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Roles of top management support and compatibility in big data predictive analytics for supply chain collaboration and supply chain performance
Abstract
With the global digitalisation, big data has received growing attention from academicians and practitioners. However, only a few empirical studies examined the benefits of big data predictive analytics (BDPA) and its influence on supply chain collaboration (SCC) and supply chain performance (SCP). Addressing the identified gaps of the implementation of organisational information processing theory (OIPT), the current study provided the foundation to develop a conceptual framework. All relevant data were collected from 197 employees in the Chinese logistics industry. Partial least squares-structural equation modelling technique was performed. The obtained empirical results supported top management support and compatibility as critical factors for the adoption of BDPA. Moreover, BDPA exhibited positive influence on SCC and SCP. Additionally, SCC mediated the relationship between BDPA and SCP. This study presented significant theoretical contributions and provided guidelines that can benefit policymakers and organisations in the efforts of implementing BDPA for enhanced SCP. After all, improving SCP would benefit customers and the society in the case of reduction and wastage of resources.
... Extant literature has mainly focused on top management support, highlighting it as a critical determinant of BA adoption (Shafique et al., 2024). Top management support strongly influences digital-enabled task performance, highlighting the importance of transformational supervisory leadership on employee performance in BA contexts (Shao et al., 2024;Wee et al., 2023). ...
... Top management support also moderates the association between the regulatory environment and BA adoption intentions . It has often been researched alongside technological factors to understand BA adoption in organizations and has been shown to mediate the effects of technological and organizational factors on BA adoption, including compatibility, competitiveness, and organizational readiness (Maroufkhani et al., 2023;Shafique et al., 2024). ...
... This support can help align the BA system with the organization's strategic goals and employees' work practices, enhancing perceived compatibility. OST suggests that employees are more likely to perceive BA systems as compatible when they receive clear and consistent support from top management (Maroufkhani et al., 2023;Shafique et al., 2024). Therefore, we propose Hypothesis 1b (H1b): Top management support positively influences employees' perceived compatibility with BA tools. ...
This study investigates the impact of perceived organizational support (POS) on employees' intentions to continue using business analytics (BA) tools. By integrating Organizational Support Theory (OST) and technology adoption models, the research highlights the critical roles of technical and top management support in influencing perceived compatibility and usefulness, which drive BA continuance intentions. Data were collected between August and October 2021 from employees across various industries in Ireland, Finland, and Sweden who used BA tools in their work. Using Partial Least Squares Structural Equation Modeling (PLS-SEM) to analyze data, the findings reveal significant relationships between the organizational support types and continuance intentions, with technical support being particularly crucial for long-term use. This study extends OST by emphasizing the importance of technical support and confirms the relevance of perceived compatibility and usefulness in technology continuance.
... Key technologies for logistics 4.0 and digital supply chain include the Internet of Things, Blockchain, digitalization, artificial intelligence, machine learning, cyberphysical, augmented reality, virtual reality, robotics, additive manufacturing, and cloud computing [8][9][10][11][12]. However, studies on logistics 4.0 and digital supply chains have often focused on individual technologies such as artificial intelligence [13][14][15], blockchain [16], Internet of Things [17][18][19], big data [15,[20][21][22], supply chain analytics [23], and wearable technologies [24]. The literature found inconsistencies in the use of information technology in digital supply chain and logistics 4.0. ...
... The study explored the relationship among logistics 4.0, digital supply chain, artificial intelligence, Internet of Things, digitalization, big data, circular economy, green logistics, and green supply chain management. The focus was only on four key technologies: Artificial intelligence [13][14][15], Internet of Things [17][18][19], big data [15,[20][21][22], and digitalization [51] These technologies are common in logistics 4.0 and digital supply chains, and most literature focuses on their economic, environmental, and social benefits. ...
... The adoption of more sustainable new technologies and process improves not only productivity but reduces also ecological footprints. Nonetheless, this process depends not only on the adoption of new technologies but also on managerial decisions since managers make significant decisions and give the pace of the transition (Kharb et al. 2024;Liao and Zhang 2020;Shafique et al. 2024). The role of the BOD is particularly noteworthy, as such transition also reflects corporate strategic decisions. ...
... The positive moderating effect of NQP on D1, indicates that in companies with a higher proportion of top managers, NQP plays a supportive role by fostering an environment that promotes innovation. This could be evidenced form Shafique et al.'s (2024) study that top managers had the ability and strategic vision to leverage resource effectively, driving the adoption of innovative practices and technologies. At the meantime, as suggested by Jiang et al. (2024) and , NQP was usually associated with TFP, which represented a transformation and upgrading of productivity, typically providing top managers with greater opportunities to exert their influence. ...
This study explores the relationship between five types of board diversity and environmental innovation (EI), with a focus on the moderating role of new quality productivity (NQP). Using an entire sample from China's A‐share market (2013–2022), the study examines how board compositions—specifically top managers, independent directors, female directors, supervisory directors, and blockholder directors—influence EI performance. The entropy method is adopted to measure NQP, while robustness and endogeneity checks, alongside two‐step system GMM estimation, reinforce the credibility of the findings. Our findings suggest that while board diversity generally benefits EI, only certain types, such as top managers and female directors, positively impact EI. In contrast, diversity among supervisory and blockholder directors has negative effects, and independent directors show no significant influence. NQP can amplify these effects, although its impact varies by board member type. This study emphasizes that aligning board diversity with environmental objectives is essential, not just merely increasing diversity. Instead, specific board compositions and roles determine whether diversity effectively drives EI, answering that “more is not always better” regarding diversity's role in environmental goals. Future research could examine additional mechanism and explore NQP's impact on sustainability across various contexts.
... Regarding methodological approach, the study results show a tendency to shift from conventional statistical models to machine learning and data science-based approaches (Jiang et al., 2024). Classical models such as linear regression, ARIMA, and exponential smoothing are still widely used, but more and more studies are adopting neural networks, decision trees, and random forest models to improve prediction accuracy (Bag et al., 2022;Shafique et al., 2024). The machine learning approach is considered more adaptive to big data and variable complexity, although it still faces interpretability challenges and high computational resource requirements (Kayıkçı et al., 2025). ...
... In terms of methods, there is a tendency to transition from traditional statistical models to machine learning and data science-based approaches (Jiang et al., 2024;Town et al., 2022). Although classical models such as ARIMA, linear regression, and exponential smoothing are still used, more and more studies are applying methods such as neural networks, decision trees, and random forests (Bag et al., 2022;Lee & Trimi, 2021;Mayer & Yang, 2023;Shafique et al., 2024). Machine learning-based models are considered superior in managing extensive data and complex variables, although they face challenges in interpretability and consumption of computing resources (Paul et al., 2021). ...
This study aims to systematically review the academic literature related to the role of forecasting in managerial planning, using the Systematic Literature Review (SLR) approach. This review was conducted on 47 articles published between 2015 and 2024, collected from reputable databases such as Scopus and Web of Science. The focus of the study includes methodological approaches, key findings, research trends, and geographical and sectoral distribution of the analyzed studies. Following the PRISMA protocol, this study successfully identified key topics such as prediction accuracy, forecasting applications in strategic decision making, and challenges in applying forecasting in a dynamic business environment. The benefits of this study include strengthening the theoretical and practical foundations in forecasting for more accurate and objective decision-making. Academics can use the study results as material for curriculum development and further research directions, while practitioners obtain guidance in strategically implementing forecasting. This study also fills the gap in the literature related to the integration of forecasting theory and practice. It contributes to educating organizations to shift from an intuitive approach to a data-driven approach in future planning.
... Big data is used by scientists not only in the academic sphere but also in practice. Scientific works also examine the effects of predictive analytics of big data on supply chain performance (Shafique et al., 2024). Classical forecasting is being replaced by predictive analytics, and its knowledge can create competition (Korayim et al., 2024). ...
... In accordance with other authors (Korayim et al., 2024), the importance of big data and subsequent demand forecasting is confirmed in this article. Predictive analytics applied in this article is an integral part of modern business management, which is in line with other studies (Shafique et al., 2024). ...
... Top management support plays a crucial role in facilitating Big Data adoption by providing strategic direction, allocating necessary resources, and fostering a data-driven culture within organizations (Shafique et al., 2024). When leadership actively champions Big Data initiatives, employees are more likely to perceive the value of such technologies and align their efforts accordingly, leading to improved organizational performance (Prakash, 2024). ...
This study examines the factors influencing Big Data adoption and its impact on organizational performance within Malaysia Digital Status Companies, particularly in the Global Business Services (GBS) sector. Grounded in the Technology-Organization-Environment (TOE) framework and Resource-Based View (RBV) theory, the study explores the roles of data quality management, data security, ease of use, and top management support, with training acting as a moderating variable. Based on 272 survey responses analyzed using Partial Least Squares Structural Equation Modelling (PLS-SEM), the findings reveal that data quality management, ease of use, and top management support contribute significantly to organizational performance, whereas data security does not exhibit a significant effect. Furthermore, training enhances the influence of ease of use, highlighting the importance of intuitive technology and skill development. This study supports the Malaysia Digital Economy Blueprint by advancing data-driven strategies, strengthening digital infrastructure, and boosting economic competitiveness
... Managerial support in supply chain management describes the degree of understanding, approval, and encouragement by senior management or supply chain managers towards adopting and using technological capabilities like BDA (Shafique et al., 2024). This includes providing the needed resources, guidance, and steering required for the successful implementation and execution of strategic initiatives across the entire supply chain (Xu and Liu, 2024). ...
Purpose
This research examines the influence of big data analytics (BDA) on the competitive performance of firms in the United Arab Emirates (UAE). Focused on the linkages of IT-enabled dynamic capabilities, managerial support, data driven culture, environmental uncertainty and supply chain resilience, the study aims to evaluate the mechanisms through which BDA contributes to competitive advantage.
