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Big Data and Predictive Analytics for Supply Chain Sustainability: A Theory-driven Research Agenda
Abstract
Big data and predictive analytics (BDPA) tools and methodologies are leveraged by businesses in many ways to improve operational and strategic capabilities, and ultimately, to positively impact corporate financial performance. BDPA has become crucial for managing supply chain functions, where data intensive processes can be vastly improved through its effective use. BDPA has also become a competitive necessity for the management of supply chains, with practitioners and scholars focused almost entirely on how BDPA is used to increase economic measures of performance. There is limited understanding, however, as to how BDPA can impact other aspects of the triple bottom-line, namely environmental and social sustainability outcomes. Indeed, this area is in immediate need of attention from scholars in many fields including industrial engineering, supply chain management, information systems, business analytics, as well as other business and engineering disciplines. The purpose of this article is to motivate such research by proposing an agenda based in well-established theory. This article reviews eight theories that can be used by researchers to examine and clarify the nature of BDPA’s impact on supply chain sustainability, and presents research questions based upon this review. Scholars can leverage this article as the basis for future research activity, and practitioners can use this article as a means to understand how company-wide BDPA initiatives might impact measures of supply chain sustainability.
... It identifies three types of pressures: i) coercive pressures from regulations and governmental entities, ii) normative pressures, driven by customers and stakeholders, and iii) mimetic pressures from competitive forces. Caldera, Desha, and Dawes, 2019;Sardana et al., 2020;Wilhelm et al., 2015;Behl et al., 2022;Govindan, 2018;Hazen et al., 2016;Varsei et al., 2014;Glover et al., 2014 Natural resourcebased view ...
... A central concept in this theory is power, which is defined as the ability to control critical resources. Carter and Rogers, 2008;Grimm, Hofstetter, and Sarkis, 2014;Wilhelm et al., 2015;Dania, Xing, and Amer, 2018;Hazen et al., 2016;Wolf, 2014;Esfahbodi, Zhang, and Watson, 2016;Hajmohammad and Vachon, 2015;Touboulic, Chicksand, and Walker, 2014;Jawaad and Zafar, 2019 Transaction cost economics This theory posits that an optimal organizational structure can be attained by minimizing exchange costs. Carter and Rogers, 2008;Jraisat et al., 2021;Grimm, Hofstetter, and Sarkis, 2014;Nayal et al., 2021;Hazen et al., 2016;Alghababsheh and Gallear, 2020;Varsei et al., 2014;Pagell, Wu, and Wasserman 2010;Ahmadi-Gh and Bello-Pintado, 2022;Meinlschmidt, Schleper, and Foerstl, 2018 ...
... Carter and Rogers, 2008;Grimm, Hofstetter, and Sarkis, 2014;Wilhelm et al., 2015;Dania, Xing, and Amer, 2018;Hazen et al., 2016;Wolf, 2014;Esfahbodi, Zhang, and Watson, 2016;Hajmohammad and Vachon, 2015;Touboulic, Chicksand, and Walker, 2014;Jawaad and Zafar, 2019 Transaction cost economics This theory posits that an optimal organizational structure can be attained by minimizing exchange costs. Carter and Rogers, 2008;Jraisat et al., 2021;Grimm, Hofstetter, and Sarkis, 2014;Nayal et al., 2021;Hazen et al., 2016;Alghababsheh and Gallear, 2020;Varsei et al., 2014;Pagell, Wu, and Wasserman 2010;Ahmadi-Gh and Bello-Pintado, 2022;Meinlschmidt, Schleper, and Foerstl, 2018 ...
The objective of this paper is to examine the progression of research literature on sustainable supply chain management and present the prevailing theoretical lenses employed in this field. Through a systematic literature review, this paper identifies: i) dominant theories, ii) the roles played by dominant theories and their combination, and iii) the correlation between dominant theories and research methodologies. The goal is to assist researchers in approaching sustainable supply chain management studies by familiarizing them with the current state-of-the-art theoretical perspectives commonly employed in the field. Additionally, this review develops research propositions and a “theoretical toolbox” to empower scholars in gaining a more profound comprehension of these theoretical perspectives.
... A framework proposed to reduce supply risk and enhance environmental sustainability taking into account basic research, technical development, application, and re-phase across a product life cycle 6 Hazen, et al. [45] Big data and predictive analytics for supply chain sustainability: A theory-driven research agenda 2016 ...
... Technology leapfrogging is seen as the radical adoption of advanced technology where immediate prior technology has been absent [62]. For developing countries, this would allow companies to leapfrog foreign competitors [45]. Leapfrogging can be linked with a form of innovation and obtaining marketplace while being environmentally compliant. ...
... Leapfrogging can be linked with a form of innovation and obtaining marketplace while being environmentally compliant. Hazen, et al. [45] believe that by leveraging big data and predictive analytics and applying this to resources and firm expertise, a marketplace advantage can be obtained. Furthermore, a decision support system becomes a crucial component when evaluating sustainability metrics, as explored by. ...
This research identifies the relationship between industrial engineering and environmental sustainability knowledge components. A combination of a systematic literature review (SLR) and applied thematic analysis (ATA) is employed to uncover the pertinent literature associated with the purpose of this research. Whilst various forms of strategies, theories, methods, and practices were uncovered in each of the knowledge components, only a few were overlapping. These overlapping components include green supply chain operations, circular economy, and technology management. This study is the first in a series of studies contextualising industrial engineering knowledge in terms of its applicability to environmental sustainability. These results reveal concepts from industrial engineering and environmental sustainability knowledge components that can be used to systematically design methodologies or practically implement them in an industry or organisation. Methods and practices were a dedicated theme in the analysis, and these can be used by practitioners. A circle packing diagram is crafted using the IISE Body of Knowledge as a means of categorisation. This study considered industrial engineering as a catalyst in creating new forms of transdisciplinary knowledge areas. It also considers how industrial engineering knowledge can contribute to meeting environmental challenges.
... The introduction of technology-based sensory systems in operations provides value for firms (Hazen et al., 2016). The networking of interrelated devices (either through sensors, software, and other technologies) has been successfully applied in production process monitoring (Hopkins and Hawking, 2018), logistics tracking (Khan et al., 2019), and even in warehouse operations (da Silva et al., 2018). ...
... While research examining digital technologies has been relevant for some time, recently, there has been emergent interest in the adoption of these technologies at a supply chain level (Hazen et al., 2016). Experience seems to suggest that the adoption of digital infrastructure capabilities offers organizations several benefits. ...
... Several authors also confirm the value of digital infrastructure capabilities in supply chain operations (Ivanov and Dolgui, 2020). Digital technology venturing facilitates the management of supply chain relationships (Hopkins and Hawking, 2018), and also improves inventory systems (Hazen et al., 2016), resource allocation optimization (da Silva et al., 2018), and customer demand forecasting (Ambulkar et al., 2015). In other research (Pathak, 2023) digital technologies such as machine learning and artificial intelligence positively impacted the relationships between buyers and suppliers. ...
... BA has the capacity and potential to evaluate an organisation's strategic action in order to achieve successful business planning. Big data is a source of value creation and a CA since it allows for the detection of performance patterns, supports operational processes, facilitates better control of data (Avelar-Sosa et al., 2014;Yu et al., 2018) and can be used to enhance the operational and financial performance of firms (Akter et al., 2016;Hazen et al., 2016). BDA can also be instrumental in improving SSCM performance (Bag et al., 2020). ...
... A green SC may be implemented with enhanced data quality management and integrated data capture (Zhao et al., 2017). Several studies on BDA have been made in terms of supplier performance assessment (Addo-Tenkorang & Helo, 2016), SC analytics (Arunachalam et al., 2018), SC agility (Fosso Wamba et al., 2018), SC sustainability (Hazen et al., 2016;, SC innovation (Hazen et al., 2016;Papadopoulos et al., 2017), and SC chain innovation capability (Tan et al., 2015). BDA helps with capital budgeting, financial planning, and optimising investment return (Fosso Wamba et al., 2020). ...
... A green SC may be implemented with enhanced data quality management and integrated data capture (Zhao et al., 2017). Several studies on BDA have been made in terms of supplier performance assessment (Addo-Tenkorang & Helo, 2016), SC analytics (Arunachalam et al., 2018), SC agility (Fosso Wamba et al., 2018), SC sustainability (Hazen et al., 2016;, SC innovation (Hazen et al., 2016;Papadopoulos et al., 2017), and SC chain innovation capability (Tan et al., 2015). BDA helps with capital budgeting, financial planning, and optimising investment return (Fosso Wamba et al., 2020). ...
The food supply chain (FSC) is becoming more sustainable as companies aim to meet demand with lower waste and emissions. Big data analytics (BDA) can help achieve sustainability goals by extracting meaningful information from past data to help create sustainable strategies. However, in the sustainability literature, BDA's role in enabling sustainable FSC innovations is not explored. Thus, this study investigates how data-driven analytics might improve FSC innovation by adopting creative tactics in every triple bottom line (TBL) component-green, corporate social responsibility (CSR), and financial-to gain a competitive edge. A resource-based view (RBV) perspective was used to evaluate the links between supply chain (SC) innovation capabilities and competitive advantage (CA) in FSC innovation and sustainability. Indian food processing enterprises were surveyed using a questionnaire to collect data from 200 respondents. Adopting a structural equation modelling (SEM) approach, six hypotheses were evaluated for significance on the surveyed data using AMOS V.20. Since both goodness and badness fit indices were above cutoff values, the measurement model was robustly evaluated and found to fit the survey data well. Structural model findings supported all study hypotheses. The results indicate that BDA strongly impacts food supply chain TBL and FSC innovation. Data-driven innovative TBL methods were shown to boost FSC competitiveness. With the growing demand for value-added innovation in FSC sustainable development, this study uniquely contributes to the current literature by linking BDA and TBL practice innovation to FSC CA.
... Other research investigates the relationship between big data analytics and financial performance of organisations Wamba et al., 2017). Only very limited studies examine the impact of big data on supply chain sustainability (Hazen et al., 2016;Jeble et al., 2018). ...
... Moreover, this domain has yet to earn significant interest in academia. According to Hazen et al. (2016), studies that address the relationship between big data analytics and sustainability in the context of supply chains are contemporarily relevant and quite scant which requires further attention from practitioners and academia. This is particularly true considering the COVID-19 outbreak. ...
Sustainability of supply chains are current buzzwords globally and are now more important than ever. Big data analytics holds many promises to address supply chain issues. However, the potential of big data analytics to address COVID-19 pandemic issues, particularly supply chain sustainability, remains vague and understudied. This study highlights the challenges facing supply chains resulting from the COVID-19 outbreak and investigates the role of big data analytics in promoting supply chain sustainability. Our findings revealed that big data analytics can play a vital role in overcoming supply chain disruptions and enhancing the sustainability of supply chains during the COVID-19 pandemic.
