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Data Analytics for Sustainable Business: Practical Insights for Measuring and Growing Impact
Abstract
The current landscape indicates that sustainability is gaining traction as one of the core business strategies. The use of data analytics to monitor and improve sustainability measures in organizations has remained one of the most effective approaches. Thus, this study examines the impact of Big Data Analytics (BDA) capabilities on process eco-innovation and sustainability performance across industries. We focus on four core capabilities—information technology, personnel expertise, management, and BDA—and their role in achieving sustainability goals. Our results reveal that predictive analytics can significantly reduce carbon emissions by 15% over five years, with emissions projected to drop from 100 metric tons in 2024 to 65 metric tons by 2030. Additionally, energy consumption accounts for 33% of overall resource usage, followed by carbon emissions (33%), water usage (24%), and waste generation (10%). Comparative metrics indicate a 30-40% reduction in carbon emissions, water consumption, and waste generation after adopting sustainability practices, underscoring the importance of data-driven innovation. Our findings highlight the varying needs across industries: the financial sector demands real-time decision- making, healthcare focuses on cost optimization, and retail prioritizes customer satisfaction and operational efficiency. Furthermore, regulatory compliance and resource heterogeneity shape BDA adoption, influencing organizational performance. This study offers practical insights into how industries can align analytics with eco-innovation, driving sustainable growth and operational excellence. These results emphasize the transformative potential of predictive analytics in enhancing sustainability, making BDA a critical component of future industrial strategies. The current landscape indicates that sustainability is gaining traction as one of the core business strategies. The use of data analytics to monitor and improve sustainability measures in organizations has remained one of the most effective approaches. Thus, this study examines the impact of Big Data Analytics (BDA) capabilities on process eco-innovation and sustainability performance across industries. We focus on four core capabilities—information technology, personnel expertise, management, and BDA—and their role in achieving sustainability goals. Our results reveal that predictive analytics can significantly reduce carbon emissions by 15% over five years, with emissions projected to drop from 100 metric tons in 2024 to 65 metric tons by 2030. Additionally, energy consumption accounts for 33% of overall resource usage, followed by carbon emissions (33%), water usage (24%), and waste generation (10%). Comparative metrics indicate a 30-40% reduction in carbon emissions, water consumption, and waste generation after adopting sustainability practices, underscoring the importance of data-driven innovation. Our findings highlight the varying needs across industries: the financial sector demands real-time decision- making, healthcare focuses on cost optimization, and retail prioritizes customer satisfaction and operational efficiency. Furthermore, regulatory compliance and resource heterogeneity shape BDA adoption, influencing organizational performance. This study offers practical insights into how industries can align analytics with eco-innovation, driving sustainable growth and operational excellence. These results emphasize the transformative potential of predictive analytics in enhancing sustainability, making BDA a critical component of future industrial strategies.
... Traditional fire and gas detection technologies, while effective to some extent, suffer from limitations such as high false alarm rates, delayed response times, and susceptibility to environmental interferences. These challenges necessitate the integration of artificial intelligence (AI), Internet of Things (IoT), sensor fusion, and predictive analytics to develop more robust, intelligent, and proactive detection mechanisms (Alasa, 2020;Alasa, 2021;Alasa et al., 2024;Rahaman et al., 2024;Islam et al. 2024). This research has demonstrated that AI-enhanced detection systems, incorporating machine learning algorithms, computer vision techniques, and deep neural networks, significantly improve hazard identification by distinguishing between true threats and false positives (Zhang et al., 2024;Hossain et al., 2023). ...
Fire and gas hazards pose significant threats to industrial environments, necessitating the implementation of advanced detection and mitigation systems. Traditional flame and gas detectors, while effective, often suffer from limitations such as false alarms, delayed responses, and environmental interferences. Smart optimization of these detectors using artificial intelligence (AI), machine learning (ML), sensor fusion, and predictive analytics has emerged as a transformative approach to enhancing industrial safety. This paper explores various smart optimization techniques, including AI-driven classification models, real-time monitoring through the Internet of Things (IoT), and deep learning-based fire detection strategies. The study also highlights industrial applications of optimized flame and gas detection systems, evaluating their effectiveness compared to conventional models. Finally, future research directions in edge computing, digital twins, and sustainable safety technologies are discussed to provide a roadmap for further advancements in this domain.
... Traditional fire and gas detection technologies, while effective to some extent, suffer from limitations such as high false alarm rates, delayed response times, and susceptibility to environmental interferences. These challenges necessitate the integration of artificial intelligence (AI), Internet of Things (IoT), sensor fusion, and predictive analytics to develop more robust, intelligent, and proactive detection mechanisms (Alasa, 2020;Alasa, 2021;Alasa et al., 2024;Rahaman et al., 2024;Islam et al. 2024). This research has demonstrated that AI-enhanced detection systems, incorporating machine learning algorithms, computer vision techniques, and deep neural networks, significantly improve hazard identification by distinguishing between true threats and false positives (Zhang et al., 2024;Hossain et al., 2023). ...
