Journal articles: 'Big quality'

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Moore, Alison. "Thinking big on quality." Nursing Standard 30, no. 3 (September 16, 2015): 22–23. http://dx.doi.org/10.7748/ns.30.3.22.s23.

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Batini, Carlo, Anisa Rula, Monica Scannapieco, and Gianluigi Viscusi. "From Data Quality to Big Data Quality." Journal of Database Management 26, no. 1 (January 2015): 60–82. http://dx.doi.org/10.4018/jdm.2015010103.

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This article investigates the evolution of data quality issues from traditional structured data managed in relational databases to Big Data. In particular, the paper examines the nature of the relationship between Data Quality and several research coordinates that are relevant in Big Data, such as the variety of data types, data sources and application domains, focusing on maps, semi-structured texts, linked open data, sensor & sensor networks and official statistics. Consequently a set of structural characteristics is identified and a systematization of the a posteriori correlation between them and quality dimensions is provided. Finally, Big Data quality issues are considered in a conceptual framework suitable to map the evolution of the quality paradigm according to three core coordinates that are significant in the context of the Big Data phenomenon: the data type considered, the source of data, and the application domain. Thus, the framework allows ascertaining the relevant changes in data quality emerging with the Big Data phenomenon, through an integrative and theoretical literature review.

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ParkJooSeok, Hocheol Ryu, Jangho Lee, 이준용, 김승현, and 이준기. "Applying Service Quality to Big Data Quality." Korea Journal of BigData 2, no. 2 (December 2017): 87–93. http://dx.doi.org/10.36498/kbigdt.2017.2.2.87.

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Ge, Mouzhi, and Vlastislav Dohnal. "Quality Management in Big Data." Informatics 5, no. 2 (April 16, 2018): 19. http://dx.doi.org/10.3390/informatics5020019.

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Virgolino, Zirvaldo Z., Osvaldo Resende, Douglas N. Gonçalves, Kaique A. F. Marçal, and Juliana de F. Sales. "Physiological quality of soybean seeds artificially cooled and stored in different packages." Revista Brasileira de Engenharia Agrícola e Ambiental 20, no. 5 (May 2016): 473–80. http://dx.doi.org/10.1590/1807-1929/agriambi.v20n5p473-480.

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ABSTRACT The aeration of seeds with artificially cooled air and their storage in big-bag packages aim to lengthen the shelf life while maintaining the quality. Thus, the objective was to study the effects of cooling before storage on germination and vigor of soybean seeds stored in trifoliate kraft paper bag and big bag in non-air-conditioned environment. Seeds of soybean cultivar NA 7337 RR were mechanically harvested in March 2013, holding an average moisture content of 18% w.b. In June, 16,000 kg of seeds were processed and cooled to 18 °C. Equal amount of non-cooled seeds was used as a control. Equally divided in kraft paper and in big bags, and combining cooling and packaging, the seeds were stored for three months and analyzed for moisture content, germination and vigor. Cooling to room temperature and different packaging types had similar effects on seed quality. Big bags packing showed better efficiency in retaining the moisture content of cooled seeds. No direct effects of cooling could be identified prior to storage on the germination and vigor of soybean seeds.

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Park, Sorah. "Audit Quality And Accrual Quality: Do Big 4 Auditors Indeed Enhance Accrual Quality Of ‘Powerful’ Clients?" Journal of Applied Business Research (JABR) 33, no. 2 (March 1, 2017): 343–50. http://dx.doi.org/10.19030/jabr.v33i2.9908.

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External auditors are considered ‘watchdogs’ which closely monitor corporate financial reporting process and provide guidelines for investors and financial institutions. However, recent accounting scandals in Korea indicate that external auditors may cater their audit reports to their clients’ needs. Based on a sample of listed companies on the Korea Stock Exchange from 2001 to 2010, this study finds the evidence consistent with such conjecture. First, large business conglomerates in Korea (so called ‘chaebols’) audited by Big 4 have lower accrual quality than the others, indicating that Big 4 auditors may not serve as watchdogs to enhance accrual quality of ‘powerful’ clients. However, powerful clients who pay greater non-audit service fees to Big 4 auditors have higher accrual quality than the others. This result suggests that non-audit services provided by Big 4 may not necessarily harm the quality of accounting information, contrary to the traditional view in the literature.

