The role of big data analytics capabilities in bolstering supply chain resilience and firm performance: a dynamic capability view

Impact of fiscal policies and green financing on firm innovation and firm value for green economic recovery

The worldwide spread of the COVID-19 epidemic has led to a rise in the costs of natural resources, which has increased production prices, slowed productivity, and threatened financial development. To stimulate the growth of sustainable economies, fiscal and monetary strategies must adopt a prioritized approach towards fostering innovation and development. The study investigates into recovery strategies by examining the influence of minute taxation reductions on power and exploring the incentives and mechanisms that drive innovation. We can estimate and deduce several outcomes by employing a variance-variance method to analyze quarterly data from Chinese companies listed in the market between Q1 2019 and Q2 2021. Enhancing energy efficiency through tax incentives can immensely benefit a company's innovative endeavors, as innovation serves to recover and expand market share. Furthermore, our research suggests that tax credits promoting energy efficiency can alleviate financial barriers and foster increased investment in innovation. Lastly, by endorsing artistic ventures, businesses can reduce costs and bolster internal cash flow. The implications of these findings are insignificant, as they propose that ineffective eco-design fiscal policies may serve as a negligible component of a limited business transformation plan for the post-COVID-19 era.

The communication path and improvement strategy of symbolic culture of sneaker consumption culture using the big data analysis

With the emergence of Artificial Intelligence technology and the advancement of science and technology, the current mainstream path of social development is continuously updating and improving various industries using technology. Therefore, in order to promote the development of sneaker consumer culture, this study explores the use of technological means to improve the dissemination effect of symbolic culture in sneaker consumer culture. Firstly, the development concept and mainstream direction of sneaker consumer culture in the era of big data are discussed, and the application principle of big data technology is introduced. Then, a sneaker culture dissemination model based on big data technology is designed. Finally, the model is optimized using a Convolutional Neural Network (CNN), and its effectiveness is evaluated. The results show that the Convolutional Neural Network-Big Data (CNN-BD) model designed in this study has the highest fitting degree of 93% and a lowest fitting degree of 78% in the UT-Zap50K dataset. In the Ai2 dataset, the highest fitting degree of the big data classification model is 94%, and the lowest is 76%. In the Kaggle Women's Shoe dataset, the highest fitting degree of the big data classification model is 92%, and the lowest is 77%. In the Kaggle Men's Shoe dataset, the highest fitting degree of the big data classification model is 94%, and the lowest is 79%. The designed model has the highest accuracy rate of 93% in sneaker classification, while other models have the highest accuracy rate of around 82% in sneaker classification. Compared with traditional big data technology, the designed model has greatly improved and can adapt to more working environments. This study not only provides technical support for the application of big data technology but also contributes to improving the dissemination effect and promoting the comprehensive development of sneaker consumer culture.

Big Data Analytics Capability and Firm Performance in the Hotel Industry: The Mediating Role of Organizational Agility

The emergence of the Covid-19 pandemic and restrictions on international mobility have negatively impacted the tourism market. Tourism players, particularly the hotel industry, have turned to big data analytics to mitigate uncertainties and offer better products and services. Nonetheless, the central question for researchers and practitioners is how the usage of big data analytics can help the hotel industry improve firm performance. Drawing on the resource-based view and dynamic capability theories, this study analyses the relationship between big data analytics capability and firm performance in the hotel industry. This study expands the current research by examining the role of organizational agility in mediating the relationship between big data analytics capability and firm performance. To empirically test the research model, the author used survey data from 115 star-rated hotels throughout Malaysia. Through partial least square equation modeling, the findings revealed that big data analytics capability positively affects organizational agility and firm performance. The result also demonstrated that organizational agility mediates the relationship between big data analytics capability and firm performance. This study can also guide hoteliers to identify resources required to build big data analytics capability and further highlight the significance of organizational agility in improving firm performance in the hotel industry.

