Search results

Growing attention has been paid to bricolage as a strategic means to overcome resource constraints in small and medium-sized enterprises (SMEs). In the industrial market, a bricolage strategy and ambidextrous action may help firms to remain competitive by responding quickly to the business-to-business marketing. Despite its paramount importance, questions as to how bricolage is strengthened and how bricolage improves innovation ambidexterity have remained unanswered. This study aims to develop an integrated model for the relationships among environmental turbulence, learning orientation, ambidexterity and performance, with a particular focus on the mediation of bricolage.

Building on the literature review regarding the key constructs, hypotheses were developed. Data were collected using questionnaires from 229 SMEs in South Korea. To test hypothesis, structural equation modeling and Monte Carlo method for assessing mediation were performed.

Results reveal that environmental turbulence and learning orientation are positively associated with bricolage, which sequentially affects ambidextrous action as a driver of performance. The findings also indicate that bricolage significantly mediates the relationship between its antecedents and ambidexterity.

This research contributes to advancing our understanding of the role of a bricolage strategy for innovation ambidexterity and performance in SMEs. This study is the first to examine the mediation of bricolage between environmental factors and ambidexterity for improved performance.

This study is a targeted review of some of the major changes in European regulation that guided energy policy decisions in the Iberian Peninsula and how they may have aggravated the problem of lack of flexibility. This study aims to assess some of the proposed short-term solutions to address this issue considering the underlying root causes and suggests a different course of action, that in turn, could help alleviate future market strains.

The evolution of the most important (macro) energy and price-related variables in both Portugal and Spain is assessed using market and grid operator data. In addition, the authors present critical viewpoints on some of the most recent EU and national regulation changes (official document analysis).

The Iberian energy policy and regulatory agenda has successfully promoted a rapid adoption of renewables (main goal), although with insufficient diversification of generation technologies. The compulsory closings of thermal plants and an increased tax (mainly carbon) added pressure toward more environmentally friendly thermal power plants. However, inevitably, this curbed the bidding price competitiveness of these producers in an already challenging market framework. Moving forward, decisions must be based on “a bigger picture” that does not neglect system flexibility and security of supply and understands the specificities of the Iberian market and its generation portfolio.

This work provides an original account of unprecedented spikes in energy prices in 2021, specifically in the Iberian electricity market. This acute situation worries consumers, industry and governments. Underlining the instability of the market prices, for the first time, this study discusses how some of the most important regulatory changes, and their perception and absorption by involved parties, contributed to the current environment. In addition, this study stresses that if flexibility is overlooked, the overall purpose of having an affordable and reliable system is at risk.

For some years now, Cameroon has seen a significant increase in its electricity demand, and this need is bound to grow within the next few years owing to the current economic growth and the ambitious projects underway. Therefore, one of the state's priorities is the mastery of electricity demand. In order to get there, it would be helpful to have reliable forecasting tools. This study proposes a novel version of the discrete grey multivariate convolution model (ODGMC(1,N)).

Specifically, a linear corrective term is added to its structure, parameterisation is done in a way that is consistent to the modelling procedure and the cumulated forecasting function of ODGMC(1,N) is obtained through an iterative technique.

Results show that ODGMC(1,N) is more stable and can extract the relationships between the system's input variables. To demonstrate and validate the superiority of ODGMC(1,N), a practical example drawn from the projection of electricity demand in Cameroon till 2030 is used. The findings reveal that the proposed model has a higher prediction precision, with 1.74% mean absolute percentage error and 132.16 root mean square error.

These interesting results are due to (1) the stability of ODGMC(1,N) resulting from a good adequacy between parameters estimation and their implementation, (2) the addition of a term that takes into account the linear impact of time t on the model's performance and (3) the removal of irrelevant information from input data by wavelet transform filtration. Thus, the suggested ODGMC is a robust predictive and monitoring tool for tracking the evolution of electricity needs.

Energy security has been talked about by governments and policymakers because the global energy market is unstable and greenhouse gas emissions threaten the long-term health of the global environment. One of the most potent ways to cut CO₂ emissions is through the production and consumption of renewable energy. Thus, the purpose of this paper is to highlight the drivers that, if ambitious environmental policies are implemented, might improve energy security or prevent its deterioration.

The study uses a balanced panel data set for Indonesia, Malaysia, the Philippines, Singapore, Thailand and Vietnam that covers a period of 30 years (1990–2020). The pooled panel dynamic least squares is used in this study.