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Che, Limei, Ole-Kristian Hope, and John Christian Langli. "How Big-4 Firms Improve Audit Quality." Management Science 66, no. 10 (October 2020): 4552–72. http://dx.doi.org/10.1287/mnsc.2019.3370.

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This paper studies whether and how Big-4 firms provide higher-quality audits than non-Big-4 firms. Specifically, we first examine a Big-4 effect and then explore three sources of the Big-4 effect. To test the Big-4 effect, we use a unique data set of individual audit partners for a large sample of private companies and a novel research design exploiting the fact that auditees may follow the auditor who switches affiliation from a non-Big-4 firm to a Big-4 firm. Thus, we compare audit quality and audit fees of the same partner–auditee pairs before and after the switch. The results show that the Big-4 effect exists in the private-firm segment. More important, we find evidence for three sources of the Big-4 effect. First, Big-4 firms are able to recruit non-Big-4 partners who deliver higher audit quality than other non-Big-4 partners in the preswitch period. Second, enhanced learning has taken place after the switch. Third, the increased audit quality can also be attributed to stronger incentives/monitoring. These are new findings to the literature. This paper was accepted by Suraj Srinivasan, accounting.

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Escobar, Carlos A., Jeffrey A. Abell, Marcela Hernández-de-Menéndez, and Ruben Morales-Menendez. "Process-Monitoring-for-Quality — Big Models." Procedia Manufacturing 26 (2018): 1167–79. http://dx.doi.org/10.1016/j.promfg.2018.07.153.

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Singer, Peter, Pavan Sukhdev, Hon-Lam Li, Madhu Suri Prakash, Hellmuth Lange, Susanna Baltscheffsky, and ElIzabeth Peredo. "The Big Question: Quality of Life." World Policy Journal 28, no. 2 (2011): 3–6. http://dx.doi.org/10.1177/0740277511415049.

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Goodchild, Michael F. "The quality of big (geo)data." Dialogues in Human Geography 3, no. 3 (November 2013): 280–84. http://dx.doi.org/10.1177/2043820613513392.

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Montgomery, Douglas C. "Big Data and the Quality Profession." Quality and Reliability Engineering International 30, no. 4 (May 22, 2014): 447. http://dx.doi.org/10.1002/qre.1669.

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Kim, Jeong-Bon, and Byron Y. Song. "Auditor Quality and Loan Syndicate Structure." AUDITING: A Journal of Practice & Theory 30, no. 4 (November 1, 2011): 71–99. http://dx.doi.org/10.2308/ajpt-10144.

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SUMMARY This study investigates whether and how the quality of external auditors hired by borrowers has an impact on loan syndicate structure. Our empirical analyses, using a sample of U.S. syndicated loans from 1996 to 2009, show the following findings: First, a larger number of banks participate in syndicated loans to borrowing firms with Big 4 (or previously Big 5 or Big 6) auditors than to those with non-Big 4 auditors. Second, the percentage of a syndicated loan retained by the lead bank(s) is smaller when the borrower is a client of a Big 4 auditor than when the borrower is a client of a non-Big 4 auditor. Third, the effect of auditor quality (Big 4 versus non-Big 4) on loan ownership structure is less pronounced when lenders are able to gather more information about the borrower prior to the loan deal. Overall, our results suggest that auditor quality plays an important role in loan syndication by alleviating information asymmetries between lead banks and non-lead participant banks. JEL Classifications: G21; G32; M42. Data Availability: Data are publicly available from sources identified in the paper.

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Otokiti, Ahmed. "Using informatics to improve healthcare quality." International Journal of Health Care Quality Assurance 32, no. 2 (March 11, 2019): 425–30. http://dx.doi.org/10.1108/ijhcqa-03-2018-0062.

