executive team information system and financial … · and financial reporting competencies, and...
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Executive Team Information System
and Financial Reporting Competencies, and Voluntary Adoption of XBRL Reporting
Presented by
Dr Jap Efendi
Assistant Professor University of Texas-Arlington
#2013/14-06
The views and opinions expressed in this working paper are those of the author(s) and not necessarily those of the School of Accountancy, Singapore Management University.
Executive Team Information System and Financial Reporting Competencies,
and Voluntary Adoption of XBRL Reporting
J. EFRIM BORITZ, University of Waterloo
JAP EFENDI, University of Texas-Arlington
American Accounting Association (AAA), the 2013 Canadian Academic Accounting Association (CAAA), the
2013 Pacific Asia Conference on Information Systems (PACIS), the 2013 International Symposium on Accounting
Information Systems (ISAIS), the 6th
University of Kansas International Conference on XBRL and Information
Systems, the 2013 AAA Information Systems Section Mid-Year Meeting, the 25th
XBRL International Conference,
as well as the research seminars at City University of Hong Kong, KonKuk University and University of Waterloo.
We are grateful to Won Gyun No and Lev Timoshenko for their assistance with data gathering. We also wish to
acknowledge the funding provided by the UW-SSHRC 4A research grant program and the University of Waterloo
Centre for Information Integrity and Information Systems Assurance, sponsored by CPA Canada, CaseWare IDEA
Inc. and ISACA (Toronto Chapter).
JEE-HAE LIM, University of Waterloo
* We would like to thank Christine Wiedman, participants, discussants and anonymous reviewers at the 2013
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EXECUTIVE TEAM INFORMATION SYSTEM AND FINANCIAL REPORTING
COMPETENCIES, AND VOLUNTARY ADOPTION OF XBRL
ABSTRACT
The issue of determinants of voluntary XBRL adoption has drawn considerable attention from the
academic community around the world. Specifically, higher profitability and stronger corporate
governance have been found to be significant factors associated with voluntary XBRL adoption in
the U.S. We focus on the characteristics of the executive team (CEO and CFO) to investigate
whether they are associated with the voluntary adoption of XBRL technology beyond the effect of
firm characteristics. We also evaluate whether these characteristics (financial and information
system competencies) within the executive team affected the quality of the XBRL-tagged filings,
such as the use of customized extensions, errors rates, and reporting lag. We find that higher levels
of information systems (IS) competencies were positively associated with early adoption of XBRL
but, surprisingly, we find that higher levels of financial expertise were negatively associated with
early adoption of XBRL. We also find that for voluntary adopters the two areas of expertise are
differentially associated with XBRL filing quality. IS competency is negatively associated with
the excessive use of extension taxonomies, errors and warning in the instance document, whereas
financial expertise is negatively associated with taxonomy errors and warnings. These results
extend the literature on the influence of management on corporate decisions and can be used as a
guide for investigating voluntary adoption of other financial reporting technologies, voluntary
XBRL filings in other jurisdictions and other voluntary disclosures such as sustainability reporting
and voluntary standardized business reporting in XBRL in jurisdictions where such reporting is
not mandatory.
Keywords: Extended business reporting language (XBRL); voluntary disclosure; information
systems (IS) competency; financial reporting competency.
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1. Introduction
The U. S. Securities and Exchange Commission (SEC) initiated its XBRL Voluntary Filing
Program (VFP) on the EDGAR System by its Final Rule on March 16, 2005 (SEC 2005). Under
the VFP, SEC registrants could voluntarily submit supplemental XBRL-tagged financial
information, with this information serving as “exhibits” to a number of mandatory EDGAR filings
until April 13, 2009 when the SEC made the use of XBRL mandatory (SEC 2009). The VFP
offered an opportunity to a firm’s management to make a choice of whether to participate in the
VFP by adopting the innovative financial reporting technology represented by XBRL. The vast
majority of companies chose not to participate in the VFP; however, over the course of the VFP,
165 participants voluntarily adopted this new financial reporting technology to provide XBRL-
tagged information to the SEC, the financial markets and other interested parties through 692
XBRL filings1 that paralleled their traditional filing processes.
Existing academic and professional literature suggests that the introduction of XBRL
offers substantial advantages to the adopting companies and certain aspects of the financial
reporting environment by aligning financial reporting with organizational plans, facilitating
communication among market players as well as enhancing the quality of stakeholders decisions
(Hodge et al. 2004). It can strengthen external reporting practices by improving disclosure through
the use of XBRL taxonomies (Bovee et al. 2002; Bonson et al. 2009a). Thus, bringing about the
benefits associated with increased disclosure such as lower cost of capital (Botosan 1997; Li et al.
2012) and higher market capitalization (Yoon et al. 2011; Kim et al. 2012).
XBRL is a financial reporting innovation that has the potential to significantly improve
corporate reporting by streamlining internal financial reporting practices by creating consistent
definitions of financial statement items, reducing the need for multiple spreadsheets in the
1 Officially, these were considered furnishings rather than filings (SEC 2005) but in this paper we
use the term filings. Available at: http://www.sec.gov/Archives/edgar/monthly/
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preparation of financial statements and rectifying the errors and inconsistencies associated with
copying and pasting information to and from multiple documents, leading to cost savings and
improved efficiency and effectiveness in the financial reporting and finance function, as well as
enhanced internal control (Amrhein et al. 2009). The use of XBRL thus promised to revolutionize
financial reporting by providing improved consistency, access to and transparency of data
contained in financial reports. However, the introduction of a new technology into the financial
reporting process can be risky, disruptive and costly and the promised benefits cannot be taken for
granted. A number of papers prior to and during the VFP (Debreceny et al. 2005; Boritz and No
2008) questioned many aspects of the XBRL implementations under the VFP. A recent study
(Harris and Morsfield 2012) by Columbia University questions whether the promise of XBRL can
ever be achieved. Without top executive support, it is unlikely that firm personnel would
voluntarily agree to participate in what was still an unproven financial reporting technology.
Therefore, there is reason to believe that only executives expecting a reasonable probability of
widespread XBRL adoption, with stronger signaling incentives and a positive attitude towards
XBRL would strive to take advantage of the opportunity provided by the VFP to learn how to take
early advantage of the benefits offered by XBRL adoption. These benefits include preserving and
further improving the advantageous position they had compared to their peers.
Hambrick and Mason (1984) theorize that organizational outcomes reflect the
characteristics of the entity’s upper echelons (top managers). Bamber et al. (2010) and Ge et al.
(2011) apply this theory to voluntary disclosure of management forecasts and accounting choices,
respectively, and find that certain characteristics of senior managers are associated with corporate
outcomes such as the frequency and quality of management forecasts and earnings quality.
Hameed and Counsell (2012) suggest that companies’ adoption of IT innovations is influenced by
top management’s role in such decisions, shaped by their knowledge, experience, expertise and
attitudes. These research findings imply that the characteristics of top executives not only
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determine whether a company decides to participate in the VFP, but also can have a pervasive
impact on the effectiveness of the XBRL implementation process and the reliability of XBRL
filings (Janvrin and No 2012).
This study examines how key members of the executive team (CEO and CFO) were
associated with their firms’ decision to participate in the VFP and the quality of their XBRL
implementations as reflected in the quality of their filings. Because XBRL is both a technological
and financial reporting innovation, our research questions revolve around whether executives with
both financial reporting and IS competencies influenced, promoted and supported the adoption of
XBRL. First, we examine executives’ IS- related and financial reporting/accounting- related
competencies to explain firms’ early adoption of XBRL through their participation in the VFP.
Second, since the competencies may be proxies for executives’ interests in, attitudes about and
resourcing of the XBRL implementation we investigate whether the competencies of the executive
team affect the quality of the XBRL implementation in terms of the frequency of customized
extensions and errors occurring in the filings, and the timeliness of those filings. We do not
propose that members of the executive team personally participated in the project; but rather, that
they lent their power, influence and resources to affect such outcomes. This effect is analogous to
that exerted by boards of directors, audit committees and “tone at the top” to promote financial
reporting quality.
Using a sample of VFP adopters from 2005 to 2009, we find that executives’ IS- related
competencies are associated with firms’ decision to participate in the VFP. The CEO’s
competencies are the most significant predictors of voluntary adoption, both IS- and financial
competencies-, with a more modest role being played by the CFO, despite the fact that XBRL is a
financial reporting innovation. Higher levels of IS competencies among executives are positively
associated with voluntary adoption of XBRL. Surprisingly, we find higher levels of financial
reporting competencies on the part of both the CEO and CFO are negatively associated with
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voluntary adoption of XBRL. This may be due to conservatism on their part, weaker belief in the
strategic benefits of participating in the VFP or weaker belief in the financial reporting benefits of
XBRL. We also find that for participants in the VFP the two sets of competencies are
differentially associated with XBRL filing quality: IS competencies are negatively associated with
errors and warnings in instance documents, the excessive use of taxonomy extensions and
reporting lag, whereas financial reporting competencies are negatively associated with errors and
warnings in the taxonomy. These results are robust to tests aimed at endogeneity considerations as
well as different dependent and control variable specification.
This study makes several contributions. First, we extend the literature on voluntary
adoption of financial reporting innovations by considering the case of XBRL, which represents
both a technological and a financial reporting innovation. Although prior research has investigated
many aspects of the adoption of information technologies in organizations (Hameed and Counsell
2012), there has been limited research on the adoption of financial reporting innovations.
Voluntary reporting and disclosure technologies that have been adopted during the past decade
include companies’ use of investor relations web sites for corporate disclosures (Debreceny et al.
2002; Trabelsi et al. 2008; Cormier et al. 2009;), the use of Web conference calls to disseminate
corporate information (Bushee et al. 2003), and the use of social media such as Facebook and
Twitter to deliver corporate news (Sprenger and Welpe 2010). However, the research on these
technologies has not considered the impact of executive team competencies on their adoption.
Second, we extend the literature on the influence of mangers’ characteristics on firm
behavior such as participation in the VFP and production of high quality XBRL filings. Prior
research shows a positive association between a firm’s propensity to provide voluntary
disclosures, strength of corporate governance, firm profitability and voluntary adoption of the
XBRL technology (Premuroso and Bhattacharya 2008; Timoshenko and Boritz 2013). However,
prior research on the VFP does not consider the impact of the knowledge, experience and expertise
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of the executive team on VFP participation and XBRL. Our study demonstrates that executives’
competencies or associated interests and attitudes that are proxied by competencies influenced
decisions about voluntary adoption of XBRL and implementation quality beyond the observable
effects of firm characteristics. These findings contribute to a deeper understanding of the
characteristics of firms and their management teams that influence their adoption of innovative
technologies and financial reporting innovations such as XBRL and their outcomes.