Design/methodology/approach
This research employs an empirical investigation to address questions regarding the influence of BDA on the competitive performance of the supply chain industry in the UAE. The research involved the distribution of a structured questionnaire to the employees ( n = 400) across diverse supply chain units in the UAE. The proposed framework was evaluated through SPSS and AMOS. Additionally, the researchers utilized the Process Macro to reveal the mediating and moderating dynamics.
Findings
The study finding emphasizes the impact of BDA on both supply chain resilience and competitive performance with IT-enabled capabilities playing a mediating role. Furthermore, managerial support was found to positively moderate the relationship between BDA and IT-enabled capabilities.
Research limitations/implications
This study contributes to the improvisation of existing literature in the field providing an understanding of how study variables collectively influence competitive performance within the specific context of UAE firms.
Practical implications
The findings provide insights for industry practitioners, highlighting the strategic importance of integrating BDA into supply chain management to boost operational efficiency and sustain competitive performance.
Originality/value
The study findings provide opportunities for scholars as well as managers for optimizing their strategic developments to build sustainable competitive performance by processing data analytic processes, resilient activities and efficient managerial support.
... Operational agility is vital in there, manufacturing organizations have to learn continuously in this volatile, uncertain, complex and ambiguous (VUCA) world. Specifically, although entrepreneurship orientation (EO) has been recognized as an influencer of innovative practices, its function in fostering the adoption of BDA-AI and its effect on operational performance have remained uncertain (Shafique et al., 2024;Tan et al., 2024;Zhang et E-ISSN: 3063-0304, P-ISSN: 3063-1092 Researcher Academy Innovation Data Analysis (RAIDA) Vol 1, No. 3, December 2024, Page 140-252 https://doi.org/10. 69725/raida.v1i3.170 ...
Objective: This study investigates the relationships between Entrepreneurial Orientation (EO), BDA-AI adoption, Operational Performance (OP), and the moderating role of Environmental Dynamics (ED). The study aims at exploring the impact of strategic orientation and advanced technologies on organizational capabilities in the uncertain contexts.Methods: We employed a quantitative analysis using Structural Equation Modeling (SEM) with Warp PLS to test the hypothesized relationships. The measurement model was then checked for reliability and validity and fit indices were calculated to ascertain robustness. Because this was a multi-year initiative, data were collected over time and included a number of metrics associated with operational improvement and technology adoption.Results: The result showed a significant and positive association of EO, BDA-AI adoption, and OP. These relationships were greatly strengthened by ED, highlighting ED as an engine of organizational adaptability and performance in both dynamic contexts and organizations. The proposed structural model was able to explain quite a lot of the variance in the data and fit her extremely well according to the model fit indices.Novelty: Therefore, this study proposed ED as a crucial mediating variable to help unite the excess between the strategic education and innovative performance. It offers a unique lens through which to view the ways in which firms use EO and advanced analytics to maintain competitive advantage under conditions of environmental turbulence.Implications for Research: The study provides a conceptual basis for future empirical research on the strategic coupling of EO and BDA-AI in sectors. It opens up avenues for consideration of environmental and organizational influences that enable or inhibit the performance-induced benefits of technology innovations
... The literature has focused on artificial intelligence-big data analytical capabilities (AI-BDACs) from the perspective of supply chain agility [9]. Additionally, big data analytics (BDA) has sharpened the supply chain performance and overall organizational performance [10,11]. ...
The industrial shift from Industry 4.0 to Industry 5.0 has transformed organizational thinking, moving the focus from purely technological implementation to a more human-centered approach. The current study has focused on the Industry 5.0 technological capabilities to bring into circular economy practices aligned with sustainable development goals, aiming to enhance sustainable performance. Moreover, the resource-based theory has grounded the development of the comprehensive framework on Industry 5.0 technological capabilities (artificial intelligence capabilities, big data analytical capabilities, Internet of Things capabilities, machine learning capabilities, and blockchain technology capabilities) and circular economy practices (eco-design, management system, and investment recovery) to achieve sustainable performance (environmental performance, social performance, and economic performance). Data have been collected from 179 respondents from the Chinese manufacturing industry. Additionally, data have been analyzed using the structural equation modeling technique. The results showed that Industry 5.0 technological capabilities directly affect sustainable performance. Moreover, circular economy practices played a dual, moderating, and mediating role between Industry 5.0 technological capabilities and sustainable performance. The current study has contributed to filling a gap in the literature on Industry 5.0 capabilities, especially in the circular economy and sustainable performance perspective. The practical contribution recommended is that if organizations focused on their Industry 5.0 technological capabilities, it would boost circular economy practices and sustainable performance to achieve sustainable development goals.
... Compatibility is an aspect of an organisation's capacity to adopt new technologies [55]. The significance of the metaverse compatibility with marketing for retail SMEs is as follows: with the ability to catapult brand-consumer interactions into a more colourful, engaging, and immersive domain than traditional digital platforms, the metaverse heralds a new era of digital engagement. ...
Small and medium-sized enterprises (SMEs) rely on a thriving distribution network and digital technologies, including the metaverse, to remain competitive. The retail industry is perceived as a portfolio-based and granular business. Hence, retail SMEs can be a key source of innovation and a major source of growth that need technical flexibility to react quickly to changing customer needs. Therefore, grounded in the TOE framework, this study is undertaken to explore the drivers of metaverse adoption for enhancing the marketing capabilities of retail SMEs. Online surveys were used to gather primary data from 300 marketing practitioners employed in retail SMEs. Subsequently, the data were analysed using quantitative research methodology. The finding reveals that the metaverse is gaining attention from retail SMEs due to its technological, organisational, and environmental drivers, as well as its ability to provide immersive experiences to customers. Thereafter, metaverse adoption enhances the marketing capabilities of SMEs with new sales channels, tailored marketing, and increased customer reach with an understanding of the marketplace focused on customer relations, market research, and cross-management capabilities.
... A phased approach also provides the opportunity to test and refine the technologies before full-scale deployment, ensuring that any issues are addressed early on. Integrating AI and BDA with existing systems may require custom solutions and middleware to ensure compatibility and seamless data flow (Shafique, Yeo, and Tan, 2024). To manage the high costs associated with AI and BDA implementation, organizations should conduct a comprehensive cost-benefit analysis. ...
This review paper examines the use of artificial intelligence (AI) and big data analytics in solving supply chain management (SCM) issues and enhancing future operational efficiency. The primary objective is to synthesize existing research and provide a comprehensive overview of how these technologies are revolutionizing SCM. The paper systematically reviews recent literature on AI and big data applications in SCM, focusing on key areas such as demand forecasting, inventory management, and logistics optimization. By analyzing various studies and case examples, it highlights the transformative effects of these technologies on supply chain processes. The review covers a range of AI techniques including machine learning, deep learning, and predictive analytics, as well as the role of big data in capturing and processing large volumes of supply chain-related information. Key findings from the literature indicate significant improvements in supply chain visibility, decision-making accuracy, and operational efficiency. AI and big data analytics enable more precise demand forecasting, better inventory control, and optimized logistics, leading to cost reductions and enhanced responsiveness to market fluctuations. The review also discusses the challenges and considerations for implementing these technologies, such as data quality, integration complexity, and the need for specialized skills. The paper emphasizes the critical role of AI and big data analytics in addressing contemporary SCM issues and fostering future-ready supply chains. It underscores the necessity for organizations to adopt these technologies to stay competitive and achieve long-term operational excellence. The insights provided serve as a valuable resource for researchers and practitioners aiming to leverage AI and big data for supply chain innovation. Keywords: Artificial Intelligence (AI), Big Data Analytics (BDA), Supply Chain Management (SCM), Predictive Analytics, Internet of Things (IoT), Blockchain Technology, Automation, Machine Learning, Inventory Management, Logistics Optimization, Supply Chain Visibility, Real-time Monitoring, Data Quality, System Integration, Risk Management, Operational Efficiency, Strategic Decision-making, Sustainability, Supply Chain Resilience, Emerging Technologies.
... The results of the analysis of the consistency show that the coefficients of the scales were within the acceptable values and the reliability of the questionnaire was high, so the next step of validation could be conducted. [71]. Therefore, it is necessary to compare the response from early and late respondents. ...
Currently, an increasing number of elderly people are choosing to live in elderly care communities, where they have access to smart health services. Despite the availability of these services, the utilization rate among the elderly remains low, resulting in the elderly not fully experiencing the benefits of this advanced technology. To ensure more elderly people to receive these benefits, this study constructed a model of the mechanisms affecting the use of smart health services by elderly people in retirement communities based on the TAM theory and collected data through questionnaires. A total of 1,292 valid questionnaires were collected from 8 retirement communities in several cities in China, and the data were analyzed by PLS-SEM. This study finds that different types of trust differ in how they influence the elderly’s perception of smart health services; perceived ease of use cannot directly affect the elderly’s willingness to adopt smart health services but should be achieved through the influence of health expectations; various types of social support play an important role in the elderly’s perceived usability of smart health services; and perceived usefulness plays an important role in the adoption of smart health services. In addition, through a multi-cohort comparative analysis, this study found differences in gender, age, income, and education in the adoption of health services by the elderly. The findings support the advancement and implementation of smart healthcare services among the elderly.
... This aligns with the existing literature (Kaartemo and Nyström, 2021;Sukkird and Shirahada, 2015;Tan et al., 2015) which points that a hospital is more likely to process and analyze real-time clinical and operational data accurately with healthcare 4.0 technologies to execute supply chain operational and innovative tasks under uncertainties, which in turn yield high performance. This is an essential finding because, to improve performance, healthcare organizations must increasingly make data-driven supply chain decisions (Ali and Kannan, 2022;Govindan et al., 2020;Shafique et al., 2024). ...
