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Purpose The purpose of this paper is to suggest how firms could use big data to facilitate product innovation processes, by shortening the time to market, improving customers’ product adoption and reducing costs. Design/methodology/approach The research is based on a two-step approach. First, this research identifies four potential key success factors for organisations to integrate big data in accelerating their product innovation processes. The proposed factors are further examined and developed by conducting interviews with different organisation experts and academic researchers. Then a framework is developed based on the interview outputs. The framework sets out the key success factors involved in leveraging big data to reduce lead times and costs in product innovation processes. Findings The three determined key success factors are: accelerated innovation process; customer connection; and an ecosystem of innovation. The authors believe that the developed framework based on big data represents a paradigm shift. It can help firms to make new product development dramatically faster and less costly. Research limitations/implications The proposed accelerated innovation processes demand a shift in traditional organisational culture and practices. It is, though, meaningful only for products and services with short life cycles. Moreover, the framework has not yet been widely tested. Practical implications This paper points to the vital role of big data in helping firms to accelerate product innovation processes. First of all, it allows organisations to launch new products to market as quickly as possible. Second, it helps organisations to determine the weaknesses of the product earlier in the development cycle. Third, it allows functionalities to be added to a product that customers are willing to pay a premium for, while eliminating features they do not want. Last, but not least, it identifies and then prioritises customer needs for specific markets. Originality/value The research shows that firms could harvest external knowledge and import ideas across organisational boundaries. An accelerated innovation process based on big data is characterised by a multidimensional process involving intelligence efforts, relentless data collection and flexible working relationships with team members.

Purpose - Assessing a measure of sustainable supply chain management (SSCM) performance is currently a key challenge. The literature on SSCM is very limited and performance measures need to have a systematic framework. The recently developed balanced scorecard (BSC) is a measurement system that requires a balanced set of financial and non-financial measures. The purpose of this paper is to evaluate the SSCM performance based on four aspects i.e. sustainability, internal operations, learning and growth, and stakeholder. Design/methodology/approach - This paper developed a BSC hierarchical network for SSCM in a close-loop hierarchical structure. A generalized quantitative evaluation model based on the Fuzzy Delphi Method (FDM) and Analytical Network Process (ANP) were then used to consider both the interdependence among measures and the fuzziness of subjective measures in SSCM. Findings - The results of this study indicate that the top-ranking aspect to consider is that of stakeholders, and the top five criteria are green design, corporate sustainability, strategic planning for environmental management, supplier cost-saving initiatives and market share. Originality/value - The main contributions of this study are twofold. First, this paper provides valuable support for supply chain stakeholders regarding the nature of network hierarchical relations with qualitative and quantitative scales. Second, this paper improves practical performance and enhances management effectiveness for SSCM.

This study provides a data-driven analysis that illustrates a clear renewable energy depiction in sustainable energy security and unveils the regional issues due to the literature solely occupies energy security concept in the descriptions view, and renewable energy differences related to regions are rarely discussed. A hybrid method is proposed to valid those indicators and shows the trend of future studies. This study enriches the challenges and opportunities by contributing to understand the fundamental knowledge of renewable energy in sustainable energy security frontier, conveyance directions for future study and investigation, and assessment on global renewable energy position and regional disparities. There are valid 19 indicators, in which energy demand, energy policy, renewable resources, smart grid, and uncertainty representing the future trends. World regional comparison includes 115 countries/territories and categorized into five geographical regions. The result shows that those indicators have addressed different issues in the world regional comparison.

Purpose – The purpose of this paper is to enhance the understanding of sustainable supply chain management (SSCM) and provide a comprehensive and quantitative method to assess performance. Design/methodology/approach – The study applied interval-valued triangular fuzzy numbers associated with grey relational analysis to improve the insufficient information and overcome the incomplete system under uncertainty. Findings – The findings support the argument that the triple bottom line is insufficient to cover the entire concept of SSCM; in particular, the aspects of operations, stakeholders and resilience have not been addressed in previous studies. Research limitations/implications – The results reveal that the triple bottom line concept is insufficient to illustrate the principles of SSCM and to provide an extensive basis for theory development. The aspects and criteria considered in the study only relate to the studied company and may need to be reviewed when applied to other industries. Practical implications – The methodology and findings of the study demonstrate the core applications of criteria ranking and identify priority areas that utilize less investment but that may maintain the studied company’s current performance. Suggestions for the prioritization of criteria to enhance SSCM performance are provided. Originality/value – The present study provides three valuable contributions. First, it adopts collaboration theory to furnish a theoretical foundation for SSCM. Second, the proposed hybrid method is able to overcome uncertainty and subsequently evaluate SSCM while utilizing incomplete and imprecise information. Third, the evaluation provides significant results for consideration in decision making by the studied company.