Fire and gas hazards pose significant threats to industrial environments, necessitating the implementation of advanced detection and mitigation systems. Traditional flame and gas detectors, while effective, often suffer from limitations such as false alarms, delayed responses, and environmental interferences. Smart optimization of these detectors using artificial intelligence (AI), machine learning (ML), sensor fusion, and predictive analytics has emerged as a transformative approach to enhancing industrial safety. This paper explores various smart optimization techniques, including AI-driven classification models, real-time monitoring through the Internet of Things (IoT), and deep learning-based fire detection strategies. The study also highlights industrial applications of optimized flame and gas detection systems, evaluating their effectiveness compared to conventional models. Finally, future research directions in edge computing, digital twins, and sustainable safety technologies are discussed to provide a roadmap for further advancements in this domain.
... This study attempts to address this gap by exploring the intersection of AI and blockchain in business through a bibliometric and content analysis of the existing literature. The study focuses on identifying the key trends, ML, AI, and data analytics that recently emerging research areas, and technological advancements to illustrate the growing importance of this integration for businesses seeking to gain a competitive edge Rahaman et al., 2024b). ...
Artificial intelligence (AI), Machine Learning (ML), and blockchain technology are revolutionizing businesses by fostering creativity, efficiency, and security across a range of sectors. This paper explores the mutually beneficial relationship between these technologies using bibliometrics and content analysis, shedding light on their emerging applications and new research directions. We identify key industries like banking, supply chain management, and healthcare where blockchain and artificial intelligence are significantly influencing these disciplines by looking at a broad range of academic and commercial publications. Findings indicate that combining blockchain's decentralized security characteristics with AI-driven predictive analytics enhances automated decision-making, fraud detection, and transparent transactions. In a similar vein, supply chain management employs AI to forecast demand and maximize inventory, while smart contracts driven by blockchain technology streamline transportation. Blockchain and AI integration are used in healthcare applications to improve diagnosis, safeguard patient information, and facilitate interoperable medical records. Despite these advancements, problems with scalability, regulatory ambiguity, and technical complexity are impeding widespread adoption. Multidisciplinary collaboration, innovative policymaking, and advancements in blockchain and AI infrastructures are required to address these issues. By mapping the most significant papers, organizations, and academics that are affecting the field, this study offers valuable information for future research and business endeavors in this transformative sector.
Fire hazards in Gas-to-Liquids (GTL) processing facilities present substantial safety and operational challenges due to the intrinsic nature of these environments, which involve highly flammable gaseous materials, elevated temperatures, and intricate industrial processes. The convergence of these factors creates a high-risk setting where even minor ignition sources can lead to catastrophic consequences. Traditional fire detection systems, such as smoke and heat detectors, often fall short in such complex settings due to delayed response times, susceptibility to false alarms, and interference from environmental conditions like dust, humidity, or vapor emissions. To address these limitations and enhance fire safety protocols, there is a growing emphasis on deploying advanced early warning systems equipped with cutting-edge technologies. These include artificial intelligence (AI) for intelligent pattern recognition, Internet of Things (IoT) frameworks for continuous, networked data collection, and multispectral imaging for enhanced flame detection across various wavelengths. This paper provides a comprehensive analysis of the inherent fire detection challenges specific to GTL facilities, critically assesses the shortcomings of conventional systems, and highlights the potential of next-generation fire monitoring technologies. Through the integration of AI-driven flame recognition algorithms, real-time sensor networks, and predictive analytics, modern fire detection systems can significantly increase the speed and accuracy of incident response. Furthermore, this study presents real-world case studies and practical applications of these advanced technologies, illustrating their role in minimizing fire-related risks and reinforcing operational safety across GTL plants. Ultimately, the implementation of intelligent, proactive fire detection solutions represents a paradigm shift in safeguarding critical infrastructure within the energy sector.