Cloud computing represents a groundbreaking technological change that transforms traditional IT operational paradigms, driving significant improvements in supply chain efficiency and unlocking new value through digital capabilities. Despite its growing influence, empirical research on this subject remains limited, with unclear explanations of the specific ways cloud computing enhances supply chain operations. The precise mechanisms through which it influences supply chain dynamics are yet to be fully explored. This study employs survey data from Chinese enterprises utilizing cloud computing, applying Smart PLS 3.0 for partial least squares structural equation modeling (PLS-SEM) to assess how cloud-based technical competencies affect supply chain outcomes. Grounded in IT capability theory, we conceptualize cloud computing’s technical dimensions as Flexible IT Infrastructure and Cloud/Business Synergy while incorporating supply chain governance as a mediator and market uncertainty as a moderator to clarify the relationship between cloud capabilities and performance. Our findings advance both scholarly and managerial perspectives on cloud computing’s role in modern supply chains.
The research substantiates the advantages and develops scientific and practical recommendations for using the Internet of Things (IoT) as a technology for collecting big data for interactive managerial decision-making by today’s enterprises. Using the top 30 countries in management automation in 2023 as an example, the authors employed a regression analysis method to model the impact of IoT and big data utilization on the target outcomes of interactive managerial decision-making in contemporary business management. As a result, the model revealing the influence of IoT and big data usage on the target outcomes of interactive managerial decision-making uncovered previously unknown causal relationships in the automation of contemporary business management. The model identified advantages such as increased export activity of enterprises, optimized logistics, enhanced innovation activity, and accelerated and simplified corporate reporting to government regulators. The demonstrated perspective of maximizing the benefits of interactive managerial decision-making in Russia holds practical significance because it will enhance the accuracy and reliability of planning, forecasting, and state and corporate management of the automation processes in Russian business management. The authors developed a technology for collecting big data using IoT. The managerial significance of the developed technology is confirmed by the following advantages for interactive decision-making: acceleration of big data collection and improvement of its objectivity, greater systematization of big data and more precise causal relationships among its elements, and increased completeness and reliability of big data due to a broader spectrum of its sources.
Purpose
The purpose of this study is to provide a holistic understanding of the factors that either promote or hinder the adoption of artificial intelligence (AI) in supply chain management (SCM) and operations management (OM). By segmenting the AI lifecycle and examining the interactions between critical success factors and critical failure factors, this study aims to offer predictive insights that can help in proactively managing these factors, ultimately reducing the risk of failure, and facilitating a smoother transition into AI-enabled SCM and OM.
Design/methodology/approach
This study develops a knowledge graph model of the AI lifecycle, divided into pre-development, deployment and post-development stages. The methodology combines a comprehensive literature review for ontology extraction and expert surveys to establish relationships among ontologies. Using exploratory factor analysis, composite reliability and average variance extracted ensures the validity of constructed dimensions. Pearson correlation analysis is applied to quantify the strength and significance of relationships between entities, providing metrics for labeling the edges in the resource description framework.
Findings
This study identifies 11 dimensions critical for AI integration in SCM and OM: (1) setting clear goals and standards; (2) ensuring accountable AI with leadership-driven strategies; (3) activating leadership to bridge expertise gaps; (4) gaining a competitive edge through expert partnerships and advanced IT infrastructure; (5) improving data quality through customer demand; (6) overcoming AI resistance via awareness of benefits; (7) linking domain knowledge to infrastructure robustness; (8) enhancing stakeholder engagement through effective communication; (9) strengthening AI robustness and change management via training and governance; (10) using key performance indicators-driven reviews for AI performance management; (11) ensuring AI accountability and copyright integrity through governance.
Originality/value
This study enhances decision-making by developing a knowledge graph model that segments the AI lifecycle into pre-development, deployment and post-development stages, introducing a novel approach in SCM and OM research. By incorporating a predictive element that uses knowledge graphs to anticipate outcomes from interactions between ontologies. These insights assist practitioners in making informed decisions about AI use, improving the overall quality of decisions in managing AI integration and ensuring a smoother transition into AI-enabled SCM and OM.
In a world marked by constant change—politically, economically, socially, and
technologically—research boundaries have become increasingly fluid and dynamic. The rigid
structures that once defined disciplines and their corresponding methodologies are no longer
adequate for addressing the complex problems and challenges facing contemporary societies.
Problems such as climate change, global health crises, and socio-economic inequality
demand novel, integrated approaches that draw from multiple disciplines. The boundary lines
between fields of study are becoming less distinct, and in many cases, are being actively
redefined. This volume, Rethinking Research Boundaries: Multidisciplinary Perspectives in a
Changing World, brings together thought leaders, scholars, and practitioners from a wide
array of fields to explore the evolving nature of research in an interconnected world. The
primary aim of this work is to challenge traditional academic silos and promote a more
holistic understanding of knowledge production, one that recognizes the benefits of
interdisciplinary collaboration and cross-sector partnerships.
The book is organized around key themes that reflect the urgency and relevance of
interdisciplinary work in contemporary research. Topics range from the intersection of
science and technology with social sciences and humanities, to new methodologies and
epistemologies that embrace complexity, uncertainty, and diverse perspectives. These
contributions seek not only to push the boundaries of traditional research but to envision how
these boundaries can be redefined in ways that are responsive to the pressing challenges of
our time.
As you embark on this exploration of multidisciplinary, you will find that the very notion of
"boundaries" is no longer a straightforward concept. Instead, it is an evolving terrain that
requires us to rethink how knowledge is produced, communicated, and applied. This book is,
in essence, a call to action—an invitation to break free from outdated frameworks and to
engage in research that is both innovative and relevant to the changing world around us.
Purpose
This study aims to examine how big data adoption (BA) helps to improve innovation capability, supply chain integration, resilience and organizational performance through direct and mediating mechanisms.
Design/methodology/approach
This study uses a combination of meta-analytic approaches (meta-structural equation modeling and meta-regression) using 205 effect sizes from 76 prior empirical studies. It leverages the organization information processing theory as a theoretical lens to analyze the proposed relationships. This study estimates heterogeneity in the relationship between BA and innovation capability based on the meta-regression by considering different types of moderators: digital competitiveness score (DCS), national culture, type of economies and gross domestic product (GDP) per capita.
Findings
The findings indicate that BA improves the innovation capability of the organization, supply chain integration and resilience, which consequently drives organizational performance. The results show that the innovation capability mediating effect is higher between BA and supply chain integration than between BA and supply chain resilience link. However, supply chain resilience and integration are equally effective in translating innovation capability influence to organizational performance. The authors find that developing countries reap more benefits from BA in driving innovation, and country culture plays a vital role in driving innovations.
Research limitations/implications
This study offers multiple theoretical implications. First, deriving from organization information processing theory, the authors recognized that BA and innovation capability complement each other, which improves the information processing capacity of the organizations, enabling supply chain integration, resilience and organizational performance (Bahrami et al. , 2022; Gupta et al. , 2020; Chatterjee et al. , 2022). This study is one of few that analyzed how BA and innovation capability work together to drive supply chain integration, resilience and organizational performance, which was not collectively studied in existing studies, meta-analyses or reviews to ascertain the direct and mediating mechanisms (Aryal et al. , 2020; Oesterreich et al. , 2022; Ansari and Ghasemaghaei, 2023; Bag and Rahman, 2023; Alvarenga et al. , 2023). Second, our study offers integrated and more definitive results regarding identified relationships. More precisely, the study provides statistically significant direct effects with the help of meta-analysis and meta-structural equation modeling to remove the ambiguity in the literature. Third, apart from the above definitive relationships, mediation analysis contributes to academia in identifying significant mediating mechanisms related to innovation capability, supply chain integration and resilience. Innovation capability partially and significantly mediates between BA and supply chain integration/resilience. Fourth, meta-regression provides valuable insights related to DCS, national culture and type of economies in the supply chain context. In fact, this study is the first one to examine the effects of DCS and all dimensions of national culture on the BA−INV relationship and overcome certain limitations that exist in the literature (Oesterreich et al. , 2022; Ansari and Ghasemaghaei, 2023; Nakandala et al. , 2023).
Practical implications
Big data is captured through evolving digital technologies such as intelligent sensors, radio frequency identification tags, global positioning system (GPS) locations and social media, which generate large data sets. Thus, managers must extract value from such a large data set and transition from big data to BA. This transition encompasses retrieving unknown patterns and insights from big data, its interpretations and extracting meaningful actions (Gupta et al. , 2020; Hallikas et al. , 2021). This study confirms that organizational capabilities in terms of BA and innovation enable supply chain integration and resilience. Managers must concentrate on BA and innovation capability simultaneously rather than making a trade-off between capabilities (Morita and Machuca, 2018) to drive supply chain integration, resilience and performance. For example, Morita and Machuca (2018) study revealed that many companies are doing trade-offs between capabilities and innovation. Hence, the findings clarified confusion among practitioners and confirmed that BA improves innovation capability, consequently enabling higher supply chain integration and resilience. Thus, managers investing in innovation capability will be more confident about integration, resilience and performance outcomes.
Originality/value
This is one of the early studies that examine the underlying mechanisms of innovation capability, supply chain integration and resilience between BA and organizational performance. Moderation analysis with a DCS, national culture, type of economies and GDP per capita explains the heterogeneity between the BA and innovation capability relationship.
The intricate dynamics of supply chain collaboration within the aquaculture industry underscore the need for a comprehensive understanding of how various forms of capital and information sharing influence collaborative efforts. This study addresses this necessity by integrating intellectual capital theory and social capital theory as the theoretical foundation for the proposed conceptual research framework. Utilizing structural equation modeling (SEM), this research analyzed data collected from 180 shrimp farmers in East Java, Indonesia, from October 2021 to January 2022. The findings reveal that intra-organizational human capital, structural capital, inter-organizational relational capital and information sharing positively affect supply chain collaboration. Additionally, the study demonstrates that supply chain collaboration enhances both collaborative advantage and organizational performance. It also highlights the nuanced moderating role of information sharing in strengthening the impact of structural capital on collaboration. This research provides new insights into the aquaculture industry’s collaborative dynamics, offering valuable implications for practitioners and policymakers aiming to foster effective collaboration and achieve beneficial performance outcomes.