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Purpose The purpose of this paper is to provide insights into contemporary challenges associated with applying informatics and big data to healthcare quality improvement. Design/methodology/approach This paper is a narrative literature review. Findings Informatics serve as a bridge between big data and its applications, which include artificial intelligence, predictive analytics and point-of-care clinical decision making. Healthcare investment returns, measured by overall population health, healthcare operation efficiency and quality, are currently considered to be suboptimal. The challenges posed by informatics/big data span a wide spectrum from individual patients to government/regulatory agencies and healthcare providers. Practical implications The paper utilizes informatics and big data to improve population health and healthcare quality improvement. Originality/value Informatics and big data utilization have the potential to improve population health and service quality. This paper discusses the challenges posed by these methods as the author strives to achieve the aims.

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Willey, Ronald J. "Big data is it a big deal?" Process Safety Progress 34, no. 3 (July 30, 2015): 211. http://dx.doi.org/10.1002/prs.11761.

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Francis, Jere R., and Michael D. Yu. "Big 4 Office Size and Audit Quality." Accounting Review 84, no. 5 (September 1, 2009): 1521–52. http://dx.doi.org/10.2308/accr.2009.84.5.1521.

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ABSTRACT: Larger offices of Big 4 auditors are predicted to have higher quality audits for SEC registrants due to greater in-house experience in administering such audits. We test this prediction by examining a sample of 6,568 U.S. firm-year observations for the period 2003–2005 and audited by 285 unique Big 4 offices. Results are consistent with larger offices providing higher quality audits. Specifically, larger offices are more likely to issue going-concern audit reports, and clients in larger offices evidence less aggressive earnings management behavior. These findings are robust to extensive controls for client risk factors and to controls for other auditor characteristics. While the evidence suggests audit quality is higher on average in larger Big 4 offices, we make no claims that audit quality is unacceptably low in smaller offices.

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Lillrank, Paul. "Small and big quality in health care." International Journal of Health Care Quality Assurance 28, no. 4 (May 11, 2015): 356–66. http://dx.doi.org/10.1108/ijhcqa-05-2014-0068.

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Purpose – The purpose of this paper is to clarify healthcare quality’s ontological and epistemological foundations; and examine how these lead to different measurements and technologies. Design/methodology/approach – Conceptual analysis. Findings – Small quality denotes conformance to ex ante requirements. Big quality includes product and service design, based on customer requirements and expectations. Healthcare quality can be divided into three areas: clinical decision making; patient safety; and patient experience, each with distinct measurement and improvement technologies. Practical implications – The conceptual model is expected to bring clarity to constructing specific definitions, measures, objectives and technologies for improving healthcare. Originality/value – This paper claims that before healthcare quality can be defined, measured and integrated into systems, it needs to be clearly separated into ontologically and epistemologically different parts.

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Sukumar, Sreenivas R., Ramachandran Natarajan, and Regina K. Ferrell. "Quality of Big Data in health care." International Journal of Health Care Quality Assurance 28, no. 6 (July 13, 2015): 621–34. http://dx.doi.org/10.1108/ijhcqa-07-2014-0080.

