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Enhancing Supply Chain: Exploring and Exploiting AI Capabilities
... The main obstacle to their spread is conflicting business interests, but aligning these interests can yield substantial savings for all parties. Besides, in most of the cases there is a digital divide among partner firms (Sharma et al., 2024). ...
The digital divide is one of the biggest challenges of our time. There are significant differences in the digital maturity of individuals, organizations/companies and regions/countries, which increase the inequalities between them and result in a persistent and growing digital divide. The digital divide can be attributed to a number of factors, and its scientific definition is constantly evolving. It was first attributed more to technological factors, such as internet speed. Today, economic and social factors, such as the ability to use technology, are increasingly important, both at the individual, organizational and governmental levels. The causes and opportunities of digital divide are explored through this theoretical paper. Different indicator systems have been developed over the last decades to measure digital divide, and their logic can be used to identify the factors of digital divide and to propose a way to bridge the gap. By creating a conceptual framework, the causes of the digital divide can be grouped into three dimensions; (1) structural, (2) cognitive and (3) motivational. Along these three dimensions, proposals can also be made to overcome the digital divide at individual, organizational/company and regional/country levels.
... The application of AI in supply chain management (SCM) is shaped by technology trends, organizational systems, ecological factors, and human interactions, wherein AI aids in better decision-making, operational advancements, and sustainable solutions (23). Additionally, explorative AI capabilities bolster supply chain resilience, while exploitative AI capabilities enhance efficiency but may adversely affect resilience, thereby emphasizing the necessity of balancing these capabilities (24). In summation, AI functions as a catalyst for the promotion of sustainable practices within supply chains, providing a framework for strategic integration that can inform policy development and managerial decision-making aimed at optimizing supply chain efficiency, sustainability, and resilience across various industries and regions. ...
Sustainable Supply Chain Management [SSCM] focuses on integrating environmentally sustainable practices into supply chains to balance economic growth with environmental care and social responsibility. The adoption of Artificial Intelligence [AI] in SSCM is revolutionizing supply chain operations by offering new opportunities for efficiency, transparency, and sustainability. The research paper is basically done to find out the field's trends and current research status. Further, the aim of the research is to derive the major concepts and social collaboration on study. The study highlights the emergence of block chain technology as a significant area of interest within SSCM and AI, alongside the importance of green supply chain practices and big data utilization for enhancing sustainability and management practices. The paper underscores the potential of AI-driven technologies, such as predictive analytics and machine learning, to improve supply chain management by enhancing decision-making accuracy, reducing inefficiencies, and promoting sustainable practices. The most used terms in the research are Artificial Intelligence, Supply Chain, Decision Support Systems, Decision Making, and performance. The paper explores three main themes; these include the emergence of block chain technology, the interplay between AI and green supply chain practices, and the significance of management practices alongside big data and sustainability considerations. This thematic analysis suggests areas with potential for further research and development. This paper underscores the critical role of AI in advancing sustainable supply chain practices and outlines the current state of research, key contributors, and future directions in this interdisciplinary field.
This research examines the transformative potential of artificial intelligence (AI) in general and Generative AI (GAI) in particular in supply chain and operations management (SCOM). Through the lens of the resource-based view and based on key AI capabilities such as learning, perception, prediction, interaction, adaptation, and reasoning, we explore how AI and GAI can impact 13 distinct SCOM decision-making areas. These areas include but are not limited to demand forecasting, inventory management, supply chain design, and risk management. With its outcomes, this study provides a comprehensive understanding of AI and GAI's functionality and applications in the SCOM context, offering a practical framework for both practitioners and researchers. The proposed framework systematically identifies where and how AI and GAI can be applied in SCOM, focussing on decision-making enhancement, process optimisation, investment prioritisation, and skills development. Managers can use it as a guidance to evaluate their operational processes and identify areas where AI and GAI can deliver improved efficiency, accuracy, resilience, and overall effectiveness. The research underscores that AI and GAI, with their multifaceted capabilities and applications, open a revolutionary potential and substantial implications for future SCOM practices, innovations, and research.
Interorganizational collaboration and the use of new digital technologies, such as artificial intelligence (AI), big data analytics, internet of things (IoT), or blockchain technology, are regarded as key enablers in implementing sustainability and circular economy‐oriented practices. While this is reflected in a few conceptual and case studies, statistical analyses on the topic are rare. No study so far has focused on collaboration, and digital technologies have only been studied in isolation. Therefore, the purpose of this study is to investigate the effect of interorganizational collaboration practices on a firm's circular economy practices and on outcomes (sustainability performance and economic performance), as well as the potentially facilitative role of new digital technologies on both. The research is based on a deductive approach, using a random sample of 112 Austrian manufacturing companies. The study employs partial least squares structural equation modeling (PLS‐SEM), features a multiple‐respondent design, and uses the dynamic capabilities view as a theoretical foundation. The study finds that interorganizational collaboration practices have a strong positive effect on the implementation of sustainability and CE practices, while the use of new digital technologies and general dynamic capabilities do not. The use of digital technologies positively affects only interorganizational collaboration, while general dynamic capabilities serve as an antecedent for both the use of digital technologies and interorganizational collaboration. Regarding the outcomes of CE implementation, the study finds a positive impact on firm‐level sustainability and economic performance. From a theoretical point of view, the study provides a new perspective on the prerequisites for successful CE implementation, highlights the importance of collaboration, and contextualizes the role of new digital technologies and dynamic capabilities. From a practical point of view, based on the positive outcomes found, the study supports arguments in favor of company engagement in CE activity. It also serves to motivate purposive digitization and systems thinking in order to create efficient CE collaboration networks.
This study evaluated the sensitivity of maximum likelihood (ML)- generalized least squares (GLS) - and asymptotic distribution-free (ADF)-based fit indices to model misspecification under conditions that varied sample size and distribution. The effect of violating assumptions of asymptotic robustness theory also was examined. Standardized root-mean-square residual (SRMR) was the most sensitive index to models with misspecified factor covariance(s) and Tucker–Lewis Index (1973; TLI)Bollen's fit index (1989; BL89) relative noncentrality index (RNI) comparative fit index (CFI) and the ML- and GLS-based gamma hat McDonald's centrality index (1989; Mc) and root-mean-square error of approximation (RMSEA) were the most sensitive indices to models with misspecified factor loadings. With ML and GLS methods we recommend the use of SRMR supplemented by TLI BL89 RNI CFI gamma hat Mc or RMSEA (TLI Mc and RMSEA are less preferable at small sample sizes). With the ADF method we recommend the use of SRMR supplemented by TLI BL89 RNI or CFI. Finally most of the ML-based fit indices outperformed those obtained from GLS and ADF and are preferable for evaluating model fit.
