Predicting US growth performance went from 43% for

Predicting the businessvalue of IT on Organizational productivity: A comparison of DEA, Decision Treeand Neural Networks IntroductionA significant growth in productivity cements thebasis for improvements in the standard of living(Niebel & Mannheim, 2014).

Heavy Investments and effective use of InformationTechnology (IT) or Information Systems (IS) are seen as a key driver ofproductivity growth (Niebel & Mannheim, 2014). According toa report by the Development & Research Center  in 2004 ,enterprises that effectively useIT in their business process and operations experience  greater productivity leading to greatercompetitiveness that promotes sustainable economic growth which is aprerequisite for poverty reduction. IT have drastically changed society in the lastquarter of the century inducing unexpected qualitative and quantitative changes(Biagi, 2013).A lot of research studies has established on theimportance of ICT for the US growth rebirth observed between years 1995 and2006. For instance ,Jorgenson et al (2008) estimate thatthe share attributable to ICT in US growth performance went from 43% for theperiod 1971-1995 to 59% for the period 1995-2000 (Biagi, 2013).

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This acceleration in productivity growth in the U.S.prompted an eruption of massive academic research on both sides of the Atlantic(Ark, Mcguckin, & Inklaar, 2003; Biagi, 2013;Oulton, 2002).A vast majority of the research centered within theUS economy has resolved that ICT was accountable for much of the accelerationin productivity growth as compared to that of Europe  where attention wasfocused on the slower growth and how much of it could be tied to differences inICT diffusion relative to the U.S(Ark et al., 2003).

According to Dedrick, Gurbaxani, & Kraemer, (2003),there has beena long-running discussion in both the business media and the informationsystems and economics literature over whether information technology (IT) waspaying off in higher productivity. The first studies, conducted in the 1980s, found noconnection between IT investment and productivity at the level of firms,industries, or the economy as a whole Loveman 1994; Roach 1987, 1989, 1991;Strassmann 1990.Dedrick et al., 2013 indicated thatsome authors also suggested strong evidence that the returns to IT investmentare positive and significant for organizations, industries, and for economylike U.

S.Brynjolfsson and Yang concluded that althoughresearchers analyzed statistics extensively during the 1980s, they found littleevidence that information technology significantly increased productivity(Erik Brynjolfsson & Yang, 1996).This inconsistency in recognizing the real businessvalue of IT on productivity brought about the famous phrase “IT productivityparadox” stated by the famous economics Robert Solow in 1987(BAUMOL & SOLOW, 1998; Hutchinson, 2008; Triplett,1999).

Biagi, (2013) attributed thedelay in recognizing the significance of ICT in accounting for labourproductivity growth to the following; a lack of accurate quantitative measuresfor the output and value created by ICT and secondly, measuring productivity inthe service sector ( a heavy user of ICT) is also very difficult.Information Technology (IT) poses a serious dilemmafor management today. On one hand, continuing IT innovations have the potentialof changing the competitive game for many organizations.

On the other hand, thesize of the IT investment puts increasing pressure on managers to assess itsbusiness value and results of recent studies are at best inconclusive (Mukhopadhyay, Lerch, & Mangal, 1997)(Mendoncaet la.,2008)(Abri& Mahmoudzadeh, 2014).Outcome of myLiterature ReviewMain aim/Specificobjectives of the Literature Review/purpose·        The present study contributes to therelevant literature by extending the conceptualized framework to explore theICT impact on productivity.

·        provide both qualitative andquantitative information about the vast literature on IT and productivity  ·        givesa sizeable and up to update survey of the subject matter (1990-2017) .Toachieve this, the authors included working papers, studies report, student’sthesis, conference papers and journal articles in the literature survey.·        Identifygaps in existing studies which will give directions to future studies·        Identifythe various methodology used in assessing or predicting the impact of IT onproductivity.The strength and weakness of these methodologies would also beassess.·        Toknow the continental distribution of studies done the subject matter.

