Do Companies Adopt Big Data as Determinants of Sustainability: Evidence from Manufacturing Companies in Jordan

Information and communication technology make it easier for managers to gather customer data quickly and efficiently. However, managing, analysing, and utilizing the vast amount of data for sustainability decision are not easy. Therefore, this study aims to examine the readiness of manufacturing firms in adopting big data analytics in sustainable development. Moreover, this study employed the Partial Least Square Structural Equation Modelling (PLS-SEM) technique and analyses the data collected from 172 respondents working in different organizations in Amman and Jordan. The results reveal that there is a significant relationship between top management support and competitive pressures and intentions to adopt big data analytics. However, the moderating influence of perceived risk on the relationship between intention and actual use of big data has not been proved. The study provides fresh findings on determinants of intention to adopt big data analytics, actual use, and moderating role of perceived risk within the model to develop sustainability. Furthermore, the study has a number of theoretical and practical implications. Our main findings provide a deeper understanding of the enablers of BDA adoption through the development of a framework that includes direct and moderating constructs, as well as recommendations to practitioners on how to enhance BDA adoption based on eight BDA enablers

Predictive Analytics Model for Power Consumption in Manufacturing

AbstractA Smart Manufacturing (SM) system should be capable of handling high volume data, processing high velocity data and manipulating high variety data. Big data analytics can enable timely and accurate insights using machine learning and predictive analytics to make better decisions. The objective of this paper is to present big data analytics modeling in the metal cutting industry. This paper includes: 1) identification of manufacturing data to be analyzed, 2) design of a functional architecture for deriving analytic models, and 3) design of an analytic model to predict a sustainability performance especially power consumption, using the big data infrastructure. A prototype system has been developed for this proof-of-concept, using open platform solutions including MapReduce, Hadoop Distributed File System (HDFS), and a machine-learning tool. To derive a cause-effect relationship of the analytic model, STEP-NC (a standard that enables the exchange of design- to-manufacturing data, especially machining) plan data and MTConnect machine monitoring data are used for a cause factor and an effect factor, respectively

Opening the black box of big data sustainable value creation: the mediating role of supply chain management capabilities and circular economy practices

Purpose – This article examines the mechanisms through which big data analytics capabilities (BDAC) contribute to creating sustainable value and analyzes the mediating roles that supply chain management capabilities (SCMC), as well as circular economy practices (CEP), play through their impact on sustainable performance. Design/methodology/approach – Following a literature review, a serial mediation model is presented. Hypotheses regarding direct and mediating relationships are tested to determine their potential for sustainability impact and circularity. Partial least squares structural equation modeling (PLS-SEM) has been applied for causal and predictive purposes. Findings – The results indicate that big data analytics capabilities do not have a direct positive impact on sustainable performance but influence indirectly through SCMC and CEP. Originality/value – Although some authors have addressed the associations between IT business value, supply chain (SC), and sustainability, this paper provides empirical evidence related to these relationships. Additionally, this study performs novel predictive analyses.Junta de Andalucía US-126445

THE IMPACT OF SUSTAINABLE BRANDING USING BIG DATA AND BUSINESS ANALYTICS IN THE MARKET RESEARCH INDUSTRY

Abstract Aim: The research aimed to explore how sustainable branding and big data analytics could enhance brand equity and sustainability in the market research industry. It reviewed existing literature, analysed branding strategies of data-driven companies, identified key attributes for sustainable positioning, used qualitative research methods to investigate competitive advantage, and created a theoretical framework to demonstrate how sustainable branding could improve performance in data-driven companies using big data and analytics. Methodology: This research used qualitative methods for a systematic review of sustainability, branding, and business analytics in the market research industry. It involved semi-structured interviews with 38 senior managers and directors from 24 companies across 8 countries. Despite the impact of COVID-19 on data collection due to changes in working patterns, this study showcased the potential of modern qualitative methods such as the 'inductive a priori' model. It utilized advanced technologies and multi-disciplinary research to tackle complex industry concepts. The research sought to bring about sustainable change in the market research industry. Results: The results of the study indicated that sustainable branding was positively related to consumer behaviour, corporate reputation, and financial performance. Big data and business analytics offered valuable insights into consumer preferences, attitudes, and behaviour which helped companies to develop and manage successful sustainable branding strategies. The study provided a comprehensive framework for understanding the role of sustainable branding, big data, and business analytics in the market research industry. Contribution to knowledge: The contribution of the study lies in identifying the importance of sustainable branding and its relationship with big data and business analytics. The study highlighted the potential benefits of integrating sustainability practices into branding strategies and suggested practical implications for companies to adopt sustainable branding approaches. The findings of the study offered insights into the value of big data and business analytics in the market research industry and provided a basis for future research in this field

