Contemporary issues in Performance Measurement and Productivity: A review of research issues,...

UNIVERSITY OF GHANA BUSINESS SCHOOL

RESEARCH DESIGN AND QUALITATIVE RESEARCH

UGBS 653

Contemporary issues in Performance Measurement and

Productivity: A review of research issues, conceptual approaches,

methods and gaps

MPHIL OPERATIONS MANAGEMENT GROUP

2013/2014 ACADEMIC YEAR-SEMESTER ONE

25/10/2013

Dr. Richard Boateng

1

Group List

No. Name Index

Number

Email Address Phone Number

1 Banuro, Joseph Kwaku

10277402 [email protected] 0240267716

2 Ofori, Charles Gyamfi

10283050 [email protected] 0207279230

3 Turkson, Charles

10442287 [email protected] 0208751735

4 Opoku Mensah, Richard

10443466 [email protected] 0246232968

5 Anagba, K. Kingsley

10443877 [email protected] 0243512308

6 Gakpey, Victor Sosu

10444001 [email protected] 0244813354

7 Asare, K.A. Jones 10444145 [email protected] 0247817537

2

Table of Contents

Group List .................................................................................................................................. 1

List of Figures ............................................................................................................................ 2

List of Tables ............................................................................................................................. 2

List of Abbreviations ................................................................................................................. 3

ABSTRACT ............................................................................................................................... 4

INTRODUCTION ..................................................................................................................... 4

FRAMING AND METHODOLOGY ....................................................................................... 6

PRESENTATION OF FINDINGS ............................................................................................ 9

Performance Measurement in Operations Management ...................................................... 10

Firm Performance ............................................................................................................. 11

Financial Performance ...................................................................................................... 12

Labour productivity .......................................................................................................... 12

Trends in research on performance measurement ................................................................ 13

Industrial trends in performance measurement ................................................................ 13

Contextual trends in performance measurement .............................................................. 15

Method trends in performance measurement ................................................................... 16

RESEARCH GAPS ................................................................................................................. 18

Gaps in research issue .......................................................................................................... 18

Gaps in conceptual approaches ............................................................................................ 18

Gaps in context of study ....................................................................................................... 19

Gaps in research methods used ............................................................................................ 19

FUTURE RESEARCH DIRECTIONS ................................................................................... 20

References ................................................................................................................................ 21

Appendix A: List of Articles Reviewed ................................................................................... 22

Appendix B: The Steps in a Systematic Review ..................................................................... 27

List of Figures

Figure 1: Number of articles in each topic category 8

Figure 2: Distribution of articles (Continent) 15

List of Tables

Table 1: Initial set of Journals considered ................................................................................ 7

Table 2: Descriptive statistics of works reviewed ..................................................................... 9

Table 3: Mapping of Research Issues ...................................................................................... 11

Table 4: Binomial Test of Proportions..................................................................................... 14

Table 5: Types of Service businesses ...................................................................................... 14

Table 6: Distribution of papers (Country level) ....................................................................... 15

Table 7: Methods used in the papers........................................................................................ 16

Table 8: Results of goodness of fit test .................................................................................... 17

Table 9: Statistical Methods Used ........................................................................................... 17

3

List of Abbreviations

1 European Journal Of Operations Research :EJOR

2 International Journal Of Production Research :IJPR

3 International Journal Of Operations And Production Management :IJOPM

4 Production Planning and Control :PPC

5 Supply Chain Management, International Journal :SCMIJ

6 Journal Of Operations Management :JOM

7 Omega - International Journal Of Management Science :OIJMS

8 Journal Of Operational Research Society :JORS

9 Naval Research Logistics :NRL

10 Decision Sciences :DS

11 Journal of Forecasting :JOF

12 Journal of Scheduling :JOS

13 Management Science :MS

14 Informs :INT

15 Operations Management :OM

16 Association of Business Schools :ABS

17 SCImago Journal Ranking :SJR

18 Journal Citation Report :JCR

4

Contemporary issues in Performance Measurement and Productivity: A review of

research issues, conceptual approaches, methods and gaps

ABSTRACT

Although there has been some considerable amount of review of knowledge in Operations

Management, these papers either generically look at all topics in Operations Management or

draw inferences from a single journal. They are thus either too broadly focussed or have a

narrow coverage. This study therefore assesses an aspect of operations management from

various academic journals. The study evaluates contemporary knowledge in performance

measurement and productivity in operations management. This allows for the assessment of

trends and identification of gaps in productivity research. A systematic literature review

process was implemented to aid in a more scientific review. This paper focused on

publications of three top journals in operations research from 2011 to 2013. In all 69 articles

were reviewed. The study revealed that, there are no publications with Africa as the study

area coupled with a relatively lower number of publications targeting developing economies.