... Fortune 1000 companies, due to their immense size and operational complexity, present unique opportunities and challenges in the application of big data analytics and business intelligence (Maroufkhani et al., 2019). These organizations operate on a large scale, often spanning multiple countries and markets, which results in the generation of extensive data from various sources such as sales transactions, customer interactions, and supply chain operations (Hazen et al., 2016;Wang et al., 2016). By effectively harnessing this data, Fortune 1000 companies can enhance their operational efficiency, improve customer experiences, and drive innovation. ...
... The impact of Walmart on local economies and its use of big data analytics to enhance operations have been extensively studied, highlighting both the positive and negative consequences of its expansive presence. Hazen et al. (2016) reviewed the literature on Walmart's influence on local economic development, examining the effects on retail and nonretail businesses, employment, wages, and community welfare. Their analysis reveals a dichotomy where Walmart's presence boosts job creation, tax revenues, and consumer savings through lower prices, yet simultaneously threatens small local businesses and potentially depresses wages (Erevelles et al., 2016). ...
This study explores the transformative impact of big data analytics and business intelligence on the operations and strategic decision-making of Fortune 1000 companies, with a particular focus on Walmart. Walmart's integration of advanced data analytics tools has significantly optimized various business areas, including inventory management, customer engagement, and supply chain operations. By leveraging big data, Walmart has gained profound insights into customer behavior, enabling accurate demand forecasting and streamlined operations that enhance operational efficiency and competitive advantage. The study highlights Walmart's use of predictive analytics to improve inventory management and supply chain efficiency. Analyzing purchasing patterns and customer preferences has reduced stockouts and excess inventory, boosting customer satisfaction and minimizing costs. Despite its advanced infrastructure, Walmart faces challenges in data integration and real-time analytics due to data silos created by its vast operations. Enhancing real-time analytics integration and data governance practices is crucial to ensure data quality, security, and compliance. Additionally, the study examines Walmart's strategic use of dynamic pricing algorithms to adjust prices in real-time based on market conditions, effectively maximizing sales and profitability. This aligns with previous research on the benefits of dynamic pricing in retail. Furthermore, the broader economic implications of Walmart's data-driven strategies are discussed, noting that while Walmart's efficient operations and lower prices benefit consumers, they also pose challenges for small local businesses. This study provides a detailed analysis of Walmart's leverage of big data analytics and business intelligence to sustain its competitive advantage and drive business success. It offers valuable insights for other Fortune 1000 companies on the importance of technology, organizational culture, and governance in achieving sustained business success.
... Fortune 1000 companies, due to their immense size and operational complexity, present unique opportunities and challenges in the application of big data analytics and business intelligence (Maroufkhani et al., 2019). These organizations operate on a large scale, often spanning multiple countries and markets, which results in the generation of extensive data from various sources such as sales transactions, customer interactions, and supply chain operations (Hazen et al., 2016;Wang et al., 2016). By effectively harnessing this data, Fortune 1000 companies can enhance their operational efficiency, improve customer experiences, and drive innovation. ...
... The impact of Walmart on local economies and its use of big data analytics to enhance operations have been extensively studied, highlighting both the positive and negative consequences of its expansive presence. Hazen et al. (2016) reviewed the literature on Walmart's influence on local economic development, examining the effects on retail and nonretail businesses, employment, wages, and community welfare. Their analysis reveals a dichotomy where Walmart's presence boosts job creation, tax revenues, and consumer savings through lower prices, yet simultaneously threatens small local businesses and potentially depresses wages (Erevelles et al., 2016). ...
This study investigates the transformative impact of big data analytics and business intelligence on the operations and strategic decision-making of Fortune 1000 companies, with a focus on Walmart. Walmart's integration of advanced data analytics tools has enabled significant optimization across various business areas, including inventory management, customer engagement, and supply chain operations. Leveraging big data, Walmart has gained deep insights into customer behavior, allowing for accurate demand forecasting and streamlined operations, which enhance operational efficiency and competitive advantage. The study highlights Walmart's use of predictive analytics to improve inventory management and supply chain efficiency, demonstrating how analyzing purchasing patterns and customer preferences reduces stockouts and excess inventory, thus boosting customer satisfaction and minimizing costs. Despite its advanced infrastructure, Walmart faces challenges in data integration and real-time analytics due to data silos created by its vast operations. Enhancing real-time analytics integration and data governance practices is crucial to ensure data quality, security, and compliance. Additionally, the study examines Walmart's strategic use of dynamic pricing algorithms to adjust prices in real-time based on market conditions, effectively maximizing sales and profitability, aligning with previous research on dynamic pricing benefits in retail. Furthermore, the broader economic implications of Walmart's data-driven strategies are discussed, noting that while Walmart's efficient operations and lower prices benefit consumers, they also pose challenges for small local businesses. This study provides a detailed analysis of Walmart's leverage of big data analytics and business intelligence to sustain its competitive advantage and drive business success, offering valuable insights for other Fortune 1000 companies on the importance of technology, organizational culture, and governance in achieving sustained business success.
... As such, the organizational exterior environment was defined as the institutional environment, and institutionalization appeared when there was common figuration of habitual activities and operations by definite participants in this environment (Berger and Luckmann, 1966). There have been thought-provoking chances to explore big data via institutional theory as a theoretical lens (Braganza et al., 2017;Hazen et al., 2016). Particularly, institutional theory possessed the conceivable to investigate the way that big data analytics could demonstrate its effect on financial, social, and environmental performance approaches (Hazen et al., 2016). ...
... There have been thought-provoking chances to explore big data via institutional theory as a theoretical lens (Braganza et al., 2017;Hazen et al., 2016). Particularly, institutional theory possessed the conceivable to investigate the way that big data analytics could demonstrate its effect on financial, social, and environmental performance approaches (Hazen et al., 2016). As such, Institutional theory could also allow the researchers to grasp which burdens had the significant effect on the dissemination and application of big data analyses in particular areas. ...
The current research conceptualizes and validates a model concentrating on how policy initiatives foster the big data management capabilities (BDMC) to achieve sustainability. Additionally, it also pursues to delve into the mediation mechanism of Global brain reflective management accounting practices (GBAP) in the linkage between BDMC and sustainability. Outstandingly, it makes several endeavors to deepen insight on whether the extent of the effect of BDMC on GBAP and the effect of GBAP on sustainability vary resting on specific degree of innovation human resource management (IHRM). The statistical data of a convenient and snowball sample of 612 participants was gathered from a structured and close-ended questionnaire survey. In order to bring forth the proposed hypothesized interconnections, the fundamental analytical instrument utilized was structural equation modeling (SEM). Additionally, multi-group SEM analysis was also applied to corroborate the moderating effects of IHRM. Beyond ameliorating the insight into how intersection of accounting practices and new technologies could make a huge contribution to BDMC enhancement to reach the sustainability paradigm, the observations of this research gave rise to the practical implications for the practitioners in organizational management and policy-makers in promulgating rules in relation to digital transformation implementation within small and medium enterprises.
... Strong applications and systems are essential for real-time tracking, logistics, operations, sales, production, and financial data for performance management (Jalil et al., 2019). Businesses that use analytics decision-making outputs in SCs to increase profitability (Hazen et al., 2016). Procedures for SC operation planning are outlined in the plan processes. ...
Business performance depends on supply chain management (SCM), which can be enhanced through digital transformation to enable quick decisions to increase market share. By enabling informed decision-making, business intelligence (BI) can facilitate this improvement in SCM effectiveness and competitive edge. Real-time production and inventory level monitoring is made possible by BI capabilities, which help organisations adapt to changing market trends and meet customer demands. Particularly, BI technology can increase the SCM of enterprises in the consumer products sector by providing value-added and financial gains, enabling speedy industry adoption and rapid industry growth. The study employed a quantitative methodology for gathering data, and a simple random sampling method was employed to prevent sample bias. The study considered linear curve estimation (LCE) as a method of data analysis. The results of the model estimations showed that BI recorded positive and significant influence on SC visibility, flexibility, and resilience in the Nigerian consumer goods sector. The study concludes that BI increased the effectiveness of supply chain management (SCM) by improving visibility, anticipating demand, increasing resilience, optimising processes, and boosting productivity. SCM effectiveness depends on the efficient handling of disruptions and uncertainty. The study highlights the practical policy implications following the findings of the study.
... Considering resources, some researchers have argued that the use of the dynamic capabilities view, an extension of the resource-based view, is relevant for managers to reorganise resources easily to satisfy consumer demand, which is the most critical factor in achieving SCS (Teece et al., 1997). The resource-based view or the dynamic capabilities view have been applied by researchers to explore potential approaches to SCS, such as supply chain risk management (SCRM) (Nisar et al., 2022), supply chain finance (SCF) , supply chain members' relationships (Kumar and Rahman, 2016) and technological adoption (Hazen et al., 2016). ...
Purpose
This paper aims to investigate the triangular interconnections among supply chain finance (SCF), supply chain risk management (SCRM) and supply chain sustainability (SCS) within the context of small and medium-sized enterprises (SMEs) under the theoretical foundation of dynamic capabilities view.
Design/methodology/approach
A total of 319 valid data sets were gathered from SMEs in China to evaluate the research model. This study uses partial least square structural equation modelling and necessary condition analysis as the two statistical methodologies for the assessment.
Findings
The findings indicate that SCF positively impacts on both SCRM and SCS, whereas SCRM also positively influences SCS. Furthermore, it has been observed that SCRM partially mediates the connection between SCF and SCS.
Research limitations/implications
The findings contribute to the literature of SCS by empirically validating the direct and mediating impacts of SCRM on SCS.
Practical implications
The results provide valuable insights that can assist SME stakeholders, owners and managers in developing strategies to effectively incorporate SCF and SCRM practices, thereby enhancing SCS performance.
Originality/value
This study expands the existing research on SCF and SCRM in the context of promoting SCS, specifically from the viewpoint of an Asian developing country.
... Lastly, studies like (Raut et al. 2019;Hazen et al. 2016) recap the importance of big data and predictive analytics in making supply chains more sustainable. Through a survey in the manufacturing sector, one study (Raut et al. 2019) shows that predictive models enable organizations to anticipate demand variations, which in turn reduces waste and overproduction. ...
This study aims to analyze trends, pioneers, emerging issues, and potential future research in the field of digital technologies such as blockchain, artificial intelligence, big data, fintech, and digital transformation for corporate sustainability. Using VOSviewer, R-studio, and BiblioMagika, this bibliometric review analyses 1251 articles published between 1995 and 2024 from the Scopus database. It highlights gaps in the knowledge and possible areas for further research in digital technologies and sustainability. Based on the findings, it can be determined that recent scholarly work has focused on topics such as digitalisation and sustainability, AI and sustainable development, blockchain and environmental technology, financial technology and green innovation, and energy policy and carbon emissions. This study is useful in helping emerging scholars identify and understand current trends in digital technologies and sustainability.
... Other studies have investigated the relationship between data analytics and the financial performance of companies . Only a few of these studies examine how data analytics can be leveraged to enhance environmental sustainability (Hazen et al., 2016;Jeble et al., 2018). ...
