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There is an increasing recognition in the public audit profession that the emergence of big data as well as the growing use of business analytics by audit clients has brought new opportunities and challenges. That is, should more complex business analytics beyond the customary analytical procedures be used in the engagement and if so, where? Which techniques appear to be most promising? This paper starts the process of addressing these questions by examining extant external audit research. 301 papers are identified that discuss some use of analytical procedures in the public audit engagement. These papers are then categorized by technique, engagement phase, and other attributes to facilitate understanding. This analysis of the literature is categorized into an External Audit Analytics (EAA) framework, the objective of which is to identify gaps, to provide motivation for new research, and to classify and outline the main topics addressed in this literature. Specifically, this synthesis organizes audit research, thereby offering guidelines regarding possible future research about approaches for more complex and data driven analytics in the engagement.

The purpose of this study is to map current knowledge on big data analytics (BDA) for supply chain risk management (SCRM) while providing future research needs.

The research team systematically reviewed 53 articles published between 2015 and 2021 and further contrasted the synthesis of these articles with four in-depth interviews with BDA startups that provider solutions for SCRM.

The analysis is framed in three perspectives. First, supply chain visibility – i.e. the number of tiers in the solutions; second, BDA analytical approach – descriptive, prescriptive or predictive approaches; third, the SCRM processes from risk monitoring to risk optimization. The study underlines that the forefront of innovation lies in multi-tiered, multi-directional solutions based on prescriptive BDA to support risk response and optimization (SCRM). In addition, we show that research on these innovations is scant, thus offering an important avenue for future studies.

This study makes relevant contributions to the field. We offer a theoretical framework that highlights the key relationships between supply chain visibility, BDA approaches and SCRM processes. Despite being at forefront of the innovation frontier, startups are still an under-explored agent. In times of major disruptions such as COVID-19 and the emergence of a plethora of new technologies that reshape businesses dynamically, future studies should map the key role of such actors to the advancement of SCRM.

Business analytics (BA) has attracted growing attention mainly due to the phenomena of big data. While studies suggest that BA positively affects organizational performance, there is a lack of academic research. The purpose of this paper, therefore, is to examine the extent to which top- and bottom-performing companies differ regarding their use and organizational facilitation of BA.

Hypotheses are developed drawing on the information processing view and contingency theory, and tested using multivariate analysis of variance to analyze data collected from 117 UK manufacture companies.

Top- and bottom-performing companies differ significantly in their use of BA, data-driven environment, and level of fit between BA and data-drain environment.

Extensive use of BA and data-driven decisions will lead to superior firm performance. Companies wishing to use BA to improve decision making and performance need to develop relevant analytical strategy to guide BA activities and design its structure and business processes to embed BA activities.

This study provides useful management insights into the effective use of BA for improving organizational performance.

This work consists of a critical reflection on the extent to which hospitality and tourism management scholars have accurately used the term “analytics” and its five types (i.e. descriptive, exploratory, predictive, prescriptive and cognitive analytics) in their research. Only cognitive analytics, the latest and most advanced type, is based on artificial intelligence (AI) and requires machine learning (ML). As cognitive analytics constitutes the cutting edge in industry application, this study aims to examine in depth the extent cognitive analytics has been covered in the literature.

This study is based on a systematic literature review (SLR) of the hospitality and tourism literature on the topic of “analytics”. The SLR findings were complemented by the results of an additional search query based on “machine learning” and “deep learning” that was used as a robustness check. Moreover, the SLR findings were triangulated with recent literature reviews on related topics (e.g. big data and AI) to generate additional insights.

The findings of this study show that: there is a growing and accelerating body of research on analytics; the literature lacks a consistent use of terminology and definitions related to analytics. Specifically, publications rarely use scientific definitions of analytics and their different types; although AI and ML are key enabling technologies for cognitive analytics, hospitality and tourism management research did not explicitly link these terms to analytics and did not distinguish cognitive analytics from other forms of analytics that do not rely on ML. In fact, the term “cognitive analytics” is apparently missing in the hospitality and tourism management literature.

This study generates a set of eight theoretical and three practical implications and advance theoretical and methodological recommendations for further research.

To the best of the authors’ knowledge, this is the first study that explicitly and critically examines the use of analytics in general, and cognitive analytics in particular, in the hospitality and tourism management literature.

This study aims to provide an in-depth understanding of big data analytics (BDA) in human resource management (HRM). The emergence of digital technology and the availability of large volume, high velocity and a great variety of data has forced the HRM to adopt the BDA in managing the workforce.

This paper evaluates the past, present and future trends of HRM through the bibliometric analysis of citation, co-citation and co-word analysis.

Findings from the analysis present significant research clusters that imply the knowledge structure and mapping of research streams in HRM. Challenges in BDA application and firm performances appear in all three bibliometric analyses, indicating this subject’s past, current and future trends in HRM.

Implications on the HRM landscape include fostering a data-driven culture in the workplace to reap the potential benefits of BDA. Firms must strategically adapt BDA as a change management initiative to transform the traditional way of managing the workforce toward adapting BDA as analytical tool in HRM decision-making.

This study presents past, present and future trends in BDA knowledge structure in human resources management.

The reported study aims at connecting the two crucial aspects of manufacturing of future, i.e. advanced analytics and digital simulation, with an objective to facilitate real-time control of manufacturing operations. The work puts forward a framework for designing prescriptive decision support system for a multi-machine manufacturing environment.

The schema of the decision support system design begins with the development of a simulation model for a manufacturing shop floor. The developed model facilitates prediction followed by prescription. As a connecting link between prediction and prescription mechanism, heuristics for intervention have been proposed. Sequential design and simulation-based demonstration of activities that span from development of a multi-machine shop floor model; a prediction mechanism and a scheme of intervention that ultimately leads to prescription generation are the highlights of the current work.

The study reveals that the effect of intervention on the observed predictors varies from one another. For a machine under observation, subject to same intervention scheme, while two of the predictive measures namely penalty and desirability stabilize after a certain point, a third measure, i.e. complexity, shows either an increase or decrease in percent change. The work objectively establishes that intervention plans have to be evaluated for every machine as well as for every environmental variable and emphasizes the need for dynamic evaluation and control mechanism.

The proposed prescriptive control mechanism has been demonstrated through a case of a high pressure die casting (HPDC) manufacturer.

The purpose of this paper is twofold. First, to combine a holistic model – in our case the balanced scorecard – with the time-driven activity-based costing model. The inspiration for this stems both from Kaplan and Norton and from the intense discussions and use of business analytics (BA) and performance management (PM). Second, to use numerical experiments – more specifically Monte Carlo simulation – to design and explore four hypothetical scenarios within such a holistic model. The paper also aims to emphasise the role played by statistics in increasing the confidence in using such a framework.

The author runs four numerical experiments using different assumptions to show how a decision-maker can improve the outcome by making small changes in the key performance indicator (KPI) input variables.

The paper gives recommendations for the assumptions that each decision-maker has to consider when setting out to conduct this kind of analysis. Small changes in some input variables may completely change the output and hence the decision result.

The paper shows why practitioners and researchers need to better understand the limitations of deterministic analysis to make realistic models when combining more accounting models. To choose the relevant probability distributions for the input resources is an important issue for the decision-maker as they have a very large impact on the result.

The real value of the paper lies in making students and practitioners as well as researchers aware of the opportunities for stochastic modelling and also to point at the problems and limitations of combining elements from BA with performance measurement and management.

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.