As new quality productivity (NQP) emerges as a rising star of productivity that can effectively leverage technological innovation and sustainability, this study aims to explore the relationship between NQP and environmental innovation, with a particular focus on the roles of managerial empowerment and board centralization within the context of China A-share listed companies. Utilizing the entire sample of China A-share market from 2013 to 2022, the study analyses the effectiveness of various dimensions reflecting innovation engagement among Chinese listed companies. For measuring NQP, the entropy method is employed to calculate the weights. By controlling for industry and year effects, the study examines both the main and moderating effects of managerial empowerment and board centralization. Additionally, heterogeneity tests, robustness checks, and two-stage least squares (2SLS) estimation were conducted to address endogeneity concerns. The results demonstrate that NQP significantly enhances environmental innovation, with managerial empowerment supporting this positive effect and board centralization obstructing it. The positive effect of NQP is particularly evident in state-owned enterprises, while in heavily polluting industries, the anticipated positive moderating effect of top managers disappears due to strict regulatory environments. Furthermore, board centralization negatively moderates environmental innovation, especially in lightly polluting sectors where internal governance is more sensitive. The study underscores the importance for policymakers to tailor regulations that balance managerial empowerment and board centralization to enhance the transformation of NQP into environmental innovation. Future research is needed to further investigate different background settings and the mechanisms through which NQP influences sustainability.
Supply chain capabilities are fundamental in driving superior supply chain performance in diverse business environments. This study evaluates the impact exerted by supply chain capabilities on supply chain performance in the fast-growing apparel manufacturing sector in Bangladesh. Focusing on innovativeness, visibility, and collaboration as emerging components of supply chain capabilities, this research examines the associations among these factors and supply chain performance based on the dynamic capability theory. Additionally, the moderating role of digital supply chain deployment is tested in the research model. A total of 159 samples were collected from higher and lower-level supply chain managers and merchandisers from the apparel industry in Bangladesh, through an online survey. Data were analyzed using PLS-SEM to validate the research hypotheses. Findings indicate a significant positive relationship between firms’ innovativeness, visibility, and supply chain performance, while collaboration did not exhibit significant relationship with performance in this study. This indicates the lack of supply chain collaboration within the apparels industry in Bangladesh. The findings also highlight the critical importance of digital supply chain integration to increase both supply chain capacity and performance in this industry. As digital supply chain integration has received much attention in recent literatures, this study contributes to the current theory by refining the relationships between capabilities and performance in supply chain, for the lucrative apparel industry in a developing economy. Consequently, this research adds valuable contribution to the existing body of literature within the realms of supply chain management and Information Technology management. The study also provides meaningful practical insights for managers in the apparel sector to enhancing their supply chain capabilities, which could significantly improve their business performance and competitiveness.
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.
The Sustainable Development Goals and circular economy are two critical aspects of the 2030 Agenda for Sustainable Development. They both seek to reduce the waste of natural resources and enhance society’s social, economic, and environmental goals. This study aims to identify, develop, test, and verify the significant antecedents that affect the adoption of supply chain analytics and its consequences for achieving the circular economy. We have divided the conceptual framework into two parts. In the first part, the relationship among data integration and scalability, organizational readiness, and policies and regulations as Technological–Organizational–Environmental factors as antecedents in adopting supply chain analytics. In the second part, the dynamic capabilities view grounded the relationship among supply chain analytics, supply chain integration, and sustainable supply chain flexibility effect directly and indirectly on the circular economy. Data have been collected using the survey method from 231 respondents from the manufacturing industry in Pakistan. Data have been analyzed using (i) partial least square structure equation modeling (ii) and artificial neural network approaches. The empirical findings proved that antecedents (data integrity and scalability, organizational readiness, and policy and regulation) and consequences (supply chain integration and sustainable supply chain flexibility) of supply chain analytics adoption would improve the circular economy performance. Additionally, artificial neural networks have supported these relationships. The adoption of supply chain analytics will enable organizations to supply chain integration. Additionally, organizations with more integration and analytics in their operations tend to have more flexibility and a circular economy. Moreover, organizations and society will obtain social, economic, and environmental benefits and reduce wastage and negative environmental impacts.
Artificial intelligence (AI) and big data are two emerging technological concepts in supply chain management. The current study has merged AI and big data as artificial intelligence-driven big data analytics capability (AI-BDAC). The integration of resource-based theory (RBT) and contingency theory (CT) has provided to formulate conceptual framework. The efficient use of AI-BDAC in the supply chain improves internal integration (II) organizational processes and achieves supply chain agility (SCA). The ultimate goal for utilizing AI-BDAC is to increase supply chain performance (SCP). Data has been collected data from Chinese logistic companies having sample size 150 using survey method. Partial least square-structural equation modeling (PLS-SEM) results showed that AI-BDAC has direct effect on SCP. Moreover, II and SCA have mediating effects between AI-BDAC and SCP. It is concluded that using advanced technologies like big data and AI will support organizations to improve their II to attain SCA, which will boost SCP, especially in emerging economies. Additionally, the improvement in SCP will also increase speed and efficiency in logistic industry, and reduce the wastage, and cost; these are social benefits.KeywordsSupply chain performanceArtificial intelligenceBig data analyticsSupply chain integrationSupply chain agility
Academics and practitioners have shown growing interests in big data analytics and artificial intelligence (BDA-AI) in recent years. Despite this, research on the application of BDA-AI for green supply chain collaboration (GSCC) and its influence on environmental performance (EP) is still limited. The current research addresses this gap and extends organizational information processing theory by incorporating BDA-AI and exploring top management commitment (TMC) as a moderator. The current study developed a moderated mediation model based on 402 samples of data from Turkish manufacturing firms. The result revealed that the application of BDA-AI has a positive impact on GSCC and EP. The results also indicated that GSCC has a positive impact on EP. Our findings revealed that GSCC mediated the association between BDA-AI and EP. The results also revealed that TMC moderated the positive relationship between BDA-AI and GSCC, such that the strength of the positive relationship is further intensified at higher levels of TMC. The results also show that TMC moderated the positive relationship between BDA-AI and EP, such that the strength of the positive relationship is dampened at lower levels of TMC; significant findings have not been outlined in the extant literature. The current research will assist supply chain and logistics managers and top management in deploying BDA-AI technology to support GSCC and improve EP.
The lack of collaboration among the members of the supply chain is one of the main reasons for the functional weakness of a supply chain. In today’s innovative world, organizations have started to collaborate through new ways in the supply chain. This paper examines the relationship between the collaboration in the supply chain and its performance and evaluates the mediating role of blockchain technology and moderator role of supply chain resilience in improving both collaboration and performance. A model was developed with data collected from 250 employees of different industries with similar supply chains in Iran. Testing of research hypotheses was done using structural equation modeling using SmartPLS software. The results showed that the collaboration in the supply chain led to improving the performance of the chains with a coefficient of 0.520. In addition, one feature of blockchain technology, namely, transparency, has the highest impacts on mediating collaboration impacts on supply chain performance. The authors’ findings also highlight that among the performance metrics of supply chain management, “speed” is highly increasing when blockchain technology is used for collaboration. Furthermore, supply chain resilience enhances the relationship between collaboration and supply chain performance as a moderator variable. The use of block chain technology in the supply chain creates a decentralized system with high security, reliability, and transparency for the development of collaboration and better performance. This reflects that blockchain technology solutions increase the quality, speed, reliability, and flexibility by removing intermediaries and improving collaboration among the members of the chain, and will improve the chain performance by reducing costs which leads to a better supply chain performance. On the other hand, resilience can conquer unexpected disruptions in supply chain and reinforces the adaptability to become ready for unexpected possible events.
Purpose: In today’s world, with the presence of huge volumes of data, although organizations have faced many problems, using big data analysis has been able to significantly improve their efficiency and integrate information in the supply chain through the topic of computing. Cloud and big data achieve coordination between components and improve communication. On the other hand, Internet of Things (IoT) technology tools are one of the most important sources of big data production, and understanding and correct use of this data and their timely analysis using big data analysis techniques and technologies based on artificial intelligence can be effective steps to improve supply chain processes. Also, the use of these technologies can play an important role in process agility and, as a result, supply chain resilience.
Methodology: In this study, the dimensions and key components of the use of large data obtained from the Internet of Things (IoT) in an industry's supply chain are investigated as a case study. Finally, a model for implementing an agile and lean supply chain based on IoT data analysis to improve the supply chain performance of these industries during emergency drug distribution during critical conditions is presented.
Findings: This study shows that these technologies can be used as a powerful enabler, especially in the distribution of fast-acting pharmaceutical products.
Originality/Value: In this paper a model for implementing an agile and lean supply chain based on IoT data analysis to improve the supply chain performance of these industries during emergency drug distribution during critical conditions is presented.
The Covid-19 pandemic has resulted in a slowdown in the global economy, and the Indonesian economy has also been affected. The implementation of industry 4.0 is considered the right strategy to revitalize the manufacturing sector. This study aims to determine and understand the effect of information technology implementation on supply chain performance through information sharing and supply chain collaboration. This study uses a quantitative approach to measure an independent variable effect on mediation and dependent variables to test the predetermined hypotheses. The data was collected using a non-probability sampling technique with a purposive sampling method through a questionnaire instrument with the total number of respondents of 70 manufacturers in Surabaya. Then, the data were analyzed using Partial Least Square software and Structural Equation Modeling based on components or variants. The results indicate that the relationship between supply chain collaboration and information sharing creates competitive advantages. Consequently, the existence of precise, accurate, and qualified information can facilitate joint decision-making. Furthermore, the increasing adoption of supply chain collaboration with information technology can improve company performance. In conclusion, a manufacturing company can maintain its supply chain performance during the Covid-19 pandemic by implementing information technology, information sharing and collaboration with partners in a supply chain. This study provides insight and guidance for managers on ways to improve supply chain performance. This study can also contribute to current research in supply chain management theory.
