the data mindset and its effects on auditor …

THE DATA MINDSET AND ITS EFFECTS ON AUDITOR PERFORMANCE

by

Jared D. Cline

Submitted in partial fulfillment of the

requirements for Departmental Honors in

the Department of Accounting

Texas Christian University

Fort Worth, Texas

May 2, 2016

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THE DATA MINDSET AND ITS EFFECTS ON AUDITOR PERFORMANCE

Project Approved:

Supervising Professor: Renee M. Olvera, Ph.D.

Associate Professor of Professional Practice

Department of Accounting

Ronald L. Pitcock, Ph.D.

J. Vaughn & Evelyne H. Wilson Honors Fellow

Director of Prestigious Scholarships

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ABSTRACT

Data is quickly becoming an enormous asset for companies, and businesses are making

significant capital expenditures to enhance their data-capturing capabilities for both customer-

facing and backend processes, creating Big Data sets. The availability of this data is already

affecting the way that auditors approach risk assessment and evidence-gathering, so it is

important to understand how the data analytics audit procedures used with Big Data change the

way auditors perform. Prior research has not explored how data analytics and Big Data affect the

auditor in a practical setting, so this study using student auditors examines the effects of using a

data analytics mindset versus a traditional audit mindset when performing an audit procedure.

The study examines the dependent variables of auditor performance, information overload,

professional skepticism, confidence and calibration and finds that auditor mindset has an

inconclusive effect on all of the tested dependent variables. The study also offers conclusions

and areas of possible future research.

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TABLE OF CONTENTS

INTRODUCTION .......................................................................................................................... 1

LITERATURE REVIEW ............................................................................................................... 4

Origins of Big Data Analytics............................................................................................. 4

The Data Analytics Mindset ............................................................................................... 7

Information Overload.......................................................................................................... 8

State Professional Skepticism ........................................................................................... 10

Auditor Confidence and Calibration ................................................................................. 12

METHODOLOGY ....................................................................................................................... 13

Sample............................................................................................................................... 13

Experimental Design and Procedure ................................................................................. 13

Experimental Task ............................................................................................................ 14

Instrument ......................................................................................................................... 14

Variables ........................................................................................................................... 16

Independent Variable – Auditor Mindset ......................................................................... 16

Dependent Variables - Performance, Information Overload, State Professional

Skepticism, Confidence, and Calibration .......................................................................... 16

RESULTS ..................................................................................................................................... 17

Framing Check .................................................................................................................. 17

Hypothesis Testing............................................................................................................ 18

CONCLUSION ............................................................................................................................. 20

REFERENCES ............................................................................................................................. 23

APPENDIX A: ILLUSTRATIONS, CHARTS, GRAPHS, AND TABLES ............................... 27

Figure 1: Big Data Source Expansion ............................................................................... 27

Table 1: Demographic Information .................................................................................. 27

Exhibit 1: H1 Testing ........................................................................................................ 28




APPENDIX B: INSTRUMENT ................................................................................................... 31

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INTRODUCTION

The purpose of this study is to examine whether a data analytics mindset affects auditor’s

performance. Specifically, the study investigates whether the data analytics mindset affects

auditors’ perceptions of information overload during an audit task, their self-reported state

professional skepticism, and ultimately their confidence in performing an audit task. Ernst &

Young indicates that having an analytics mindset means having the ability to synthesize data

from multiple sources into meaningful information that changes how audits are performed and

gives the auditor evidence for audit conclusions (E&Y, 2015, p. 2). A data analytics mindset

implies the need for auditors to incorporate Big Data into their audit procedures. The term Big

Data is being used to describe the constant collection of data points from a seemingly endless list

of sources. Today, “data are continuously collected at an exponentially increasing rate, aided by

the existence of various information systems and the decreasing cost of storage” (Brown-Liburd,

Issa, & Lombardi, 2015, p. 1). Everywhere we turn, from social media to buyer behavior to

medical research, people and companies are gathering data points to improve their processes.

While the specific characteristics of Big Data can vary in different contexts, there are

specific features of Big Data that have come to define the concept. Known as the 3 V’s, those

features are: the overall Volume of the data to be managed, the Velocity of data being

continuously generated and added, and the Variety of “incompatible data formats, non-aligned

data structures, and inconsistent data semantics” (Laney, 2001). Recently a fourth V describing

Big Data was added—uncertain Veracity (IBM, 2012). Especially due to volume and velocity,

hardware and software companies have been forced to develop completely new methodologies

for creating the technological tools used for processing this data.

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Corporations have been able to leverage these new capabilities to drive business process

improvements and create competitive advantages. They have changed their information systems

from traditional data processing to automated data capture, which helps automate management

and production (Vasarhelyi, Kogan, & Tuttle, 2015). For example, Netflix and Amazon use

machine learning combined with enormous data sets of clickstream information and user reviews

to recommend new products or films to customers. Each of these companies has assembled an

unprecedented stockpile of data to drive machine learning—Amazon uses catalogued product

information and Netflix uses thorough tagging of millions of movies and TV shows (Milian,

2014; Madrigal, 2014). On the other hand, companies are also using Big Data analytics to cut

costs. Pratt & Whitney is attempting to cut down on unplanned aircraft engine maintenance by

partnering with IBM to analyze the petabytes of in-flight data generated by new-generation

engines (Morgan, 2015).

In order for corporations to create these improvements, the data collected through the

new automated processes must be analyzed in a way that provides insights about the information

available. Developers create business intelligence (BI) platforms to perform this analysis.

Gartner defines BI platforms as software platforms that deliver four core analysis capabilities,

along with other integration and information delivery capabilities (Richardson, Schlegel, Sallam,

& Hostmann, 2009). These four types of data analysis are: online analytical processing (OLAP),

advanced visualization, predictive modeling and data mining, and scorecards (Richardson et al.,

2009). These methods, which rely on software to perform complex statistical analysis and

visualization, allow people to make decisions using the Big Data sets that corporations now

collect.

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Similar to how corporations utilize Big Data techniques to drive performance

improvements, the audit profession has begun to lean on analytics to enhance audit quality.

Brown-Liburd, Issa, and Lombardi (2015, p. 452) state:

“while most individuals will concur that the ability to collect, manage, and analyze data

more effectively has the potential to lead to better judgment and decision making, Big

Data has the potential to dramatically change the way auditors make decisions (i.e., risk

assessments) and collect audit evidence.”

This dramatic change created by analytics has been most adopted in internal audit (IA), which

uses analytics to “examine an entire population of data…and focus on potential issues.” By

looking at larger data sets, IA can “discern relationships and correlations that were never before

visible” (EY Risk Advisory, 2014, p. 2). While external audit lags behind in analytics adoption,

it will see the same benefits from utilizing Big Data procedures, since auditors must manage and

analyze large corporate data sets to make decisions about companies’ financial statements.

However, even with all of the automation in corporations, audits are still performed by

people, and auditors will never gain the full benefits of data analytics unless they choose to

utilize the tools available. The audit procedures that create a sense of confidence and comfort for

the auditor with his or her results are the audit procedures that firms will continue to utilize,

regardless of the enhanced analytical capabilities available to them. Thus this study aims to

determine how utilizing new data analytics procedures will affect auditor performance as well as

auditor levels of information overload, state professional skepticism, and judgement accuracy,

confidence, and calibration.

For the study, we use E&Y’s previously mentioned definition of data mindset, “the

ability to assimilate data and information from various sources to produce relevant insights that

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impact the nature, timing and extent of the audit and enable the auditor to evaluate the evidence

to draw appropriate audit conclusions” (2015, p. 2). Having this mindset is key because it will

drive the adoption of analytics technologies in the future and help bring external audits back up

to speed with the business information technology landscape.

With the advent of Big Data being incorporated into the traditional audit, auditors must

effectively assimilate data from financial and non-financial sources to perform risk assessment,

evaluate fraud risks, and perform historically traditional audit tasks. Existing research suggests

that the use of Big Data may improve efficiency and effectiveness of the audit, however auditors

must overcome cognitive and processing weaknesses to achieve these results (Brown-Liburd et

al., 2015). Our study extends existing research by drilling down into how the data analytics

mindset affects auditor performance, which directly affects audit quality. This information will

help guide audit firms in the future as they make the transition to using more data analytics in

their audits while still emphasizing the importance of the auditor as a person.

The remainder of this paper proceeds as follows. The next section provides a background

on the ideas and procedures behind the data analytics mindset, followed by a discussion of the

dependent variables measured in our study—information overload, state professional skepticism,

and auditor confidence. The fourth section develops a hypothesis and explains the methodology

of the experiment and analysis. The fifth section discusses the results of the study, and the last

section offers concluding analysis and opportunities for future research.

LITERATURE REVIEW

Origins of Big Data Analytics

As the way businesses interact with their customers and gain insight into their own

processes changes, the information those businesses generate changes as well. Shifting

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transactions, manufacturing, and other business processes to computer-driven media has allowed

businesses to capture significantly more detailed data on their activities, and the availability of

external online data has grown as well. Big Data grows out of these shifts and “a technological

environment in which almost anything can be recorded, measured, and captured digitally, and

thereby turned into data” (Cao et al., 2015, pp. 423-424). In addition to traditional ERP system

data, companies can now automatically collect information from mobile and web sources, such

as click-path data and multi-URL analysis. Figure 1 in Appendix A illustrates the expanding

sources of data flow for corporations. The increasing number of data feeds that companies use

requires a process of combining them to gain meaningful insights.

Big Data analytics is then the “process of inspecting, cleaning, transforming, and

modeling Big Data to discover and communicate useful information and patterns, suggest

conclusions, and support decision making” (Cao et al., 2015, p. 424). In addition, it often

involves combining several sources of data (Cao et al., 2015). One example of this process is

how Bollen, Mao, and Zeng (2011) measured public sentiment via Twitter and used the data to

predict stock market movements. Using Google’s Profile of Mood States and the OpinionFinder

tool developed by Wilson et al. (2005), the researchers generated time series data on mood shifts

based upon millions of tweets by millions of users. They then used this data to predict stock

market movements up to four days ahead. The effectiveness of Big Data analytics used in the

stock market use case is corroborated with the success of other analysis projects undertaken by

companies such as Wal-Mart, which used weather data combined with transactional history data

to better manage inventories in disaster-threatened locations (Hays, 2004; Cao et al., 2015).

The capabilities of data analytics are changing the way users—particularly financial

statement auditors—think about using data. Cao et al. (2015) identify three of these capabilities:

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1. It is now possible to analyze all the data points during substantive procedures rather than

just a small, user-chosen sample, which can lead to more robust audit conclusions.

2. It is now possible to make use of correlations that may not identify the fundamental

causes of a phenomenon, but may provide indication of likely future events. These

correlations can be used in risk assessment and testing for material misstatements of

financial statements.

3. It is now possible to employ correlation models in continuous auditing, which may

ultimately enhance effectiveness when compared to auditing at specified intervals.

These characteristics and the benefits they provide open the door for widespread use of Big Data

analytics in the external audit context, but thus far the industry has been slow to adopt the new

processes.

Brown-Liburd et al. (2015) assume that external audit’s use of Big Data analytics tools

will expand moving forward and theorize potential challenges that auditing firms and their

personnel will face while adapting to these tools. Since “decisions made based on information

derived from Big Data still involve interpretation and judgment” (pp. 454-455), the researchers

discuss the behavioral implications of using these tools. They argue that “while data analytic

tools make it possible to extract large volumes of data, analysis and interpretation of results may

be problematic for auditors because the output still produces an overwhelming amount of data”

(Brown-Liburd et al., 2015; Issa & Kogan, 2014). The increased information load then leads to

the inclusion of irrelevant data and auditors’ inability to recognize important patterns. However,

the study also suggests tools to help mitigate the information overload associated with the use of

Big Data analytics, such as decision models that can be implemented while generating the output

of an analytics process.

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The Data Analytics Mindset

In psychology literature, a mindset is a “cognitive orientation that promotes task

completion” (Gollwitzer, 1990). This construct has been used to explain ideas such as goal

pursuit, inference making, interpersonal relationships, stereotyping, and fairness, and is thus

widely invoked in different studies (Hamilton, Vohs, Sellier, & Meyvis, 2011). Mindset theories

assume that in different situational cues, such as the demands of particular tasks, individuals shift

from using one mindset to another, and that it is difficult to use multiple mindsets at once

(Hamilton et al., 2011).

A data analytics mindset is a cognitive orientation that promotes using data to identify

issues, model predictions, and complete tasks. Because this term is relatively new, to our

knowledge, no studies explore how a data analytics mindset affects individuals in audit contexts.