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Purpose – The current trend in Big Data analytics and in particular health information technology is toward building sophisticated models, methods and tools for business, operational and clinical intelligence. However, the critical issue of data quality required for these models is not getting the attention it deserves. The purpose of this paper is to highlight the issues of data quality in the context of Big Data health care analytics. Design/methodology/approach – The insights presented in this paper are the results of analytics work that was done in different organizations on a variety of health data sets. The data sets include Medicare and Medicaid claims, provider enrollment data sets from both public and private sources, electronic health records from regional health centers accessed through partnerships with health care claims processing entities under health privacy protected guidelines. Findings – Assessment of data quality in health care has to consider: first, the entire lifecycle of health data; second, problems arising from errors and inaccuracies in the data itself; third, the source(s) and the pedigree of the data; and fourth, how the underlying purpose of data collection impact the analytic processing and knowledge expected to be derived. Automation in the form of data handling, storage, entry and processing technologies is to be viewed as a double-edged sword. At one level, automation can be a good solution, while at another level it can create a different set of data quality issues. Implementation of health care analytics with Big Data is enabled by a road map that addresses the organizational and technological aspects of data quality assurance. Practical implications – The value derived from the use of analytics should be the primary determinant of data quality. Based on this premise, health care enterprises embracing Big Data should have a road map for a systematic approach to data quality. Health care data quality problems can be so very specific that organizations might have to build their own custom software or data quality rule engines. Originality/value – Today, data quality issues are diagnosed and addressed in a piece-meal fashion. The authors recommend a data lifecycle approach and provide a road map, that is more appropriate with the dimensions of Big Data and fits different stages in the analytical workflow.

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Kuenzler, Adrian. "ADVANCING QUALITY COMPETITION IN BIG DATA MARKETS." Journal of Competition Law & Economics 15, no. 4 (December 2019): 500–537. http://dx.doi.org/10.1093/joclec/nhz021.

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Abstract The European Commission in its decision in Google Search (Shopping) found that a dominant search engine abused its market power by giving preferential treatment to its own related services over those of rivals. Conventional market mechanisms, it is supposed, can no longer correct the harm arising from such conduct. But there is disagreement in legal scholarship as to what this harm actually represents. This article maintains that the practice of self-favouring is best understood as an attempt to ward off product quality degradations in digital markets, which are difficult to repair purely by means of the consumer’s sole ability to switch. Framed against Albert Hirschman’s well-known work on Exit, Voice, and Loyalty, the Commission’s ruling must be understood as recognition that rivalry stemming from smaller market actors will not necessarily prevent large platforms from degrading product quality, despite the consumer’s ability to gain access to a variety of services that are only a click away. The article explicates the market’s functioning and the available methods of recuperation against the backdrop of the available case law in this area, and expounds the Commission’s findings in light of Hirschman’s theory.

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Connolly, J. V. "Bringing up the big guns of quality." Manufacturing Engineer 70, no. 9 (1991): 9. http://dx.doi.org/10.1049/me:19910186.

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AMINA ONYEABOR, GRACE, and AZMAN TA'A. "DATA QUALITY EVALUATION FRAMEWORK FOR BIG DATA." i-manager’s Journal on Cloud Computing 5, no. 2 (2018): 27. http://dx.doi.org/10.26634/jcc.5.2.15692.

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Richardson, Annette. "‘The big three’: quality, safety & cost." Nursing in Critical Care 16, no. 5 (August 8, 2011): 220–21. http://dx.doi.org/10.1111/j.1478-5153.2011.00469.x.

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Choi, Sang-Kyoon, and Soon-Cheon Jeon. "A propose of Big data quality elements." Journal of Korea Navigation Institute 17, no. 1 (February 28, 2013): 9–15. http://dx.doi.org/10.12673/jkoni.2013.17.01.009.

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Janssen, Marijn, Haiko van der Voort, and Agung Wahyudi. "Factors influencing big data decision-making quality." Journal of Business Research 70 (January 2017): 338–45. http://dx.doi.org/10.1016/j.jbusres.2016.08.007.

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عبدالوهاب, مروة جابر, Wessam Elbehaidy, and أمل السید ابو طبل. "Dimensionality for Big Data Quality A review." النشرة المعلوماتیة فی الحاسبات والمعلومات 3, no. 2 (July 1, 2021): 1–10. http://dx.doi.org/10.21608/fcihib.2021.74561.1045.

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Karim, Mohammad A., and Sayan Sarkar. "Auditors’ quality, footnotes, and earnings persistence." Managerial Finance 46, no. 2 (May 13, 2019): 267–82. http://dx.doi.org/10.1108/mf-11-2018-0569.

