GENERAL RESEARCH

The impact of early XBRL adoption on analysts’ forecastaccuracy - empirical evidence from China

Chunhui Liu & Lee Jian Yao & Choon Ling Sia & Kwok Kee Wei

Received: 14 February 2011 /Accepted: 25 March 2013# Institute of Information Management, University of St. Gallen 2013

Abstract Developing common standards, such as eXtensibleBusiness Reporting Language (XBRL), to smoothen informa-tion sharing in the value chain is considered the leading issuefor releasing the potential of e-business. Data standards suchas XBRL play a critical role in an increasingly networkedenvironment. Despite promises of XBRL to improve dataaccuracy, few empirical studies have tested the impact of earlyXBRL adoption on the quality of information. Theoriesexplaining information technology (IT) productivity paradoxindicate that value realization from IT innovations may expe-rience time lag due to the need for technology refinement anddiffusion. This study examines the impact of early adoption ofXBRL on analysts’ forecast accuracy with empirical data ofChinese firms. The uncertainty related to the unproven tech-nology, such as information errors, has decreased analysts’forecast accuracy during the early adoption period amongfirms in an economy with little public information on listedfirms. Our findings have practical implications that will facil-itate the quality improvement of financial information in anetworked business environment. Our findings highlight theimportance of quality assurance and policy enforcementfor value realization from XBRL adoption to regulators,

filers, information consumers, the accounting profession andother stakeholders.

Keywords XBRL adoption . Innovation diffusion .

e-Business IT business value . Online financial reporting

JEL classification M150 . M410 . O330

Introduction

The quality of information plays a critical role in enablingbusiness networking (Otto et al. 2011a) and is a key parameterfor the success of online service systems (Loonam andO’Loghlin 2008) such as online systems that disclose busi-nesses’ financial information. Online disclosure of businesses’financial information has become increasingly important foreasier and broader circulation of financial data (Debreceny etal. 2002). Among organized public bodies, the Toronto StockExchange in Canada, the Securities and Exchange Commission(SEC) in US, and Companies House in the UK have developedlarge scale databases like SEDAR, EDGAR, and CompaniesHouse Direct to allow electronic filing and retrieval via theInternet (Xiao et al. 2004).

Ranked as one of top ten technologies for accounting andauditing professionals by the American Institute of CertifiedPublic Accountants (Peng and Chang 2010), eXtensibleBusiness Reporting Language (XBRL) is a standard XMLreporting language recently developed to remove duplicativedata entry, to improve accuracy of electronic communicationof business financial information (Yoon et al. 2011; Teo et al.2003) and to facilitate the automated production and con-sumption of large volumes of business performance informa-tion by combining the immediacy and reach of the Web withthe ability of information consumers to incorporate corporateinformation directly into their data warehouses and decisionmodels (Debreceny et al. 2010). XBRL is a semantic standard

Responsible Editor: Xin Luo

C. Liu (*)Department of Business and Administration,The University of Winnipeg, 515 Portage Avenue,Winnipeg, Manitoba R3B 2E9, Canadae-mail: m.liu@uwinnipeg.ca

L. J. YaoJ. A. Butt College of Business, Loyala University New Orleans,6363 St. Charles Ave., Box 15,New Orleans, LA 70118, USA

C. L. Sia :K. K. WeiDepartment of Information Systems, The City Universityof Hong Kong, Tat Chee Avenue,Kowloon, Hong Kong SAR

Electron MarketsDOI 10.1007/s12525-013-0132-8

(Folmer et al. 2011) and addresses the system layer of businessnetworking (Otto et al. 2011b).

This research investigates the impact of XBRL on thequality of financial information as reflected in analyst fore-cast accuracy. Assessing the quality of semantic standardscan be useful to improve the quality of individual standardsso as to advance interoperability in networked business(Folmer et al. 2011). Financial analysts are important andinfluential users of financial reports (Yu 2010) and playimportant roles as information intermediaries and economicagents whose actions affect security pricing (Rock et al.2001). Research suggests that financial statements are a vitalsource of information for analysts in determining their fore-casts (Acker et al. 2002; Chang and Most 1985; Peek 2005;Vergoossen 1993). Lang and Lundholm (1996) indicate thatfirms with more informative disclosure policies have moreaccurate analyst earnings forecasts. Since financial analystsin the capital market can be used as signals of informationasymmetry because of their superior information processingcapabilities (e.g. Roulstone 2003), examining how the adop-tion of XBRL affects analyst forecast accuracy can uncoverthe impact of XBRL adoption on the quality of information(Liu et al. 2013). Implications from research on value real-ization from XBRL adoption have immediate benefits forregulators, filers, information consumers, the accountingprofession and other stakeholders.