Our third major contribution is that executive teams with higher IS competencies are
more strongly associated with the voluntary adoption of XBRL than executive teams with higher
financial reporting competencies despite the fact that XBRL is a financial reporting innovation as
well as a technological innovation. In other words, it appears that the champions behind the
voluntary adoption decisions have technological rather than financial reporting rationales for them
and this is true even for executive members with non-IT specific responsibilities such as the CEO
and CFO. These findings should be very useful to “those responsible for selecting and developing
upper level executives” (Hambrick and Mason 1984: 193) since they shed light on the choices
made by top managers with certain types of competencies and the outcomes associated with those
competencies. Understanding the impact of key decision makers on XBRL adoption and
implementation choices may also help competitors understand the choices made by their
counterparts. Our findings should also be useful to policy makers, regulators and XBRL
proponents who may be targeting the wrong members of the executive team when developing an
XBRL adoption strategy.
Fourth, we contribute to the identification of antecedents of XBRL quality. Feedback on
the SEC’s XBRL initiative suggests that there is agreement on the potential benefits of XBRL, but
significant concerns raised over the cost versus benefits of XBRL and the quality of XBRL filings
(Harris and Morsfield, 2012). Our study documents that executive team members’ organizational
roles, competency types and competency levels are associated with XBRL implementation quality.
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Our results further demonstrate that executives’ IS and financial reporting competencies influence
the quality of financial reporting differently. These findings can be very important to standard-
setters, regulators and users of XBRL data interested in understanding the determinants of the
quality of XBRL data.
These results can be used as a guide for investigating voluntary XBRL filings in other
jurisdictions and other voluntary financial reporting initiatives such as XBRL filings for
sustainability reporting and voluntary standardized business reporting using XBRL in jurisdictions
where such reporting is not mandatory. The results can also be applied to the mandatory phase of
XBRL reporting to determine whether and how executives’ competency sets contribute to the
quality of mandatory filings.
The remainder of the paper is organized as follows. Section 2 outlines the motivation of
the study and provides a review of the extant literature on the topic of voluntary XBRL adoption
in the U.S. Also, it contains a discussion of relevant theory and hypotheses development. Section 3
describes our sample and research design. Section 4 presents empirical findings of the study.
Section 5 provides the summary of results, the discussion of limitations, as well as possible
directions for future research in the area.
2. Background and hypothesis development
The SEC’s XBRL VPF
The U. S. Securities and Exchange Commission (SEC) initiated its XBRL Voluntary Financial
Reporting Program (VFP) on the EDGAR System by its Final Rule on March 16, 2005 (SEC,
2005) until April 13, 2009. The primary goal of XBRL adoption is to eliminate time-consuming,
labor-consuming, labor-intensive and error-prone practices that are currently used for generating
and exchanging financial reports. XBRL (eXtensible Reporting Language) is a standardized
approach to tagging business information such as financial statements, so that it can be imported
into users’ analytical software to perform a variety of analyses without any need for the user to
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manually process the information. The computer readable XBRL data is easy to access and
analyze company in a timely fashion, adding to the transparency of company filings. Furthermore,
XBRL facilitates continuous auditing, thereby maximizing the frequency with which financial
information is reviewed and facilitating the enforcement of corporate accountability legislation
(SEC, 2009).
The two main components of an XBRL tagging process are the structured list (called a
Taxonomy) of authoritative, standardized tags corresponding to business concepts such as Cash,
Revenues, etc. and the relationships among them, and the rules governing the tagging process
(called a Specification) based on XML, a widely-used internet-oriented language for tagging
information. In each jurisdiction a body with the authority to do so (e.g. in the U.S. it is the FASB)
establishes the taxonomy and makes it available on a website for users to download into their
tagging system. If some relevant tags are not included in the taxonomy, companies can add their
own tags by creating customized taxonomy extensions, which is one of the features of extensible
languages such as XBRL. Company-specific XBRL files (called Instance documents) are
developed by mapping companies’ financial statement line items to the official XBRL taxonomies
and the customized extension taxonomies and adding the tags from those taxonomies to the line
items as if they were bar codes on grocery items. Typically, preparers use tagging software to
apply the tags or outsource this process to financial printers and make their instance documents
available on regulators’ websites (e.g., in the U.S. it is the SEC’s Edgar) or their own websites for
users to download into their analytical applications.
VFP research
Since its inception in 2005, the VFP has generated considerable interest from accounting
academics. Debreceny et al. (2005) strongly support the SEC’s proposal and consider XBRL
adoption vital in the democratization of markets. One important research stream focuses on firm-
specific characteristics of voluntary adopters of XBRL, such as their corporate governance
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(Premuroso and Bhattacharya 2008), the accuracy of their initial XBRL filings (Bartley et al.
2010), and the information content of their filings (Efendi et al. 2011). The voluntary nature of
electing to file in XBRL means that these studies may be given to a self-selection bias. 2
However,
because of the ultimate corporate benefits associated with early adoption of XBRL through
participation in VFP, when a firm submits its financials in XBRL format, it can be viewed as a
signal to the market that management teams are committed to transparency, thereby increasing the
market’s perceived legitimacy of the adopting firm.
Several small sample interview-based studies have investigated executives’ attitudes
towards XBRL. For example, Pinsker and Li (2008) study the perceived and actual benefits of
voluntary XBRL adoption by interviewing four executives involved in XBRL adoption cases in
Canada, Germany, South Africa, and the US who reported that both the anticipated adoption
benefits and the actual benefits involved cost savings through increased processing capability,
higher efficiency (decreased data redundancy), and lower cost of bookkeeping. They also consider
XBRL to be a key emerging technology in financial reporting and expected a “first-mover”
advantage in the market that would create a competitive advantage over their rivals who did not
use XBRL and would help to reduce the cost of capital (Botosan 1997; Li et al. 2012).
Using the Delphi technique to investigate the possible motives that led companies to
participate in the VFP, Bonson et al. (2009b) report that according to XBRL experts, the most
prominent motivation for companies to participate in the VFP was to obtain a deeper knowledge
of XBRL in order to benefit from its advantages. This motivation could be viewed as the
underlying reason for all of the others, including firms’ desire to achieve a corporate image as a
pioneer in technology, to leverage XBRL metadata, including business rules, which allow for
2 In considering relevant firm-specific characteristics, several studies have produced mixed results
(Premuroso and Bhattacharya 2008; Callaghan and Nehmer 2009; Efendi et al. 2011) possibly due
to small sample sizes and other limitations.
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better re-use of financial information and better integration with software applications and to
improve their image in the financial markets. Behavioral studies of users indicate that users prefer
XBRL to data in pdf format if they have access to tools that enable them to take advantage of the
features of XBRL and have more favorable impressions of companies that issue financial
statements in XBRL format (Pinsker and Wheeler 2009). However, not all market participants
would welcome the improvements in financial reporting associated with the facilitated access to
corporate data and increased transparency and would therefore not choose to participate in the
VFP.
Extant research and professional guidance (COSO, 1992) suggest that the tone at the top
established by upper management (CEO and CFO) and VFP participation should be positively
associated because the quality of corporate governance that influences tone at the top is one of the
factors that is positively associated with participation in the VFP (Premuroso and Bhattacharya,
2008). Therefore, executives with IS and/or financial reporting-oriented competencies would be
expected to champion early adoption and high quality of XBRL to gain the competitive
advantages from doing so. They also would be expected to exert their executive power to
influence the decision to participate in the VFP and to provide resources for the project to ensure
its success (Finkelstein, 1992).
Executives’ IS competency and VFP participation
Researchers and practitioners have argued that the management of IT and leadership in IT must be
a shared endeavor between the top executive team and IS professionals (Sambamurthy and Zmud
1999; Bassellier et al. 2003), especially new innovative technology adoption. Prior research
documents that IT competence can build competitive advantages when the competence is
comprised of a synergistic relation between experience and knowledge (Bassellier et al. 2001).
Experience in IT can be gained at the level of IT projects and IT organizational management over
time or from previous positions. In fact, as IT experience increases the management team’s
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understanding of IT problems and solutions, their IT leadership is strengthened (Bassellier et al.
2003). Overall IT experience is found to have an effect on the process of new innovations
(Kollmann et al. 2009), such as e-business, ERP, and XBRL, etc. While experience is the basis to
create and develop tacit knowledge, education provides declarative or explicit knowledge for IT
experts.
Often IS- related explicit knowledge from formal academic degrees allows executives to
exhibit IT leadership that better prepares the executive to cope with complex problems and
develop innovative strategic solutions (Wang and Alam 2007; Wiersema and Bantel 1992). IS-
related education, expert, or experience are more likely to complement each other over the years,
thus, senior IT executives with greater IS- related education or experience attract public
recognition for their firm’s IT capability reputation (Lim et al. 2012), foster the creation of a cycle
of positive reciprocity with their IT executives (Lim et al. 2013a), and further enhance higher
synergetic relationship between the CEO and senor IT executives (Lim et al. 2013b). Therefore,
experience and expertise in the IT domain are accompanied by IS competencies that may represent
the executive teams’ competencies in a holistic manner and eventually improve a firm’s ability to
achieve competitive technological advantages.
Given that IS competencies are important in choosing to adopt an IT innovation such as
XBRL, when the manager responsible for the adoption decision perceives a high level of
usefulness and exercises a favorable attitude towards technology, that manager’s employer
(executives) is more likely to adopt XBRL (Davis 1989; Rogers 1995; Pinsker 2008). We expect
that executives with higher IS competencies are an important influence in choosing to adopt an IT
innovation. This leads to the following hypothesis:
HYPOTHESIS 1. Firms with executive team members with higher levels of information
system competencies are more likely to participate in the voluntary XBRL filing
program.
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Executives’ financial reporting competency and VFP participation
XBRL is a key element in improving the transparency of corporate financial reporting and market
efficiency (Bergeron 2003; Pinsker and Li 2008; Kim et al. 2012). Successful implementation of
this technology may require members of the executive team to promote high levels of information
quality. While there is no consistent definition of information quality in the accounting literature,
relevance and faithful representation are the two key dimensions cited by the FASB (2010) in its
conceptual framework. In the context of XBRL, these concepts relate to the quality of the XBRL
taxonomy used to prepare the XBRL instance documents, the completeness of the XBRL files,
mapping of the source information to the taxonomy and justification of taxonomy extensions,
consistency of the tagged information with the source files and proper structure of the XBRL files
(AICPA 2012).
An emerging line of accounting research indicates that the accounting and/or financial
reporting related competency of executives plays an important role in determining the quality of
financial reporting. Specifically, a professional certification or other accounting and financial
reporting related work experience generally contributes more significantly to the effectiveness of
the executives, such as CFO (Aier et al. 2005; Li et al. 2010), or the audit committee (e.g.,
DeZoort and Salterio 2001; McDaniel et al. 2002; Naiker and Sharma, 2009) than members
without such qualifications or experience. In addition, Aier et al. (2005) argue that financial
literacy is negatively associated with financial reporting errors (using restatements as a proxy).
