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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 progressive increase in the size of datasets has given life to the so-called big data that provides researchers with the opportunity to extract a greater amount of useful information in many sectors, especially in the tourism industry.

The chapter aims to demonstrate that sustainable tourism (ST) could be particularly favored by using big data and a data-driven approach. Furthermore, as ST appears in line with a new type of responsible entrepreneurship, called Humane Entrepreneurship (HumEnt), this chapter investigates the link between ST and HumEnt and the impact of big data and data-oriented approaches on ST and HumEnt.

The research adopts a qualitative approach, applying the case study technique. The authors conducted ten semi-structured interviews with key informants from a specific form of hospitality: Albergo Diffuso. Findings show the advantages of the data-driven approach to tourism and entrepreneurship highlighting how using data creates new opportunities for decision making in ST and HumEnt.

Online learning can present challenges and barriers for students, especially when it comes to self-motivation and discipline. Non-traditional learners and those who may be underprepared are often the students most likely to seek virtual learning options. As a result, methods of supporting online learners must be intentional and robust to stay attentive to students’ needs. The American Women’s College (TAWC) at Bay Path University designed its Social Online Universal Learning (SOUL) model to promote degree completion through a constellation of evidence-based practices that cultivate student engagement in a personalized online learning environment. SOUL employs an innovative adaptive technology approach with Universal Design for Learning (UDL) principles to promote accessibility and affordability. Foundational to these frameworks is a commitment to leveraging technology to gather data that drives action-oriented analytics, triggering interventions by faculty and staff and generating predictive models to inform wrap-around support. SOUL’s high-tech, high-touch attributes give students agency over their unique learning paths and provide instructors and administrators the meaningful insights needed to target efforts in a personalized yet scalable way, to promote and positively impact student success. Lessons learned in the process of developing data-driven “high-tech, high-touch” practices are presented.

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.

A trend in modern supply chain management has been to substitute information for inventory. In this chapter, an approach to how information and communication technology can be used to achieve this in a maritime logistics context is outlined and described based upon a bulk shipping case.

The approach used is based on data-driven modeling and analysis, in which current logistics and commodity storage costs are benchmarked against a “best possible solution.”

To make a new solution operative, a change should be made based upon an analytical decision-making approach, ICT infrastructure development, and inter-organizational development. Thus, the proper use of analytical and transactional information and communication technology in maritime logistics would enable logistics chain stakeholders to track stock levels and ultimately allocate vessels to move cargo when that is logistically most cost effective. Further, this could support a development in the contractual relationships between producer and shipping line changing from a Contract of Affreightment to a Service Level Agreement relationship.

There is room for enhanced use of information and communication technology to provide decision and operational support at strategic, tactical, and operational levels within maritime logistics. This chapter explains some of the driving forces for this, together with a tested approach and method for this, given into a specific, practical case.

This article reviews the quantitative marketing literature on artificial intelligence (AI) through an economics lens. We apply the framework in Prediction Machines: The Simple Economics of Artificial Intelligence to systematically categorize 96 research papers on AI in marketing academia into five levels of impact, which are prediction, decision, tool, strategy, and society. For each paper, we further identify each individual component of a task, the research question, the AI model used, and the broad decision type. Overall, we find there are fewer marketing papers focusing on strategy and society, and accordingly, we discuss future research opportunities in those areas.

There has been a lack of focus on multi-level issues within leadership research. Dionne and Dionne (2009) address this gap in the research by presenting a Monte Carlo simulation examining leadership at four levels of analysis within a group decision-making context. While their work makes a strong contribution to the sciences of leadership, group decision making, and team complexity, many aspects of the research demonstrate potential for great expansion and improvement. Toward this purpose, this commentary discusses and provides suggestions regarding the topics of computer simulation in team research, group decision-making theory, and the modeling of team complexity. It is intended to stimulate continued critical thinking and more innovative, practical, and carefully designed research efforts.

Increasing availability of the financial data has opened new opportunities for quantitative modeling. It has also exposed limitations of the existing frameworks, such as low accuracy of the simplified analytical models and insufficient interpretability and stability of the adaptive data-driven algorithms. I make the case that boosting (a novel, ensemble learning technique) can serve as a simple and robust framework for combining the best features of the analytical and data-driven models. Boosting-based frameworks for typical financial and econometric applications are outlined. The implementation of a standard boosting procedure is illustrated in the context of the problem of symbolic volatility forecasting for IBM stock time series. It is shown that the boosted collection of the generalized autoregressive conditional heteroskedastic (GARCH)-type models is systematically more accurate than both the best single model in the collection and the widely used GARCH(1,1) model.

Big data is a buzzword of our times, and yet the awareness of what big data is, how it permeates our daily lives, and how it is applied either in the policy-making process or in the business sector remains relatively low. From a different perspective, while specialists, that is, practitioners and researchers, dealing with the technical facets of big data successfully uncover new features, new domains, and new opportunities related to big data, there is a need of evaluating and examining these findings through the lens of social sciences and management. This chapter offers an insight into key issues and developments that shape the broad and multi-directional big data debate. To this end, the content of the book is elaborated and the key findings are highlighted. In this way, this chapter serves as a very useful guide into the question of how big data is applied across issues and domains and how it is valid and relevant to all of us today.