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Digitalization accelerates the need of tourism and hospitality ecosystems to reframe business models in line with a data-driven orientation that can foster value creation and innovation. Since the question of data-driven business models (DDBMs) in hospitality remains underexplored, this paper aims at (1) revealing the key dimensions of the data-driven redefinition of business models in smart hospitality ecosystems and (2) conceptualizing the key drivers underlying the emergence of innovation in these ecosystems.

The empirical research is based on semi-structured interviews collected from a sample of hospitality managers, employed in three different accommodation services, i.e. hotels, bed and breakfast (B&Bs) and guesthouses, to explore data-driven strategies and practices employed on site.

The findings allow to devise a conceptual framework that classifies the enabling dimensions of DDBMs in smart hospitality ecosystems. Here, the centrality of strategy conducive to the development of data-driven innovation is stressed.

The study thus developed a conceptual framework that will serve as a tool to examine the impact of digitalization in other service industries. This study will also be useful for small and medium-sized enterprises (SMEs) managers, who seek to understand the possibilities data-driven management strategies offer in view of stimulating innovation in the managers' companies.

The paper reinterprets value creation practices in business models through the lens of data-driven approaches. In this way, this paper offers a new (conceptual and empirical) perspective to investigate how the hospitality sector at large can use the massive amounts of data available to foster innovation in the sector.

Drawing on the knowledge-based view, this study investigates how IT–business alignment influences business model design via organizational learning and examines the moderating role of data-driven culture in the relationship between IT–business alignment and business model design via organizational learning.

Using multi-respondent survey data collected from 597 Chinese firms, mediation and moderated mediation analyses were used to examine this study's hypotheses.

The mediation test results revealed organizational learning served as a mediator between IT–business alignment and two types of business model design (i.e. novelty- and efficiency-centered). In addition, data-driven culture strengthened the indirect effects of IT–business alignment on these two types of business model design via organizational learning.

This study extends current understandings of the relationship between IT–business alignment and business model design by revealing the mediating role of organizational learning and investigating its indirect effects under various degrees of data-driven culture. As such, it contributes to the literature on the business model and IT–business alignment and provides insights for managers seeking to achieve the expected business model design.

In today’s digital world, data-driven digital artefacts pose challenges for education, as many students lack an understanding of data and feel powerless when interacting with them. This paper aims to address these challenges and introduces the data awareness framework. It focuses on understanding data-driven technologies and reflecting on the role of data in everyday life. The paper also presents an empirical study on young school students’ data awareness.

The study involves a teaching unit on data awareness framed by a pre- and post-test design using a questionnaire on students’ awareness and understanding of and reflection on data practices of data-driven digital artefacts.

The study’s findings indicate that the data awareness framework supports students in understanding data practices of data-driven digital artefacts. The findings also suggest that the framework encourages students to reflect on these data practices and think about their daily behaviour.

Students learn a model about interactions with data-driven digital artefacts and use it to analyse data-driven applications. This approach appears to enable students to understand these artefacts from everyday life and reflect on these interactions. The work contributes to research on data and artificial intelligence literacies and suggests a way to support students in developing self-determination and agency during interactions with data-driven digital artefacts.

Despite data being a hot topic, little is known about how data can be successfully used in interactions in business-to-business relationships, specifically in the boundary spanning contexts of firms working together to use data and create value. Hence, this study aims to investigate the boundary spanning context of data-driven customer value projects to understand the outcomes of such activities, including the types of value created, how resulting value is shared between the interacting firms, the types of capabilities required for firms to deliver value from data and in what contexts different outcomes are created and different capabilities required.

Three abductive case studies were undertaken with firms from different business-to-business domains. Data were coded in NVivo and interpreted using template analysis and cross-case comparison. Findings were sense checked with the case study companies and other practitioners for accuracy, relevance and resonance.

The findings expand our understanding of firm interactions when extracting value from data, and this study presents 15 outcomes of value created by the firms in the study. This study illustrates the complexity and intertwined nature of the process of value creation, which emphasises the need to understand distinct types of outcomes of value creation and how they benefit the firms involved. This study goes beyond this by categorising these outcomes as unilateral (one actor benefits), developmental (one actor benefits from the other) or bilateral (both actors benefit).