Within the context of external audit, a data analytics mindset is the cognitive orientation that

promotes performing audit procedures through the incorporation of data larger than a user-

defined sample. It functions in opposition to the traditional audit mindset, which promotes using

other methods approved by the Public Company Accounting Oversight Board (PCAOB) such as

audit sampling and analytical procedures investigating “significant differences from expected

amounts” (PCAOB, 2010). Public accounting firms are now looking to encourage the data

analytics mindset in their employees and are thus redefining the mindset in more practical terms.

E&Y (2015, p. 2) synthesizes the definitions of Big Data analytics and mindset in the audit

context, defining the data analytics mindset as “the ability to assimilate data and information

from various sources to produce relevant insights that impact the nature, timing and extent of the

audit and enable the auditor to evaluate the evidence to draw appropriate audit conclusions.”

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Studies have shown that task performance can vary depending on which mindset an

individual is using. For example, Oskar Külpe reported that in an experiment testing subjects’

ability to recall details from a stimulus display, individuals gave highly accurate answers to

questions for which they were prepared but inaccurate answers to questions that did not match

their instructions (Gollwitzer, 1990). Similarly, Chapman (1932) observed that subjects gave

more accurate reports when their instructions matched the questions they were asked after a

presentation. Thus, a mindset should prepare the individual so that material can be analyzed

efficiently, resulting in proper task completion (Gollwitzer, 1990). In the context of audit

procedures, the auditor’s mindset should prepare the auditor to efficiently analyze financial

information to identify the client’s risks. Testing the overall performance of the data analytics

mindset leads to the following hypothesis:

H1: Auditors using a data analytics mindset identify a greater number of risky

accounts during an analytical procedures task than auditors using a traditional

mindset.

Information Overload

Information overload is defined as simply receiving too much information in a given

amount of time to effectively process (Brown-Liburd et al., 2015; Eppler & Mengis, 2004).

Decision makers have a limited ability to process large quantities of complex information, and

previous studies on the abilities of individuals to combine information sources have shown poor

outcomes (Brown-Liburd et al., 2015, p. 455; Benbasat & Taylor, 1982; Iselin, 1988;

Kleinmuntz, 1990).

Information overload is typically categorized into three situations: information retrieval,

organization, and analysis processes; decision processes; and communication processes (Eppler

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& Mengis, 2004). These processes all involve scenarios where individuals experience a

heightened information load, or quantity of information to be processed, which functions

alongside an individual’s information processing capacity, or “ability to perform information

processing activities such as collection, processing, and use of information” (Schick, Gordon, &

Haka, 1990, p. 203). Schroder et al. (1967) introduced the view that information load and

processing capacity are not independent, since high information load can lead to increased

processing capacity up to a certain point (Eppler & Mengis, 2004).

Previous studies have found that in the audit context, information overload leads to poor

judgements (Brown-Liburd et al., 2015). The bulk of this research on information overload in the

area of accounting focuses on how an individual’s performance, in terms of satisfactory decision

making, varies with the amount of information an individual is provided (Eppler & Mengis,

2004; Schick et al., 1990). Researchers have found that performance correlates positively with

the amount of information provided up to a point of rapidly diminishing returns. After this point

information provided will no longer be used in the decision-making process and the individual

enters a state of information overload (Eppler & Mengis, 2004). In the context of data analytics,

Brown-Liburd et al. (2015) suggest that “the nature of analysis of Big Data and the resulting

output could still potentially result in reduced audit quality due to information overload.”

Data analytics requires the use of vast amounts of data that may be too large to

incorporate into the traditional spreadsheet software that auditors typically use. As such, the use

of this vast amount of data will require auditors to organize the data into a format that can

provide value to the audit and ultimately into a format that is relevant and trustworthy (Brown-

Liburd et al., 2015). In addition, auditors must conduct analyses on the data to identify outlying

information or anomalies, which contributes to a heightened analysis process. Therefore auditors

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using a data analytics mindset are more likely to experience information overload because of the

quantities of data presented and the required analyses to convert the data into a valuable source

of information for planning the audit. Conversely, auditors engaged in a traditional mindset do

not have an emphasis on organizing or evaluating vast amounts of data. Instead, their intention is

to evaluate the summarized information presented by the client to identify any significant

fluctuation from a pre-defined expectation (often prior year’s audited amounts). Therefore,

auditors in the traditional audit mindset are less likely to experience information overload during

an audit planning task even when the data are available to them for consideration. As such,

hypothesis two states:

H2: Auditors using a data analytics mindset are more likely to experience

information overload when performing an audit planning task than auditors using

a traditional audit mindset.

State Professional Skepticism

Professional standards give auditors the duty to remain skeptical in their engagements

(AICPA, 2002). Standard-setters and researchers have created various definitions for

professional skepticism (PS), such as an attitude that includes a questioning mind and a critical

assessment of evidence (AICPA, 2007), the ability to detect fraud (Choo & Tan, 2000) , the

opposite of trust (Shaub, 1996), conservatism bias in audit judgement (McMillan & White,

1993), the equivalent of independence (Kadous, 2000), and presumptive doubt (Nelson, 2009).

For this study, we use the AICPA definition of PS, including a questioning mind and a critical

assessment of evidence.

There are then two components of professional skepticism: trait PS, a stable personality

characteristic, and state PS, a temporary and context-dependent measure (Robinson, Curtis, &

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Robertson, 2013). Robinson et al. (2013) explain the need to focus on state PS in research as

follows:

“Distinguishing trait PS from state PS is essential to the goal of improving PS in

practice since the most likely path to improving audit quality is through factors

that can be modified by firm practices or regulation, given the difficulties of

changing established personality traits.”(McCrae & Costa Jr, 1990; Church,

2000)(McCrae & Costa Jr, 1990; Church, 2000)(McCrae & Costa Jr, 1990;

Church, 2000)(McCrae & Costa Jr, 1990; Church, 2000)

Robinson et al.’s research was the first to explore the relationship between the trait and

state components of PS, and we build upon that study by examining PS in the context of

data analytics.

Data analytics takes place in the changing information technology environment. PS has

not been studied extensively in the context of the growing and ubiquitous use of accounting

information systems (AIS) in the audit environment, where it is possible that many auditors take

information generated from such systems as infallible. This study looks to bridge this gap by

exploring how the data analytics mindset specifically affects state PS when the auditor is

surrounded by multiple data sources and Big Data that is completely electronically generated.

In addition to their work on trait and state skepticism, the Robinson et al. study

determined that different constructs could affect state PS. The study found that time pressure

improves auditors’ state PS, indirectly affecting skeptical behaviors. This study suggests that

Robinson et al.’s conclusion will translate when using the construct of a data analytics mindset,

leads to the following hypothesis:

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H3: Auditors using a data analytics mindset have higher levels of state

professional skepticism than auditors using a traditional audit mindset.

Auditor Confidence and Calibration

For auditor decision-making, confidence is necessary as well as accuracy, since negative

results of an audit can be challenging to convey to a client. Confidence is referred to as a ‘second

order judgement’ with judgement itself being the ‘first-order judgement’ (Arkes, 1991). “By

stating his/her confidence, a decision-maker is pronouncing a judgement on the first-order

judgement (Chung & Monroe, 2000, p. 135). The appropriateness of this second-order

judgement of confidence, or calibration, is the relationship between judgement accuracy and

confidence (Chung & Monroe, 2000).

Researchers have studied auditor confidence and calibration in numerous contexts,

arriving at differing conclusions. Chung & Monroe (2000) state that “some studies reported that

auditors were underconfident (e.g., Mladenovic and Simnett, (1994); Dilla et al., (1991);

Solomon et al., (1985); Tomassini et al., (1982)), whereas Waller and Felix (1984) reported that

auditors were over-confident, and other auditing studies (e.g., Pincus (1991); Moeckel and

Plumlee (1989)) reported that auditors were both under- and over-confident.” However, to date

no research has been done on auditor confidence and calibration in the context of the data

analytics mindset or an AIS with Big Data. The studies above focus on how appropriateness of

confidence is affected by factors such as task difficulty and level of experience (Chung &

Monroe, 2000), and this study will extend previous research by determining how auditor

confidence is affected by the use of the data analytics mindset and Big Data analysis procedures

and how appropriate the outcome levels of confidence are. If confidence and calibration are

affected then there are numerous implications for auditors moving forward implementing data

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analytics in auditing procedures, since “auditors may make inappropriate decisions if their

confidence levels are not appropriate” (Chung & Monroe, 2000, p. 136). For example, if auditors

tend to become overconfident when presented with evidence generated from audit procedures

using data analytics, audit firms need to be wary of solely focusing on analytics for any one

portion of the audit. This discussion leads to the following hypotheses:

H4: Auditors using a data analytics mindset are more confident about their

planning procedures than auditors using a traditional audit mindset.

H5: Auditors using a data analytics mindset show more appropriate levels of

calibration, or appropriateness of confidence, than auditors using a traditional

audit mindset.

METHODOLOGY

Sample

The study’s sample consists of 49 student auditors from Texas Christian University.

Participation was anonymous, and no personally identifying information was collected. The

participants consisted of twenty-eight males and twenty-one females, all of whom were

undergraduate college seniors. The focus of this case was performing a planning analytical

procedure that identified risky income statement accounts for further investigation, which did not

require any specific financial reporting knowledge. Participants had previously taken an average

of 5 undergraduate accounting courses, and all participants had an understanding of the basics of

financial statements. See Table 1 in Appendix A.

Experimental Design and Procedure

The experiment was a 2 x 1 experimental design where the independent variables were

traditional audit mindset and data analytics mindset and the dependent variables were number of

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risky accounts identified, levels of information overload, levels of state professional skepticism,

confidence level, and confidence calibration. I manipulated each individual’s mindset by

defining and describing the mindset the individual was asked to use, and I verified the resulting

framing shift by asking a multiple choice question with answers corresponding to each of the

described mindsets.

Experimental Task

The task given to the participants is a modified version of the task created by Chui,

Curtis, and Pike (2013) with the addition of supporting data modified from the EY Foundation

Operations and Academic Resource Center’s Analytic Mindset exercise. The task requires

participants to identify risky income statement accounts as a planning analytical procedure. I

identified this procedure as a good candidate for use with Big Data and data analytics since the

task itself requires the individual to make an analysis based on financial information, which in

the past has been limited to summarized data presented on the face of the financial statements

and in the somewhat more detailed trial balance. With the use of more granular data that Big

Data collection can now provide, one can perform this analytical procedure using data analytics

with a deeper understanding of the information behind the financial statements.

Instrument

The experimental study was conducted in three phases. In Phase One, participants

completed the 30-question Hurtt (2010) trait skepticism scale to provide a measure of their level

of trait PS. Similar to prior research, the study measured trait PS prior to completing the case

study, ensuring that the measure is not affected by the case content.

Phase Two included the mindset framing as well as the instrument task itself. Participants

were asked to perform the task using either the traditional audit mindset, described as following

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authoritative guidance issued by the American Institute of Certified Public Accountants

(AICPA), or the data analytics mindset, described as using data and information from various

sources to produce relevant insights. The sample was split in half, chosen randomly. The

instructions then described methods that generic users of each mindset would likely use to

perform the task. Participants were asked to answer a framing check question that verified their

understanding of the mindset. The experimental task itself was the task from Chui, Curtis, and

Pike’s (2013) Lakeview Lumber case, which asks participants to identify risky income statement

accounts from a fictitious company’s financial statements. This study added to the original task

by adding a modified version of a data set from the EY Foundation Operations and Academic

Resource Center’s Analytic Mindset exercise, which was a complete sales data set for use by

users of the data analytics mindset. The financial statement numbers were updated to match the

new data, keeping the overall account comparisons the same on a percentage basis. The sales

data set included in the modified study showed significant risks in the sales account if broken

down to the product line level, providing additional insight for participants using the data

analytics mindset. These insights are similar to those gained by real-world professionals if they

were to use this level of detail when analyzing a risky account.

Phase Three was a case reflection, including the collection of demographic data.

Participants identified the accounts they thought were risky, specifically notating their assessed

risk of the sales account. Participants then responded to information overload questions,

confidence level questions, state PS questions from Robinson et al. (2013), and case interest and

skill questions such as level of adoption of technology and overall interest. Lastly, participants

answered basic demographic questions regarding age, year in school, and number of accounting

courses taken.