Advancing supply chain management from agility to hyperagility: a dynamic capability view

Research and practice emphasize the criticality of supply chain agility in responding to external disruptions. However, many organizations struggled to respond at enhanced speed to the global supply chain shocks caused by the COVID-19 pandemic. Organizations need hyperagile supply chains to survive and remain competitive in an environment characterized by unexpected and sudden disruptions such as the COVID-19 pandemic. We propose that supply chain hyperagility (SCH) is a distinctive organization-specific capability. It enables organizations to effectively manage demand shocks at extreme speeds and under extreme time pressures. We advance the concept of supply chain hyperagility and establish its antecedents, taking the dynamic capability perspective. This study operationalizes the SCH construct for the first time and investigates its antecedents using structural equation modeling. The results highlight the significance of data analytical capabilities, market orientation, entrepreneurial orientation, and supply chain integration in shaping supply chain hyperagility. The study offers practical insights for managers regarding designing supply chains that can navigate hyperagile environments and benefit from the opportunities presented by such environments.

Big data and big disaster: a mechanism of supply chain risk management in global logistics industry

Purpose

Although big data may enhance the visibility, transparency, and responsiveness of supply chains, whether it is effective for improving supply chain performance in a turbulent environment, especially in mitigating the impact of COVID-19, is unclear. The research question the authors addressed is: How do logistics firms improve the supply chain performance in COVID-19 through big data and supply chain integration (SCI)?

Design/methodology/approach

The authors used a mixed-method approach with four rounds of data collection. A three-round survey of 323 logistics firms in 26 countries in Europe, America, and Asia was first conducted. The authors then conducted in-depth interviews with 55 logistics firms.

Findings

In the first quantitative study, the authors find mediational mechanisms through which big data analytics technology capability (BDATC) and SCI influence supply chain performance. In particular, BDATC and SCI are two second-order capabilities that help firms develop three first-order capabilities (i.e. proactive capabilities, reactive capabilities, and resource reconfiguration) and eventually lead to innovation capability and disaster immunity that allow firms to survive in COVID-19 and improve supply chain performance. The results of the follow-up qualitative analysis not only confirm the inferences from the quantitative analysis but also provide complementary insights into organizational culture and the institutional environment.

Originality/value

The authors contribute to supply chain risk management by developing a three-level hierarchy of capabilities framework and finding a mechanism with the links between big data and big disaster. The authors also provide managerial implications for logistics firms to address the new management challenges posed by COVID-19.

Artificial intelligence and big data analytics for supply chain resilience: a systematic literature review

Artificial Intelligence (AI) and Big Data Analytics (BDA) have the potential to significantly improve resilience of supply chains and to facilitate more effective management of supply chain resources. Despite such potential benefits and the increase in popularity of AI and BDA in the context of supply chains, research to date is dispersed into research streams that is largely based on the publication outlet. We curate and synthesise this dispersed knowledge by conducting a systematic literature review of AI and BDA research in supply chain resilience that have been published in the Chartered Association of Business School (CABS) ranked journals between 2011 and 2021. The search strategy resulted in 522 studies, of which 23 were identified as primary papers relevant to this research. The findings advance knowledge by (i) assessing the current state of AI and BDA in supply chain literature, (ii) identifying the phases of supply chain resilience (readiness, response, recovery, adaptability) that AI and BDA have been reported to improve, and (iii) synthesising the reported benefits of AI and BDA in the context of supply chain resilience.

Big data analytics capability and supply chain performance: the mediating roles of supply chain resilience and innovation

Purpose

Big data analytics (BDA) capabilities can affect supply chain performance in several ways. The main purpose of this study was to understand how BDA capabilities could affect supply chain performance through supply chain resilience and supply chain innovation.

Design/methodology/approach

The study adopted a cross-sectional approach to collect survey-based responses to examine the hypotheses. Accordingly, 187 responses were collected and analyzed using partial least squares (PLS) in the SmartPLS3.

Findings

The results showed that BDA capabilities improve supply chain performance through resilience and innovation of the supply chain.

Originality/value

The present study also contributed to the existing literature by demonstrating the mediating role of supply chain resilience and supply chain innovation between BDA capabilities and supply chain performance. In this context, some theoretical and managerial implications were proposed and discussed.

Big Data Analytics in Supply Chain Management: A Systematic Literature Review and Research Directions

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.