Banker, Rajiv

Chang, Hsihui

Kao, Yi-Ching

English

AIS Electronic Library (AISeL)

Association for Information SystemsAIS Electronic Library (AISeL)AMCIS 2001 Proceedings Americas Conference on Information Systems(AMCIS)December 2001Investigation of Lagged Impacts of InformationTechnology Expenditures in a Public AccountingFirmRajiv BankerUniversity of Texas at DallasHsihui ChangUniversity of Texas at DallasYi-Ching KaoUniversity of Texas at DallasFollow this and additional works at: http://aisel.aisnet.org/amcis2001This material is brought to you by the Americas Conference on Information Systems (AMCIS) at AIS Electronic Library (AISeL). It has been acceptedfor inclusion in AMCIS 2001 Proceedings by an authorized administrator of AIS Electronic Library (AISeL). For more information, please contactelibrary@aisnet.org.Recommended CitationBanker, Rajiv; Chang, Hsihui; and Kao, Yi-Ching, "Investigation of Lagged Impacts of Information Technology Expenditures in aPublic Accounting Firm" (2001). AMCIS 2001 Proceedings. 277.http://aisel.aisnet.org/amcis2001/2772001  Seventh Americas Conference on Information Systems 1431INVESTIGATION OF LAGGED IMPACTS OFINFORMATION TECHNOLOGY EXPENDITURESIN A PUBLIC ACCOUNTING FIRMRajiv D. BankerUniversity of Texas at Dallasrbanker@utdallas.eduHsihui ChangUniversity of Texas at Dallasswchang@utdallas.eduYi-ching KaoUniversity of Texas at Dallasyiching@utdallas.eduIntroductionRecent empirical research has documented positive impacts of information technology (IT) investments (Brynjolfsson 1993).However, most IT productivity studies have relied largely on cross-sectional evidence.  In this paper, we study IT productivityimpacts using time series data for 36 months from one of the Big Five public accounting firm in Taiwan.  We examine the  ITimpacts not only at the time when the investments are made but also in the subsequent periods.  This longitudinal study allowsus to evaluate causality as well as investigate the potential lagged impacts of IT.  Moreover, it enables analysis of the longitudinalpattern of IT productivity impacts to assess the organizational learning and adjustment costs for IT adoption before productivityimprovement is manifest.While IT productivity has been studied in several  industries, our study is perhaps the first focused on the professional servicesindustry in which information and knowledge work play a prominent role.  The public accounting profession has been changingrapidly in recent years to leverage information technology in its audit and taxation services (Elliot 2000).  Therefore, theestimation of the productivity impacts of IT expenditures in a public accounting firm is of considerable interest to both academiaand practice.We collected a panel of data over 36 months for 5 branches of the public accounting firm.  Data from branches of a single firmhelps control for unmeasured organizational and cultural factors that may influence productivity, although  it also limits broadgeneralizations to other contexts.  Access to senior management enabled us to obtain qualitative information to enrich the insightsobtained from our quantitative analysis.Research QuestionsLiterature ReviewThe inability of early research on IT productivity to document positive impact of IT on firm performance was referred to as the“IT productivity paradox”.  One of the explanations for the IT productivity paradox phenomenon is the lag in productivity gainbecause of organizational learning and adjustment (Brynjofsson 1993).  Yet, very few empirical studies have addressed the lagproblem.  Devaraj and Kohli (2000) examined the impact of IT investment on hospital performance assuming a 3-month lag forIT labor and a 4-month lag for IT capital.  Loveman (1994) used a distributed lag model to examine the pattern of lagged impactsof IT, but there were only 4 time points available in his data set.In the operations management literature, there is empirical evidence (Hayes and Clark 1986, Chew et al. 1990, Banker et al. 2001)documenting a U-shaped curve for the longitudinal  process for non-IT capital investments: plant productivity often decreasesinitially when investment in new equipment is made, but then increases over a longer period.  This lag in the productivity impactof manufacturing investment reflects the time required by an organization to learn and adapt to the new technology.  This researchis to verify if IT has similar characteristics to other capital investment in its longitudinal impacts.IS and S/W Design, Development, and Use1432 2001  Seventh Americas Conference on Information SystemsProduction in a Public Accounting FirmProfessional staff is the primary and often the constraining input in the professional services industry.  Therefore, our objectiveis to analyze the impacts of IT expenditure on the profitability per professional in a public accounting firm over time.  Whileaccounting professionals are the major driver for the firm’s service output, the professionals need to incur some operating costs(such as office rent, office suppli s, travel) to support their work.  