Purpose
Although scholars argue that artificial intelligence (AI) represents a tool to potentially smoothen the bullwhip effect in the supply chain, only little research has examined this phenomenon. In this article, the authors conceptualize a framework that allows for a more structured management approach to examine the bullwhip effect using AI. In addition, the authors conduct a systematic literature review of this current status of how management can use AI to reduce the bullwhip effect and locate opportunities for future research.
Design/methodology/approach
Guided by the systematic literature review approach from Durach et al. (2017), the authors review and analyze key attributes and characteristics of both AI and the bullwhip effect from a management perspective.
Findings
The authors' findings reveal that literature examining how management can use AI to smoothen the bullwhip effect is a rather under-researched area that provides an abundance of research avenues. Based on identified AI capabilities, the authors propose three key management pillars that form the basis of the authors' Bullwhip-Smoothing-Framework (BSF): (1) digital skills, (2) leadership and (3) collaboration. The authors also critically assess current research efforts and offer suggestions for future research.
Originality/value
By providing a structured management approach to examine the link between AI and the bullwhip phenomena, this study offers scholars and managers a foundation for the advancement of theorizing how to smoothen the bullwhip effect along the supply chain.
In 2021, the third edition of our introductory book A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) was published (Hair, Hult, Ringle, & Sarstedt, 2022). The book covers the latest developments in the ield, including recent advances in model evaluation (e.g., inference testing in discriminant validity assessment, predictive
power assessments using PLS predict comparisons), improved guidelines for minimum sample sizes, and new complementary methods and concepts such as necessary condition analysis and endogeneity. The book has been highly successful as evidenced
in its citation count of more than 24,000 times according to Google Scholar (as of August
2021), and the translations into seven other languages, including in German (Hair et al.,
2017), Italian (Hair et al., 2020), and Spanish (Hair et al., 2019). One of the book’s features
that has likely contributed to its popularity is our strong focus on pedagogical elements,
most notably our reliance on a single running case study and the commercial SmartPLS 3 software (Ringle, Wende, & Becker, 2015), which stands out due to its frictionless design, allowing novice researchers to quickly specify and estimate PLS path models (Memon et al., 2021; Sarstedt & Cheah, 2019).
In recent times, the concepts of IT-enabled agility and digital resilience have gained increased attention. The COVID-19 pandemic has brought a turbulent environment in its wake, thus providing an opportunity to study organizations’ agility and digital resilience. This study examines retailers’ strategies and IT’s role in facilitating responsiveness to uncertainties in the marketplace. An analysis of the quarterly earnings call transcripts shows the increased reliance of organizations on IT to drive and sustain their strategies. This study supports the oft-made claim that IT capabilities can make an organization more responsive and agile in the face of disruptions.
Purpose
Many supply chains have faced disruption during Covid-19. Artificial intelligence (AI) is one mechanism that can be used to improve supply chain resilience by developing business continuity capabilities. This study examines how firms employ AI and consider the opportunities for AI to enhance supply chain resilience by developing visibility, risk, sourcing and distribution capabilities.
Design/methodology/approach
The authors have gathered rich data by conducting semistructured interviews with 35 experts from the e-commerce supply chain. The authors have adopted a systematic approach of coding using open, axial and selective methods to map and identify the themes that represent the critical elements of AI-enabled supply chain resilience.
Findings
The results of the study highlight the emergence of five critical areas where AI can contribute to enhanced supply chain resilience; (1) transparency, (2) ensuring last-mile delivery, (3) offering personalized solutions to both upstream and downstream supply chain stakeholders, (4) minimizing the impact of disruption and (5) facilitating an agile procurement strategy.
Research limitations/implications
The study offers interesting implications for bridging the theory–practice gap by drawing on contemporary empirical data to demonstrate how enhancing dynamic capabilities via AI technologies further strengthens supply chain resilience. The study also offers suggestions for utilizing the findings and proposes a framework to strengthen supply chain resilience through AI.
Originality/value
The study presents the dynamic capabilities for supply chain resilience through the employment of AI. AI can contribute to readying supply chains to reduce their risk of disruption through enhanced resilience.
This paper makes an initial attempt to develop a theory of supply chain resilience through ambidexterity in the context of the COVID-19 pandemic. We conducted a single-case analysis focusing on Zong-Teng Group, one of the biggest cross-border e-commerce enterprises in China, as our sample. Data were mainly collected from interviews with Zong-Teng managers and public online resources. Through case analysis, this paper identifies that a fit between the information processing requirements of a firm and its information processing capability leads to greater ambidexterity for exploitation and exploration, which in turn improves supply chain resilience (SCR) in the form of agility, redundancy and flexibility. In addition, ambidexterity in terms of morality improves SCR culture. This paper may be the first to adopt information processing theory to examine SCR and consider the role of ambidexterity, noting that crises such as COVID-19 impose an exponential increase in information processing requirements, to which many firms fail to respond effectively.
Purpose
The aim of the study is to examine the impact of the big data analytics capabilities (BDAC) on the organizational performance. The study also examines the mediating role of ambidexterity and the moderating role of business value of big data (BVBD) analytics in the relationship between the big data analytics capabilities and the organizational performance.
Design/methodology/approach
This study collected primary data based on a questionnaire survey among the large manufacturing firms operating in UAE. A total of 650 questionnaires were distributed among the manufacturing firms and 295 samples were used for final data analysis. The survey was conducted from September to November in 2019, and data were analyzed based on partial least squares structural equation modeling (PLS-SEM).
Findings
The big data analysis (BDA) scalability is supported by the findings on the performance of firm and its determinants such as system, value of business and quality of information. The roles of business value as a moderator and ambidexterity as mediator are found significant. The results reveal that there is a need for managers to consider the business value and quality dynamics as crucial strategic objectives to achieve high performance of the firm.
Research limitations/implications
The study has significant policy implication for practitioners and researchers for understanding the issues related to big data analytics.
Originality/value
This is an original study based on primary data from UAE manufacturing firms.
This editorial aims to present the papers accepted for the special issue (SI) 'Industry experiences of Artificial Intelligence (AI): benefits and challenges in operations and supply chain management.' First, we provide a brief introduction considering the relationship between AI and operations and supply chain management (OSCM) by highlighting some companies already using and practical insights. In sequence, we introduce the papers selected for the SI. The last section gives some intriguing and challenging research directions for scholars and industry practitioners by highlighting potential topics, research opportunities, and possible benefits. Through this SI, we look forward to helping industry practitioners, policy-makers, scholars, and all interested in this field to gain more knowledge about AI applications and insights in relation to OSCM.
