Adoption of business analytics in accounting

Araújo, Leticia; Behr, Ariel; Schiavi, Giovana Sordi

doi:10.1590/1808-057x20231771.en

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Revista Contabilidade & Finanças

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Original Article • Rev. contab. finanç. 34 (93) • 2023 • https://doi.org/10.1590/1808-057x20231771.en copy

Adoption of business analytics in accounting

Authorship SCIMAGO INSTITUTIONS RANKINGS

ABSTRACT

The aim of this study is to analyze feasible business analytics (BA) adoption initiatives in accounting and their potential antecedents and effects, according to experts and early adopters. BA solutions help professionals explore data and gain insights for decision making. Although there is a clear relationship between BA and accounting, there is a gap between the theory and its adoption in practice. The research is relevant to academia and the market because it presents the main antecedents, effects, uses, techniques, and data sources, as well as the characteristics of the task, technology, and individual that impact the fit between BA and accounting. The main antecedents of adoption and good practices in early adopter projects using BA are highlighted, reducing the theory-practice gap and bringing new elements to promote its adoption among professionals and organizations. A qualitative-exploratory study was conducted using semi-structured interviews with 20 professionals from different accounting areas. The results highlight the main BA usage initiatives: identifying improper transactions, analyzing larger volumes of data, and performing predictive analyses. Efficiency, quality, and improved decision making were the main effects of using BA. The feasibility of BA initiatives was analyzed using the task-technology fit (TTF) model and the antecedents of adoption were analyzed using the technology-organization-environment (TOE) model, identifying characteristics of the task, technology, and individual as well as technological, organizational, and environmental factors that increase the fit between BA and accounting. The study contributes to highlighting the barriers (regulation and data availability) that affect the adoption of BA and concludes that the purposes of use, the depth of adoption, and the effects differ according to the accounting area, as the main effects of the use of BA primarily impact the accounting area itself, followed by other stakeholders.

Keywords:
business analytics; adoption; antecedents; effects; accounting

	Initiative	Target audience	Frequency of use	Approach, technique, algorithm
EA1-T	Conference routines in the tax area that consolidate information from various legacy systems and indicate records for analysis; tax and transfer pricing metrics dashboards with a general and detailed view of all inbound and outbound operations from all business units.	External stakeholders, business areas: commercial, supply chain and procurement, industry bodies	Online with daily updates	Visualization, clustering, statistical techniques using Python and R
EA2-T	A tool that analyzes purchase order history and indicates what the tax code should be for a given item being purchased; a model to optimize income tax and social security contribution projections for the next 12 months.	Tax compliance, tax area	Monthly and quarterly	Supervised, regression, heat map, decision tree, visualization, time series
EA3-A	Dashboards used to analyze accounting accounts for revenues, data on both budget and asset expenses and payroll and line items; tool for detecting anomalies based on historical analysis of spending behavior; issue alerts for auditors to perform their analysis.	Internal auditors, government bodies, society	Online with daily and monthly updates	Descriptive statistics, visualization
EA4-G	Dashboards used directly for various analyses, such as contract profitability analysis, and also as an intermediate consolidated database for other analyses and reports.	Accounting and commercial areas, managers, directors, and superintendents	Online with monthly updates	Descriptive statistics, visualization
EA5-F	Dashboards for monitoring accounting process statistics and volume and tax analysis.	Business and corporate/back-office areas	Online with monthly updates	Visualization
EA6-O	Identifying hidden relationships between organizations, legal entities, and individuals based on the supplier register.	External stakeholder, external audit	On demand	Supervised, descriptive statistics, classification, clustering, text mining
EA7-O	In the area of compliance, monitoring sensitive transactions or transactions with third parties to identify patterns; identifying companies with some similarity by cross-referencing supplier and customer records with proprietary databases, selecting potential records for in-depth investigation.	Compliance, internal auditing or risk management, legal and forensic areas	Monthly, quarterly, biannual, or annual updates and on demand	Clustering, classification, visualization, fuzzy matching, e-discovery tool
EA8-F	Monitoring the evolution of the general and detailed portfolio; forecasting profits and losses, and measuring risks based on historical data.	Commercial area, accounting area, and management	Online and on demand	Supervised, regression, decision tree
EA9-F	Preparation of financial statements.	Accounting area	Annual	Visualization
EA9-G	Disclosure of results to partners and monitoring of profitability with a focus on project management by client.	Managers and coordinators	Weekly and monthly	Visualization
EA10-F	Control of the accounting area’s activities and a dashboard that shows the outliers of divergences between related parties and allows the monitoring of legal balances.	Accounting area	Monthly	Visualization

