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This paper analyses the use of big data techniques in auditing, and finds that the practice is not as widespread as it is in other related fields. We first introduce contemporary big data techniques to promote understanding of their potential application. Next, we review existing research on big data in accounting and finance. In addition to auditing, our analysis shows that existing research extends across three other genealogies: financial distress modelling, financial fraud modelling, and stock market prediction and quantitative modelling. Auditing is lagging behind the other research streams in the use of valuable big data techniques. A possible explanation is that auditors are reluctant to use techniques that are far ahead of those adopted by their clients, but we refute this argument. We call for more research and a greater alignment to practice. We also outline future opportunities for auditing in the context of real-time information and in collaborative platforms and peer-to-peer marketplaces.

This study aimed to present the methodology of the text data analysis to establish marketing strategies for fintech companies in a practical way. Specifically, the methodology was presented to convert customers' review data, which consisted of the text data (unstructured data), to the numerical data (structured data) by using a text mining algorithm “Global Vectors for Word Representation,” abbreviated as “GloVe”; additionally, the authors presented the methodology to deploy the numerical data for marketing strategies with eliminate-reduce-raise-create (ERRC) value factor analytics.

First, the authors defined the background, features and contents of fintech services based on a review of related literature review. Additionally, they examined business strategies, the importance of social media for fintech services and fintech technology trends based on the literature review. Next, they analyzed the similarity between fintech-related keywords, which represent the trends in fintech services, and the text data related to fintech corporations and their services posted on Facebook and Twitter, which are two of the most popular social media globally, during the period 2017–2019. The similarity was then quantified and categorized in terms of the representative global fintech companies and the status of each fintech service sector. Furthermore, the similarity was visualized, and value elements were rebuilt using ERRC strategy analytics.

This study is meaningful in that it quantifies the degree of similarity between customers' responses, experiences and expectations regarding the rapidly growing global fintech firms' services and trends in fintech services.

This study suggests a practical way to apply in business by providing a method for transforming unstructured text data into structured numerical data it is measurable. It is expected that this study can be used as the basis for exploring sustainable development strategies for the fintech industry.

Accounting education is re-inventing itself as technology impacts the practical aspects of accounting in the real world and education tries to keep up. Big Data and data analytics have begun to influence elements of accounting including audit, accounting preparation, forensic accounting and general accountancy consulting. The purpose of this paper is to qualitatively analyse the current skills provision in accounting Masters courses linked to data analytics compared to academic and professional expectations of the same.

The academic expectations and requirements of the profession, related to the impact of Big Data and data analytics on accounting education were reviewed and compared to the current provisions of this accounting education in the form of Masters programmes. The research uses an exploratory, qualitative approach with thematic analysis.

Four themes were identified of the skills required for the effective use of Big Data and data analytics. These were: questioning and scepticism; critical thinking skills; understanding and ability to analyse and communicating results. Questioning and scepticism, as well as understanding and ability to analyse, were frequently cited explicitly as elements for assessment in various forms of accounting education in the Masters courses. However, critical thinking and communication skills were less explicitly cited in these accounting education programmes.

The research reviewed and compared current academic literature and the requirements of the professional accounting bodies with Masters programmes in accounting and data analytics. The research identified key themes relevant to the accounting profession that should be explicitly developed and assessed within accounting education for Big Data and data analytics at both university and professional levels. Further analysis of the in-depth curricula, as opposed to the explicitly stated topic coverage, could add to this body of research.

This paper considers the potential combined role of professional qualification examinations and master’s degrees in skills provision for future practitioners in accounting and data analysis. This can be used to identify the areas in which accounting education can be further enhanced by focus or explicit mention of skills that are both developed and assessed within these programmes.

The paper considers the interaction between academic and professional practice in the areas of accounting education, highlighting skills and areas for development for students currently considering accounting education and data analytics.

While current literature focusses on integrating data analysis into existing accounting and finance curricula, this paper considers the role of professional qualification examinations with Masters degrees as skills provision for future practitioners in accounting and data analysis.

A vast amount of complex data is being generated in the business environment, which enables support for decision-making through information processing and insight generation. The purpose of this study is to propose a process model for data-driven decision-making which provides an overarching methodology covering key stages of the business analytics life cycle. The model is then applied in two small enterprises using real customer/donor data to assist the strategic management of sales and fundraising.

Data science is a multi-disciplinary subject that aims to discover knowledge and insight from data while providing a bridge to data-driven decision-making across businesses. This paper starts with a review of established frameworks for data science and analytics before linking with process modelling and data-driven decision-making. A consolidated methodology is then described covering the key stages of exploring data, discovering insights and making decisions.

Representative case studies from a small manufacturing organisation and an independent hospice charity have been used to illustrate the application of the process model. Visual analytics have informed customer sales strategy and donor fundraising strategy through recommendations to the respective senior management teams.

The scope of this research has focused on customer analytics in small to medium-sized enterprise through two case studies. While the aims of these organisations are rather specific, they share a commonality of purpose for their strategic development, which is addressed by this paper.

Data science is shown to be applicable in the business environment through the proposed process model, synthesising micro- and macro-solution methodologies and allowing organisations to follow a structured procedure. Two real-world case studies have been used to highlight the value of the data-driven model in management decision-making.

The present study aims to investigate the influence of key factors on the success of women micro-entrepreneurs in select states of India.