Supply chain resilience (SCRes) and performance have become increasingly important in the wake of the recent supply chain disruptions caused by subsequent pandemics and crisis. Besides, the context of digitalization, integration, and globalization of the supply chain has raised an increasing awareness of advanced information processing techniques such as Artificial Intelligence (AI) in building SCRes and improving supply chain performance (SCP). The present study investigates the direct and indirect effects of AI, SCRes, and SCP under a context of dynamism and uncertainty of the supply chain. In doing so, we have conceptualized the use of AI in the supply chain on the organizational information processing theory (OIPT). The developed framework was evaluated using a structural equation modeling (SEM) approach. Survey data was collected from 279 firms representing different sizes, operating in various sectors, and countries. Our findings suggest that while AI has a direct impact on SCP in the short-term, it is recommended to exploit its information processing capabilities to build SCRes for long-lasting SCP. This study is among the first to provide empirical evidence on maximizing the benefits of AI capabilities to generate sustained SCP. The study could be further extended using a longitudinal investigation to explore more facets of the phenomenon.
Operations research (OR) has been at the core of decision making since World War II, and today, business interactions on different platforms have changed business dynamics, introducing a high degree of uncertainty. To have a sustainable vision of their business, firms need to have a suitable decision-making process at each stage, including minute details. Our study reviews and investigates the existing research in the field of decision support systems (DSSs) and how artificial intelligence (AI) capabilities have been integrated into OR. The findings of our review show how AI has contributed to decision making in the operations research field. This review presents synergies, differences, and overlaps in AI, DSSs, and OR. Furthermore, a clarification of the literature based on the approaches adopted to develop the DSS is presented along with the underlying theories. The classification has been primarily divided into two categories, i.e. theory building and application-based approaches, along with taxonomies based on the AI, DSS, and OR areas. In this review, past studies were calibrated according to prognostic capability, exploitation of large data sets, number of factors considered, development of learning capability, and validation in the decision-making framework. This paper presents gaps and future research opportunities concerning prediction and learning, decision making and optimization in view of intelligent decision making in today’s era of uncertainty. The theoretical and managerial implications are set forth in the discussion section justifying the research questions.
Prior research on the evolution of digital infrastructure has paid considerable attention to effective strategies for resolving contradictory tensions, yet what we still do not understand is the role of higher-level organizational capabilities that help balance the contradictory tensions that emerge during this evolution. In addressing this gap, two related questions guided our investigation: (1) How do organizations experience and resolve contradictory tensions throughout the evolution of digital infrastructure? and (2) What can we learn about the organizational capabilities that drive strategic actions in resolving these contradictory tensions? We approach these questions using an in-depth case study at RE/MAX LLC, a global real estate franchise. Based on our findings, we propose a theoretical model of digital infrastructure ambidexterity. The model recognizes three pairs of capabilities (identifying and germinating, expanding and legitimizing, and augmenting and implanting) and two supporting factors (leadership and structure) that are key to resolving contradictory tensions during this evolution. This study responds to a recent research call for dynamic process perspectives at multiple levels of analysis. We discuss the implications of this model for research and practice and offer observations for future research.
With the emergence of information and communication technologies, organizations worldwide have been putting in meaningful efforts towards developing and gaining business insights by combining technology capability, management capability and personnel capability to explore data potential, which is known as big data analytics (BDA) capability. In this context, variables such as sensing capability—which is related to the organization’s ability to explore the market and develop opportunities—and analytics culture—which refers to the organization’s practices and behavior patterns of its analytical principles—play a fundamental role in BDA initiatives.
However, there is a considerable literature gap concerning the effects of BDA-enabled sensing capability and analytics culture on organizational outcomes (i.e., customer linking capability, financial performance, market performance, and strategic business value) and on how important the organization’s analytics culture is as a mediator in the relationship between BDA-enabled sensing capability and organizational outcomes.
Therefore, this study aims to investigate these relationships. And to attain this goal, we developed a conceptual model supported by dynamics capabilities, BDA, and analytics culture. We then validated our model by applying partial least squares structural equation modeling. The findings showed not only the positive effect of the BDA-enabled sensing capability and analytics culture on organizational outcomes but also the mediation effect of the analytics culture. Such results bring valuable theoretical implications and contributions to managers and practitioners.
The paper presents a conceptual framework for extending our understanding of resilience in complex adaptive systems (CAS) such as supply chains using the adaptive cycle framework. The adaptive cycle framework may help explain change and the long-term dynamics and resilience in supply chain networks. Adaptive cycles assume that dynamic systems such as supply chain networks go through stages of growth, development, collapse, and reorientation. Adaptive cycles suggest that the resilience of a complex adaptive system such as a supply chain is not fixed, but expand and contract over time, and resilience requires such systems to navigate each of the cycles’ four stages successfully.
As a framework, the adaptive cycle can explain the mechanisms that support or prevent resilience in supply chains.
A key insight is that the adaptive cycle concept can broaden our understanding of SCRES beyond focal scales, including cross-scale resilience. Adaptive cycles may also give us new insights into the sort of competences required to avoid stagnation, promote system renewal as resilience expands and contracts over time. In addition, the adaptive cycle may move our discussion of resilience beyond engineering and ecological resilience to include evolutionary resilience. While the first two presently dominate theorizing on SCRES, evolutionary resilience may be more insightful than both are. Adaptive cycles capture the idea of change, adaptation, and transformation and allow us to explore cross-scale resilience.
We argue that knowing how to prepare for and overcome key pathologies associated with each stage of the adaptive cycle can broaden our repertoire of strategies for managing SCRES across time. Another key insight is that human agency is important for preventing complex adaptive systems such as supply chain networks from crossing critical thresholds into imminent collapse. More importantly, disruptions may present an opportunity for innovation and renewal for building more resilient supply chains.
Viability is the ability of a supply chain (SC) to maintain itself and survive in a changing environment through a redesign of structures and replanning of performance with long-term impacts. In this paper, we theorize a new notion—the viable supply chain (VSC). In our approach, viability is considered as an underlying SC property spanning three perspectives, i.e., agility, resilience, and sustainability. The principal ideas of the VSC model are adaptable structural SC designs for supply–demand allocations and, most importantly, establishment and control of adaptive mechanisms for transitions between the structural designs. Further, we demonstrate how the VSC components can be categorized across organizational, informational, process-functional, technological, and financial structures. Moreover, our study offers a VSC framework within an SC ecosystem. We discuss the relations between resilience and viability. Through the lens and guidance of dynamic systems theory, we illustrate the VSC model at the technical level. The VSC model can be of value for decision-makers to design SCs that can react adaptively to both positive changes (i.e., the agility angle) and be able to absorb negative disturbances, recover and survive during short-term disruptions and long-term, global shocks with societal and economical transformations (i.e., the resilience and sustainability angles). The VSC model can help firms in guiding their decisions on recovery and re-building of their SCs after global, long-term crises such as the COVID-19 pandemic. We emphasize that resilience is the central perspective in the VSC guaranteeing viability of the SCs of the future. Emerging directions in VSC research are discussed.
