Predicting US growth performance went from 43% for

Predicting the business
value of IT on Organizational productivity: A comparison of DEA, Decision Tree
and Neural Networks

 

Introduction

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A significant growth in productivity cements the
basis for improvements in the standard of living(Niebel & Mannheim, 2014).

Heavy Investments and effective use of Information
Technology (IT) or Information Systems (IS) are seen as a key driver of
productivity growth (Niebel & Mannheim, 2014).

 According to
a report by the Development & Research Center  in 
2004 ,enterprises that effectively use
IT in their business process and operations experience  greater productivity leading to greater
competitiveness that promotes sustainable economic growth which is a
prerequisite for poverty reduction.

IT have drastically changed society in the last
quarter of the century inducing unexpected qualitative and quantitative changes
(Biagi, 2013).

A lot of research studies has established on the
importance of ICT for the US growth rebirth observed between years 1995 and
2006.

For instance ,Jorgenson et al (2008) estimate that
the share attributable to ICT in US growth performance went from 43% for the
period 1971-1995 to 59% for the period 1995-2000 (Biagi, 2013).

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 the
US economy has resolved that ICT was accountable for much of the acceleration
in productivity growth as compared to that of 
Europe  where attention was
focused on the slower growth and how much of it could be tied to differences in
ICT diffusion relative to the U.S(Ark et al., 2003).

According to Dedrick, Gurbaxani, & Kraemer, (2003),there has been
a long-running discussion in both the business media and the information
systems and economics literature over whether information technology (IT) was
paying off in higher productivity.

The first studies, conducted in the 1980s, found no
connection 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 that
some authors also suggested strong evidence that the returns to IT investment
are positive and significant for organizations, industries, and for economy
like U.S.

Brynjolfsson and Yang concluded that although
researchers analyzed statistics extensively during the 1980s, they found little
evidence that information technology significantly increased productivity(Erik Brynjolfsson & Yang, 1996).

This inconsistency in recognizing the real business
value of IT on productivity brought about the famous phrase “IT productivity
paradox” stated by the famous economics Robert Solow in 1987(BAUMOL & SOLOW, 1998; Hutchinson, 2008; Triplett,
1999).

Biagi, (2013) attributed the
delay in recognizing the significance of ICT in accounting for labour
productivity growth to the following; a lack of accurate quantitative measures
for the output and value created by ICT and secondly, measuring productivity in
the service sector ( a heavy user of ICT) is also very difficult.

Information Technology (IT) poses a serious dilemma
for management today. On one hand, continuing IT innovations have the potential
of changing the competitive game for many organizations. On the other hand, the
size of the IT investment puts increasing pressure on managers to assess its
business value and results of recent studies are at best inconclusive (Mukhopadhyay, Lerch, & Mangal, 1997)(Mendonca
et la.,2008)(Abri
& Mahmoudzadeh, 2014).

Outcome of my
Literature Review

Main aim/Specific
objectives of the Literature Review/purpose

·        
The present study contributes to the
relevant literature by extending the conceptualized framework to explore the
ICT impact on productivity.

·        
provide both qualitative and
quantitative information about the vast literature on IT and productivity 

·        
gives
a sizeable and up to update survey of the subject matter (1990-2017) .To
achieve this, the authors included working papers, studies report, student’s
thesis, conference papers and journal articles in the literature survey.

·        
Identify
gaps in existing studies which will give directions to future studies

·        
Identify
the various methodology used in assessing or predicting the impact of IT on
productivity.The strength and weakness of these methodologies would also be
assess.

·        
To
know the continental distribution of studies done the subject matter.

Scope of
Literature Review

This review examines about 150 empirical studies
based on economic analysis that have appeared between  1990 to 2017.

The review concentrates mainly on studies whose
results have been published in high standard peer-reviewed scholarly journals,
working papers, institutional report and student’s theses. These journals are
known for having high standards for review and acceptance and therefore are
most appropriate for a critical review that seeks to achieve an objective and
balanced perspective on the research.

Methodology

Data
Collection

The
electronic databases searched in this review included;

·        
Journals: Elsevier, Springer,
Taylor& Francis, MIS Quarterly, IEEE, Emerald, Wiley, the MIT Press, OECD
Publications, ACM etc.

·        
Conference papers.

·        
Working Papers such as one from The Bank
of England’s working paper series.

·        
Reports and articles from recognized
institutions like IMF, World Bank etc.

 

Selection of Literatures

In
the selection of the various literatures for this study, the Preferred
Reporting Items for

Systematic
Review and Meta-Analysis, PRISMA Flow Diagram was adapted (Knobloch, Yoon, & Vogt, 2011; Moher et al., 2015;
Moher, Liberati, Tetzlaff, Altman, & Group, 2009)

The
flow diagram shown below depicts the flow of information through the different
phases of a systematic review.

It
maps out the number of records identified, included and excluded, and the reasons
for exclusions.

Moher et al., (2009) defined a
systematic review as a review of a clearly formulated question that uses systematic
and explicit methods to identify, select, and critically appraise relevant
research, and to collect and analyze data from the studies that are included in
the review.

Statistical
methods (meta-analysis) may or may not be used to analyze and summarize the results
of the included studies.

Meta-analysis
refers to the use of statistical techniques in a systematic review to integrate
the results of included studies.