An empirical study is carried out to understand the influence of the psychological, socio-cultural, skill and resource-related factors on the success of women entrepreneurs. To achieve the set goal, a comprehensive questionnaire is developed for collecting data and is analyzed using the t-test, the chi-square test and structural equation modeling.

The proposed model is validated using structural equation modeling, and the fitness values indicate that the model is fit to explain the entrepreneurial success of women entrepreneurs in India.

The result advocates that the participation of women entrepreneurs may be increased to not only improve national growth but also empower women in India.

In the context of the women micro-entrepreneurs, no such study covering such a vast area of India has been carried out.

Machine learning algorithms are useful to effectively analyse, and therefore automatically classify online reviews. The purpose of this paper is to demonstrate a novel text-mining framework and its potential for use in the classification of unstructured hotel reviews.

Well-known data mining methods (i.e. boosted decision trees (BDT), classification and regression trees (C&RT) and random forests (RF)) in conjunction with incorporating five-fold cross-validation are used to predict the star rating of the hotel reviews. To achieve this goal, extracted features are used to create a composite variable (CV) to deploy into machine learning algorithms as the main feature (variable) during the learning process.

BDT outperformed the other alternatives in the exact accuracy rate (EAR) and multi-class accuracy rate (MCAR) by reaching the accuracy rates of 0.66 and 0.899, respectively. Moreover, phrases such as “clean”, “friendly”, “nice”, “perfect” and “love” are shown to be associated with four and five stars, whereas, phrases such as “horrible”, “never”, “terrible” and “worst” are shown to be associated with one and two-star hotels, as it would be the intuitive expectation.

To the best of the knowledge, there is no study in the existent literature, which synthesizes the knowledge obtained from individual features and uses them to create a single composite variable that is powerful enough to predict the star rates of the user-generated reviews. This study believes that the proposed method also provides policymakers with a unique window in the thoughts and opinions of individual users, which may be used to augment the current decision-making process.

The main purpose of this paper is to review the related literature and propose a new decision‐support‐system (DSS) framework for marketing in the business‐to‐business (B2B) arena based on customer‐relationship management (CRM) and knowledge‐driven marketing to help related‐field graduate students and marketing managers.

Reviews a range of the most important works published between 1966 and 2004 in order to demonstrate both practical and theoretical aspects. The main method of this research is analytical and conceptual and the approach to this subject was to investigate the gap between marketing DSSs and analytical CRM.

Provides information about a customized marketing DSS in a B2B context, indicates related literature and frameworks and, finally, tests the ideas with a case study.

Outcomes and applications are identified for developing new activities in improving marketing decision making and marketing planning based on customer orientation and customer satisfaction.

Despite such interdependencies, the research in the fields of DSSs and CRM solutions has not adequately considered the integration of such systems. The novel contribution of this paper lies in integrating marketing DSSs and CRM with regard to knowledge‐driven marketing in B2B marketing, in both theoretical and practical aspects.

This paper aims to seek answers to a primary question: “How much do divergent leverage factors account for fluctuations in time-varying financial leverage in leading hospitality sub-sectors decomposed by four exclusive sub-portfolios?” In the path of seeking answers, this paper investigated the effects of both firm-specific and macroeconomic indicators to firms’ varying financial leverage in those primary sub-sectors overtime.

In each sub-sector portfolios, firms were sorted based on market-to-book values (Mktbk _it ) with median breakpoint percentiles. For hypothesis testing, this paper constructed panel regression models with firm fixed-effects to layout fluctuant financial leverage phenomenon engaged with a set of 11 leverage factors in each Mktbk _it sorted sub-sector portfolios.

Results exhibited assorted evidences. The bottom line was: firms with different market capitalization rates in each portfolio acted differently in regard to the magnitude of financial leverage across time.

The final sample of 415 firms in four sub-sector portfolios sufficiently embraced financial leverage composition in the hospitality industry across time. However, by reason of lack of data in the other intra-hospitality industries, such as gaming and/or cruise lines, findings did not represent the firms operated in those sub-industries.

This paper departed from the established context of the previous literature in the manner that it expects to add to the literature by demonstrating the core drivers causing the deviations in financial structure in four exclusive, hospitality industry sub-sector portfolios with varying leverage proxies overtime.

This study aims to explore the implementation of data analytics in the Big-Four accounting firms, including the extent to which a digital transformation is changing the work of financial auditors, why it is doing so and how these firms are managing the transformation process.

The authors conducted 23 interviews with 20 participants across four hierarchical levels from three of the Big-Four accounting firms in New Zealand.

The firms have entered the era of “smart audit systems”, in which auditors provide deep business insights that are communicated more effectively through data visualisation. The full potential, however, of data analytics depends not only on the transformation process within accounting firms but also on improvement in the quality of IT systems in client companies. The appointment of transformation managers, the recruitment of technology-savvy graduates and the provision of extensive training are helping to embed data analytics in the Big-Four firms. Accounting graduates in financial audit now need to show that they have the aptitude to become “citizen data scientists”.

The findings explain how data analytics is being embraced in the Big-Four auditing firms and underline the implications for those who work in them.

The findings challenge the “technological reluctance” thesis. In contrast, the authors observe a climate of positive attitudes towards new technology and accompanying actions in the Big-Four firms. The authors show how branches of the Big-Four firms operating distantly from their global headquarters, and with smaller economies of scale, are implementing the new technologies that characterise their global firms.