This research is exploratory in nature. This study provides a basis for further exploration of how firm interactions surrounding the implementation of data-driven customer value projects can benefit the firms involved and offers some transferable knowledge which is of particular relevance to practitioners.

This research contributes to the understanding of data-driven customer-focused projects and offers some practical management tools. The identification of outcomes helps define project goals and helps connect these goals to strategy. The organisation of outcomes into themes and contexts helps managers allocate appropriate human resources to oversee projects, mitigating the impacts of a current lack of talent in this area. Additionally, using the findings of this research, firms can develop specific capabilities to exploit the project outcomes and the opportunities such projects provide. The findings can also be used to enhance relationships between firms and their customers, providing customer value.

This work builds on research that explores the creation of value from data and how value is created in boundary spanning contexts. This study expands existing work by providing greater insight into the mechanics and outcomes of value creation and by providing specific examples of value created. This study also offers some recommendations of capability requirements for firms undertaking such work.

Data transformation has prompted enterprises to rethink their strategic development. Scholars have frequently acknowledged the vast potential value of supply chain data and realised that simply owning data resources cannot guarantee excellent innovation performance (IP). Therefore, this study focussed on the mediating and moderating issues between data-driven supply chain orientation (DDSCO) and IP. More specifically, the purpose was to explore (1) whether DDSCO promotes enterprise innovation through dynamic and improvisational capabilities and (2) how information complexity (INC) plays a moderating role between capabilities and performance.

An empirical study was performed using the results of a questionnaire survey, and a literature review was used to build the premises of this study. A sample was conducted on 296 Chinese enterprises, and the data collected were used to test the hypothesis by successive regression.

This research has implications for the theoretical development of DDSCO, as well as the dynamic capabilities (DC) and improvisation capabilities (IC) in innovation strategic literature. The empirical results show that DDSCO has a direct, positive impact on both DC and IC, which thus positively impact IP. Meanwhile, IC has a negative moderating effect on the path joining DC and IP. Conversely, IC has a positive moderating effect on the path joining IC and IP.

Although this study has limitations, it also creates opportunities for future research. The survey comes from different industries, so the possibility of unique influences within industries cannot be ruled out. Second, the authors' survey is based on cross-sectional data, which allow for more comprehensive data verification in the future. Third, this study also provides opportunities for future research, because it proves that DC and IC, as partial mediators of DDSCO and IP, can mine other paths of the data-driven supply chain in IP. For example, the perspective of the relationship between supply chain members, knowledge perspective, etc.

The research findings offer a novel perspective for enterprise managers. First, enterprises can leverage supply chain data to gain competitive advantages in innovation. Second, it is imperative for enterprises to acknowledge the significance of developing dynamic and IC. This also requires enterprises to acknowledge innovations in DDSCO necessitate a focus on dynamic and IC. Third, it is recommended that managers take into account both sides of IC and encourage enterprises to prioritise the utilisation of IC.

Empirical research results revealed how DDSCO improves IP and is an extension of digital transformation in the supply chain field, providing new opportunities and challenges for enterprise innovation. It can also expand the enterprise's understanding of DDSCO. Second, based on resource-based theory, it is possible to develop and test theoretical arguments regarding the importance of dynamic and IC as intermediaries in the DDSCO-IP. Third, the authors conducted simulations of highly dynamic data environments to develop and test theoretical arguments about the importance of IC as a moderator of capabilities-performance relationships.

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.

The production planning problem with fine-grained information has hardly been considered in practice. The purpose of this study is to investigate the data-driven production planning problem when a manufacturer can observe historical demand data with high-dimensional mixed-frequency features, which provides fine-grained information.

In this study, a two-step data-driven optimization model is proposed to examine production planning with the exploitation of mixed-frequency demand data is proposed. First, an Unrestricted MIxed DAta Sampling approach is proposed, which imposes Group LASSO Penalty (GP-U-MIDAS). The use of high frequency of massive demand information is analytically justified to significantly improve the predictive ability without sacrificing goodness-of-fit. Then, integrated with the GP-U-MIDAS approach, the authors develop a multiperiod production planning model with a rolling cycle. The performance is evaluated by forecasting outcomes, production planning decisions, service levels and total cost.