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Variables

Independent Variable – Auditor Mindset

To vary the mindset under which each student auditor approached the case, participants

were placed in two groups. Each group was presented with a description of a different mindset—

one group was presented with information about the traditional audit mindset, and the other

group was presented with information about the data analytics mindset. In the mindset

descriptions, participants were told how an individual using the specific mindset would work

through the case. The data analytics group was told to consider data and information from

various sources to produce insights, including letting an analysis of a large volume of data tell

the story. The traditional audit group was told to perform the audit in accordance with

authoritative guidance issued by the American Institute of Certified Public Accountants

(AICPA), which in this case often translates to comparing expectations for account fluctuations

with actual changes. Then both groups were given examples of what applying these mindsets

would look like in examining the repairs and maintenance expenses account in an audit. For the

data analytics group, the example was to analyze trends in an overall data download of the

account, and for the traditional audit group the example was to obtain documentation for a

sampling of the items.

Dependent Variables - Performance, Information Overload, State Professional Skepticism,

Confidence, and Calibration

This study investigated 5 different dependent variables: performance, information

overload, state professional skepticism, confidence, and calibration.

To measure performance, participants were asked to identify which income statement

accounts they thought should be investigated further. Performance, therefore, was defined as the

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number of accounts the participant indicated—the more accounts labeled as risky, the higher the

performance. There were 9 possible income statement accounts: Sales, Cost of Sales, Gross

Profit, Depreciation, Bad Debts Expense, Warranty Expense, Other Selling Expenses, and

General & Administrative Expense.

To measure state professional skepticism, the study used questions from the study by

Robinson et al. (2013), capturing skepticism behaviors that occurred during the case. Responses

to the questions were on a scale of 1-7, 1 indicating “Strongly disagree” and 7 “Strongly agree.”

The responses were then totaled and scaled to a one hundred-point scale. Since the study wanted

to specifically look at state PS and not trait PS, the Hurtt (2010) scale results from Phase 1 of the

study were used as a controlling variable.

To measure information overload, participants were asked to what extent they felt

overwhelmed by the information in the case, as well as to what extent they felt they were

provided with more information than necessary to complete the task. Participants responded on a

scale of 1-10, with 1 indicating “very little” and 10 “to a great extent.”

To capture data on confidence levels, the study had participants rate their confidence

level in their risk assessment by showing their agreement with the statement “I am confident

about the risk assessment level I provided” on a scale of 1-10, where 1 indicated “strongly

disagree” and 10 “strongly agree.” Calibration is simply a function of confidence compared to

performance, so it was easily calculated with these two previously discussed measures.

RESULTS

Framing Check

To assess whether the mindset framing procedure presented in Phase 1 was effective in

changing the way participants thought about approaching the case, the study asked participants a

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check question, which simply asked them to recall the mindset they approached the case with

once they completed the task. The question asked participants to answer true or false to the

statement “I am asked to perform this analytical risk assessment task using a data analytics

mindset.” Seven out of the 49 responses answered the manipulation check question incorrectly,

so those results were excluded from the study’s analysis.

Hypothesis Testing

In order to investigate hypothesis 1 (H1), which stated that auditors using a data analytics

mindset identify a greater number of risky accounts during an analytical procedures task than

auditors using a traditional mindset, the study asked participants to identify which accounts they

thought were risky. Totaling the number of identified accounts, the study found that using the

data analytics mindset (M = 2.878) did not statistically affect auditor performance compared to

the traditional audit mindset (M = 2.882). Results of a t-test show insignificant relationships for

either mindset on the performance variable (p = 0.927). See Exhibit 1 in Appendix A.

Testing of hypothesis 2 (H2), which states that auditors using a data analytics mindset are

more likely to experience information overload when performing an audit planning task than

auditors using a traditional audit mindset, similarly produced insignificant results. Participants’

responses to the two information overload questions were combined into a composite score that

could range from 2-20. The traditional audit mindset group showed higher information overload

(M = 11.824) than the data analytics group (M = 10.360), but results of a t-test show

insignificant results (p = 0.297). See Exhibit 2 in Appendix A.

Hypothesis 3 (H3) stated that auditors using a data analytics mindset have higher levels

of state professional skepticism than auditors using a traditional audit mindset. In order to test

H3, the study created composite scores for all participants for both trait and state professional

19

skepticism. The composite score for trait skepticism was generated by totaling the responses to

the 30-part questionnaire from Phase 1 and scaling the responses to a 100-point scale. Similarly,

the state composite score combined scores from the 7-part Robinson questionnaire in Phase 3

and scaled the responses to 100. After controlling for the influences of trait skepticism in a two-

variable regression, the study found that mindset has no significant effect on state PS, since there

was no significant change between the traditional audit mindset (M = 59.664) and the data

analytics mindset (M = 60.899) (p = 0.934).

Both hypothesis 4 (H4) and hypothesis 5 (H5) function off of the results collected by the

confidence assessment question, where participants rated their confidence in their risk

assessment on a scale of 1-10. H4 states that auditors using a data analytics mindset are more

confident about their planning procedures than auditors using a traditional audit mindset, and H5

states that auditors using a data analytics mindset show more appropriate levels of calibration, or

appropriateness of confidence, than auditors using a traditional audit mindset. In testing how

confidence levels were affected by the change in mindset, the study found no significant

relationships between confidence and mindset; traditional audit mindset had a mean of M =

5.353, and data analytics mindset had a mean of M = 5.480, with a p value of 0.8635. See

Exhibit 3 in Appendix A. In order to calculate calibration, responses were graphed with

performance, rated as number of risky accounts identified, on one axis, and confidence on

another. The ideal calibration line is a straight line from (1 confidence, 0 accounts listed) to (10

confidence, 3 accounts listed), since the correct risk assessment for the case was 3 accounts.

Calibration scores, therefore, were the graphical distances from each response to the perfect

calibration line, where a lower distance equals better calibration. After performing this

manipulation, the study found no significant link between a change in mindset and increased

20

calibration, since the traditional audit mindset had a mean calibration of M = 0.874 and the data

analytics mindset had a mean of M = 0.810 with a p-value of 0.749. See Exhibit 4 in Appendix

A.

CONCLUSION

The purpose of this study is to extend existing research into a more practical realm,

investigating a real-world simulated relationship between the data analytics mindset and auditor

performance. Specifically, we investigated whether the data analytics mindset affects their

perception of information overload during the task, their self-reported state professional

skepticism, and ultimately their confidence in performing an audit task.

For today’s companies, generating information is becoming one of the most important

value-adding processes, helping to increase visibility into cost, customer, and industry trends.

Big Data, known for its characteristics of volume, variety, and velocity of data flow, is

transforming the way that the corporate landscape keeps track of its information, including

financial transaction information. Today, we have access to real-time, transactional-level data on

transactions that affect each part of the balance sheet and income statement, information which

was not readily available just 5 years ago. This availability of information is changing the way

that auditors, both internal and external, approach their duties in investigating financial

statements and controls. Indeed, Brown-Liburd, Issa, and Lombardi state that “Big Data has the

potential to dramatically change the way auditors make decisions (i.e., risk assessments) and

collect audit evidence” (2015, p. 452).

The biggest hurdle audit firms face when incorporating Big Data into audits is how to

effectively teach auditors to use the information available to them. Computer technologies called

data analytics allow people to properly interpret larger data sets, but auditors have to make the

21

choice and the effort to incorporate these tools into their audits. This study attempted to replicate

a practical way in which data analytics could be used in an audit and gauge how the mindset shift

from traditional auditing to using data analytics changed the auditor’s tangible and intangible

performance.

The experiment utilized student auditors in a 3-phase study which asked the participants

to perform an audit task using either the traditional audit mindset or the data analytics mindset.

Using a modified version of the case in Chui, Curtis, and Pike’s (2013) study supplemented with

data from the EY Foundation Operations and Academic Resource Center’s Analytic Mindset

exercise, the study had participants assess the risk of income statement accounts using a

specified mindset, either data analytics or traditional audit. The case gave participants attributes

of their specified mindset, stating that the data analytics mindset would lead auditors to let the

data tell the story and incorporate large data sets and the traditional audit mindset would lead

auditors to follow guidance from the AICPA and perform standard audit procedures like year-

over-year change analysis and sampling. After performing the task, participants completed

questions on which accounts were deemed risky, their perceptions of information overload, state

skepticism questions, and confidence questions.

The five hypotheses stated that a shift from traditional audit mindset to data analytics

mindset would increase performance, measured by number of risky accounts identified, increase

information overload, increase state professional skepticism, and increase confidence and

calibration. Results from this experiment show insignificant relationships between the mindset

independent variable and all of our dependent variables, however the experiment was subject to

several limitations that may have affected results. For example, the study attempted to recreate a

data analytics environment with a Microsoft Excel file. While this recreation is decent and many

22

auditors use Excel to analyze data, it is not a true replication of the information that auditors

would receive in a Big Data context. Thus the impact of using data analytics in the experiment

may be limited. Similarly, the way in which information was presented to the participants may

have reduced the distinctions between the two groups; both mindsets were presented with the

same information sets, including the sales data breakdown. In a real-world scenario, it is unlikely

that an auditor with a traditional audit mindset would have obtained a dataset for the entire sales

account. This difference may have caused confusion for the traditional audit group.

From this study, I offer several suggestions for future research in this area—the

intersection of Big Data and auditing. As audit firms continue to explore using data analytics in

their audits, it is increasingly important to understand how these technologies affect auditors’

tangible and intangible performance. The dependent variables this study examines—overall

performance, information overload, state professional skepticism, confidence, and calibration—

can serve as a baseline group of attributes to continue to observe in practical settings so that

firms and auditors themselves can have a better understanding of where and how to use Big Data

and data analytics in the future.

23

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27

APPENDIX A: ILLUSTRATIONS, CHARTS, GRAPHS, AND TABLES

Figure 1: Big Data Source Expansion

Adapted from Moffitt and Vasarhelyi (2013).

Table 1: Demographic Information

Number in

Sample

Percentage in

Sample

Male 28 57%

Female 21 43%

Total 49 100%

28

Exhibit 1: H1 Testing


y = -0.0424x + 2.8824R² = 0.0002

1

2

3

4

5

6

7

8

9

10

Number of Accounts to Investigate Further

Traditional Audit Mindset

Data AnalyticsMindset



y = -1.4635x + 11.824R² = 0.0272

2

4

6

8

10

12

14

16

18

20

Perceived Information Overload





29



y = 0.1271x + 5.3529R² = 0.0007

1

2

3

4

5

6

7

8

9

10

Confidence





y = -0.0647x + 0.8743R² = 0.0026

0

0.5

1

1.5

2

2.5

3

Calibration of Confidence





30

1

2

3

4

5

6

7

8

9

10

0 1 2 3

Co

nfi

den

ce

Accuracy

Confidence vs. Accuracy

Traditional Data Analytics

31

APPENDIX B: INSTRUMENT

See following pages.

General Instructions and Phase 1

General Instructions Thank you for taking your time to participate in this study. The purpose of this study is to betterunderstand auditor’s judgements.

This is a three-phase study.1. In the first phase you will respond to questions about yourself.2. In the second phase you are presented with Company background information from Lakeview

Lumber. During this phase, you must review the background material and then access the excel fileincluded in the e-mail to you and complete the simple audit task and perform planning analyticalprocedures associated with a risk assessment.

3. In the third and final phase you will respond to questions about your perceptions of the case andquestions about yourself.

The information included in this case is not intended to be completely representative of what would beavailable to you when performing an actual audit. That type of detail would require more time tocomplete that necessary. Please base your responses solely on the information provided.

Phase 1: Questions about yourselfInstructions: Statements that people use to describe themselves are given below. Please circle theresponse that indicates how you generally feel. There are no right or wrong answers. Do not spend toomuch time on any one statement.

1 - StronglyDisagree 2 2 3 4 5

6 - StronglyAgree

I often accept other people'sexplanations without furtherthought.

I feel good about myself.

I wait to decide on issues untilI can get more information.

The prospect of learningexcites me.

I am interested in what causespeople to behave the way theydo.

I am confident of my abilities.

I often reject statementsunless I have proof that theyare true.

Discovering new information isfun.

I take my time when makingdecisions.

I tend to immediately acceptwhat other people tell me.

Other people's behavior doesnot interest me.

I am self-assured.

My friends tell me that Iusually question things that Isee or hear.

I like to understand the reasonfor other people's behavior.

I think that learning isexciting.

I usually accept things I see,read, or hear at face value.

I do not feel sure of myself.

I usually notice inconsistenciesin explanations.

Most often I agree with whatthe others in my group think.

I dislike having to makedecisions quickly.

I have confidence in myself.

