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This study aims to build an integrated model for information technology (IT)/information system (IS) team exploration and exploitation innovation in the business-to-business (B2B) enterprise context by empirically investigating the mediating role of tacit-explicit knowledge co-creation and exploring the behavior approach of servant leaders for IT/IS team exploration-exploitation innovation.

The authors' analysis was supported by 182 enterprise-IT/IS teams (403 participants) in Taiwan. The authors used a questionnaire and Structural Equation Model (SEM)-SmartPLS to validate the development model. This study examines IT/IS exploration-exploitation innovation using a combination of quantitative survey research and qualitative case studies.

The specific roles of direct and mediating effects for two innovations of IT/IS team exploration and exploitation were investigated. The findings show a direct effect of knowledge creation (tacit and explicit) on IT/IS team exploration-exploitation innovation. Servant leader behavior positively influences tacit-explicit knowledge co-creation practices, IT/IS team exploration and exploitation. Moreover, knowledge creation (tacit and explicit) successfully mediates the correlation between servant leaders and IT/IS team innovations (for exploration and exploitation).

Managers, IT/IS consultants and enterprises at the executive level are suggested to encourage knowledge co-creation practices, both tacit and explicit to support their IT/IS team innovation. The greater the degree of explicit knowledge (i.e. socialization and internalization) and tacit knowledge creation (i.e. externalization and combination), the greater will be the opportunities for meeting the enterprise-IT/IS team exploration and exploitation innovation goals. The project manager may follow servant leadership behavior to promote effective knowledge co-creation process on the IT/IS team.

This effort contributes to greater and new understanding of how ambidexterity capability, tacit-explicit knowledge co-creation (mediators) and servant leaders for IT/IS team exploration-exploitation innovation in the B2B enterprise context and new foundations for future studies on a cross-enterprise IT/IS team. This research is also the first empirical effort to understand how a servant perspective leadership contributes through the knowledge co-creation process for IT/IS exploration-exploitation innovation.

This study aims to provide a new overview and opportunities of blockchain (BCT), financial technology (fintech) and knowledge management (KM) over the past ten years. Its focus is on their potential to drive new value creation and innovation processes within the digital landscape of the tourism and hospitality.

This systematic literature review and sociotechnical approach employs a literature analysis, analyzing and synthesizing 62 relevant articles published in the past decade form e-databases (Web of Science and Scopus).

This study reveals that researchers frequently discuss the potential advantages and challenges of BCT, fintech and KM in this industry. These include establishing systems that prioritize transparency and traceability, addressing blockchain security concerns, enhancing financial transaction efficiency and trustworthiness, and promoting innovation and improvement through KM strategies. Furthermore, this review suggests that the application of blockchain, fintech and KM has the potential to create new markets and opportunities in the tourism and hospitality industry. This study provides insights into the state and implementation of technology-based and knowledge-based for tourism and hospitality in times of crisis and digitization era.

Shifting to new lens (refers to sociotechnical theory), from technology adoption strategy, it is important to stay updated with emerging technologies such as BCT and fintech and upcoming technologies trends must align with tourism and hospitality business objectives, customer expectations and market demands. From the socio-dimension, KM is not confined to technological tools alone. Instead, it is a strategic approach that emphasizes fostering a culture of open communication, collaboration and knowledge sharing within the team of tourism and hospitality industry.

Through a literature review approach, this study establishes a new foundation in tourism and hospitality such as analyzing research gaps, understanding benefits and challenges, supporting methodologies/theoretical frameworks and informing the future research opportunities. Additionally, a novel contribution is the inclusion of sociotechnical approach that is allocated into socio or knowledge resources perspective (knowledge management), and technical or technology perspective (blockchain and fintech) that drives tourism and hospitality innovation.

While previous research has demonstrated the positive effects of digital business strategies on operational efficiency, financial performance and value creation, little is known about how such strategies influence innovation performance. To address the gap, this paper aims to investigate the impact of a firm’s digital business strategy on its innovation performance.

Drawing on the dynamic capability view, this study examines the mechanism through which a digital business strategy affects innovation performance. Data were collected from 215 firms in China and analyzed using multiple regression and structural equation modeling.

The empirical analysis reveals that a firm’s digital business strategy has positive impacts on both product and process innovation performance. These impacts are partially mediated by knowledge-based dynamic capability. Additionally, a firm’s digital business strategy interacts positively with its entrepreneurial orientation in facilitating knowledge-based dynamic capability. Moreover, market turbulence enhances the strength of this interaction effect. Therefore, entrepreneurial-oriented firms operating in turbulent markets can benefit more from digital business strategies to enhance their knowledge-based dynamic capabilities and consequently improve their innovation performance.