This research work is an assessment of how data analytics contributes to environmental sustainability. The objectives of the study were: To determine how data analytics affects environmental sustainability, To find out barriers to data analytics and environmental sustainability, To discover opportunities for data analytics and environmental sustainability, and To ascertain how data analytics aligns with environmental conservation goals and values. This research adopts a quantitative questionnaire approach as the method of data collection. Questionnaires were administered to 50 persons; the study used descriptive and inferential statistics to analyse the data gathered, and regression analysis was used to generate inferences from the data, using Statistical Packages for Social Sciences (SPSS). The regression model shows that there is an overall statistical significance between the independent variables (effect of data analytics, barriers of data analytics, and opportunities of data analytics) and the dependent variable (environmental sustainability with a corresponding p-value of 0.038). Therefore, the study recommends that organisations and governments invest in robust data analytics infrastructure to effectively collect, process and analyse environmental data. Efforts should be made to address the barriers identified in the analysis, such as implementation challenges, data quality issues, and scalability concerns, and enhancing data literacy and building analytical skills among environmental professionals is crucial for maximising the potential of data analytics.
... According to the resource-based view (Barney, 1991), a theoretical approach deemed suitable for examining big data and its impact on sustainability (Hazen et al., 2016) argues that while physical technology can be easily replicated, the strategic exploitation of technology using complex social resources can yield a competitive advantage (Barney, 1991). Nevertheless, the exploitation of technologies generally involves the use of socially complex resources to reach its potential, meaning that a firm can obtain a sustained competitive advantage if it can exploit the technology better than other firms. ...
... This section describes the results of the content analysis. The nine literature reviews included in the sample (see 9,10,17,21,22,37,44,50,62 in the Appendix) have been excluded from this phase. Therefore, the total number of articles analysed was 54. ...
... According to the resource-based view (Barney, 1991), a theoretical approach deemed suitable for examining big data and its impact on sustainability (Hazen et al., 2016) argues that while physical technology can be easily replicated, the strategic exploitation of technology using complex social resources can yield a competitive advantage (Barney, 1991). Nevertheless, the exploitation of technologies generally involves the use of socially complex resources to reach its potential, meaning that a firm can obtain a sustained competitive advantage if it can exploit the technology better than other firms. ...
Sustainability is one of the greatest challenges for industry today. The purpose of this paper is to study the influence of sustainability orientation on product innovation in the European manufacturing sector, with a particular focus on the direct and mediating effect of industrial big data use, something that has been largely neglected so far. The data used for the purpose of the present study were collected from the European Manufacturing Survey (EMS) 2018 edition, consisting of 1,123 surveys administered in Austria, Spain, Croatia, Lithuania, Slovakia, Slovenia and Serbia. Binary logistic regressions and Hayes mediation models are used to test
the hypotheses. Results suggest that sustainability orientation practices and industrial big data use positively influence product innovation, and that industrial big data use mediates the relation between sustainability orientation and product innovation. The findings have implications for both theory and practice.
... In the context of supply chain resilience, where rapid and informed decision-making is critical, prescriptive analytics offers direct pathways to action, making it a crucial area for indepth investigation . Prescriptive analytics has a direct impact on decision-making, whereas, descriptive and predictive analytics provide necessary insights and forecasts (Hazen et al. 2016), they often stop short of suggesting specific actions. In contrast, prescriptive analytics leverages AI to not only predict future scenarios but also recommend specific decisions and actions to achieve optimal outcomes. ...
Supply chains are inherently complex, multifaceted, and dynamically changing systems that are highly susceptible to exogenous shocks. At the same time, Artificial Intelligence (AI) and prescriptive analytics are increasingly being reported as an approach to developing sustainable and resilient supply chains. However, Research is largely fragmented into streams based on different types of AI technologies across several supply chain contexts and through varying disciplinary perspectives. We curate and synthesise this fragmented body of knowledge by conducting a systematic literature review of AI research in supply chains that have been published in 3* and 4* Chartered Association of Business Schools (CABS) ranked journals between 2000 and 2023. The search strategy resulted in 508 studies, of which 76 were identified as primary papers relevant to this study. The study contributes to the accumulative building of knowledge by (i) identifying relationships between AI and descriptive, predictive, and prescriptive analytics in supply chain research, (ii) exploring specificities of AI for prescriptive analytics in supply chains, (iii) outlining implications for research and practice, and (iv) providing a research agenda for future supply chain researchers to enhance the utility of these technologies as enablers of supply chain resilience.
... The effectiveness of the literature review is contingent on the comprehensiveness of the data sources and the efficacy of the search strategies applied. The inclusion of diverse and reputable sources aimed to provide a holistic view of the current landscape of predictive analytics in SCM (Hazen et al., 2016). In addition to academic journals, conference proceedings, and books, industry reports and whitepapers were considered to incorporate real-world applications and insights. ...
Supply chain management (SCM) is a critical component of modern business operations, and the integration of
predictive analytics has emerged as a transformative force in enhancing efficiency, decision-making, and overall
performance. This paper presents a comprehensive review of the applications and benefits of predictive analytics in
supply chain management, exploring its role in demand forecasting, inventory optimization, and supply chain visibility.
The literature review provides a historical perspective on the evolution of predictive analytics in SCM, delving into key
concepts and definitions. Drawing upon existing research, the paper analyzes real-world applications, case studies, and
successful implementations in areas such as demand forecasting, inventory management, and supply chain visibility.
Methodologically, the paper outlines the criteria for selecting relevant studies, details the search strategies employed,
and highlights the sources contributing to the comprehensive understanding of predictive analytics in SCM. The
exploration of applications focuses on how predictive analytics is revolutionizing demand forecasting, optimizing
inventory levels, and enhancing supply chain visibility. Through case studies and examples, the paper illustrates the
practical implications of implementing predictive analytics in these key areas. Real-world examples and data-driven
insights underscore the transformative impact of predictive analytics on SCM processes. Despite its numerous
advantages, challenges and limitations exist in the implementation of predictive analytics. This paper examines common
hurdles and proposes strategies to overcome these challenges, offering a balanced perspective on the practical
implications of integrating predictive analytics into supply chain management. Looking towards the future, the paper
discusses emerging trends and technologies in predictive analytics, anticipating advancements that will further shape
the landscape of SCM analytics. It summarizes key findings, outlines implications for practitioners and researchers, and
suggests avenues for future research in the dynamic field of predictive analytics in supply chain management.
Keywords: Predictive analytics; Chain management; Applications; Benefits
... The inherent difficulties of industrial supply chains, such as the distance between customers and suppliers, cultural differences, and limited visibility into the supply base, create an atmosphere that is conducive to the concealment of behaviours that are socially and ecologically unsustainable. In order to accomplish sustainability objectives, it is necessary to take a proactive approach that is backed by cutting-edge technology [16]. These technologies should make it possible for all actors in a supply chain to gather, analyse, and share data in a seamless manner [17]. ...
Sustainable supply chain management education has assumed critical importance in today's context of evolving environmental, economic, and social sustainability objectives. The growing complexity of supply chain management underscores the necessity for acquiring new competencies to navigate these challenges. Furthermore, as businesses are compelled to enhance their practices with heightened awareness of environmental issues, the imperative for sustainable management becomes increasingly evident. Future business leaders or supply chain managers, particularly higher learning institution's students, should be able to demonstrate the ability to navigate and harness the collective environmental intelligence within their supply networks, promoting the principles of environmental sustainability. This study aims to examine the impact of immersive learning (artificial intelligence, augmented reality, and gamification) towards education, in the context of understanding sustainable supply chain management (SSCM) practices and concepts. The study used a cross-sectional survey approach with a purposive sampling technique to collect data from 204 respondents. The findings of this study suggest that immersive learning techniques are significant and positive factors that contribute to SSCM education. The evidence presented suggests that artificial intelligence and gamification serve as transformative tools, enhancing students' comprehension of SSCM concepts and fostering a genuine interest in adopting more sustainable business practices. In essence, this research reinforces the indispensability of sustainable supply chain education in equipping future business leaders with the knowledge and skills required to navigate the complexities of contemporary SC management while championing environmental, economic, and social sustainability goals.
... Decisions made by management have been aided by big data analytics on engineering activities throughout the operational planning phase, which usually involves request preparation, obtaining, manufacturing, recording, and logistics. Future research on the properties of big data analytics on all company performance outcomes is crucial because it is recognized as a viable essential in an engineering practice [17]. ...
The purpose of this research is to comprehend how big data analytics affect engineering performance. The industrial part especially the engineering practice is among the most significant and delicate in the world. Gathering and manufacturing have a huge social impact on the economies of the nations and, consequently, on the lives of individuals all over the world. The potential for big data to completely alter engineering practice and enhance ongoing engineering projects. Many organizations appear to be aware of the advantages big data can bring to their performance in engineering practice, particularly its significant possible worth, but they encounter a number of challenges when implementing it, primarily because they are having trouble figuring out how to use the derived insights for their development. The development of new strategies and services is a crucial engineering activity, and it has been demonstrated to significantly affect an organization’s viability. If these insights are monetized, Organizations aiming for an improved engineering practice can build brand-new, customer-centered, and data-driven projects or both goods and services, providing a long-lasting competitive advantage and new revenue streams. According to empirical research, companies that have engineering practice incorporated with a data-driven approach that can show how big data contributes to improved performance, while those that have not yet instilled the entire organization struggle with an absence of comprehension on how to use big data technology to create potential value and accomplish their organizational goals. Due to the enormous strategic potential of big data, this article tries to conceptualize and investigate its effects on corporate performance. It also explores the impacts of big data on engineering performance because of its high strategic potential. Finally, it explores whether and how the creation of new engineering services and projects makes use of big data and related technologies. An in-depth SWOT, binary Logistic Regression analysis, and the use of grounded theory combine previous big data studies with several enterprises in Lagos, Nigeria’s Iganmu industrial layout area. The caliber of data gathered, data availability, legal considerations of data confidentiality and safekeeping, and highly qualified individuals working with big data are additional critical factors that influence the use of a data-driven approach. Therefore, in order for companies to achieve effectiveness and efficiency, they need to reflect on and make strategic decisions utilizing a comprehensive perspective on big data.
... Previous studies illustrated the importance of technological adoption to collaborate all SC partners (Hazen et al., 2016). Moreover, the integration and collaboration between SC partners (suppliers and customers) can be enhanced through adoption of technologies which facilitates the transformation and exchange of information along SCM processes and overcome SC barriers (Cagliano & Mangano, 2021;Jain et al., 2021;Yang et al., 2021). ...
The aim of this research is to empirically investigate the influence of supply chain management practices (SCMPs) on customer satisfaction through the mediating roles of flexibility and technology adoption. A questionnaire was used to collect data from organizational consumers in the Egyptian context, in which hypotheses were analyzed through covariance‐based structural equation modeling for 1009 usable questionnaires. The findings revealed the positive relationship between SCMPs and flexibility, technology adoption, and customer satisfaction. Moreover, it revealed the positive relationship between technology adoption and customer satisfaction, however, the direct impact of flexibility on customer satisfaction is not significant. Empirical evidence also illustrated that technology adoption can significantly mediate the relationship between SCMPs and customer satisfaction.
