Search results

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.

The purpose of this paper is to analyze the inadequacies of current business education in the tackling of the educational challenges inherent to the advent of a data-driven business world. It presents an analysis of the implications of digitization and more specifically big data analytics (BDA) and data science (DS) on organizations with a special emphasis on decision-making processes and the function of managers. It argues that business schools and other educational institutions have well responded to the need to train future data scientists but have rather disregarded the question of effectively preparing future managers for the new data-driven business era.

The approach involves analysis and review of the literature.

The development of analytics skills shall not pertain to data scientists only, it must rather become an organizational cultural component shared among all employees and more specifically among decision makers: managers. In the data-driven business era, managers turn into manager-scientists who shall possess skills at the crossroad of data management, analytical/modeling techniques and tools, and business. However, the multidisciplinary nature of big data analytics and data science (BDADS) seems to collide with the dominant “functional silo design” that characterizes business schools. The scope and breadth of the radical digitally enabled change, the author are facing, may necessitate a global questioning about the nature and structure of business education.

For the sake of transparency and clarity, academia and the industry must join forces to standardize the meaning of the terms surrounding big data. BDA/DS training programs, courses, and curricula shall be organized in such a way that students shall interact with an array of specialists providing them a broad enough picture of the big data landscape. The multidisciplinary nature of analytics and DS necessitates to revisit pedagogical models by developing experiential learning and implementing a spiral-shaped pedagogical approach. The attention of scholars is needed as there exists an array of unexplored research territories. This investigation will help bridge the gap between education and the industry.

The findings will help practitioners understand the educational challenges triggered by the advent of the data-driven business era. The implications will also help develop effective trainings and pedagogical strategies that are better suited to prepare future professionals for the new data-driven business world.

By demonstrating how the advent of a data-driven business era is impacting the function and role of managers, the paper initiates a debate revolving around the question about how business schools and higher education shall evolve to better tackle the educational challenges associated with BDADS training. Elements of response and recommendations are then provided.

Significant advances in digital technologies impact both organisations and knowledge workers alike. Organisations are now able to effectively analyse significant amounts of data, while accomplishing actionable insight and data-driven decision-making through knowledge workers that understand and manage greater complexity. For decision-makers to be in a position where sufficient information and data-driven insights enable them to make informed decisions, they need to better understand fundamental constructs that lead to the understanding of deep knowledge and wisdom. In an attempt to guide organisations in such a process of understanding, this research study focuses on the design of an organisational transformation framework for data-driven decision-making (OTxDD) based on the collaboration of human and machine for knowledge work. The OTxDD framework was designed through a design science research approach and consists of 4 major enablers (data analytics, data management, data platform, data-driven organisation ethos) and 12 sub-enablers. The OTxDD framework was evaluated in a real-world scenario, where after, based on the evaluation feedback, the OTxDD framework was improved and an organisational measurement tool developed. By considering such an OTxDD framework and measurement tool, organisations will be able to create a clear transformation path to data-driven decision-making, while applying the insight from both knowledge workers and intelligent machines.

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.

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 purpose of this paper is to explore the role of storytelling in data interpretation, decision-making and individual-level adoption of business analytics (BA).

Existing theory is extended by introducing the concept of BA data-driven storytelling and by synthesizing insights from BA, storytelling, behavioral research, linguistics, psychology and neuroscience. Using theory-building methodology, a model with propositions is introduced to demonstrate the relationship between storytelling, data interpretation quality, decision-making quality, intention to use BA and actual BA use.

BA data-driven storytelling is a narrative sensemaking heuristic positively influencing human behavior towards BA use. Organizations deliberately disseminating BA data-driven stories can improve the quality of individual data interpretation and decision-making, resulting in increased individual utilization of BA on a daily basis.

To acquire a deeper understanding of BA data-driven storytelling in behavioral operational research (BOR), future studies should test the theoretical model of this study and focus on exploring the complexity and diversity in individual attitudes toward BA.

This study provides practical guidance for business practitioners who struggle with interpreting vast amounts of complex data, making data-driven decisions and incorporating BA into daily operations.

This cross-disciplinary study develops existing BOR, storytelling and BA literature by showing how a novel BA data-driven storytelling approach can facilitate BA adoption in organizations.