I do not like to decide until I'velooked at all of the readilyavailable information.

I like searching for knowledge.

I frequently question thingsthat I see or hear.

It is easy for other people toconvince me.

I seldom consider why peoplebehave in a certain way.

I like to ensure that I'veconsidered most availableinformation before making adecision.

I enjoy trying to determine ifwhat I read or hear is true.

I relish learning.

The actions people take andthe reasons for those actionsare fascinating.

Data Analytics Mindset

Phase 2: Analytical Procedures & Risk Assessment

During this phase of the study, you are provided with information about a company including theircomparative financial statements.

Data Analytics Mindset

Please complete the analytical procedures and risk assessment using a data analytics mindset. This meansthat you are expected to consider data and information from various sources to produce relevant insightsduring the planning phase risk assessment for a fictitious company. In addition, a data analytics mindsetimplies the need for you to incorporate analysis of large volumes of data into your audit procedures.

For example, when considering methods for an entity to evaluate the potential for issues in capitalizationof repairs and maintenance expenses an auditor with a data analytics mindset may obtain a download ofall repairs and maintenance expenses for the period under audit and analyze trends in the expenses suchas timing, dollar amount, or location of charges.

Individuals who use a data analytics mindset often consider relationships among data to predict or

What data should I request from the client to investigate whether sales in the U.S. increased more significantly thansales from Mexico?sal

Who is the company's current CEO?Wh

Does the company have appropriate internal controls over financial reporting?DoDo

Why is there an increase in net sales compared to prior year?

understand fluctions. In addition, individuals who use a data analytics mindset often allow the data totell the story and have no preconceived notions regarding what the data will communicate to them.

Please respond to the following question:

From the list of questions provided below, identify the question that most likely represents the type ofmindset you are asked to use in completing this project?

Next you are presented with information for Lakeview Lumber Company.

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Background Information on Lakeview LumberLakeview Lumber is located in the city of Lakeview, a metropolitan area of approximately 200,000people. Lakeview Lumber sells between 30,000 and 35,000 different kinds of building materials, lawn andgarden products, and home improvement supplies to retail customers, as well as to contractors and otherbuilding professionals. Retail customers are required to pay in cash or by a major credit card at the timeof their purchase. However, the vast majority of contractors and building professionals has establishedcredit accounts and are billed on a monthly basis. Lakeview Lumber's main competitors in the area areThe Home Depot and Eagle Hardware & Garden.

Your firm has been the auditors for Lakeview Lumber for the past several years.

Key PersonnelLakeview Lumber’s management team consists of the following key executives. Based on your firm’s priorinteraction with these key executives, you have some basic knowledge of their background.

John Coleman, the controller – John started his career as an auditor with your firm. John was assignedto the audit of Lakeview Lumber each of the six years he worked for your firm. He has been theController for Lakeview Lumber since 2010. John usually arrives at work with his sports car. He and hiswife have recently purchase a new home in an upscale neighborhood.

Pat Anderson, the accounting manager – Pat has a B.A. in accounting and has been with LakeviewLumber for four years. Prior to working for Lakeview Lumber, Pat was the night auditor for a local hotel.Pat has extensive and detailed knowledge of the Lakeview Lumber’s accounting systems and theirweaknesses. Pat believes that less formality in controls would allow the Lakeview Lumber’s accountingdepartment to operate more efficiently and effectively, with fewer constraints.

Managerial CompensationLakeview Lumber compensates its key personnel primarily through a fixed salary schedule. In a recentboard meeting, Lakeview Lumber’s board of directors approved the motion to award all key personnel a

What data should I request from the client to investigate whether sales in the U.S. increased more significantly thansales from Mexico?sal

Who is the company's current CEO?Wh

Does the company have appropriate internal controls over financial reporting?DoDo

Why is there an increase in net sales compared to prior year?

cash bonus at the end of each year. Each key personnel will receive a cash bonus based on apredetermined percentage of the company’s reported net income.

Lakeview Lumber's Accounting EnvironmentBased on your firm’s prior year audits, Lakeview Lumber appeared to have some issues in its accountingsystems and internal controls. However, these issues did not appear to allow material errors into thecompany’s financial reporting process. Lakeview Lumber management reassured your firm that LakeviewLumber will take appropriate actions to upgrade its accounting systems.

The financial statements for Lakeview Lumber are provided in the excel file that you received alongwith this e-mail. Please review the financial information contained within the excel file and completethe task outlined in the excel file before you continue.

Remember that you are asked to perform your procedures using a Data Analytics Mindset


Phase 2: Analytical Procedures & Risk Assessment

During this phase of the study, you are provided with information about a company including theircomparative financial statements.


Please complete the analytical procedures and risk assessment using a traditional audit mindset. Thismeans that you are expected to perform the audit in accordance with authoritative guidance issued bythe American Institute of Certified Public Accounts (AICPA). Those standards require that you plan andperform the audit to obtain reasonable assurance regarding whether the financial statements are free ofmaterial misstatement.

For example, when considering methods for an entity to evaluate the potential for issues in capitalizationof repairs and maintenance expenses an auditor with a traditional audit mindset may obtain a sample ofrepairs and maintenance expenses for the period under audit and examine supporting documentation toensure appropriate treatment of selected items.

Individuals who use a traditional audit mindset often develop expectations for fianancial statement lineitems and compare those expectations to the unaudited current year amounts.

Please respond to the following question:

From the list of questions provided below, identify the question that most likely represents the type ofmindset you are asked to use in completing this project?

Next you are presented with information for Lakeview Lumber Company.

Background Information on Lakeview LumberLakeview Lumber is located in the city of Lakeview, a metropolitan area of approximately 200,000people. Lakeview Lumber sells between 30,000 and 35,000 different kinds of building materials, lawn andgarden products, and home improvement supplies to retail customers, as well as to contractors and otherbuilding professionals. Retail customers are required to pay in cash or by a major credit card at the timeof their purchase. However, the vast majority of contractors and building professionals has establishedcredit accounts and are billed on a monthly basis. Lakeview Lumber's main competitors in the area areThe Home Depot and Eagle Hardware & Garden. Your firm has been the auditors for Lakeview Lumber for the past several years. Key PersonnelLakeview Lumber’s management team consists of the following key executives. Based on your firm’s priorinteraction with these key executives, you have some basic knowledge of their background. John Coleman, the controller – John started his career as an auditor with your firm. John was assignedto the audit of Lakeview Lumber each of the six years he worked for your firm. He has been theController for Lakeview Lumber since 2010. John usually arrives at work with his sports car. He and hiswife have recently purchase a new home in an upscale neighborhood. Pat Anderson, the accounting manager – Pat has a B.A. in accounting and has been with LakeviewLumber for four years. Prior to working for Lakeview Lumber, Pat was the night auditor for a local hotel.Pat has extensive and detailed knowledge of the Lakeview Lumber’s accounting systems and theirweaknesses. Pat believes that less formality in controls would allow the Lakeview Lumber’s accountingdepartment to operate more efficiently and effectively, with fewer constraints. Managerial CompensationLakeview Lumber compensates its key personnel primarily through a fixed salary schedule. In a recentboard meeting, Lakeview Lumber’s board of directors approved the motion to award all key personnel acash bonus at the end of each year. Each key personnel will receive a cash bonus based on apredetermined percentage of the company’s reported net income. Lakeview Lumber's Accounting EnvironmentBased on your firm’s prior year audits, Lakeview Lumber appeared to have some issues in its accountingsystems and internal controls. However, these issues did not appear to allow material errors into thecompany’s financial reporting process. Lakeview Lumber management reassured your firm that LakeviewLumber will take appropriate actions to upgrade its accounting systems. The financial statements for Lakeview Lumber are provided in the excel file that you received alongwith this e‐mail. Please review the financial information contained within the excel file and completethe task outlined in the excel file before you continue.

Remember that you are asked to perform your procedures using a Traditional Audit Mindset

Phase 3: After

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Phase 3: Please respond to the following questions. You may not return to your excel file to respond to

TrueTru

False

TrueTru

False

these questions.

True or False: I am asked to perform this analytical risk assessment task using an audit mindset.

True or False: I am asked to perform this analytical risk assessment task using a data analytics mindset.

Please answer the following:

1 - Verylittle 2 3 4 5 6 7 8 9

10 - Toa greatextent

To what extent did you feeloverwhelmed by the quantityof information provided to youin the analytical procedurestask?

To what extent do you believethat you were provided withmore information thannecessary to complete theanalytical procedures task?

To what extent did you feeloverwhelmed by the quantityof information provided to youin the analytical procedurestask?

Based on the analytical procedures I performed in phase 2 and instructions provided by my auditmanager, I have selected the following accounts for further investigation: (check all accounts that apply)

SalesInvestigate further?stigate furt

Cost of SalesInvestigate further?stigate furtstigate furt

Gross ProfitInvestigate further?stigate furtstigate furt

DepreciationInvestigate further?stigate furtstigate furt

Bad Debts ExpenseInvestigate further?stigate furtstigate furt

Warranty ExpenseInvestigate further?stigate furtstigate furt

Other Selling ExpensesInvestigate further?stigate furtstigate furt

General & AdministrativeExpense

Investigate further?stigate furt

Income TaxesInvestigate further?stigate furtstigate furt

Please answer the following:

1 - Verylittle 2 3 4 5 6 7 8 9

10 - Toa greatextent

In order to determine thoseaccounts that you choose toinvestigate further, in additionto calculating changes in theaccount balance from theprior year, to what extent didyou perform additionalanalysis?

If you performed additional analysis, describe that analysis below:

Please answer the following:1 - Notrisky at

all 2 3 4 5 6 7 8 9

10 -Extremely

Risky

Based on all the information Ihave reviewed aboutLakeview Lumber, myassessment of risk associatedwith the sales account is...

Based on all the information Ihave reviewed aboutLakeview Lumber, myassessment of the overall riskfor this is...

Please answer the following:1 -

Stronglydisagree 2 3 4 5 6 7 8 9

10 -Stronglyagree

I am confident about the riskassessment level I providedabove.

For each of the following statements below select “1” if you strongly disagree and “7” if you stronglyagree:

1 - Stronglydisagree 2 3 4 5 6

7 - Stronglyagree

While working on this case, Ifrequently questioned things

MaleMa

Female

FreshmanFre

SophomoreSopSo

JuniorJuJu

SeniorSeSe

Other

that I saw or read.

While working on this case, Ihad a tendency to rejectstatements unless I had proofthat they were true.

While working on this case, Itook my time makingdecisions.

I did not like to make decisionsquickly while working on thecase.

While working on this case, Itried to ensure that I hadconsidered the most availableinformation before making adecision.

I tended to perform additionalanalysis before deciding whichaccounts to investigate duringthis case.

In the case, I used all of theresources available to me toget all of the information Icould.

Audit sampling may not be aneffective way to identify riskyaccounts in the planning phaseof an audit.

This case was interesting.

I put a lot of effort intocompleting this case.

I find Excel easy to use.

The use of new technologyenhances my taskeffectiveness.

I find new technology useful.

Your gender is?

What is your year in school?

How many accounting classes have you completed? (Enter a number)

Which accounting classes have you completed?

Please provide any comments you have about this study in the space below.

PLEASE UPLOAD YOUR EXCEL FILE TO THE ECOLLEGE DROPBOX TITLED ???

Thank you for your participation!

