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The purpose of this paper is to analyze, from a dynamic capabilities perspective, the role of big data analytics in supporting firms' innovation processes.

Relevant literature is reviewed and critically assessed. An interpretive methodology is used to analyze empirical data from interviews of big data analytics experts at firms within digitally related sectors.

This study shows how firms leverage big data to gain “richer” and “deeper” data at the inter-sections between the digital and physical worlds. The authors provide evidence for the importance of counterintuitive strategies aimed at developing innovative products, services or solutions with characteristics that may initially diverge, even significantly, from established customer/user needs.

The authors’ findings offer insights to help practitioners manage innovation processes in the physical world while taking investments in big data analytics into account.

The authors provide insights into the evolution of scholarly research on innovation directed toward opportunities to create a competitive advantage by offering new products, services or solutions diverging, even significantly, from established customer demand.

The concept of modularity has gained considerable traction in technology studies as a way to conceive, describe, and innovate complex systems, such as product design or organizational structures. In the recent literature, technological modularity has often been intertwined with business model innovation, and scholarship has started investigating how modularity in technology affects changes in business models, both at the cognitive and activity system levels. Yet we still lack a theoretical definition of what modularity is in the business model domain. Business model innovation also encompasses different possibilities of modelling businesses, which are not clearly understood nor classified. We ask when, how, and if modularity theory can be extended to business models in order to enable effective and efficient modelling. We distinguish theoretically between modularity for technology and for business models, and investigate the key processes of modularization and manipulation. We introduce the basic operations of business modelling via modular operators adapted from the technological modularity domain, using iconic examples to develop an analogical reasoning between modularity in technology and in business models. Finally, we discuss opportunities for using modularity theory to foster the understanding of business models and modelling, and develop a challenging research agenda for future investigations.

Digital business models are often designed for rapid growth, and some relatively young companies have indeed achieved global scale. However, despite the visibility and importance of this phenomenon, analysis of scale and scalability remains underdeveloped in management literature. When it is addressed, analysis of this phenomenon is often over-influenced by arguments about economies of scale in production and distribution. To redress this omission, this paper draws on economic, organization, and technology management literature to provide a detailed examination of the sources of scaling in digital businesses. We propose three mechanisms by which digital business models attempt to gain scale: engaging both non-paying users and paying customers; organizing customer engagement to allow self-customization; and orchestrating networked value chains, such as platforms or multi-sided business models. Scaling conditions are discussed, and propositions developed and illustrated with examples of big data entrepreneurial firms.

This study tests whether environmental awareness affects firm creation by using Google Trends data and a novel region-level data set from Italy.

Forward-looking entrepreneurs drive firm creation. The authors hypothesize that more environmentally conscious entrepreneurs will emerge as environmental awareness rises, increasing the number of green and energy firms. The authors test the prediction using Google Trends data and a novel region-level data set from Italy.

The authors find that not only the number of green and energy-innovative firms but also that of all innovative start-ups increases with rising environmental consciousness. The results imply some “innovation spillover” effects from green sectors to other industries with rising environmental awareness.

The paper hypothesizes that as environmental awareness rises, more environmental-conscious entrepreneurs will emerge, which would increase the number of green and energy firms. Robustness and falsification tests are also offered.

Although research on smart technologies explains their critical importance in sustainable business models (SBMs) (Mikalef et al., 2017), it remains unclear how organisations can embrace smart technologies to create and/or improve their sustainable business models. The purpose of this paper is to unravel and address the challenges of smart technologies to build and maintain a sustainable business model for organisations.

The research develops an empirical analysis through a case study approach. We have investigated the case of ENAV – an Italian air navigation service provider – and how this firm uses smart technologies in the creation of its successful SBM. After constructing a basic theory, the authors moved to evidence collection. The data analysis has adopted a qualitative approach based on a thematic analysis of the transcripts and related documents.

The findings from the case study support the idea that the business value and the strategic relevance of smart technologies still remain largely underestimated in SBM adoption (Mikalef et al., 2017). Case study findings suggest that until today smart technologies have played a minimal role in SBM adoption. However, the smart technologies show the potential to inform the SBM adoption process by contributing to corporate communication for external stakeholders and to the main dimensions of SBMs such as safety and security or the respect for social and environmental criteria in the supply chain.

This study seeks to support organisations and their directors to build and improve sustainable business models through smart technologies to maintain their competitive advantages. Specifically, our findings suggest that smart technologies can help organisations bridge the design–implementation gap of sustainable business models.

This research advances our understanding of the role of smart technologies by explaining how they can enhance sustainable business model adoption. Indeed, we offer a comprehensive view of the integration of insights from three different but related literature streams such as sustainability strategies, smart technologies and change management studies.