Gas-to-Liquids (GTL) processing facilities face critical fire safety challenges due to the high flammability of gases, elevated temperatures, and complex industrial processes. Traditional fire detection systems-such as smoke, heat, and optical detectors-often underperform in such environments because of delayed response times, false alarms, and interference from dust, humidity, or vapors. To overcome these limitations, advanced technologies like artificial intelligence (AI), Internet of Things (IoT), and multispectral imaging are being integrated to enable real-time, accurate fire detection and prevention. AI-based systems, equipped with deep learning algorithms and predictive analytics, can process multispectral data and thermal imaging to detect early ignition signs with high precision. Simultaneously, IoT-enabled sensors create a continuous data network, allowing 24/7 surveillance and automated response. Computer vision and edge computing further enhance detection in obstructed or confined environments by analyzing complex thermal patterns in real time. Implementation strategies include sensor fusion, system optimization, and adaptive model training to ensure accuracy and minimize false positives. Real-world case studies illustrate the effectiveness of these technologies in mitigating risks and ensuring operational safety in GTL plants. The integration of AI and IoT marks a significant advancement in industrial fire prevention, enabling a proactive and resilient approach to safety in the energy sector.
Fire outbreaks pose significant risks to lives and property, necessitating the development of accurate and efficient detection systems. Traditional fire detection methods, such as smoke and heat sensors, often suffer from high false alarm rates and slow response times. To address these limitations, this study explores the integration of deep learning techniques with heat signature analysis to improve fire detection accuracy and reliability. The proposed system employs convolutional neural networks (CNNs) trained on a dataset comprising both infrared and visible-spectrum images, allowing for early fire detection with minimized false positives. Additionally, the study investigates the impact of environmental factors on detection performance and explores the potential of real-time monitoring using edge computing. The experimental results demonstrate that deep learning models outperform conventional fire detection techniques, offering improved precision, recall, and computational efficiency. This research provides valuable insights into the application of artificial intelligence in fire safety and highlights future directions for advancing AI-driven fire detection systems.
Fire hazards pose a significant risk to human lives, infrastructure, and the environment, necessitating the development of efficient fire detection and localization systems. Traditional methods, including smoke and heat sensors, suffer from high false alarm rates and delayed response times. In recent years, deep learning has emerged as a transformative approach, leveraging computer vision to enhance accuracy in fire detection and localization. This paper provides a comprehensive survey of deep learning techniques employed for fire detection, including Convolutional Neural Networks (CNNs), object detection models such as YOLO and Faster R-CNN, and hybrid approaches integrating multimodal data. The study further explores publicly available fire datasets, preprocessing techniques, and performance evaluation metrics. Additionally, key challenges such as real-time processing constraints, environmental variability, and model generalization are discussed. The paper concludes with an outlook on future advancements, including lightweight AI models, multi-sensor fusion, and synthetic dataset generation for robust fire detection systems.
Globally, approximately one-third of the total food produced is wasted. The environmental effect of such wastage is the contribution to greenhouse gas emissions, and economically, money that could have been reinvested in the economy is lost. This research showcases the ability of IoT devices to gather data on wastage and management as they happen, as well as big data analytics in facilitating data acquisition for enhanced decision-making. The research proposal seeks to show how the use of these technologies can support waste minimization, efficiently utilize resources, and promote sustainability in the agro-food chain. Thus, a literature survey will be applied to outline the meth-odologies currently in use, the holes in current practice, and new ideas on how to incorporate IoT and big data in waste management. Furthermore, several case studies 'success stories' are highlighted to explain and quantify an actual application of these technologies in different sectors of the agro-food chain with tangible results in terms of reduction of the number of wastes and gains in efficiency. The application of Internet of Things (IoT) technologies and big data analytics for real-time handling of agro-food waste is a major problem in the agro-food sector. The conclusions highlight the positive effect of IoT and big data in tackling agro-food waste; at the same time, they give practical suggestions for the key players throughout the supply chain. Thus, our study gives insights into a shift in the management of agro-food waste through efficient technology and rallying policymakers, industry experts, and researchers to fully capture technological improvements for development.
The evaluation report explores how the University of Arkansas at Pine Bluff (UAPB) Small Farm Program supports small-scale farmers in Arkansas by providing them with financial, production, and marketing assistance. This study examines the program's effectiveness in helping farmers improve their agricultural practices, profitability, and sustainability. The survey, conducted with randomly selected farmers, reveals how well the program delivers practical solutions in these areas. On financial and business planning assistance, most farmers said the training improved their ability to create farm business plans, with 73.3% noting that it helped them maintain accurate records and understand crop insurance. However, less than half felt that the program helped them secure loans, suggesting a need for more targeted financial training. In production assistance, 80% of respondents reported that the training improved their understanding of production techniques, leading to higher yields and productivity. Two-thirds experienced increased production, and 60% noticed a boost in agricultural productivity. To enhance the program’s long-term impact, future efforts should focus on strengthening farmer networks, improving financial access, and keeping pace with market trends. Expanding farmer association memberships can provide better networking and collective market opportunities. Regular feedback from participants will keep the program agile, addressing evolving needs and ensuring continued relevance. This approach supports sustainable growth, helping farmers turn market reach into lasting success. Overall, the UAPB Small Farm Program has positively impacted small-scale farmers, enhanced their agricultural practices, and helped them navigate financial and marketing challenges. The recommendations aim to further boost the program’s reach and effectiveness.