The findings show that renewable energy consumption is positively related to gross domestic product per capita, energy intensity per capita and renewable energy installed capacity. Wherein renewable energy use is inversely related to per capita electricity consumption, CO₂ emissions and the use of fossil fuel electricity.

There is a lack of research identifying the factors influencing energy security in the ASEAN region. Therefore, this study focuses on the drivers that influence energy security, which are explained by the proportion of renewable energy in final energy consumption. Without identifying the demand and supply sources of energy, especially electricity production based on renewable energy techniques, it is hard for policymakers to achieve the desired renewable energy-based outcome.

This paper aims to study the motives that led to conflict between two groups – Employees of state-owned power distribution companies and the Government – over permitting parallel licensing to a private company for power distribution services in selected areas of the Maharashtra state in India. The study also seeks to comprehend the reconciliation process and the role of leadership in thwarting the strike that could have impacted common citizens.

The research is built on a case-based approach to analyze the pre- and poststrike environment along with the impact of the power shortage during the few hours of the strike. A semi-structured interview method wherein government and employee union representatives were interviewed to understand their version of the incident is used. Related literature, reports and news were reviewed to realize the impacts and consequences of similar situations in the past.

The strike was called off within a few hours with the intervention of state government to resolve the issue, promising the union the government’s intention not to privatize but to invest INR 500bn in the three government companies. The parallel licensing may impact government-owned power distribution companies as well as customers in the future.

It will pave the way for lessons related to such incidence where the Government and the Unions are at loggerheads over issues like privatization or ownership of the company and help the involved and other parties to seek a viable solution. The role of resilient leadership demonstrated by both parties led to a win-win solution within a few hours of the strike.

The paper is a case study on an issue that is very contemporary; the role of leadership and its swiftness in decision-making that led to a solution to a very complex situation is something that was not done earlier in the context of the State vs Union issue.

Carbon leakage – where multinational enterprises (MNEs) transfer carbon-intensive production activities to countries with laxer emissions constraints for cost purposes – is one of the main mechanisms through which international business (IB) contributes to climate change. This chapter discusses a new policy initiative called the Carbon Border Adjustment Mechanism (CBAM) that the European Union (EU) introduced in May 2023 to fight carbon leakage. The authors analyze the logic of CBAM and discuss how it will likely influence IB both in industries that are directly targeted by CBAM and related industries that will face spillover effects.

Accurate prediction of seasonal power consumption trends with impact disturbances provides a scientific basis for the flexible balance of the long timescale power system. Consequently, it fosters reasonable scheduling plans, ensuring the safety of the system and improving the economic dispatching efficiency of the power system.

First, a new seasonal grey buffer operator in the longitudinal and transverse dimensional perspectives is designed. Then, a new seasonal grey modeling approach that integrates the new operator, full real domain fractional order accumulation generation technique, grey prediction modeling tool and fruit fly optimization algorithm is proposed. Moreover, the rationality, scientificity and superiority of the new approach are verified by designing 24 seasonal electricity consumption forecasting approaches, incorporating case study and amalgamating qualitative and quantitative research.

Compared with other comparative models, the new approach has superior mean absolute percentage error and mean absolute error. Furthermore, the research results show that the new method provides a scientific and effective mathematical method for solving the seasonal trend power consumption forecasting modeling with impact disturbance.

Considering the development trend of longitudinal and transverse dimensions of seasonal data with impact disturbance and the differences in each stage, a new grey buffer operator is constructed, and a new seasonal grey modeling approach with multi-method fusion is proposed to solve the seasonal power consumption forecasting problem.

The highlights of the paper are as follows:

1. A new seasonal grey buffer operator is constructed.

2. The impact of shock perturbations on seasonal data trends is effectively mitigated.

3. A novel seasonal grey forecasting approach with multi-method fusion is proposed.

4. Seasonal electricity consumption is successfully predicted by the novel approach.

5. The way to adjust China's power system flexibility in the future is analyzed.

A new seasonal grey buffer operator is constructed.

The impact of shock perturbations on seasonal data trends is effectively mitigated.

A novel seasonal grey forecasting approach with multi-method fusion is proposed.

Seasonal electricity consumption is successfully predicted by the novel approach.

The way to adjust China's power system flexibility in the future is analyzed.