FY2014 FY14-FY13 FY2013 FY13-FY12 FY2012 FY2014 FY14-FY13 FY2013 FY13-FY12 FY2012

Unaudited % change Audited % change Audited Unaudited % change Audited % change Audited

Sales $4,886 $4,898 0% $4,894 Assets:

Cost of Sales $3,155 $3,128 1% $3,101 Cash and Cash Equivalents $1,523 35% $1,132 88% $601

Gross Profit $1,731 $1,770 -1% $1,793 Accounts Receivable $327 1% $325 22% $267

Less: Allowance ($134) -12% ($152) 6% ($144)

Depreciation $184 $174 3% $169 Inventory $1,102 9% $1,009 2% $989

Bad Debts Expense $83 $125 6% $118 Other Current Assets $89 -5% $94 -32% $139

Warranty Expense $85 $113 -3% $117 Total Current Assets $2,907 21% $2,408 30% $1,852

Other Selling Expenses $366 $351 3% $342 Property, Plant & Equipment $5,411 1% $5,351 3% $5,219

Total Selling Expenses $718 $763 2% $746 Less: Accumulated Depreciation ($1,987) 10% ($1,803) 11% ($1,629)

Total Assets $6,331 6% $5,956 9.45% $5,442

General & Administrative

Expenses $235 $224 4% $215 Liabilities:

Total Expenses $953 $987 3% $961 Accounts Payable $595 -22% $765 -4% $798

Income Before Income Estimated Liability for Warranties $98 -10% $109 -5% $115

Taxes $778 $783 -6% $832 Accrued Salaries and Wages $87 5% $83 -2% $85

Income Taxes $128 $130 -6% $138 Income Taxes Payable $19 12% $17 13% $15

Net Income $650 $653 -6% $694 Total Current Liabilities $799 -18% $974 -4% $1,013

Long-term Debt $300 -25% $400 -20% $500

Total Liabilities $1,099 -20% $1,374 -9% $1,513

Stockholders' Equity:

Retained Earnings $3,632 22% $2,982 28% $2,329

Common Stock, par value $0.01

Authorized 500,000 shares; issued

and outstanding—100,000 shares $1,600 0% $1,600 0% $1,600

Total Stockholders' Equity $5,232 14% $4,582 17% $3,929

Total Liabilities and

Stockholders' Equity $6,331 6.29% $5,956 9.45% $5,442

Please perform a planning analytical procedure for the Lakeview Lumber's income

statement. The Company's balance sheet is provided only as background

information. Sam, the audit manager, suggests that to perform analytical procedures

you compare the FY2014 account balance to the FY 2013 account balance and

calculate the % change from prior year. The purpose of this analytical procedure is

to identify accounts that you would like to investigate further.

NOTE: The client has provided a download of sales information. That information is

contained in the "Data" tab of this worksheet.

Lakeview Lumber

Balance Sheets

(amounts in thousands)

Lakeview Lumber

Income Statements

(amounts in thousands, except per share data)

For Informational use only -- please perform analytical procedures on the Income Statement

Only

Country Product Month Year Time SalesCanada Appearance Boards January 2012 Jan-12 22,292$ Canada Appearance Boards February 2012 Feb-12 23,145$ Canada Appearance Boards March 2012 Mar-12 22,686$ Canada Appearance Boards April 2012 Apr-12 22,363$ Canada Appearance Boards May 2012 May-12 22,677$ Canada Appearance Boards June 2012 Jun-12 22,126$ Canada Appearance Boards July 2012 Jul-12 21,937$ Canada Appearance Boards August 2012 Aug-12 22,021$ Canada Appearance Boards September 2012 Sep-12 22,913$ Canada Appearance Boards October 2012 Oct-12 22,978$ Canada Appearance Boards November 2012 Nov-12 22,748$ Canada Appearance Boards December 2012 Dec-12 22,928$ Canada Decking January 2012 Jan-12 45,317$ Canada Decking February 2012 Feb-12 44,825$ Canada Decking March 2012 Mar-12 45,519$ Canada Decking April 2012 Apr-12 45,100$ Canada Decking May 2012 May-12 44,991$ Canada Decking June 2012 Jun-12 45,533$ Canada Decking July 2012 Jul-12 46,179$ Canada Decking August 2012 Aug-12 45,587$ Canada Decking September 2012 Sep-12 44,755$ Canada Decking October 2012 Oct-12 44,436$ Canada Decking November 2012 Nov-12 43,996$ Canada Decking December 2012 Dec-12 44,719$ Canada Fencing January 2012 Jan-12 44,367$ Canada Fencing February 2012 Feb-12 46,610$ Canada Fencing March 2012 Mar-12 44,357$ Canada Fencing April 2012 Apr-12 46,819$ Canada Fencing May 2012 May-12 45,895$ Canada Fencing June 2012 Jun-12 45,891$ Canada Fencing July 2012 Jul-12 45,675$ Canada Fencing August 2012 Aug-12 44,718$ Canada Fencing September 2012 Sep-12 44,972$ Canada Fencing October 2012 Oct-12 45,161$ Canada Fencing November 2012 Nov-12 45,206$ Canada Fencing December 2012 Dec-12 43,820$ Canada Framing Lumber & Studs January 2012 Jan-12 44,958$ Canada Framing Lumber & Studs February 2012 Feb-12 46,640$ Canada Framing Lumber & Studs March 2012 Mar-12 45,787$ Canada Framing Lumber & Studs April 2012 Apr-12 43,473$ Canada Framing Lumber & Studs May 2012 May-12 45,081$ Canada Framing Lumber & Studs June 2012 Jun-12 44,635$ Canada Framing Lumber & Studs July 2012 Jul-12 45,650$ Canada Framing Lumber & Studs August 2012 Aug-12 46,313$ Canada Framing Lumber & Studs September 2012 Sep-12 46,020$ Canada Framing Lumber & Studs October 2012 Oct-12 43,902$ Canada Framing Lumber & Studs November 2012 Nov-12 45,387$ Canada Framing Lumber & Studs December 2012 Dec-12 46,091$ Canada Lattice January 2012 Jan-12 23,011$ Canada Lattice February 2012 Feb-12 22,737$ Canada Lattice March 2012 Mar-12 21,981$ Canada Lattice April 2012 Apr-12 22,710$ Canada Lattice May 2012 May-12 22,470$ Canada Lattice June 2012 Jun-12 22,904$ Canada Lattice July 2012 Jul-12 23,166$ Canada Lattice August 2012 Aug-12 22,449$ Canada Lattice September 2012 Sep-12 22,264$ Canada Lattice October 2012 Oct-12 22,133$ Canada Lattice November 2012 Nov-12 22,725$ Canada Lattice December 2012 Dec-12 24,389$ Canada Paneling January 2012 Jan-12 22,921$ Canada Paneling February 2012 Feb-12 22,935$ Canada Paneling March 2012 Mar-12 22,855$ Canada Paneling April 2012 Apr-12 23,245$ Canada Paneling May 2012 May-12 21,970$ Canada Paneling June 2012 Jun-12 21,925$ Canada Paneling July 2012 Jul-12 22,055$ Canada Paneling August 2012 Aug-12 22,725$ Canada Paneling September 2012 Sep-12 22,465$ Canada Paneling October 2012 Oct-12 22,359$ Canada Paneling November 2012 Nov-12 23,132$ Canada Paneling December 2012 Dec-12 22,443$ Canada Plywood January 2012 Jan-12 43,609$ Canada Plywood February 2012 Feb-12 46,037$ Canada Plywood March 2012 Mar-12 45,474$ Canada Plywood April 2012 Apr-12 45,535$ Canada Plywood May 2012 May-12 45,745$ Canada Plywood June 2012 Jun-12 44,771$ Canada Plywood July 2012 Jul-12 45,953$ Canada Plywood August 2012 Aug-12 44,989$ Canada Plywood September 2012 Sep-12 45,014$

Country Product Month Year Time SalesCanada Plywood October 2012 Oct-12 45,002$ Canada Plywood November 2012 Nov-12 44,514$ Canada Plywood December 2012 Dec-12 44,696$ Canada Pressure Treated Lumber January 2012 Jan-12 22,202$ Canada Pressure Treated Lumber February 2012 Feb-12 22,737$ Canada Pressure Treated Lumber March 2012 Mar-12 22,593$ Canada Pressure Treated Lumber April 2012 Apr-12 22,908$ Canada Pressure Treated Lumber May 2012 May-12 21,771$ Canada Pressure Treated Lumber June 2012 Jun-12 22,774$ Canada Pressure Treated Lumber July 2012 Jul-12 23,015$ Canada Pressure Treated Lumber August 2012 Aug-12 21,728$ Canada Pressure Treated Lumber September 2012 Sep-12 23,230$ Canada Pressure Treated Lumber October 2012 Oct-12 23,191$ Canada Pressure Treated Lumber November 2012 Nov-12 22,579$ Canada Pressure Treated Lumber December 2012 Dec-12 22,183$ Canada Ready-To-Assemble Kits January 2012 Jan-12 22,803$ Canada Ready-To-Assemble Kits February 2012 Feb-12 23,626$ Canada Ready-To-Assemble Kits March 2012 Mar-12 23,457$ Canada Ready-To-Assemble Kits April 2012 Apr-12 22,286$ Canada Ready-To-Assemble Kits May 2012 May-12 23,249$ Canada Ready-To-Assemble Kits June 2012 Jun-12 22,574$ Canada Ready-To-Assemble Kits July 2012 Jul-12 23,198$ Canada Ready-To-Assemble Kits August 2012 Aug-12 22,729$ Canada Ready-To-Assemble Kits September 2012 Sep-12 22,467$ Canada Ready-To-Assemble Kits October 2012 Oct-12 22,450$ Canada Ready-To-Assemble Kits November 2012 Nov-12 22,718$ Canada Ready-To-Assemble Kits December 2012 Dec-12 21,596$ Canada Shims & Wood Shingles January 2012 Jan-12 21,672$ Canada Shims & Wood Shingles February 2012 Feb-12 22,404$ Canada Shims & Wood Shingles March 2012 Mar-12 22,849$ Canada Shims & Wood Shingles April 2012 Apr-12 22,450$ Canada Shims & Wood Shingles May 2012 May-12 22,092$ Canada Shims & Wood Shingles June 2012 Jun-12 22,608$ Canada Shims & Wood Shingles July 2012 Jul-12 22,313$ Canada Shims & Wood Shingles August 2012 Aug-12 23,246$ Canada Shims & Wood Shingles September 2012 Sep-12 22,256$ Canada Shims & Wood Shingles October 2012 Oct-12 23,145$ Canada Shims & Wood Shingles November 2012 Nov-12 22,207$ Canada Shims & Wood Shingles December 2012 Dec-12 21,808$ Canada Timber January 2012 Jan-12 23,623$ Canada Timber February 2012 Feb-12 22,134$ Canada Timber March 2012 Mar-12 22,826$ Canada Timber April 2012 Apr-12 21,707$ Canada Timber May 2012 May-12 22,911$ Canada Timber June 2012 Jun-12 23,191$ Canada Timber July 2012 Jul-12 22,430$ Canada Timber August 2012 Aug-12 22,481$ Canada Timber September 2012 Sep-12 23,709$ Canada Timber October 2012 Oct-12 22,689$ Canada Timber November 2012 Nov-12 21,889$ Canada Timber December 2012 Dec-12 21,785$ Canada Appearance Boards January 2013 Jan-13 22,300$ Canada Appearance Boards February 2013 Feb-13 22,682$ Canada Appearance Boards March 2013 Mar-13 23,174$ Canada Appearance Boards April 2013 Apr-13 22,183$ Canada Appearance Boards May 2013 May-13 21,870$ Canada Appearance Boards June 2013 Jun-13 22,244$ Canada Appearance Boards July 2013 Jul-13 21,393$ Canada Appearance Boards August 2013 Aug-13 21,818$ Canada Appearance Boards September 2013 Sep-13 22,712$ Canada Appearance Boards October 2013 Oct-13 22,706$ Canada Appearance Boards November 2013 Nov-13 23,081$ Canada Appearance Boards December 2013 Dec-13 22,932$ Canada Decking January 2013 Jan-13 45,299$ Canada Decking February 2013 Feb-13 45,813$ Canada Decking March 2013 Mar-13 44,879$ Canada Decking April 2013 Apr-13 45,609$ Canada Decking May 2013 May-13 46,535$ Canada Decking June 2013 Jun-13 45,841$ Canada Decking July 2013 Jul-13 45,635$ Canada Decking August 2013 Aug-13 44,945$ Canada Decking September 2013 Sep-13 45,313$ Canada Decking October 2013 Oct-13 45,771$ Canada Decking November 2013 Nov-13 44,676$ Canada Decking December 2013 Dec-13 44,747$ Canada Fencing January 2013 Jan-13 45,419$ Canada Fencing February 2013 Feb-13 44,974$ Canada Fencing March 2013 Mar-13 45,060$ Canada Fencing April 2013 Apr-13 44,512$ Canada Fencing May 2013 May-13 44,149$ Canada Fencing June 2013 Jun-13 45,354$