Firms are less likely to restate their earnings, especially when CFOs have more years of work
experience, advanced degrees, and professional certification (like a CPA). This study extends the
literature on background characteristics of executives by focusing on the financial reporting
competencies of the executive teams in companies that participated in the VFP.
As noted, XBRL is a financial reporting technology. As such, one would expect that
financial reporting executives and other executives with financial reporting competencies would
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be involved in the decision whether to participate in the VFP and what resources to invest in the
project. If executives with financial reporting competencies view XBRL as an innovative and
effective financial reporting technology, then they might be expected to push for adoption of
XBRL during the VFP. However, if the financial reporting side is uninformed, unenthusiastic or
conservative in its view of the benefits of XBRL, then it might be hesitant to promote the adoption
of this technology (Troshani and Rao 2007; Pinsker and Li 2008). This leads to the following
hypothesis:
HYPOTHESIS 2. Firms with executive team members with higher levels of financial
reporting competencies are more likely to participate in the voluntary XBRL filing
program.
Executives’ IS and financial reporting competencies and VFP quality (extensions)
Under the SEC’s approach, filers can create customized extension elements when there are no
elements in the U.S. GAAP taxonomy to support the concepts contained in their financial
statements and supporting note disclosures (SEC 2009). At the same time, the SEC sets out a
decision hierarchy that filers must follow when making the decision to create the customized
extensions. Necessary extensions that comply with the SEC’s various rules have implications for
information users.
Several studies have addressed extensions as a proxy for data quality of XBRL filings.
Chou (2006) and Chou and Chang (2008) use the proportion of company-defined elements used
over total elements used in XBRL instances to measure data quality, so that a smaller percentage
represents a better fit or simpler reporting.3 For their respective samples, Chou (2006) and Chou
and Chang (2008) report fit rates in the interval from 0 to 71% and from 0 to 89%. A low rate
implies that the standard taxonomy is more complete, requiring fewer customized extensions and
contributing to comparability among filers, whereas a higher rate will make automated corporate
3 The fit variable from Chou (2006) and Chou and Chang (2008) and the completeness variable
from Zhu and Fu (2009) and Zhu and Wu (2010a and 2010b) sum up to 1 for a given company:
Fit + Completeness = 1.
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filings comparisons among peers more difficult. In a similar vein, Debreceny et al. (2011)
investigate extensions to the 2009 U.S. GAAP Taxonomy in the first year of XBRL filings under
the SEC’s mandatory XBRL filing program to assess the impact of extensions on the quality and
comparability of the XBRL-tagged data. They conclude that about 40% of the extensions were
unnecessary. However, none of these studies explain why some firms have the unnecessary
extensions when others do not, and what role, if any, the competencies of the executive team play
in contributing to the use of such unnecessary extensions or in ensuring high levels of information
quality. This, in turn, suggests that it is necessary to extend the literature on both IS and financial
reporting competencies that influence the quality of financial reporting (e.g. extensions) by
focusing on the competencies of the members of the executive team. This leads to the following
hypothesis:
HYPOTHESIS 3. The quality of the voluntary XBRL filings, as measured by the number
of extensions used, varies according to the level of executives IS and financial reporting
competencies.
Executives’ IS and financial reporting competencies and VFP quality (instance and taxonomy
errors/warnings)
Another XBRL quality metric focuses on types of XBRL errors, including validation warnings in
taxonomy extensions and instance documents. For example, Boritz and No (2008) check all of the
304 XBRL filings submitted by 74 participants in the VFP from its initiation in 2005 to December
31, 2007 with regard to their conformity with the suggested XBRL taxonomies, specifications, and
the requirements for XBRL filings. Using two types of validation software, they find that 272
(89.5%) of the filings passed the taxonomy validation tests, while only 104 (34.2%) passed the
instance document validation test.
Extension taxonomy related metrics of quality would be more likely to fall under the
purview of the financial reporting function than the IT function as they relate to mapping the
company’s financial statement items to the standard taxonomy and creating extension taxonomies
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to add items deemed to be missing or insufficiently precise. Such assessments would require
financial expertise and would be influenced and supported by members of the executive team with
such expertise. In contrast, instance document related metrics of quality such as validation errors
and warnings would more likely fall under the purview of individuals with IS competencies as
they relate to using instance creation software to produce an instance document consistent with the
XBRL specifications and best practices. Such activities would be influenced and supported by
members of the executive team with IT expertise. As mentioned earlier, previous studies of XBRL
quality describe and document the frequency of various types of errors, but do not explain the
reasons behind them. We suggest that IS and financial reporting competencies could be proxies for
the types of emphasis the entity’s executives and the organizational units that they lead place on
the implementation quality of the XBRL filing. This leads to the following hypothesis.
HYPOTHESIS 4. The quality of the voluntary XBRL filings as measured by total
problems, which consists of the number of taxonomy related and instance related errors
and warnings, vary according to the level of executives’ IS and financial reporting
competencies.
Another measure of the quality of XBRL filings is their timeliness. Information delays
are associated with weaker quality of information (Krishnan and Yang 2009). Although there is no
deadline specified for filings under the VFP, we expect executives with higher level of interest in
the success of XBRL would manage or influence their XBRL filings so that they would be
submitted in a more timely manner; i.e., submitted fewer days after a fiscal period end (e.g.,
reporting lag). This leads to the following hypothesis.
HYPOTHESIS 5. The quality of the voluntary XBRL filings as measured by the
reporting lag varies according to the level of executives’ IS and financial reporting
competencies.
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3. Method
Sample selection
All filings made during the VFP are obtained from the U.S. Securities and Exchange Commission
(SEC) Interactive Financial Report Viewer website4 from 2005 to 2009. For our sample period,
we identify 692 VFP interactive submissions from 138 firms. A subsequent cross-match with
Compustat results in a sample of 102 firms with available financial data for our adoption analysis
and 483 submissions for our VFP quality analysis. We observe a steady increase in VFP
participation over the years, except in the year 2009.5 Panel A of Table 1 summarizes our sample
selection procedures and the sample distribution by years.
For the 102 VFP sample, we construct a matched-pair control group using the propensity
score matching approach (PSM, hereafter) to generate a sample of control firms that mimic our
sample of VFP firms based on a broad range of firm level characteristics that have been known to
affect VFP participation. The PSM procedure allows us to efficiently match along multiple
dimensions at the firm level, while not making assumptions at the executive level with regard to
VFP participation. We start with the 102 sample firms, plus 42,349 non-VFP firms in the same
year with available financial data. Using this sample of 42,349 observations, we estimate a logistic
regression where the dependent variable, VFP, is equal to one if the firm participated in the VFP,
and 0 otherwise. We use as covariates those firm level characteristics that have been identified in
prior literature to potentially impact the VFP participation decision, including SIZE, Profitability
(ROA), and MTB ratio. The logistic regression has reasonable explanatory power (Woolridge
2002), with adjusted Pseudo R2 of 18.2%. Using this model, a propensity score is derived for each
observation based on its predicted probability of VFP participation (VFP-XBRL). We then match
each sample firm without replacement to one unique control firm by identifying pairings within
4 available at: http://www.sec.gov/Archives/edgar/monthly/
5 The SEC announced mandatory XBRL adoption in the same year, April 15, 2009 which marked
the end of the VFP.
17
the same two-digit SIC code that result in the smallest propensity score differences (Non-VFP-
XBRL).
To identify executives’ IS and financial reporting competencies in both sample and
control firms, we search proxy statements, such as Form 10-K and DEF-14A, from the U.S. SEC.
In order to enhance the completeness and accuracy of our data, we also conduct a subsequent
manual review of each IT executive’s biographical information via Lexis-Nexis, as well as fifteen
online information sources.6 These overall comprehensive searches from various sources provide
information about executives’ biography, title, tenure, and other information.
Panel A of Table 1 describes the quality of the 483 VFP filings submitted. The mean
proportion of extension usage (EXT) is 0.24 (Extension Element / Total Element), which suggests
that VFP participants had issues using the established taxonomy. Although Instance and
Taxonomy Problems exist, the total problems (TOTALPB) are relatively small in number. Mean
Reporting Lag (RLAG) indicates that on average it takes 12.6 weeks after fiscal-period-ends for
firms to submit their XBRL reports.
Panel B of Table 1 provides a summary of the sample distribution based on industry
distribution by two-digit SIC code. The 102 VFP-XBRL firms cover ten industry groups. Among
them, the Metal, Machinery, Equipment, and Instruments industry has the highest number of VFP
firms, followed by the Banking and Finance Service, Transpiration, and Chemicals, Petroleum and
Coal, Rubber and Plastic industries.
[Insert Table 1]
Dependent variables
To examine the effect of the executive team’s IS and/or financial reporting competencies on VFP
participation (HYPOTHESES 1 and 2), our dependent variable is VFP, which equals one if the
6 Boardroominsiders.com, Businessweek People, Career Advantage.net, Company Press Release,
Evanta.com, Factiva, Forbes People Tracker, Informwationweek, Linkedin, Marketwatch,
Mergent Online, Reuters, Resource.Bnet.Com, Womenworthwatching.com, and Zoominfo People.
18
firm participated in the VFP and 0 otherwise. Furthermore, to investigate the effect of the
executive team’s IS- and/or financial reporting competencies on VFP quality (HYPOTHESES 3,
4, and 5), our dependent variables present several aspects of VFP Quality7 in the participants’ first
VFP filings. VFP Quality is calculated in the following ways: 1) the proportion of customized
extensions usage of the number of total elements (EXT); 2) the number of instance validation
errors and warnings (INSTANCE); 3) the number of taxonomy validation errors and warnings
(TAXANOMY); 4) the sum of both instance and taxonomy problems (TOTALPB); and 5) the
number of weeks it takes a company to submit an XBRL filing after a fiscal period end (RLAG).
Independent variables
IS competency
According to IS strategic leadership literature, IS- related explicit knowledge and experience
enable IS executives to exhibit IS leadership and to leverage the business value of IT. Consistent
with the recent research (Lim et al. 2012, 2013a & b), IS competencies have been measured by
formal IS- related education, prior IS- related employment, and years of IS- related experience.
First, IS- related education provides the declarative or explicit knowledge that better prepares the
executive to cope with complex problems and develop innovative strategic solutions (Wiersema
and Bantel 1992; Wally and Baum 1994; Geletkanycz and Boyd 2011; Lim et al. 2012, 2013a &
b). Second, IS- related experience forms the basis for improved understanding of IT and business
problems that help a firm obtain a competitive advantage (Bassellier et al. 2001; Bassellier et al.
2003; Lim et al. 2012, 2013a & b) and serves as a signal of the ability of an executive team to
implement the firm’s IS strategy (Chatterjee et al. 2001; Lim et al. 2012, 2013a & b).
