Conceptualization, operationalization, and validation of the digital data stream Readiness Index

This article describes how in their search for value creation, companies are investing considerable resources in so-called "Big Data" initiatives. A peculiar aspect of these initiatives is the increasing availability of real-time streams of data. Successfully leveraging these streams to extract value is emerging as a critical competence for the modern firm. Despite the significant attention received, scholarly research on Digital Data Stream (DDS) remains insufficient. More importantly, there are no specialized definitions and measurement instruments that can move the field forward by initiating a cumulative research tradition. This article can provide clarification on key definitions, differentiating DDS from Big Data. Drawing on the organizational readiness concept, the DDS readiness index develops as a measure of organizational readiness to exploit real-time digital data. This article will conceptualize, define, operationalize and validate the index. By identifying the four dimensions of mindset, skillset, dataset and toolset as the elements of the DDS readiness index and discussing its managerial and research implications

How to react to a shock? Effects of Airbnb hosts' choices and market segmentation at the time of Covid-19

We investigate the way service providers who operate on an online peer-to-peer (P2P) platform readapted their marketing choices to face the Covid-19 pandemic. Through an empirical investigation on a large dataset of Airbnb properties in Rome, observed from January 2018 to December 2020, we provide a threefold contribution by investigating how Airbnb hosts reacted to the Covid-19 pandemic shock, in terms of marketing choices, such as price adjustments and flexible cancellation policies; the direct effects of these choices on their economic returns; and how service providers on Airbnb reacted to address the new needs of their customers during the Covid-19 pandemic. The findings provide useful insights for researchers and practitioners and show that the adoption of combined marketing choices led to more than proportional effects on performances as it allowed Airbnb hosts to exploit profitable market segmentation mechanisms

Replication-induced DNA secondary structures drive fork uncoupling and breakage

Sequences that form DNA secondary structures, such as G-quadruplexes (G4s) and intercalated-Motifs (iMs), are abundant in the human genome and play various physiological roles. However, they can also interfere with replication and threaten genome stability. Multiple lines of evidence suggest G4s inhibit replication, but the underlying mechanism remains unclear. Moreover, evidence of how iMs affect the replisome is lacking. Here, we reconstitute replication of physiologically derived structure-forming sequences to find that a single G4 or iM arrest DNA replication. Direct single-molecule structure detection within solid-state nanopores reveals structures form as a consequence of replication. Combined genetic and biophysical characterisation establishes that structure stability and probability of structure formation are key determinants of replisome arrest. Mechanistically, replication arrest is caused by impaired synthesis, resulting in helicase-polymerase uncoupling. Significantly, iMs also induce breakage of nascent DNA. Finally, stalled forks are only rescued by a specialised helicase, Pif1, but not Rrm3, Sgs1, Chl1 or Hrq1. Altogether, we provide a mechanism for quadruplex structure formation and resolution during replication and highlight G4s and iMs as endogenous sources of replication stress

Managing changes initiated by industrial big data technologies : a technochange management model

With the adoption of Internet of Things and advanced data analytical technologies in manufacturing firms, the industrial sector has launched an evolutionary journey toward the 4th industrial revolution, or so called Industry 4.0. Industrial big data is a core component to realize the vision of Industry 4.0. However, the implementation and usage of industrial big data tools in manufacturing firms will not merely be a technical endeavor, but can also lead to a thorough management reform. By means of a comprehensive review of literature related to Industry 4.0, smart manufacturing, industrial big data, information systems (IS) and technochange management, this paper aims to analyze potential changes triggered by the application of industrial big data in manufacturing firms, from technological, individual and organizational perspectives. Furthermore, in order to drive these changes more effectively and eliminate potential resistance, a conceptual technochange management model was developed and proposed. Drawn upon theories reported in literature of IS technochange management, this model proposed four types of interventions that can be used to copy with changes initiated by industrial big data technologies, including human process intervention, techno-structural intervention, human resources management intervention and strategic intervention. This model will be of interests and value to practitioners and researchers concerned with business reforms triggered by Industry 4.0 in general and by industrial big data technologies in particular