Evidence of the effects	Effects
“I can do this more efficiently, for example, by combining an unsupervised technique where I can review a database of a million emails and, from the 10,000 I've reviewed, I can project to the rest of the database which emails or which of those 990,000 would have a high probability of being relevant, I can substantially direct my effort and be much more efficient and accurate in my review.” (EX11-O)	Experts and early adopters from all the accounting areas studied highlighted “efficiency” as the main effect. The words productivity, time, process, agility, bigger, and better stood out.
“Better efficiency in directing resources to increase sales in a specific region and optimize operational resources.” (EX5-G)
“It’s optimizing the process, so in a way, we can do it with more productivity, ask less of the team, and improve the speed of taking that credit. It improves the process and takes that credit faster.” (EA2-T)
“It’s accuracy, accuracy in classification, so you can predict for the organization that's calculating, you can predict a scenario more accurately, have actions, so to speak, to minimize unpredictability.” (EX12-F)	Experts and early adopters from all the accounting areas studied highlighted the effect of “quality.” The words greater accuracy, coverage, precision, and security stood out.
“The most sensitive thing, the first thing is the accuracy of the data, right, reliability, agility in getting the data, right; I focus on reliability because I generally don’t know what the percentage is, but the vast majority of errors that occur that result in fines from the tax authorities are due to data errors.” (EX8-T)
“There’s an improvement in the quality of information, knowing how much we're going to grow, how much we're going to lose, and then being able to invest better.” (EA8-F)
“Effects, here, is a support for corporate decision making, because with the result of this analysis, the company will know that it has to take some action plan to improve.” (EX3-A)	Experts and early adopters from all the accounting areas studied highlighted “improved decision-making” as one of the effects, derived from better conditions for planning and managing the business based on reliable and easily accessible information.
“I think there are n possibilities when it comes to forecasting, but the main ones are these: it improves decision making in the company.” (EX5-G)
“There’s another front that helps me make decisions, calling the sales department, for example, and saying: look, we're going to change the pricing policy for this item, this item, this item, and the return for the company is going to be this, we're going to optimize the tax chain by this much, etc.” (EA1-T)
“The best decision making, really, if we think about optimizing financial gain, I’m going to buy, I'm going to sell, I'm going to negotiate based on the model’s response, right, if it’s falling I'm going to buy, well, for that kind of decision making, for gain.” (EX12-F)	With the exception of the forensic area, experts and early adopters highlighted “increased profitability” as one of the effects, with the financial and management areas being the most prominent.
“The economic gain in the capital market is because with these techniques the auditor can audit better, can do his job better, and that protects the market in general, resulting in better quality accounting information.” (EX1-A)
“We can increase the company’s profitability and really increase the people doing what they should be doing.” (EA9-G)
“Greater control over transactions, over operations, over sensitive interactions, according to the company’s vision of risk.” (EX11-O)	Compliance is one of the effects present in the research. It is strongly related to the forensic, auditing, and tax areas. The main words associated with it are fraud, control, supplier, and detection.
“We can often stop a tender that’s overpriced or something like that before it happens, because we already get the data on a daily basis, we can detect it by generating alerts.” (EA3-A)
“The company doesn’t like positive surprises or negative surprises, it likes predictability, which in a country like Brazil is complicated; it likes to be in compliance [...] So this analytics front seeks to bring a little more tax coherence to our table.” (EA1-T)
“Basically, you’re going to have a more rounded company, I'd say, you're going to detect fraud, you're going to improve controls, you're going to inhibit fraud.” (EX10-O)	“Effectiveness” is one of the effects highlighted by experts.
“I think that the effect is to strengthen the internal control structure [...] you improve your control so that it doesn't happen again next time.” (EX1-A)
“We could bring new business to the company, new possibilities, open up customers, launch new products.” (EX2-A)	Experts believe that some initiatives have the effect of “anticipating market trends.”
“Bringing in new customer perspectives that can shape new products and bringing in new ideas that can add to the company’s portfolio.” (EX5-G)
“An understanding of the market perhaps in that sense, a greater understanding [...] of the market that we operate in, you know, niche markets that we operate in to promote new business and opportunities.” (EX9-F)

