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This research investigates the critical role of data governance (DG) in shaping a data-driven culture (DDC) within organizations, recognizing the transformative potential of data utilization for efficiency, opportunities, and productivity. The study delves into the influence of DG on DDC, emphasizing the mediating effect of data literacy (DL).

The study empirically assesses 125 experienced managers in Indonesian public service sector organizations using a quantitative approach. Structural Equation Modeling (SEM) analysis was chosen to examine the impact of DG on DDC and the mediating effects of DL on this relationship.

The findings highlight that both DG and DL serve as antecedents to DDC, with DL identified as a crucial mediator, explaining a significant portion of the effects between DG and DDC.

Beyond unveiling these relationships, the study discusses practical implications for organizational leaders and managers, emphasizing the need for effective policies and strategies in data-driven decision-making.

This research fills an important research gap by introducing an original model and providing empirical evidence on the dynamic interplay between DG, DL, and DDC, contributing to the evolving landscape of data-driven organizational cultures.

Drawing on social capital theory, this study aims to explore the effect of corporate social responsibility (CSR) on organizational resilience. The research investigates the mediating role of relationship quality in the association of CSR with organizational resilience, and the moderating role of data-driven culture in the association between CSR and relationship quality.

Data were collected from Chinese agricultural firms with a sample of 241 senior or middle executives and structural equation modeling was used to test the research model and hypotheses.

The results indicate that CSR positively affects the relationship quality between agribusinesses and farmers, which in turn positively affects both proactive resilience and reactive resilience. Relationship quality has a partial mediating role in the association of CSR with proactive resilience and reactive resilience. Data-driven culture has a positive moderating effect on the relationship between CSR and relationship quality.

By arguing for CSR toward organizational resilience and analyzing its underlying mechanism, this study enriches the literature on CSR and organizational resilience and expands the existing knowledge on the roles of relationship quality and data-driven culture. This study also provides practical insights into how to improve organizational resilience.

This study aimed to examine the role of big data analytics capabilities (BDAC) in fostering organizational innovation capabilities and, consequently, in achieving economic, social and environmental sustainability.

Through the lens of dynamic capability theory, this study surveyed 115 hotels using purposive sampling to gain in-depth insights regarding the factors affecting organizational sustainability in the hospitality industry. The data analysis was conducted using partial least squares-structural equation modeling (PLS-SEM).

The findings reported a substantial impact of seven core dimensions (i.e. technology, data, basic resources, technological skills, managerial skills, organizational learning and data-driven culture) in building BDAC among hotels. Moreover, BDAC was also revealed to significantly influence innovation capabilities, positively impacting all three sorts of sustainability performance. Innovation capability also mediated the relationship between BDAC and all sustainability factors.

The findings will assist policymakers and practitioners in developing effective initiatives to enhance the adoption and implementation of data science and technologies, substantially contributing to the “National IR 4.0 Policy” and “Malaysia Digital Economy Blueprint” and achieving sustainable development goals (SDGs).

The originality of this study is established by investigating the interplay between BDAC, innovation capability and sustainability performance, particularly in the context of the hotel industry, whereas the existing studies focus on exploring the advantages of BDA.

This study attempts to explore the linkages between reliable big and cloud data analytics capabilities (RB&CDACs) and the comparative advantage (CA) that applies in the manufacturing sector in the countries located in North Africa (NA). These are considered developing countries through generating green product innovation (GPI) and using green process innovations (GPrLs) in their processes and functions as mediating factors, as well as the moderating role of data-driven competitive sustainability (DDCS).

To achieve the aim of this study, 346 useable surveys out of 1,601 were analyzed, and valid responses were retrieved for analysis, representing a 21.6% response rate by applying the quantitative methodology for collecting primary data. Convergent validity and discriminant validity tests were applied to structural equation modeling (SEM) in the CB-covariance-based structural equation modeling (SEM) program, and the data reliability was confirmed. Additionally, a multivariate analysis technique was used via CB-SEM, as hypothesized relationships were evaluated through confirmatory factor analysis (CFA), and then the hypotheses were tested through a structural model. Further, a bootstrapping technique was used to analyze the data. We included GPI and GPrI as mediating factors, while using DDCS as a moderated factor.