Similarly, service industry seems to be of major concern to operations researchers. Also,

much research has been conducted on firm performance relative to labour and financial

performances in organizations. Coupled with these, there seem to be over-reliance on

quantitative techniques to the neglect of qualitative techniques. It is suggested that further

studies need to evaluate performance systems in developing economies and more research

directed towards labour and financial performance especially in the manufacturing industry.

Keywords: Performance measurement, Productivity, Firm performance, Operations

Management

INTRODUCTION

Performance measurement is an important aspect of Operations Management (OM), which

comprises a whole spectrum of research areas like Supply Chain Management, Management

Science, Quality Management, Scheduling and others. Generally OM aims at ensuring

efficient use of scarce resources to produce goods and services to satisfy customer needs or

requirements and enhance competiveness (Lehmann & Koelling, 2010). It also deals with

ways by which organisational activities can be managed in the production and delivery of

goods and services as required by customers (Radnor & Barnes, 2007). Performance

measurement is linked with several other research areas in the field of operations research.

Russell and Taylor (2011) posit that improving product design, quality of materials and parts,

job designs or production process will all lead to increased productivity.

Performance Measurement and Productivity are closely linked and difficult to distinguish.

Neely, Gregory and Platts (1995) identified that Performance Measurement, although often

used is barely defined. They outlined two fundamental dimensions for Performance

Measurement – Efficiency and Effectiveness. Whereas efficiency measures economic

utilisation of firms’ resources at a given level of customer satisfaction, effectiveness looks at

5

the extent to which the customer requirements are met. Mathematically, efficiency and

productivity are expressed as the ratio of the work output to input. By definition, productivity

is that aspect of performance measurement that focuses on efficiency. However, for the

purpose of this paper, these two concepts are used interchangeably. Performance

measurement became a field of study in academic institutions about three decades ago (Busi

& Bititici, 2006).

Performance measurement has been of concern to and applied by government agencies,

corporate bodies as well as non-profit making organisations (Vanichchinchai, 2012; Halligan,

Sarricco & Rhodes, 2012; and Shafer & Moella, 2012). Whereas Vanichchinchai, (2012)

applies it in Supply Chain Management, Halligan, Sarricco and Rhodes (2012) linked

performance measurement to Public Sector Governance. Shafer and Moella (2012) also

looked at performance monitoring at the corporate level. This shows the importance of

performance and productivity measurement in various fields of study.

Due to the importance of performance measurement, there is the need for a critical

understanding of the extent of research in this area of Operations Management. Over the past

decades, various reviews have been conducted in Operations Management. Scudder and Hill

(1998) provided a review of empirical studies in Operations Management in a ten-year

interval spanning from the year 1986 to 1995. Cohen and Kunreuther (2007) outlined the

contributions made by Kleindorfer to Operations management. Kouvelis, Chambers and

Wang (2007) also reviewed manuscripts that were focused on Supply Chain Management

published in the Production and Operations Management Journal in a fifteen-year period

(1992-2006). Similarly, Gupta, Verma and Victorino (2006) assessed empirical research

published in the same journal over the period 1992 to 2005. To the best knowledge of

researchers, only Lehmann and Koelling (2010) have reviewed works on productivity.

However, despite these important contributions of these researches, these reviews have three

main limitations: too broadly focused (on a lot of topic areas in Operations Management); too

narrowly covered (on only one journal or the contribution of an individual) and lack currency

(not enough reviews in the last three years).

This paper is therefore undertaken to provide a synthesis of literature that relates to current

research works that have been carried out in the area of productivity and performance

measurement and to provide directions for future research. It is thus undertaken to achieve

the following specified objectives:

1. Provide a review of literature and an analysis of research and results related to

productivity in Operations Management literature.

2. Identify trends in the research related to context, methods and industrial focus in

Performance Measurement

3. Identify gaps and provide directions for further research in the area of performance

measurement based on contexts, issues and methodologies.

The subsequent sections of this work are divided into four main sections. The first section

presents the methods applied in the review process. The second, third and fourth sections

reviews the research. Whereas the second section reviews current knowledge in productivity,

the third section examines the trend in research on productivity and performance

6

measurement. The fourth section finally takes a look at the gaps in the research and suggests

areas for further research.

FRAMING AND METHODOLOGY

A systematic literature review process was used to understand the extent and trends in

research in performance measurement and productivity. This is a means of identifying,

evaluating and interpreting all available research relevant to a particular question, topic area

or phenomenon of interest (Kitchenham, 2007). Here, the researcher identifies and plans the

review process before it is undertaken.

Unlike traditional reviews, a systematic review has been seen to synthesise existing work in a

manner that is more scientific, fair and seen to be fair, transparent, more rigorous and

minimizes bias (Kitchenham, 2007; Lehmann & Koelling, 2010). This is because there is the

need for a predefined search strategy that allow for completeness of the papers to be assessed.