This study contributes to the understanding of how a firm’s digital business strategy interacts with entrepreneurial orientation in turbulent markets to shape knowledge-based dynamic capability, which in turn enhances the firm’s innovation performance.

This study aims to examine Big Data and the management of libraries in the era of the Fourth Industrial Revolution and its implications for policymakers in Nigeria.

A qualitative methodology was used, involving the administration of open-ended questionnaires to librarians from six selected federal universities located in Southwest Nigeria.

The findings of this research highlight that a significant proportion of librarians are well-acquainted with the relevance of big data and its potential to positively revolutionize library services. Librarians generally express favorable opinions concerning the relevance of big data, acknowledging its capacity to enhance decision-making, optimize services and deliver personalized user experiences.

This study exclusively focuses on the Nigerian context, overlooking insights from other African countries. As a result, it may not be possible to generalize the study’s findings to the broader African library community.

To the best of the authors’ knowledge, this study is unique because the paper reported that librarians generally express favorable opinions concerning the relevance of big data, acknowledging its capacity to enhance decision-making, optimize services and deliver personalized user experiences.

The purpose of this study is to review the role of knowledge management (KM) in disaster management and crisis. Disaster causes many detrimental impacts on human lives through loss of life and damage to properties. KM has been shown to dampen the impact of the disaster on the utilization of knowledge among agencies involved and the local communities impacted by disasters.

Through a bibliometric methodology (co-citation, bibliographic coupling and co-word analysis), this study presents significant themes in the past, current and future predictions on the role of KM in disaster management. In this review paper, 437 publications were retrieved from the Web of Science and analyzed through VOSviewer software to visualize and explore the knowledge map on the subject domain.

Findings suggest that the significant themes derived are centralized to disaster preparedness during disaster and disaster postrecovery. This review presents a state-of-art bibliometric analysis of the crucial role of KM in building networks and interconnection among relevant players and stakeholders involved in disaster management.

The main implication of this study is how the authorities, stakeholders and local community can integrate the KM system within the three stages of disasters and the crucial role of technologies and social media in facilitating disaster management.

To the best of the authors’ knowledge, this is the first study to present a bibliometric analysis in mapping KM’s past, present and future trends in disaster management.

Despite the escalating significance and intricate nature of supply chains, there has been limited scholarly attention devoted to exploring the cognitive processes that underlie supply chain management. Drawing on cognitive-behavioral theory, the authors propose a moderated-mediation model to investigate how paradoxical leadership impacts manufacturing supply chain resilience.

By conducting a two-wave study encompassing 164 supply chain managers from Chinese manufacturing firms, the authors employ partial least squares structural equation modeling (PLS-SEM) to empirically examine and validate the proposed hypotheses.

The findings indicate that managers' paradoxical cognition significantly affects supply chain resilience, with supply chain ambidexterity acting as a mediating mechanism. Surprisingly, the study findings suggest that big data analytics negatively moderate the effect of paradoxical cognition on supply chain ambidexterity and supply chain resilience, while positively moderating the effect of supply chain ambidexterity on supply chain resilience.

These findings shed light on the importance of considering cognitive factors and the potential role of big data analytics in enhancing manufacturing supply chain resilience, which enriches the study of behavioral operations.

The results offer managerial guidance for leaders to use paradoxical cognition frames and big data analytics properly, offering theoretical insight for future research in manufacturing supply chain resilience.

This is the first empirical research examining the impact of paradoxical leadership on supply chain resilience by considering the role of big data analytics and supply chain ambidexterity.

Taking an organizational perspective, this paper aims to understand how organizations respond to such strong and concurrent societal effects, and to answer the question, “How should researchers conceptualize the symbiotic relationship between society and business during a catastrophic societal event?”

The authors highlight through numerous examples, the impact of COVID-19 on society is well-evidenced in the research. They also draw on such evidence of the effects of catastrophic societal events like COVID-19 to support the appropriateness of this conceptualization.

The authors found that organizations that use both short- and long-term activities concurrently are better able to tackle the concurrent short- and long-term effects of catastrophic events like COVID-19.

The authors use ambidexterity theory, supported by evidence derived from organizational responses to COVID-19, to offer a new and more comprehensive conceptualization that frames the concurrent and interrelated short-term and long-term organizational response to a catastrophic societal event. Further, they highlight the importance of studying such organizational responses in the context of the organization’s referent groups.