 

 

The process involved:

1.
Defining the scope of the review, developing questions and inclusion/exclusion
criteria;

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 inclusion
criteria

A
number of further criteria were specified to select appropriate studies for
inclusion in the literature survey.

To
be included in the review, papers had to

Studies
have be writing in English

Studies
should have well-structured scientific methodology

(a)
Include empirical evidence relating to the impacts of IT/ICT or Information
System adoption on productivity or the economy. This was done to address the
main aim of the study.

(b)
Date from 1990 to 2017,

(c)
Include studies done using data from any of the following continents; Both
North and South America, Asia, Europe and Africa.

In
the search for the literature, the authors realize that a lot of studies was
also done within the context of developing countries and because most countries
in Africa are grouped under the developing category, the authors decided to
include studies also done within developing countries. This was done in order
to have a clear picture of the various studies done within the African
continent.

 

 

 

 

 

 

 

 

 

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)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

PRISMA
Flow Diagram .Source (Moher et al., (2009))

 

 

 

Classification of the
studies 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 The
Literatures Using Saunders et al Research Onion

It
is normally not unusual for a researcher to begin thinking about a research by
considering as to whether to, administer a questionnaire or conduct interviews.

Concerns
of this issue has been thoroughly addressed by Saunders, Lewis, & Thornhill, (2009)  and also cited by (Knox, 2004) using the
research “Onion” model shown in figure below.

According
to the authors, before coming to this central point in any study there are
important layers of the onion that need to be peeled away.

The
classification was done under the following heading: Technique, Time-Horizon
and Strategy

 

 

 

 

 

 

 

 

 

The
Saunders et al Research Onion

 

Source:(Saunders et al., 2009)

 

Meta –Analysis of the
Selected Literature

The 141 selected studies that were included were quantitatively
analyses using the SPSS.

The Results /output
of the Analysis

 

Number of studies and
location (continent)

 

 

 Number of studies and Strategy adopted

 

 

 

 

 

Studies and year of
publication

 

Study location and
strategy adopted

 

 

Conclusions drawn from both the
qualitative and quantitative Review of the Literature and Research directions

Kendrick,
 (1986) suggested that the problem of
productivity has been an issue of measurement. (Kendrick
& Vaccara, 1980; Triplett & Bosworth, 2003) also stated that measurement of productivity
is very challenging and can only be done is indirectly through the use other
variable and the productivity is intern calculated from these parameters or
variable

Major
concerns in measurements dates from that fact that inputs and outputs are just
not difficult to explain but are also difficult in quantification. (E
Brynjolfsson, 1993; Hatry, 1972; Sink, Tuttle, & DeVries, 1984).

Most investigations have
only used econometric models to assess the impact of IT on productivity.

The main strength of econometric analysis does not use
strict assumptions enforced by growth accounting and can easily be differentiated
between the short and the long-term effects of ICT investment and diffusion.

The study also reveals that the
growth-accounting method had some form of methodological difficulties. Thus is
centered on the hypothesis of constant economies of scale and absence of
externalities. This means that the impact of ICT on productivity projected by
growth accounting is over estimated if in certainty one has economies of scale
under-estimated if investment in IT produces progressive external effects.

The
lack of good quantitative measures still remains a hurdle for analysing the
value created by IT and the effect on firm performance (Brynjolfsson, 1993;
Brynjolfsson and Hitt, 1996).

 

Parametric
models have failed to provide a comprehensive viewpoint by focusing on some
specific settings, and impose functional assumptions on many IT- and
non-IT-related variables (Shafer and Byrd, 2000).

 

Most
studies of IT and productivity has been centered developed countries especially
in America and Europe leaving developing countries mostly located in Africa
behind (Abri
& Mahmoudzadeh, 2014)
(Mendonca et al., 2008) .This means that there is huge  literature gap n between Africa and other
continents.

To the best of our knowledge, there is limited no
studies concerning the correlation between IT adoption and labor productivity
using firm-level data from Ghana.

A lot of studies adopted the survey (84) and case
studies (51) leaving the experimental (5) strategy behind.

The very few studies that were conducted in Africa
were mostly survey and case studies with no experimental study in Africa.

The study also concluded that
gamification has a potential impact of improving organization productivity but
little study has been done in that respect. This means that more extensive
works needs to done in this area.

The study also concluded
that because of issues with IT productivity measurement methods , quality of
data 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 scientific
methodology should be used in the study of IT and productivity impacts.

EXPECTED OUTCOME OF
THE WHOLE THESIS

·        
The present study contributes to the
relevant literature by extending the conceptualized framework to explore the
ICT impact on productivity.

·        
provide both qualitative and
quantitative information about the vast literature on IT and productivity 

·        
gives
a sizeable and up to update survey of the subject matter (1990-2017) .To
achieve this, the authors included working papers, studies report, student’s
thesis, conference papers and journal articles in the literature survey.

·        
Contributing
to the existing literature on the subject matter by bring the African
experience(data) on board

·        
Use
current methodologies such as DEA, DT and ANN to study the subject matter.

·        
Compare
the results of each methodologies in order to draw a meaningful conclusion

·        
Developed
a generic methodology and model by combing two or more of the  stated methodologies

·        
Identify
the various methodology used in assessing or predicting the impact of IT on
productivity .The strength and weakness of these methodologies would also be
assess.

·        
Attempt
to fill the gap between the various strategies by using an Experimental
strategy.

 

Publications: At least two conference papers
and 3 journal articles

 

x

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