Purpose
Though business analytics capability continues to attract considerable industrial and scholarly attention, its holistic performance implications, especially in the post-COVID-19 period, have not been fully understood. Thus, there have been calls for a full understanding of the implications of BAC for achieving holistic, sustainable outcomes among firms. This study therefore examines the influence of BAC on the three dimensions of sustainable performance. We also proposed the mediating role of circular economy implementation.
Design/methodology/approach
We tested the proposed model using survey data from 246 managers of manufacturing firms in Ghana. Partial least squares structural equation modelling was employed to validate the model.
Findings
Our findings showed that BAC significantly enhances both sustainable performance and circular economy implementation. We also found a significant association between CEI and sustainable performance. We further found significant partial mediation of CEI in the BAC sustainable performance nexus.
Practical implications
Our study offers thoughtful insights for managers, policymakers and the academic community that firms should simultaneously implement circular models alongside building analytics competencies in the quest to achieve balanced performance outcomes.
Originality/value
To the best of our knowledge, our study is among the very few attempts to understand the mechanism that channels the benefits of BAC for a holistic, sustainable outcome.
Recently, governments and organizations have repeatedly pressed manufacturing enterprises to promote the ethical and transparent use of natural resources, lessen their negative effects on national and international ecosystems, and safeguard people and the environment. In this context, enhancing the various stages of the product/service life cycle to fulfill sustainability requirements and foster sustainable value creation is a key area of interest for researchers and professionals. This emphasis reflects the growing recognition of the importance of minimizing the environmental impact of products and services, while also maximizing their positive contributions to society, economy, and environment. To this end, this research work addresses how manufacturing enterprises benefit from life cycle sustainability assessment (LCSA) thinking to incorporate the environmental and social criteria into the product/service life cycle strategies. To do so, a novel approach based on environmental priority strategy (EPS) as an LCSA method for impacts monetization coupling with Big Data Analytics (BDA) techniques and tools is developed to evaluate and analyze the manufacturing enterprises’ impacts on the environment and society. Moreover, the developed approach evaluates manufacturing enterprises’ progress toward sustainable development goals (SDGs). Finally, to demonstrate the applicability of the developed approach, a case study from the corporate environmental impact database is used, and the obtained numerical results are analyzed showing its efficiency and added value.
Purpose
The advancements in internet technologies and the use of sophisticated digital devices in supply chain operations incessantly generate enormous amounts of data, which is termed as big data (BD). The BD technologies have brought about a paradigm shift in the supply chain decision-making towards profitability and sustainability. The aim of this work is to address the issue of implementation of the big data analytics (BDA) in sustainable supply chain management (SSCM) by identifying the relevant factors and developing a structural model for this purpose.
Design/methodology/approach
Through a comprehensive literature review and experts’ opinion, the crucial factors are found using the PESTEL framework, which covers political, economic, social, technological, environmental and legal factors. The structural model is developed based on the results of the total interpretive structural modelling (TISM) procedure and MICMAC analysis.
Findings
The policy support regarding IT, culture of data-based decision-making, inappropriate selection of BDA technologies and the laws related to data security and privacy are found to affect most of the other factors. Also, the company’s vision towards environmental performance and willingness for material and energy optimization are found to be crucial for the environmental and social sustainability of the supply chain.
Research limitations/implications
The study is focused on the manufacturing supply chain in emerging economies. It may be extended to other industry sectors and geographical areas. Also, additional factors may be included to make the model more robust.
Practical implications
The proposed model imparts an understanding of the relative importance and interrelationship of factors. This may be useful to managers to assess their strengths and weaknesses and ascertain their priorities in the context of their organization for developing a suitable investment plan.
Social implications
The study establishes the importance of BDA for conservation and management of energy and material. This is crucial to develop strategies for enhancing eco-efficiency of the supply chain, which in turn enhances the economic returns for the society.
Originality/value
This study addresses the implementation of BDA in SSCM in the context of emerging economies. It uses the PESTEL framework for identifying the factors, which is a comprehensive framework for strategic planning and decision-making. This study makes use of the TISM methodology for model development and deliberates on the social and environmental implications too, apart from theoretical and managerial implications.