The academic inquiry of operational capabilities (OCs) has claimed focal interest in mainstream strategy research. Recent theoretical advances suggest these capabilities are a fundamental trigger to the identification and exploitation of entrepreneurial opportunities. However, the extant literature has been, at best, partial with regard to empirical insights that integrate OCs with entrepreneurial opportunities. Addressing this theoretical lacuna from the standpoint of organisational learning theory, the purpose of this paper is to investigate the interplay between OCs and entrepreneurial opportunities and their overall impact on exporting SME’s growth.

To realise the empirical aims a descriptive research design employing a survey methodology was used. The authors are generated data from a sample of 117 exporting small and medium-sized enterprises (SME) in Croatia. Ordinary least squares regression was employed to test the conceptual model and five derived hypotheses.

The findings demonstrate that market-sensing capabilities are vital in enhancing exporting SME’s opportunity recognition capacity and the rate of international opportunity exploitation that leads to increased firm growth. Also, study findings show that the link between the increased rate of international opportunity exploitation contributes more to the growth when exporting SMEs have highly developed adaptive and innovation capabilities.

This study brings to surface some novel insights about how exporting SMEs can better design their export marketing strategy. The results suggest, OCs occupy key role in the exporting SMEs international venturing efforts by delivering higher growth.

The study contributes to the export marketing strategy field by offering empirical evidence that both capability and opportunity-based views should be assessed simultaneously in explaining exporting SME’s competitiveness. Finally, we offer valuable theoretical and practical implications as well as avenues for further research that should extend our knowledge in the field.

Decentralized finance (DeFi), enabled by blockchain, could bring about a new financial system, where peers will interact directly, with little or no place for traditional intermediation. However, some crucial tasks cannot be left solely to an algorithm and, consequently, most DeFi applications still require human decisions. The aim of this research is to assess the role of intermediation in the light of DeFi, analysing how humans and algorithms will interact.

The authors based their work on a twofold qualitative methodology, first analysing publicly available secondary data, particularly from white papers and DeFi Pulse (a website providing data on DeFi solutions) and then running two focus group discussions.

DeFi does not eliminate financial intermediation, but enables it to be performed in new ways, where decentralization means that no single entity can hold too much power or monopoly. DeFi has, however, inherited risks from the underlying technologies that unintentionally facilitate illegal behaviour and can hamper the authorities’ supervision. The complex duality algorithm- vs human-based actions will not be solved indisputably in favour of the former, as DeFi solutions can range from requiring algorithms to play a dominant role, to enabling greater human interaction by actively involving more people.

This research contributes to the emerging debate between algorithm- and human-based intermediation, especially in relation to the standing literature on financial intermediation, where considerations made in the light of the newest theories on blockchain and DeFi are still scarce.

The firms’ adoption and improvement of big data analytics capabilities to improve economic and environmental performance have recently increased. This makes it important to discover the underlying mechanism influencing the association between big data analytics (BDA) and economic and environmental performance, which is missing in the existing literature. The present study discovers the indirect effect of green innovation (GI) and the moderating role of corporate green image (CgI) on the impact of BDA capabilities, including big data management capability (MC) and big data talent capability (TC), on economic and environmental performance.

A time-lagged design was employed to collect data from 417 manufacturing firms, and study hypotheses were evaluated using Mplus.

The empirical outcomes indicate that both BDA capabilities of firms significantly influence green innovation (GI), which significantly mediates the relationship between BDA and economic and environmental performance. Our findings also revealed that CgI strengthened the effect of GI on economic and environmental performance. The empirical evidence provides important theoretical and practical repercussions for manufacturing SMEs and policymakers.

This study contributes to the literature on BDA by empirically exploring the effects of MC and TC on improving the EcP and EnP of manufacturing firms. It does so through the indirect impact of GIs and the moderating effect of CgI, thereby extending the Dynamic capabilities view (DCV) paradigm.

This paper aims to analyse the bibliometric characteristics of the ISO 50001 publication, map the state of the art of the research topic and identify future research issues.

This research is a bibliometric study. The data were collected from Scopus. Both performance and science mapping analysis were performed.

The research results showed the top author, paper and country of ISO 50001 publications. There are four author collaboration clusters and five country collaboration clusters. Eight research themes were mapped into four quadrants based on the density and centrality. The bibliometric coupling analysis showed six research clusters. Finally, the research issues were mapped. The implications were discussed.

This research gave several implications for researchers, practitioners and public policymakers. For researchers, the bibliometric analysis provides several research issues that can be followed up by future research. For practitioners, the bibliometric analysis showed that applied tools and methods that can assist the implementation of ISO 50001-based energy management have been developed. For public policymakers, the bibliometric analysis offered the knowledge structure on ISO 50001 that can be used in public policymaking development. The author collaboration cluster and the bibliometric coupling cluster can be used to trace the scientific information that is needed as the foundation of public policy.

Many ISO 50001 studies have been performed. However, based on the search in several main academic scientific paper databases, there is no bibliometric study on the research topic. This is the first bibliometric study on ISO 50001 publication. This study takes a holistic approach combining performance analysis and science mapping analysis that includes elaborated thematic mapping and evolution analysis.