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We theorize a notion of a digital supply chain (SC) twin – a computerized model that represents network states for any given moment in real time. We explore the conditions surrounding the design and implementation of the digital twins when managing disruption risks in SCs. The combination of model-based and data-driven approaches allows uncovering the interrelations of risk data, disruption modeling, and performance assessment. The SC shocks and adaptations amid the COVID-19 pandemic along with post-pandemic recoveries provide indisputable evidences for the urgent needs of digital twins for mapping supply networks and ensuring visibility. The results of this study contribute to the research and practice of SC risk management by enhancing predictive and reactive decisions to utilize the advantages of SC visualization, historical disruption data analysis, and real-time disruption data and ensure end-to-end visibility and business continuity in global companies.
This study explores digital business transformation through the lens of four emerging technology fields: artificial intelligence, blockchain, cloud and data analytics (i.e., ABCD). Specifically, the study investigates the operations and value propositions of these distinct but increasingly converging technologies. Due to the dynamic nature of innovation, the potential of this ABCD hybridization, integration, recombination and convergence has yet to be considered. Using a multidisciplinary approach, the findings of the study show wide-reaching and diverse applications among a variety of vertical sectors, presenting exploratory research avenues for future investigation. The study also highlights the practical implications of these new technologies.
Digital divide centers on access to various dimensions of information and communication technology (ICT) including physical access, motivation, skills, and actual usage of digital technologies. This divide tends to be even wider in the context of developing countries. Yet, there is a lack of literature on the digital divide among the faculty who teach in higher education settings. Thus, as a preliminary effort, by using a 57-item Faculty’s ICT Access (FICTA) scale, we investigated the digital inequalities (at the physical, motivational, skills, and usage levels) among Pakistani faculty in respect of their personal and positional categories. We also examined the relationship between faculty’s instructional usage of ICT and other dimensions of their ICT access. The findings revealed that there were significant differences in the faculty’s access to technology at the four levels in respect of their personal and positional categories. Further, the findings of the study shed light on the theoretical implications of the framework of successive kinds of ICT access suggested by van Dijk (The deepening divide: inequality in the information society, 2005).
Abstract
The importance of big data and predictive analytics has been at the forefront of research for operations and manufacturing management. Literature has reported the influence of big data and predictive analytics for improved supply chain and operational performance, but there has been a paucity of literature regarding the role of external institutional pressures on the resources of the organization to build big data capability. To address this gap, this paper draws on the resource-based view of the firm, institutional theory and organizational culture to develop and test a model that describes the importance of resources for building capabilities, skills, and big data culture and subsequently improving cost and operational performance. We test our research hypotheses using 195 surveys, gathered using a pre-tested questionnaire. Our contribution lies in providing insights regarding the role of external pressures on the selection of resources under moderating effect of big data culture and their utilisation for capability building, and how this capability affects cost and operational performance.
Keywords: Big Data, Predictive Analytics, Institutional Theory, Resource Based View, Big Data Culture, Manufacturing Performance, PLS SEM
This study investigates the impact of pre-COVID-19 organizational ambidexterity, digital capabilities, and organizational resilience on firms’ innovation behavior post the second COVID-19 wave. Utilizing World Bank business surveys from 2019 and COVID follow-up surveys in 2020 and 2021 across 21 countries (8,928 firms), we employ partial least squares structural equation modeling and necessary condition analysis. Findings reveal that organizational ambidexterity and digital capabilities positively influence innovation. Organizational ambidexterity indirectly impacts innovation through digital capabilities. The study emphasizes the crucial role of pre-COVID-19 organizational ambidexterity in catalyzing innovation, highlighting its synergy with digital capabilities. Recognizing and cultivating organizational ambidexterity, along with enhancing digital capabilities, are recommended for firms seeking competitive advantages. The study underscores the importance of leaders fostering ambidexterity and knowledge-sharing practices to drive innovation.
Purpose
The topic of artificial intelligence (AI) has been expanding rapidly in recent years, gaining the attention of academics and practitioners. This study provides a structured literature review (SLR) on AI and management decisions (MDs) by analysing the scientific output and defining new research topics.
Design/methodology/approach
The study uses a rigorous methodological approach to summarise the state of the art of the past literature. The authors used Scopus as the database for data collection and utilised the Bibliometrix R package. In total, 204 peer-reviewed English articles were collected and analysed.
Findings
The results showed that literature in this field is emerging. Studies are focused on using AI as forecasting and classification for management decision-making, AI as a tool to improve knowledge management in organisations and extract information. The cluster analysis revealed the presence of five thematic clusters of studies on the topic.
Originality/value
The study’s originality lies in providing a new perspective on AI for MDs. In particular, the analysis reveals a new classification of research streams and provides fruitful research questions to continue research on the topic.
Purpose
The purpose of this paper is to identify the themes that emerged from retail supply chain (RSC) literature during the coronavirus disease 2019 (COVID-19) pandemic that inform future mitigation and recovery strategies.
Design/methodology/approach
This study analyses contributions in the RSC literature using four databases: Emerald, Elsevier (Science Direct), Wiley and Taylor & Francis. The systematic review approach resulted in identifying 74 articles covering 2020 to 2022.
Findings
Four themes emerged from the RSC literature on COVID-19. The first theme highlighted the factors that exacerbated the effects of the COVID-19 pandemic on the RSC. The second theme focussed on the types of disruptions that occurred in the RSC during the pandemic. The third theme demonstrated the recovery strategies used to reduce the impact of COVID-19 on the RSC. The fourth theme identified proposed mitigation strategies for the RSC post-COVID-19 outbreak.
Practical implications
The study provides a deeper understanding of how RSC managers could successfully reduce the effects of the COVID-19 pandemic by dealing with interruptions. Based on the reviewed studies and the four themes that evolved from RSC literature on COVID-19 throughout 2020–2022, 11 key RSC strategies and lessons have been recommended to decision-makers in the retail industry.
Originality/value
This is the first study to identify the themes that emerged from RSC literature during the COVID-19 pandemic to inform future mitigation and recovery strategies. The resulting themes add to the existing body of knowledge and establish the need for further research into other sectors that might be affected by future pandemics.
