Big data and data analytics in auditing: in search of legitimacy

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Federica De Santis ◽  
Giuseppe D’Onza
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Audit Quality ◽  
Empirical Studies ◽  
Innovation Process ◽  
Structured Interviews ◽  
Senior Managers ◽  
Content Type ◽  
Audit Strategy ◽  
Audit Standards

Purpose This study aims to analyze the utilization of big data and data analytics (BDA) in financial auditing, focusing on the process of producing legitimacy around these techniques, the factors fostering or hindering such process and the action auditors take to legitimate BDA inside and outside the audit community. Design/methodology/approach The analysis bases on semi-structured interviews with partners and senior managers of Italian audit companies. Findings The BDA’s legitimation process is more advanced in the audit professional environment than outside the audit community. The Big Four lead the BDA-driven audit innovation process and BDA is used to complement traditional audit procedures. Outside the audit community, the digital maturity of audit clients, the lack of audit standards and the audit oversight authority’s negative view prevent the full legitimation of BDA. Practical implications This research highlights factors influencing the utilization of BDA to enhance audit quality. The results can, thus, be used to enhance the audit strategy and to innovate audit practices by using BDA as a source of adequate audit evidence. Audit regulators and standards setters can also use the results to revise the current auditing standards and guidance. Originality/value This study adds to the literature on digital transformation in auditing by analyzing the legitimation process of a new audit technique. The paper answers the call for more empirical studies on the utilization of BDA in financial auditing by analyzing the application of such techniques in an unexplored operational setting in which auditees are mainly medium-sized enterprises and family-run businesses.

Factors affecting the diffusion of integrated reporting – a UK FTSE 100 perspective

2015 ◽  
Vol 6 (2) ◽  
pp. 190-223 ◽  
Author(s):  
Fiona Ann Robertson ◽  
Martin Samy
Keyword(s):  
Diffusion Of Innovation ◽  
Empirical Studies ◽  
Small Sample ◽  
Relative Advantage ◽  
Research Approach ◽  
Structured Interviews ◽  
Integrated Reporting ◽  
Senior Managers ◽  
Content Type ◽  
Reporting Practices

Purpose – This study seeks to investigate the likely adoption of integrated reporting (IR), in addition to highlighting the limitations of current reporting practices. In particular, the analysis in this study used the characteristics of diffusion of innovation theory to investigate how perceptions of IR as offering a relative advantage over existing practices; its compatibility to existing organisational values, past experiences and needs; and its perceived complexity impacted on the adoption and diffusion of IR. Design/methodology/approach – Methodology was based on a content analysis of 22 UK FTSE 100 annual and sustainability reports across industries. To build a phenomenological triangulation research approach, semi-structured interviews were conducted with ten senior managers to ascertain their perceptions of current SR practices and IR. Findings – The analysis in this study revealed that low/medium levels of linkages exist between the majority of reports in the sample, thus limiting their usefulness. Based on these findings, this study suggests that senior managers perceive IR as having a relative advantage over existing practice. Overall, the senior managers interviewed were supportive of IR and this research revealed that many companies are starting to integrate their reporting along IR guidelines. This study further identifies factors that are likely to impact on more widespread diffusion of IR. Research limitations/implications – The sample size to assess linkages between reports was based on a sample of company reports across industries to give a balanced view of reporting practices. This could be viewed as a limitation as it was not a representative sample of the population as a whole. Another limitation of this research study was the small sample of organisations that participated in the interview process, and the single country focus. Practical implications – This study has identified several factors that were likely to impact on wider spread diffusion of IR, which should be of interest to practitioners in this field as well as those considering the adoption of IR. Originality/value – As an emerging phenomenon, there are few empirical studies exploring IR practices and perceptions. To the best of the authors' knowledge, this is the first paper that provides some insights into IR from a UK perspective.

Big data for business management in the retail industry

2019 ◽  
Vol 57 (8) ◽  
pp. 1980-1992 ◽  
Author(s):  
Gabriele Santoro ◽  
Fabio Fiano ◽  
Bernardo Bertoldi ◽  
Francesco Ciampi
Keyword(s):  
Big Data ◽  
Empirical Studies ◽  
Business Management ◽  
Data Gathering ◽  
Secondary Data ◽  
Retail Industry ◽  
Structured Interviews ◽  
Content Type ◽  
Potential Benefits ◽  
Shed Light

Purpose The purpose of this paper is to shed light on how big data deployment transforms organizational practices, thereby generating potential benefits, in a specific industry: retail. Design/methodology/approach To achieve the paper’s goal, the authors have conducted several semi-structured interviews with marketing managers of four retailers in Italy, and researched secondary data to get a broader picture of big data deployment in the organizations. Findings Data analysis helped identify specific aspects related to big data deployment, data gathering methods, required competences and data sharing approaches. Originality/value Despite the growing interest in big data in various fields of research, there are still few empirical studies on big data deployment in organizations in the management field, and even fewer on specific sectors. This research provides evidence of specific areas of analysis concerning big data in the retail industry.