Though earnings forecasts are more than ever a crucial topicfor capital markets investors and researchers (Coen et al. 2009;Frankel et al. 2006), the demand for forecast is particularlyhigh where weak investor protection results in earnings that areless likely to reflect economic performance (DeFond and Hung2007). The literature characterizes China as an emerging equitymarket with relatively weak investor protection (Zhou 2003;Chen et al. 2001). Despite the increasing importance of theChinese financial markets, little research has investigated earn-ings’ forecasts in China (Barniv 2009). China as the firstcountry to formally adopt XBRL for financial reporting(Kernan 2008) offers a great opportunity to examine the effectof early XBRL adoption on analysts’ forecast also because itsadoption does not coincide with major changes to accountingstandards. This study examines the impact of early adoption ofXBRL on analysts’ forecast accuracy based on empirical dataof Chinese firms between 2001 and 2006.

This research is important and valuable to security regu-lators, businesses and practitioners. The study empiricallyexamines the impact of mandatory XBRL adoption in Chinawhich can be informative to security regulators who areconsidering the mandatory adoption of XBRL in other ju-risdictions with similar legal and institutional environments.The finding of a negative association between XBRL man-date and forecast accuracy highlights the need for improve-ments by practitioners and businesses to assure informationquality in XBRL adoption before full value realization of

XBRL can be achieved in terms of improving the quality ofinformation in a networked business environment for betterdecision-making (Zhu and Wu 2011).

Global adoption of XBRL

Charles Hoffman, a CPA, initiated the conception of XBRL in1998. Since 2001, many XBRL jurisdictions have supportedthe development of XBRL. China started to assess XBRLadoption as early as 2002. Formal annual report filing withXBRL started with 2004 annual reports in China. Spainmandated the switch to XBRL to coincide with the adoptionof International Financial Reporting Standards (IFRS) on July2005 (O’Kelly 2010). South Korea developed a mandatoryfiling program since October 2007 (Yoon et al. 2011).Singapore mandated XBRL filing since November 2007(O’Kelly 2010). Israel also chose to coincide its switch toXBRL with its adoption of IFRS in January 2008 (O’Kelly2010). Belgium mandated XBRL for reporting since January2008 (O’Kelly 2010). Luxembourg started XBRL mandatoryfiling from January 2008 (O’Kelly 2010). Japan switched toXBRL filing since April 2008 (O’Kelly 2010). India started aphase-in adoption by mandating XBRL filing for the top 100Indian companies in January 2008 (O’Kelly 2010). The USSEC mandated a phase-in process for essential reporting withXBRL to begin for a fiscal period ending on or after June 15,2009 for the roughly top 500 public firms (Srivastava andKogan 2010). The second phase required all companies with apublic float of more than $700 million to use XBRL by June15 of 2010. The third phase required all US public companiesto use XBRL starting June 2011. Standard Business Reportingfrom businesses to the government in Australia started to useXBRL since July 2010 (O’Kelly 2010). XBRL became man-datory for firm reports in Denmark and UK in 2011 (O’Kelly2010). Regulators in many jurisdictions such as CanadianSecurities Administrators are still assessing the costs andbenefits from XBRL adoption.

The level of XBRL adoption is sometimes considered tobe disappointing when compared with the early predictionsmade for XBRL’s success (Keeling and Domingo 2004).One possible reason is the lack of empirical evidence on theimpact of the unproven technology and the uncertaintyresulted thereof. This study contributes by investigatingearly XBRL adoption’s impact based on empirical data.