In a short span of time since its introduction, generative artificial intelligence (AI) has garnered much interest at both personal and organizational levels. This is because of its potential to cause drastic and widespread shifts in many aspects of life that are comparable to those of the Internet and smartphones. More specifically, generative AI utilizes machine learning, neural networks, and other techniques to generate new content (e.g. text, images, music) by analyzing patterns and information from the training data. This has enabled generative AI to have a wide range of applications, from creating personalized content to improving business operations. Despite its many benefits, there are also significant concerns about the negative implications of generative AI. In view of this, the current article brings together experts in a variety of fields to expound and provide multidisciplinary insights on the opportunities, challenges, and research agendas of generative AI in specific industries (i.e. marketing, healthcare, human resource, education, banking, retailing, the workplace, manufacturing, and sustainable IT management).
The purpose of this article is to propose a novel classification of the interrelationships between I4.0 technologies and CE principles that highlights the most conclusive findings and extant gaps in the relevant research. A Systematic Literature Review has been developed to locate, select and evaluate relevant contributions made to CE interrelationships with I4.0 technologies. Studies have been analysed and classified according to the specific I4.0 technology and CE principle addressed
(10Rs). The articles have been clustered into three main groups: (i) useful application of materials; (ii) extending the lifespan of products and their parts, and (iii) smarter product use and manufacture. A mind map of the investigated articles has been used to establish the interrelationships between individual technologies and each CE principle at the supply chain
level. Based on this classification, a focus group interview (FGI) was held with experts to dig deeper into the interrelationships between I4.0 technologies and CE principles. The FGI results have identified how each as yet unexplored I4.0 technology could be linked to each CE principle.
A Fuzzy Delphi (FD) study was also applied to identify the most relevant I4.0 technologies for improving CE principles and closing gaps in the literature regarding the 10R CE principles. In addition, guidelines have been established to assist with practical applications and generate a
research agenda on the interrelationships between I4.0 technologies and CE principles at the supply chain level. Implications for theory include the extension of view from the research gaps between I4.0 technologies and the 10Rs identified in the literature; also, an FGI and FD were performed based on the detected research gaps to identify future lines of research for academics and offer useful guidance to directors and managers on I4.0 technology interrelationships for improving at least one of the 10R CE principles. The contribution to practice aims to enable managers to easily identify which technology from the I4.0 domain should be used to advance any given CE principle. Lastly, we provide useful guidance on the application of as-yet-unused technologies to improve CE principles.
The digital transformation (DT) is reshaping the economy and society. In supply chains (SCs), DT involves adopting digital technologies to collaborate. DT opportunities are particularly diverse in globally distributed manufacturing networks (GDMN) and SCs. Here, DT influences both internal and external collaboration activities, i.e., configuration and coordination of the intra-firm network and network relationships, structure, and governance in the inter-firm network. This study investigates if and how DT changes relationship dynamics and collaboration efficiency in SCs and distributed manufacturing networks through information sharing and jointly used digital technologies. While existing studies have mainly focused on individual digital technologies and their potential for SCs and manufacturing networks, this study contributes to a better understanding of the adoption process and the change in relationship dynamics through DT and joint use of digital technologies. The methodology follows a qualitative approach in a multi-case study setting with six multi-national manufacturing companies operating extensive intra-firm and inter-firm networks. A theoretical framework based on organisational information processing theory guides the study. Data is collected in semi-structured interviews and enriched by secondary data from internal company documents and publicly available sources. The results indicate that digital tools are triggering a centralisation trend in intra-firm networks that leverages efficiencies but is met with stakeholder scepticism. SC collaboration is becoming increasingly dynamic through digital tools, which the SC partners often promote. Non-adopters are not being dropped yet, but the pressure of digital transformation is increasing and becoming more of a threat to small businesses.
Purpose
As global supply chains continue to develop, uncertainty grows and supply chains are frequently threatened with disruption. Although big data technology is being used to improve supply chain resilience, big data technology's role in human–machine collaboration is shifting between “supporters” and “substitutes.” However, big data technology's applicability in supply chain management is unclear. Choosing appropriate big data technology based on the enterprise's internal and external environments is important.
Design/methodology/approach
This study built a three-factor structural model of the factors “management support,” “big data technology adoption” and “supply chain resilience”. Big data technology adoption was divided into big data-assisted decision-making technology (ADT) and big data intelligent decision-making technology (IDT). A survey was conducted on more than 260 employees from supply chain departments in Chinese companies. The data were analyzed through structural equation modeling using Analyze of Moment Structures (AMOS) software.
Findings
The study's empirical results revealed that adopting both ADT and IDT improved supply chain resilience. The effects of both types of big data were significant in low-dynamic environments, but the effect of IDT on supply chain resilience was insignificant under high-dynamic environments. The authors also found that government support had an insignificantly effect on IDT adoption but significantly boosted ADT adoption, whereas management support factors promoted both ADT and IDT adoption.
Originality/value
By introducing two types of big data technology from the perspectives of the roles in human–machine collaborative decision-making, the research results provide a theoretical basis and management implications for enterprises to reduce the supply chain risk of enterprises.
Purpose
The purpose of this paper is to explore the nature of a competitive action and its impact on the response of rivals in the digital market. Specifically, this paper introduces the concept of action complexity and action variation to delineate the configuration characteristics of each digital competitive action and empirically investigates how these action characteristics further affect rivals’ response speed.
Design/methodology/approach
This paper uses structural content analysis methods to code competitive actions based on the news of Chinese online travel agencies (OTAs) from 2010 to 2015. The cox proportional hazards regression models are employed to test the hypotheses.
Findings
The results indicate that action complexity of the focal firm is negatively associated with rivals’ response speed as it constrains their interpretation (awareness), motivation and capability to respond, while action variation of the focal firm is positively associated with rivals’ response speed as it enhances their attention (awareness) and motivation to respond. Furthermore, the negative relationship between action complexity and response speed is weaker when action variation is high.
Originality/value
Further to advancing competitive dynamics theory, this paper proposes an action-configuration perspective to explore the particular content and quality of each digital competitive action. The discussion of competitive rivalry between OTAs also enriches the application of competitive dynamics in the digital market. Meanwhile, this paper further clarifies the decision-making process of rivalry drawing on the awareness–motivation–capability (AMC) framework.
