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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.

The study queries the drivers of innovation management in contemporary data-driven organizations/companies. It is argued that data-driven organizations that integrate a strategic orientation grounded in data, human abilities and proactive management are more effective in triggering innovation.

Research reported in this paper employs constructivist grounded theory, Gioia methodology, and the abductive approach. The data collected through semi-structured interviews administered to 20 Italian start-up founders are then examined.

The paper identifies the key enablers of innovation development in data-driven companies and reveals that data-driven companies may generate different innovation patterns depending on the kind of capabilities activated.

The study provides evidence of how the combination of data-driven culture, skills' enhancement and the promotion of human resources may boost the emergence of innovation.

In today's business setting, the business analytic capability, data-driven culture and product development features are highly pronounced in light of the firm's competitive advantage. Though widespread attention has been given to the above concepts, there hasn't been much research done on how it could support achieving competitive advantage.

This research strongly lies on the theoretical background and empirically tests the hypothesized relationships. The primary survey of 272 responses was analysed by using the partial least squares structural equation modelling (PLS-SEM).

The findings of this study show a significant relationship for the constructs in the research model except for the third hypothesis. Accordingly, the firm's data-driven culture does not have a significant impact on new product newness.

This study empirically tests the business analytics capability, data-driven culture, and new product development features in the context of a firm's competitive advantage. The findings of this study contribute to the theoretical, practical and managerial aspects of this field.

The growing turbulence of the external environment has progressively led to the necessity by organizations of exploiting new opportunities provided by data-driven approaches for supporting the even more complex decision-making processes. The new digital environment has led to the development and adoption of innovative approaches; also in the urban context which has always been characterized by different, interconnected, and dynamic dimensions. Urban governance models have been enhanced by smart technologies, which act as enablers of advanced services and foster connections between citizens, public and private organizations, and decision-makers. In this context, the objective of this chapter is to examine the role of data-driven approaches in the urban context during the chaotic and high variable circumstances related to the diffusion of the Coronavirus disease 2019 (Covid-19). Thanks to the adoption of the co-evolutionary perspective, a cycle in urban governance decision-making approach based on digital technologies is depicted and its contribution for managing the ongoing Covid-19 is traced. The results of the analysis highlight how the data-driven approach supports urban decision-making process and shed light on the co-evolutionary perspective as heuristic device to map the interactions settled in the networks between local governments, data-driven technologies, and citizens. In this sense, this chapter offers interesting insights, potentially capable of generating useful implications for both researchers and professionals in the public sector.

The purpose of this study is to identify, analyze and explain the ethical implications that can result from the datafication of service.

This study uses a midrange theorizing approach to integrate currently disconnected perspectives on technology-enabled service, data-driven business models, data ethics and business ethics to introduce a novel analytical framework centered on data-driven business models as the general metatheoretical unit of analysis. The authors then contextualize the framework using data-intensive insurance services.

The resulting midrange theory offers new insights into how using machine learning, AI and big data sets can lead to unethical implications. Centered around 13 ethical challenges, this work outlines how data-driven business models redefine the value network, alter the roles of individual actors as cocreators of value, lead to the emergence of new data-driven value propositions, as well as novel revenue and cost models.

Future research based on the framework can help guide practitioners to implement and use advanced analytics more effectively and ethically.

At a time when future technological developments related to AI, machine learning or other forms of advanced data analytics are unpredictable, this study instigates a critical and timely discourse within the service research community about the ethical implications that can arise from the datafication of service by introducing much-needed theory and terminology.

The circular economy (CE) model is seen as an alternative model to the linear economy models, which seem to be reaching their physical limits. The CE business model aims to reuse materials and decrease the need for virgin materials. This requires the implementation of a reverse supply chain, close collaboration between actors, as well as well-organized logistics. For this reason, the CE companies have typically high demand for digitalized processes and the utilization of data on both operational and business development dimensions. Also the utilization of big data collected from the companies’ business environment can provide new opportunities for business development in CE. Despite the fact that utilization of data collected from the business environment and operations enables data-driven approaches for various decision-making functions in companies, many companies still struggle to figure out how to use analytics to take advantage of their data. In the small- and medium-sized enterprises (SMEs), in particular, the managers are facing difficulties with ever-increasing amounts of data and sophisticated analytics. Indeed, prior research identified several kinds of barriers to the effective utilization of data in SMEs. Still, research on data-driven decision-making remains scarce in CE context. This chapter presents a case study consisting of seven cases, all representing SMEs operating in the field of CE in Finland. In the case study, the barriers and practical challenges for data-driven decision-making in CE SMEs are investigated. Based on the case study results, this chapter proposes that utilization of data, lack of resources, lack of capabilities, and regulation are the main barriers to data-driven decision-making in CE SMEs.