Big data analytics has been successfully used for various business functions, such as accounting, marketing, supply chain, and operations. Currently, along with the recent development in machine learning and computing infrastructure, big data analytics in the supply chain are surging in importance. In light of the great interest and evolving nature of big data analytics in supply chains, this study conducts a systematic review of existing studies in big data analytics. This study presents a framework of a systematic literature review from interdisciplinary perspectives. From the organizational perspective, this study examines the theoretical foundations and research models that explain the sustainability and performances achieved through the use of big data analytics. Then, from the technical perspective, this study analyzes types of big data analytics, techniques, algorithms, and features developed for enhanced supply chain functions. Finally, this study identifies the research gap and suggests future research directions.
Due to SARS-CoV-2 pandemic, higher education institutions are challenged to continue providing quality teaching, consulting, and research production through virtual education environments. In this context, a large volume of data is being generated, and technologies such as big data analytics are needed to create opportunities for open innovation by obtaining valuable knowledge. The purpose of this paper is to investigate the factors that influence the adoption of big data analytics and evaluate the relationship it has with performance and knowledge management, taking into consideration that this technology is in its initial stages and that previous research has provided varied results depending on the sector in focus. To address these challenges, a theoretical framework is developed to empirically test the relationship of these variables; 265 members of universities in Latin America were surveyed, and structural equation modeling is used. The findings identify compatibility, an adequate organizational data environment, and external support as factors required to adopt big data analytics, and their positive relationship is tested with knowledge management processes and organizational performance. This study provides practical guidance for decision-makers involved in or in charge of defining the implementation strategy of big data analytics in higher education institutions.
The recent technological inclusions in supply chains are encouraging practitioners to continuously rethink and redesign these supply chains. Organizations are trying to implement sustainable manufacturing and supply chain practices to utilize their resources to the full extent in order to gain a competitive advantage. Circular supply chain management acts as the main pathway to achieve optimal circular business models; however, research in this area is still in its infancy and there is a need to study and analyze how the benefits of technology can be leveraged in conventional models to impact circular supply chains and build smart, sustainable, circular business models. To gain better familiarity with the future research paradigms, a detailed systematic literature review was conducted on this topic to identify the dynamics of this field and domains deserving further academic attention. A holistic and unique review technique was used by the authors to capture maximal insights. A total of 96 publications from 2010 to 2021 were selected from the Web of Science core collection database through strict keyword search codes and exclusion criteria, with neat integration of systematic and bibliometric analyses. The findings of this study highlight the knowledge gaps and future research directions, which are presented at the end of this paper.
This research contributes to the debate about the relevance of Industry 4.0 technologies in improving environmental performance in the manufacturing industry. We employed a qualitative–quantitative approach in which 19 Italian operations managers were interviewed and 260 managers responded to an online questionnaire. The effects of various technologies were ranked using ordinal regression. Comments and suggestions gave context to the quantitative results. Sensors, radio‐frequency identification, artificial intelligence and analytics were found to be most relevant in improving environmental performance, whereas simulation software contributed moderately. Additive manufacturing, cobots, robots, automated mobile robots and automated guided vehicles had a negative effect, augmented reality had no effect and other technologies indirectly affected environmental performance. We also found a lack of knowledge and application as well as scepticism about technologies such as artificial intelligence and augmented reality. Finally, there was concern about the disposal of electrical and electronic waste produced by these technologies.
Big data is heralded as the next big thing for organizations to gain competitive advantages. New data-driven firms need to control key resources in order to develop the new data-driven capabilities they need. The present paper analyzes the relationships between process innovation capability, management innovation and big data analytics capability, covering aspects related to a better understanding of how firms can obtain benefit from their investments in big data. PLS-SEM models with data from 195 firms are used. The main results suggest that management innovation and process innovation capabilities have an important role in the development of big data analytics capability. Big data analytics capability is much more than just investing in technology, collecting vast amounts of data, and allowing the technology department to experiment with analytics. The outcomes of this study present evidence on how innovative managers who promote innovations in process as well as innovations in different aspects of the organization favor the development of capabilities in big data analytics.
Big data is an essential aspect of innovation which has recently gained major attention from both academics and practitioners. Considering the importance of the education sector, the current tendency is moving towards examining the role of big data in this sector. So far, many studies have been conducted to comprehend the application of big data in different fields for various purposes. However, a comprehensive review is still lacking in big data in education. Thus, this study aims to conduct a systematic review on big data in education in order to explore the trends, classify the research themes, and highlight the limitations and provide possible future directions in the domain. Following a systematic review procedure, 40 primary studies published from 2014 to 2019 were utilized and related information extracted. The findings showed that there is an increase in the number of studies that address big data in education during the last 2 years. It has been found that the current studies covered four main research themes under big data in education, mainly, learner’s behavior and performance, modelling and educational data warehouse, improvement in the educational system, and integration of big data into the curriculum. Most of the big data educational researches have focused on learner’s behavior and performances. Moreover, this study highlights research limitations and portrays the future directions. This study provides a guideline for future studies and highlights new insights and directions for the successful utilization of big data in education.
The momentum has been building toward the realisation of the United Nations Development Programme's Sustainable Development Goals (SDGs). In this regard, technological upgrading through the adoption of innovative technologies, as in big data analytics (BDA), can be seen as a key enabler of helping to address societal challenges. While there is some evidence for realising value created from BDA adoption, organisational issues associated with societal challenges, specifically those targeting the SDGs, are yet to be appreciated. This study utilises a technology-organisation-environment framework to examine the role of top management support in facilitating value creation from BDA adoption for the realisation SDGs. Based on a survey of 320 UK managers, this study found that the technological driver of BDA coupled with top management support can significantly help in the adoption process. Therefore, crafting the value needed for effectively supporting the realisation of these goals.
Abstract Drawing from tenets of the resource-based theory, we propose and test a model that examines the relationship between the application of big data analytics (ABDA) and organizational performance (OP) in small and medium enterprises (SMEs). Further, this study examines the mediating role of knowledge management practices (KMP) in relation to the ABDA and OP. Data were collected from respondents working in SMEs through an adapted instrument. This research study adopts the Baron–Kenny approach to test the mediation. The results indicated that the ABDA had a positive and significant impact on OP. Also, KMP had partially mediated the relationship between ABDA and OP in SMEs. The dataset was solely comprised of SMEs from Pakistan administered Kashmir and may not reflect the insights from other regions. Hence limits the generalizability of the results. Findings highlight both strategic and practical implications related to decision making in organizations for top management, particularly in developing countries. This study attempts to contribute to the literature through novel findings and recommendations. These fallouts will help the top management during the key decision-making process and encourage practitioners who seek competitive advantage through enhanced organizational performance in SMEs.
Environmental problems are one of the major concerns in China due to booming industries and large-scale production. Manufacturing industries must change the traditional approaches of production and supply chain management practices to cope with environmental issues. For this purpose, a multidisciplinary and empirical study has been conducted in China. In the manufacturing industries around the world, there is a noticeable transition in human resource management approaches toward green human resource management (GHRM). However, green training could be considered as a critical element of GHRM and could be used as an instrument to trigger emotional intelligence (EI) of the employees, which could make them more responsible toward the external environment and result in improved green supply chain management (GSCM) practices. Similarly, regulatory pressures could also enforce manufacturing industries to contribute positively to GSCM practices. In addition, taking into account the two critical functions of GSCM practices, that is, green purchasing and eco-design, this research study attempted to bring under consideration that GSCM practices could be enhanced through EI as well. For this purpose, data were collected from 250 manufacturing industries of China through a convenient sampling technique, and structural equation modeling was applied through Smart-PLS version 3.2.7 to measure and validate the model of this study. The findings of the study revealed that GSCM practices could be improved through regulatory pressures, and EI could be used as a mediating factor to enhance the relationship between GHRM (green training) and GSCM practices.
Big data is expected to drive the new digital transformation of society, government, and business. The proportion of firms implementing and utilizing big data, however, is not high. There are many challenges and obstacles related to firm adoption of big data. The purpose of this study is to identify the factors influencing the adoption of big data among Korean firms using the technology-organization-environment (TOE) framework. The results of the analytic hierarchy process (AHP) and regression analysis from data collected from 50 experts and 226 companies, respectively, are as follows: the benefits from big data, technological capabilities, financial investment competence, and data quality and integration are perceived by the experts and the firms as the strongest determinants of adoption. Management support for big data, security and privacy, and government support and policy are also influential factors of firm big data adoption.
Supply chain information collaboration refers to a relationship in which supply chain partners organically integrate, coordinate, and develop resources, business processes, and organizations in order to achieve common goals. The goal is to create and coordinate efforts in all aspects of the supply chain. The overall value of the supply chain is greater than the simple sum of the value of each link, thereby improving the competitiveness of the entire supply chain. However, information sharing in supply chain information collaboration has always had problems such as information distortion, information loss and information delay. The effective coordination of information has become a difficult point in supply chain management. This paper mainly uses the Internet of Things and big data technology to build a simulation model of the supply chain bullwhip effect based on the mathematical model of the bullwhip effect, and uses the simulation method to simulate and study the key factors in the model. The image of the simulation results objectively clarifies the value of information collaboration in the supply chain.