Following the COVID-19 outbreak, a wide range of scholars and practitioners have come to recognize the potential of Additive Manufacturing (AM) technology in building supply chain resilience and efficiency. However, it remains unclear how AM technology might be able to simultaneously build supply chain efficiency and resilience, given the often conflicting nature of these qualities. This paper employs an ambidextrous perspective on dynamic capability theory to investigate the potential of AM technology to solve this resilience-efficiency dilemma at the supply chain level. The research design involves a hybrid approach, combining focus groups and multiple case studies, with particular attention paid to the African supply chain context. The findings indicate that AM technology presents the potential to develop ambidextrous dynamic capabilities, leading to the reconciliation of resilience and efficiency at the supply chain level. Some determinants, such as data-driven systems, supply chain collaboration, innovation agility and knowledge are found to be critical to enable the development of those capabilities around AM-enabled manufacturing systems. The study contributes to the preparation of the global supply chain for the post-COVID era, where digital technologies such as AM will be fundamental for both building resilience and efficiency simultaneously. Practitioners in emerging economies may benefit directly from the outcomes of this study. Furthermore, managers and policy-makers in developed countries may be made aware of the significance of using AM technology in emerging countries to enhance the performance of the global supply chain.
Rural nanostores are the dominant source of packaged products for consumers in rural China, thus the more these stores order from reliable suppliers, the better the product quality available to rural areas. To improve merchandise circulation in rural areas, the Chinese government embarked on the large-scale “Thousands of Villages” (ToV) program in 2005. One of its key components was the implementation of an information technology (IT) procurement system to facilitate transactions between rural nanostores and the ToV program’s certified consumer packaged goods (CPG) suppliers. The nanostores’ adoption of the ToV procurement system was encouraged yet voluntary. We study how this system was used initially and how it evolved over time. If effective, this program has the potential to address the growing social disparity between rural and developed areas in China. We first embarked on an exploratory analysis (March 2012) to understand the ToV program from the perspectives of nanostore owners, the ToV’s CPG suppliers, and the government. We then collected interview data. In Period 1 (2013-2014), when the technology was still nascent, we found nanostore owner trust in the ToV’s CPG suppliers, and system value, played key roles for nanostore owners to use the ToV procurement system. In Period 2 (2018-2019), three contextual factors emerged—population demographic shift, improved technology infrastructure, and trust in new [non-ToV] purchasing platforms—each hindering the ToV procurement system’s use. We observed strong government support during early phases of ToV, but that support evolved from subsidizing the ToV platform and offering associated training (Period 1), to also providing credibility to competing non-ToV procurement platforms (Period 2). Collectively, the findings identify idiosyncratic challenges that arise when public policies attempt to address developing-region problems by reengineering supply chains via IT. We provide implications for IT research about technology management in rural developing areas and for managers to recognize potential pitfalls of managing IT projects in supply bases unfamiliar with advanced IT.
The purpose of this systematic literature review is to answer the following questions: 1) Which topics show the most promise as emerging themes within the intersection of Information Systems (IS) and Operations and Supply Chain Management (OSCM)? 2) Which theories are used in IS/OSCM research? This systematic literature review is discipline-based, focusing on the overlapping research between the fields of Operations and Supply Chain Management and Information Systems. Results from this review provide interdisciplinary IS/OSCM researchers with potential research topics and corresponding theories and publication outlets within the SCM research area. The review covers 10 years, 103 theories, 29 journals, and 155 research papers, thereby providing researchers with both a holistic and detailed view of the IS/OSCM research field.
The purpose of this study is to empirically assess the combined impact that Blockchain technology (BC), the Industrial Internet of Things (IIoT) technologies, and agile production have on supply chain performance. This study is narrowly based on the analysis of data from 303 U.S. manufacturing managers. The proposed theoretical model is analyzed following a covariance-based structural equation modeling methodology (CB/SEM). This is the first study to empirically assess the combined impact of Blockchain technology, IIoT technologies, and agile production on supply chain performance. The results support the conclusion that Blockchain and IIoT technologies are complementary and yield greater agility. Managers are provided with evidence that combining Blockchain, IIoT technologies, and agile production will lead to greater improvements in supply chain performance than using these technologies stand-alone. © 2022 International Association for Computer Information Systems.
Digitalization has altered many assumptions underpinning research on innovation management. At the early innings of exploring how digital innovation management stands out, there is a need for further studies in this area. Previous research on how firms use artificial intelligence has distinguished between automation and augmentation of human activities. In this paper, we explore how firms implement artificial intelligence within research and development. Utilizing an international news database spanning 956 articles from 122 newspapers published in 2020, we find that artificial intelligence is primarily adopted to augment human activities (55%) within research and development, rather than to automate matters (11%). We observe differences across sectors where automation is more common in government, information and communication technology (ICT), and technology and software. Our systematic coding shows that artificial intelligence is primarily adopted for exploration research and development (64%), rather than exploitation (5%). Based on these findings, we conclude that research and development from artificial intelligence primarily focuses on novel markets and areas of operations, rather than enhancing existing product markets and activities. Moreover, it augments human labor rather than replaces it; hence, job losses related to artificial intelligence do not seem to be taking place within research and development.
Based on organizational information processing theory (OIPT), this study examines how and when business networks exert a positive influence on firms’ organizational resilience capacity. Using data collected from 409 Chinese manufacturing firms during the COVID-19 pandemic, and by disaggregating business networks into two dimensions—network breadth and network depth—our findings show, firstly, that both network breadth and network depth are positively correlated to the organizational resilience capacity of firms; secondly, that these relationships are mediated by firms’ ambidextrous learning; and thirdly, that the positive effects of network breadth and network depth on organizational resilience capacity are stronger when the firms’ digital technology levels are higher. Furthermore, through additional analysis, we find that the positive impact of business networks on organizational resilience capacity is stronger for non-state-owned enterprises (non-SOEs) than it is for SOEs, and also that the moderating effect of digital technology on the relationship between business networks and organizational resilience capacity is greater for non-SOEs than it is for SOEs. These findings provide new insight into how a firm's business network, in combination with its ambidextrous learning and level of digital technology, affects its organizational resilience capacity development, which helps it survive a crisis for future sustainable development.
This article provides a systematic review of the digital divide, a phenomenon which refers to disparities in Information and Communications Technology access, usage, and outcomes. It uniquely identifies the factors affecting the digital divide that have emerged in recent years (2017–2021) as well as investigate if there are new forms or levels of the divide that have surfaced in recent literature. The findings, based on 50 included studies, show that the factors affecting the digital divide can be classified into three different segments and nine main categories: sociodemographic, socioeconomic, personal elements, social support, type of technology, digital training, rights, infrastructure, and large-scale events. Out of all factors, education has been linked to the digital divide the most. The majority of recent literature have studied Level 2 of the divide. Also, only one article in the sample has considered the digital divide at the firm level. Findings also show that a new form, type-of-internet access, and two potential new levels of the digital divide, algorithmic awareness and data inequalities, have been identified in the contemporary literature. The results contribute to the understanding and development of the different perspectives of the digital divide concept. They also contribute to the stream of literature on the determinants of the divide and to the social inequalities and digital inclusion literature. This review can be seen as a guide for managers to realize and understand the forms that the divide can take and to delve into their organizational capabilities on the digitalization front and evaluate where further development is needed within their organizations to help diminish the divide.