Country Product Month Year Time SalesCanada Fencing July 2013 Jul-13 46,246$ Canada Fencing August 2013 Aug-13 44,498$ Canada Fencing September 2013 Sep-13 45,459$ Canada Fencing October 2013 Oct-13 45,439$ Canada Fencing November 2013 Nov-13 46,051$ Canada Fencing December 2013 Dec-13 44,102$ Canada Framing Lumber & Studs January 2013 Jan-13 46,270$ Canada Framing Lumber & Studs February 2013 Feb-13 44,127$ Canada Framing Lumber & Studs March 2013 Mar-13 43,685$ Canada Framing Lumber & Studs April 2013 Apr-13 44,975$ Canada Framing Lumber & Studs May 2013 May-13 43,843$ Canada Framing Lumber & Studs June 2013 Jun-13 44,398$ Canada Framing Lumber & Studs July 2013 Jul-13 46,205$ Canada Framing Lumber & Studs August 2013 Aug-13 43,681$ Canada Framing Lumber & Studs September 2013 Sep-13 44,358$ Canada Framing Lumber & Studs October 2013 Oct-13 46,275$ Canada Framing Lumber & Studs November 2013 Nov-13 44,573$ Canada Framing Lumber & Studs December 2013 Dec-13 45,209$ Canada Lattice January 2013 Jan-13 22,986$ Canada Lattice February 2013 Feb-13 22,466$ Canada Lattice March 2013 Mar-13 22,673$ Canada Lattice April 2013 Apr-13 22,276$ Canada Lattice May 2013 May-13 22,880$ Canada Lattice June 2013 Jun-13 23,285$ Canada Lattice July 2013 Jul-13 22,835$ Canada Lattice August 2013 Aug-13 23,182$ Canada Lattice September 2013 Sep-13 23,561$ Canada Lattice October 2013 Oct-13 22,665$ Canada Lattice November 2013 Nov-13 23,126$ Canada Lattice December 2013 Dec-13 24,048$ Canada Paneling January 2013 Jan-13 21,925$ Canada Paneling February 2013 Feb-13 22,876$ Canada Paneling March 2013 Mar-13 23,466$ Canada Paneling April 2013 Apr-13 23,037$ Canada Paneling May 2013 May-13 22,957$ Canada Paneling June 2013 Jun-13 23,595$ Canada Paneling July 2013 Jul-13 22,489$ Canada Paneling August 2013 Aug-13 22,365$ Canada Paneling September 2013 Sep-13 24,137$ Canada Paneling October 2013 Oct-13 22,176$ Canada Paneling November 2013 Nov-13 23,709$ Canada Paneling December 2013 Dec-13 22,513$ Canada Plywood January 2013 Jan-13 45,829$ Canada Plywood February 2013 Feb-13 45,503$ Canada Plywood March 2013 Mar-13 45,393$ Canada Plywood April 2013 Apr-13 45,563$ Canada Plywood May 2013 May-13 45,481$ Canada Plywood June 2013 Jun-13 46,149$ Canada Plywood July 2013 Jul-13 43,856$ Canada Plywood August 2013 Aug-13 44,937$ Canada Plywood September 2013 Sep-13 47,049$ Canada Plywood October 2013 Oct-13 45,030$ Canada Plywood November 2013 Nov-13 45,018$ Canada Plywood December 2013 Dec-13 43,878$ Canada Pressure Treated Lumber January 2013 Jan-13 21,984$ Canada Pressure Treated Lumber February 2013 Feb-13 21,940$ Canada Pressure Treated Lumber March 2013 Mar-13 21,752$ Canada Pressure Treated Lumber April 2013 Apr-13 22,772$ Canada Pressure Treated Lumber May 2013 May-13 22,495$ Canada Pressure Treated Lumber June 2013 Jun-13 22,163$ Canada Pressure Treated Lumber July 2013 Jul-13 22,175$ Canada Pressure Treated Lumber August 2013 Aug-13 22,394$ Canada Pressure Treated Lumber September 2013 Sep-13 22,941$ Canada Pressure Treated Lumber October 2013 Oct-13 22,594$ Canada Pressure Treated Lumber November 2013 Nov-13 22,459$ Canada Pressure Treated Lumber December 2013 Dec-13 21,499$ Canada Ready-To-Assemble Kits January 2013 Jan-13 23,213$ Canada Ready-To-Assemble Kits February 2013 Feb-13 23,519$ Canada Ready-To-Assemble Kits March 2013 Mar-13 23,119$ Canada Ready-To-Assemble Kits April 2013 Apr-13 22,005$ Canada Ready-To-Assemble Kits May 2013 May-13 23,177$ Canada Ready-To-Assemble Kits June 2013 Jun-13 22,584$ Canada Ready-To-Assemble Kits July 2013 Jul-13 21,985$ Canada Ready-To-Assemble Kits August 2013 Aug-13 22,799$ Canada Ready-To-Assemble Kits September 2013 Sep-13 22,753$ Canada Ready-To-Assemble Kits October 2013 Oct-13 22,817$ Canada Ready-To-Assemble Kits November 2013 Nov-13 22,099$ Canada Ready-To-Assemble Kits December 2013 Dec-13 22,796$ Canada Shims & Wood Shingles January 2013 Jan-13 21,287$ Canada Shims & Wood Shingles February 2013 Feb-13 22,128$ Canada Shims & Wood Shingles March 2013 Mar-13 22,366$

Country Product Month Year Time SalesCanada Shims & Wood Shingles April 2013 Apr-13 22,115$ Canada Shims & Wood Shingles May 2013 May-13 22,443$ Canada Shims & Wood Shingles June 2013 Jun-13 23,692$ Canada Shims & Wood Shingles July 2013 Jul-13 22,906$ Canada Shims & Wood Shingles August 2013 Aug-13 22,222$ Canada Shims & Wood Shingles September 2013 Sep-13 23,409$ Canada Shims & Wood Shingles October 2013 Oct-13 23,524$ Canada Shims & Wood Shingles November 2013 Nov-13 22,729$ Canada Shims & Wood Shingles December 2013 Dec-13 23,326$ Canada Timber January 2013 Jan-13 22,790$ Canada Timber February 2013 Feb-13 22,916$ Canada Timber March 2013 Mar-13 22,237$ Canada Timber April 2013 Apr-13 23,094$ Canada Timber May 2013 May-13 22,050$ Canada Timber June 2013 Jun-13 22,686$ Canada Timber July 2013 Jul-13 22,824$ Canada Timber August 2013 Aug-13 22,473$ Canada Timber September 2013 Sep-13 22,466$ Canada Timber October 2013 Oct-13 22,244$ Canada Timber November 2013 Nov-13 23,041$ Canada Timber December 2013 Dec-13 22,796$ Canada Appearance Boards January 2014 Jan-14 21,318$ Canada Appearance Boards February 2014 Feb-14 20,067$ Canada Appearance Boards March 2014 Mar-14 20,823$ Canada Appearance Boards April 2014 Apr-14 20,380$ Canada Appearance Boards May 2014 May-14 20,617$ Canada Appearance Boards June 2014 Jun-14 21,781$ Canada Appearance Boards July 2014 Jul-14 21,075$ Canada Appearance Boards August 2014 Aug-14 21,550$ Canada Appearance Boards September 2014 Sep-14 21,123$ Canada Appearance Boards October 2014 Oct-14 20,266$ Canada Appearance Boards November 2014 Nov-14 21,136$ Canada Appearance Boards December 2014 Dec-14 20,165$ Canada Decking January 2014 Jan-14 37,496$ Canada Decking February 2014 Feb-14 37,881$ Canada Decking March 2014 Mar-14 44,040$ Canada Decking April 2014 Apr-14 39,731$ Canada Decking May 2014 May-14 38,635$ Canada Decking June 2014 Jun-14 42,484$ Canada Decking July 2014 Jul-14 38,303$ Canada Decking August 2014 Aug-14 38,891$ Canada Decking September 2014 Sep-14 41,679$ Canada Decking October 2014 Oct-14 38,996$ Canada Decking November 2014 Nov-14 39,992$ Canada Decking December 2014 Dec-14 40,106$ Canada Fencing January 2014 Jan-14 39,267$ Canada Fencing February 2014 Feb-14 38,439$ Canada Fencing March 2014 Mar-14 39,250$ Canada Fencing April 2014 Apr-14 40,275$ Canada Fencing May 2014 May-14 38,045$ Canada Fencing June 2014 Jun-14 38,643$ Canada Fencing July 2014 Jul-14 38,880$ Canada Fencing August 2014 Aug-14 39,506$ Canada Fencing September 2014 Sep-14 40,157$ Canada Fencing October 2014 Oct-14 39,972$ Canada Fencing November 2014 Nov-14 39,272$ Canada Fencing December 2014 Dec-14 39,004$ Canada Framing Lumber & Studs January 2014 Jan-14 40,159$ Canada Framing Lumber & Studs February 2014 Feb-14 39,828$ Canada Framing Lumber & Studs March 2014 Mar-14 40,280$ Canada Framing Lumber & Studs April 2014 Apr-14 39,209$ Canada Framing Lumber & Studs May 2014 May-14 40,693$ Canada Framing Lumber & Studs June 2014 Jun-14 38,970$ Canada Framing Lumber & Studs July 2014 Jul-14 40,703$ Canada Framing Lumber & Studs August 2014 Aug-14 39,476$ Canada Framing Lumber & Studs September 2014 Sep-14 39,138$ Canada Framing Lumber & Studs October 2014 Oct-14 40,998$ Canada Framing Lumber & Studs November 2014 Nov-14 40,398$ Canada Framing Lumber & Studs December 2014 Dec-14 40,444$ Canada Lattice January 2014 Jan-14 20,469$ Canada Lattice February 2014 Feb-14 19,938$ Canada Lattice March 2014 Mar-14 24,454$ Canada Lattice April 2014 Apr-14 19,210$ Canada Lattice May 2014 May-14 19,026$ Canada Lattice June 2014 Jun-14 23,874$ Canada Lattice July 2014 Jul-14 20,211$ Canada Lattice August 2014 Aug-14 19,629$ Canada Lattice September 2014 Sep-14 19,458$ Canada Lattice October 2014 Oct-14 19,346$ Canada Lattice November 2014 Nov-14 20,175$ Canada Lattice December 2014 Dec-14 21,816$

Country Product Month Year Time SalesCanada Paneling January 2014 Jan-14 21,322$ Canada Paneling February 2014 Feb-14 20,959$ Canada Paneling March 2014 Mar-14 20,933$ Canada Paneling April 2014 Apr-14 20,345$ Canada Paneling May 2014 May-14 19,861$ Canada Paneling June 2014 Jun-14 25,836$ Canada Paneling July 2014 Jul-14 21,526$ Canada Paneling August 2014 Aug-14 21,699$ Canada Paneling September 2014 Sep-14 23,460$ Canada Paneling October 2014 Oct-14 21,358$ Canada Paneling November 2014 Nov-14 20,642$ Canada Paneling December 2014 Dec-14 22,843$ Canada Plywood January 2014 Jan-14 34,823$ Canada Plywood February 2014 Feb-14 33,845$ Canada Plywood March 2014 Mar-14 33,688$ Canada Plywood April 2014 Apr-14 33,448$ Canada Plywood May 2014 May-14 34,057$ Canada Plywood June 2014 Jun-14 32,579$ Canada Plywood July 2014 Jul-14 35,788$ Canada Plywood August 2014 Aug-14 34,838$ Canada Plywood September 2014 Sep-14 33,260$ Canada Plywood October 2014 Oct-14 33,787$ Canada Plywood November 2014 Nov-14 34,516$ Canada Plywood December 2014 Dec-14 33,546$ Canada Pressure Treated Lumber January 2014 Jan-14 24,371$ Canada Pressure Treated Lumber February 2014 Feb-14 23,995$ Canada Pressure Treated Lumber March 2014 Mar-14 23,993$ Canada Pressure Treated Lumber April 2014 Apr-14 23,416$ Canada Pressure Treated Lumber May 2014 May-14 23,824$ Canada Pressure Treated Lumber June 2014 Jun-14 22,344$ Canada Pressure Treated Lumber July 2014 Jul-14 23,181$ Canada Pressure Treated Lumber August 2014 Aug-14 22,738$ Canada Pressure Treated Lumber September 2014 Sep-14 23,560$ Canada Pressure Treated Lumber October 2014 Oct-14 25,041$ Canada Pressure Treated Lumber November 2014 Nov-14 24,760$ Canada Pressure Treated Lumber December 2014 Dec-14 23,463$ Canada Ready-To-Assemble Kits January 2014 Jan-14 35,654$ Canada Ready-To-Assemble Kits February 2014 Feb-14 34,526$ Canada Ready-To-Assemble Kits March 2014 Mar-14 34,923$ Canada Ready-To-Assemble Kits April 2014 Apr-14 35,632$ Canada Ready-To-Assemble Kits May 2014 May-14 34,891$ Canada Ready-To-Assemble Kits June 2014 Jun-14 35,373$ Canada Ready-To-Assemble Kits July 2014 Jul-14 34,527$ Canada Ready-To-Assemble Kits August 2014 Aug-14 34,684$ Canada Ready-To-Assemble Kits September 2014 Sep-14 34,896$ Canada Ready-To-Assemble Kits October 2014 Oct-14 34,762$ Canada Ready-To-Assemble Kits November 2014 Nov-14 35,154$ Canada Ready-To-Assemble Kits December 2014 Dec-14 34,230$ Canada Shims & Wood Shingles January 2014 Jan-14 21,236$ Canada Shims & Wood Shingles February 2014 Feb-14 20,679$ Canada Shims & Wood Shingles March 2014 Mar-14 21,174$ Canada Shims & Wood Shingles April 2014 Apr-14 21,300$ Canada Shims & Wood Shingles May 2014 May-14 20,058$ Canada Shims & Wood Shingles June 2014 Jun-14 20,372$ Canada Shims & Wood Shingles July 2014 Jul-14 21,303$ Canada Shims & Wood Shingles August 2014 Aug-14 21,130$ Canada Shims & Wood Shingles September 2014 Sep-14 20,862$ Canada Shims & Wood Shingles October 2014 Oct-14 20,402$ Canada Shims & Wood Shingles November 2014 Nov-14 21,086$ Canada Shims & Wood Shingles December 2014 Dec-14 21,842$ Canada Timber January 2014 Jan-14 21,597$ Canada Timber February 2014 Feb-14 21,217$ Canada Timber March 2014 Mar-14 20,336$ Canada Timber April 2014 Apr-14 21,197$ Canada Timber May 2014 May-14 20,830$ Canada Timber June 2014 Jun-14 21,319$ Canada Timber July 2014 Jul-14 20,844$ Canada Timber August 2014 Aug-14 20,794$ Canada Timber September 2014 Sep-14 20,604$ Canada Timber October 2014 Oct-14 20,536$ Canada Timber November 2014 Nov-14 21,743$ Canada Timber December 2014 Dec-14 21,559$ US Appearance Boards January 2012 Jan-12 4,581$ US Appearance Boards February 2012 Feb-12 4,989$ US Appearance Boards March 2012 Mar-12 4,815$ US Appearance Boards April 2012 Apr-12 5,005$ US Appearance Boards May 2012 May-12 4,432$ US Appearance Boards June 2012 Jun-12 4,422$ US Appearance Boards July 2012 Jul-12 4,537$ US Appearance Boards August 2012 Aug-12 4,195$ US Appearance Boards September 2012 Sep-12 4,195$

