Search results

This paper aims to conceptualize the digital behavior of startups and investigate the emerging behaviors about digital strategies of the Italian startup firms enrolled in the Startup Act policy initiative. Digital technologies were divided into intra- and inter-organizational digital infrastructures, and this categorization offers startups the opportunity to identify a set of enabling technologies that could be used to improve their digital strategies.

An empirical analysis has been conducted to investigate the degree of adoption of digital intra- and inter-organizational digital infrastructures in the entire population of 6,178 Italian firms listed in the Register of Innovative Startups.

The paper proposes a taxonomy bringing together four startup behaviors for adopting digital technologies: digital follower, technical influencer, social influencer and digital leader. From the perspective of policy makers, considering the financial efforts that public authorities are supporting in the last decade, implications are mainly concerned with policy measures aimed both to reinforce the overall adoption of digital technologies and to develop a balanced adoption of intra- and inter-organizational digital infrastructures.

Measures addressed to support female and foreign entrepreneurship could be useful to support a more dynamic and well-balanced cultural and racial contamination, thus improving the adoption of digital tools.

The purpose of this study is to contribute to research concerning the role of digital infrastructure in digital government. This is done by answering the research question: how does digital infrastructuring constrain ambidexterity in public sector organizations?

The research is designed as a clinical inquiry in a large Swedish municipality, involving data collection in the form of interviews and internal documents. The method of analysis involves both exploring generative mechanisms in digital infrastructuring and theorizing on the findings based on previous literature.

The findings identify four generative mechanisms through which stability and change in digital infrastructuring constrain ambidexterity in terms of both efficiency (exploitation) and innovation (exploration).

This study’s limitations are related to international and intersectoral transferability and risks associated with its approach to clinical inquiry. The main implications are its contribution to the literature on how stability counteracts not only innovation but also efficiency and how change counteracts not only efficiency but also innovation.

This study identifies clear generative mechanisms that should be avoided by managers striving for digital government, and it offers clear recommendations for said managers regarding how to avoid them.

This study offers implications for national-level digital infrastructure policy and contributes to efforts to increase the capabilities of digital government.

As two of the four identified generative mechanisms are novel contributions, this study offers a concrete addition to existing research. This study has resulted in factual change in the studied organization as well as at the national level through successful dissemination of the findings for both policy and practice in other public sector organizations.

The paper aims to explore the value generated by a specific configuration of a smart city's infrastructure by proposing a comparison between a silos configuration versus a crowd configuration at the data storage and processing level.

A system dynamics simulation is adopted to determine and compare the value created by the two configurations of smart city's infrastructure. The simulation outlines the flow of data and their positive and negative feedback that reinforce and hinder the smart city value generation.

The results demonstrate the huge impact of the availability of data for App developers when crowdsourcing configuration is adopted. Furthermore, results unveil the potential in value generation of a crowdsourcing smart city platform configuration compared to a silos architecture.

The authors have proposed a comparison between two alternative smart city digital platform configurations. The paper seeks to test the magnitude of the pros and cons of a crowdsourcing approach in setting up a smart city digital platform. The paper provides new guidelines for improving the data management of smart cities.

The aim of this study was to identify interventions for and mechanisms of the digital transformation of purchasing and supply management (PSM) processes. The digital transformation of tactical and operational PSM processes has often progressed slowly despite the solid knowledge of advanced technologies.

This study used a qualitative exploratory approach based on 14 interviews with PSM executives from firms that are continuously working toward using advanced technologies in their PSM processes but have not yet gained full strategic benefits from digital transformation.

This study formulates five propositions regarding interventions and mechanisms that can positively influence the digital transformation of PSM processes. The main intervention in this regard is the renewal of data infrastructure, including platforms. PSM-related data should meet needs from both tactical and operational viewpoints. When applications serve as a source of data, they support digital transformation. Mechanisms such as supplier measurement and process improvement are outcomes of the digital transformation of PSM processes.

This study highlights the importance of common data sets for tactical and operational purchasing. These purchasing data should be owned and served by a cross-functional team. To create this interoperability, a firm needs global governance of open standards.

This study makes a theoretical contribution to the discussion of what kind of interventions positively influence on the digital transformation of PSM processes. Specifically, this study explains the integration needs of data and applications.

As the size of the population is growing and the capacity of the planet Earth is limited, human beings are searching for sustainable and technology-enabled solutions to support society, ecology and economy. One of the solutions has been developing smart sustainable cities. Smart sustainable cities are cities as systems, where their infrastructure, different subsystems and different functional domains are virtually connected to the information and communication technologies (ICT) and internet via sensors and devices and the Internet of Things (IoT), to collect and process real-time Big Data and make efficient, effective and sustainable solutions for a democratic and liveable city for its various stakeholders. This chapter explores the concepts and practices of sustainable smart cities across the globe and explores the use of technologies such as IoT, Blockchain technology and Cloud computing, etc. their challenges and then presents a view on business models for sustainable smart cities.