Scope ofLiterature ReviewThis review examines about 150 empirical studiesbased on economic analysis that have appeared between  1990 to 2017.The review concentrates mainly on studies whoseresults have been published in high standard peer-reviewed scholarly journals,working papers, institutional report and student’s theses. These journals areknown for having high standards for review and acceptance and therefore aremost appropriate for a critical review that seeks to achieve an objective andbalanced perspective on the research.MethodologyDataCollectionTheelectronic databases searched in this review included;·        Journals: Elsevier, Springer,Taylor& Francis, MIS Quarterly, IEEE, Emerald, Wiley, the MIT Press, OECDPublications, ACM etc.·        Conference papers.·        Working Papers such as one from The Bankof England’s working paper series.·        Reports and articles from recognizedinstitutions like IMF, World Bank etc. Selection of LiteraturesInthe selection of the various literatures for this study, the PreferredReporting Items forSystematicReview and Meta-Analysis, PRISMA Flow Diagram was adapted (Knobloch, Yoon, & Vogt, 2011; Moher et al.

, 2015;Moher, Liberati, Tetzlaff, Altman, & Group, 2009)Theflow diagram shown below depicts the flow of information through the differentphases of a systematic review. Itmaps out the number of records identified, included and excluded, and the reasonsfor exclusions. Moher et al., (2009) defined asystematic review as a review of a clearly formulated question that uses systematicand explicit methods to identify, select, and critically appraise relevantresearch, and to collect and analyze data from the studies that are included inthe review. Statisticalmethods (meta-analysis) may or may not be used to analyze and summarize the resultsof the included studies. Meta-analysisrefers to the use of statistical techniques in a systematic review to integratethe results of included studies.   The process involved:1.

Defining the scope of the review, developing questions and inclusion/exclusioncriteria; 2.Identification of potential studies through literature searches using keywords;3.Screening of abstracts and papers to meet inclusion criteria; 4.Characterization of articles for mapping by keywords. 5.

The Meta-Analysis.The inclusioncriteriaAnumber of further criteria were specified to select appropriate studies forinclusion in the literature survey. Tobe included in the review, papers had to Studieshave be writing in EnglishStudiesshould have well-structured scientific methodology(a)Include empirical evidence relating to the impacts of IT/ICT or InformationSystem adoption on productivity or the economy. This was done to address themain aim of the study. (b)Date from 1990 to 2017, (c)Include studies done using data from any of the following continents; BothNorth and South America, Asia, Europe and Africa. Inthe search for the literature, the authors realize that a lot of studies wasalso done within the context of developing countries and because most countriesin Africa are grouped under the developing category, the authors decided toinclude studies also done within developing countries. This was done in orderto have a clear picture of the various studies done within the Africancontinent.

          Studies included in quantitative synthesis (meta-analysis) (n =141   )   Records excluded (n = 102)   Records after duplicates removed (n = 407)   THE PRISMA FLOW DIAGRAM Records identified through database searching (n = 750)   Full-text articles excluded, with reasons (n 164   )   Studies included in qualitative synthesis (n = 141)                            PRISMAFlow Diagram .Source (Moher et al., (2009))   Classification of thestudies on continental bases Themes No. of Studies Identified IT and Productivity studies in Europe 43 IT and productivity studies in both North and South America 44 IT and Productivity  Studies in Asia 23 IT and Organizations Productivity in Developing Countries   7 IT and Productivity  Studies in Africa 11 Total 141  Classification of TheLiteratures Using Saunders et al Research Onion Itis normally not unusual for a researcher to begin thinking about a research byconsidering as to whether to, administer a questionnaire or conduct interviews.Concernsof this issue has been thoroughly addressed by Saunders, Lewis, & Thornhill, (2009)  and also cited by (Knox, 2004) using theresearch “Onion” model shown in figure below.Accordingto the authors, before coming to this central point in any study there areimportant layers of the onion that need to be peeled away.