Sustainability has profound implications for environmental competitiveness, yet little has been done to study the feasibility of sustainable supply chain management (SSCM) practices as a predictor of organizational performance (operational and environmental performance). By integrating stakeholder theory and dynamic capability theory, this study aims to determine the impact of corporate social responsibility (CSR) on SSCM practices and assess its impact on organizational performance. This research also investigates the role of big data analytical capabilities (BDAC) in mediating the relationship between SSCM practices and organizational performance. The authors collected data online, examined 320 valid responses, and tested research hypotheses. The findings suggest that CSR (both internal and external CSR) positively promotes SSCM practices and contributes to expanding dynamic capacity theory in the context of BDA capabilities. BDAC is also a key mediator between SSCM practices and organizational performance. These results contribute to and improve the research on stakeholder theory and SSCM practice and provide a new perspective for scholars to further study this issue.
How do information systems and big data analytics help to enable a sustainable future? This question is investigated in nine papers in this special issue that examine the issue of big data analytics for sustainability from a variety of perspectives. Broadly, these papers can be considered in four main areas: health, online behavior and consumption, safety and the environment, and methods to improve understanding of sustainability issues. Recent advances in data-driven decision-making analytics research focusing on different aspects of sustainability are discussed in these papers, including air pollution management, online health consultation services, gamification of exercise and health, sustainable urban mobility, the sustainable use of resources in hospitals, the design of anticrime information support systems, the interdependence effects among mobile social apps, networks of sustainable development goals, and the spillover effect of sustainable consumption.
With attempts being made by both governments and organizations to reduce environmental hazards, blockchain technology is becoming a crucial tool for attaining supply chain sustainability in small and medium enterprises (SME). Recognizing this fact, this study investigates the effect of blockchain technology on sustainable supply chain practices to improve organizational performance. In this investigation, data was gathered from 364 respondents, including middle and upper-level managers from SME manufacturing companies in China and Pakistan. The relationships among the endogenous and exogenous variables were tested by employing partial least squares structural equation modeling (PLS-SEM). The primary results suggest that blockchain technology and green information systems positively influence sustainable supply chain practices. Moreover, green information systems and sustainable supply chain practices possess a significant positive association. Sustainable supply chain practices have a positive and significant relationship with sustainability; i.e., operational, environmental, and economic performance. The findings also reveal that three dimensions of organizational sustainability—i.e., operational, environmental, and economic performance—have a significant effect on organizational performance. Additionally, the results of this study provide valuable insights into blockchain technology and offer policy implications for manufacturers and legislators regarding the implementation and promotion of green supply chain practices.
Digital technologies are growing in importance for accelerating firms’ circular economy transition. However, so far, the focus has primarily been on the technical aspects of implementing these technologies with limited research on the organizational resources and capabilities required for successfully leveraging digital technologies for circular economy. To address this gap, this paper explores the business analytics resources firms should develop and how these should be orchestrated towards a firm-wide capability. The paper proposes a conceptual model highlighting eight business analytics resources that, in combination, build a business analytics capability for the circular economy and how this relates to firms’ circular economy implementation, resource orchestration capability, and competitive performance. The model is based on the results of a thematic analysis of 15 semi-structured expert interviews with key positions in industry. Our approach is informed by and further develops, the theory of the resource-based view and the resource orchestration view. Based on the results, we develop a deeper understanding of the importance of taking a holistic approach to business analytics when leveraging data and analytics towards a more efficient and effective digital-enabled circular economy, the smart circular economy.
Today, most organizations are undergoing a digital transformation. At the same time, the gravity of environmental issues has put sustainability and the circular economy at the top of corporate agendas. To this end, information systems, in particular business analytics, are being highlighted as essential enablers of an accelerated circular economy transition. However, effectively managing this joint transformation is a challenge. Firms struggle to identify which organizational resources they should target and how those should be leveraged towards a firm-wide business analytics capability for circular economy. To address these questions, this study draws on recent literature dealing with smart circular economy and business analytics capabilities along with the resource-based and resource orchestration view to (1) create an instrument to measure firms’ business analytics capability for circular economy, and (2) examine the relationship among a circular economy-specific business analytics capability, circular economy implementation, resource orchestration capability, and firm performance. The proposed research model was tested using partial least squares structural equation modeling of survey data from 125 top-level managers at companies across Europe. The results show that firms with a strong business analytics capability have an increased resource orchestration capability and a greater ability to excel in the circular economy, resulting in improved organizational performance in building a more sustainable competitive advantage in an increasingly competitive business landscape. The effect of business analytics capability on firm performance is not direct but fully mediated through resource orchestration capability and circular economy implementation. The results empirically validate the proposed research model and offer pathways to future information systems research streams to support the operationalization of circular strategies. The study provides the first empirical evidence of a business analytics capability for circular economy and its effect on firm performance.