The technological transformation has led to the introduction of mini computers as wearable computers which facilitate users to use the Internet, different applications, messaging, and for calls all on one platform. The focus of these wearable computers in earlier studies has been on consumers and technical aspects, and their application has not been explored from an organizational perspective. This means that the recent introduction of wearable technology has great potential, which is untapped in the organizational context, especially for supply chain collaboration (SC). In this study, wearable technology is thought to offer huge potential for organizations in SC. Therefore, the purpose of this study is to empirically examine the role of wearable technology i.e. Smartwatch in enhancing collaboration among supply chain members with mediating roles of green training (GT) and emotional intelligence (EI). The population of this study is the pharmaceutical industry in Pakistan. Simple random sampling method has been used to collect data from 150 sample size. Data have been collected through offline and online survey method. Data have been analyzed through partial least square-structure equation modeling (PLS-SEM) technique. The results of this study have empirically tested the established conceptual framework. Continuance intention to use smartwatch has a positive effect on SC. Furthermore, GT and EI have mediating roles between continuance intention to use smartwatch and SC. It is concluded that the continuance intention to use smartwatch will increase SC. GT and EI will strengthen the relationship between continuance intention to use smartwatch and SC. The use of wearable technology will motivate employees to communicate rapidly and to monitor their supply chain activities using web-based and other applications. This study has practical implications in organizations and theoretical contribution to literature.
Background: It is understood that improving performance has become an important objective of supply chains. As such, strategies have been adopted as an effort towards increasing performance. Amongst these strategies, partnership, collaboration and integration have been identified. A mutual advantage of these approaches is that they facilitate cohesion between parties where knowledge and resources are pooled together and shared with the purpose of achieving optimum results. However, it is observed that the extent to which partnership, collaboration and integration affect supply chain performance collectively is a research void.
Objectives: This study intended to examine the influence of partnership, collaboration and integration on supply chain performance, particularly within the small and medium enterprise (SME) sector. The theory of relational view was adopted to support the study’s conceptual model.
Method: The study used SmartPLS to analyse the data.
Results: Three hypotheses were empirically substantiated using a sample of 271 SMEs situated in Gauteng.
Conclusion: The findings revealed that the research constructs partnership, collaboration and integration influence supply chain performance in a positive way. Implications of the study are further provided.
ABSTRACT Increasing supply chain performance has always been a common phenomenon in the history of organizations. Recent technological advancements (including radio frequency identification (RFID) in combination with big data predictive analytics (BDPA)) have been able to significantly leverage supply chain performance. In this respect, there is an urgent need for acceptance of RFID technology as well as big data predictive analytics to be empirically investigated in order to better understand the enhancement of supply chain performance. Therefore, this study aims at establishing and empirically investigating the relationship between big data predictive analytics (BDPA) acceptance, RFID acceptance and supply chain performance (SCP) respectively. The population of this study comprises the pharmaceutical logistics industry in China. The sample was obtained from employees through the use of a simple random sampling method. Data were collected through an adapted questionnaire. SmartPLS 3.0 software was used for structural equation modeling analysis. Partial least square - Structural equation modeling (PLS – SEM), the value of path coefficient, was found to be 0.700. This showed a direct positive effect of big data predictive analytics acceptance on supply chain performance. Moreover, RFID acceptance was also tested through the “variance accounted for” (VAF) method of mediation. Results showed a partial mediation of 57% between BDPA and SCP. Theoretically, this study fills the knowledge gap by finding relationships between BDPA and SCP via a mediation role of RFID technology. Practically, it would be beneficial for supply chain managers in the pharmaceutical industry to adopt and implement BDPA and RFID technology so as to enhance supply chain performance.
The Internet of Things (IoT) is the next generation of internet-connected information communication technologies (ICT). IoT typically integrates supply chain activities to enhance green supply chain performance (GSCP). Since every organization has different IoT capabilities in comparison with other organizations, GSCP can enable supply chain integration activities for enhanced performance. The implementation of an IoT system can reduce the consumption of organizational resources like energy, electricity, and time and can increase the operational speed to gain better logistics and, ultimately, improved supply chain performance. This study has developed and empirically tested the relationship between IoT capabilities, energy consumption behavior (ECB), supply chain integration, green training (GT), and supply chain practices. Such a multidisciplinary relationship has not previously been established in the literature. The proposed study can fulfill the literature gap and opens new horizons for interdisciplinary research. Data used in this study are collected through offline and online survey methods. A total number of 250 out of 400 respondents participated in the survey. Data has been analyzed through partial least square—structure equation modeling (PLS—SEM) technique. The results of this study empirically test the developed model. IoT has a positive effect on supplier integration (SI), and customer integration (CI). Furthermore, SI and CI have a mediating role between IoT and GSCP, and GT has a positive impact on GSCP. It is concluded that the implementation of IoT can integrate CI and SI to increase GSCP. GT and ECB can ultimately improve GSCP. Additionally, the use of technology and GT can motivate employees to save energy and protect the environment to increase GSCP.
Purpose
This paper provides a comprehensive, yet concise, overview of the considerations and metrics required for PLS-SEM analysis and result reporting. Preliminary considerations are summarized first, including reasons for choosing PLS-SEM, recommended sample size in selected contexts, distributional assumptions, use of secondary data, statistical power, and the need for goodness-of-fit testing. Next, the metrics, as well as the rules of thumb, that should be applied to assess the PLS-SEM results are covered. Besides covering established PLS-SEM evaluation criteria, the overview includes new guidelines for applying (1) PLSpredict, a novel approach for assessing a model’s out-of-sample prediction, (2) metrics for model comparisons, and (3) several complementary methods for checking the results’ robustness.
Design/methodology/approach
This paper provides an overview of previously and recently proposed metrics, as well as rules of thumb, for evaluating the results of research, based on the application of PLS-SEM.
Findings
Most of the previously applied metrics for evaluating PLS-SEM results are still relevant, but scholars need to be knowledgeable about recently proposed metrics (e.g., model comparison criteria) and methods (e.g., endogeneity assessment, latent class analyses, PLSpredict) and when and how to apply them.
Research limitations/implications
Methodological developments associated with PLS-SEM are rapidly emerging. The metrics reported in this paper are useful for current applications, but scholars need to continuously seek the latest developments in the PLS-SEM method.
Originality/value
In light of more recent research and methodological developments in the PLS-SEM domain, guidelines for the method’s use need to be continuously extended and updated. This paper is the most current and comprehensive summary of the PLS-SEM method and the metrics applied to assess its solutions.
Purpose
Green supply chain management (GSCM) and the circular economy (CE) overlap but also differ. The purpose of this paper is to clarify linkages between these two concepts. It identifies mutual theory applications used to study GSCM and CE.
Design/methodology/approach
A systematic literature review is conducted to identify theories from GSCM and CE studies. A critical analysis explores the theories that can provide mutual applications between GSCM and CE fields. Propositions are developed.
Findings
In all, 12 theories are applied in both GSCM and CE studies. Several theories are only applied in GSCM studies, but can help to advance CE study. These theories include complexity, transaction cost economics, agency, and information theories. Each of the eight theories only applied to CE can potentially advance GSCM study.
Research limitations/implications
The findings contribute to further theory development for both GSCM and CE study. A methodological review can advance theoretical development and cross-pollination in both fields.
Originality/value
This work is the first study to explicitly explore linkages of GSCM and CE from a theoretical perspective.
Despite the growing interest towards big data within higher education institutions (HEI), research on big data analytics capability within the HEI context is rather limited. The main objective of this study is to have a better understanding of the utilization of big data analytics capability for data-driven decision making in order to achieve better performance from Malaysian HEIs. This study validates an integrative model by combining information processing theory and resource-based view theory. Unlike extant literature, this study proposed methodology involving dual-stage analysis involving of Partial Least Squares Structural Equation Modelling and evolving Artificial Intelligence named deep learning (Artificial Neural Network) were performed. The application of deep ANN architecture is able to predict 83% of accuracy for the proposed model. Besides, the outcome of data-driven decision making from the relationship between big data analytic capability and data-driven decision making towards the performance of HEIs has significant findings. Results revealed that data-driven decision making could positively play an essential role in the relationship between big data analytic capability and performance of HEIs. Theoretically, the newly integrated theoretical model that incorporates information processing theory and resource-based view provides useful guidelines to HEI’s about the crucial capabilities and resources that must be put into place in order to reap the benefits associated with big data implementations in the wake of Industry Revolution 4.0.
Purpose: The aim of this study is to evaluate Big Data Analytics (BDA) drivers in the context of food supply chains (FSC) for transition to a Circular Economy (CE) and Sustainable Operations Management (SOM).
Design/methodology/approach: Ten different BDA drivers in FSC are examined for transition to CE; these are Supply Chains (SC) Visibility, Operations Efficiency, Information Management & Technology, Collaborations between SC partners, Data-driven innovation, Demand management & Production Planning, Talent Management, Organizational Commitment, Management Team Capability and Governmental Incentive. An interpretive structural modelling (ISM) methodology is used to indicate the relationships between identified drivers to stimulate transition to CE and SOM. Drivers and pair-wise interactions between these drivers are developed by semi-structured interviews with a number of experts from industry and academia.
Findings: The results show that Information Management & Technology, Governmental Incentive and Management Team Capability drivers are classified as independent factors; Organizational Commitment and Operations Efficiency are categorized as dependent factors. SC Visibility, Data-driven innovation, Demand management & Production Planning, Talent Management and Collaborations between SC partners can be classified as linkage factors. It can be concluded that Governmental Incentive is the most fundamental driver to achieve BDA applications in FSC transition from linearity to CE and SOM. In addition, Operations Efficiency, Collaborations between SC partners and Organizational Commitment are key BDA drivers in FSC for transition to CE and SOM.
Originality/value: The main contribution of the study is to analyse BDA drivers in the context of FSC for transition to CE and SOM. This study is unique in examining these BDA drivers based on FSC. We hope to find sustainable solutions to minimize losses or other negative impacts on these SC.
Research Implications: The interactions between these drivers will provide benefits to both industry and academia in prioritising and understanding these drivers more thoroughly when implementing BDA based on a range of factors. This study will provide valuable insights. The results from this study will help in drawing up regulations to prevent food fraud, implementing laws concerning government incentives, reducing food loss and waste, increasing tracing and traceability, providing training activities to improve knowledge about BDA and focusing more on data analytics.
