Elucidation of big data analytics in banking : a four-stage Delphi study

Purpose In today's networked business environment, a huge amount of data is being generated and processed in different industries, which banking is amongst the most important ones. The aim of this study is to understand and prioritize strategic applications, main drivers, and key challenges of implementing big data analytics in banks. Design/methodology/approach To take advantage of experts' viewpoints, the authors designed and implemented a four-round Delphi study. Totally, 25 eligible experts have contributed to this survey in collecting and analyzing the data. Findings The results revealed that the most important applications of big data in banks are “fraud detection” and “credit risk analysis.” The main drivers to start big data endeavors are “decision-making enhancement” and “new product/service development,” and finally the focal challenge threatening the efforts and expected outputs is “information silos and unintegrated data.” Originality/value In addition to stepping forward in the literature, the findings advance our understanding of the main managerial issues of big data in a dynamic business environment, by proposing effective further actions for both scholars and decision-makers

A person‐centred view of citizen participation in civic crowdfunding platforms: A mixed‐methods study of civic backers

AbstractCrowdfunding platforms have emerged as a promising contemporary means for mobilising collective civic actions to address local or social issues, improve community cohesion and develop the public good. This empirical study taps into the understudied civic crowdfunding platforms (CCP) developed to facilitate such actions, proposing, supporting and funding public‐interest projects through crowdsourcing and microfinancing. Previous studies have shown that individuals' characteristics affect their level of civic engagement with social issues. Considering the diversity of contributor motivations, we aim to shed light on the dynamics of emergent subpopulations of citizens who participate in CCPs. To this end, we use a sequential mixed‐methods approach to integrate our fuzzy set Qualitative Comparative Analysis (fsQCA) findings with the results of an in‐depth qualitative study, to gain rich and robust inferences and meta‐inferences. In Study 1 (n = 316), we used fsQCA to explore five distinctive configural profiles that display the heterogeneity of civic backers' motivations, including civic champions, prosocial advocates, normative supporters, reward seekers and regret‐averse contributors. In Study 2, we corroborated and complemented our fsQCA inferences through an extreme‐case study and identified four boundary conditions. Taken together, our inferences and meta‐inferences address the heterogeneity of motivations for participating in CCPs, by understanding and theorising about diverse profiles of citizen backers. Finally, we offer practical implications for successful civic crowdfunding initiatives.</jats:p

Semantic structures of business analytics research : applying text mining methods

Introduction. Business analytics has grown exponentially over the last decade, combining technologies, systems, practices and applications. It has attracted both practitioners and academics based on its capabilities to analyse critical business data to gain new insights about business operations and the market. The research goal of this paper is to identify major research topics and trends using text mining techniques. Method. We applied text mining methods such as co-word analysis and topic modelling to 1,024 published research documents in the business analytics field found in the Web of Science and Scopus databases. Analysis. We used term co-occurrence maps and latent Dirichlet allocation to mine and visualise data. Results. Findings showed that the knowledge structure of business analytics consists of three main themes: analytical methods, business analytics in practice, and business analytics value creation. Big data analytics, machine learning, and data science techniques are major topics. Further, business analytics research topics were identified and clustered into four thematic groups. Conclusions. The findings present a context for business analytics research development. They show recent trends in the field, namely a predominant interest in big data analytics, social networks, business value, the health sector, and customer retention