Business leaders and policymakers within service economies are placing greater emphasis on well-being, given the role of workers in such settings. Whilst people’s well-being can lead to economic growth, it can also have the opposite effect if overlooked. Therefore, enhancing subjective well-being (SWB) is pertinent for all organisations for the sustainable development of an economy. While health conditions were previously deemed the most reliable predictors, the availability of data on people’s personal lifestyles now offers a new dimension into well-being for organisations. Using open data available from the national Annual Population Survey in the UK, which measures SWB, this research uncovered that among several independent variables to predict varying levels of people's perceived well-being, long-term health conditions, one's marital status, and age played a key role in SWB. The proposed model provides the key indicators of measuring SWB for organisations using big data.
Business model innovation is considered key for organizations to achieve sustainability. However, there are many problems involving the operationalization of business model innovation. We used a design science methodology to develop an artifact to assist business model innovation efforts. The artifact uses performance measurement indicators of the company’s business model, which are powered by Big Data analytics to endow customer-driven business model innovation. Then, we applied the artifact in a critical case study. The selected company is a fashion ecommerce that proposes a vegan and sustainable value using recycled plastic bottle yarn as raw material, and ensures that no material with animal origin is used. Our findings show that the artifact successfully assists a proactive and continuous effort towards business model innovation. Although based on technical concepts, the artifact is accessible to the context of small businesses, which helps to democratize the practices of business model innovation and Big Data analytics beyond large organizations. We contribute to the business model innovation literature by connecting it to performance management and Big Data and providing paths for its operationalization. Consequently, in practice, the proposed artifact can assist managers dealing with business model as a dynamic element towards a sustainable company.
Increased greenhouse gas (GHG) emissions in the past decades have created concerns about the environment. To stymie global warming and the deterioration of the natural environment, global CO2 emissions need to reach approximately 1.3 tons per capita by 2050. However, in Malaysia, CO2 output per capita—driven by fossil fuel consumption and energy production—is expected to reach approximately 12.1 tons by the year 2020. GHG mitigation strategies are needed to address these challenges. Cleaner production, through eco-innovation, has the potential to arrest CO2 emissions and buttress sustainable development. However, the cleaner production process has been hampered by lack of complete data to support decision making. Therefore, using the resource-based view, a preliminary study consisting of energy and utility firms is undertaken to understand the impact of big data analytics towards eco-innovation. Linear regression through SPSS Version 24 reveals that big data analytics could become a strong predictor of eco-innovation. This paper concludes that information and data are key inputs, and big data technology provides firms the opportunity to obtain information, which could influence its production process—and possibly help arrest increasing CO2 emissions.
This paper investigates the organizational challenges raised by Big Data and its impact on the business environment with a focus on performance management. We investigate managers’ perceptions, understanding, and attitudes relating to Big Data and its analytics, in terms of opportunities, extent, limitations, challenges, and implications, with specific reference to performance management. The research methodology we adopt is grounded theory: we develop a reflection guide based on research questions covering the impact and challenges of a data-driven culture on business, and the impact on performance management and the decision-making process. The results obtained from senior executives from 21 Romanian companies leads to a conceptual model that distils the major areas arising from the responses and the interrelationships between them. These reveal several key areas of managerial relevance and suggest fruitful action. In particular, we find that the most critical areas requiring intervention lie in the area of awareness and understanding, goal setting, assessing benefits and limitations, learning to trust data, and commitment to an embedded data-driven culture. In addition to changes within organizations themselves, there are also implications for other stakeholders, such as education providers.
Background: Big data and predictive analytics could improve the ability to help with the sustainability of sourcing decisions. Sustainability has become a necessary goal for businesses and a powerful strategy for competitive advantage. There’s a need for sustainable innovations along the supply chain to enable companies to have a strong market presence. Developing absorptive capacity both in firms and in supply chains are also integral to responding to dynamic markets and customer needs. The main objective of this paper is to identify the features of big data and predictive analytics applied to sustainable supply chain innovation, and to analyze the role of absorptive capacity.
Methods: A literature review investigates how absorptive capacity affects the impact of the utilization of big data and predictive analytics on sustainable supply chain innovation.
Results: This paper proposes a conceptual framework linking the different elements. It also proposes a synthesis of the existing definitions of the used concepts. In particular, the role of absorptive capacity as enabler on Big Data and Predictive Analytics on sustainable supply chain innovation is stressed.
Conclusions: The paper investigates the emerging paradigm of big data and predictive analytics. The conceptual framework use theoretical foundation of absorptive capacity, and the extant literature on Big Data and predictive analytics. This framework will help us to build a research model for sustainable supply chain innovation applications. Further work is required to develop an action research methodology for validating the framework in depth within a company.
Purpose
Despite the current progress in realizing how Big Data Analytics can considerably enhance the Sustainable Manufacturing Supply Chain (SMSC), there is a major gap in the storyline relating factors of Big Data operations in managing information and trust among several operations of SMSC. This study attempts to fill this gap by studying the key enablers of using Big Data in SMSC operations obtained from the internet of Things (IoT) devices, group behavior parameters, social networks and ecosystem framework.