Literature review and hypothesis development

Low penetration of electronic interconnection standards isbelieved to hinder e-business (Wigand et al. 2005).However, implementing a standard like XBRL incurs costs.The U.S. SEC estimates that the direct costs to a companysubmitting its first interactive financial statements with XBRLwith block-text footnotes and schedules could average

C. Liu et al.

$40,510 with an upper bound of $82,220 while the costs forsubsequent block-text filings could average $13,450 with anupper bound of $21,340 (Sledgianowski et al. 2010). ThoughXBRL is expected to evolve into the global data standard forfinancial reporting (Chang and Jarvenpaa 2005) with im-proved quality of information (Wigand et al. 2005), fewempirical studies have tested actual value realization ofXBRL adoption in terms of improved quality of information.

Great promises of XBRL have attracted research into thetechnical aspects of XBRL (Locke and Lowe 2007), thecharacteristics of early voluntary XBRL adopters(Premuroso and Bhattacharya 2008), the fit betweenexisting XBRL taxonomy and reporting practices (Bonsonet al. 2009), and the impact of XBRL adoption on informa-tion asymmetry (Yoon et al. 2011). Many unanswered ques-tions call for more research (Debreceny and Gray 2001). Forexample, empirical studies are needed to investigate wheth-er claimed benefits of XBRL have been realized.

One major expected value from XBRL is the reduction ofinformation asymmetry and the improvement to informationquality in an increasingly networked business environment(Marquardt and Wiedman 1998; Yoon et al. 2011).Analysts’ forecast accuracy is a good proxy for financialinformation quality (Roulstone 2003; Yu 2010). Many stud-ies reveal positive association between the quality of disclo-sures and forecast accuracy (Acker et al. 2002; Barron et al.1999; Hope 2003; Hope 2004; Higgins 1998; Lang andLundholm 1996; Vanstraelen et al. 2003). Since tags andtaxonomy of XBRL provide a straightforward searching andanalyzing capability, XBRL is expected to improve analystforecast (Yoon et al. 2011). Hunton and McEwen (1997)reveal that directive information search strategy where ana-lysts select specific information from a given informationset instead of viewing the material in its original sequence,which is enabled by XRBL, is associated with accurateanalysts forecast. Investigating the impact of XBRL adop-tion on analyst forecast accuracy may provide insight on theimpact of XBRL on quality of information.

Many researchers have studied the extent to whichapplications of IT lead to improved performance or busi-ness value. Hitt and Brynjolfsson (1996) analyze 370firms over the period 1988–1992 to find no correlationbetween IT investment and performance, such as totalshareholder return. Based on over 2,000 observations of624 firms, Sircar et al. (2000) examine 624 firms over1988–1993 to find no significant link between IT invest-ments and net income. Prior research has often referredto such a weak link between IT and value as an ‘ITproductivity paradox’.

One theory explaining the IT productivity paradox is thatIT investments take time to realize their business value as timeis needed to fine tune a new technology, to properly learn thetechnology, and to readjust it in an organization (Rai et al.

1997). Kivijarvi and Saarinen (1995) find that investment ininformation systems has long-term delayed effects on profit-ability. Im et al. (2001) find the existence of productivityparadox in the 1980s, but a positive market reaction to ITinvestment announcements in the early 1990s. With morerecent data over 1998–2000, Yao et al. (2010) also uncoverimproved productivity from IT investment.

Recent studies of early XBRL adoption reveal that XBRL,as an unproven technology, has yet to be fine-tuned to realizevalue above cost. Boritz and No (2009) find several problemareas such as redundant elements, inconsistent labels, missingtotals, and misspellings in XBRL-rendered documents.Debreceny et al. (2010) investigate calculation errors in thefirst round of filings of quarterly reports made in XBRLformat under the SEC XBRL mandate to catch an averageof 1.8 errors per filing with three quarters of the filings beingfrom errors. The study reports a median error of $9.1 millionper filing with the maximum exceeding $7 billion. In addition,adopting a new information technology may disrupt the cur-rent operations and create uncertainty (Doolin and Troshani2007; Hwang 2009; Hwang 2005; Li and Pinsker 2005). Suchunresolved challenges in XBRL implementation may havenegative impact on the quality of information. Errors inXBRL-tagged financial statements can increase informationasymmetry and reduce forecast accuracy (Liang and Huang1998; Liu et al. 2008) because financial statements are a vitalsource of information for analysts in determining their fore-casts (e.g. Acker et al. 2002). Therefore, this study hypothe-sizes that early XBRL adoption results in decreased forecastaccuracy. Given such existing issues yet to be solved, it isunclear as to whether the value from XBRL adoption such asimproved quality of financial information or improved ana-lysts’ forecast accuracy has beenmaterialized. This study addsto the literature with empirical assessment over the valuerealization of early XBRL adoption.