The adoption of Artificial Intelligence (AI) in the food supply chains (FSC) can address unique challenges of food safety, quality and wastage by improving transparency and traceability. However, the technology adoption literature in FSC is still the in infancy stage, meaning little is known about the critical success factors (CSFs) that could affect the adoption of AI in FSC. Therefore, this study makes a pioneering attempt by examining the CSFs influencing the adoption of AI in the Food Supply Chain (FSC). A conceptual framework based on TOEH (Technology-Organisation-Environment-Human) theory is used to determine the CSFs influencing AI adoption in the context of Indian FSC. The rough-SWARA technique was used to rank and prioritise the CSFs for AI adoption using the relative importance weights. The results of the study indicate that technology readiness, security, privacy, customer satisfaction, perceived benefits, demand volatility, regulatory compliance, competitor pressure and information sharing among partners are the most significant CSFs for adopting AI in FSC. The findings of the study would be useful for AI technology providers, supply chain specialists and government agencies in framing appropriate policies to foster the adoption of AI in FSC the sector. ARTICLE HISTORY
In the era of digitalisation, the role of Big Data is proliferating, receiving considerable attention in all sectors and domains. The domain of operations and supply chain management (OSCM) is no different since it offers multiple opportunities to generate a large magnitude of data in real-time. Such extensive opportunities for data generation have attracted academics and practitioners alike who are eager to tap different elements of Big Data application in OSCM. Despite the richness of prior studies, there is limited research that extensively reviews the extant findings to present an overview of the different facets of this area. The current study addresses this gap by conducting a systematic literature review (SLR) to uncover the existing research trends, distil key themes, and identify areas for future research. For this purpose, 116 studies were identified through a stringent search protocol and critically analysed. The key outcome of this SLR is the development of a conceptual framework titled the Dimensions-Avenues-Benefits (DAB) model for BDA adoption as well as potential research questions to support novel investigations in the area, offering actionable implications for managers working in different verticals and sectors.
Manufacturing incumbents find it difficult to integrate AI in their traditional business models. This paper draws on the research question: How does manufacturing incumbents use AI for enabling business model innovation in industrial ecosystems? We use qualitative method in order to study four large global manufacturing incumbents that are transforming their business models with AI. We performed more than 30 semi-structured in-depth interviews with strategic key personnel in order to understand how they have succeeded with implementing AI and transforming business models. Our main contribution establishes the need for AI business-model innovation to be aligned with ecosystem innovation. Specifically, in short-term incumbents may use ecosystem reconfiguration strategy, whereas long-term strategies relate to ecosystem revitalization, and resilience. Thus, we contribute by connecting organizational microelements with ecosystem macro dimensions and provide an evolutionary model envisioning how incumbents attempt to promote strategic transitions in their firms and ecosystems.
This study provides an overview of state-of-the-art research on Artificial Intelligence in the business context and proposes an agenda for future research. First, by analyzing 404 relevant articles collected through Web of Science and Scopus, this article presents the evolution of research on AI in business over time, highlighting seminal works in the field, and the leading publication venues. Next, using a text-mining approach based on Latent Dirichlet Allocation, latent topics were extracted from the literature and comprehensively analyzed. The findings reveal 18 topics classified into four main clusters: societal impact of AI, organizational impact of AI, AI systems, and AI methodologies. This study then presents several main developmental trends and the resulting challenges, including robots and automated systems, Internet-of-Things and AI integration, law, and ethics, among others. Finally, a research agenda is proposed to guide the directions of future AI research in business addressing the identified trends and challenges.
There is widespread concern that the growth of the Internet is exacerbating inequalities between the information rich and poor. Digital Divide examines access and use of the Internet in 179 nations world-wide. A global divide is evident between industrialized and developing societies. A social divide is apparent between rich and poor within each nation. Within the online community, evidence for a democratic divide is emerging between those who do and do not use Internet resources to engage and participate in public life. Part I outlines the theoretical debate between cyber-optimists who see the Internet as the great leveler. Part II examines the virtual political system and the way that representative institutions have responded to new opportunities on the Internet. Part III analyzes how the public has responded to these opportunities in Europe and the United States and develops the civic engagement model to explain patterns of participation via the Internet.
The union between Industry 4.0 and the circular economy (CE) appears relatively recent. In this sense, new trading zones for sharing a common scenario among academics and practitioners are needed. The paper aims to investigate the link between Industry 4.0 and the CE by understanding how Industry 4.0 can foster the impact of the CE on companies. The study proposes a broader perspective that includes thematic and content analysis gathering data on professional documents based on business cases, newspaper articles, press releases and specialised blogs, as well as scientific papers. The joint academic-practitioners view highlights how Industry 4.0 has the potential to impact on the CE through countless actions: increasing waste disposal; promoting remanufacturing; enhancing the efficiency of critical resources such as water, energy, gas and CO 2 ; and improving business models and the mission of companies. However, barriers still exist in its adoption, stressing the need for holis-tic and integrated design and a proactive environment of collaboration among stake-holders. Results lead to practical as well as research implications. K E Y W O R D S business models, circular economy, Industry 4.0, remanufacturing, resource efficiency, waste management
Purpose
Supply chain resilience (SCR) is a key concept for managers who wish to develop the capacity to enhance their supply chain’s (SC’s) ability to cope with unexpected turbulence. SC digital tools are often seen as a solution that provides more visibility, anticipation and collaboration (SCR capability factors). The purpose of this paper is to investigate the link between SCR and SC digitalisation
Design/methodology/approach
A sample was considered with 300 managers in the field of SCM, and the results were analysed using factor analysis and structural equation modelling (SEM). SEM was employed to test the impact of the degree of digital maturity and SC digital tools on SCR.
Findings
SC digitalization is characterised by the degree of digital maturity and the adoption of SC digital tools. The degree of digital maturity has a strong influence on digital tool adoption. SCR is positively impacted by both the degree of digital maturity and the adoption of digital tools.
Research limitations/implications
The findings do not indicate which tools contribute the most to SCR.
Practical implications
Managers should reflect on the need to continue digitalizing their SCs if they want greater SCR in the current uncertain environment.
Originality/value
This is the first quantitative study that focuses on assessing the impact of the degree of digital maturity and the SC digital tools adopted on SCR. Validation of the hypotheses model confirms the positive impact of SC digitalisation on SCR for researchers and managers.