	Antecedents of adoption	Evidence
TECHNOLOGICAL FACTORS	Technological competence: infrastructure that facilitates adoption and experts, i.e., people who have the knowledge to perform the tasks (Zhu & Kraemer, 2005).	“It makes all the difference to have two people on the tax team who have a lot of expertise in technology, it’s certainly also a differentiator.” (EA2-T)
		“Each superintendency has a data science team assigned to it, which speeds up deliveries and provides a better understanding of the business area.” (EA5-F)
		“It was done by the accounting team, but with advice from the data team, which is a productive team in the company; and when we have any doubts, we call them too, but the implementation was all done by the team itself.” (EA9-G)
	Relative advantage: defined as “the degree to which an innovation is perceived as better than the idea it replaces” (Sun et al., 2016).	“Increased productivity. The teams are getting leaner and there’s a need for rapid delivery.” (EA4-G)
	Observability: technology features are perceived as beneficial after observing how other organizations use them.	“So we started discussing this and we visited the ... cube here to evaluate new fronts; we benchmarked with other companies and then we realized that we were on the right track.” (EA1-T)
ORGANIZATIONAL FACTORS	Management support, or management commitment, is the degree to which a company’s management invests in technological innovation (Cohen & Sayag, 2010); it is explained as “the degree to which management understands the importance of technology and the extent to which it is involved in related initiatives” (Park et al., 2015).	"And then, without the support of our head office here, and this very good relationship we have with the head office, we would hardly be able to do it, because it’s a project that takes time, it’s something new for the area, and even in the market there isn't much benchmarking." (EA2-T)
		“Initially, it was even a stimulus that came from the organization’s own management for us to start investing in these new data analysis technologies.” (EA3-A)
	Effectiveness of change: this is “the extent to which members of the organization are psychologically and behaviorally prepared to implement organizational change” (Weiner et al., 2008).	“What motivated the initial adoption was the issue of visibility. We were able to get the information from the sector and that thing of everyone working together for the company’s results.” (EA9-G)
	Decision-making culture, e.g., evidence-based, decision-making standards.	“There’s a strategic driver in the organization, which is data-driving decision making; as a result, each superintendency has a data science team assigned to it.” (EA5-F)
ENVIRONMENTAL FACTORS	External pressure: these are “the influences of the external environment” (Verma et al., 2017), regulatory pressures, competitive pressures (Lin, 2014), encouragement from professional bodies.	“In terms of good practices and thinking about how the market has adopted these solutions, has adopted these tools, has thought about this issue in order to continue or remain competitive in the future, so we need to adopt these practices.” (EA8-F)
		“[Our country] is today the country that’s most at the forefront of this tax transformation initiative, not because we’re better, not because we’re faster, but because it’s a very complex country in the tax area; we saw this as an opportunity to make it less complex.” (EA1-T)
	External support: defined as the “availability of support for implementing and using an information system” (Premkumar & Roberts, 1999); readiness of the commercial partner.	“About two years ago, we started working together with a supplier, which we signed a mentoring contract with, and then we developed these tools internally with them in the organization to speed things up and to really be able to analyze all the data.” (EA3-A)
		“We’d need external support, for example, a Big Four, a ..., in short, companies that work in this area.” (EA1-T)

Universidade de São Paulo, Faculdade de Economia, Administração e Contabilidade, Departamento de Contabilidade e Atuária Av. Prof. Luciano Gualberto, 908 - prédio 3 - sala 118, 05508 - 010 São Paulo - SP - Brasil, Tel.: (55 11) 2648-6320, Tel.: (55 11) 2648-6321, Fax: (55 11) 3813-0120 - São Paulo - SP - Brazil
E-mail: recont@usp.br

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