Country Product Month Year Time SalesUS Appearance Boards October 2012 Oct-12 4,502$ US Appearance Boards November 2012 Nov-12 4,948$ US Appearance Boards December 2012 Dec-12 4,363$ US Decking January 2012 Jan-12 8,626$ US Decking February 2012 Feb-12 8,372$ US Decking March 2012 Mar-12 9,111$ US Decking April 2012 Apr-12 9,118$ US Decking May 2012 May-12 9,014$ US Decking June 2012 Jun-12 9,209$ US Decking July 2012 Jul-12 9,600$ US Decking August 2012 Aug-12 9,775$ US Decking September 2012 Sep-12 8,618$ US Decking October 2012 Oct-12 8,151$ US Decking November 2012 Nov-12 8,657$ US Decking December 2012 Dec-12 8,544$ US Fencing January 2012 Jan-12 9,530$ US Fencing February 2012 Feb-12 10,040$ US Fencing March 2012 Mar-12 8,090$ US Fencing April 2012 Apr-12 10,028$ US Fencing May 2012 May-12 9,644$ US Fencing June 2012 Jun-12 10,337$ US Fencing July 2012 Jul-12 9,753$ US Fencing August 2012 Aug-12 9,274$ US Fencing September 2012 Sep-12 9,113$ US Fencing October 2012 Oct-12 9,632$ US Fencing November 2012 Nov-12 9,244$ US Fencing December 2012 Dec-12 8,387$ US Framing Lumber & Studs January 2012 Jan-12 9,612$ US Framing Lumber & Studs February 2012 Feb-12 10,180$ US Framing Lumber & Studs March 2012 Mar-12 8,721$ US Framing Lumber & Studs April 2012 Apr-12 8,046$ US Framing Lumber & Studs May 2012 May-12 8,318$ US Framing Lumber & Studs June 2012 Jun-12 8,764$ US Framing Lumber & Studs July 2012 Jul-12 8,894$ US Framing Lumber & Studs August 2012 Aug-12 9,501$ US Framing Lumber & Studs September 2012 Sep-12 9,828$ US Framing Lumber & Studs October 2012 Oct-12 7,850$ US Framing Lumber & Studs November 2012 Nov-12 9,529$ US Framing Lumber & Studs December 2012 Dec-12 9,731$ US Lattice January 2012 Jan-12 5,064$ US Lattice February 2012 Feb-12 5,055$ US Lattice March 2012 Mar-12 4,151$ US Lattice April 2012 Apr-12 4,454$ US Lattice May 2012 May-12 4,479$ US Lattice June 2012 Jun-12 5,060$ US Lattice July 2012 Jul-12 4,935$ US Lattice August 2012 Aug-12 4,239$ US Lattice September 2012 Sep-12 4,358$ US Lattice October 2012 Oct-12 3,981$ US Lattice November 2012 Nov-12 4,506$ US Lattice December 2012 Dec-12 5,158$ US Paneling January 2012 Jan-12 4,179$ US Paneling February 2012 Feb-12 4,496$ US Paneling March 2012 Mar-12 4,821$ US Paneling April 2012 Apr-12 5,054$ US Paneling May 2012 May-12 4,024$ US Paneling June 2012 Jun-12 4,147$ US Paneling July 2012 Jul-12 4,430$ US Paneling August 2012 Aug-12 4,555$ US Paneling September 2012 Sep-12 4,957$ US Paneling October 2012 Oct-12 4,528$ US Paneling November 2012 Nov-12 5,028$ US Paneling December 2012 Dec-12 4,846$ US Plywood January 2012 Jan-12 8,544$ US Plywood February 2012 Feb-12 10,280$ US Plywood March 2012 Mar-12 8,901$ US Plywood April 2012 Apr-12 9,168$ US Plywood May 2012 May-12 9,656$ US Plywood June 2012 Jun-12 9,062$ US Plywood July 2012 Jul-12 9,023$ US Plywood August 2012 Aug-12 9,070$ US Plywood September 2012 Sep-12 9,374$ US Plywood October 2012 Oct-12 9,498$ US Plywood November 2012 Nov-12 8,268$ US Plywood December 2012 Dec-12 8,095$ US Pressure Treated Lumber January 2012 Jan-12 3,957$ US Pressure Treated Lumber February 2012 Feb-12 4,876$ US Pressure Treated Lumber March 2012 Mar-12 4,065$ US Pressure Treated Lumber April 2012 Apr-12 4,982$ US Pressure Treated Lumber May 2012 May-12 3,961$ US Pressure Treated Lumber June 2012 Jun-12 4,659$

Country Product Month Year Time SalesUS Pressure Treated Lumber July 2012 Jul-12 4,544$ US Pressure Treated Lumber August 2012 Aug-12 4,014$ US Pressure Treated Lumber September 2012 Sep-12 4,591$ US Pressure Treated Lumber October 2012 Oct-12 4,873$ US Pressure Treated Lumber November 2012 Nov-12 4,863$ US Pressure Treated Lumber December 2012 Dec-12 4,278$ US Ready-To-Assemble Kits January 2012 Jan-12 4,785$ US Ready-To-Assemble Kits February 2012 Feb-12 5,268$ US Ready-To-Assemble Kits March 2012 Mar-12 4,984$ US Ready-To-Assemble Kits April 2012 Apr-12 4,117$ US Ready-To-Assemble Kits May 2012 May-12 5,231$ US Ready-To-Assemble Kits June 2012 Jun-12 4,567$ US Ready-To-Assemble Kits July 2012 Jul-12 4,600$ US Ready-To-Assemble Kits August 2012 Aug-12 4,977$ US Ready-To-Assemble Kits September 2012 Sep-12 4,177$ US Ready-To-Assemble Kits October 2012 Oct-12 4,496$ US Ready-To-Assemble Kits November 2012 Nov-12 5,268$ US Ready-To-Assemble Kits December 2012 Dec-12 3,967$ US Shims & Wood Shingles January 2012 Jan-12 4,290$ US Shims & Wood Shingles February 2012 Feb-12 4,310$ US Shims & Wood Shingles March 2012 Mar-12 5,118$ US Shims & Wood Shingles April 2012 Apr-12 4,898$ US Shims & Wood Shingles May 2012 May-12 4,178$ US Shims & Wood Shingles June 2012 Jun-12 4,683$ US Shims & Wood Shingles July 2012 Jul-12 4,871$ US Shims & Wood Shingles August 2012 Aug-12 5,161$ US Shims & Wood Shingles September 2012 Sep-12 4,597$ US Shims & Wood Shingles October 2012 Oct-12 5,344$ US Shims & Wood Shingles November 2012 Nov-12 4,614$ US Shims & Wood Shingles December 2012 Dec-12 3,996$ US Timber January 2012 Jan-12 5,036$ US Timber February 2012 Feb-12 4,395$ US Timber March 2012 Mar-12 5,019$ US Timber April 2012 Apr-12 3,823$ US Timber May 2012 May-12 5,015$ US Timber June 2012 Jun-12 4,843$ US Timber July 2012 Jul-12 4,135$ US Timber August 2012 Aug-12 4,824$ US Timber September 2012 Sep-12 5,168$ US Timber October 2012 Oct-12 4,771$ US Timber November 2012 Nov-12 4,387$ US Timber December 2012 Dec-12 4,341$ US Appearance Boards January 2013 Jan-13 4,541$ US Appearance Boards February 2013 Feb-13 5,187$ US Appearance Boards March 2013 Mar-13 4,560$ US Appearance Boards April 2013 Apr-13 4,338$ US Appearance Boards May 2013 May-13 3,788$ US Appearance Boards June 2013 Jun-13 4,470$ US Appearance Boards July 2013 Jul-13 3,794$ US Appearance Boards August 2013 Aug-13 4,533$ US Appearance Boards September 2013 Sep-13 4,722$ US Appearance Boards October 2013 Oct-13 4,562$ US Appearance Boards November 2013 Nov-13 4,852$ US Appearance Boards December 2013 Dec-13 4,901$ US Decking January 2013 Jan-13 9,682$ US Decking February 2013 Feb-13 9,823$ US Decking March 2013 Mar-13 8,596$ US Decking April 2013 Apr-13 9,507$ US Decking May 2013 May-13 10,084$ US Decking June 2013 Jun-13 9,484$ US Decking July 2013 Jul-13 9,283$ US Decking August 2013 Aug-13 9,153$ US Decking September 2013 Sep-13 9,094$ US Decking October 2013 Oct-13 10,161$ US Decking November 2013 Nov-13 9,283$ US Decking December 2013 Dec-13 9,827$ US Fencing January 2013 Jan-13 9,638$ US Fencing February 2013 Feb-13 8,668$ US Fencing March 2013 Mar-13 8,769$ US Fencing April 2013 Apr-13 9,092$ US Fencing May 2013 May-13 8,350$ US Fencing June 2013 Jun-13 9,064$ US Fencing July 2013 Jul-13 9,300$ US Fencing August 2013 Aug-13 9,069$ US Fencing September 2013 Sep-13 9,506$ US Fencing October 2013 Oct-13 9,210$ US Fencing November 2013 Nov-13 9,475$ US Fencing December 2013 Dec-13 8,704$ US Framing Lumber & Studs January 2013 Jan-13 9,870$ US Framing Lumber & Studs February 2013 Feb-13 8,901$ US Framing Lumber & Studs March 2013 Mar-13 8,011$