Numerical results show that the key variables influencing market demand can be completely recognized through the GP-U-MIDAS approach; in particular, the selected accuracy of crucial features exceeds 92%. Furthermore, the proposed approach performs well regarding both in-sample fitting and out-of-sample forecasting throughout most of the horizons. Taking the total cost and service level obtained under the actual demand as the benchmark, the mean values of both the service level and total cost differences are reduced. The mean deviations of the service level and total cost are reduced to less than 2.4%. This indicates that when faced with fluctuating demand, the manufacturer can adopt the proposed model to effectively manage total costs and experience an enhanced service level.

Compared with previous studies, the authors develop a two-step data-driven optimization model by directly incorporating a potentially large number of features; the model can help manufacturers effectively identify the key features of market demand, improve the accuracy of demand estimations and make informed production decisions. Moreover, demand forecasting and optimal production decisions behave robustly with shifting demand and different cost structures, which can provide manufacturers an excellent method for solving production planning problems under demand uncertainty.

This study attempts to examine the relationship between reliable big and cloud data analytics capabilities (RB&CDACs) and comparative advantages (CA) of manufacturing firms (MFs) in the Middle East region as developing countries using green product innovation (GPI) and green process innovations (GPrI) mediating factors, further assess the role of data-driven competitive sustainability factor as a moderated factor.

436 useable online surveys were analyzed using the quantitative approach for the data-gathering process, applying structural equation modeling in the Smart-PLS program as an analysis tool. The sample unit for analysis included all middle- and senior-level managers and employees within MFs. The authors performed convergent validity and discriminant validity tests, bootstrapping also was applied. The authors included GPI and GPrI as mediating factors while using data-driven competitive sustainability as a moderated factor.

The findings of this study indicated that there is a positive significant effect in the relationship between reliable big and cloud data analytics capabilities and comparative advantages, which is supported by the formulated hypothesis. Furthermore, the findings confirmed that there was a positive and significant effect through the mediating factors (i.e. GPI and GPrI) on comparative advantage, additionally, it confirmed and supported that the moderating factor represented by data-driven competitive advantage suitability has significant effect as well.

This study has some limitations represented by using only one type of methodological approach (i.e. quantitative), further, it was conducted on only Asian countries in the Middle East region.

This piece of work improved the proposed conceptual research model and included several factors such as reliable big and cloud data analytics capabilities, comparative advantage, data-driven competitive sustainability, GPI and GPrI. This research offered new and valuable information and findings for managers, practitioners and decision-makers in the MFs in the Middle East region as a road map and gaudiness for the importance to apply these factors in their firms for enhancing the comparative advantages in their firms. Further, this research fills the gap in SCM literature and makes a bridge of knowledge and contribution to the existence of previous studies.

Previous research assumes that technologies 4.0, particularly big data, may be highly relevant for organizations to increase human resources (HR) communication strategies, but the research provides little or no evidence on whether and how these tools are applied in employees and labor market relations. This study intends to offer a first insight on the adoption of data-driven HR/talent management approach, contributing to the ongoing debate on the Industry 4.0. This study aims to investigate the use of 4.0 technologies in HR and talent management functions, focusing also on the adoption of big data analytics for internal and recruitment communication.

The analysis of the literature enables to define the research questions and an exploratory web survey was carried out through a structured questionnaire. The analysis unit of the empirical survey includes the communication and marketing managers of 90 organizations in Italy, examined in the Mediobanca Report on the “Main Italian Companies.”

Findings highlight a lack of the use of 4.0 technologies and big data analytics in employee and labor market relations and reveal some sectoral differences in the adoption of 4.0 technologies. Moreover, the study points out that the development of HR analytics is hampered by short-term perspective, data quality problems and the lack of analytics skills.

Due to the exploratory research design and the circumscribed sample from a single country (Italy), further cross-national evidence is needed. This study provides digital communication managers with useful insights to improve the data-driven HR/talent management approach, which is a strategic asset for ensuring a sustainable competitive advantage and optimizing business performance.

The study offers an overview about the use of big data analytics in internal and recruitment communications. Considering the alignment between Italian and European trends in the use of big data and in the adoption of HR analytics, the study can provide insights also for other European organization.

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.