Keywords: Food Supply Chains, Circular Economy, Sustainable Operations Management, Big Data Analytics, Drivers, Interpretive Structural Modelling
Abstract
Purpose
This paper aims to examine the effect of inventory information sharing on inventory efficiency and its intervening effect of information technology (IT) capability in manufacturing firms.
Design/methodology/approach
Stratified random sampling and filter questions selected targeted respondents, and an online survey collected 124 completed questionnaires from Malaysian manufacturing firms. partial least squares structural equation modeling (PLS-SEM) examined the structural model and hypothesis statement. An analysis of importance-performance map analysis (IPMA) test identified the relative importance drivers of inventory efficiency.
Findings
The findings showed that enhanced IT capabilities in manufacturing firms mediate a positive relationship between inventory sharing and inventory efficiency.
Research limitations/implications
This study portrays the relationship between inventory level, demand and information sharing. The research was carried out only within Malaysian manufacturing firms.
Practical implications
These findings will enable the management of manufacturing firms to design and visualise their inventory levels and share best practices across supply chain networks to achieve effective and optimised inventory planning.
Social implications
This study illustrates an intervention model that offers a direct and indirect impact of IT capabilities that allow scholars to close inventories productivity gaps in research.
Originality/value
This paper extends the limited literature on the sharing of inventory information and inventory productivity, notably from a strategic management perspective. The findings help scholars clearly understand the information systems capability and its mediating impact on information sharing and inventory efficiency’s relationship in the manufacturing sector. Moreover, demand information sharing affected the dynamic supply chain.
Big data analytics and artificial intelligence (BDA-AI) technologies have attracted increasing interest in recent years from academics and practitioners. However, few empirical studies have investigated the benefits of BDA-AI in the supply chain integration process and its impact on environmental performance. To fill this gap, we extended the organizational information processing theory by integrating BDA-AI and positioning digital learning as a moderator of the green supply chain process. We developed a conceptual model to test a sample of data from 168 French hospitals using a partial least squares regression-based structural equation modeling method. The findings showed that the use of BDA-AI technologies has a significant effect on environmental process integration and green supply chain collaboration. The study also underlined that both environmental process integration and green supply chain collaboration have a significant impact on environmental performance. The results highlight the moderating role of green digital learning in the relationships between BDA-AI and green supply chain collaboration, a major finding that has not been highlighted in the extant literature. This article provides valuable insight for logistics/supply chain managers, helping them in mobilizing BDA-AI technologies for supporting green supply processes and enhancing environmental performance.
Despite increasing research interest in big data analytics, exploring its important role in implementing supply chain management practices in healthcare organisations is still one of the major challenges for both academics and practitioners. We propose a research model theoretically grounded on organizational information processing theory (OIPT) to investigate the roles of big data analytics capability (BDAC) in developing hospital supply chain integration (SCI) and operational flexibility. The results from our analysis of survey data from a sample of 105 senior executives from the Chinese hospitals reveal that BDAC has a significant impact on three dimensions of hospital SCI: inter-functional integration, hospital-patient integration, and hospital-supplier integration; and that hospital-patient integration and hospital-supplier integration fully mediate the relationship between inter-functional integration and operational flexibility. These findings extend and validate OIPT within the context of big data-driven hospital supply chains, while also providing useful and timely guidance to healthcare practitioners in developing data-driven SCI for better operational flexibility, especially to respond to the unprecedented disruption caused by the COVID-19 outbreak.
The significance of big data analytics-powered artificial intelligence has grown in recent years. The literature indicates that big data analytics-powered artificial intelligence has the ability to enhance supply chain performance, but there is limited research concerning the reasons for which firms engaging in manufacturing activities adopt big data analytics-powered artificial intelligence. To address this gap, our study employs institutional theory and resource-based view theory to elucidate the way in which automotive firms configure tangible resources and workforce skills to drive technological enablement and improve sustainable manufacturing practices and furthermore develop circular economy capabilities. We tested the research hypothesis using primary data collected from 219 automotive and allied manufacturing companies operating in South Africa. The contribution of this work lies in the statistical validation of the theoretical framework, which provides insight regarding the role of institutional pressures on resources and their effects on the adoption of big data analytics-powered artificial intelligence, and how this affects sustainable manufacturing and circular economy capabilities under the moderating effects of organizational flexibility and industry dynamism.
Over recent years, organizations have started to capitalize on the significant use of Big Data and emerging technologies to analyze, and gain valuable insights linked to, decision-making processes. The process of Competitive Intelligence (CI) includes monitoring competitors with a view to delivering both actionable and meaningful intelligence to organizations. In this regard, the capacity to leverage and unleash the potential of big data tools and techniques is one of various significant components of successfully steering CI and ultimately infusing such valuable knowledge into CI strategies. In this paper, the authors aim to examine Big Data applications in CI processes within organizations by exploring how organizations deal with Big Data analytics, and this study provides a context for developing Big Data frameworks and process models for CI in organizations. Overall, research findings have indicated a preference for a rather centralized informal process as opposed to a clear formal structure for CI; the use of basic tools for queries, as opposed to reliance on dedicated methods such as advanced machine learning; and the existence of multiple challenges that companies currently face regarding the use of big data analytics in building organizational CI.
Big data analytics (BDA) adoption is a game-changer in the current industrial environment for precision decision making and optimal performance. Nonetheless, the determinants or consequences of its adoption in small and medium enterprises remain unclear, hence the objective of this study. Data analysis of 171 Iranian small and medium manufacturing firms revealed that complexity, uncertainty and insecurity, trialability, observability, top management support, organizational readiness, and external support affect significantly on BDA adoption. The findings confirm the strong impact of BDA adoption in small to medium-sized enterprises, marketing and financial , performance enhancement. Understanding the drivers of BDA adoption helps managers to employ appropriate initiatives that are vital for effective implementation. The results enable BDA service providers to attract and diffuse BDA in small to medium-sized enterprises.
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.
Big data has increasingly appeared as a frontier of opportunity in enhancing firm performance. However, it still is in early stages of introduction and many enterprises are still un-decisive in its adoption. The aim of this study is to propose a theoretical model based on integration of Human-Organization-Technology fit and Technology-Organization-Environment frameworks to identify the key factors affecting big data adoption and its consequent impact on the firm performance. The significant factors are gained from the literature and the research model is developed. Data was collected from top managers and/or owners of SMEs hotels in Malaysia using online survey questionnaire. Structural Equation Modelling (SEM) is used to assess the developed model and Adaptive Neuro-Fuzzy Inference Systems (ANFIS) technique is used to prioritize adoption factors based on their importance levels. The results showed that relative advantage, management support, IT expertise, and external pressure are the most important factors in the technological, organizational, human, and environmental dimensions. The results further revealed that technology is the most important influential dimension. The outcomes of this study can assist the policy makers, businesses and governments to make well-informed decisions in adopting big data.
Big data adoption is a process through which businesses find innovative ways to enhance productivity and predict risk to satisfy customers need more efficiently. Despite the increase in demand and importance of big data adoption, there is still a lack of comprehensive review and classification of the existing studies in this area. This research aims to gain a comprehensive understanding of the current state-of-the-art by highlighting theoretical models, the influence factors, and the research challenges of big data adoption. By adopting a systematic selection process, twenty studies were identified in the domain of big data adoption and were reviewed in order to extract relevant information that answers a set of research questions. According to the findings, Technology–Organization–Environment and Diffusion of Innovations are the most popular theoretical models used for big data adoption in various domains. This research also revealed forty-two factors in technology, organization, environment, and innovation that have a significant influence on big data adoption. Finally, challenges found in the current research about big data adoption are represented, and future research directions are recommended. This study is helpful for researchers and stakeholders to take initiatives that will alleviate the challenges and facilitate big data adoption in various fields.
This article aims to empirically investigate the factors that affects the adoption of big data analytics by firms (adopters and non-adopters). The current study is based on three feature that influence BDA adoption: technological context (relative advantage, complexity, compatibility), organizational context (top management support, technology readiness, organizational data environment), and environmental context (competitive pressure, and trading partner pressure). A structured questionnaire-based survey method was used to collect data from 231 firm managers. Relevant hypotheses were derived and tested by partial least squares. The results indicated that technology, organization and environment contexts impact firms' adoption of big data analytics. The findings also revealed that relative advantage, complexity, compatibility, top management support, technology readiness, organizational data environment and competitive pressure have a significant influence on the adopters of big data analytics, whereas relative advantage, complexity and competitive pressure have a significant influence on the non-adopters of big data analytics.
Big data analytics is becoming very popular concept in academia as well as in industry. It has come up with new decision tools to design data-driven supply chains. The manufacturing industry is under huge pressure to integrate sustainable practices into their overall business for sustainable operations management. The purpose of this study is to analyse the predictors of sustainable business performance through big data analytics in the context of developing countries. Data was collected from manufacturing firms those have adopted sustainable practices. A hybrid Structural Equation Modelling - Artificial Neural Network model is used to analyse 316 responses of Indian professional experts. Factor analysis results shows that management and leadership style, state and central-government policy, supplier integration, internal business process, and customer integration have a significant influence on big data analytics and sustainability practices. Furthermore, the results obtained from structural equation modelling were feed as input to the artificial neural network model. The study findings shows that management and leadership style, state and central-government policy as the two most important predictors of big data analytics and sustainability practices. The results provide unique insights into manufacturing firms to improve their sustainable business performance from an operations management viewpoint. The study provides theoretical and practical insights into big data implementation issues in accomplishing sustainability practices in business organisations of emerging economies.