... Using BDA would encourage organisationand environmental responsibility and enhance the accuracy of funding decisions (Zhao and Wang, 2020). Due to their corporate obligation to environmental benignancy, companies are ready tocreate and market green products in the BD era, but they also have to ensure profitability (Hazen et al., 2016). Innovation and learning performance affect SSC efficiency and are significantly moderate . ...
... La théorie de l'acteur-réseau fournit une manière systématique de considérer l'infrastructure entourant les réalisations technologiques qui traite les relations sociales comme des effets de réseau (Law, 1992). Cette théorie soutient que les événements ne doivent pas être considérés dans le vide, mais qu'ils sont plutôt influencés par les facteurs environnants (Hazen et al., 2016). (2001) suggèrent que cette théorie est viable pour une application en Supply Chain Management (Gammelgaard, 2004). ...
La crise de la COVID-19 a accéléré la digitalisation, en particulier des entreprises des produits de consommation, avec le développement du e-commerce et les restrictions du monde physique. La digitalisation apparait parfois comme « allant de soi », alors que la mise en œuvre de l’innovation est souvent très complexe et a fait l’objet de différents modèles en sciences de gestion. Au-delà de la complexité de la gestion technologique et des acteurs, une autre approche, issue de la sociologie des sciences permet de comprendre le rôle qui doit être tenu par les acteurs humains et techniques pour garantir le succès de l’innovation. Dans cet article nous mobilisons le modèle de l’acteur-réseau pour analyser la mise en œuvre de la digitalisation de la Supply Chain de L’Oréal.
... Gandomi and Haider (2015) demonstrated how BD predictive analytics aids in measuring supply chain sustainability. Hazen et al. (2016) discovered a connection between BD predictive analytics and sustainable supply chain management. ...
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).
... Financial capability is a key capability that is required for Industry 4.0 technologies, as investments in new equipment and updates to existing ones are essential for success. Predictive capability is an essential advantage gained from implementing Industry 4.0, as it can pinpoint future demand and supply changes (Gunasekaran et al., 2017;Hazen et al., 2016;Ilie-Zudor et al., 2015). When Industry 4.0 takes full advantage of its inimitable information and knowledge resources, as well as advanced data analysis technology, to predict unexpected demands and events, it will produce a superior resilient advantage in operation (Sheffi & Rice, 2005). ...
The linear economic business model was deemed unsustainable, necessitating the emergence of the circular economy (CE) business model. Due to resource scarcity, increasing population, and high food waste levels, the food sector has been facing significant sustainability challenges. Small and medium-sized enterprises (SMEs), particularly those in the food sector, are making efforts to become more sustainable and to adopt new business models such as the CE, but adoption rates remain low. Industry 4.0 and its associated technological applications have the potential to enable CE implementation and boost business competitiveness. In the context of emerging economies facing significant resource scarcity constraints and limited technology availability, CE principles need to be adapted. CE could create a new job economy in emerging economies, bringing scale and a competitive advantage. This study explores the enablers of and barriers to Industry 4.0 adoption for CE implementation in fruit and vegetable SMEs in India from a resource-based perspective. The purpose is to develop an evidence-based framework to help inform theory and practice about CE implementation by SMEs in emerging economies. Fifteen semi-structured interviews were conducted with experts in food SMEs. The interview transcripts were first subjected to thematic analysis. The analysis was then complemented with sentiment and emotion analyses. Subsequently, hierarchical cluster analysis, k-means analysis, and linear projection analysis were performed. Among others, the findings suggest that Industry 4.0 plays a key role in implementing CE in SMEs in emerging economies such as India. However, there are specific enablers and barriers that need to be considered by SMEs to develop the resources and capabilities needed for CE competitive advantage.
... Access improves decision-making and affects behaviors. Chiu et al. (2006) and Hazen et al. (2016) argue that interactions within a social network led to a standard of acceptable social norms which have an impact on health behaviors, such as substance abuse, alcoholism, prostitution, etc. Social capital enhances access to healthcare through lobbying while providing psychosocial support networks important for the physical and mental well-being of members. There exist two elements of social networks (a) Social relations (Portes, 1998) (b) Quantity and quality of resources available within a network (Bourdieu, 1985) Kankanhalli et al. (2005) highlight the importance of social capital in the creation of a conducive environment for knowledge exchange and information flow. ...
Many factors influence the utilization of reproductive healthcare services in Kenya. Despite the effort by the government and other stakeholders to improve access and utilization of these services, there remains a major challenge in reaching out to marginalized segments of society. The study aims to examine the factors affecting the utilization of modern contraceptives by homeless women in Nairobi, Kenya, and draw policy recommendations based on the findings. The study utilized the logit model to analyze determinants of contraceptive utilization by homeless women in Nairobi using primary data collected from 196 households within Nairobi. The number of children per woman, age at first birth, living with a partner, drug abuse by the respondent, drug abuse by respondents’ partner, poverty, child planning, health facility delivery, neonatal death incidence, knowledge of male sterilization, never attending school, primary school attendance, secondary school attendance, operating of small business and contraceptive spending significantly affect the utilization of modern contraceptives by homeless women in Nairobi, Kenya. The majority of homeless women in Nairobi utilized injectibles (26.63%) and implants (24.07%) as a form of contraception. The government should therefore provide a contraceptive mix that incorporates these forms of contraception to ensure maximum utilization.
... The term BDA refers to the collecting of data, analytical tools, computer algorithms and methodologies used to draw significant insights and patterns from enormous data sets (Jeble et al., 2018). BDA may also be used for prediction by using the big data predictive analytics framework, substantially enhancing operational and strategic capabilities in the supply chain (Hazen et al., 2016). BDA entails the application of advanced analytical tools to extract meaningful information from vast amounts of data to aid decision-making (Tsai et al., 2015). ...
Purpose
The purpose of this paper is to develop a theoretical model that explains the impact of big data analytics capabilities (BDAC) on company's supply chain innovation capabilities and sustainable supply chain performance. BDAC is represented through two dimensions of big data technological capabilities (BDTC) and big data personal capabilities (BDPC). Moreover, the relationships between BDTC and BDPC with sustainable supply chain performance through the mediation effect of supply chain innovation capabilities are examined.
Design/methodology/approach
The study used a quantitative research approach. A survey of 400 Jordanian manufacturing companies was carried out to conduct this research. However, the responses of 207 managers were valid to be used in the analysis. In this study, the SmartPLS software was used to perform structural equation modeling using a partial least squares approach (PLS-SEM) and to examine the measurement and structural model's validity and reliability.
Findings
According to the results of this study, BDPC has a significant positive impact on supply chain innovation capabilities. Furthermore, the findings indicate that supply chain innovation capabilities are the most influential predictor of sustainable supply chain performance and act as a positive significant mediator in the relationship between BDPC and firm sustainable performance. Surprisingly, the study found that BDTC had no significant effect on supply chain innovation capabilities. Besides that, no significant relationship exists between BDTC and firm sustainable performance via the mediation effect of supply chain innovation capabilities.
Originality/value
This study provides an integrated research model that incorporates BDAC, supply chain innovation capabilities, and sustainable supply chain performance in order to analyze supply chain innovation and sustainable supply chain performance. This suggests that the scope of the study is broader in terms of predicting sustainable supply chain performance. As a result, the study intends to fill a gap in the literature by explaining how BDAC affects supply chain innovation capabilities and firms sustainable performance. In addition, the role of supply chain innovation capabilities as a mediator between BDAC and sustainable supply chain performance is investigated.
... Data is essential for society and the economy because of its potential for innovation in the public and private sectors (Hofman & Rajagopal, 2014), associated with creating environmental, social, and economic value. In this regard, big data is increasingly changing how impacts on the environment are measured and mapped, contributing to SD by being used to measure carbon emissions (Hazen et al., 2016;Huang et al., 2017;Seles et al., 2018), to improve social and environmental sustainability in supply chains (Dubey et al., 2019), to expand the informational landscape of smart sustainable cities (Bibri, 2018), and to improve the allocation and utilisation of natural resources (Song et al., 2017), among other applications. Within this context, data-intensive methods are increasingly being employed to answer environmental questions relevant to sustainability (Pennington et al., 2020). ...
Despite the abundance of studies focused on how higher education institutions (HEIs) are implementing sustainable development (SD) in their educational programmes , there is a paucity of interdisciplinary studies exploring the role of technology , such as data science, in an SD context. Further research is thus needed to identify how SD is being deployed in higher education (HE), generating positive exter-nalities for society and the environment. This study aims to address this research gap by exploring various ways in which data science may support university efforts towards SD. The methodology relied on a bibliometric analysis to understand and visualise the connections between data science and SD in HE, as well as reporting on selected case studies showing how data science may be deployed for creating SD impact in HE and in the community. The results from the bibliometric analysis unveil five research strands driving this field, and the case studies exemplify them. This study can be considered innovative since it follows previous research on artificial intelligence and SD. Moreover, the combination of bibliometric analysis and case studies provides an overview of trends, which may be useful to researchers and decision-makers who wish to explore the use of data science for SD in HEIs. Finally, the findings highlight how data science can be used in HEIs, combined with a framework developed to support further research into SD in HE.
... Nevertheless, BDPA in the context of sustainable development and within the limits of big data availability have gained much attention (Bag et al., 2022;Hazen et al., 2016;Singh & El-Kassar, 2019). BDPA has been shown to promote performance in sustainable manufacturing (Ma et al., 2022;Tayal et al., 2020), sustainable tourism (Agrawal et al., 2022), human well-being (Weerakkody et al., 2021), renewable energy (Ifaei et al., 2017), sustainable agriculture (Ifaei et al., 2017), sustainable supply chain (Kusi-Sarpong et al., 2021;Peng et al., 2022), and food waste (Ciccullo et al., 2022); it has been employed for smart cities development, especially in contexts of management strategies of different sources of data . ...
... For example, Schmidt & Wagner, (2019) explores the capabilities of digital technology to reduce transaction costs. Studies support that digitization and market-oriented governance structures for buyer-supplier transactions can reduce transaction costs ( eg. Hazen et al., 2016;Sanders et al., 2019) Similarly, Roeck et al., (2020) has an impact on organizational processes and activities afterward. ...
It is undeniable that the coronavirus (COVID-19) pandemic has affected the global economy and environment. The main victims of the COVID-19 outbreak are agricultural Micro and Small Enterprises MSEs in KWT, especially in developing countries, where the use of digital media is still limited. This paper uses literature and personal insights to provide lessons on digitalization the COVID-19 pandemic for the development of MSEs in sustainable KWTs from a technology-for-social perspective. researchers develop work concepts to support digital transformation after COVID-19 for the sustainable development of KWT MSEs. The fact is that digital payments, especially mobile money, must be an important digital transformation priority for MSEs in KWT. In addition, institutions must support the resources and capabilities of MSEs in KWT adopting digital for sustainable business, production and consumption. This study shows that the Chair of the KWT UMK and other stakeholders to re-research their business strategy, combine crisis scenarios and business plans as an effort to retain customers virtually so that they can increase sustainable agricultural businesses. We also propose further research areas to enhance the transformation of digital KWT MSEs after COVID-19.