Therefore, we use various combinations of variables for representing IS competency
characteristics of the following executives: CEO and CFO. The variable (ITacd) is one if the
executive members have an IS- related academic degree, zero otherwise. The variable (ITjob) is
7 We acknowledge the dataset provided by Boritz and No (2008).
19
one if the executive members have held an IS- related jobs8, zero otherwise. The variable (ITfirm)
is one if the executive members have IS- industry related experience, zero otherwise. We code
CEOIS (CFOIS) equal to one if a CEO (CFO) has any of the three IT competency characteristic
one year before VFP adoptione, zero otherwise.
Financial reporting competency
Similarly, we measure executives’ accounting and financial reporting related competencies. The
variable (ACCpro) is one if the executives have a professional certification (e.g. CPA, CA, and
other accounting- and/or financial reporting related certifications), zero otherwise. The variable
(ACCexp) is one if the executives have experience as a public accountant, auditor, principal or
chief financial officer, controller, treasurer, or principal or chief accounting officer, zero
otherwise. We code CEOACC (CFOACC) equal to one when a CEO (CFO) has any of the two
described financial reporting competencies one year before VFP adoption, zero otherwise.
Control variables (CVs)
Senior IT executives’ technical and business knowledge are essential to successful innovation
(Earl and Feeny 1994), more powerful senior IT executives, such as Chief Information Officer
(CIO) are more likely to succeed in promoting their vision among members of the firm’s top
management team (Chatterjee et al. 2001; Lim et al. 2013b), in sustaining IT capability reputation
(Lim et al. 2012 & 2013a), and in justifying the need for allocation of resources for strategic IT
projects (Armstrong and Sambamurthy 1999). Therefore, we control for the effectiveness of senior
IT executives and the company’s IT strategy. We first identify if a firm has a senior IT executive,
such as CIO or other types of IT executives, and code ITEXE equal to one, zero otherwise.
8 Consistent with recent research (Lim et al. 2012, 2013a & b), IS- related jobs (e.g. 317 different
official titles for senior IT executives) were considered: e.g., Chief Information Officer (CIO),
Chief Technology Officer (CTO), Sr. V.P. of Information Systems (IS)/Information Technology
(IT)/Computing Information Systems (CIS)/Management Information Systems (MIS); V.P. of
IS/IT/CIS/MIS; Dir. of IS/IT/CIS/MIS; Exec. Dir. of IS/IT/CIS/MIS; Managing Director of
IS/IT/CIS/MIS; Pres. Dir. of IS/IT/CIS/MIS.
20
Second, following the recommendation by Dehning et al. (2003), we define three broad categories
depending on the strategic role of IT: The (INFO) variable is one if firms engage in informative IT
strategic role (i.e., providing new information about business activities to senior management, to
employees across the firm, and to customers for better decision making), zero otherwise; the
(AUTO) variable is one if firms engage in automative IT strategic role (i.e., replacing human labor
by automating business processes), zero otherwise. The (TRANS) variable, transformative strategy
(i.e., redefining business and industry processes and relationships) is used as a baseline.
To evaluate the effect of the executive team’s competencies on VFP participation, we
control for the size of the firm (SIZE), using the natural logarithm of market capitalization at the
end of the quarter (Kothari et al. 2009). Prior studies have consistently shown that larger firms are
more likely to participate in VFP (Premuroso and Bhattacharya 2008; Callaghan and Nehmer
2009, etc.) thus it is necessary to include SIZE in the regression analysis. The market to book ratio
(MTB) captures the growth potential perceived in the market, which is related to the information
risk. MTB is calculated as the market capitalization divided by total shareholders’ equity at the end
of the quarter to control for firm growth (Kothari et al. 2009). We control for a firm’s financial
loss (LOSS), measured as one if the net income is negative, zero otherwise. The need to control for
the past financial performance of a firm (ROA) is twofold: (1) a firm's strategic IT choice could be
a function of its past performance (Santhanam and Hartono, 2003; Lim et al. 2012, 2013a & b),
and (2) it is likely that the selection of firms with superior IT by industry experts might be
influenced by the firm's past performance (Bharadwaj, 2000). In addition, financial risks increase
as a firm’s financial leverage increases (Kothari et al. 2009), and highly leveraged firms tend to
disclose more financial information to reassure creditors and to signal their confidence to the
public security markets (Malone et al. 1993); thus, we include the leverage ratio (LEV). Each
financial variable was winsorized at the 1% and 99% levels to control for any potential outliers.
21
Since effective overall governance at the firm-level can lead to effective IT adoption and
governance, we proxy for the effectiveness of board monitoring using two measures, the board
size (BOARDSIZE) and the CEO duality (CEOCHAIR).9 The size of the board represents expertise
and the monitoring scheme in mitigating agency costs (Anderson et al. 2004). When a CEO also
serves as the chair of the Board, it is viewed as a proxy for the degree of CEO influence, as well as
the relationship between the CEO chair’s and the Board’s monitoring function (Alexander et
al.1993, Dechow et al. 1996, Li et al. 2007). Finally, we control for the fixed industry and year
effects.
Instrmental variables (IVs)
We include two instrumental variables for use in later robustness tests: R&D and ITINT. R&D is a
proxy for the firm’s general level of investment in IT innovation as a proportion of its sales. High
tech IT intensive firms (ITINT) would be expected to have executives with IT competencies.
Research design
Probit regression models in Equations (1-3) are used to test Hypothesis 1 and Hypothesis 2, which
assess the effects of the executive team’s IS- and financial reporting- related competencies on VFP
participation. To examine the executive team’s IS- and financial reporting- related competencies
separately, we use Equation (1) for IS competencies and Equation (2) for financial reporting
competencies:
VFP = β0 + β1CEOIS + β2CFOIS + β3ITEXE + β4AUTO + β5INFO + β6SIZE + β7MTB
+ β8LOSS + β9ROA + β10LEV + β11BOARDSIZE + β12CEOCHAIR + β13INDUSTRY
+ β14YEAR + ε (1)
VFP = β0 + β1CEOACC + β2CFOACC + β3ITEXE + β4AUTO + β5INFO + β6SIZE + β7MTB
+ β8LOSS + β9ROA + β10LEV + β11BOARDSIZE + β12CEOCHAIR + β13INDUSTRY
+ β14YEAR + ε (2)
9 As a sensitivity analysis, we also consider to include (1) the number of independent board and
(2) the frequency of board meetings, and find similar results (see the additional tests section).
22
As a full model, we use Equation (3) to test how the executive team’s IS- and financial reporting-
related competencies simultaneously influence the firm’s VFP participation:
VFP = β0 + β1CEOIS + β2CFOIS + β3CEOACC + β4CFOACC + β5ITEXE + β6AUTO + β7INFO
+ β8SIZE + β9MTB + β10LOSS + β11ROA + β12LEV + β13BOARDSIZE
+ β14CEOCHAIR + β15INDUSTRY + β16YEAR + ε (3)
As stated previously, our dependent variable for Hypothesis 1 and Hypothesis 2 is VFP, which
equals one if the firm participated in the VFP, and zero otherwise. All other variables are as
defined above. We compare the effect of the executive team’s IS and/or financial reporting
competency level on VFP- vs. Non-VFP firms.
To examine Hypothesis 3, Hypothesis 4 and Hypothesis 5, the impact of the executive
team’s competencies on XBRL filing quality during the VFP, we use the logistic regression model
in Equation (4):
VFP Quality = β0 + β1CEOIS + β2CFOIS + β3CEOACC + β4CFOACC + β5ITEXE + β6AUTO
+ β7INFO + β8SIZE + β9MTB + β10LOSS + β11ROA + β12LEV
+ β13BOARDSIZE + β14CEOCHAIR + β1510-K + β16REPORTNUM
+ β17INDUSTRY + β18YEAR+ e (4)
VFP Quality is calculated in five ways: 1) the rates of customized extensions usage (EXT) over the
number of total elements; 2) the number of instance validation errors and warnings (INSTANCE);
3) the number of taxonomy validation errors and warnings (TAXANOMY); 4) the sum of both
instance and taxonomy problems (TOTALPB); and 5) the number of weeks it takes a company to
submit an XBRL filing after a fiscal period end (RLAG). Since annual reports typically contain
more elements and take longer to produce compared to quarterly reports (e. g., Qi et al. 2000;
Asthana and Balsam 2001; Griffin 2003), we include an annual report (10-K) dummy variable,
which equals to one for a 10-K filing, otherwise zero. As learning curves vary in XBRL endeavors
over the years, we also consider the cumulative experience level of the firm by including,
23
REPORTNUM, which corresponds to the Nth
number of the XBRL filing produced by a VFP firm.
All other variables are as defined previously.
4. Results
Table 2 presents descriptive statistics of the sample, comparing the split between VFP-XBRL vs.
Non-VFP-XBRL firms. Consistent with our expectation, VFP-XBRL firms with CEOs with higher
levels of IS competencies (e.g., CEOs that have obtained IS related academic degree, and had IS
related jobs, or worked in an IS/IT firm before) are more likely to participate in the VFP than Non-
VFP-XBRL firms. About a quarter of VFP firms have CEOs with IT competencies compared to
12.9% for control firms. Surprisingly, CEO and CFO financial reporting competencies are lower
for VFP-XBRL firms. Only 3.9% (57.4%) of VFP firms have CEO (CFO) with financial
competencies compared to 12.8% (75.2%) for control firms. This indicates that executives with
higher accounting- and financial reporting- competencies are less likely to participate, perhaps
because those who have financial reporting competencies tend to be more risk averse (more
conservative) with regard to new technology adoption or doubt its benefit/cost value. Importantly,
the number of CIOs or other IT executives is significantly higher for VFP-XBRL firms (p-value <
0.01). This may be a reflection of the comparatively high proportion of high tech firms among the
VFP firms.
Table 2 also shows means and medians for our control variables. At the company level,
our sample firms (VFP-XBRL) are larger (SIZE), lower loss (LOSS), and more profitable (ROA),
compared to the control sample (Non-VFP-XBRL). Among the governance variables, our sample
firms have larger board size and greater CEO duality (e.g. when the CEO and chairman of board,
positions are held by the same person, this individual often wields significant power). The results
for the control variables are consistent with prior studies.
[Insert Table 2]
24
Table 3 presents the correlation table. It is interesting to observe that IS competencies are
correlated. CEO IS Competencies (CEOIS) have significant positive correlations with CFO IS
Competencies (CFOIS) and the presence of a CIO or IT expert position in a company. However,
CFO IS Competencies are not significantly correlated with the presence of the latter. This can be
interpreted as the importance of the CEO in driving the IT direction of a firm. We also observe
CEO financial reporting competencies (CEOACC) are correlated with CFO financial reporting
competencies (CFOACC). None of the variance inflation factors (VIFs) in our regressions is
greater than 5, which is well below the suggested multicollinearity problem threshold of 10
(Marquandt, 1980; Gujarati, 1995). Our examination of the standard errors and size of the
coefficients also shows that they are not sensitive to the inclusion or exclusion of the highly
correlated variables, indicating that multicollinearity is unlikely to be problematic (Hosmer and
Lemeshow, 1989).