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Purpose The purpose of this paper is to investigate the role of auditors in financial statement readability. Using a simple proxy for financial statement obfuscation (number of footnotes), the authors examine the relationship between auditor quality, financial statement readability and earnings persistence. Design/methodology/approach The authors use regression analysis to test two hypotheses. In the first hypothesis, the authors investigate whether firms audited by Big 4 auditors have a lower number of footnotes than firms audited by non-Big 4 auditors. In the second hypothesis, the authors show that the firms with more footnotes have less earning persistence in comparison to the firms with less footnotes. Findings The authors find that firms audited by Big 4 auditors have fewer footnotes than firms audited by non-Big 4 auditors, and a larger number of footnotes reduces earnings persistence one-year and two-years ahead of the financial statement, although a larger number of footnotes does not reduce earning persistence when firms use Big 4 auditors. Overall, firms that use non-Big 4 auditors tend to obfuscate annual reports by using more footnotes and, in turn, reduce earnings persistence. Originality/value This is the first paper that has used number of footnotes in 10Ks as a proxy for financial statement readability. This paper shows how auditors’ reputation plays a key role in the readability of the financial statement. Prior studies related to readability have ignored the importance of auditors’ quality with respect to the readability of financial statements.

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Behn, Bruce K., Jong-Hag Choi, and Tony Kang. "Audit Quality and Properties of Analyst Earnings Forecasts." Accounting Review 83, no. 2 (March 1, 2008): 327–49. http://dx.doi.org/10.2308/accr.2008.83.2.327.

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Under the assumption that audit quality relates positively to unobservable financial reporting quality, we investigate whether audit quality is associated with the predictability of accounting earnings by focusing on analyst earnings forecast properties. The evidence shows that analysts' earnings forecast accuracy is higher and the forecast dispersion is smaller for firms audited by a Big 5 auditor. We further find that auditor industry specialization is associated with higher forecast accuracy and less forecast dispersion in the non-Big 5 auditor sample but not in the Big 5 auditor sample. Overall, our results suggest that high-quality audit provided by Big 5 auditors and industry specialist non-Big 5 auditors is associated with better forecasting performance by analysts.

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Jiang, John (Xuefeng), Isabel Yanyan Wang, and K. Philip Wang. "Big N Auditors and Audit Quality: New Evidence from Quasi-Experiments." Accounting Review 94, no. 1 (March 1, 2018): 205–27. http://dx.doi.org/10.2308/accr-52106.

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ABSTRACT Whether Big N auditors provide higher audit quality than non-Big N auditors remains a debate. We add new evidence to this debate by utilizing the setting of Big N auditors' acquisitions of non-Big N auditors. We identify 331 treatment firms that switched to Big N auditors due to the exogenous shocks imposed by Big N acquisitions. Our difference-in-differences analyses show that treatment firms' audit quality improves after switching to Big N auditors. In comparison, mergers or acquisitions among non-Big Ns have little impact on audit quality. Our cross-sectional analyses suggest the audit quality improvement among treatment firms is more likely due to Big N auditors' general competence rather than their industry-specific expertise. Finally, we find that treatment firms experience no significant market reactions around the announcements of Big N acquisitions, indicating that the capital markets may not attach any premium to the improved audit quality associated with Big N auditors. JEL Classifications: M41; M49.

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Wang, Chen-Chin, Fan-Hua Kung, and Kai-Hsun Lin. "Does audit firm size contribute to audit quality? Evidence from two emerging markets." Corporate Ownership and Control 11, no. 2 (2014): 108–19. http://dx.doi.org/10.22495/cocv11i2p8.

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This study investigated whether the Big N audit firms in emerging markets can provide audits of high quality and mitigate information risk, by comparing the audit quality of Big N audit firms in Taiwan with those in China. The two countries share a similar cultural background and engage in frequent economic exchange; however, they have different legal systems and institutional environments. This study followed previous research in the use of bid-ask spread and discretionary accruals as proxy variables for information asymmetry and audit quality. Our results indicate that politico-economic differences between Taiwan and China influence the effectiveness of independent auditors when it comes to the mitigation of information asymmetry. Big N audit firms in Taiwan helped to mitigate information asymmetry and provided audit services of higher quality, whereas Big N firms in China were better able to constrain earnings management. Our results indicate that market concentration and market share have a stronger influence on reputation incentive and audit quality than does the size of an audit firm.