Purpose
This paper analyses the role of green human resources management (GHRM) practices on the application of logistics social responsibility (LSR) practices and examines the moderating effect of big data analytics (BDA) utilisation levels within these relationships.
Design/methodology/approach
Based on quantitative research methodology using survey data from 404 managers in the logistics service providers (LSPs) industry in the Philippines, PLS-SEM technique was used to test hypotheses formulated in this research.
Findings
Empirical results achieved suggest that GHRM practices have a significant positive impact on LSR. Among all individual GHRM practices, green training and development did not have any influence on LSR. While the results also revealed that BDA assimilation acts as a moderator of the relationship between GHRM and LSR, no support was found for the moderation effect of BDA acceptance or adoption on this relationship.
Originality/value
The study fills a gap in the logistics literature by introducing dynamic capabilities theory to the nexus between GHRM and SLR for the first time, which reveals previously unknown answers on effects of GHRM practices on LSR. The study also introduces BDA assimilation as an important moderator that can strengthen positive impact of GHRM on LSR.
The integration of Artificial Intelligence (AI) in the textile industry is driving significant advancements across various domains, including manufacturing, design, quality control, and sustainability. This review explores how AI technologies, such as machine learning, computer vision, and predictive analytics, are transforming traditional processes to enhance efficiency, reduce waste, and offer innovative solutions for smart textiles and wearable technology. AI-driven automation is optimizing production workflows, while AI-powered quality control ensures higher accuracy in defect detection and fabric inspection. Additionally, AI is enabling predictive analytics in supply chains, helping manufacturers anticipate demand and manage resources more effectively. The review also highlights AI's role in sustainable textiles, where it supports eco-friendly practices by minimizing waste, promoting resource efficiency, and enabling circular fashion. Despite its transformative potential, AI implementation presents challenges, such as ethical concerns, data quality, and integration with traditional systems. This article discusses the current trends, applications, and future directions of AI in textiles, underscoring its pivotal role in reshaping the industry's future.
Purpose
The theoretical background bases on the big data analytics-artificial intelligence (BDA-AI) technologies and supply chain ambidexterity (SCAX) in the firms to assess their sustainability endeavors such as green supply chain management (GSCM) to improve their green communication and corporate image.
Design/methodology/approach
Around 220 participants in the manufacturing firms are participants' industry expertise, diverse roles, and representation as key stakeholders.
Findings
The results show BDA-AI and SCAX affected on GSCM and found the significant relationships with green communication and corporate image. Green communication was discovered to impact corporate image significantly.
Originality/value
Prior studies are neglected to address the relationship among the AI, powered by rapid computational and BDA breakthroughs, redefines cognitive tasks, achieving feats previously deemed impossible-making implicit judgments, simulating emotions, and driving operations. This study selects manufacturing firms as respondents due to their forefront of BDA-AI and supply chain ambidexterity adoption to benefit the operational efficiency and competitiveness. The firms intricate supply chains, diverse stakeholders, and strategic emphasis on corporate image make it an ideal context to examine the nuanced impact of these technologies.
The study aims to conduct a systematic literature review and bibliography analysis to explore the role of big data analytics in transforming supply chain management. The systematic literature review was conducted according to the PRISMA guidelines extended into a three-phase approach. The articles were reviewed from different databases like Scopus, Web of Science, and ABDC. 239 articles were reviewed through abstract screening, and 191 articles were finally selected after full-text screening. The results of the analysis reflected the publication trend from January 2011 to January 2024, keyword analysis, co-citation and network analysis, and theme identification from the domain. Moreover, the study theoretically contributes by suggesting growing trends in the field of supply chain management, and the managerial implications of the study suggest the benefits of implementing big data analytics in supply chain management.
This study investigates the technology adoption of Raspberry Pi-Powered Learning Management System (RPP-LMS) in the post-pandemic educational landscape. Extending the UTAUT2 model, we integrate Institutional Adoption and Academic Support from the Model of Distance Education. Our findings from analyzing 302 distance education participants reveal significant relationships between various constructs, including Performance Expectancy, Effort Expectancy, Habit, and Academic Support, and their impact on adopting RPP-LMS. Notably, Habit emerges as a critical determinant, significantly influencing use behavior. Moreover, our study explains the moderating effects of Age and Gender on these relationships, providing valuable insights into demographic disparities in technology adoption. We propose a tailored Technology Deployment Plan that offers practical strategies for educators, administrators, and policymakers to optimize distance learning experiences and promote the effective integration of technology in the aftermath of global disruptions.
The United Nations’ 17 Sustainable Development Goals stress the importance of global and local efforts to address inequalities and implement sustainability. Addressing complex, interconnected sustainability challenges requires a systematic, interdisciplinary approach, where technology, AI, and data-driven methods offer potential solutions for optimizing resources, integrating different aspects of sustainability, and informed decision-making. Sustainability research surrounds various local, regional, and global challenges, emphasizing the need to identify emerging areas and gaps where AI and data-driven models play a crucial role. The study performs a comprehensive literature survey and scientometric and semantic analyses, categorizes data-driven methods for sustainability problems, and discusses the sustainable use of AI and big data. The outcomes of the analyses highlight the importance of collaborative and inclusive research that bridges regional differences, the interconnection of AI, technology, and sustainability topics, and the major research themes related to sustainability. It further emphasizes the significance of developing hybrid approaches combining AI, data-driven techniques, and expert knowledge for multi-level, multi-dimensional decision-making. Furthermore, the study recognizes the necessity of addressing ethical concerns and ensuring the sustainable use of AI and big data in sustainability research.
The global supply chains sustainability and viability has been severely impacted due to the emergence of black swan events such as COVID‐19. Previous studies have examined how to enhance supply chain sustainability from a single internal capability perspective or an external relationship perspective, few studies have examined the combined effects of internal and external factors to enhance supply chain sustainability. To fill this research gap, this study investigates the multivariate relationship from internal capabilities and external relationships mutually complementary perspective to improve supply chain sustainability. Grounded on dynamic capabilities theory and relational view, we put forward the supply chain dynamic capability construct under the internal perspective, and relationship construct with supply chain members under the external perspective. Based on the configuration theory, 270 data from enterprises were analyzed using fsQCA (fuzzy set qualitative comparative analysis), and the results shows that: first, there is no necessary condition for generating high supply chain sustainability, which means that individual factors do not lead to high supply chain sustainability; second, this study identified two paths to achieve high supply chain sustainability: capability‐driven and relationship‐driven, which reflects the multiple paths and complex mechanisms to achieve high supply chain sustainability; third, this study finds the important role of supply chain dynamic reconfiguring capability in forming high supply chain sustainability. These results can provide some theoretical guidance for enterprises to improve supply chain sustainability and achieve high quality economic development.
This paper presents a dynamic closed-loop supply chain (CLSC) model, incorporating a manufacturer, a retailer, and an internet recycling platform (IRP), utilizing differential game theory while considering the forgetting effect of consumers. The model encompasses factors such as the quality level of used products and Big Data marketing (BDM), comparing optimal equilibriums under decentralized and cooperative decision scenarios. To effectively coordinate the dynamic CLSC at each time point, we propose a revenue-sharing and cost-sharing (RSCS) combined contract. In addition to ensuring reasonable sharing of revenues and costs, this contract allows the manufacturer to flexibly adjust wholesale prices for final products and transfer prices for used products in order to distribute profits appropriately and achieve Pareto optimality within the CLSC system. Furthermore, our results indicate that there exists a threshold for Big Data marketing efficiency; high-efficiency BDM not only facilitates increased recycling on Internet platforms but also reduces unit recycling costs for enterprises. Interestingly, when implementing the combined contract, Big Data marketing efficiency does not impact the transfer price paid by manufacturers to Internet recycling platforms.
Many textile companies have come to the realization that data is crucial for improving sales and revenue margins. Clothing brands and retailers must develop manufacturing and sales styles that appeal to customers. In recent decades, with advancements in various categories of data analytics and artificial intelligence techniques (e.g., machine learning), the value of data-driven applications has been well acknowledged by textile clothing retailers. They use predictive software outputs for regular operational decision-making. This chapter reviews retail businesses and their products' manufacturing data analytics. It presents a scalable business intelligence framework using a graph data model and its management system. The chapter also highlights that big data technologies and related supporting resources (e.g., the Internet of Things) enable real-time data capture, storage, processing, and sharing. This helps businesses make operational decisions faster and more effectively. This chapter presents an algorithm for extracting knowledge from stored business data to exemplify the analytical value of the graph database model for business intelligence.
This study aims to determine fraud prevention strategies with a risk management approach from the perspective of the Quran. The methodology in this research is a literature study with a qualitative approach to the analysis of deductive and inductive processes. Primary data sources consist of verses of the Quran which are used as references with explanations from several commentators, and secondary data sources consist of relevant previous scientific research works. The Al-Quran interpretation method used for analysis is the al-maudhu'i interpretation method. Fraud is an act of crime and according to sharia, there must be prevention efforts according to ability. Organizational activities always contain uncertainty which is synonymous with risk, including the risk of fraud. Management has the responsibility to manage the risks that will be faced. Risk management and internal control contribute to the implementation of good governance, with the implementation of an adequate risk management and internal control system. Several verses of the Quran and hadith can be used to implement the principles of good governance: transparency, accountability, responsibility, independence and fairness. Several risk management principles are: integrated, structured and comprehensive, tailored to user needs, inclusive, dynamic, best available information, cultural and human factors, and continuous improvement. Regarding the application of the principles of fraud prevention, verses in the Al-Quran have a perspective that is not limited to textual and contextual, but also a broader and comprehensive perspective materially and immaterially.
With the explosion of data in the digital age, Big Data Analytics (BDA) has been embraced as a powerful technology for businesses to gain insights and make data-driven decisions. Its impact is evident across various industries, from fast-food chains utilizing it for personalized marketing to healthcare institutions employing it for improved patient care. This research explores the burgeoning field of BDA concerning sustainability by employing case studies to illuminate the powerful ways BDA empowers companies to move towards sustainability, aiming to demonstrate its transformative potential in achieving environmental and social goals. A humongous amount of data is generated, estimated at 2.5 quintillion bytes daily, which provides a fertile ground for BDA applications. Harnessing this data will help companies gain valuable insights into their environmental footprint, optimize resource utilization, and frame ingenious solutions for sustainability challenges. As a case study, we will be delving into Allbirds, a company that has sustainability as a core concept, that has identified pivotal areas for enhancement and incorporated data-driven solutions to optimize resource utilization and minimize the environmental impact to cope with issues such as climate changes and global warming. As another case study, we will explore Siemens which demonstrates the transformative power of big data in achieving broader environmental and social goals using their MindSphere platform.
