Driving Innovation through Big Open Linked Data (BOLD): Exploring Antecedents using Interpretive Structural Modelling

YesInnovation is vital to find new solutions to problems, increase quality, and improve profitability. Big open linked data (BOLD) is a fledgling and rapidly evolving field that creates new opportunities for innovation. However, none of the existing literature has yet considered the interrelationships between antecedents of innovation through BOLD. This research contributes to knowledge building through utilising interpretive structural modelling to organise nineteen factors linked to innovation using BOLD identified by experts in the field. The findings show that almost all the variables fall within the linkage cluster, thus having high driving and dependence powers, demonstrating the volatility of the process. It was also found that technical infrastructure, data quality, and external pressure form the fundamental foundations for innovation through BOLD. Deriving a framework to encourage and manage innovation through BOLD offers important theoretical and practical contributions

A Sensemaking Approach to Europe\u27s Data Strategy

The use of data-driven tools provides a plethora of benefits and chal- lenges from a data policy-making perspective. This holds implications at organ- izational, national, and regional levels. At regional level the development of high- quality data-driven tools, among others, involve geo-political implications as they contribute to the region’s competitive advantage. In Europe, the European Commission has made attempts towards the formulation of a regional policy on data, aiming at fostering Europe’s global competitiveness and data sovereignty. Despite its geo-political impact, academic research on data strategy formulation at regional level remains scarce. While existing IS scholars have largely empha- sized on data strategies, the focus of these studies has been mainly at organiza- tional level. This paper motivates the need to go beyond data policies the organ- izational level and deepen our understanding on how data policies are formulated at regional level. Focusing on the case of the European regional area and the Eu- ropean Data Act formulation, the proposed research aims to shed light on how stakeholders make sense of the forthcoming data policy in Europe. The paper reflects on existing literature on data governance and availability and discusses its relevance to data policy formulation at regional level. It proposes sensemaking as a theoretical lens for this research and describes the methodology for the pro- posed research

Data-driven Innovation: Understanding the Direction for Future Research

In the contemporary age of information, organisations have realised the importance of “data” to innovate and thereby attain a competitive advantage. As a result, firms are more focused on understanding the potential to achieve data-driven innovation (DDI). Researchers too have focused on examining this novel phenomenon in a broader scope. In this study, we conducted a systematic and comprehensive review of the literature to understand the DDI phenomenon. The findings of this study benefit scholars in determining the gaps in the current body of knowledge as well as for practitioners to improve their data strategy to enhance and develop innovation capabilities

Developing human resource data risk management in the age of big data

In recent years, a great deal of attention has been devoted to trying to understand the risk challenges that arise in information management, and most recently, challenges that arise due to big data. In this article, the complexities of big data for employers are explored, drawing on a risk management on Human Resources (HR) perspective and normal accident theory (NAT) to illustrate the evolving characteristics of these complexities. The paper concludes with a series of recommendations that focus on education, design in data collection, and risk management, in the hope that these recommendations enable employers to better anticipate and address emerging big data challenges

Developing a Framework using Interpretive Structural Modeling for the Challenges of Digital Financial Services in India

Digital financial services (DFS) can expand the delivery of basic financial services to the poor through innovative technologies like mobile-phone-enabled solutions, electronic money models and digital payment platforms. By 2020, it is estimated that the mobile will have the potential to serve about 250 million people for financial services in India. Yet there remains a long way for India to go in digital finance. Realizing this, the objectives of the current research are to recognize various key challenges of DFS, to find contextual relationships between various challenges and to develop a hierarchy of challenges to promote DFS in India. The findings revealed 45 contextual relationships among the key challenges using experts’ inputs. Implementing interpretive structural modelling (ISM) indicated “Lack of literacy/digital literacy (C4)” and “Universal unavailability of Internet (C8)” as the key driving challenges coming on the way of using DFS

A Bibliometric Study of Social Media as a e-Government Public Services

Social media is used as an essential tool of government communication to the public and a form of service from e-government. This paper aims to identify the scientific development of social media themes in public communication services. This paper used bibliometric technic to investigate the broad overview of media studies on government public services. The analysis includes a citation and co-citation analysis, bibliographical coupling, and co-occurrence analysis. The data is taken from the Scopus database for 2010- 2021 with the source of the journal article type. The analysis results show the dynamics of increasing and decreasing the number of government social media studies. The bibliometric analysis also shows the most contributing journal sources, the most productive authors, the most cited journal articles, co-citations between authors, and clusters of themes that develop from social media themes used by the government in communicating and serving the public

Open data for open innovation:managing absorptive capacity in SMEs

Open Data (OD) utilisation has been encouraged by governments because of its potential to fuel digital innovation. Despite this, there is a paucity of study into the role of OD for SMEs, in contrast to the growing literature that has focused on the collection and sharing of OD by the public sector. As such, our study contributes to open innovation research by analysing the main capabilities needed to overcome existing barriers to successfully manage OD in SMEs. Building upon the recent SME-oriented OI literature and adopting an interpretative absorptive capacity framework, we analyse the data collected from 30 semi-structured interviews with experts working in UK organisations adopting OD-based OI strategies. We find a number of core factors that shape OD acquisition, assimilation, transformation and exploitation by SMEs. Results show that without the specific OD capabilities identified in our study, it will be difficult for SMEs to successfully use OD, which may explain why the uptake of OD by SMEs more broadly has so far been limited. These unique OD capabilities need to be better developed by OD using SMEs, if this \u2018raw material\u2019 for the digital economy is to be fully exploited

Driving innovation through big open linked data (BOLD): Exploring antecedents using interpretive structural modelling

Innovation is vital to find new solutions to prob- lems, increase quality, and improve profitability. Big open linked data (BOLD) is a fledgling and rapidly evolving field that creates new opportunities for innovation. However, none of the existing literature has yet considered the interrelation- ships between antecedents of innovation through BOLD. This research contributes to knowledge building through utilising interpretive structural modelling to organise nineteen factors linked to innovation using BOLD iden- tified by experts in the field. The findings show that almost all the variables fall within the linkage cluster, thus having high driving and dependence powers, dem- onstrating the volatility of the process. It was also found that technical infrastructure, data quality, and external pressure form the fundamental foundations for innovation through BOLD. Deriving a framework to encourage and man- age innovation through BOLD offers important theoretical and practical contributions

Exploring barriers of m-commerce adoption in SMEs in the UK: Developing a framework using ISM

YesIn the modern business era, mobile commerce (m-commerce) is changing the way the business is conducted using the Internet. However, the prominence of m-commerce among small and medium-sized enterprises (SMEs) in the UK is minimal. The purpose of this study is to evaluate the existing literature and to extend the research surrounding the barriers that prevent the adoption of m-commerce amongst SMEs. The study uses an Interpretive Structural Modelling (ISM) and MICMAC approach for guiding and helping managers of SMEs. Data was collected from an expert participant group each of whom had extensive knowledge of m-commerce. The findings represent the unstable nature of variables in the context of their impact on each other, their relationships, and themselves. The listed factors in the proposed framework and the interrelationships between them highlight the multi-dimensional element of m-commerce adoption prevention. This observation proves criticality of analysing data as a collective entity rather than viewing the barriers in isolation. The findings also indicated ‘perceived risk’ being a key barrier that demonstrates how personal opinions of the concept of adoption can have a great significance on the outcome and whether other variables will come into effect