It has been applied in wide variety of academic areas like software engineering, medicine and

management research. For example, Lehmann and Koelling (2010), have applied this

methodology in operations research in reviewing productivity of services. This study adopted

the Khan, Kunz, Kleijnen and Antes (2003) five steps plan to conducting a systematic review.

This is outlined in Appendix 2.

Step 1: Framing questions for review

The aim of this paper is to review research on performance measurement and productivity

during the three year period, from 2011 to 2013, that had been published in three top journals

in Operations Management. The paper therefore sought to answer the questions: What are the

new development in performance measurement? What techniques of researchers using to

evaluate performance? and What are the gaps in research?

Step 2: Identifying relevant work

Systematic review requires that works relied on are extensive and from multiple sources,

ensuring that reasons for inclusion and exclusion are recorded (Khan, Kunz, Kleijnen &

Antes, 2003).

This requirement was followed in conducting this review. To ensure availability of extensive

research from multiple sources, a pool of fifteen (15) discipline-relevant journals based on

four (4) metrics: Thomson Reuters’ 2012 Journal Citation Report, Association of Business

Schools’ 2010 Academic Journal Quality Guide, the SCImago Journal Rank and discussions

with faculty. These were journals in operations and management science that have high

impact factor ratings and highly recommended by experts in operations management. These

have been identified in Table 1.

7

Table 1: Initial set of Journals considered

Journal Publisher ABS SJR

1. IJPR Taylor and Francis 4 1.303

2. JOM Elsevier 4 4.997

3. OR Informs 4 3.925

4. MS Informs 4 2.906

5. EJOR Elsevier 3 2.596

6. IJOPM Emerald 3 1.784

7. OIJMS Elsevier 3 3.162

8. JORS Palgrave Macmillan 3 1.367

9. JOS Wiley 3 1.857

10. PPC Taylor and Francis 3 0.697

11. NRL Wiley 3 1.268

12. DS Wiley 3 1.292

13. SCMIJ Emerald 3 1.265

14. JOF Wiley 3 0.575

15. INT Informs 2 1.187

Source: Authors’ Construct

After a thorough assessment and discussion with experts, the following criteria was used to

select three top journals for review:

1. The Journal must be in Grade 3 or 4 of the ABS quality ranking

2. The Journal must be ranked in all three rankings (JCR, ABS & SJR) used for the

study

3. The Journal must have a relatively high impact factor

4. In order to ensure broader coverage, no three journals from the same publisher can

be selected.

The final three selected Journals were therefore: Journal of Operations Management (JOM);

Omega International Journal of Management Sciences (OIJMS); and Supply Chain

Management International Journal (SCMIJ). These are top grade journals with high impact

factors. Whereas JOM is a grade 4 journal (ABS), OIJMS and SCMIJ are grade 3 papers.

Other statistics also indicate a rising dependence on articles from these three journals by other

researchers.

Emphasis of this review was on contribution made on performance measurement from 2011

to 2013, to identify contemporary research issues that are trending in operations research.

Although the focus of the work was to understand the extent and trends of research on

performance measurement and productivity, there was the need to consider other topics that

have had prominence in these journals over the years under consideration. There were 557

articles in the three journals including editorials. These were subsequently categorized into

research topics to identify topics in operations research that have been researched on

extensively.

Step 3: Assessing the quality of studies

The selection criteria used in identifying the three top journals was robust at ensuring that

only highly ranked journals were selected. The Thomson’s JCR, ABS ranking and SCImago

8

rankings are all acknowledged to be good indicators of quality publishing. Therefore the

quality of papers from these journals was assured.

Also, to avoid bias in categorization, all seven (7) members of the group were part of the

panel for approval of article summary by individual members. This means that the article

summary by individual members had to go through scrutiny by all members for approval

before it could be accepted.

Step 4: Summarizing the evidence

The 557 articles identified were categorized based mainly on the topic categories in the

advice to contributors in Journal of Operations Management. This categorization has been

applied by Scudder and Hill (1998). The results of the topic categorization are presented in

Figure 1.

Figure 1: Number of articles in each topic category

From Figure 1, it is evident that issues on Supply Chain Management had been well

researched because one of the journals had it as its main focus. This is followed by

Performance Measurement and Productivity. Disaster Management in operations research has

had the least number of publications. The category “other” refer to articles that cannot be

placed under any of the listed categories since they are not operations research papers. These

papers may have applied operations research models in other academic disciplines.

