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While social media data are used increasingly in studies of social movements, social media evolves far more rapidly than academic research and publication. This chapter argues that researchers should adopt historical and archival approaches to social media data. Treating social media data as an “instant archive” – one that is self-curated, is co-constituted, and changes rapidly – we caution researchers to pay attention to the features of this archive and their implications for working with the data therein. Applying insights from recent discussions of archival methods for social science research to the specific features of social media data, we explore how platform features, repressive effects, and user innovations affect the content of the instant archive. We then offer strategies for researchers' methodological approaches, including how best to select units of analysis and platforms, how to collect and interpret archival materials, and how to identify silences in the data.

Artificial intelligence (AI) is the key technology used and is gradually affecting all aspects of the organisations in their pursuit of digital transformation. In this study, the authors investigated the influence of AI on work, people and the firm. The authors adopted a qualitative approach to the study. The findings of the study indicated the pervasiveness of AI, the emergence of new forms of work, the threat to some of the existing jobs and the emergence of new skill sets. The data also suggested that with AI not every aspect of work is going to change; particularly the human interaction and capabilities for solving multivariate and complex problems are going to stay even with AI. As the new sets of skills are emerging, so the need for continuous skill development also emerges as relevant to the industry. Another set of findings suggested that new forms of organisations might evolve with the usage of AI and the technology could play a key role irrespective of the industry. The data also reflected that human capital processes like talent management and talent development would act as the integration mechanisms between the changing work, the emerging skill sets of people and the changing forms of the organisations.

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.

In this paper, we examine the occupational distribution of individuals who hold bachelor degrees in particular fields in the United States using data from the various waves of the National Survey of College Graduates. We propose and calculate indices that describe two related aspects of the occupational distribution by major field of study: distinctiveness (how dissimilar are the occupations of a particular major when compared with all other majors) and variety (how varied are the occupations among those who hold that particular major). We discuss theoretical properties of these indices and statistical properties of their estimates. We show that the occupational variety has increased since 1993 for most major fields of study, particularly between the 1993 and 2003 waves of the survey. We explore reasons for this broadening of the occupation distribution. We find that this has not led to an increase in reported mismatch between degree and occupation.

In this chapter, the authors review emerging literature on multidimensional, information age-related phenomena across different disciplines to derive common themes and topics. The authors then proceed to analyse recent developments in these fields to provide an interdisciplinary overview of the most disruptive challenges for multinational companies (MNCs) competing in the modern information age. These challenges include more efficient peer-to-peer communication between stakeholders, crowd-organisation, globalisation of value chains and the need to organise knowledge resources. The aim of the chapter is not to review all age research, but to identify fundamental uncertainties for MNCs and discuss strategies of tackling such information age phenomena from an international business perspective.

This chapter introduces the KontoSensor, a digital service offered by Deutsche Bank since September 2018, as an example of data processing using predictive analytics. We present the motivation behind this digital service, the use cases and methods currently implemented, the way they have been created, and measures to increase the usage of the KontoSensor. With KontoSensor, Deutsche Bank offers a digital service to its clients to analyze their transactions on their current accounts using methods from predictive analytics and to inform them when irregularities are found. Twelve months after the start, 90,000 clients are already using this service and experiencing the results of data science firsthand.

Motivated by empirical features that characterize cryptocurrency volatility data, the authors develop a forecasting strategy that can account for both model uncertainty and heteroskedasticity of unknown form. The theoretical investigation establishes the asymptotic optimality of the proposed heteroskedastic model averaging heterogeneous autoregressive (H-MAHAR) estimator under mild conditions. The authors additionally examine the convergence rate of the estimated weights of the proposed H-MAHAR estimator. This analysis sheds new light on the asymptotic properties of the least squares model averaging estimator under alternative complicated data generating processes (DGPs). To examine the performance of the H-MAHAR estimator, the authors conduct an out-of-sample forecasting application involving 22 different cryptocurrency assets. The results emphasize the importance of accounting for both model uncertainty and heteroskedasticity in practice.