Research method

Chinese firms mandated to use XBRL are used as thesample mainly because China is the first country mandatingXBRL for business report filing and is thus an appropriatetarget market to examine the initial effect of XBRL adop-tion. Another major reason is that unlike firms elsewhere(e.g. Spain), XBRL adoption in China does not coincidewith the conversion from local accounting standards toInternational Financial Reporting Standards (IFRS). Thus,the change in forecast accuracy identified among Chinesemandatory adopters will not be a result of multiple majorchanges to the reporting process. Mandatory adoption ofXBRL is studied to avoid self-selection bias. Chinese firmsare analyzed also because the Chinese financial marketshave become increasingly important for investors aroundthe world (Barniv 2009) and because analysts’ forecast

XBRL adoption impacts on analysts’ forecast accuracy

plays a particularly important role in equity market withrelatively weak investor protection (DeFond and Hung2007) such as China (Zhou 2003; Chen et al. 2001).

Since 1992, China issued four sets of accounting regula-tions (1992, 1998, 2001, and 2006) with each replacing theprevious one (Chen et al. 2002; Peng et al. 2008). TheXBRL adoption mandate occurred during the period be-tween 2001 and 2006 when the third set of accountingregulations was in effect. Therefore, data are collected be-tween 2001 and 2006 to screen out potential interveningeffects due to accounting regulation change. Organizationsadopting XBRL before the 2004 mandate are removed fromthe sample because these organizations adopted XBRL on avoluntary basis. The 2001~2003 data reflect pre-adoptionperiod while the years 2004~2006 data reflect post-adoption period. Information on analyst forecasts isobtained from the I/B/E/S International database while fi-nancial accounting information and stock information areobtained from CSMAR database. Firms with missing vari-able values are removed to result in 129 sample firms with672 firm year observations. The sample includes firms fromdifferent industries: 53% manufacturing industrials, 24%public utilities; 9% conglomerates, 7% properties/realestate, 4% commerce, and 3% finance.

Following Barniv (2009) and other studies on determi-nants of forecast accuracy (e.g. Alford and Berger 1999;Brown 1997; Frankel et al. 2006; Hope and Kang 2005;Kross et al. 1990; Richardson et al. 2004), the researchmodel identifies the impact of XBRL adoption on analysts’forecast accuracy while controlling variables previouslyfound to influence the forecast accuracy as follows:

FACCjt ¼ a0 þ a1 � Post adoption periodþ a2 � NANAjt

þ a3 � STDjt þ a4 � EPSjt þ a5 �MVEjt

þ a6 � LOSSjt þ a7 � FPIjt þ a8 � Industryjt þ "jt

ð1ÞWhere FACCjt is the forecast accuracy for firm j in year t,

defined as FACCjt=−AFEPjt as per (Barniv 2009; Hope2003; Hope and Kang 2005; Lang and Lundholm 1996)while AFEPjt is the forecast error (Coen et al. 2009).AFEPjt is obtained through deflating the absolute differencebetween actual earnings per share (EPS) and consensusforecast EPS by year-end stock price. The stock price isused to deflate the difference between actual and forecastedEPS to facilitate comparisons across firms (Hope 2003).FACC is calculated as follows:

� Actual EPS�Mean Estimated EPSj jBeginning�of�year stock price

ð2Þ

Post adoption period is a dummy variable equal to 1 if afirm-year (a specific firm in a certain year) observes manda-tory adoption of XBRL and 0 otherwise. NANAjt is the