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Lane, T. R. A., A. C. Shepherd, M. Gohel, I. J. Franklin, and A. H. Davies. "Big Veins, Big Deal - Vein Diameter Affects Disease Severity, not Quality of Life." Journal of Vascular Surgery: Venous and Lymphatic Disorders 1, no. 1 (January 2013): 101. http://dx.doi.org/10.1016/j.jvsv.2012.10.008.

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Durham, Danielle D., Franziska S. Rokoske, Laura C. Hanson, John G. Cagle, and Anna P. Schenck. "Quality Improvement in Hospice: Adding a Big Job to an Already Big Job?" American Journal of Medical Quality 26, no. 2 (March 2011): 103–9. http://dx.doi.org/10.1177/1062860610379631.

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Cho, Tae－Hyun. "Audit Quality of Big 4 Auditors as Measured by Accruals Quality." Korea Association of Business Education 32, no. 1 (February 24, 2017): 163–85. http://dx.doi.org/10.23839/kabe.2017.32.1.163.

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Tran, Manh Dung, Khairil Faizal Khairi, and Nur Hidayah Laili. "A longitudinal study of audit quality differences among independent auditors." Journal of Economics and Development 21, no. 2 (October 7, 2019): 234–46. http://dx.doi.org/10.1108/jed-10-2019-0040.

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Purpose The purpose of this paper is to investigate the differences of audit quality of financial statements among auditors, including Big 4 and non-Big 4 auditors. Design/methodology/approach By employing cross-sectional analysis of compliance (a proxy of audit quality) of goodwill impairment testing of listed firms in the context of Hong Kong, the variation of audit quality of financial statements of auditees has been shown. Findings Audit quality of Big 4 auditors is viewed to be higher than that of non-Big 4 audit firms and the homogeneity of audit quality among Big 4 auditors is not long accepted, but variation. Practical implications Even though unqualified opinions have been given on the auditors’ reports, the quality of financial statements audit is a skeptical issue because of the high level of non-compliance of goodwill impairment testing under International Financial Reporting Standards. Originality/value This study does emphasize the higher audit quality of financial statements of Big 4 auditors than that of non-Big 4 auditors and stresses the variation of audit quality among Big 4 auditors.

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Kristensen, Thomas. "The Big Picture on Big Flaws." Network Security 2003, no. 9 (September 2003): 19–20. http://dx.doi.org/10.1016/s1353-4858(03)00911-5.

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Kristensen, Thomas. "The Big Picture on Big Holes." Network Security 2003, no. 10 (October 2003): 19–20. http://dx.doi.org/10.1016/s1353-4858(03)01010-9.

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Kristenson, Thomas. "The Big Picture on Big Holes." Network Security 2003, no. 11 (November 2003): 15–17. http://dx.doi.org/10.1016/s1353-4858(03)01109-7.

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Kristenson, Thomas. "The big picture on big holes." Network Security 2004, no. 1 (January 2004): 19–20. http://dx.doi.org/10.1016/s1353-4858(04)00027-3.

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Kristensen, Thomas. "The big picture on big holes." Network Security 2004, no. 2 (February 2004): 15. http://dx.doi.org/10.1016/s1353-4858(04)00037-6.

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Kristenson, Thomas. "The big picture on big holes." Network Security 2004, no. 3 (March 2004): 19. http://dx.doi.org/10.1016/s1353-4858(04)00055-8.

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Balle, Jakob. "The big picture on big holes." Network Security 2004, no. 5 (May 2004): 18–20. http://dx.doi.org/10.1016/s1353-4858(04)00082-0.

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Kristensen, Thomas. "The big picture on big holes." Network Security 2004, no. 7 (July 2004): 19–20. http://dx.doi.org/10.1016/s1353-4858(04)00107-2.