Theclassification was done under the following heading: Technique, Time-Horizonand Strategy         TheSaunders et al Research Onion Source:(Saunders et al., 2009) Meta –Analysis of theSelected LiteratureThe 141 selected studies that were included were quantitativelyanalyses using the SPSS.The Results /outputof the Analysis Number of studies andlocation (continent)   Number of studies and Strategy adopted     Studies and year ofpublication Study location andstrategy adopted  Conclusions drawn from both thequalitative and quantitative Review of the Literature and Research directionsKendrick, (1986) suggested that the problem ofproductivity has been an issue of measurement. (Kendrick& Vaccara, 1980; Triplett & Bosworth, 2003) also stated that measurement of productivityis very challenging and can only be done is indirectly through the use othervariable and the productivity is intern calculated from these parameters orvariableMajorconcerns in measurements dates from that fact that inputs and outputs are justnot difficult to explain but are also difficult in quantification. (EBrynjolfsson, 1993; Hatry, 1972; Sink, Tuttle, & DeVries, 1984).Most investigations haveonly used econometric models to assess the impact of IT on productivity.The main strength of econometric analysis does not usestrict assumptions enforced by growth accounting and can easily be differentiatedbetween the short and the long-term effects of ICT investment and diffusion.

The study also reveals that thegrowth-accounting method had some form of methodological difficulties. Thus iscentered on the hypothesis of constant economies of scale and absence ofexternalities. This means that the impact of ICT on productivity projected bygrowth accounting is over estimated if in certainty one has economies of scaleunder-estimated if investment in IT produces progressive external effects.Thelack of good quantitative measures still remains a hurdle for analysing thevalue created by IT and the effect on firm performance (Brynjolfsson, 1993;Brynjolfsson and Hitt, 1996).

 Parametricmodels have failed to provide a comprehensive viewpoint by focusing on somespecific settings, and impose functional assumptions on many IT- andnon-IT-related variables (Shafer and Byrd, 2000). Moststudies of IT and productivity has been centered developed countries especiallyin America and Europe leaving developing countries mostly located in Africabehind (Abri& Mahmoudzadeh, 2014)(Mendonca et al., 2008) .This means that there is huge  literature gap n between Africa and othercontinents.

To the best of our knowledge, there is limited nostudies concerning the correlation between IT adoption and labor productivityusing firm-level data from Ghana.A lot of studies adopted the survey (84) and casestudies (51) leaving the experimental (5) strategy behind.The very few studies that were conducted in Africawere mostly survey and case studies with no experimental study in Africa.The study also concluded thatgamification has a potential impact of improving organization productivity butlittle study has been done in that respect. This means that more extensiveworks needs to done in this area.The study also concludedthat because of issues with IT productivity measurement methods , quality ofdata etc. as cited by (Biagi,2013; Cardona et al.

, 2013; Dedrick et al., 2003; Brynjolfsson , 1996) which has led to  apparently inconsistent conclusions (Draca,Sadun, & Van Reenen, 2009),new and better scientificmethodology should be used in the study of IT and productivity impacts.EXPECTED OUTCOME OFTHE WHOLE THESIS·        The present study contributes to therelevant literature by extending the conceptualized framework to explore theICT impact on productivity.·        provide both qualitative andquantitative information about the vast literature on IT and productivity  ·        givesa sizeable and up to update survey of the subject matter (1990-2017) .Toachieve this, the authors included working papers, studies report, student’sthesis, conference papers and journal articles in the literature survey.·        Contributingto the existing literature on the subject matter by bring the Africanexperience(data) on board ·        Usecurrent methodologies such as DEA, DT and ANN to study the subject matter.

·        Comparethe results of each methodologies in order to draw a meaningful conclusion ·        Developeda generic methodology and model by combing two or more of the  stated methodologies·        Identifythe various methodology used in assessing or predicting the impact of IT onproductivity .The strength and weakness of these methodologies would also beassess.·        Attemptto fill the gap between the various strategies by using an Experimentalstrategy. Publications: At least two conference papersand 3 journal articles