[Insert Table 3]
Table 4 reports a summary of probit regression results. To facilitate comparative
analyses, we report results of the following four models: the base model is in column (1) with only
firm-specific characteristics; with strategic IT role in column (2); with IT executives in column
(3); and the fully specified model with sets of controls, including corporate governance variables
is in column (4). Column (1) of Table 4 presents the probit regressions using the firm level
characteristics similar to the models used in prior research. Larger firms and firms with higher
R&D intensity are more likely to participate in the VFP. As shown Column (1), our basic model
has a Pseudo R2 of 33.4%, which is significantly better than the Pseudo R
2 of 18.2% obtained in a
logistic regression using all Non-VFP-XBRL firms with available financial data (within the PSM
selection process). In column (2), the results show that firms with an informative strategy (INFO)
are more likely to participate in the VFP (compared to a transformative strategy used as a
baseline). Adding these IT strategy variables into the basic model also increases Pseudo R2 to
25
36.5% and increases prediction accuracy from 80.5% to 81.6% correctly classified. Consistent
with our expectation, the results of columns (3) and (4) show that the presence of senior IT
executives is also positively associated with VFP participation before and/or after controlling for
BOARDSIZE and CEOCHAIR.
[Insert Table 4]
Table 5 presents results of probit regressions combining IS and financial reporting
competencies in the models. In column (1), we find that CEO IS competency has a statistically
significant coefficient (CEOIS coefficient = 0.795; p-value < 0.01), suggesting that CEOs’ IS
related knowledge, expertise, and experience were associated with firms’ decision to participate in
the VFP. The reported marginal effect suggests that CEOs with IS competency are 21.5% more
likely to participate in the VFP. However, CEO financial reporting competency has a statistically
significant but negative coefficient (CEOACC coefficient = –1.159; p-value < 0.01). The marginal
effect indicates that CEOs with financial competencies are 31.4% less likely to join VFP. This is a
very interesting finding, since it indicates that although the roles of executive team members are
important factors in the adoption of innovative technologies such as XBRL, their competencies are
also important.
CEOs with financial reporting expertise behaved differently than CEOs with IS expertise.
Financial reporting competencies of CFOs also have significant positive coefficients for IS
expertise and negative coefficients for financial expertise in column (2). Finally, in column (3)
examining both executives’ IS and financial reporting competencies, the result shows that both
CEOs and CFOs with IS competencies were more likely to participate in the VFP, while CEOs
and CFOs with financial reporting competencies were less likely to do so. Among members of the
executive team, it appears that CEO competencies are clearly the dominant driver of the decision
to participate in the VFP. Overall the empirical analysis supports Hypothesis 1, but not Hypothesis
2.
26
[Insert Table 5]
Table 6 presents the OLS results from the analysis of usage of customized extensions,
errors, and warnings from Instance or Taxonomy documents (as proxies for XBRL
Implementation Quality) associated with the 483 VFP filings made by the 102 voluntary XBRL
firms. In column (1), we find that CEO IS competencies are less likely to be associated with the
use of customized extensions. The results indicate that the CEO may discourage the use of
customized extensions because they reduce standardization and make interoperability more
difficult, while also potentially causing a firm’s reporting conventions to deviate from official
taxonomies.
As shown in columns (2), there is also some evidence that CEO IS competencies are
negatively associated with INSTANCE, whereas in column (3) CFO financial reporting
competencies are negatively associated with TAXONOMY in the VFP filings. These findings may
reflect the fact that taxonomy issues related to mapping the company’s financial statements to the
standard and extension taxonomies are a financial reporting competency, whereas instance
validation issues relate to following technical XBRL specifications for creating XBRL documents,
which are more of a technical competency. In column (4) there is strong evidence that CFO
financial competencies are negatively associated with all instance and taxonomy problems
(TOTALPB), suggesting that even if they were less likely to adopt XBRL, in firms that did adopt
the executives with financial reporting expertise influenced the quality of the XBRL
implementation.
Accounting information has to be timely to be relevant; investors want information to be
made available as soon as possible. As shown in column (5) we find that firms having CEOs with
IS competencies submit their XBRL filing about two and a half weeks earlier on average than
otherwise. This result suggests that although there is no deadline for the VFP reporting, executives
with higher level of IS reporting competencies influenced their XBRL filing process to submit
27
their XBRL filing in a more timely manner, or with fewer reporting delays. Interestingly, we find
that the 10-K VFP form (10-K) is significantly related to customized extensions and reporting time
effects. This is in part due to the fact that 10-K filings generally contain more information as
compared with other SEC filings. We also find that the quality of XBRL filings has improved with
filing experience. REPORTNUM, a variable indicating XBRL filing number (first, second, third,
and so on), has a significantly negative coefficient in four of the five quality measures. There is a
learning curve in which firms continue to improve the quality of their VFP filings over time.
[Insert Table 6]
Additional tests
Endogeneity concern
Executives’ IS competencies are likely to be endogenously determined. The regression estimators
are neither consistent nor efficient when determinants of executive IS competencies are left out
from the right-hand side of the regression model being estimated. To address the possibility of
endogeneity, we use a specific instrumental variable (IV) method for binary dependent variables in
Panel A of Table 7. As shown in Table 3, our instruments R&D and ITFIRM are highly correlated
with CEOIS and CFOIS, and not correlated with VFP.
We evaluate the validity and strength of these IVs following the suggestions from
Larcker and Rusticus (2010). The strength of the correlation between the instruments and the
problematic variable is examined by calculating the partial F-value. The partial F-tests reported in
Panel A of table 8 are significant at < 0.001 level. Next we regress the error terms from the main
models on the instruments and verify that they are not correlated. Since the number of instruments
used here is more than the number of problematic variables, we can apply the Sargan
overidentifying test to determine whether the instruments are correlated with the residuals. The p-
values from the Sargan tests are not significant at 0.10 level, thus, failing to reject the hypothesis
that the instruments are correlated with the error terms, and therefore are valid instruments. The
28
two tests indicate that R&D and ITINT are good instruments.
Because the dependent variables from both stages are binary, the typical two-stage least
squares (2SLS) method using probit or logit estimators in both stages may result in inconsistent
parameter estimates (Woolridge 2002). To account for the binary nature of VFP and CEOIS
(CFOIS), and to address the potential endogeneity of CEOIS (CFOIS), we jointly estimate the two
equations using a bivariate probit regression approach. In this approach, we estimate both
equations simultaneously as a system of two equations where the error terms from the first and the
second stage are assumed to be: (a) independent of the instruments and the control variables, and
(b) distributed as bivariate normal with mean zero. This estimation method allows us to test
whether the endogenous variable CEOIS (CFOIS) is significantly related to the unobserved error
term from the main model. A significant correlation between CEOIS (CFOIS) and ε from the main
models (i.e. ρ ≠ 0) suggests that estimating the main equation using a single-equation probit
regression would not provide consistent parameter estimates.
Panel B of Table 7 presents the result of the regression of VFP adoption on the residual
variables. In columns (1) and (2), the regression results show that firms with higher CEO and CFO
IS competencies are associated with earlier adoption of XBRL than the benchmark firms (p-value
< 0.10). In contrast, when the CEO and CFO IS competency is lower than the benchmark, the firm
is not associated with VFP XBRL adoption. These two variables are the same variables that were
reported as having a significant relationship with VFP XBRL adoption in columns (1) and (2) of
Table 5. Similarly, we also find when the CEO and CFO IS competency is higher than the
benchmark, the firm is associated with VFP XBRL adoption in column (3) after combining both
IS and financial reporting competencies for CEO and CFO. Overall, the two-stage regression
results for the full model are consistent with the logistic regression results (see column (3) of
Table 5). The tests of ρ’s on Table 8 show that we fail to reject the null hypothesis that there is no
endogeneity issue in our bivariate and trivariate (regression with two endogeneous variables)
29
probit models. Additionally a comparison of coefficients for each independent variable in Table 6
and Table 8 shows that they are relatively close in value.
[Insert Table 7]
To mitigate the causal-effect issue, we also re-run our single-equation probit models
using lag variables of CEOIS (CFOIS) and CEOACC (CFOACC) in year t–2, and the results
remain consistent. Column (1) of Table 8 shows that coefficient CEOISt-1 and CFOISt-1 are
positive and significant, and coefficient CEOACCt-1 is negative and significant.
As voluntary adopters may self-select into the XBRL filing program, it is possible that
our results are contaminated by a self-selection bias. To address the issue of self-selection bias, we
calculate the Inverse Mills Ratio (MILLSRATIO) from the first stage probit model and include the
ratio in the second stage. Column (2) of Table 9 shows that the coefficient for MILLSRATIO is
not significant at p-value of 0.10, suggesting no significant self-selection bias.
[Insert Table 8]
Abnormal IS competency
Following the approach by Laksmana (2008), we use the first stage regression as a prediction
model for CEOIS competency. Thus the residuals from these regressions indicate whether a firm’s
executive IS competency is above or below the expectation. For example, if a residual from the
regression CEO IS competencies is positive, variable CEOISRESID+ is equal to the value of the
residual, and zero otherwise. If the residual value is negative, then CEOISRESID– takes on the
value of the residual, and zero otherwise. By allowing these new variables to indicate different
slopes, we can examine the potentially different impact of firms with higher or lower CEO IS
competencies on voluntary XBRL adoption. We repeat this process for CFO IS competencies,
obtaining CFOISRESID+ and CFOISRESID–.
30
Table 9 presents the result of the regression of VFP adoption on the residual variables. In
columns (1) and (2), the regression results show that firms with higher CEO and CFO IS
competencies are associated withearly adoption of XBRL more than the benchmark firms (p-value
< 0.10). In contrast, when the CEO and CFO IS competency is lower than the benchmark, then the
firm is not associated with VFP XBRL adoption. These two variables are the same variables that
were reported as having significant relationship with VFP XBRL adoption in columns (1) and (2)
of Table 5. Consistently, we also find when the CEO and CFO IS competency is higher than the
benchmark, then the firm is associated with VFP XBRL adoption in column (3) after combining
both IS and financial reporting competencies for CEO and CFO. Overall the two-stage regression
results for the full model are consistent with the logistic regression results (see column (3) of
Table 6).
[Insert Table 9]
Since members of the executive team have a variety of competencies regardless of the
organizational role they play (CEO and CFO), we further examine whether the impetus for the
adoption of XBRL stems from the technological or financial reporting side of the executive team.
Table 9 presents results from estimating Equations (1) and (2) regarding IS- and financial
reporting- related competencies. In columns (1) and (2), we see that firms with higher CEO IS
competencies are more likely to participate in the VFP after controlling for all the firm level
factors (coefficient = 0.772; p-value < 0.05), and CFO IS competencies are marginally significant.