The COVID-19 pandemic has disrupted the ‘real’ world and substantially impacted the virtual world and thus the Internet ecosystem. It has caused a significant exogenous shock that offers a wealth of natural experiments and produced new data about broadband, clouds, and the Internet in times of crisis. In this chapter, we characterise and evaluate the evolving impact of the global COVID-19 crisis on traffic patterns and loads and the impact of those on Internet performance from multiple perspectives. While we place a particular focus on deriving insights into how we can better respond to crises and better plan for the post-COVID-19 ‘new normal’, we analyse the impact on and the responses by different actors of the Internet ecosystem across different jurisdictions. With a focus on the USA and Europe, we examine the responses of both public and private actors, with the latter including content and cloud providers, content delivery networks, and Internet service providers (ISPs). This chapter makes two contributions: first, we derive lessons learned for a future post-COVID-19 world to inform non-networking spheres and policy-making; second, the insights gained assist the networking community in better planning for the future.

The purpose of this paper is to identify sensing, seizing and reconfiguring routines of dynamic capabilities that enable digital transformation in firms.

A qualitative approach is used. Representatives from a firm going through digital transformations are interviewed, and focus groups have been carried out with a consultancy firm experienced in giving advice to firms going through digital transformation.

Six routines identified as relevant specifically for digital transformation are identified. These are cross-industrial digital sensing, inside-out digital infrastructure sensing, digital strategy development, determination of enterprise boundaries, decomposition of digital transformation into specified projects and creation of a unified digital infrastructure.

The authors provide direction for managers on how to approach digital transformation. In relation to previous research, the authors provide more specific guidance regarding how to reconfigure the organization in digital transformation.

The paper uses a novel context for digital transformation and complements the very few studies available using dynamic capabilities to understand digital transformation.

Digital markets are increasingly constructed by an interplay between (non)human market actors, i.e. through algorithms, but, simultaneously, fragmented through platformization. This study aims to explore how interactional dynamics between (non)human market actors co-codify markets through expressive and networked content across social media platforms.

This study applies digital methods as cross-platform analysis to analyze two data sets retrieved from YouTube and Instagram using the keywords “sustainable fashion” and #sustainablefashion, respectively.

The study shows how interactional dynamics between (non)human market actors, co-codify markets across two social media platforms, i.e. YouTube and Instagram. The authors introduce the notion of sticky market webs of connection, illustrating how these dynamics foster cross-platform market codification through relations of exteriority.

Research implications highlight the necessity to account for all involved entities, including digital infrastructure in digital markets and the methodological potential of cross-platform analyses.

Practical implications highlight considerations managers should take into account when designing market communication for digital markets composed of (non)human market actors.

Social implications highlight the possible effects of (non)human market co-codification on markets and consumer culture, and corresponding countermeasures.

This study contributes to an increased understanding of digital market dynamics by illuminating interdependent market co-codification dynamics between (non)human market actors, and how these dynamics (de)territorialize digital market assemblages through relations of exteriority across platforms.

Artificial intelligence (AI) has started to receive attention in the field of digital entrepreneurship. However, few studies propose AI-based models aimed at assisting entrepreneurs in their day-to-day operations. In addition, extant models from the product design literature, while technically promising, fail to propose methods suitable for opportunity development with high level of uncertainty. This study develops and tests a predictive model that provides entrepreneurs with a digital infrastructure for automated testing. Such an approach aims at harnessing AI-based predictive technologies while keeping the ability to respond to the unexpected.

Based on effectuation theory, this study identifies an AI-based, predictive phase in the “build-measure-learn” loop of Lean startup. The predictive component, based on recommendation algorithm techniques, is integrated into a framework that considers both prediction (causal) and controlled (effectual) logics of action. The performance of the so-called active learning build-measure-predict-learn algorithm is evaluated on a data set collected from a case study.

The results show that the algorithm can predict the desirability level of newly implemented product design decisions (PDDs) in the context of a digital product. The main advantages, in addition to the prediction performance, are the ability to detect cases where predictions are likely to be less precise and an easy-to-assess indicator for product design desirability. The model is found to deal with uncertainty in a threefold way: epistemological expansion through accelerated data gathering, ontological reduction of uncertainty by revealing prior “unknown unknowns” and methodological scaffolding, as the framework accommodates both predictive (causal) and controlled (effectual) practices.

Research about using AI in entrepreneurship is still in a nascent stage. This paper can serve as a starting point for new research on predictive techniques and AI-based infrastructures aiming to support digital entrepreneurs in their day-to-day operations. This work can also encourage theoretical developments, building on effectuation and causation, to better understand Lean startup practices, especially when supported by digital infrastructures accelerating the entrepreneurial process.