The empirical findings indicated that the proposed moderated-mediation model was accepted due to the relationships between the constructs being statistically significant. Further, the findings showed that there is a significant positive effect in the relationship between reliable BCDA capabilities and CAs as well as a mediating effect of GPI and GPrI, which is supported by the proposed formulated hypothesis. Additionally, the findings confirmed that there is a moderating effect represented by data-driven competitive advantage suitability between GPI, GPrI and CA.

One of the main limitations of this study is that an applied cross-sectional study provides a snapshot at a given moment in time. Furthermore, it used only one type of methodological approach (i.e. quantitative) rather than using mixed methods to reach more accurate data.

This study developed a theoretical model that is obtained from reliable BCDA capabilities, CA, DDCS, green innovation and GPrI. Thus, this piece of work bridges the existing research gap in the literature by testing the moderated-mediation model with a focus on the manufacturing sector that benefits from big data analytics capabilities to improve levels of GPI and competitive advantage. Finally, this study is considered a road map and gaudiness for the importance of applying these factors, which offers new valuable information and findings for managers, practitioners and decision-makers in the manufacturing sector in the NA region.

Despite the growing interest regarding companies' sustainability, its social dimension has mostly been neglected by academics and practitioners. Consequently, this study aims to address this issue by investigating if the adoption of human resource (HR) analytics can positively influence the impact of social sustainable operations practices (SSOP) on employees' motivation and engagement and the effect of these lasts on organizational retention.

Data were collected through online questionnaires addressed to 281 HR managers of heterogeneous companies from Europe and analyzed through a structural equation modeling (SEM) technique.

The findings confirmed the positive effect of SSOP on employees’ motivation and engagement, and of these last on employees’ retention. Furthermore, they confirmed that the usage of HR analytics positively moderates the relationship between SSOP and employees’ motivation and engagement.

This study contributes to both sustainable operations management and HR management literature streams. First, it adopts a multidisciplinary perspective which also considers evidence from HR management literature, allowing the authors to concentrate on the social dimension of sustainability. Second, it provided further insight regarding the adoption of a data-driven approach in relation to social sustainable operations management. Finally, it contributes to HR analytics-related literature by demonstrating its impact also on organizational aspects that are not directly controlled by the HR department.

The tourist industry has to adopt a big data-driven foresight approach to enhance decision-making in a post-COVID international landscape still marked by significant uncertainty and in which some megatrends have the potential to reshape society in the next decades. This paper, considering the opportunity offered by the application of the quantitative analysis on internet new data sources, proposes a prediction method using Google Trends data based on an estimated transfer function model.

The paper uses the time-series methods to model and predict Google Trends data. A transfer function model is used to transform the prediction of Google Trends data into predictions of tourist arrivals. It predicts the United States tourism demand in Italy.

The results highlight the potential expressed by the use of big data-driven foresight approach. Applying a transfer function model on internet search data, timely forecasts of tourism flows are obtained. The two scenarios emerged can be used in tourism stakeholders’ decision-making process. In a future perspective, the methodological path could be applied to other tourism origin markets, to other internet search engine or other socioeconomic and environmental contexts.

The study raises awareness of foresight literacy in the tourism sector. Secondly, it complements the research on tourism demand forecasting by evaluating the performance of quantitative forecasting techniques on new data sources. Thirdly, it is the first paper that makes the United States arrival predictions in Italy. Finally, the findings provide immediate valuable information to tourism stakeholders that could be used to make decisions.

This study aims to unveil the pivotal components and implementation pathways in the digital innovation of smart tourism destinations, while constructing a theoretical framework from a holistic perspective.