Business Analytics (BA) - powered transformation for environmental and social sustainability in organisations: A dynamic capabilities perspective

The impetus to address issues of global warming, pollution, and social inclusiveness continues to grow, forcing organisations to focus on their environmental and social sustainability. The sustainability imperative has a direct impact on how organisations operate and define their competitive advantage, this study will provide insights into the BA-powered capabilities leveraged by organisations to achieve their sustainability goals. Previous studies have explored the role of big data analytics capabilities in strengthening dynamic capabilities (DC), and the positive relationship between DC, environmental, social, and economic sustainability, yet have neglected to analyze the BA-powered capabilities that transform organisations for sustainability. This study examines how BA can facilitate the development of socio-technical capabilities to enable organisations to adapt, reconfigure and transform their internal processes to achieve sustainability and understand capabilities required to (i) unlock sustainability-related insights from analytics, and (ii) transform insights into value-creating activities that help attain sustainability goals within organisations

Essential Micro-foundations for Contemporary Business Operations: Top Management Tangible Competencies, Relationship-based Business Networks and Environmental Sustainability

Although various studies have emphasized linkages between firm competencies, networks and sustainability at organizational level, the links between top management tangible competencies (e.g., contemporary relevant quantitative-focused education such as big data analytics and data-driven applications linked with the internet of things, relevant experience and analytical business applications), relationship-based business networks (RBNs) and environmental sustainability have not been well established at micro-level, and there is a literature gap in terms of investigating these relationships. This study examines these links based on the unique data collected from 175 top management representatives (chief executive officers and managing directors) working in food import and export firms headquartered in the UK and New Zealand. Our results from structural equation modelling indicate that top management tangible competencies (TMTCs) are the key determinants for building RBNs, mediating the correlation between TMTCs and environmental sustainability. Directly, the competencies also play a vital role towards environmental practices. The findings further depict that relationship-oriented firms perform better compared to those which focus less on such networks. Consequently, our findings provide a deeper understanding of the micro-foundations of environmental sustainability based on TMTCs rooted in the resource-based view and RBNs entrenched in the social network theory. We discuss the theoretical and practical implications of our findings, and we provide suggestions for future research

Big Data Sustainability: An Environmental Management Systems Analogy

Today, organizations globally wrestle with how to extract valuable insights from diverse data sets without invading privacy, causing discrimination, harming their brand, or otherwise undermining the sustainability of their big data projects. Leaders in these organizations are thus asking: What management approach should businesses employ sustainably to achieve the tremendous benefits of big data analytics, while minimizing the potential negative externalities? This Paper argues that leaders can learn from environmental management practices developed to manage the negative externalities of the industrial revolution. First, it shows that, along with its many benefits, big data can create negative externalities that are structurally similar to environmental pollution. This suggests that management strategies to enhance environmental performance could provide a useful model for businesses seeking sustainably to develop their personal data assets. Second, this Paper chronicles environmental management’s historical progression from a back-end, siloed approach to a more proactive and collaborative “environmental management system” method. An approach modeled after environmental management systems—a Big Data Management System approach—offers an effective model for managing data analytics operations to prevent negative externalities. Finally, this Paper shows that a Big Data Management System approach aligns with: (A) Agile software development and DevOps practices that companies use to develop and maintain big data applications, (B) best practices in Privacy by Design and Privacy Engineering, and (C) emerging trends in organizational management theory. At this critical, formative moment when organizations begin to leverage personal data to revolutionary ends, we can readily learn from environmental management systems to embrace sustainable big data management from the outset

Fuzzy VIKOR approach for selection of big data analyst in procurement management

Background: Big data and predictive analysis have been hailed as the fourth paradigm of science. Big data and analytics are critical to the future of business sustainability. The demand for data scientists is increasing with the dynamic nature of businesses, thus making it indispensable to manage big data, derive meaningful results and interpret management decisions. Objectives: The purpose of this study was to provide a brief conceptual review of big data and analytics and further illustrate the use of a multicriteria decision-making technique in selecting the right skilled candidate for big data and analytics in procurement management. Method: It is important for firms to select and recruit the right data analyst, both in terms of skills sets and scope of analysis. The nature of such a problem is complex and multicriteria decision-making, which deals with both qualitative and quantitative factors. In the current study, an application of the Fuzzy VIsekriterijumska optimizacija i KOmpromisno Resenje (VIKOR) method was used to solve the big data analyst selection problem. Results: From this study, it was identified that Technical knowledge (C1), Intellectual curiosity (C4) and Business acumen (C5) are the strongest influential criteria and must be present in the candidate for the big data and analytics job. Conclusion: Fuzzy VIKOR is the perfect technique in this kind of multiple criteria decisionmaking problematic scenario. This study will assist human resource managers and procurement managers in selecting the right workforce for big data analytics