Design/methodology/approach
Adaptive Prospects (Improving SC performance, combating counterfeits, Productivity, Transparency, Security and Safety, Asset Management and Communication) are the constructs that this research first conceptualizes, defines and then evaluates in studying Big Data Analytics based operations in SMSC considering best worst method (BWM) technique.
Findings
To begin, two situations are explored one with Big Data Analytics and the other without are addressed using empirical studies. Second, Big Data deployment in addressing MSC barriers and synergistic role in achieving the goals of SMSC is analyzed. The study identifies lesser encounters of barriers and higher benefits of big data analytics in the SMSC scenario.
Research limitations/implications
The research outcome revealed that to handle operations efficiently a 360-degree view of suppliers, distributors and logistics providers' information and trust is essential.
Practical implications
In the Post-COVID scenario, the supply chain practitioners may use the supply chain partner's data to develop resiliency and achieve sustainability.
Originality/value
The unique value that this study adds to the research is, it links the data, trust and sustainability aspects of the Manufacturing Supply Chain (MSC).
Networked enterprises (NEs) in the current business are constantly under pressure from stakeholders and government restrictions to encourage ethical and transparent behavior in using natural resources, and their impacts on nearby and global ecosystems, people, and communities. In addition, NEs face vulnerable economical challenges including, market changes, personalized consumer trends, as well as, environmental and social restrictions. In this context, this paper addresses the problem of sustainable NEs vulnerabilities. To do so, a big data analytics-based approach is developed to drive sustainable NEs flexibility and robustness. More specifically, flexibility refers to the network’s ability to respond quickly to changes and risks. While robustness concerns the development of optimum and long-term strategies enabling the network to cope with severe environmental risks and economical costs. Moreover, even if the literature is rich with Big Data models and frameworks developed for sustainable enterprises, there is a real need to scale and extend existing models to cover all sustainability pillars (i.e., social, environmental, and economical) and sustainable value creation (SVC). Accordingly, flexibility and robustness coupling with big data analytics (BDA) levels (i.e. descriptive analytics, diagnostic analytics, predictive analytics, prescriptive analytics) will enable NEs to grow sustainability in order to create sustainable value. Finally, to demonstrate the applicability of the developed approach, the corporate environmental impact (CEI) database is used to evaluate the sustainable development goals (SDGs) of NEs. The obtained numerical results show the efficiency of our approach.
Despite the increasing number of companies deploying big data analytics (BDA) in corporate social responsibility (CSR) activities, few studies have investigated how the use of BDA in CSR activities affects CSR performance. Drawing on the resource-based view, we propose that the impact of BDA use in CSR initiatives (BDA-enabled CSR) on CSR performance depends on a firm's ability to provide data-driven insights through big data management and big data analytics (big data analytics capability, BDAC). We further show that the positive interaction effect of BDA-enabled CSR and BDAC on CSR performance is pronounced in the CSR performance categories of environmental impact, employee relations, product safety, and corporate governance, but not in community relations, human rights, and workplace diversity. This study contributes to BDA literature as well as CSR literature by empirically demonstrating how BDA-enabled CSR and BDAC influence CSR performance. This study provides practical implications to strategy managers, social entrepreneurs, venture capitalists or investors, and policy makers.
Big data analytics (BDA) is a revolutionary approach for sound decision-making in organizations that can lead to remarkable changes in transforming and supporting the circular economy (CE). However, extant literature on BDA capability has paid limited attention to understanding the enabling role of data-driven insights for supporting decision-making and, consequently, enhancing CE performance. We argue that firms drive decision-making quality through data-driven insights, business intelligence and analytics (BI&A), and BDA capability. In this study, we empirically investigated the association of BDA capability with CE performance and examined the mediating role of data-driven insights in the relationship between BDA capability and decision-making. Data were collected from 109 Czech manufacturing firms, and partial least squares structural equation modeling was applied to analyze the data. The results reveal that BDA capability and BI&A are positively associated with decision-making quality. This effect is stronger when the manufacturer utilizes data-driven insights. The results demonstrate that BDA capability drives decision-making quality in organizations, and data-driven insights do not mediate this relationship. BI&A is associated with decision-making quality through data-driven insights. These findings offer important insights to managers, as they can act as a reference point for developing data-driven insights with the CE paradigm in organizations.