Country Product Month Year Time SalesUS Framing Lumber & Studs April 2013 Apr-13 8,990$ US Framing Lumber & Studs May 2013 May-13 8,248$ US Framing Lumber & Studs June 2013 Jun-13 8,431$ US Framing Lumber & Studs July 2013 Jul-13 10,310$ US Framing Lumber & Studs August 2013 Aug-13 8,820$ US Framing Lumber & Studs September 2013 Sep-13 8,745$ US Framing Lumber & Studs October 2013 Oct-13 9,568$ US Framing Lumber & Studs November 2013 Nov-13 8,802$ US Framing Lumber & Studs December 2013 Dec-13 9,033$ US Lattice January 2013 Jan-13 4,607$ US Lattice February 2013 Feb-13 4,692$ US Lattice March 2013 Mar-13 3,790$ US Lattice April 2013 Apr-13 4,165$ US Lattice May 2013 May-13 5,044$ US Lattice June 2013 Jun-13 4,674$ US Lattice July 2013 Jul-13 4,887$ US Lattice August 2013 Aug-13 4,906$ US Lattice September 2013 Sep-13 5,032$ US Lattice October 2013 Oct-13 4,089$ US Lattice November 2013 Nov-13 4,722$ US Lattice December 2013 Dec-13 5,386$ US Paneling January 2013 Jan-13 4,582$ US Paneling February 2013 Feb-13 4,893$ US Paneling March 2013 Mar-13 4,458$ US Paneling April 2013 Apr-13 5,073$ US Paneling May 2013 May-13 4,707$ US Paneling June 2013 Jun-13 4,330$ US Paneling July 2013 Jul-13 4,561$ US Paneling August 2013 Aug-13 3,905$ US Paneling September 2013 Sep-13 4,934$ US Paneling October 2013 Oct-13 4,274$ US Paneling November 2013 Nov-13 5,138$ US Paneling December 2013 Dec-13 4,390$ US Plywood January 2013 Jan-13 9,423$ US Plywood February 2013 Feb-13 9,166$ US Plywood March 2013 Mar-13 9,850$ US Plywood April 2013 Apr-13 9,768$ US Plywood May 2013 May-13 8,992$ US Plywood June 2013 Jun-13 9,954$ US Plywood July 2013 Jul-13 8,387$ US Plywood August 2013 Aug-13 8,513$ US Plywood September 2013 Sep-13 9,945$ US Plywood October 2013 Oct-13 8,506$ US Plywood November 2013 Nov-13 9,744$ US Plywood December 2013 Dec-13 8,222$ US Pressure Treated Lumber January 2013 Jan-13 4,155$ US Pressure Treated Lumber February 2013 Feb-13 4,010$ US Pressure Treated Lumber March 2013 Mar-13 4,044$ US Pressure Treated Lumber April 2013 Apr-13 3,995$ US Pressure Treated Lumber May 2013 May-13 4,680$ US Pressure Treated Lumber June 2013 Jun-13 4,254$ US Pressure Treated Lumber July 2013 Jul-13 4,499$ US Pressure Treated Lumber August 2013 Aug-13 4,739$ US Pressure Treated Lumber September 2013 Sep-13 4,989$ US Pressure Treated Lumber October 2013 Oct-13 4,822$ US Pressure Treated Lumber November 2013 Nov-13 4,397$ US Pressure Treated Lumber December 2013 Dec-13 3,764$ US Ready-To-Assemble Kits January 2013 Jan-13 4,996$ US Ready-To-Assemble Kits February 2013 Feb-13 5,149$ US Ready-To-Assemble Kits March 2013 Mar-13 5,094$ US Ready-To-Assemble Kits April 2013 Apr-13 4,240$ US Ready-To-Assemble Kits May 2013 May-13 5,095$ US Ready-To-Assemble Kits June 2013 Jun-13 4,934$ US Ready-To-Assemble Kits July 2013 Jul-13 4,069$ US Ready-To-Assemble Kits August 2013 Aug-13 4,545$ US Ready-To-Assemble Kits September 2013 Sep-13 5,106$ US Ready-To-Assemble Kits October 2013 Oct-13 4,796$ US Ready-To-Assemble Kits November 2013 Nov-13 4,568$ US Ready-To-Assemble Kits December 2013 Dec-13 4,844$ US Shims & Wood Shingles January 2013 Jan-13 3,858$ US Shims & Wood Shingles February 2013 Feb-13 4,077$ US Shims & Wood Shingles March 2013 Mar-13 4,087$ US Shims & Wood Shingles April 2013 Apr-13 3,825$ US Shims & Wood Shingles May 2013 May-13 3,901$ US Shims & Wood Shingles June 2013 Jun-13 5,210$ US Shims & Wood Shingles July 2013 Jul-13 5,082$ US Shims & Wood Shingles August 2013 Aug-13 4,850$ US Shims & Wood Shingles September 2013 Sep-13 5,071$ US Shims & Wood Shingles October 2013 Oct-13 4,921$ US Shims & Wood Shingles November 2013 Nov-13 5,240$ US Shims & Wood Shingles December 2013 Dec-13 5,243$

Country Product Month Year Time SalesUS Timber January 2013 Jan-13 4,730$ US Timber February 2013 Feb-13 4,607$ US Timber March 2013 Mar-13 4,356$ US Timber April 2013 Apr-13 4,996$ US Timber May 2013 May-13 4,526$ US Timber June 2013 Jun-13 4,723$ US Timber July 2013 Jul-13 4,423$ US Timber August 2013 Aug-13 4,607$ US Timber September 2013 Sep-13 4,181$ US Timber October 2013 Oct-13 4,382$ US Timber November 2013 Nov-13 4,730$ US Timber December 2013 Dec-13 4,192$ US Appearance Boards January 2014 Jan-14 6,412$ US Appearance Boards February 2014 Feb-14 5,219$ US Appearance Boards March 2014 Mar-14 6,281$ US Appearance Boards April 2014 Apr-14 5,092$ US Appearance Boards May 2014 May-14 5,595$ US Appearance Boards June 2014 Jun-14 6,607$ US Appearance Boards July 2014 Jul-14 6,331$ US Appearance Boards August 2014 Aug-14 6,303$ US Appearance Boards September 2014 Sep-14 5,884$ US Appearance Boards October 2014 Oct-14 5,050$ US Appearance Boards November 2014 Nov-14 6,244$ US Appearance Boards December 2014 Dec-14 5,349$ US Decking January 2014 Jan-14 12,470$ US Decking February 2014 Feb-14 11,975$ US Decking March 2014 Mar-14 15,067$ US Decking April 2014 Apr-14 13,875$ US Decking May 2014 May-14 14,332$ US Decking June 2014 Jun-14 15,277$ US Decking July 2014 Jul-14 12,565$ US Decking August 2014 Aug-14 13,607$ US Decking September 2014 Sep-14 13,536$ US Decking October 2014 Oct-14 13,413$ US Decking November 2014 Nov-14 13,468$ US Decking December 2014 Dec-14 14,362$ US Fencing January 2014 Jan-14 13,906$ US Fencing February 2014 Feb-14 12,177$ US Fencing March 2014 Mar-14 13,282$ US Fencing April 2014 Apr-14 14,027$ US Fencing May 2014 May-14 12,099$ US Fencing June 2014 Jun-14 12,407$ US Fencing July 2014 Jul-14 12,465$ US Fencing August 2014 Aug-14 12,320$ US Fencing September 2014 Sep-14 14,559$ US Fencing October 2014 Oct-14 13,572$ US Fencing November 2014 Nov-14 13,762$ US Fencing December 2014 Dec-14 12,958$ US Framing Lumber & Studs January 2014 Jan-14 15,350$ US Framing Lumber & Studs February 2014 Feb-14 15,427$ US Framing Lumber & Studs March 2014 Mar-14 15,670$ US Framing Lumber & Studs April 2014 Apr-14 14,264$ US Framing Lumber & Studs May 2014 May-14 17,328$ US Framing Lumber & Studs June 2014 Jun-14 16,357$ US Framing Lumber & Studs July 2014 Jul-14 16,803$ US Framing Lumber & Studs August 2014 Aug-14 15,468$ US Framing Lumber & Studs September 2014 Sep-14 14,203$ US Framing Lumber & Studs October 2014 Oct-14 17,406$ US Framing Lumber & Studs November 2014 Nov-14 16,981$ US Framing Lumber & Studs December 2014 Dec-14 16,424$ US Lattice January 2014 Jan-14 5,499$ US Lattice February 2014 Feb-14 5,114$ US Lattice March 2014 Mar-14 5,804$ US Lattice April 2014 Apr-14 4,923$ US Lattice May 2014 May-14 4,876$ US Lattice June 2014 Jun-14 5,665$ US Lattice July 2014 Jul-14 5,431$ US Lattice August 2014 Aug-14 5,150$ US Lattice September 2014 Sep-14 4,809$ US Lattice October 2014 Oct-14 4,685$ US Lattice November 2014 Nov-14 5,087$ US Lattice December 2014 Dec-14 6,018$ US Paneling January 2014 Jan-14 6,477$ US Paneling February 2014 Feb-14 6,440$ US Paneling March 2014 Mar-14 5,653$ US Paneling April 2014 Apr-14 5,566$ US Paneling May 2014 May-14 5,286$ US Paneling June 2014 Jun-14 6,876$ US Paneling July 2014 Jul-14 6,409$ US Paneling August 2014 Aug-14 6,571$ US Paneling September 2014 Sep-14 5,493$

Country Product Month Year Time SalesUS Paneling October 2014 Oct-14 6,459$ US Paneling November 2014 Nov-14 5,304$ US Paneling December 2014 Dec-14 6,186$ US Plywood January 2014 Jan-14 8,140$ US Plywood February 2014 Feb-14 6,863$ US Plywood March 2014 Mar-14 6,653$ US Plywood April 2014 Apr-14 7,315$ US Plywood May 2014 May-14 6,733$ US Plywood June 2014 Jun-14 6,700$ US Plywood July 2014 Jul-14 8,485$ US Plywood August 2014 Aug-14 8,207$ US Plywood September 2014 Sep-14 6,908$ US Plywood October 2014 Oct-14 7,864$ US Plywood November 2014 Nov-14 7,801$ US Plywood December 2014 Dec-14 7,845$ US Pressure Treated Lumber January 2014 Jan-14 9,794$ US Pressure Treated Lumber February 2014 Feb-14 8,831$ US Pressure Treated Lumber March 2014 Mar-14 8,100$ US Pressure Treated Lumber April 2014 Apr-14 8,322$ US Pressure Treated Lumber May 2014 May-14 8,779$ US Pressure Treated Lumber June 2014 Jun-14 7,734$ US Pressure Treated Lumber July 2014 Jul-14 8,880$ US Pressure Treated Lumber August 2014 Aug-14 7,779$ US Pressure Treated Lumber September 2014 Sep-14 8,667$ US Pressure Treated Lumber October 2014 Oct-14 9,940$ US Pressure Treated Lumber November 2014 Nov-14 9,823$ US Pressure Treated Lumber December 2014 Dec-14 8,459$ US Ready-To-Assemble Kits January 2014 Jan-14 2,408$ US Ready-To-Assemble Kits February 2014 Feb-14 1,744$ US Ready-To-Assemble Kits March 2014 Mar-14 2,375$ US Ready-To-Assemble Kits April 2014 Apr-14 2,424$ US Ready-To-Assemble Kits May 2014 May-14 2,428$ US Ready-To-Assemble Kits June 2014 Jun-14 2,126$ US Ready-To-Assemble Kits July 2014 Jul-14 1,866$ US Ready-To-Assemble Kits August 2014 Aug-14 1,880$ US Ready-To-Assemble Kits September 2014 Sep-14 1,812$ US Ready-To-Assemble Kits October 2014 Oct-14 1,823$ US Ready-To-Assemble Kits November 2014 Nov-14 2,404$ US Ready-To-Assemble Kits December 2014 Dec-14 1,769$ US Shims & Wood Shingles January 2014 Jan-14 5,976$ US Shims & Wood Shingles February 2014 Feb-14 6,071$ US Shims & Wood Shingles March 2014 Mar-14 6,382$ US Shims & Wood Shingles April 2014 Apr-14 6,322$ US Shims & Wood Shingles May 2014 May-14 5,490$ US Shims & Wood Shingles June 2014 Jun-14 5,328$ US Shims & Wood Shingles July 2014 Jul-14 5,996$ US Shims & Wood Shingles August 2014 Aug-14 6,043$ US Shims & Wood Shingles September 2014 Sep-14 6,208$ US Shims & Wood Shingles October 2014 Oct-14 5,377$ US Shims & Wood Shingles November 2014 Nov-14 5,828$ US Shims & Wood Shingles December 2014 Dec-14 6,611$ US Timber January 2014 Jan-14 6,224$ US Timber February 2014 Feb-14 6,272$ US Timber March 2014 Mar-14 5,966$ US Timber April 2014 Apr-14 5,799$ US Timber May 2014 May-14 5,906$ US Timber June 2014 Jun-14 5,716$ US Timber July 2014 Jul-14 6,114$ US Timber August 2014 Aug-14 6,246$ US Timber September 2014 Sep-14 5,020$ US Timber October 2014 Oct-14 5,448$ US Timber November 2014 Nov-14 6,567$ US Timber December 2014 Dec-14 6,128$

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