Keywords
Big-data analytics, Ecological-economic-social sustainability, Green practices, Sustainable Operations management, Structural equation modelling-artificial neural network, Emerging economies
Theoretical contribution is a process which is based on the theory development and advancement in existing theory with some logics and facts. This study has focused on some theoretical contribution related question and their answers through the narrative review of literature. This study will highlight what is the theory? And what are the major building blocks of theory? How authors can contribute in theory? The answers for these questions during theoretical studies will enhance the impact of paper and also increase the chance of publication. This study also suggested how theoretical concepts can be practical implemented in the society and organizations to enhance organizational performance and validate the theory. Â
The main objective of the study is to understand how big data analytics capability (BDAC) as an organizational culture can enhance trust and collaborative performance between civil and military organizations engaged in disaster relief operations. The theoretical framework is grounded in organizational information processing theory (OIPT). We have conceptualized an original theoretical model to show, using the competing value model (CVM), how BDAC, under a moderating influence of organizational culture, affects swift trust (ST) and collaborative performance (CP). We used WarpPLS 6.0 to test the proposed research hypotheses using multi-respondent data gathered through an email questionnaire sent to managers working in 373 organizations, including the military forces of different countries, government aid agencies, UN specialized agencies, international non-government organizations (NGOs), service providers, and contractors. The results offer four important implications. First, BDAC has a positive, significant effect on ST and CP. Second, flexible orientation (FO) and controlled orientation (CO) have no significant influence on building ST. Third, FO has a positive and significant moderating effect on the path joining BDAC and CP. Finally, CO has negative and significant moderating effect on the path joining BDAC and CP. The control variables: temporal orientation (TO) and interdependency (I) have significant effects on ST and CP. These results extend OIPT to create a better understanding of the application of information processing capabilities to build swift trust and improve collaborative performance. Furthermore, managers can derive multiple insights from this theoretically-grounded study to understand how BDAC can be exploited to gain insights in contexts of different management styles and cultures. We have also outlined the study limitations and provided numerous future research directions.
The purpose of this study is to examine the extent of sustainable capabilities driven by corporate commitment resulting from the integration of big data technologies, green supply chain management, and green human resource management practices, and the extent to which these capabilities can enhance the broader firm performance. The study was also designed to investigate the degree to which green human resource management practices influence the integration of big data technologies with processes and enhance the relationships between green supply chain management practices, both internal and external, as well as their influence on sustainable performance. We used dynamic capabilities theory and proposed a conceptual research model which was tested empirically. The findings of our study establish the influence of big data driven strategies on business growth in terms of sustainable performance by considering internal processes that constitute sustainable capabilities. The study recommends the integration of green supply chain management, green human resource management practices, and big data management to enhance firms’ sustainable capabilities that lead to better sustainable performance.
In marketing applications of structural equation models with unobservable variables, researchers have relied almost exclusively on LISREL for parameter estimation. Apparently they have been little concerned about the frequent inability of marketing data to meet the requirements for maximum likelihood estimation or the common occurrence of improper solutions in LISREL modeling. The authors demonstrate that partial least squares (PLS) can be used to overcome these two problems. PLS is somewhat less well-grounded than LISREL in traditional statistical and psychometric theory. The authors show, however, that under certain model specifications the two methods produce the same results. In more general cases, the methods provide results which diverge in certain systematic ways. These differences are analyzed and explained in terms of the underlying objectives of each method.
Purpose
Big data analytics (BDA) guarantees that data may be analysed and categorised into useful information for businesses and transformed into big data related-knowledge and efficient decision-making processes, thereby improving performance. However, the management of the knowledge generated from the BDA as well as its integration and combination with firm knowledge have scarcely been investigated, despite an emergent need of a structured and integrated approach. The paper aims to discuss these issues.
Design/methodology/approach
Through an empirical analysis based on structural equation modelling with data collected from 88 Italian SMEs, the authors tested if BDA capabilities have a positive impact on firm performances, as well as the mediator effect of knowledge management (KM) on this relationship.
Findings
The findings of this paper show that firms that developed more BDA capabilities than others, both technological and managerial, increased their performances and that KM orientation plays a significant role in amplifying the effect of BDA capabilities.
Originality/value
BDA has the potential to change the way firms compete through better understanding, processing, and exploiting of huge amounts of data coming from different internal and external sources and processes. Some managerial and theoretical implications are proposed and discussed in light of the emergence of this new phenomenon.
This article sought to identify the drivers of Big Data adoption within the manufacturing and services sectors in India. A questionnaire-based survey was used to collect data from manufacturing and service sector organizations in India. The data was analyzed using exploratory and confirmatory factor analyses. Relevant hypotheses were then derived and tested by SEM analysis. The findings revealed that the following factors are important for both sectors: relative advantage, compatibility, complexity, organizational size, top management support, competitive pressure, vendor support, data management and data privacy. Statistically significant differences between the service and the manufacturing sectors were found. In other words, the relative importance of the factors for Big Data adoption differs between the sectors. The only exception was complexity, which was found to be insignificant in regard to the manufacturing sector. The factors identified can be used to facilitate Big Data adoption outcomes in organizations.
Purpose: – With the rapid economic development of nations across the globe, there is proportionate increment in corresponding carbon footprint. There are numerous counter measures proposed to mitigate it in terms of legislation and policy framing. However, they have a shortsighted vision of predominantly focusing on manufacturing and transportation industry thereby neglecting one of the significant contributor of global emissions- agricultural industry. Among all the agri-food products, beef has the highest carbon footprint and majority of its emission are generated in beef farms. The issue is more intensive in developing nations where most of global cattle are raised and simultaneously farmers are less informed and aware of resources/technology to address emissions from their farms. Therefore, there is need to raise awareness among farmers and thereby incorporate carbon footprint as a major cattle supplier selection attribute by abattoir and processor and integrate it as a standard practice in procurement of cattle. Design/methodology: A novel framework based on big data cloud computing technology is developed for eco-friendly cattle supplier selection. It is capable of measuring greenhouse gas emissions in farms and assimilate into the cattle supplier selection process. Fuzzy AHP, DEMATEL and TOPSIS method is employed to make an optimum tradeoff between conventional quality attributes and carbon footprint generated in farms to select the most appropriate supplier. Findings: The proposed framework would assist in shedding the environmental burden of beef supply chain as the majority of carbon footprint is generated in beef farms. Moreover, the vertical coordination in the supply chain among farmers and abattoir, processor would be strengthened. The execution of the framework is depicted in case study section. Originality: The literature is deficient of ecofriendly supplier selection in the agri-food sector particularly in developing countries. This study bridges the gap in the literature by proposing a novel framework to incorporate carbon footprint into traditional supplier selection process via an amalgamation of big data, ICT and Operations Research. The proposed framework would assist in mitigating the carbon footprint of beef products as they have highest emissions among all agri-food products. This framework is generic in nature and can be implemented in any food supply chain.
Purpose
Although big data analytics (BDA) have great benefits for higher education institutions (HEIs), due to lack of sufficient evidence on how BDA investment can pay off, it is tough for HEIs practitioners to realize value from such adoption. The purpose of this paper is to propose a big data academic and learning analytics enabled business value model to explain BDA potential benefits and business value which can be obtained by developing such analytics capabilities in HEIs.
Design/methodology/approach
The study examined 47 case descriptions from 26 HEIs to investigate the causal association between the BDA current and potential benefits and business value creation path for big data academic and learning analytics success in HEIs.
Findings
The pressure of compliance with all legal and regulatory requirements and competition had pushed HEIs hard to adopt BDA tools. However, the study found out that application of risk and security and predictive analytics to higher education fields is still in its infancy. Using this theoretical model, the results provide new insights to higher education administrators on ways to create BDA capabilities for HEIs transformation and suggest an empirical foundation that can lead to more thorough analysis of BDA implementation.
Originality/value
A distinctive theoretical contribution of this study is its conceptualization of understanding business value from BDA in the typical setting of higher education. The study provides HEIs with an all-inclusive understanding of BDA and gives insights on how it helps to transform HEIs. The new perspectives associated with the big data academic and learning analytics enabled business value model will contribute to future research in this area.
Harnessing the power of technology is one of the key measures of effective leadership. Leadership Strategies in the Age of Big Data, Algorithms, and Analytics will help leaders think and act like strategists to maintain a leading-edge competitive advantage. Written by a leading expert in the field, this book provides new insights on how to successfully transition companies by aligning an organization's culture to accept the benefits of digital technology. The author emphasizes the importance of creating a team spirit with employees to embrace the digital age and develop strategic business plans that pinpoint new markets for growth, strengthen customer relationships, and develop competitive strategies. Understanding how to deal with inconsistencies when facts generated by data analytics disagree with your own experience, intuition, and knowledge of the competitive situation is key to successful leadership.
Purpose
This paper aims to recognise the current state of big data analytics (BDA) on different organisational and supply chain management (SCM) levels in Brazilian firms. Specifically, it focuses on understanding BDA awareness in Brazilian firms and proposes a framework to analyse firms’ maturity in implementing BDA projects in logistics/SCM.
Design/methodology/approach
A survey on SCM levels of 1000 firms was conducted via questionnaires. Of the 272 questionnaires received, 155 were considered valid, representing a 15.5% response rate.
Findings
The knowledge of Brazilian firms regarding BDA, the difficulties and barriers to BDA-project adoption, and the relationship between supply chain levels and BDA knowledge were identified. A framework was proposed for the adoption of BDA projects in SCM.
Research limitations/implications
This study does not offer external validity due to restrictions for the generalisation of the results even in the Brazilian context, which stems from the conducted sampling. Future studies should improve the comprehension in this research field and focus on the impact of big data on supply chains or networks in emerging world regions, such as Latin America.
Practical implications
This paper provides insights for practitioners to develop activities involving big data and SCM, and proposes functional and consistent guidance through the BDA-SCM triangle framework as an additional tool in the implementation of BDA projects in the SCM context.
Originality/value
This study is the first to analyse BDA on different organisational and SCM levels in emerging countries, offering instrumentalisation for BDA-SCM projects.