Across the board we observe that firms with an informative IT strategy (INFO) are more likely to
join the VFP. This implies that firms with this strategic approach may focus on providing new
information, including financial information, to internal as well as external decision makers.
Interestingly, in columns (3) and (4), we find that coefficients of financial reporting competencies
for CEOs and CFOs are significantly negative (CEOACC coefficient = –1.112; p-value < 0.01;
31
CFOACC coefficient = –0.316; p-value < 0.10, respectively). These suggest that firms with
executives with more financial reporting competencies were less likely to embark on the VFP.
[Insert Table 9]
Alternative IT- and financial reporting competency measures
Motivated by the robustness of the results based on the aggregate form of IS and financial
reporting competencies, we consider the effect of individual attributes of an IT executive’s IS/IT
competencies. We still find a positive association between the detailed levels of CEOs’ IT
competencies, such as the IS- related practical experience (ITfrm) and their firm’s VFP
participation. However, the individual components of IS/IT competencies (ITad, ITjob, and
ITfirm) are either marginally significant or insignificant. Similar to current research (Lim et al.
2012, 2013a & b), our results also indicate that while IS competencies can be attributed to
multiple sources, there is no single value that appears to be more important than the others.
We still find a negative association between the detailed levels of CFOs’ financial
reporting competencies, such as any professional certification (ACCpro), or the financial
reporting- related practical experience (ACCexp); although, the individual components of financial
reporting competencies (ACCpro and ACCexp) are marginally significant.
We also consider alternative IS- and financial reporting- competencies specifications and
repeat our analysis using the following specifications. The first CEOISct (CFOISct) variable is the
count of the three IS competency characteristics possessed by the CEOISct (CFOISct) (minimum
count = zero; maximum count = three), whereas the second CEOACCct (CFOACCct) variable is
the count of the two financial reporting competency characteristics possessed by the CEO (CFO).
While all coefficient signs are consistent, some have a decreased level of significance.
Alternative VFP XBRL quality
To test the generalizability of our VFP XBRL Quality measures, we consider additional
constraints in three ways. In an untabulated test, the extension (EXTalt) variable is re-defined as
32
the number of customized extension elements / (number of extension elements + number of
official elements). The replication of the econometric analysis based on the conservative definition
of extension produces similar results: CEO IS competencies (coefficient –0.062; p-value < 0.01)
are less likely to lead to the use of extensions. Combining both Instance and Taxonomy problems
(TOTALPB), we find that the coefficient for CEO financial reporting competencies is highly
significant at –0.255 and p-value < 0.01.
Accounting for corporate governance
In untabulated tests we examine whether our results are sensitive to other governance
characteristics. These characteristics are not included in the main analysis because we do not have
complete data for our 204 sample and control firms. First, board independence (BDIND) is
included because voluntary disclosure is increased by more independent boards (Cheng and
Courtenay 2006). Second, we include the frequency of board meetings (BMEET), which could
reflect the magnitude of problems being faced by the firm (Goh 2009). We find that board
independence and frequency of board meetings are positively associated with XBRL adoption but
they do not affect the results presented above. Instead of the individual governance variables, we
also check the governance score (Gompers et al., 2003)10
, and this G-index score does not change
our main results.
With respect to prior literature, we include executive characteristics such as age and
tenure (Murphy, 1998). Using a reduced sample of 162 complete observations we find that the
CEOs (CFOs) at the VFP-XBRL firms are relatively older (AGE) and have longer tenure
(TENURE) than those at the Non-VFP-XBRL firms. When we control for both AGE and TENURE
of CEO and CFO, the overall results remain unchanged.
Alternative industry effects
10
G-index score is available at http://www.robinson.gsu.edu/accountancy/gov_score.html.
33
Since several studies have discussed the need to focus on specific industries, we included
five Fama-French broad industry control dummy variables11
(Consumer, Manufacturing, High
Tech, Healthcare, and Other). Overall, there does not seem to be a difference in the VFP
participation across the different industries in our sample.
5. Conclusions
This study focuses on IS and financial reporting competencies since IS and financial
reporting are the two aspects that are the essence of the innovation represented by XBRL.
Specifically, we examine the following question: “Did the impetus for the adoption of XBRL stem
from the technological or financial side of the executive team?” Consistent with our expectations,
we find that executives’ organizational roles and competencies play a significant role in the
adoption of new financial reporting technology. Unsurprisingly, CEOs have the strongest
influence on voluntary adoption of XBRL, both positive and negative. CEOs with IS competencies
have a strong positive influence on voluntary adoption of XBRL. Surprisingly, and ironically,
financial reporting competencies appear to be a negative rather than a positive factor in the
voluntary adoption of XBRL, regardless of whether those competencies are possessed by the CEO
or CFO. This finding has important implications for policy makers, regulators and proponents of
XBRL seeking to obtain the support of executives to adopt XBRL in non-mandatory XBRL
jurisdictions or new voluntary programs such as the use of inline XBRL12
that might be initiated
by the SEC in the future. These stakeholders should concentrate on communicating with
executives (both CEOs and CFOs) with financial reporting competencies to persuade them of the
benefits of XBRL for financial reporting. Otherwise, there is a hitherto unrecognized risk that
12
Inline XBRL or iXBRL is an new version of XBRL that combines HTML and XBRL in a single
filing that has been adopted in the U.K. and is being contemplated by the SEC as part of a new
voluntary XBRL filing program.
34
executives who might be assumed to be aligned with the financial reporting objectives of XBRL
could in fact be barriers to its adoption and proliferation.
This study also contributes to the academic literature on voluntary adoption of innovative
technologies, particularly financial reporting technologies. Although XBRL is a financial
reporting technology, it is noteworthy that participation in the VFP was positively associated with
companies’ having executives with IS competencies. Support from such quarters might be
surprising to some and suggests that researchers investigating other innovative financial reporting
technologies should incorporate executives’ competencies into their models. This study also
demonstrates that members of the executive team possess multiple competencies. Accordingly, a
combination of executives’ roles and their respective competencies determines the influence that
they exert on a firm’s decisions. This is particularly evident in the role played by the CEO that
reflects the CEO’s competencies. Thus we extend the literature on the influence of managers’
characteristics on firms’ financial reporting choices and outcomes by demonstrating the
association between managers’ competencies and the quality of their XBRL implementation as
evidenced by the quality attributes of their XBRL filings. This also contributes to our
understanding of antecedents of data quality in XBRL filings. It suggests that executives with IS
competencies can influence some, but not all, aspects of XBRL data quality; likewise for
executives with financial reporting competencies. An implication for practice is that responsibility
for XBRL data quality is a shared responsibility on the part of the executive team.
Our study is subject to some caveats. First, the sample used in this study suffers from a
potential survivorship bias problem. Because the empirical test requires background data from
publically available data sources, small firms or those with very low executive rank (e.g. some
CFO, CIO, or other IT executives) may not have made the final sample used in empirical tests,
biasing the results in favor of the hypotheses. Second, other factors such as IT maturity (e.g. the
current state of IT or several new areas of IT investments) could play a critical role in determining
35
the costs or benefits of IT investments. Examining such additional factors might be a fruitful
research endeavor in the future.
To date, the quality of XBRL-tagged information has been determined ex post and no
systematic ex ante determinants of the quality of XBRL-tagged data (such as management
financial reporting and IS/IT expertise) have been identified. Our results therefore can be used to
predict the quality of XBRL-tagged information and not just used to describe it after the fact.
Accordingly, these results can be used as a guide for investigating voluntary XBRL filings in other
jurisdictions and other voluntary disclosures such as XBRL filings for sustainability reporting and
voluntary standardized business reporting in XBRL in jurisdictions where such reporting is not
mandatory. Even in jurisdictions where XBRL reporting is mandatory, our results can help predict
and explain which firms’ filings will be of a higher quality due to the executive team’s financial
reporting and IS competencies.
36
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41
TABLE 1
Panel A: Sample selection procedure
Details Filings Firms
Step 1 Starts from 10-Q or 10-K XBRL filings collected from the
U.S. Securities and Exchange Commission Interactive
Financial Report Viewer website (available at:
http://www.sec.gov/Archives/edgar/monthly/) from 2005 to
2009.
692 138
Step 2 Exclude due to
missing values from Compustat (209) (36)
Step 3 VFP-XBRL (2005-2009) 483 102
Year EXT INSTANCE TAXONOMY TOTALPB RLAG 483 102
Mean
(Median)
Mean
(Median)
Mean
(Median)
Mean
(Median)
Mean
(Median)
2005 0.276
(0.262)
0.571
(1.000)
0.000
(0.000)
0.555
(1.000)
13.5
(10.5)
30 8
2006 0.306
(0.287)
0.645
(1.000)
0.156
(0.000)
0.802
(1.000)
15.4
(10.0)
96 19
2007 0.312
(0.307)
0.723
(1.000)
0.092
(0.000)
0.815
(1.000)
14.8
(10.0)
141 21
2008 0.151
(0.117)
0.572
(1.000)
0.072
(0.000)
0.645
(1.000)
9.7 (8.0) 192 41
2009 0.127
(0.108)
0.400
(0.000)
0.050
(0.000)
0.450
(0.000)
9.9 (7.0) 24 13
Panel B: Distribution of sample by 2-digit SIC
2-digit SIC Industry No. %
10-19 Mining, Oil and Gas, and others 9 8.8
20-27 Food, Kindred, Printing and Publishing 7 6.9
28-29 Chemicals, Petroleum and Coal, Rubber and Plastics 12 11.8
33-38 Metal, Machinery and Equipment, Instruments 25 24.5
44-49 Transportation 15 14.7
52-58 Whole Sale, Retails 3 2.9
60-67 Banking, Financial Service 19 18.6
73 Business Service 9 8.8
80& 87 Health, Engineering and Management Service 2 2
99 Others 1 1
Total 102 100
42
TABLE 2
Panel A: Descriptive statistics
VFP-XBRL
(N=102)
Non-VFP-XBRL
(N=102)
Test of Difference
Mean Median s.d. Mean Median s.d. Mean Median
Independent variables
CEOIS 0.247 0.000 0.043 0.129 0.000 0.033 0.118 *** 0.000 **
CEOACC 0.039 0.000 0.019 0.128 0.000 0.033 -0.089 ** 0.000 **
CFOIS 0.217 0.000 0.041 0.178 0.000 0.038 0.039 0.000
CFOACC 0.574 1.000 0.049 0.752 1.000 0.043 -0.178 *** 0.000 ***
Control variables
ITEXE 0.653 1.000 0.047 0.376 0.000 0.048 0.277 *** 0.000 ***
SIZE 9.047 9.446 0.217 6.705 6.816 0.193 2.341 *** 2.578 ***
MTB 6.913 2.862 2.385 5.143 2.644 1.214 1.769 0.108
LOSS 0.108 0.000 0.031 0.245 0.000 0.042 -0.137 *** 0.000 **
ROA 0.041 0.064 0.015 -0.067 0.022 0.048 0.108 ** 0.030 ***
LEV 0.240 0.159 0.053 0.230 0.104 0.030 0.010 0.003
BOARDSIZE 11.09 11.00 0.356 8.290 8.000 0.234 2.792 *** 3.000 ***
COECHAIR 0.574 1.000 0.049 0.436 0.000 0.050 0.139 ** 0.000 **
Instrumental variables (for robustness tests)
R&D+ 0.033 0.000 0.005 0.028 0.000 0.006 0.004 0.000
ITFIRM+ 0.188 0.000 0.039 0.168 0.000 0.037 0.020 0.000
Notes:
***, **, * Significant at the 0.01, 0.05, and 0.10 levels using a two-tailed test, respectively.
43
TABLE 2 (Continued)
Panel B: Variable definitions:
VFP is one if the firm participated in the VFP, and zero otherwise;
VFP Quality
is calculated in five ways: 1) the rate of customized extensions usage over
the number of total elements (EXT); 2) the number of instance validation
errors and warnings (INSTANCE); 3) the number of taxonomy validation
errors and warnings (TAXANOMY); 4) the sum of both instance and
taxonomy problems (TOTALPB); and 5) the number of weeks it takes a
company to submit an XBRL filing after a fiscal period end (RLAG);
CEOIS/CFOIS
is one if a CEO/CFO has IS- related competencies, such as ITad, ITjob,
or ITfirm, zero otherwise;
ITad is one if the executive members have an IS- related academic degree, zero
otherwise;
ITjob is one if the executive members have held an IS- related jobs, zero
otherwise;
ITfirm is one if the executive members have worked at an IT- intensive firm,
zero otherwise;
CEOACC/CFOACC is one if a CEO/CFO has financial reporting- related competencies, such
as ACCpro, or ACCexp, zero otherwise;
ACCpro
is one if the CEO/CFO has a professional certification (e.g. CPA, CA,
and other accounting- and/or financial reporting- related certifications),
zero otherwise;
ACCexp
is one if the CEO/CFO has experience as a public accountant, auditor,
principal or chief financial officer, controller, or principal or chief
accounting officer, zero otherwise;
ITEXE is one if the company has a senior IT executive, such as CIO (Chief
Information Officer), or other type of IT expert (Lim et al. 2012, 2013a &
b), zero otherwise;
AUTO is one if firms engage in automative IT strategic role, zero if firms engage
in transformative strategy (Dehning et al. 2003);
INFO is one if firms engage in informative IT strategic role, zero if firms
engage in transformative strategy (Dehning et al. 2003);
SIZE is a natural logarithm of Total Assets;
MTB is Fiscal Closing Price / Book Value Per Share;
LOSS equals to one if Net Income is < 0, zero otherwise;
ROA is Net Income / Total Assets;
LEV is long-term debt divided by total assets;
BOARDSIZE number of directors;
CEOCHAIR is one if the current CEO serves a chairman of the board, zero otherwise;
R&D is R&D Expenses / Sales (Miller, 2006; Scott and Pascoe, 1987);
ITINT is one if a firm is a member of an IT producing company (e.g., three digit
SIC codes are 357 (Computers and Office Equipment), 366
(Communications Equipment), 367 (Electronic Components), and
737(Computer Services), 0 otherwise (Chatterjee et al. 2002; Jorgenson et
al. 2005; Yang et al. 2012);
10-K is one if an XBRL filing is related to an annual report (10-K); zero
44
otherwise;
REPORTNUM is the number of the Nth
XBRL filing produced by a company (a proxy
for the cumulative XBRL experience level);
Industry includes industry fixed effects using 2-digit SIC codes;
Year includes year effects.
45
TABLE 3
Correlations
(1)
VFP
(2)
CEOIS
(3)
CFOIS
(4)
ITEXE
(5)
CEO
ACC
(6)
CFO
ACC
(7)
INFO
(8)
AUTO
(9)
SIZE
(10)
LOSS
(11)
ROA
(12)
MTB
(13)
LEV
(14)
BOARD
SIZE
(15)
CEO
CHAIR
(16)
R&D
(17)
ITINT
(1) 0.151 0.049 0.274 -0.172 -0.176 0.019 0.000 0.489 -0.180 0.132 0.038 0.011 0.417 0.137 0.040 0.025
(2) 0.151 0.208 0.192 -0.060 0.129 0.001 -0.068 -0.041 0.010 0.038 0.121 -0.094 -0.013 -0.085 0.459 0.571
(3) 0.049 0.208 0.015 0.020 0.144 -0.053 -0.070 -0.050 -0.002 0.050 0.087 0.020 -0.051 -0.034 0.294 0.257
(4) 0.275 0.192 0.015 -0.144 -0.121 -0.182 -0.144 0.422 -0.035 0.175 -0.023 -0.065 0.301 0.039 0.153 0.248
(5) -0.172 -0.060 0.020 -0.144 0.149 0.091 0.081 -0.006 0.037 0.013 -0.077 -0.052 -0.036 -0.041 -0.144 -0.144
(6) -0.176 0.129 0.144 -0.121 0.149 -0.191 0.101 -0.134 0.059 0.014 0.059 -0.074 -0.101 -0.121 0.155 0.140
(7) 0.020 -0.001 -0.053 -0.182 0.091 -0.191 -0.166 -0.161 0.135 -0.096 -0.081 -0.118 -0.173 0.056 -0.022 -0.177
(8) 0.000 -0.068 -0.070 -0.144 0.081 0.101 -0.166 -0.048 0.120 -0.098 -0.000 -0.028 -0.031 -0.003 -0.072 -0.065
(9) 0.520 -0.040 -0.060 0.426 -0.012 -0.154 -0.150 -0.046 -0.235 0.286 -0.038 -0.107 0.591 0.222 -0.074 -0.082
(10) -0.180 0.010 -0.002 -0.035 0.037 0.059 0.135 0.120 -0.212 -0.691 0.019 0.153 -0.178 -0.241 0.071 0.021
(11) 0.258 0.119 0.074 0.126 -0.088 0.059 0.006 -0.058 0.089 -0.660 -0.012 -0.293 0.122 0.165 0.051 0.062
(12) 0.027 0.222 0.115 0.105 -0.215 0.067 -0.082 0.050 -0.112 -0.063 0.264 -0.006 0.034 -0.115 0.081 0.097
(13) 0.036 -0.190 -0.025 0.010 -0.056 -0.117 0.137 0.003 0.284 0.062 -0.181 -0.188 -0.089 -0.116 -0.115 -0.075
(14) 0.464 -0.007 -0.026 0.322 -0.037 -0.155 -0.145 -0.033 0.716 -0.183 0.097 0.021 0.163 0.053 0.084 0.544
(15) 0.137 -0.085 -0.034 0.039 -0.041 -0.121 0.056 -0.003 0.227 -0.241 0.099 -0.029 0.005 0.110 0.092 -0.073
(16) 0.106 0.436 0.305 0.234 -0.191 0.118 -0.036 -0.069 0.032 0.006 0.232 0.317 -0.152 -0.059 0.035 -0.086
(17) 0.025 0.571 0.257 0.248 -0.144 0.141 -0.177 -0.065 -0.093 0.021 0.141 0.189 -0.170 -0.059 -0.086 0.588
Notes: Spearman correlations are reported on the lower left and Pearson correlations are reported on the upper right. Variables defined in Table
2. Significance level at 5 percent level is depicted with bold font.
46
TABLE 4
Probit regression: IT area
Exp.
sign (1) (2) (3) (4)
Coeff. p-stat. Coeff. p-stat. Coeff. p-stat. Coeff. p-stat.
Intercept -2.606 *** -2.800 *** -2.903 *** -3.365 ***
SIZE (+) 0.315 *** 0.344 *** 0.313 *** 0.225 ***
MTB (?) 0.005 0.005 0.006 0.005
LOSS (-) -0.550 * -0.653 ** -0.796 ** -0.688 **
ROA (+) -0.601 -0.750 -1.047 -0.659
LEV (?) 0.294 0.244 0.233 0.278
AUTO (+) 0.963 1.150 * 1.146 *
INFO (+) 0.632 ** 0.663 *** 0.709 ***
ITEXE (+) 0.424 ** 0.382 *
BOARDSIZE (+) 0.106 ***
CEOCHAIR (-) 0.106
Industry Included Included Included Included
Year Included Included Included Included
N 204 204 204 204
Pseudo R2 0.334 0.365 0.381 0.410
Model chi-square 46.5 *** 49.8 *** 53.1 *** 55.6 ***
Correctly classified 80.5 81.6 82.1 83.5
Notes: All variables are as defined in Table 2. ***, **, * Significant at the 0.01, 0.05, and 0.10 levels
using a two-tailed test, respectively. One-tailed tests are shown for variables with a
signed prediction. Two-tailed test are shown for variables without a signed prediction or
when the coefficient sign is the opposite to our prediction.
47
TABLE 5
Probit regression: Executives’ IS- and financial reporting competencies
Exp.
sign
(1) (2) (3)
Coeff. Mar.
Effect
p-
stat.
Coeff. Mar.
Effect
p-
stat.
Coeff. Mar.
Effect
p-
stat.
Intercept -3.572 *** -3.246 *** -3.471 ***
CEOIS (+) 0.795 0.215 *** 0.759 0.201 **
CEOACC (-) -1.159 -0.314 *** -1.114 -0.296 ***
CFOIS (+) 0.450 0.127 ** 0.420 0.111 *
CFOACC (-) -0.360 -0.101 * -0.303 -0.080
ITEXE (+) 0.251 0.068 0.376 0.106 * 0.252 0.067
AUTO (+) 1.129 0.306 1.324 0.373 * 1.301 0.346 *
INFO (+) 0.606 0.164 ** 0.708 0.199 *** 0.617 0.164 **
SIZE (+) 0.256 0.069 *** 0.227 0.064 *** 0.257 0.068 ***
MTB (?) 0.002 0.000 0.005 0.001 0.002 0.000
LOSS (-) -0.586 -0.158 * -0.693 -0.195 ** -0.600 -0.159 *
ROA (+) -0.561 -0.152 -0.704 -0.198 -0.626 -0.166
LEV (?) 0.316 0.085 0.237 0.066 0.249 0.074
BOARDSIZE (+) 0.114 0.031 *** 0.107 0.030 *** 0.113 0.030 ***
CEOCHAIR (-) 0.060 0.016 0.105 0.029 0.060 0.016
Industry Included Included Included
Year Included Included Included
N 204 204 204
Pseudo R2 0.470 0.432 0.485
Model chi-square 58.8 *** 56.7 *** 60.2 ***
Correctly classified 85.9 84.2 86.5
Notes: All variables are as defined in Table 2. ***, **, * Significant at the 0.01, 0.05, and 0.10 levels
using a two-tailed test, respectively. One-tailed tests are shown for variables with a
signed prediction. Two-tailed test are shown for variables without a signed prediction or
when the coefficient sign is the opposite to our prediction.