The purpose of this paper is to understand the dark side of blockchain technology (BCT) adoption in small and mid-size enterprises. The focus of the authors is to decode the intricate relationship among the selected variables missing in the existing literature.

A focused group approach is initiated by the authors to identify the barriers. Total interpretive structural modeling, Matrice d'impacts croisés multiplication appliquée á un classment, that is, matrix multiplication applied to classification and decision-making trial and evaluation laboratory are used to analyze the complex relationships among identified barriers.

This study finds that implementation of BCT reduces maintenance cost by withdrawing manual effort, as BCT has better capability to quantify the internal status of the system (observability characteristic). The observability characteristic of BCT provides high compatibility to the system. This study also finds that the compatibility of BCT with the organization reduces implementation cost and facilitates project management. The findings of this study recommend analyzing maintenance cost and compatibility of BCT before implementing it. Small and mid-size enterprises can select complex BCT depending on the sophistication level of IT usage and IT project management capabilities.

This study comes with various limitations, where the model developed by the authors may not be conclusive, as it is based exclusively on expert opinion. The samples collected may not help in validating the model statistically. Though the model has its limitations, it can still be considered as a nascent initiative for further investigation using structural equation modeling.

The outcomes of the theoretical and managerial contributions of the study can be categorized into three levels. This study can be used both by the industrialists and researchers to understand the barriers and the recovery methods thereafter. Suggestions that serve as future directives are also discussed by the authors.

The purpose of this paper is to develop a theoretical model that explains the impact of big data analytics capabilities (BDAC) on company's supply chain innovation capabilities and sustainable supply chain performance. BDAC is represented through two dimensions of big data technological capabilities (BDTC) and big data personal capabilities (BDPC). Moreover, the relationships between BDTC and BDPC with sustainable supply chain performance through the mediation effect of supply chain innovation capabilities are examined.

The study used a quantitative research approach. A survey of 400 Jordanian manufacturing companies was carried out to conduct this research. However, the responses of 207 managers were valid to be used in the analysis. In this study, the SmartPLS software was used to perform structural equation modeling using a partial least squares approach (PLS-SEM) and to examine the measurement and structural model's validity and reliability.

According to the results of this study, BDPC has a significant positive impact on supply chain innovation capabilities. Furthermore, the findings indicate that supply chain innovation capabilities are the most influential predictor of sustainable supply chain performance and act as a positive significant mediator in the relationship between BDPC and firm sustainable performance. Surprisingly, the study found that BDTC had no significant effect on supply chain innovation capabilities. Besides that, no significant relationship exists between BDTC and firm sustainable performance via the mediation effect of supply chain innovation capabilities.

This study provides an integrated research model that incorporates BDAC, supply chain innovation capabilities, and sustainable supply chain performance in order to analyze supply chain innovation and sustainable supply chain performance. This suggests that the scope of the study is broader in terms of predicting sustainable supply chain performance. As a result, the study intends to fill a gap in the literature by explaining how BDAC affects supply chain innovation capabilities and firms sustainable performance. In addition, the role of supply chain innovation capabilities as a mediator between BDAC and sustainable supply chain performance is investigated.

This study aims to extend and explore patterns and trends of research in the linkage of big data and knowledge management (KM) by identifying growth in terms of numbers of papers and current and emerging themes and to propose areas of future research.

The study was conducted by systematically extracting, analysing and synthesizing the literature related to linkage between big data and KM published in top-tier journals in Web of Science (WOS) and Scopus databases by exploiting bibliometric techniques along with theory, context, characteristics, methodology (TCCM) analysis.

The study unfolds four major themes of linkage between big data and KM research, namely (1) conceptual understanding of big data as an enabler for KM, (2) big data–based models and frameworks for KM, (3) big data as a predictor variable in KM context and (4) big data applications and capabilities. It also highlights TCCM of big data and KM research through which it integrates a few previously reported themes and suggests some new themes.

This study extends advances in the previous reviews by adding a new time line, identifying new themes and helping in the understanding of complex and emerging field of linkage between big data and KM. The study outlines a holistic view of the research area and suggests future directions for flourishing in this research area.

This study highlights the role of big data in KM context resulting in enhancement of organizational performance and efficiency. A summary of existing literature and future avenues in this direction will help, guide and motivate managers to think beyond traditional data and incorporate big data into organizational knowledge infrastructure in order to get competitive advantage.

To the best of authors’ knowledge, the present study is the first study to go deeper into understanding of big data and KM research using bibliometric and TCCM analysis and thus adds a new theoretical perspective to existing literature.