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Sandblad, Andreas. "The big picture on big holes." Network Security 2004, no. 8 (August 2004): 19–20. http://dx.doi.org/10.1016/s1353-4858(04)00121-7.

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Eiram, Carsten. "The big picture on big holes." Network Security 2004, no. 9 (September 2004): 18–20. http://dx.doi.org/10.1016/s1353-4858(04)00134-5.

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Potter, Bruce. "Running Vista – big OS, big change." Network Security 2007, no. 1 (January 2007): 17–18. http://dx.doi.org/10.1016/s1353-4858(07)70008-9.

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Lin, Yu-Chun. "The consequences of audit committee quality." Managerial Auditing Journal 33, no. 2 (February 5, 2018): 192–216. http://dx.doi.org/10.1108/maj-03-2016-1350.

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Purpose This study aims to examine the consequences when audit committees have different economic incentives (i.e. incentive-based compensation) to switch auditors. Design/methodology/approach The author focuses on companies experiencing an auditor switching event (client-initiated dismissals) and uses Heckman’s (1997) two-stage estimation procedure to control endogenous bias. Audit committee quality is measured by the level of incentive-based compensation. Accrual quality and abnormal audit fees are examined over the periods of auditor switches. Findings Using 1,087 US companies between 2006 and 2014, the author found that audit committees’ incentive-based compensation is negatively (positively) associated with accruals quality (abnormal audit fees) only when companies switch from Big 4 to non-Big 4 auditors or switch within non-Big 4 auditors. For companies that switch from non-Big 4 to Big 4 auditors, she found no evidence. Research limitations/implications This study provides a detailed discussion of the consequences of audit committee quality. The findings also contribute to the literature by concluding that economic incentives are associated with ineffective oversight, particularly after auditor switches. Practical implications Sarbanes–Oxley Act and its associated regulations significantly expanded the oversight role of audit committees. However, regulators bypassed restrictions on audit committee compensation. Accordingly, the author suggests that regulators focus on the issue of economic incentives to improve audit committee quality. Originality/value Minimal research has been conducted on the role of audit committees when companies switch to a new external auditor. The author shows that when companies switch auditors, incentive-based compensation significantly affects the monitoring quality of audit committees.

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Qawqzeh, Yousef K., Ghaith Jaradat, Ali Al-Yousef, Anmar Abu-Hamdah, Ibrahim Almarashdeh, Mutasem Alsmadi, Mohammed Tayfour, Khalid Shaker, and Firas Haddad. "Applying the big bang-big crunch metaheuristic to large-sized operational problems." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 3 (June 1, 2020): 2484. http://dx.doi.org/10.11591/ijece.v10i3.pp2484-2502.

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In this study, we present an investigation of comparing the capability of a big bang-big crunch metaheuristic (BBBC) for managing operational problems including combinatorial optimization problems. The BBBC is a product of the evolution theory of the universe in physics and astronomy. Two main phases of BBBC are the big bang and the big crunch. The big bang phase involves the creation of a population of random initial solutions, while in the big crunch phase these solutions are shrunk into one elite solution exhibited by a mass center. This study looks into the BBBC’s effectiveness in assignment and scheduling problems. Where it was enhanced by incorporating an elite pool of diverse and high quality solutions; a simple descent heuristic as a local search method; implicit recombination; Euclidean distance; dynamic population size; and elitism strategies. Those strategies provide a balanced search of diverse and good quality population. The investigation is conducted by comparing the proposed BBBC with similar metaheuristics. The BBBC is tested on three different classes of combinatorial optimization problems; namely, quadratic assignment, bin packing, and job shop scheduling problems. Where the incorporated strategies have a greater impact on the BBBC's performance. Experiments showed that the BBBC maintains a good balance between diversity and quality which produces high-quality solutions, and outperforms other identical metaheuristics (e.g. swarm intelligence and evolutionary algorithms) reported in the literature.