number of analysts following a firm which previous research(Barniv 2009; Coen et al. 2009) has found to positivelyinfluence forecast accuracy because a significant number ofanalysts following a firm should induce an increase in com-petitiveness and an improvement in forecast accuracy (Alfordand Berger 1999; Coen et al. 2009; Hope 2003; O’Brien1990). STDjt is standard deviation of forecasts in the consen-sus for firm j in year t or cross-analyst dispersion which is aproxy for risk (Kross et al. 1990). It was found to have anegative relation with forecast accuracy (Barniv 2009). EPSjtis the actual earnings found to have positive relation withforecast accuracy as a proxy for the magnitude of earnings(Barniv 2009). MVEjt is the natural logarithm of the marketvalue of equity of firm j for year t as a proxy for firm size(Barniv 2009). Much research includes firm size as a controlvariable in studying analyst forecast, but the net effect of firmsize is ambiguous (Frankel et al. 2006). Loss is a dummyvariable that equals 1 if the reported EPS is negative and0 otherwise. Hope (2003) finds that firm-specific factors likeprofits vs. losses are the most important in explaining thecharacteristics of analyst forecast. Loss can be negativelyassociated with forecast accuracy (Barniv 2009; Coen et al.2009; Hope and Kang 2005) due to analysts’ well-knowntendency toward optimism (Bradshaw et al. 2006; Brown1993; Gu and Wu 2003; O’Brien 1988). FPIjt is a forecastperiod indicator. The forecast horizon is also revealed to berelated to forecast accuracy (Richardson et al. 2004). Thelonger the forecast horizon, the more difficult it is to predictearnings which are influenced by even more unforeseen fac-tors, thus, FPI and forecast accuracy is expected to be nega-tive. Industry is an indicator variable for a firm’s industrymembership also found to relate to forecast accuracy (Brown1997), possibly due to the influence of sector competitivenesson earnings (Luttman and Silhan 1995).

Research findings

Descriptive statistics

The descriptive statistics on non-dummy variables for thesample is provided in Table 1. When these variables arecompared between the period without annual XBRL filing(2001–2003) and the period (2004–2006) with mandatoryannual XBRL filing, a difference is not found in MVE,STD, or FPI. However, the Mann–Whitney test revealssignificant differences between the two periods for FACC,EPS, and NANA. The FACC, forecast accuracy is lower inthe period after the mandatory XBRL adoption as hypothe-sized. The significantly increased EPS does not contribute tothe decreased FACC because these two variables are posi-tively related. Further investigation is necessary to examinewhether the decreased FACC is mainly due to the decreaseto NANA during 2004~2006.

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Model testing

To identify correlations that may lead to multicollinearityconcerns, spearman correlation coefficients among variablesare identified and presented in Table 2. The correlationcoefficients are all far below 0.80, revealing no majorconcern for multicollinearity. Tests of tolerance (minimumtolerance=0.579) and variance inflation factor (maximumVIF=1.728) further confirm that multicollinearity is not aconcern in the dataset (Myers 1990).

FACCjt is forecast accuracy for firm j in year t; Postadoption period is a dummy variable equal to 1 if a firm-

year observes mandatory adoption of XBRL and 0 other-wise; NANAjt is the number of analysts following a firm;STDjt is standard deviation of forecasts in the consensus forfirm j in year t or cross-analyst dispersion; EPSjt is the actualearnings found to have positive relation with forecast accu-racy; MVEjt is the natural logarithm of the market value ofequity of firm j for year t; LOSS is a dummy variable thatequals 1 if the reported EPS is negative and 0 otherwise;FPIjt is a forecast period indicator; Industry is an indicatorvariable for a firm’s industry membership.

Table 3 summarizes the findings from our research model,revealing empirical evidence in support of our hypothesis as

Table 1 Descriptive statistics for non-dummy variables studied

N=672 Mean Standard deviation (Std)

FACC −0.020 0.062

EPS 0.275 0.478

FPI 1.853 0.774

MVE 15.702 1.114

NANA 6.021 5.893

STD 0.045 0.050

2001–2003 2004–2006

N = 168 504

Mean Std Median Mean Std Median

FACC −0.013 0.025 −0.005 −0.023 0.070 −0.008**

EPS 0.094 0.262 0.127 0.336 0.517 0.289**

FPI 1.845 0.781 2.000 1.855 0.773 2.000

MVE 15.790 1.217 1.482 15.672 1.077 15.590

NANA 8.167 6.937 5.000 5.306 5.319 3.000**

STD 0.044 0.044 0.030 0.046 0.051 0.030

**Indicates difference significant at p<0.01; N refers to firm years

FACCjt is forecast accuracy for firm j in year t; EPSjt is the actual earnings found to have positive relation with forecast accuracy; FPIjt is a forecastperiod indicator; MVEjt is the natural logarithm of the market value of equity of firm j for year t; NANAjt is the number of analysts following a firm;STDjt is standard deviation of forecasts in the consensus for firm j in year t or cross-analyst dispersion; Loss is a dummy variable that equals 1 if thereported EPS is negative and 0 otherwise. Industry is an indicator variable for a firm’s industry membership.