48
TABLE 6
OLS regression: Executives’ IS- and financial reporting- competencies (for VFP XBRL filers only) and XBRL Implementation Quality
(1) (2) (3) (4) (5)
Exp.
sign
EXT INSTANCE TAXONOMY TOTALPB RLAG
Coeff. p-stat. Coeff. p-stat. Coeff. p-stat. Coeff. p-stat. Coeff. p-stat.
Intercept 0.143 *** 0.605 *** 0.426 *** 1.027 *** 18.387 ***
CEOIS (-) -0.029 ** -0.202 *** 0.227 ** 0.023 -2.492 *
CEOACC (-) -0.014 0.273 * -0.055 0.219 -3.982
CFOIS (-) 0.047 -0.06 0.021 0.017 0.546
CFOACC (-) -0.002 -0.053 -0.154 *** -0.210 *** -0.893
ITEXE (-) 0.060 0.123 ** -0.076 *** 0.046 0.735
AUTO (-) 0.142 -0.278 * -0.075 -0.352 -5.267
INFO (-) 0.023 0.034 -0.111 * -0.076 -0.389
SIZE (?) 0.015 *** -0.042 *** 0.005 ** -0.036 *** 0.155
MTB (?) 0.000 -0.003 * 0.000 -0.003 * 0.047
LOSS (?) -0.075 ** -0.356 *** 0.149 ** -0.203 2.332
ROA (?) -0.324 *** -1.075 *** -0.054 -1.121 *** -11.652
LEV (?) -0.021 * -0.150 * -0.087 * -0.237 *** -1.819
BOARDSIZE (?) 0.002 0.016 ** -0.015 *** 0.001 -0.105
CEOCHAIR (?) -0.069 *** 0.061 -0.006 0.050 -1.742
10-K (+) 0.067 *** -0.035 0.006 -0.028 4.922 ***
REPORTNUM (-) -0.008 *** -0.032 *** 0.003 -0.028 *** -0.699 ***
Industry Included Included Included Included Included Year Included Included Included Included Included
N 483 483 482 483 481
F-Value 12.39 *** 8.68 *** 15.95 *** 6.01 *** 5.00 ***
Adj. R2 0.371 0.276 0.427 0.199 0.166
Notes: All variables are as defined in Table 2. ***, **, * Significant at the 0.01, 0.05, and 0.10 levels using a two-tailed test, respectively. One-tailed
tests are shown for variables with a signed prediction. Two-tailed test are shown for variables without a signed prediction or when the
coefficient sign is opposite to our prediction.
49
TABLE 7
Endogeneity concern: Multivariate probit regression
Panel A: First stage probit regression
(1) (2)
CEOIS CFOIS
Coeff. p-value Coeff. p-value
Intercept -1.892 *** -1.459 **
CEOACC/CFOACC 0.155 0.341
ITEXE 0.193 -0.205
AUTO -2.675 -4.945
INFO 0.507 ** -0.146
SIZE 0.006 0.023
MTB 0.011 0.002
LOSS -0.508 0.018
ROA -0.805 0.599
LEV -0.831 0.322 **
BOARDSIZE 0.037 -0.011
CEOCHAIR -0.261 -0.124
R&D 4.790 ** 4.370 ***
ITINT 1.591 *** 0.463 *
Industry Included Included
Year Included Included
N 204 204
Partial F-test of Instruments (p-value) 0.0001 0.0002
Sargan Overidentifying Test (p-value) 0.823 0.922
50
Panel B: Second stage probit regression
Exp.
sign
(1)
Biviariate Probit
(2)
Bivariate Probit
(3)
Trivariate Probit
Coeff. p-stat. Coeff. p-stat. Coeff. p-stat.
Intercept -3.740 *** -0.550 *** -3.559 ***
CEOIS (+) 0.746 * 0.821 *
CEOACC (-) -1.096 *** -1.045 ***
CFOIS (+) 0.347 ** 0.082
CFOACC (-) -0.115 ** -0.301
ITEXE (+) 0.245 0.122 ** 0.221
AUTO (+) 1.058 0.359 * 1.101
INFO (+) 0.516 *** 0.142 ** 0.458 **
SIZE (+) 0.252 *** 0.071 *** 0.251 ***
MTB (?) 0.002 0.002 0.002
LOSS (-) -0.613 * -0.208 ** -0.591 *
ROA (+) -0.716 -0.297 0.659
LEV (?) 0.304 0.051 0.301
BOARDSIZE (+) 0.110 *** 0.029 *** 0.109 **
CEOCHAIR (-) 0.050 0.023 0.041
Industry Included Included Included
Year Included Included Included
ρ1 0.006 -0.323 -0.033
Test ρ1 = 0 (p-val) 0.984 0.207 0.926
ρ2 0.126
Test ρ2 = 0 (p-val) 0.826
Log likelihood -157.5 -194.9 -243.1
N 204 204 204
Notes: All variables are as defined in Table 2. ***, **, * Significant at the 0.01, 0.05, and 0.10 levels
using a two-tailed test, respectively. One-tailed tests are shown for variables with a
signed prediction. Two-tailed test are shown for variables without a signed prediction or
when the coefficient sign is the opposite to our prediction.
51
TABLE 8
Alternative endogeneity concern: Probit regression
(1) (2)
LAG MODEL MILLS
RATIO
Coeff. p-stat. Coeff. p-stat.
Intercept -3.814 *** Intercept -3.355 ***
CEOISt-1 (+) 0.895 *** CEOIS (+) 0.734 **
CEOACCt-1 (-) -0.974 ** CEOACC (-) -1.113 ***
CEOISt-1 (+) 0.454 * CEOIS (+) 0.411 *
CEOACCt-1 (-) -0.018 CEOACC (-) -0.299
ITEXE (+) 0.276 ITEXE (+) 0.249
AUTO (+) 1.239 AUTO (+) 1.298 *
INFO (+) 0.607 ** INFO (+) 0.618 **
SIZE (+) 0.261 *** SIZE (+) 0.257 ***
MTB (?) 0.002 MTB (?) 0.002
LOSS (-) -0.491 LOSS (-) -0.602 *
ROA (+) -0.536 ROA (+) -0.628
LEV (?) 0.308 LEV (?) 0.338
BOARDSIZE (+) 0.119 *** BOARDSIZE (+) 0.113 ***
CEOCHAIR (-) 0.083 CEOCHAIR (-) 0.060
MILLSRATIO (?) -0.075
Industry Included Industry Included
Year Included Year Included
N 204 204
Pseudo R2 0.480 0.485
Model chi-square 59.64 *** 60.28 ***
Correctly classified 86.1 86.5
Notes: All variables are as defined in Table 2. ***, **, * Significant at the 0.01, 0.05, and 0.10 levels
using a two-tailed test, respectively. One-tailed tests are shown for variables with a
signed prediction. Two-tailed test are shown for variables without a signed prediction or
when the coefficient sign is the opposite to our prediction.
52
TABLE 9
Alternative endogeneity concern: Residuals CEOIS and CFOIS
Exp.
sign (1) (2) (3)
Coeff. p-stat. Coeff. p-stat. Coeff. p-stat.
Intercept -6.129 *** -6.366 *** -6.225 ***
CEOISRESID+ (+) 1.786 ** 1.629 *
CEOISRESID- (-) 0.453 1.326
CEOACC (-) -1.962 ***
CFOIS1RESID+ (+) 1.025 * 1.891 **
CFOIS1RESID- (-) -0.484 -2.323
CFOACC (-) -0.687 *
ITEXE (+) 0.539 * 0.617 * 0.461
AUTO (+) 2.036 * 2.234 * 2.745 *
INFO (+) 1.020 ** 1.242 *** 1.046 **
SIZE (+) 0.420 *** 0.405 *** 0.441 ***
MTB (?) 0.011 0.011 0.012
LOSS (-) -1.016 * -1.207 ** -1.136 *
ROA (+) -0.403 -0.744 -0.896
LEV (?) 0.621 0.503 0.338
BOARDSIZE (+) 0.175 ** 0.191 *** 0.203 **
CEOCHAIR (+) 0.227 0.208 0.175
Industry Included Included Included Year Included Included Included
N 204 204 204
Adj. R2 0.436 0.425 0.492
Model chi-square 47.7 *** 46.8 *** 50.0 ***
Correctly classified 84.8 83.8 86.4
Notes: All variables are as defined in Table 2. ***, **, * Significant at the 0.01, 0.05, and 0.10 levels
using a two-tailed test, respectively. One-tailed tests are shown for variables with a
signed prediction. Two-tailed test are shown for variables without a signed prediction or
when the coefficient sign is the opposite to our prediction.
53
TABLE 10
Additional test: Probit regression: IS- or financial reporting- competencies
(1) (2) (3) (4)
CEO CFO CEO CFO
Coeff. p-stat. Coeff. p-stat. Coeff. p-stat. Coeff. p-stat.
Intercept -3.529 *** -3.527 *** Intercept -3.407 *** -3.101 ***
CEOIS/CFOIS (+) 0.772 ** CEOACC/CFOACC (+) -1.112 ***
CEOIS/CFOIS (+) 0.403 * CEOACC/CFOACC (+) -0.316 *
ITEXE (+) 0.342 * 0.410 ** ITEXE (+) 0.301 0.348 *
AUTO (+) 1.090 1.249 * AUTO (+) 1.195 1.203 *
INFO (+) 0.549 ** 0.758 *** INFO (+) 0.772 *** 0.662 ***
SIZE (+) 0.240 *** 0.230 *** SIZE (+) 0.239 *** 0.222 ***
MTB (?) 0.004 0.005 MTB (?) 0.003 0.005
LOSS (-) -0.581 * -0.698 ** LOSS (-) -0.704 ** -0.683 **
ROA (+) -0.653 -0.717 ROA (+) -0.581 -0.639
LEV (?) 0.347 0.265 LEV (?) 0.248 0.255
BOARDSIZE (+) 0.104 ** 0.109 *** BOARDSIZE (+) 0.116 *** 0.105 ***
CEOCHAIR (-) 0.135 0.126 CEOCHAIR (-) 0.037 0.083
Industry Industry
Year Year
N 204 204 204 204
Pseudo R2 0.435 0.421 0.445 0.424
Model chi-square 57.2 *** 55.7 *** 57.5 *** 47.3 ***
Correctly
classified
84.8
83.8
84.5
84.1
Notes: All variables are as defined in Table 2. ***, **, * Significant at the 0.01, 0.05, and 0.10 levels using a two-tailed test, respectively. One-tailed
tests are shown for variables with a signed prediction. Two-tailed test are shown for variables without a signed prediction or when the
coefficient sign is the opposite to our prediction.