The research focuses on 31 significant urban smart tourism destinations in China. Secondary data was collected through manual search supplemented by big data scraping, whereas primary data was obtained from interviews with municipal tourism authorities. Grounded theory was used to theoretically construct the phenomenon of digital innovation in smart tourism destinations.

This research has formulated a data-driven knowledge framework for digital innovation in smart tourism destinations. Core components include digital organizational innovation, smart data platforms, multi-stakeholder digital collaborative ecosystem and smart tourism scenario systems. Destinations can achieve smart tourism scene innovation through closed innovation driven by smart data platforms or open innovation propelled by a multi-stakeholder digital collaborative ecosystem.

Based on insights from digital innovation practices, this study proposes a series of concrete recommendations aimed at assisting Destination Management Organizations in formulating and implementing more effective digital innovation strategies to enhance the sustainable digital competitiveness of destinations.

This study advances smart tourism destination innovation research from localized thinking to systemic thinking; extends digital innovation theory into the realm of smart tourism destination innovation; repositions the significance of knowledge in smart tourism destination innovation; and constructs a comprehensive framework for digital innovation in smart tourism destinations.

本研究致力于揭示智能旅游目的地数字创新中的核心组件及实施路径, 并创建一个整体视角下的理论框架。

研究选定中国31座重要城市型智能旅游目的地为研究对象。通过人工检索结合大数据抓取的方式收集二手资料, 以各市旅游主管部门为访谈对象收集一手资料。运用扎根理论对智能旅游目的地的数字创新现象进行理论构建。

本研究构建了一个数据型知识驱动的智能旅游目的地数字创新框架。其中, 核心组件包括数字组织创新、智慧数据平台、多主体数字协同生态和智慧旅游场景体系。目的地可通过智慧数据平台驱动的内生型创新或多主体数字协同生态推动的开放式创新, 实现智能旅游场景创新。

本研究将智能旅游目的地创新相关研究由局部思考推向系统思考; 将数字创新理论扩展到智能旅游目的地创新的研究中; 重新定位知识在智能旅游目的地创新中的重要地位; 以及构建了一个智能旅游目的地数字创新整体框架。

本研究基于数字创新实践洞察, 提出了一系列具体建议。旨在帮助目的地管理组织更有效地制定和实施数字创新策略, 以增强旅游目的地可持竞争力。

La investigación se centra en 31 destacados destinos turísticos urbanos inteligentes de China. Los datos secundarios se recopilaron mediante una búsqueda manual complementada con técnicas de big data, mientras que los datos primarios se obtuvieron a partir de entrevistas con las autoridades turísticas municipales. Se empleó la teoría fundamentada para construir teóricamente el fenómeno de la innovación digital en los destinos turísticos inteligentes.

Esta investigación tiene como objetivo identificar los componentes esenciales y las rutas de implementación de la innovación digital en destinos turísticos inteligentes, y construir un marco teórico desde una perspectiva holística.

Este estudio ha desarrollado un marco de conocimiento basado en datos para la innovación digital en destinos turísticos inteligentes. Los componentes centrales incluyen la innovación organizativa digital, la plataforma de datos inteligentes, el ecosistema digital colaborativo de múltiples actores y el sistema de escenarios turísticos inteligentes. Además, tanto la innovación endógena impulsada por la plataforma de datos inteligentes como la innovación abierta impulsada por el ecosistema digital colaborativo de múltiples actores contribuyen a la innovación por escenarios en destinos turísticos inteligentes.

A partir de las prácticas de innovación digital, este estudio ofrece una serie de recomendaciones dirigidas a las Organizaciones de Gestión de Destinos (DMOs) para la formulación e implementación de estrategias de innovación digital de manera más efectiva, y mejorar la competitividad digital sostenible de los destinos turísticos.

Este estudio avanza la investigación sobre innovación en destinos turísticos inteligentes desde el pensamiento localizado hasta el pensamiento sistémico; extiende la teoría de la innovación digital al ámbito de la innovación en destinos turísticos inteligentes; reposiciona la importancia del conocimiento en la innovación de destinos turísticos inteligentes; y construye un marco integral para la innovación digital en destinos turísticos inteligentes.