Table 2 Spearman Correlation Coefficients, N=672

1 2 3 4 5 6 7 8 9

Dependent variable

1 FACC 1

Independent variables

2 Post adoption period −0.129** 1

3 NANA 0.069 −0.224** 1

4 STD −0.375** −0.007 0.017 1

5 EPS −0.084* 0.413** −0.023 0.281** 1

6 MVE 0.136** −0.030 0.517** −0.028 0.178** 1

7 LOSS −0.348** −0.133** 0.051 0.141** −0.459** −0.134** 1

8 FPI −0.375** 0.006 −0.038 0.218** 0.130** 0.120** 0.014 1

9 Industry −0.125** 0.129** −0.234** 0.152** 0.049 −0.314** 0.005 −0.077* 1

*Denotes p<0.05; **denotes p<0.01

XBRL adoption impacts on analysts’ forecast accuracy

the coefficient for “Post adoption period” is significant andnegative at p<0.01 level when other variables, known toinfluence the cost of capital such as NANA, STD, EPS,MVE, LOSS, FPI and Industry, are controlled and includedin the research model. As expected from previous researchfindings, EPS is found to have significant and positive asso-ciation with forecast accuracy while STD, LOSS and FPI arefound to have significant and negative relation with forecastaccuracy.

FACCjt is forecast accuracy for firm j in year t; Postadoption period is a dummy variable equal to 1 if a firm-year observes mandatory adoption of XBRL and 0 otherwise;NANAjt is the number of analysts following a firm; STDjt isstandard deviation of forecasts in the consensus for firm j inyear t or cross-analyst dispersion; EPSjt is the actual earningsfound to have positive relation with forecast accuracy; MVEjtis the natural logarithm of the market value of equity of firm jfor year t; LOSS is a dummy variable that equals 1 if thereported EPS is negative and 0 otherwise; FPIjt is a forecastperiod indicator; Industry is an indicator variable for a firm’sindustry membership.

In summary, the empirical findings suggest that earlymandatory XBRL adoption is associated with a significantdecrease in analysts’ forecast accuracy as the hypothesisproposes.

Robustness tests

Since the XBRL adoption effect may be uncertain during thetransition period, the model is also tested with the transitionperiod (2003–2004) removed as per Li (2010) and Petersen(2009). The findings are still in support of our hypothesis asthe coefficient for “Post adoption period” is significant andnegative at p<0.01 level. Likewise, EPS is found to havesignificant and positive association with forecast accuracywhile STD, LOSS and FPI are found to have significant andnegative relation with forecast accuracy. The adjusted R2

increases to 0.64 when transition period data are removedfrom analysis. Conclusions are also the same when samplefirms from finance industry are removed from the analysis.

To remove the impact of sample firm differences acrossperiods, the model is retested by removing firms that havedata for only one period. Retest results presented in Table 4still support the hypothesis with significant and negativecoefficient for the “Post adoption period” variable at p<0.01. The NANA for these firms are not significantly dif-ferent across two periods of comparison, thus indicating thatthe significant and negative association between Postadoption period and FACC still holds even when thedecrease in NANA after XBRL period reported inTable 1 is not present.