In conjunction with the rapid development of big data technology, many big data service providers (BDSPs) have been widely integrated into closed-loop supply chains (CLSCs). This study examines the role of BDSPs in a dynamic CLSC that employs a manufacturer for recycling and remanufacturing, a retailer for retailing, and a BDSP for providing big data services such as site selection for recycling. In the presence of stochastic disturbances, the Itô process is used to describe the dynamic evolution of recycling ratios. Differential game models are presented that decentralize independently without a BDSP (scenario D) and with a BDSP (scenario B) and introduce a coordinating contract that combines wholesale price discount and cost-sharing (scenario C). Furthermore, we explore the decisional differences between the three scenarios, the contract’s effects, and sensitivity to parameters by using comparative and numerical analysis. The findings indicate that BDSPs participation in CLSCs could positively affect recycling ratios and supply chain profits, and an increase in the volatility of recycling ratios could be beneficial to manufacturers and retailers. In addition, the designed contract is likely to reduce retail prices, enhance recycling ratios, and contribute to Pareto improvements. Sustainability can be attained in a way that maximizes profits and minimizes environmental impact by properly using the proposed model. Enterprises are recommended to work with BDSPs to enhance their goodwill because BDSPs facilitate the use of big data marketing for increasing sales and recycling ratios, thereby promoting sustainable economic development and environmental protection.
Supply chain management is essential to a company's success in today's fiercely competitive business environment, regardless of the industry. Businesses are increasingly turning to big data analytics as a useful tool for supply chain improvement to meet the expanding needs of customers, save operational costs, and boost overall efficiency. In order to improve critical areas including demand forecasting, inventory management, logistics optimization, and risk reduction, research has been conducted on the collecting and use of big data in supply chain management. Big data analytics and supply chain management integration can result in more cost-effective operations, better customer service, and increased resilience to disturbances. Organisations that effectively use big data will gain a competitive advantage in the constantly changing supply chain landscape as technology and data capabilities continue to grow.
Data-supported decision-making and understanding the customer's behaviour has become an essential and challenging problem for apparel businesses to sustain their position in competitive markets. Current information communication technologies (ICT) are ushering hope to mitigate this challenge, particularly the blockchain with internet of things (IoT)-based enterprise information system framework providing relevant services in global networks that mediate effective and sustainable supply chain operations. Data collection and interpretation of collected data (known as data analytics) on business-specific value creation process is most important in this architecture. This chapter reviews recent literature on technology-driven supply chain automation and related data analytics-related issues for managing sustainability. Lastly, the chapter presents an application area of 'market basket analysis' technique that focuses on discovering patterns in retail transaction data with the help of an algorithmic data mining method.
Big data analytics has played an enormous role in supply chain operations in recent years. Today, web-applications, social media, intelligent machines, sensors, mobile phones, and other innovative information technology devices generate big data in supply chain operations. These data often provide new digitized services that improve supply chain performance. In this operating environment, heterogeneous enterprise applications, manufacturing processes, or supply chain management, either inside a single enterprise or among network enterprises, require sharing of data-driven information. Thus, data management and its analytical interpretation have become significant drivers for management, product development, and provision of relevant services in network enterprises that function as mediators in effective and sustainable supply chain operations. This chapter reviews the effect of big data analytics on the supply chain operation literature, highlights how modern supply chains can manage sustainability, and presents a conceptual information system architecture.
Purpose
This paper investigated the organizational adoption of big data analytics (BDA) in the context of supply chain planning (SCP) to conceptualize how resources are orchestrated for organizational BDA adoption and to elucidate how resources and capabilities intervene with the resource management process during BDA adoption.
Design/methodology/approach
This research elaborated on the resource orchestration theory and technology innovation adoption literature to shed light on BDA adoption with multiple case studies.
Findings
A framework for the resource orchestration process in BDA adoption is presented. The authors associated the development and deployment of relevant individual, technological and organizational resources and capabilities with the phases of organizational BDA adoption and implementation. The authors highlighted that organizational BDA adoption can be initiated before consolidating the full resource portfolio. Resource acquisition, capability development and internalization of competences can take place alongside BDA adoption through structured processes and governance mechanisms.
Practical implications
A relevant discussion identifying the capability gap and provides insight into potential paths of organizational BDA adoption is presented.
Social implications
The authors call for attention from policymakers and academics to reflect on the changes in the expected capabilities of supply chain planners to facilitate industry-wide BDA transition.
Originality/value
This study opens the black box of organizational BDA adoption by emphasizing and scrutinizing the role of resource management actions.
Strategy has been defined as “the match an ovganization makes between its internal resources and skills … and the opportunities and risks created by its external environment.” 1 During the 1980s, the principal developments in strategy analysis focussed upon the link between strategy and the external environment. Prominent examples of this focus are Michael Porter's analysis of industry structure and competitive positioning and the empirical studies undertaken by the PIMS project. 2 By contrast, the link between strategy and the firm's resources and skills has suffered comparative neglect. Most research into the strategic implications of the firm's internal environment has been concerned with issues of strategy implementation and analysis of the organizational processes through which strategies emerge. 3
Business intelligence and analytics (BI&A) has emerged as an important area of study for both practitioners and researchers, reflecting the magnitude and impact of data-related problems to be solved in contemporary business organizations. This introduction to the MIS Quarterly Special Issue on Business Intelligence Research first provides a framework that identifies the evolution, applications, and emerging research areas of BI&A. BI&A 1.0, BI&A 2.0, and BI&A 3.0 are defined and described in terms of their key characteristics and capabilities. Current research in BI&A is analyzed and challenges and opportunities associated with BI&A research and education are identified. We also report a bibliometric study of critical BI&A publications, researchers, and research topics based on more than a decade of related academic and industry publications. Finally, the six articles that comprise this special issue are introduced and characterized in terms of the proposed BI&A research framework.
The era of Big Data has begun. Computer scientists, physicists, economists, mathematicians, political scientists, bio-informaticists, sociologists, and other scholars are clamoring for access to the massive quantities of information produced by and about people, things, and their interactions. Diverse groups argue about the potential benefits and costs of analyzing genetic sequences, social media interactions, health records, phone logs, government records, and other digital traces left by people. Significant questions emerge. Will large-scale search data help us create better tools, services, and public goods? Or will it usher in a new wave of privacy incursions and invasive marketing? Will data analytics help us understand online communities and political movements? Or will it be used to track protesters and suppress speech? Will it transform how we study human communication and culture, or narrow the palette of research options and alter what ‘research’ means? Given the rise of Big Data as a socio-technical phenomenon, we argue that it is necessary to critically interrogate its assumptions and biases. In this article, we offer six provocations to spark conversations about the issues of Big Data: a cultural, technological, and scholarly phenomenon that rests on the interplay of technology, analysis, and mythology that provokes extensive utopian and dystopian rhetoric.
Over the past decade, transaction cost analysis (TCA) has received considerable attention in the marketing literature. Marketing scholars have made important contributions in extending and refining TCA's original conceptual framework. The authors provide a synthesis and integration of recent contributions to TCA by both marketers and scholars in related disciplines, an evaluation of recent critiques of TCA, and an agenda for further research on TCA.
We build on an emerging strategy literature that views the firm as a bundle of resources and capabilities, and examine conditions that contribute to the realization of sustainable economic rents. Because of (1) resource-market imperfections and (2) discretionary managerial decisions about resource development and deployment, we expect firms to differ (in and out of equilibrium) in the resources and capabilities they control. This asymmetry in turn can be a source of sustainable economic rent. The paper focuses on the linkages between the industry analysis framework, the resource-based view of the firm, behavioral decision biases and organizational implementation issues. It connects the concept of Strategic Industry Factors at the market level with the notion of Strategic Assets at the firm level. Organizational rent is shown to stem from imperfect and discretionary decisions to develop and deploy selected resources and capabilities, made by boundedly rational managers facing high uncertainty, complexity, and intrafirm conflict.
This paper aims to enable the operations management community to engage with concepts from the field of complexity theory and apply them to the issue of organisational transformation. It begins by reviewing existing work on strategic change, then provides an overview of complexity theory to show how the conditioned emergence model was developed. A brief statement on method follows, which describes our research process in terms of mode 2 knowledge production. An illustrative case study is then presented and is used to highlight aspects of the model and the overlaps and differences between conditioned emergence and other approaches. The paper concludes that organisational transformation can be viewed as an emergent process which can be accessed and influenced through three interacting gateways, i.e. order generating rules, disequilibrium and positive feedback. Finally, an appendix is included which focuses specifically on the issue of the research process. Here, it is argued that calls for managerially-relevant research will be best met through more widespread adoption of mode 2 as an approach.
Purpose
Theory is needed for a discipline to mature. This research aims to provide a summary analysis of the theories being used in contemporary logistics and supply chain management (SCM) studies.
Design/methodology/approach
A comprehensive literature review of articles appearing in five top tier logistics and SCM journals is conducted in order to identify how often theory is used and to classify the specific theories used. An analysis of the theoretical categories is presented to explain the type and frequency of theory usage.
Findings
Over 180 specific theories were found within the sampled articles. Theories grouped under the competitive and microeconomics categories made up over 40 per cent of the theoretical incidences. This does not imply all articles utilize theory. The research found that theory was explicitly used in approximately 53 per cent of the sampled articles.
Practical implications
Two implications are central. First, in the minds of editors, reviewers and authors is approximately 53 per cent theory use enough? Literature suggests there continues to be a need for theory‐based research in the discipline. A first step may be to increase our theory use, and to clearly describe the theory being used. Second, the vast majority of theories used in recent logistics and SCM research originated in other disciplines. Growth in the discipline dictates the need for greater internal theory development.
Originality/value
Despite multiple calls for the use of theory in logistics and SCM, little formal research has been produced examining the actual theories being used. This research provides an in‐depth review and analysis of the use of theory in logistics and SCM research during the period 2004‐2009.
In order to improve performance at the operational level, more and more firms are developing explicit linkages with suppliers and with customers. While the question of “what beneficial impact do linkages with suppliers and with customers have for a firm” has been addressed in numerous studies, the equally important question of “why” this beneficial impact arises deserves further discourse and explication. This paper borrows and applies the Resource-Based View of the Firm, a theoretical perspective prevalent in the strategic management literature, to develop a conceptual framework to describe, explain, and predict the advantages of a firm's linkages with entities in its supply chain on its internal operations. The proposed framework can be used to justify decisions to develop, strengthen, and protect relationships with suppliers on the upstream side and with customers on the downstream side. The framework can also be used to evaluate practices implemented to link a firm to its suppliers and customers and to provide a decision roadmap for firms to better understand how to maximize operational performance benefits from these supply chain linkages.
The concepts of a supply chain and supply chain management are receiving increased attention as means of becoming or remaining competitive in a globally challenging environment. What distinguishes supply chain management from other channel relationships? This paper presents a framework for differentiating between traditional systems and supply chain management systems. These characteristics are then related to the process of establishing and managing a supply chain. A particular focus of this paper is on the implications of supply chain management for purchasing and logistics.