Business intelligence (BI) systems and tools are deemed to be a transformative source with the potential to contribute to reshaping the way different healthcare organizations’ (HCOs) services are offered and managed. However, this emerging field of research still appears underdeveloped and fragmented. Hence, this paper aims to reconciling, analyzing and synthesizing different strands of managerial-oriented literature on BI in HCOs and to enhance both theoretical and applied future contributions.

A literature-based framework was developed to establish and guide a three-stage state-of-the-art systematic literature review (SLR). The SLR was undertaken adopting a hybrid methodology that combines a bibliometric and a content analysis.

In total, 34 peer-review articles were included. Results revealed significant heterogeneity in theoretical basis and methodological strategies. Nonetheless, the knowledge structure of this research’s stream seems to be primarily composed of five clusters of interconnected topics: (1) decision-making, relevant capabilities and value creation; (2) user satisfaction and quality; (3) process management, organizational change and financial effectiveness; (4) decision-support information, dashboard and key performance indicators; and (5) performance management and organizational effectiveness.

To the authors’ knowledge, this is the first SLR providing a business and management-related state-of-the-art on the topic. Besides, the paper offers an original framework disentangling future research directions from each emerged cluster into issues pertaining to BI implementation, utilization and impact in HCOs. The paper also discusses the need of future contributions to explore possible integrations of BI with emerging data-driven technologies (e.g. artificial intelligence) in HCOs, as the role of BI in addressing sustainability challenges.

Despite that a transformational shift has occurred in many organisations towards data-driven management, many organisations struggle to harness and translate new technology, such as “big data” into a competitive advantage. This study aims to undertake an empirical investigation into the enabling factors which lead to the practice of formulating an effective data-led strategy (EDLS). Leveraging the theoretical lenses of the resource-based view, absorptive capacity and attention-focus view, a range of various factors are hypothesised to influence EDLS.

The study takes place in South Africa and is based on primary survey data focused on the Fin-tech industry sector where the need to formulate and implement an EDLS has become urgent considering the move to technology enabled banking solutions. Partial Least Squares Structural Equation Modelling (PLS-SEM) is used to test the hypotheses.

Results highlight that several factors are related to EDLS as significant predictors, which include the data platform, technical skills, knowledge management, transformation and focus-alignment. This latter factor has the largest influence on EDLS, which suggests that the alignment of focus across multiple firm divisions both vertically and horizontally significantly enables an EDLS.

Managers need to appreciate the intricacy of the range of factors involved in enabling an EDLS. Managers are advised to grow their organisational knowledge regarding which enablers offer the best pathway towards the development of a more robust framework when putting an EDLS into practice.

The article offers new insights into better understanding the relevant antecedents which enable the successful practice of an EDLS from an African emerging market perspective.

The purpose of this research is to develop a framework of an ontology-based Asset Information Model (AIM) for a Digital Twin (DT) platform and enhance predictive maintenance practices in building facilities that could enable proactive and data-driven decision-making during the Operation and Maintenance (O&M) process.

A scoping literature review was accomplished to establish the theoretical foundation for the current investigation. A study on developing an ontology-based AIM for predictive maintenance in building facilities was conducted. Semi-structured interviews were conducted with industry professionals to gather qualitative data for ontology-based AIM framework validation and insights.

The research findings indicate that while the development of ontology faced challenges in defining missing entities and relations in the context of predictive maintenance, insights gained from the interviews enabled the establishment of a comprehensive framework for ontology-based AIM adoption in the Facility Management (FM) sector.

The proposed ontology-based AIM has the potential to enable proactive and data-driven decision-making during the process, optimizing predictive maintenance practices and ultimately enhancing energy efficiency and sustainability in the building industry.

The research contributes to a practical guide for ontology development processes and presents a framework of an Ontology-based AIM for a Digital Twin platform.