This study investigates the role of supply chain risk management (SCRM) in mitigating the effects of disruptions impacts on supply chain resilience and robustness in the context of COVID-19 outbreak. Using structural equation modeling on a survey data from 470 French firms, the results confirm the basic tenets of resource-based view and organizational information processing theories regarding the combination of dynamic resources to face disruptions' uncertainty. Furthermore, the findings reveal the mediating role of SCRM practices and the prominent role they play in fostering supply chain resilience and robustness. Overall, by providing empirical assessment of a comprehensive SCRM framework, this research contributes to the extant literature and suggests further avenues for research.
Increasing environmental uncertainty poses significant challenges for organizations. Although scholars generally agree that companies require dynamic capabilities to flexibly respond to and shape uncertain environments, only little empirical research has been conducted on the factors that facilitate the development of these capabilities. This study addresses this gap and introduces strategic foresight as an important antecedent of firms’ dynamic capabilities. The paper investigates the impact of strategic foresight on two distinct types of dynamic capabilities, namely strategic flexibility and decision rationality, and how the influence of strategic foresight is moderated by the degree of environmental uncertainty. We test our hypotheses by adopting a mixed-methods approach, using both qualitative information gathered trough five expert interviews, as well as survey data collected from 79 managers familiar with strategic foresight practices. The obtained results indicate a significant positive impact of strategic foresight on firms’ strategic flexibility and decision rationality. Furthermore, this study finds that environmental uncertainty strengthens the positive effect of strategic foresight on strategic flexibility. Contributions to strategic foresight research and managerial practice for firms trying to cope with continuously increasing levels of environmental uncertainty are discussed.
While firms are increasingly exposed to catastrophes due to global presence of their supply chains, the development of supply chain resilience becomes crucial to businesses. Thus, it is important to examine business values for supply chain resilience under different types and levels of disruptions. Drawing on the organizational information process theory, a theoretical model was developed to examine the moderating effects of the various supply chain disruptions on performance outcomes. Empirical evidence, collected from primary and secondary data sources, suggests that supply chain resilience is found to be positively associated with risk management, market, and financial performance. In particular, supply chain resilience has shown importance in contributing to the risk management and market performance when firms experience high levels of supply side, infrastructure, and catastrophic disruptions.
The fourth industrial revolution and the underlying digital transformation, known as Industry 4.0, is progressing exponentially. The digital revolution is reshaping the way individuals live and work fundamentally, and the public remains optimistic regarding the opportunities Industry 4.0 may offer for sustainability. The present study contributes to the sustainability literature by systematically identifying the sustainability functions of Industry 4.0. In doing so, the study first reviews the fundamental design principles and technology trends of Industry 4.0 and introduces the architectural design of Industry 4.0. The study further draws on the interpretive structural modelling technique to model the contextual relationships among the Industry 4.0 sustainability functions. Results indicate that sophisticated precedence relationships exist among various sustainability functions of Industry 4.0. ‘Matrice d’Impacts Croisés Multiplication Appliquée àun Classement’ (MICMAC) analysis reveals that economic sustainability functions such as production efficiency and business model innovation tend to be the more immediate outcome of Industry 4.0, which pays the way for development of more remote socioenvironmental sustainability functions of Industry 4.0 such as energy sustainability, harmful emission reduction, and social welfare improvement. This study can serve Industry 4.0 stakeholders – leaders in the public and private sectors, industrialists, and academicians – to better understand the opportunities that the digital revolution may offer for sustainability, and work together more closely to ensure that Industry 4.0 delivers the intended sustainability functions around the world as effectively, equally, and fairly as possible.
The importance of big data analytics, artificial intelligence, and machine learning has been at the forefront of research for operations and supply chain management. Literature has reported the influence of big data analytics for improved operational performance, but there has been a paucity of research regarding the role of entrepreneurial orientation (EO) on the adoption of big data analytics. To address this gap, we draw on the dynamic capabilities view of firms and on contingency theory to develop and test a model that describes the role of EO on the adoption of big data analytics powered by artificial intelligence (BDA-AI) and operational performance (OP). We tested our research hypotheses using a survey of 256 responses gathered using a pre-tested questionnaire from manufacturing firms in India with the help of the National Association of Software and Services Companies (NASSCOM) and the Federation of Indian Chambers of Commerce and Industry (FICCI). The results from our analysis indicate that EO enables an organisation to exploit and further explore the BDA-AI capabilities to achieve superior OP. Further, our results provide empirical evidence based on data analysis that EO is strongly associated with higher order capabilities (such as BDA-AI) and OP under differential effects of environmental dynamism (ED). These findings extend the dynamic capability view and contingency theory to create better understanding of dynamic capabilities of the organisation while also providing theoretically grounded guidance to the managers to align their EO with their technological capabilities within their firms.
The Fourth Industrial Revolution – also known as Industry 4.0 (i4.0) – comprises the digitalisation of the industrial sector. This paper uses the theoretical lens of supply chain innovation (SCI) to investigate the implications of i4.0 on supply chain management. For these purposes, the method of structured content analysis is applied to more than 200 use cases of i4.0-enabled SCI introduced by both established and startup companies. i4.0-enabled SCI manifests along three dimensions: process, technology, and business architecture. The key findings of this study can be summarised as follows: first, i4.0-enabled SCI extends the initial focus on productivity improvements in SC processes towards scalability and flexibility. Second, extant i4.0 solutions rely mostly on analytics and smart things while omitting smart people technology and the human-centric approach associated with the i4.0 paradigm. Third, established companies adopt i4.0 merely to sustain their existing business architectures while startup companies radically change their operating models, relying heavily on data analytics and the platform economy. Consequently, established companies pursue a problem-driven, engineering-based approach to SCI while startup companies follow an ‘asset-light’, business-driven approach. Lastly, there are two distinct approaches to digitalising operational SC processes: platform-based crowdsourcing of standard processes and on-demand provision of customised services.
Drawing upon organizational information processing theory, we investigate how explorative and exploitative information technology (IT) capabilities independently and interdependently moderate the relationship between supply chain information integration and firm performance. Data collected from 215 firms in China reveal that explorative IT capability positively moderates the relationship between information sharing and firm performance but exerts no moderating effect on the association between collaborative planning and firm performance. By contrast, exploitative IT capability positively moderates the impact of collaborative planning but negatively moderates the influence of information sharing on firm performance. Furthermore, building upon the ambidexterity perspective, we develop and test a three‐way interaction hypothesis that explorative and exploitative IT capabilities would interdependently moderate the relationship between supply chain information integration and firm performance. Our results indicate that explorative and exploitative IT capabilities are complementory in moderating the link between collaborative planning and firm performance but substitutive in moderating the relationship between information sharing and firm performance. By integrating insights from both information systems and operations management, our study thus provides an in‐depth understanding on how and when IT business value can be created in supply chain information integration context.