The peer-to-peer perspective on open data is an interesting topic to research, taking into account that data-driven innovations and related startups are often developed independently by civic and private stakeholders in a highly collaborative manner and are tentatively beginning to directly compete with traditional e-government solutions, providing arguably better services to citizens and businesses. In this regard, the paper aims to further debate on the potential of such independent data-driven collaboration not only to transform the traditional mechanisms of public sector innovations but also provide more democratic ways to ensure greater transparency of government and its responsibility before the society.

The research is based on a cross-country case study, resorting to the content analysis of three demonstrative cases in the development of open data-driven projects, which specifically promote peer-to-peer communication between its stakeholders. In this regard, the case study itself relies heavily on the analysis of rich empirical data that the author collected during his field studies in the Northern European region in 2015–2017, particularly in Estonia, Finland and Sweden. The practical research itself consists of three major parts, which reflect peer-to-peer perspectives of correspondingly civic, public and private stakeholders through manifested examples of related independent projects in the area.

The paper's results demonstrate that the use of peer-to-peer mechanisms in advancing related public sector reforms allows to transform the traditional understanding of e-government phenomena in a conceptually new way. E-government or its last more political interpretation – from the perspective of its peers could be regarded not necessarily as a platform to provide digital public services but as a source of raw material for various third party projects in, respectively, civic, government and business peer-to-peer dimensions of such reforms. As a result, open data provides an interesting playground to change the very nature of public sector innovations in the area.

The choice of countries for research was motivated by purposive and convenience sampling because all these countries are situated in one region, have both similarities and differences in historical, political and socioeconomic backgrounds and, therefore, provide an ideal playground to investigate open data as a context dependable phenomenon. In this regard, the unique political and socioeconomic contexts of these countries provide an interesting playground to debate on the potential of social democracy, egalitarian society and social equality, i.e. public values that are deeply embedded into the fabric of societies there, to benefit the open data movement in a fundamental manner.

This paper reports on unique practical approaches for peer-to-peer collaboration and cooperation in advancing open data-driven platforms among stakeholders. The results of the case studies in three Nordic countries, which are currently among global leaders in advancing the concept of open government, are presented in an intrinsically illustrative manner, which could help practitioners and policymakers to understand better the potential of such a peer-to-peer perspective on open data. In this regard, the models proposed, of citizen-to-citizen, business-to-business, government-to-government interactions, could be interesting to a wide audience of e-government stakeholders in many nations.

The paper also enters into philosophical debates about societal implications of digital peer-to-peer data-driven communication among people. Recent efforts to digitize almost every part of social life, starting from popularization of solutions for distant work and ending to online access to various public services, incentivize individual members of civil society to communicate in an inherently peer-to-peer way. This fact will definitely increase the demand for related digital services. Social distancing in a digital context will allow to paradoxically emancipate technically savvy and entrepreneurial people in creating new services, including using open data, which could meet the demand.

The research is intrinsically of an empirical character because recent e-government reforms in the public sector in many countries, including in the open data area, provide rich practical knowledge to test the limits of new technologies to advance society in socioeconomic and, more importantly, political development. In this regard, this paper provides the first research in analyzing open data from a unique peer-to-peer perspective with an ultimate goal of the whole investigation to draw the attention of other e-government scholars and initiate debates on the collaborative nature of the phenomena to empower civil society and ensure transparency of government.

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 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.

In the digital age, the use of data and analytical capabilities to guide business decisions and operations plays a strategic role for organizations to gain competitive advantage (CA). However, the paths by which analytical capabilities convey their effect to CA are not yet fully known and few studies address the role of behavioral and cultural aspects of related of analytical capabilities. The purpose of this paper is to analyze how data-driven culture (DDC) and business analytics (BA) affect CA, considering the mediating effects of big data visualization (BDV) and organizational agility (OA).

A survey was conducted with 173 managers who are BDV and BA users in Brazilian organizations of various economic segments. The data were analyzed through structural equation modeling and mediation tests.

The evidence indicates that DDC and BDV are antecedents of BA. The following complementary mediations were discovered: BDV in the relationship between DDC and BA; BA in the relationship between DDC and CA; and OA in the relationship between BA and CA. It was also discovered that OA explains the transmission of most of the effect of BA to CA.

This study can help organizations to understand the importance of cultural and behavioral aspects related to the use of the analytical capabilities. Thereby, managers can establish policies and strategies to extract value from data and leverage business agility and competitiveness through use BDV and BA.

This study fills an important research gap by developing an original research model and discussing empirical evidence on how DDC and BA affect CA, considering the mediating effects of BDV and OA.