This study analyzed the relationship between social capital and knowledge creation at the individual level. Our limited theory of knowledge creation encompasses the number and strength of the relationships that a person maintains. Hypothesis tests in a sample of biomedical research scientists strongly supported predictions. As relationships increased in number, returns to knowledge creation diminished. Increased interactions with a single other showed a similar effect. The strength of interpersonal relations had a higher marginal effect on knowledge creation than the number of relations.
A central tenet of agency theory is that there is potential for mischief when the interests of owners and managers diverge. In those circumstances, and for a variety of reasons, managers may be able to exact higher rents than are reasonable or than the owners of the firm would otherwise accord them. While that foundational element of agency theory is secure, other elements derived directly from agency theory are far less settled. Indeed, even after some 75 years of conceptualization and empirical research, the three principal approaches that have long been proposed to mitigate the fundamental agency problem remain contentious. Accordingly, we provide a review of the fundamental agency problem and its mitigation through independence, equity, and the market for corporate control.
A linking of the Resource Based View of the firm, Resource Dependency Theory and the Vroom-Yetton model of leadership is used to show that when important technical (R&D) resources are located offshore for strategic and efficiency reasons, resource-based power goes with them. The extra-national technology units that embody those strategically important resources should be managed with inclusive methods that respect that power shift. Theoretical, empirical and managerial implications are drawn from this analysis. Copyright © 2001 John Wiley & Sons, Ltd.
Over the past decade, transaction cost theory (TCT) has received considerable attention from researchers in various disciplines of business. Unfortunately, the rich theoretical base of TCT has seen limited application in the operations and supply chain management research. This article seeks to change that by providing a cogent synthesis of TCT, its assumptions, constructs, and propositions. It also summarizes existing empirical work in management and other disciplines that draws from the TCT perspective and examines relationships in manufacturing organizations. A measurement model of transaction costs is subsequently presented using data from 203 manufacturing firms in the OEM electronics industry. Guidelines and recommendations for researchers are then presented regarding both the uses of the theory and its measurement. It is hoped that this study will stimulate work in the important areas of inter‐firm relationships that draw from this rich but underutilized theoretical lens, and thereby add another perspective to the knowledge base in related areas of the operations and supply chain management fields.
Teacher resistance, teacher accommodation, and teacher conformism informed instructional strategies that Mr. Jenkins used to prevent suspension. Mr. Jenkins’s instructional strategies were impacted by his resistance to dominant PBS ideology, accommodation of system constraints related to classroom disruptions and PBS, and conformism to the dominant ideology of teaching and learning culinary arts.
This chapter proposes a model describing why firms should invest in environmental supply chain innovation or ‘green supply’ activities. It argues that large high profile companies are under pressure from a wide range of stakeholders to improve their environmental performance. In contrast, small supplier firms are under less pressure, but are highly influenced by the demands of their customers. The model attempts to demonstrate that customer firms invest in environmental supply chain innovation because suppliers with poor environmental practices can expose the customer firm to high levels of environmental risk. However, implementation is dependent upon environmental pressure, firm capabilities and the degree to which customer firms are able to control their suppliers. The model is illustrated with a case study of UK supermarket retailer J Sainsbury Plc and five of their suppliers conducted over a four-year period in the late 1990s.
We develop a framework examining the relationship between environmental change and patterns of network change. We propose four patterns of network change (network expansion, network churning, network strengthening, and network shrinking) across four environmental change scenarios. Since network evolution is the result of both environmental context and strategic action, we incorporate strategic orientation as a moderator. Focusing on network change at both the firm and network levels of analysis will guide us toward a better understanding of the principles behind the building of effective networks.
To compare and contrast institutional theories used in organizational analysis, the theoretical frameworks and arguments of leading contributors to institutional theory are reviewed and recent empirical studies using institutional arguments are examined. Both approaches reveal considerable variation in the types of concepts and arguments employed, and it is argued that further improvement and growth in institutional theory is dependent upon analysts dealing more explicitly with these differences. In addition, the relation between institutions and interests is explored to show that institutional features of organizational environments shape both the goals and means of actors. Attention is called to the two primary types of actors shaping institutional environments in modern societies- the state and professional bodies-and to the way in which their interests and mode of action shape institutional patterns and mechanisms.
Three perspectives on organizations recently have been the object of increasing interest: the resource dependence, efficiency, and population perspectives. This paper reviews the driving force, organizational concept, assumptions, and literatures of each, then identifies three points of integration across the three perspectives. The suggested meta-theoretical framework within which the perspectives can be interpreted as selection processes may provide the basis for more elaborate theory development by enhancing debate and confrontation among different theoretical perspectives.
This paper examines the relationship of actor-network theory (ANT) and economic geography, arguing that there has been a rather restrictive, sometimes ambiguous reading of ANT literature. It reviews three major lines of reception in economic geography around the themes of topological space, translation and performativity. Subsequently, the paper problematizes conflicting interpretations of ‘network’ and ‘power’ as central ANT terms. In an attempt to open up new avenues of engagement with ANT, it finally sketches an agenda around three themes that are of relevance both for economic geography and for human geography more broadly: hybridity, desire and fluidity.
We illuminate the myriad of opportunities for research where supply chain management (SCM) intersects with data science, predictive analytics, and big data, collectively referred to as DPB. We show that these terms are not only becoming popular but are also relevant to supply chain research and education. Data science requires both domain knowledge and a broad set of quantitative skills, but there is a dearth of literature on the topic and many questions. We call for research on skills that are needed by SCM data scientists and discuss how such skills and domain knowledge affect the effectiveness of an SCM data scientist. Such knowledge is crucial to develop future supply chain leaders. We propose definitions of data science and predictive analytics as applied to SCM. We examine possible applications of DPB in practice and provide examples of research questions from these applications, as well as examples of research questions employing DPB that stem from management theories. Finally, we propose specific steps interested researchers can take to respond to our call for research on the intersection of SCM and DPB.
Today’s supply chain professionals are inundated with data, motivating new ways of thinking about how data are produced, organized, and analyzed. This has provided an impetus for organizations to adopt and perfect data analytic functions (e.g. data science, predictive analytics, and big data) in order to enhance supply chain processes and, ultimately, performance. However, management decisions informed by the use of these data analytic methods are only as good as the data on which they are based. In this paper, we introduce the data quality problem in the context of supply chain management (SCM) and propose methods for monitoring and controlling data quality. In addition to advocating for the importance of addressing data quality in supply chain research and practice, we also highlight interdisciplinary research topics based on complementary theory.
Supply chain security presents numerous challenges to governments interested in defending against terrorist threats. While most approaches stress technological solutions, scholars and policy-makers tend to overlook economics, labour market issues, and industrial relations. Applying agency theory from behavioural economics, this article analyses threats to the US supply chain and opportunities for efficient solutions. Using data from a sophisticated web-based survey of owner-operator cost-of-operations, it shows that drayage drivers are among the lowest paid truck drivers and workers in the US. We provide evidence that low pay is associated with both safety and security risk. Low-wage labour and subcontracting present challenges to US and foreign supply-chain security because the market attracts workers who have few other employment options. In this environment, principals and agents currently make inefficient and inequitable contracts because markets do not reflect the complete costs associated with low-probability/high-impact events like cargo theft and transport security.
Strategic supply chains serve as a potential source of competitive advantage for firms. The ability of a strategy supply chain to engender cultural competitiveness, or joint entrepreneurship and learning aimed at filling market gaps, is a key path through which a strategic supply chain may become a competitive advantage. A balance of trust and power within the supply chain offsets uncertainty and risks associated with the behaviors underlying cultural competitiveness. Using a multi-theoretic perspective, we discuss four strategies that firms use to balance a climate of trust and power in a strategic supply chain. Identifying an authority, generating a common supply chain identity, utilizing boundary spanning ties, and providing procedural and interactive justice are the strategies we discuss.
Purpose
The purpose of this paper is to examine the constructs underpinning risk management and explores its application in the supply chain context through the development of a framework. The constructs of performance and risk are matched together to provide new perspectives for researchers and practitioners.
Design/methodology/approach
The conceptual and empirical work in the supply chain management field and other related fields is employed to develop a conceptual framework of supply chain risk management (SCRM). Risk in the supply chain is explored in terms of risk/performance sources, drivers, consequences and management responses, including initial approaches to categorization within these. Two empirical cases are used to illustrate the application of the framework.
Findings
A new framework is presented that helps to integrate the dimensions of risk and performance in supply chains and provide a categorisation of risk drivers.
Research limitations/implications
SCRM is at an early stage of evolution. The paper provides a clarification of the dimensions and constructs within this field together with directions for future research and development.
Practical implications
The focus on performance in terms of efficiency and effectiveness linked to risk drivers and risk management responses provides insights to managing and measuring risk in supply chains.
Originality/value
The paper consolidates the work in an emerging strand of supply chain management. Two key challenges facing the research community are addressed, the ability to prescribe strategies to address particular risk drivers and the interaction of risk management and performance.
Despite similarities in technology and other aspects of their operation, the typical pharmaceutical manufacturing firm is far more profitable than the typical phonograph record company. These industries are compared in terms of their institutional environments, primarily from 1950 to 1965. Strong differences in organizational effectiveness, regarding their ability to control three aspects of the environment--pricing and distribution, patent and copyright law, and external opinion-leaders--are suggested to help account for the differential performance of these industries. The utility of comparing whole industries as units of analysis in organizational research is also discussed.
Supply chains are increasingly susceptible to unplanned, unanticipated disruptions. With the implementation of the practices of lean systems, total quality management (TQM), time-based competition and other supply chain improvement initiatives, managers now realize that their supply chains are fragile, particularly to environmental disruptions outside their control. As a result of recent events including 11 September 2001, a system is now emerging in purchasing to manage supply risk characterised as having a very low probability of occurrence, difficult to predict, and with a potentially catastrophic impact on the organization. This paper presents case study research findings examining how and why firms create business continuity plans to manage this risk. Propositions are then presented from an institutional theory perspective to examine how various isomorphic pressures result in firms having similar risk management practices embedded in their supply management practices over time.
The four articles in this symposium address one of the most important questions of our day: whether national and global societies can effectively and fairly address the environmental crises that face us. We need to understand how ''another world'' might be possible, who the key actors would be in creating it, and what the main obstacles are likely to be. New avenues for control of the negative effects of globalization are emerging, and these are important articles in moving the discussion forward on voluntary, technological, and citizen-consumer efforts to institute them. We need systematic studies of when and where citizen power is greatest and least, and what happens at the points in the middle. Social scientists' role can be both critical and positive, providing analyses that facilitate realistic and strategic solutions that can be tried and improved. & 2008 Elsevier Ltd. All rights reserved.