FACCjt is forecast accuracy for firm j in year t; Postadoption period is a dummy variable equal to 1 if a firm-year observes mandatory adoption of XBRL and 0 other-wise; NANAjt is the number of analysts following a firm;STDjt is standard deviation of forecasts in the consensus forfirm j in year t or cross-analyst dispersion; EPSjt is the actualearnings found to have positive relation with forecast accu-racy; MVEjt is the natural logarithm of the market value of

Table 3 Model testing resultsfor 2001~2006 FACCjt=α0+α1 * Post adoption period+α2 * NANAjt+α3 * STDjt+α4 * EPSjt+α5 * MVEjt+α6 * LOSSjt+α7 *

FPIjt+α8 * Industryjt+εjt

N=672; Adjusted R2=0.59 Coeff. Standard error t-statistic Two-tailed p-value

Intercept 0.032 0.028 1.17 0.242

Post adoption period −0.030 0.004 −8.12 <.0001

NANA + 0.000 0.000 0.11 0.912

STD – −0.424 0.032 −13.22 <.0001

EPS + 0.083 0.004 21.70 <.0001

MVE −0.001 0.002 −0.57 0.568

LOSS – −0.021 0.007 −3.04 0.003

FPI – −0.008 0.002 −3.76 0.000

Industry −0.001 0.001 −0.51 0.611

Table 4 Model testing for firms with observations for both periods

N=121; AdjustedR2=0.49

Coeff. Standarderror

t-statistic Two-tailedp-value

Intercept −0.048 0.033 −1.46 0.147

Post adoption period −0.009 0.003 −3.28 0.001

NANA + 0.000 0.000 0.39 0.696

STD – −0.103 0.042 −2.47 0.015

EPS + 0.002 0.006 0.27 0.791

MVE 0.003 0.002 1.69 0.094

LOSS – −0.040 0.007 −5.88 <.0001

FPI – −0.006 0.002 −3.51 0.001

Industry 0.001 0.001 1.01 0.316

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equity of firm j for year t; LOSS is a dummy variable thatequals 1 if the reported EPS is negative and 0 otherwise;FPIjt is a forecast period indicator; Industry is an indicatorvariable for a firm’s industry membership.

Discussion

In agreement with prior findings on IT productivity paradox,IT innovations such as XBRL take time to realize theirbusiness value as time is necessary for fine-tuning, propertraining, and necessary adjustments. The uncertainty relatedto XBRL such as information errors can decrease analystsforecast accuracy during the early adoption period. Ourfindings have implications for standard developers, regula-tors, and taxonomy users. Regulators should implementXBRL adoption with stricter policy on quality assurance toensure quality and reliability of information reported inXBRL taxonomy. Taxonomy users should be educated oncommon causes for errors in XBRL implementation toavoid the improper use of the taxonomy for betterdecision-making in a networked business environment.Technology implementation refinement is critical to re-duce the uncertainty and increase information quality.One example is to assure proper treatment of the un-derlying debit/credit assumptions in the taxonomy(Debreceny et al. 2010).

Conclusions

This study examines the impact of the early adoption ofa new semantic standard for online business reportingon analyst forecast accuracy with empirical data fromChinese firms between 2001 and 2006. We find thatanalyst forecast accuracy decreased during the earlyadoption period among firms in an economy with littlepublic information on listed firms.

The following limitations should be considered whenusing the research findings. First, data are from oneparticular country and thus limit the generalizability ofthe research findings (Liu 2013). Former studies (e.g.Leuz et al. 2003; Burgstahler et al. 2006) show that thequality of business reporting is sensitive to the qualityof legal enforcement. However, Hope (2003) shows thatfirm-specific factors such as firm size (MVE), negativeearnings (LOSS), and industry are the most important ininvestigating forecast accuracy. In addition, the conclu-sions from this study can be strengthened if the effectof XBRL is significant after singling out the effect ofregulation changes in a sample that is subject to thesechanges during the XBRL adoption, such as Spanishfirms. Third, XBRL is continuously developed and

improved. The results uncovered by this study onlyreflect the situation before year 2007.

Future research can investigate the impact of XBRL onquality of information with data from different institutionalenvironments and with different measurements of informa-tion quality. Future studies may investigate any change inXBRL adoption’s impact on the analyst forecast accuracywith more recent data. Besides, more study is necessary fordiscovering ways for fine tuning the technology towardgreater quality of information.

Acknowledgements The authors are indebted to Dr. Xin Luo,anonymous reviewers and senior editors for their enlighteningcomments and helpful suggestions. The authors also thank theparticipants of the Annual Conference on Global Economy, Businessand Finance 2012.

A dedication The authors hope to dedicate this paper to Dr. Lee J. Yaowho passed away on Nov. 14, 2012 due to complications from cancer.

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XBRL adoption impacts on analysts’ forecast accuracy