Dave Snowden on KM and big data/analytics: interview with David J. Pauleen

2017 ◽  
Vol 21 (1) ◽  
pp. 12-17 ◽  
Author(s):  
David J. Pauleen
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Theory And Practice ◽  
Content Type ◽  
Face To Face ◽  
The Relationship ◽  
A Company ◽  
Practical Implications ◽  
Scientific Officer

Purpose Dave Snowden has been an important voice in knowledge management over the years. As the founder and chief scientific officer of Cognitive Edge, a company focused on the development of the theory and practice of social complexity, he offers informative views on the relationship between big data/analytics and KM. Design/methodology/approach A face-to-face interview was held with Dave Snowden in May 2015 in Auckland, New Zealand. Findings According to Snowden, analytics in the form of algorithms are imperfect and can only to a small extent capture the reasoning and analytical capabilities of people. For this reason, while big data/analytics can be useful, they are limited and must be used in conjunction with human knowledge and reasoning. Practical implications Snowden offers his views on big data/analytics and how they can be used effectively in real world situations in combination with human reasoning and input, for example in fields from resource management to individual health care. Originality/value Snowden is an innovative thinker. He combines knowledge and experience from many fields and offers original views and understanding of big data/analytics, knowledge and management.

Strategic issues of big data analytics applications for managing health-care sector: a systematic literature review and future research agenda

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Rajesh Kumar Singh ◽  
Saurabh Agrawal ◽  
Abhishek Sahu ◽  
Yigit Kazancoglu
Keyword(s):  
Health Care ◽  
Big Data ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Health Care Sector ◽  
Future Research ◽  
Research Gaps ◽  
Content Type

PurposeThe proposed article is aimed at exploring the opportunities, challenges and possible outcomes of incorporating big data analytics (BDA) into health-care sector. The purpose of this study is to find the research gaps in the literature and to investigate the scope of incorporating new strategies in the health-care sector for increasing the efficiency of the system.Design/methodology/approachFora state-of-the-art literature review, a systematic literature review has been carried out to find out research gaps in the field of healthcare using big data (BD) applications. A detailed research methodology including material collection, descriptive analysis and categorization is utilized to carry out the literature review.FindingsBD analysis is rapidly being adopted in health-care sector for utilizing precious information available in terms of BD. However, it puts forth certain challenges that need to be focused upon. The article identifies and explains the challenges thoroughly.Research limitations/implicationsThe proposed study will provide useful guidance to the health-care sector professionals for managing health-care system. It will help academicians and physicians for evaluating, improving and benchmarking the health-care strategies through BDA in the health-care sector. One of the limitations of the study is that it is based on literature review and more in-depth studies may be carried out for the generalization of results.Originality/valueThere are certain effective tools available in the market today that are currently being used by both small and large businesses and corporations. One of them is BD, which may be very useful for health-care sector. A comprehensive literature review is carried out for research papers published between 1974 and 2021.

Value co-creation in the B2B context: a diagnosis of knowledge management based on multiple case studies

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Andrei Bonamigo ◽  
Camila Guimarães Frech ◽  
Ana Carolina Custódio Lopes
Keyword(s):  
Empirical Studies ◽  
Structured Interviews ◽  
Content Type ◽  
Knowledge Based ◽  
Multiple Case ◽  
Multiple Case Studies ◽  
Knowledge Asymmetry ◽  
Business To Business ◽  
Practical Implications

Purpose This study aims to empirically investigate how organizations delivering services in business-to-business relations deal with the boundary paradox and knowledge asymmetry in value co-creation. Design/methodology/approach This study adopted a qualitative multiple case study strategy. Datas were gathered through 13 semi-structured interviews that were then analyzed through the content analysis. Findings The authors identified three mechanisms that organizations use to deal with the boundary paradox and two strategies to handle the knowledge asymmetry. Research limitations/implications First, no opportunities were afforded to involve more participants. Second, owning to confidentiality reasons, not all organizations provided us documents to be analyzed. Practical implications The findings guide managers in balancing the use of contracts and trust in inter-firm collaborations and fostering the learning of customers. Also, insights to protect knowledge based on the paradox of openness in value co-creation. Originality/value This study’s findings address the gap in value co-creation literature concerning the lack of empirical studies.