Companies are increasingly asked to provide innovative solutions to deep-seated problems of human misery, even as economic theory instructs managers to focus on maximizing their shareholders' wealth. In this paper, we assess how organization theory and empirical research have thus far responded to this tension over corporate involvement in wider social life. Organizational scholarship has typically sought to reconcile corporate social initiatives with seemingly inhospitable economic logic. Depicting the hold that economics has had on how the relationship between the firm and society is conceived, we examine the consequences for organizational research and theory by appraising both the 30-year quest for an empirical relationship between a corporation's social initiatives and its financial performance, as well as the development of stakeholder theory. We propose an alternative approach, embracing the tension between economic and broader social objectives as a starting point for systematic organizational inquiry. Adopting a pragmatic stance, we introduce a series of research questions whose answers will reveal the descriptive and normative dimensions of organizational responses to misery.
Ecological Modernisation (EM) theorists argue that businesses in industrialised nations are ecologically restructuring in response to market signals, and that economic actors increasingly perceive a business case for sustainability. Whilst UK policymakers urge companies to undertake environmental measures voluntarily on the basis that it will be good for business, a qualitative study exploring the environmental practices of small and medium-sized construction firms found that the opinions of owner-managers had little resonance with this ‘win–win’ rhetoric. Respondents did not perceive the financial returns to be gained from eco-efficiency measures to be significant enough to warrant the short-term investment in time and resources required to pursue them. Moreover, the market was not signalling that product value could be raised or that customers could be won by embracing environmental best practice. The paper concludes by questioning the UK government’s faith in EMs ‘win–win’ ideology to encourage the greening of industry.
This paper explores the heterogeneous processes of social and technical change, and in particular the dynamics of techno-economic networks. It starts by considering the way in which actors and intermediaries are constituted and define one another within such networks in the course of translation. It then explores, first the way in which parts of such heterogeneous networks converge to create unified spaces linking incommensurable elements, and second how some of these links achieve longevity and tend to shape future processes of translation.
We raise four challenges to the claim of ecological modernization theory (EMT) that continued modernization is necessary for ecological sustainability. First, EMT needs to go beyond merely demonstrating that societies modify their institutions in reaction to environmental problems and show that such modifications lead to ecological improvements. Second, EMT must show that late stages of modernizing processes lead to the ecological transformation of production and consumption at relatively high frequency. Third, EMT must demonstrate that industries or firms that are reducing their direct impact on the environment are not contributing to the expansion of negative impacts by other industries or firms. Fourth, EMT must show not only that economies are becoming more resource efficient but also that the pace of increase in efficiency exceeds the pace of increase in overall production. In this article, we review the existing evidence and find that EMT has insufficiently addressed these four issues.
Purpose
– The goal of this paper is to provide a broad foundation for future research in the area of strategic sourcing.
Design/methodology/approach
– The foundation is derived by drawing from various well‐established organizational theories. Specifically, strategic sourcing was viewed from the perspective of institutional theory, resource dependence theory, network theory, systems theory, resource/knowledge‐based views of the firm, transaction cost economics, agency theory, strategic choice theory, sociocognitive theory, and critical theory.
Findings
– By viewing strategic sourcing through the lens of ten organizational theories, this research provides multiple insights into many interrelated strategic sourcing questions, such as when to make, buy or ally, how many and which suppliers, and how to manage sourcing relationships. The paper offers a rich and diverse foundation to foster future theory‐building activities in sourcing and supply management research.
Originality/value
– While some of these theory bases have been utilized, to some degree, in the supply management research, the paper offers a more holistic perspective of theoretical insights for strategic sourcing. Each of these organizational theories could be utilized as a foundation for future studies. Further, the paper offers competing and/or complementary theory bases to enhance possible insights into many strategic sourcing questions such as when to make, buy or ally.
This paper makes the case that environmental sociology is in the midst of a significant shift of problematics, from the explanation of environmental degradation to the explanation of environmental reform. In this paper I suggest that there are four basic mechanisms of environmental reform or improvement: environmental activism/movements, state environmental regulation, ecological modernization, and international environmental governance. I suggest further that while "green consumerism" is one of the most frequently discussed mechanisms of environmental improvement within environmental sociology and in movement discourse, green consumerist arguments generally tend to rest on one or more of the other four mechanisms of environmental reform. One of the main tasks of environmental sociology will be to assess which of these four mechanisms is the most fundamental to environmental reform. I conclude with the hypothesis that environmental movements and activism are ultimately the most fundamental pillar of environmental reform.
Despite ubiquitous references to Pfeffer and Salancik's classic volume, The External Control of Organizations, resource dependence theory is more of an appealing metaphor than a foundation for testable empirical research. We argue that several ambiguities in the resource dependence model account in part for this and propose a reformulation of resource dependence theory that addresses these ambiguities, yields novel predictions and findings, and reconciles them with seemingly contradictory empirical evidence from past studies. We identify two distinct theoretical dimensions of resource dependence, power imbalance and mutual dependence, which in the original theory were combined in the construct of interdependence and yet have opposite effects on an organization's ability to reduce dependencies by absorbing sources of external constraint. Results from a study of interindustry mergers and acquisitions among U.S. public companies in the period 1985-2000 indicate that, while mutual dependence is a key driver of mergers and acquisitions, power imbalance acts as an obstacle to their formation. We conclude that our reformulation of the resource dependence model contributes to realizing the potential of resource dependency as a powerful explanation of interorganizational action.
In the logistics literature, it is stated that research results are produced almost entirely within a positivistic paradigm. As a consequence, there is only one school in logistics research, and it is based on the positivistic approach. It also means that the research questions are derived from the same methodological approach, which tends to produce similar questions and answers. In this paper, Arbnor and Bjerke's methodological framework is presented as a basic platform for analysing logistics research. By using the framework, it becomes evident that logistics research can be divided into two schools based on the underlying methodological approach. The schools are the analytical school, building on positivism, and the systems school, building on systems theory. Arbnor and Bjerke's framework also provides a basis for expanding the logistics discipline with yet another school, the actors school, based on sociological meta-theories. Hence, the framework provides logistics research with a solid basis for analyzing existing research and a direction for future research.
Using data collected from multiple respondents in all the business units of a large multinational electronics company, we examined the relationships both among the structural, relational, and cognitive dimensions of social capital and between those dimensions and the patterns of resource exchange and product innovation within the company. Social interaction, a manifestation of the structural dimension of social capital, and trust, a manifestation of its relational dimension, were significantly related to the extent of interunit resource exchange, which in turn had a significant effect on product innovation.
We argue that the debate regarding the performance implications of demographic diversity can be usefully reframed in terms of the network variables that reflect distinct forms of social capital. Scholars who are pessimistic about the performance of diverse teams base their view on the hypothesis that decreased network density--the average strength of the relationship among team members--lowers a team's capacity for coordination. The optimistic view is founded on the hypothesis that teams that are characterized by high network heterogeneity, whereby relationships on the team cut across salient demographic boundaries, enjoy an enhanced learning capability. We test each of these hypotheses directly and thereby avoid the problematic assumption that they contradict one another. Our analysis of data on the social networks, organizational tenure, and productivity of 224 corporate R&D teams indicates that both network variables help account for team productivity. These findings support a recasting of the diversity-performance debate in terms of the network processes that are more proximate to outcomes of interest.
Agency theory is an important, yet controversial, theory. This paper reviews agency theory, its contributions to organization theory, and the extant empirical work and develops testable propositions. The conclusions are that agency theory (a) offers unique insight into in- formation systems, outcome uncertainty, incentives, and risk and (b) is an empirically valid perspective, particularly when coupled with complementary perspectives. The principal recommendation is to in- corporate an agency perspective in studies of the many problems having a cooperative structure. One day Deng Xiaoping decided to take his grandson to visit Mao. "Call me granduncle," Mao offered warmly. "Oh, I certainly couldn't do that, Chairman Mao," the awe-struck child replied. "Why don't you give him an apple?" suggested Deng. No sooner had Mao done so than the boy happily chirped, "Oh thank you, Granduncle." "You see," said Deng, "what in- centives can achieve." ("Capitalism," 1984, p. 62)
Purpose
The purpose of this paper is to analyze the existence of differences in the implementation of environmental practices between companies that possess some form of certified environmental management system (ISO 14001 or EMS) and those that do not have any such system. This study also investigates whether companies with a certified EMS are also making additional environmental demands on their suppliers.
Design/methodology/approach
An empirical study utilizing survey data from automotive supplier organizations was completed. A total of 157 in‐person interviews were conducted with managers of Spanish companies which are automotive supplier organizations. Logistic regressions and non‐parametric tests are used to evaluate hypotheses.
Findings
A positive relation was found to exist between the possession of certified EMS, specifically ISO 14001 and eco‐management and audit scheme, and the environmental demands that these organizations impose on their suppliers. This finding implies that environmental concern spreads upstream in the supply chain. The environmental demands on suppliers increase with customer organization size, but the degree of internationalization, measured by the rates of imports and exports, does not show a significant relationship to these pressures.
Research limitations/implications
The study is limited to a single (important) industrial sector and cannot be directly extrapolated to other industries.
Originality/value
There is a paucity of research that relates the adoption of certified EMS by companies to the adoption and implementation of environmental practices in their own organizations and in supply chain partners. This is the first such study to be completed for the automotive industry.
Research concerning a supplier’s voluntary adoption of environmental practices has focused on buyer influence and supplier leadership values. These explanations are pertinent to early adopters, but other theoretical perspectives are needed to understand why later adopters, who tend to be more conservative, may or may not be likely to adopt environmental practices. Two theoretical lenses may be used to better understand later adoption processes. First, transaction cost economics examines implementation costs that have not been considered in previous research. A transaction cost economics perspective suggests that suppliers are more likely to adopt environmental practices if their information seeking, bargaining, and enforcement costs are minimized. Second, institutional theory can be applied to drivers within the supply base. The institutional theory analysis reveals that supplier adoption of environmental practices is more likely if coercive, normative, and mimetic institutional forces are in play.
Firms invest millions of dollars annually in developing their supply chains, with the broad goal of increasing their own performance. However, despite the significant resources deployed for supply chain development, the extent to which initiating, maintaining, and managing supply chain relationships contributes to firm success remains unclear. The current article provides conceptual development supporting the valuation of firm-to-firm supply chain connections from the perspective of the focal firm. Based on the social network and economics literatures, the article introduces the concept of supply chain capital, which comprises the value of both the structural configuration and relationship content of the firm's supply chain network. Following theoretical development, a non-exhaustive set of propositions are constructed illustrating multiple ways that supply chain capital can be accrued and exploited for firm-level benefit. Managerial recommendations for investment in supply chain capital are included, as are future directions for research in the area of supply chain networks.
Environmental uncertainty plays a crucial role in the implementation of strategic supply management initiatives. The current study adopts the resource dependence theory to explain the direct effect of supply chain uncertainties on strategic supply management, operationalized as a second-order construct comprising strategic purchasing, long-term relationship orientation, interfirm communication, cross-organizational teams and supplier integration. Using structural equation modeling, the 200-firm sample provided evidence that strategic supply management is driven by supply and technology uncertainty. Demand uncertainty, on the other hand, was not found to have a significant impact on strategic supply management. Findings further support the link between strategic supply management and the performance of both buying and supplying firms.