The profitability per professional can be formulated as follows:−−−==RateSalaryalProfessionIntensityITIntensityCost OperatingtyProductiviGenerationRevenuealsProfession ofNumber Salaries alProfession - eExpenditur IT -Cost  Operating -Output  Revenue  alprofessionper Profit In the context of public accounting services production, we will examine the relationship between IT intensity and revenuegeneration productivity as well as IT intensity and operating cost intensity.  Since there is no significant correlation between ITintensity and professional salary rate, we can then infer the relationship between IT intensity and profit per professional.Research HypothesesPrior research on IT investment impacts speculates that organizations require a certain period of time for learning and adjustmentbefore productivity improvement may occur (Brynolfsson 1993, Soh and Markus 1995).  Our field interviews with theprofessionals at our research site suggested that users require time to learn the new technology and find ways to use it for valuecreation.  During such learning process, the professionals may slow down their regular work and thus their revenue generationproductivity may decline.  Consequently, we posit:H1a: IT intensity initially has negative lagged impacts on revenue generation productivity.After the initial learning process, the professionals are expected to realize the benefits from IT in reducing working hours perengagement.  In addition, familiarity with new software may appeal to new clients and generate new engagements and higher fees.Hence, we conjecture:H1b: IT intensity has positive lagged impacts on revenue generation productivity in the later lagged periods.A similar learning curve pattern is expected for IT impacts on operating support costs.  Initially, the professionals may incur moreoperating costs during the trial-and-error learning process.  After  that, the IT investment will result in a reduction in operatingsupporting costs by decreasing paper, copying, telephone, postage and travel expenses.  Therefore, we posit the second set ofresearch hypotheses as follows:H2a: IT intensity initially has positive lagged impacts on operating cost intensity.H2b: IT intensity has negative lagged impacts on operating cost intensity in the later lagged periods.Combining the revenue side and the cost side impacts, we expect that IT spending has similar pattern of decreasing and increasinglagged impacts on the overall professional productivity at our research site:H2a: IT intensity initially has negative lagged impacts on overall professional labor productivity.H2b: IT intensity has positive lagged impacts on overall professional labor productivity in later lagged periods.Banker et al./Lagged Impacts of IT Expenditures2001  Seventh Americas Conference on Information Systems 1433Empirical AnalysisData DescriptionWe collected data from the monthly accounting statements for each branch at our research site.  The panel includes 36 monthlyobservations from January 1997 to December 1999 for each of 5 branches for the following variables: • Branch’s monthly IT expenditure – includes spending on IT hardware, software and labor.• Branch’s monthly revenue – from the branch monthly income statement.• Branch’s monthly operating cost – operating expense from the branch monthly income statement excluding professionalsalary and IT-related costs.• Number of professionals in each branch every monthA challenge in this context is to measure the monetary value of branch monthly output.  Due to the revenue collection methodat our research site, the revenue reported in the monthly income statements does not equal the actual monthly output.  For certainaudit jobs continuing over several months, the branches do not accrue revenue on those jobs every month, rather they recognizerevenue only in the month they bill the clients.  The practice is to bill clients for the pending unbilled jobs twice each year.  Welabel these two months as the "billing" months.  Our discussions with senior management indicated that each branch has its ownbilling months depending on its mix of clients, but in all cases the billing months are six months apart.  Assuming that a proportionρ of the output is uncollected in each non-billing month, we formulate  the relationship between the actual monthly output andrevenue reported in the monthly income statement. Estimation ModelWe construct a system of two equations to test our research hypotheses.  The first equation models the impacts of IT intensityon revenue generation productivity.  The dependent variable in the first equation is the monthly actual output scaled by the numberof professionals in the branch.  Since the branch monthly actual output is not observable, it is replaced by an expression in termsof the revenue reported in the monthly income statement and the proportion ρ of uncollected output.  The second equation modelsthe impacts of IT intensity on operating cost intensity.  The dependent variable in the second equation is the monthly operatingcost scaled by the number of number of professionals in the branch.  The two equations have the same explanatory variablesincluding contemporaneous IT intensity (IT expenditure scaled by the number of professionals in the branch), lagged ITintensities, and four branch dummy variables.  