Despite the publicity regarding big data and analytics (BDA), the success rate of these projects and strategic value created from them are unclear. Most literature on BDA focuses on how it can be used to enhance tactical organizational capabilities, but very few studies examine its impact on organizational value. Further, we see limited framing of how BDA can create strategic value for the organization. After all, the ultimate success of any BDA project lies in realizing strategic business value, which gives firms a competitive advantage. In this study, we describe the value proposition of BDA by delineating its components. We offer a framing of BDA value by extending existing frameworks of information technology value, then illustrate the framework through BDA applications in practice. The framework is then discussed in terms of its ability to study constructs and relationships that focus on BDA value creation and realization. We also present a problem-oriented view of the framework—where problems in BDA components can give rise to targeted research questions and areas for future study. The framing in this study could help develop a significant research agenda for BDA that can better target research and practice based on effective use of data resources.
Purpose
The purpose of this paper is to develop a theoretical model to explain the impact of big data and predictive analytics (BDPA) on sustainable business development goal of the organization.
Design/methodology/approach
The authors have developed the theoretical model using resource-based view logic and contingency theory. The model was further tested using partial least squares-structural equation modeling (PLS-SEM) following Peng and Lai (2012) arguments. The authors gathered 205 responses using survey-based instrument for PLS-SEM.
Findings
The statistical results suggest that out of four research hypotheses, the authors found support for three hypotheses (H1-H3) and the authors did not find support for H4. Although the authors did not find support for H4 (moderating role of supply base complexity (SBC)), however, in future the relationship between BDPA, SBC and sustainable supply chain performance measures remain interesting research questions for further studies.
Originality/value
This study makes some original contribution to the operations and supply chain management literature. The authors provide theory-driven and empirically proven results which extend previous studies which have focused on single performance measures (i.e. economic or environmental). Hence, by studying the impact of BDPA on three performance measures the authors have attempted to answer some of the unresolved questions. The authors also offer numerous guidance to the practitioners and policy makers, based on empirical results.
In the strategic management field, dynamic capabilities (DC) such as organizational agility are considered to be paramount in the search for competitive advantage. Recent research claims that IT business value research needs a more dynamic perspective. In particular, the Big Data Analytics (BDA) value chain remains unexplored. To assess BDA value, a conceptual model is proposed based on a knowledge-based view and DC theories. To empirically test this model, the study addresses a survey to a wide range of 500 European firms and their IT and business executives. Results show that BDA can provide business value to several stages of the value chain. BDA can create organizational agility through knowledge management and its impact on process and competitive advantage. Also, this paper demonstrates that agility can partially mediate the effect between knowledge assets and performance (process level and competitive advantage). The model explains 77.8% of the variation in competitive advantage. The current paper also presents theoretical and practical implications of this study, and the study's limitations.
This book presents and discusses the main strategic and organizational challenges posed by Big Data and analytics in a manner relevant to both practitioners and scholars. The first part of the book analyzes strategic issues relating to the growing relevance of Big Data and analytics for competitive advantage, which is also attributable to empowerment of activities such as consumer profiling, market segmentation, and development of new products or services. Detailed consideration is also given to the strategic impact of Big Data and analytics on innovation in domains such as government and education and to Big Data-driven business models. The second part of the book addresses the impact of Big Data and analytics on management and organizations, focusing on challenges for governance, evaluation, and change management, while the concluding part reviews real examples of Big Data and analytics innovation at the global level. The text is supported by informative illustrations and case studies, so that practitioners can use the book as a toolbox to improve understanding and exploit business opportunities related to Big Data and analytics.
Purpose
– Increased business competition requires even more rapid and sophisticated information and data analysis. These requirements challenge performance management to effectively support the decision making process. Business analytics is an emerging field that can potentially extend the domain of performance management to provide an improved understanding of business dynamics and lead to a better decision making. The purpose of this positional paper is to introduce performance management analytics as a potential extension of performance management research and practice. The paper clarifies the possible application areas of business analytics and their advantages within the context of performance management.
Design/methodology/approach
– The paper employs a literature based analysis and from this a conceptual argument is established. Finally, a business analytical model is presented to be used to undertake future research.
Findings
– The paper clarifies the possible application areas of business analytics and their advantages within the context of organizational performance management.
Originality/value
– The main implication is that the paper provides evidence of the use of business analytics for understanding organizational performance. Several insights are provided for management accounting research and education.
Linkages between big data analytics, circular economy, sustainable supply chain flexibility, and sustainable performance in manufacturing firms
- Edwin Cheng
- T C Kamble
- S S Belhadi
- A Ndubisi
- N O Lai
- K H Kharat
Edwin Cheng, T. C., Kamble, S. S., Belhadi, A., Ndubisi, N. O., Lai, K. H., & Kharat, M. G. (2022).
Linkages between big data analytics, circular economy, sustainable supply chain flexibility,
and sustainable performance in manufacturing firms. International Journal of Production
Research, 60(22), 6908-6922.