The role of big data analytics capabilities in bolstering supply chain resilience and firm performance: a dynamic capability view

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mohamad Bahrami ◽  
Sajjad Shokouhyar
Keyword(s):  
Risk Management ◽  
Big Data ◽  
Supply Chain ◽  
Firm Performance ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Content Type ◽  
Supply Chain Resilience ◽  
Management Culture

PurposeBig data analytics capability (BDAC) can affect firm performance in several ways. The purpose of this paper is to understand how BDA capabilities affect firm performance through supply chain resilience in the presence of the risk management culture.Design/methodology/approachThe study adopted a cross-sectional approach to collect survey-based responses to examine the hypotheses. 167 responses were collected and analyzed using partial least squares in SmartPLS3. The respondents were generally senior IT executives with education and experience in data and business analytics.FindingsThe results show that BDA capabilities increase supply chain resilience as a mediator by enhancing innovative capabilities and information quality, ultimately leading to improved firm performance. In addition, the relationship between supply chain resilience and firm performance is influenced by risk management culture as a moderator.Originality/valueThe present study contributes to the relevant literature by demonstrating the mediating role of supply chain resilience between the BDA capabilities relationship and firm performance. In this context, some theoretical and managerial implications are proposed and discussed.

Service innovation in the hotel industry: the dynamic capabilities view

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Babak Ziyae ◽  
Hossein Sadeghi ◽  
Maryam Golmohammadi
Keyword(s):  
Dynamic Capabilities ◽  
New Technology ◽  
Hotel Industry ◽  
Service Innovation ◽  
Innovation Process ◽  
Snowball Sampling ◽  
Structured Interviews ◽  
Content Type ◽  
Human Labor ◽  
It Training

Purpose Consistent with the dynamic capabilities view tenets, this paper aims to conceptualize a theoretical framework of service innovation in the hotel industry. Design/methodology/approach This study uses a qualitative method with a content analysis approach. The data were collected using a snowball sampling method and semi-structured interviews with 14 experts in Tehran's hotel industry. Findings The findings demonstrate that the most significant factors are using the new technology, keeping up with it, training human labor, being up-to-date and adopting new infrastructures. Results also reveal that improper management and lack of knowledge are the most critical factors behind service innovation failure in the hotel industry. Regarding the infrastructures needed to develop service innovation in the hotel industry, the results show that adopting the newest technology in diverse aspects, human infrastructure, the capital and appropriate space and place are the key factors. Originality/value This paper contributes to the literature by linking the service innovation perspective to the dynamic capabilities view. It explains how hotels can enhance service innovation to gain a competitive advantage. Therefore, both academicians and hoteliers can develop action plans by selecting and managing the service innovation process.

An experimental investigation of the impact of using big data analytics on customers’ performance measurement

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Marwa Rabe Mohamed Elkmash ◽  
Magdy Gamal Abdel-Kader ◽  
Bassant Badr El Din
Keyword(s):  
Big Data ◽  
Performance Measurement ◽  
Data Analytics ◽  
Positive Impact ◽  
Big Data Analytics ◽  
Unstructured Data ◽  
Future Research ◽  
Web Based ◽  
Content Type ◽  
The Impact

Purpose This study aims to investigate and explore the impact of big data analytics (BDA) as a mechanism that could develop the ability to measure customers’ performance. To accomplish the research aim, the theoretical discussion was developed through the combination of the diffusion of innovation theory with the technology acceptance model (TAM) that is less developed for the research field of this study. Design/methodology/approach Empirical data was obtained using Web-based quasi-experiments with 104 Egyptian accounting professionals. Further, the Wilcoxon signed-rank test and the chi-square goodness-of-fit test were used to analyze data. Findings The empirical results indicate that measuring customers’ performance based on BDA increase the organizations’ ability to analyze the customers’ unstructured data, decrease the cost of customers’ unstructured data analysis, increase the ability to handle the customers’ problems quickly, minimize the time spent to analyze the customers’ data and obtaining the customers’ performance reports and control managers’ bias when they measure customer satisfaction. The study findings supported the accounting professionals’ acceptance of BDA through the TAM elements: the intention to use (R), perceived usefulness (U) and the perceived ease of use (E). Research limitations/implications This study has several limitations that could be addressed in future research. First, this study focuses on customers’ performance measurement (CPM) only and ignores other performance measurements such as employees’ performance measurement and financial performance measurement. Future research can examine these areas. Second, this study conducts a Web-based experiment with Master of Business Administration students as a study’s participants, researchers could conduct a laboratory experiment and report if there are differences. Third, owing to the novelty of the topic, there was a lack of theoretical evidence in developing the study’s hypotheses. Practical implications This study succeeds to provide the much-needed empirical evidence for BDA positive impact in improving CPM efficiency through the proposed framework (i.e. CPM and BDA framework). Furthermore, this study contributes to the improvement of the performance measurement process, thus, the decision-making process with meaningful and proper insights through the capability of collecting and analyzing the customers’ unstructured data. On a practical level, the company could eventually use this study’s results and the new insights to make better decisions and develop its policies. Originality/value This study holds significance as it provides the much-needed empirical evidence for BDA positive impact in improving CPM efficiency. The study findings will contribute to the enhancement of the performance measurement process through the ability of gathering and analyzing the customers’ unstructured data.