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Benkhaled, Hamid Naceur, Djamel Berrabah, and Faouzi Boufares. "Data Warehouses and Big Data." International Journal of Organizational and Collective Intelligence 10, no. 3 (July 2020): 1–13. http://dx.doi.org/10.4018/ijoci.2020070101.

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Before the arrival of the Big Data era, data warehouse (DW) systems were considered the best decision support systems (DSS). DW systems have always helped organizations around the world to analyse their stored data and use it in making decisive decisions. However, analyzing and mining data of poor quality can give the wrong conclusions. Several data quality (DQ) problems can appear during a data warehouse project like missing values, duplicates values, integrity constrains issues and more. As a result, organizations around the world are more aware of the importance of data quality and invest a lot of money in order to manage data quality in the DW systems. On the other hand, with the arrival of BD, new challenges have to be considered like the need for collecting the most recent data and the ability to make real-time decisions. This article provides a survey about the exiting techniques to control the quality of the stored data in the DW systems and the new solutions proposed in the literature to face the new Big Data requirements.

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Li, J. J. "Research on the Synergy of “Big Q” System." Key Engineering Materials 567 (July 2013): 123–32. http://dx.doi.org/10.4028/www.scientific.net/kem.567.123.

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Big Q system of enterprise is a complex management system, which includes several sub-systems, and it suggests that the concept of “Big Q” should be formed in response to the needs of the growing situation through analyzing evolution in quality concept in this paper. Then, the intension and characteristics of big Q synergetic concept are proposed by analyzing the characteristics of the big Q system. Based on these studies, the relationship between the synergetic model of big Q and environment is explored, and the synergetic mechanism of the Big Q system is dissected by means of mathematical analysis. To be effective, the big quality system must be studied deeply. By applying the theory of self-organizing to study the big quality system, we can generalize features, characteristics and operation rules of the big quality system. So we can manage the big quality system scientifically and effectively to obtain an optimal function of the system.

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Singh, Harjinder, Rick Newby, and Inderpal Singh. "Should the 4 big replace the big 4? An examination of audit quality using internal audit." Corporate Ownership and Control 9, no. 2 (2012): 41–55. http://dx.doi.org/10.22495/cocv9i2art3.

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Prior research has linked audit quality with large audit firms. Consequently, a dichotomous variable, Big N/non-Big N has traditionally proxied for audit quality. Applying a different measure of audit quality than audit fee, this study investigates whether a single dummy variable for Big N is an appropriate proxy for audit quality in explaining differences in the existence of clients’ internal audit (IA) function. Results indicate that the existence of clients’ IA function is not consistent among Big 4 firms. This has important research implications for the universal use of a Big N dummy variable as a measure for audit quality.

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Borrajo, Laura, and Ricardo Cao. "Big-But-Biased Data Analytics for Air Quality." Electronics 9, no. 9 (September 22, 2020): 1551. http://dx.doi.org/10.3390/electronics9091551.

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Air pollution is one of the big concerns for smart cities. The problem of applying big data analytics to sampling bias in the context of urban air quality is studied in this paper. A nonparametric estimator that incorporates kernel density estimation is used. When ignoring the biasing weight function, a small-sized simple random sample of the real population is assumed to be additionally observed. The general parameter considered is the mean of a transformation of the random variable of interest. A new bootstrap algorithm is used to approximate the mean squared error of the new estimator. Its minimization leads to an automatic bandwidth selector. The method is applied to a real data set concerning the levels of different pollutants in the urban air of the city of A Coruña (Galicia, NW Spain). Estimations for the mean and the cumulative distribution function of the level of ozone and nitrogen dioxide when the temperature is greater than or equal to 30 ∘C based on 15 years of biased data are obtained.

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Broughman, James R., and Ronald C. Chen. "Using big data for quality assessment in oncology." Journal of Comparative Effectiveness Research 5, no. 3 (May 2016): 309–19. http://dx.doi.org/10.2217/cer-2015-0021.

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Journal articles on the topic 'Big quality'

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