Industries worldwide have been striving to serve the increasing demand of consumers alongside providing importance to environmental issues. Yet, there are concern-raising changes on the planet, such as greenhouse gas (GHG) emissions resulting in a temperature rise. India remains a vital party of the United Nations Convention on Climate Change. Henceforth, the paper aims to study the increased emissions of GHG in Puducherry, an Indian Union Territory that faces tremendous pressure owing to its denser population.

The research is designed as a case study conducted in a tyre manufacturing unit in Puducherry. The industrial sector was chosen, as it is the largest contributor (78%) of the total GHG emissions. Case studies were chosen to analyse the GHG emissions and the effects of implementing the policies and imposing interventions over time. The identified areas of improvement, proposed changes and the implemented ones with the results over a three-year period have been discussed.

The present study’s GHG inventorisation for Puducherry paved the way for preparing mitigation and adaptation plans. A total of 21 and 48 changes were incorporated to conserve fuel and power, respectively. A significant 11% reduction in power consumption and 1,113,008/litres of furnace oil was achieved. This translates to 5,115 tCO₂ and 3,306 tCO₂, respectively.

This research will help to improve the importance of climate change management in the manufacturing sector, and it will pave the way for achieving effective sustainable practices.

Such case studies could cumulatively impact the policy directives/ interventions on GHG emissions. Though this seems a small leap, putting them into practice at firm levels would contribute significantly towards achieving Sustainable Development Goals.

A time-varying grey Fourier model (TVGFM(1,1,N)) is proposed for the simulation of variable amplitude seasonal fluctuation time series, as the performance of traditional grey models can't catch the time-varying trend well.

The proposed model couples Fourier series and linear time-varying terms as the grey action, to describe the characteristics of variable amplitude and seasonality. The truncated Fourier order N is preselected from the alternative order set by Nyquist-Shannon sampling theorem and the principle of simplicity, then the optimal Fourier order is determined by hold-out method to improve the robustness of the proposed model. Initial value correction and the multiple transformation are also studied to improve the precision.

The new model has a broader applicability range as a result of the new grey action, attaining higher fitting and forecasting accuracy. The numerical experiment of a generated monthly time series indicates the proposed model can accurately fit the variable amplitude seasonal sequence, in which the mean absolute percentage error (MAPE) is only 0.01%, and the complex simulations based on Monte-Carlo method testify the validity of the proposed model. The results of monthly electricity consumption in China's primary industry, demonstrate the proposed model catches the time-varying trend and has good performances, where MAPE_F and MAPE_T are below 5%. Moreover, the proposed TVGFM(1,1,N) model is superior to the benchmark models, grey polynomial model (GMP(1,1,N)), grey Fourier model (GFM(1,1,N)), seasonal grey model (SGM(1,1)), seasonal ARIMA model seasonal autoregressive integrated moving average model (SARIMA) and support vector regression (SVR).

The parameter estimates and forecasting of the new proposed TVGFM are studied, and the good fitting and forecasting accuracy of time-varying amplitude seasonal fluctuation series are testified by numerical simulations and a case study.

Blockchain for energy trading is a trending research area in the current context. However, a noticeable gap exists in the review articles focussing on solar energy trading with blockchain technology. Thus, this study aims to systematically examine and synthesise the existing research on implementing blockchain technology in sustainable solar energy trading.

The study pursued a systematic literature review to achieve its aim. The data extraction process focussed on the Scopus and Web of Science (WoS) databases, yielding an initial set of 129 articles. Subsequent screening and removal of duplicates led to 87 articles for bibliometric analysis, utilising VOSviewer software to discern evolutionary progress in the field. Following the establishment of inclusion and exclusion criteria, a manual content analysis was conducted on a subset of 19 articles.

The results indicated a rising interest in publications on solar energy trading with blockchain technology. Some studies are exploring the integration of new technologies like machine learning and artificial intelligence in this domain. However, challenges and limitations were identified, such as the absence of real-world solar energy trading projects.

This study offers a distinctive approach by integrating bibliometric and manual content analyses, a methodology seldom explored. It provides valuable recommendations for academia and industry, influencing future research and industry practices. Insights include integrating blockchain into solar energy trading and addressing knowledge gaps. These findings advance societal goals, such as transitioning to renewable energy sources (RES) and mitigating carbon emissions, fostering a sustainable future.