The significance of sustainability is continually expanding among researchers, policymakers, and decision makers. To improve the efficiency of value chain activities such as manufacturing, distribution, and consumption, an innovative research solution has been proposed: ‘Sustainable Consumption and Production (SCP) through Information and Communication Technology (ICT)'. Sustainability through ICT is significant for the industry in terms of its sustainable effects on production processes, environment, and community. This research seeks to gauge ICT—as knowledge management—for industries in the successful adoption and execution of SCP. In so doing, potential key ICT-based factors to SCP are identified from the literature and experts' feedback. The present work suggests a decision framework for assessing the interrelationships among and between the ICT oriented factors by utilizing graph theory and matrix approach. Data for this work derives from three automotive companies operating in India. From findings, ‘Governance and Management', is the topmost factor for the adoption of sustainable consumption and production in value chains. The relationship among the index values is further evaluated using Spearman's rank correlation coefficient. This research can facilitate practitioners, government agencies, and customers for a better understanding of ICT-driven factors in managing resources, reducing waste, and improving cost, which would further help in meeting sustainable development goals of the United Nations of responsible consumption and production and innovation, industry, and infrastructure.

Sustainable corporate finance is an attractive field of study in sustainability literature; however, the literature lacks systematic bibliometric analysis that provides a comprehensive review to clarify state-of-the-art sustainable corporate finance and that discusses new opportunities and potential instructions for further studies. To address this gap, this study adopts a literature review, bibliometric analysis, network analysis and co-wording technique to systematically investigate the Scopus database. In total, 30 keywords listed at least three times are used and are divided into six clusters considering six fields of research, namely, corporate finance in corporate sustainability, sustainable competitive advantages, sustainable stakeholder engagement, circular economy, sustainable corporate finance innovation and risk management and sustainable supply chain ethics. This study contributes to examining the sustainable corporate finance bibliometric status to provide directions for future studies and practical accomplishment. The sustainable corporate finance knowledge gaps are (1) corporate finance in sustainability; (2) sustainable competitive advantages; (3) sustainable stakeholder engagement; (4) circular economy; (5) sustainable corporate finance innovation and risk management; and (6) sustainable supply chain ethics. The knowledge gaps and future directions are also discussed.

Electricity inspection is important to support sustainable development and is core to the marketing of electric power. In addition, it contributes to the effective management of power companies and to their financial performance. Continuous improvement in the penetration rate of new energy generation can improve environmental standards and promote sustainable development, but creates challenges for electricity inspection. Traditional electricity inspection methods are time-consuming and quite inefficient, which hinders the sustainable development of power firms. In this paper, a load-forecasting model based on an improved moth-flame-algorithm-optimized extreme learning machine (IMFO-ELM) is proposed for use in electricity inspection. A chaotic map and improved linear decreasing weight are introduced to improve the convergence ability of the traditional moth-flame algorithm to obtain optimal parameters for the ELM. Abnormal data points are screened out to determine the causes of abnormal occurrences by analyzing the model prediction results and the user’s actual power consumption. The results show that, compared with existing PSO-ELM and MFO-ELM models, the root mean square error of the proposed model is reduced by at least 1.92% under the same conditions, which supports application of the IMFO-ELM model in electricity inspection. The proposed power-load-forecasting-based abnormal data detection method can improve the efficiency of electricity inspection, enhance user experience, contribute to the intelligence level of power firms and promote their sustainable development.

Metropolitan economic development can yield more logistics demands and provide stronger financial support for logistics industry, and logistics development in turn can boost economic growth by reducing transaction cost and increasing economic operation efficiency. Their coordinated development between the two can better promote sustainable, steady and fast growth of metropolitan economy. However, little quantitative research has been conducted on their mutual relations. To fill this gap, three contributions are made. First, through literature review and field survey, two sets of key indicators for metropolitan logistics and economic development are proposed. Second, the entropy method is applied to objectively determine indicators’ weights to precisely evaluate the development level of metropolitan economy and logistics. Third, an evaluation model for coordinated development is constructed to scientifically measure the coordinated development degree. Empirical studies based on the measuring model have been conducted by taking five China’s metropolises as subjects. Scores can be obtained to examine which indicators are the constraints for sustained, coordinated development and which are below the average, which are of vital importance affecting governmental policy-making and investment planning.

Voluntary engagement (VE) creates a sense of coordination and harmonization to share knowledge. The eminence of knowledge sharing (KS) for supply chain (SC) innovation is undeniable to initiate development in products, services, and operations. However, KS process is undergoing challenges in sustaining KS engagement by SC partners. Hence, recent researchers call for the need to address this gap in the literature to assess VE barriers. This paper studies the causal relationship of VE barriers on two MNCs, i.e., Toyota and Suzuki, via the fuzzy DEMATEL approach. The case examination findings indicate culture's alignment as the prime cause of VE and leadership commitment has stronger interdependence. The core problems which need elimination are fear of losing the job, prominence and opportunistic behavior. The study concludes that companies need to instigate the natural attributes of employees’ VE by setting-up earnest guidelines to practice free information and knowledge flow.

Low carbon supply chain network design is a multi-objective decision-making problem that involves a trade-off between low carbon emissions and cost. This study calculates the carbon footprint, wherein the greenhouse gases (GHGs) emissions data are based on carbon footprint standards. Many firms have redesigned their supply chain networks to reduce their GHG emissions. Furthermore, the production capacities and costs are collected and evaluated by using Pareto optimal solutions. In order to achieve the optimal solutions, a normal constraint method is used to formulate a mathematical model to meet two objectives: low carbon emissions and low cost. A case study is also presented to demonstrate the predictive ability of this model. The result shows that it is possible to reduce carbon emissions and lower cost simultaneously.