The dummy variables are to capture branch differences in a fixed effectsspecification (Greene 1997, pp. 615-618).Estimation ResultsWe estimated our model with different numbers of lags, ranging from zero to eight, and found that including 6 lags is the mostappropriate specification for our model based on the Akaike Information Criterion.  According to the estimation results for thefirst equation, the lagged impacts of IT intensity on revenue generation productivity exhibit a U-shaped pattern as expected.  Thevalue of the coefficients of the lagged IT variables is close to zero (0.04)  for the first-month lag, becomes negative (-1.58 and–2.22) for the second- and third-month lags, and turns positive (0.17, 3.18, and 3.43) for the forth-, fifth- and sixth-month lags.Among them, the coefficients of the second-, third-, fifth-, and sixth-month lags are significant at conventional levels.  However,the estimation results for the second equation do not exhibit a U-shaped pattern for the lagged IT impacts on operating costintensity.  The values of the coefficients of the six lagged IT variables are all negative, between -0.01 and -0.25, and significantat conventional levels for the third- and fifth-month lags.We also evaluated the aggregate impacts from IT over the first three and the next three lagged months (Greene 1997, pp. 783-784).On the revenue generation side, statistical tests indicated the existence of learning and adjustment costs in the first three laggedmonths and substantial productivity gains in the next three lagged months.  Therefore, the research hypotheses H1a of negativeinitial lagged impacts and H1b of positive longer-term lagged impacts are confirmed.  On the operating cost side, the researchhypothesis H2a about positive initial lagged impact is not supported for the first three lagged months,  but for the next three laggedmonths there are significant cost-saving benefits, supporting the research hypothesis H2b.  IS and S/W Design, Development, and Use1434 2001  Seventh Americas Conference on Information SystemsEmploying the empirical estimates, we calculated the marginal impact of IT intensity on profit per professional.  For the first threelagged months, a $1 increase in the IT expenditure per professional results in a decrease of more than $3 in the profit perprofessional and the impact is significantly different from zero.  Therefore, our hypothesis H3a about the negative initial impactof IT on professional labor productivity is supported.  For the next three lagged months, a $1 increase in the IT expenditure perprofessional results in a more than $5 increase in the profit per professional.  This marginal impact is significantly different fromzero and thus our hypothesis H3b regarding positive lagged impact of IT on professional labor productivity in the later laggedperiod is also supported.ConclusionThis study is one of the first empirical examinations of IT productivity impacts in the professional services industry.  At themethodological level, its contribution is the development of a novel estimation model to  match the actual output and reportedrevenue of the firm and capture the special production structure in the professional service industry.  At the substantive level, thisstudy extends the IT productivity literature by providing a detailed longitudinal analysis of the lagged impacts of IT investment.Specifically, it reveals that there may be short-term pain due to organizational learning and adjustment before there are long-termgains.  The implication for managers is that the evaluation of IT investment payoffs requires a longer term perspective rather thanan emphasis on instantaneous improvements.  ReferencesBanker, R.D., Field, J. and Sinha, K., “Work Team Implementation and Trajectories of Manufacturing Quality: A LongitudinalField Study,” Manufacturing & Service Operations Management (3:1), Winter 2001.Brynjolfsson, E., “The Information Technology and the Productivity Paradox,” Communication of the ACM (36:12), 1993, pp.66-77. Chew, W.B., Leonard-Barton D., and Bohn, R.E., “Beating Murphy’s Law,” Sloan Management Review, Spring 1991, pp. 5-16.Devaraj, S., and Kohli, R., “Information Technology Payoff in the Health-Care Industry: A Longitudinal Study,” Journal ofManagement Information systems (16:4), Spring 2000, pp. 41-67. Elliott, R.K, “Who Are We As a Profession – And What Must We Become?” Journal of Accountancy, Feb. 2000, pp. 81-85.Greene, W.H., Econometric Analysis, Third Edition, Prentice Hall, 1997.Hayes, R.H. and Clark, K.B., “Why Some Factories are More Productive than Others,” Harvard Business Review, Sep.-Oct. 1986,pp. 66-73. Loveman, G.W., “An Assessment of the Productivity Impact of Information Technologies,” in Information Technology and theCorporation of the 1990s, Edited by Allen, T.J. and Morton, M.S., Oxford University Press, 1994. Soh, C., and Markus, M.L., “How IT Creates Business Value: A Process Theory Synthesis,” Proceedings of the 16th InternationalConference of Information Systems, 1995, pp. 29-41

Investigation of Lagged Impacts of Information Technology Expenditures in a Public Accounting Firm

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Investigation of Lagged Impacts of Information Technology Expenditures in a Public Accounting Firm

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