One of the leading factors that shape product and service delivery are data collected in databases and other repositories maintained by companies. The transformation of such data into knowledge and wisdom may constitute a new source of income. This paper aims to explore how small/medium-sized enterprises (SMEs) advance their business models (BMs) around data to handle data-driven products and how this contributes to their innovativeness and performance.

To investigate the phenomenon, the as-is BM of a multinational SME was mapped and its limitations were revealed through a qualitative study. The BM canvas was used. Then the data-driven approach was innovated within the facility management (FM) industry, where a high volume of operational and sensor-based data being collected creates added value in terms of new data-based products.

A data-driven business model (DDBM) blueprint for the FM industry that supports the need to complement service-driven operations with the data-driven approach is delivered. Enhanced BM equips a facility manager with additional managerial tools that enable decreasing property utilization costs and opens up new opportunities for generating revenue. This paper drafts the way to evolve from service to data-driven business and point out the attitudes that managers should adopt to promote and implement DDBM.

The DDBM constitutes a guideline that supports FM organizations in focusing their activities and resources on generating business value from data and monetizing data-driven products.

The research expands knowledge regarding BMs and their evolution. The gap regarding the DDBM innovation within the FM industry is filled.

Big data is one of the most demanding topics in contemporary marketing research. Despite its importance, the big data-based strategic orientation in international marketing is yet to be formed conceptually. Thus, the purpose of this study is to systematically review and propose a holistic framework on big data-based strategic orientation for firms in international markets to attain a sustained firm performance.

The study employed a systematic literature review to synthesize research rigorously. Initially, 2,242 articles were identified from the selective databases, and 45 papers were finally reported as most relevant to propose an integrative conceptual framework.

The findings of the systematic literature review revealed data-evolving, and data-driven strategic orientations are essential for performing international marketing activities that contain three primary orientations such as (1) international digital platform orientation, (2) international market orientation and (3) international innovation and entrepreneurial orientation. Eleven distinct sub-dimensions reflect these three primary orientations. These strategic orientations of international firms may lead to advanced analytics orientation to attain sustained firm performance by generating and capturing value from the marketplace.

The study minimizes the literature gap by forming knowledge on big data-based strategic orientation and framing a multidimensional framework for guiding managers in the context of strategic orientation for international business and international marketing activities. The current study was conducted by following only a systematic literature review exclusively in firms' overall big data-based strategic orientation concept in international marketing. Future research may extend the domain by introducing firms' category wise systematic literature review.

The study has proposed a holistic conceptual framework for big data-driven strategic orientation in international marketing literature through a systematic review for the first time. It has also illuminated a future research agenda that raises questions for the scholars to develop or extend theory in this area or other related disciplines.

This study aims to identify and outline the critical challenges affecting the inclination of executives to use data as the basis for making decisions at a local government level.

The study conducted in-depth interviews with 21 public officials comprising middle- and top-level executives from 18 agencies and offices at the Bojonegoro Regency, one of Indonesia’s most progressive regencies in pursuing open government and smart cities.

The findings demonstrate that ensuring a good quality data architecture, nurturing data culture and developing analytics capability are essential in the case of a developing country such as Indonesia. However, insufficient policies and regulations, a nonexistent evaluative framework for data quality, disruptive local tradition and the ingrained autocratic administration represent significant and unique challenges to implementing data-driven decision-making in the local government in Indonesia.

The chosen research approach may result in a need for more generalizability beyond Indonesia, accentuating the necessity for the geographical objects to include other developing countries in future research.

The findings showcase that lack of awareness and acceptance from public officials and the general public of the importance of a data-driven approach; as such, a better understanding of the change in attitudes and mindsets of public officials is invariably one of the critical practical determinants.

The findings signify the importance of creating robust accountability systems and evaluative frameworks that consider the many variables influencing decisions that capture the significance of organizational and local culture.