Big data analytics capabilities and organizational performance: the mediating effect of dual innovations

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Xiaofeng Su ◽  
Weipeng Zeng ◽  
Manhua Zheng ◽  
Xiaoli Jiang ◽  
Wenhe Lin ◽  
...  
Keyword(s):  
Big Data ◽  
Organizational Performance ◽  
Empirical Analysis ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Mediating Effect ◽  
Content Type ◽  
Mediating Role ◽  
The Relationship

PurposeFollowing the rapid expansion of data volume, velocity and variety, techniques and technologies, big data analytics have achieved substantial development and a surge of companies make investments in big data. Academics and practitioners have been considering the mechanism through which big data analytics capabilities can transform into their improved organizational performance. This paper aims to examine how big data analytics capabilities influence organizational performance through the mediating role of dual innovations.Design/methodology/approachDrawing on the resource-based view and recent literature on big data analytics, this paper aims to examine the direct effects of big data analytics capabilities (BDAC) on organizational performance, as well as the mediating role of dual innovations on the relationship between (BDAC) and organizational performance. The study extends existing research by making a distinction of BDACs' effect on their outcomes and proposing that BDACs help organizations to generate insights that can help strengthen their dual innovations, which in turn have a positive impact on organizational performance. To test our proposed research model, this study conducts empirical analysis based on questionnaire-base survey data collected from 309 respondents working in Chinese manufacturing firms.FindingsThe results support the proposed hypotheses regarding the direct and indirect effect that BDACs have on organizational performance. Specifically, this paper finds that dual innovations positively mediate BDACs' effect on organizational performance.Originality/valueThe conclusions on the relationship between big data analytics capabilities and organizational performance in previous research are controversial due to lack of theoretical foundation and empirical testing. This study resolves the issue by provides empirical analysis, which makes the research conclusions more scientific and credible. In addition, previous literature mainly focused on BDACs' direct impact on organizational performance without making a distinction of BDAC's three dimensions. This study contributes to the literature by thoroughly introducing the notions of BDAC's three core constituents and fully analyzing their relationships with organizational performance. What's more, empirical research on the mechanism of big data analytics' influence on organizational performance is still at a rudimentary stage. The authors address this critical gap by exploring the mediation of dual innovations in the relationship through survey-based research. The research conclusions of this paper provide new perspective for understanding the impact of big data analytics capabilities on organizational performance, and enrich the theoretical research connotation of big data analysis capabilities and dual innovation behavior.

Financial fraud detection and big data analytics – implications on auditors’ use of fraud brainstorming session

2019 ◽  
Vol 34 (3) ◽  
pp. 324-337 ◽  
Author(s):  
Jiali Tang ◽  
Khondkar E. Karim
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Idea Generation ◽  
Fraud Detection ◽  
Content Type ◽  
Collective Wisdom ◽  
Fraud Risk ◽  
Collection Data ◽  
Audit Team

PurposeThis paper aims to discuss the application of Big Data analytics to the brainstorming session in the current auditing standards.Design/methodology/approachThe authors review the literature related to fraud, brainstorming sessions and Big Data, and propose a model that auditors can follow during the brainstorming sessions by applying Big Data analytics at different steps.FindingsThe existing audit practice aimed at identifying the fraud risk factors needs enhancement, due to the inefficient use of unstructured data. The brainstorming session provides a useful setting for such concern as it draws on collective wisdom and encourages idea generation. The integration of Big Data analytics into brainstorming can broaden the information size, strengthen the results from analytical procedures and facilitate auditors’ communication. In the model proposed, an audit team can use Big Data tools at every step of the brainstorming process, including initial data collection, data integration, fraud indicator identification, group meetings, conclusions and documentation.Originality/valueThe proposed model can both address the current issues contained in brainstorming (e.g. low-quality discussions and production blocking) and improve the overall effectiveness of fraud detection.

Sign in / Sign up

Export Citation Format

Share Document