178

6964

37 37

2823

13 12 11 94 4 4 1

0

20

40

60

80

100

120

140

160

180

200

Scm Perf Others Mat Prod Cap Plan Env Inf Beh Qua Tech Int His Dis

Nu

mb

er

of

Art

icle

s

Topic category

Supply Chain Management Scm

Performance Measurement and Productivity Perf

Others Others

Material and Inventory Management Mat

Product and Service Design Prod

Capacity Planning and Systems Design Cap

Operations Planning, Scheduling and Control Plan

Environmental Regulatory Issues Env

Operations Information Management Inf

Behavioural Operations Management Beh

Quality Management Qua

Technology Management for Operations Tech

International and Comparative Operations Int

History And Evolution His

Disaster Management Dis

9

Step 5: Interpreting the findings

The review then focuses on Performance measurement and productivity presenting insights

through synthesis of relevant literature, tabulation of study characteristics as well as use of

statistical methods for exploring differences between studies and combining their effects.

PRESENTATION OF FINDINGS

Table 2, presents a preliminary description of data used in this work. These are from critical

assessment of all works cited in Appendix A.

Table 2: Descriptive statistics of works reviewed

Frequency Percent

Name of Journal

JOM 16 23.2

OIJMS 47 68.1

SCMIJ 6 8.7

Total 69 100.0

Year of Publication

2011 30 43.5

2012 23 33.3

2013 16 23.2

Total 69 100

Type of Paper

Empirical 51 73.9

Conceptual 15 21.7

Literature Review 3 4.3

Total 69 100

Research Issue

Firm Performance 52 72.2

Financial Performance 7 9.7

Labour Flexibility 12 16.7

Other 1 1.4

Total 72 100

Level of Analysis

Micro 6 11.1

Meso 29 53.7

Macro 12 22.2

Meta 7 13

Total 54 100


From the three journals reviewed during the period, OIJMS has published the highest number

of articles on performance measurement. It had reviewed and published 47 papers (68.1%)

during the period. JOM followed with 16 papers (23.2%). SCMIJ, on the other hand, had

published the least number of articles during the period. This may be because of its focus.

10

This Journal is primarily interested in supply chain management. It is therefore not surprising

that it had the least number of publications. However, the existence of productivity papers in

SCMIJ provide enough evidence to supports Rusell and Taylor (2011) that, issues on

performance measurement cuts across all aspects of operations management.

Table 2 presents the number of articles that were published during the years under

consideration. From the table, 30 articles out of the 69 were published in 2011, representing

43.5 percent of the total articles. This is followed by 23 articles representing 33.3 percent

published in 2012. Only 16 articles on performance measurement had been published as at

September 2013. This corresponds to 23.2 percent. The few number of articles published in

2013 may be because the year has not ended, and it is possible more articles could be

published before the year ends.

Table 2 also presents the proportions of the type of research papers published during the

review period. These papers were grouped under three categories; empirical, conceptual and

literature reviews. There were 51 papers that had gone through empirical studies: this

represents about 74 percent of total articles. Also 15 articles were conceptual papers which

develops frameworks and models but has not yet seen empirical tests to confirm or disprove.

Only 3 articles representing 4.3 percent were papers which sought to review other articles and

literatures in relation to performance management. This therefore shows that there has been

extensive empirical application of OM concepts in real-world.

On the research issues, majority of the papers (72.2%) examined performance issues related

to the firm. It was seen that 7 papers narrowed down to assess financial performance of firms.

These papers consider the financial productivity. On the other hand, 12 papers emphasized

labour performance, bringing out modules for measuring labour performance and for

improving their performance. It is important to note that some articles researched on either

two or three of these research issues. Articles with numbers [24] [31] [44] and [47], for

example, considered two of the dimensions of productivity. Articles [1] and [66] considered

all three dimensions of productivity.

A breakdown was also made based on the level of analysis. It was seen that 6 articles were

the micro level. These were studies on individual organizations. Also 29 articles concentrated

on industry level (Meso), these papers investigated performance of various organizations in

one industry. This represents 53.7 percent of the total articles. For the country-wide level, 12

articles had been written. These analyse performance between firms across industries in a

given country. Also, 13 percent of published works were on the performance of entities

across country. This could be in the same industry or across industries. It is seen then that

there is the need for more Micro and Meta level studies.

Performance Measurement in Operations Management

Performance measurement practices are common in all sectors of industry and commerce as

well as the public sector, including government departments, non-governmental organisations

and charities. Given this increased interest in performance measurement, there is an

increasing interest in comparing the performance of organisations in order to identify the

performance gaps and improvement opportunities. Consequently, we have seen a number of

articles from practitioners and researchers on the subjects of performance measurement and

performance comparisons. In this work, performance is measured from three dimensions;

11

Firm performance, Financial performance and Labour productivity. Firm productivity looks

at assessment and performance improvement programmes that are aimed at the firm as a

whole. This may be a matter of sustainability or a multi-factor productivity measures.

Financial and labour productivity are single factor productivity measures. Whereas financial

productivity looks at cost minimization and profit maximization, labour productivity bothers

on improving labour size and their capacity to perform. Table 3 shows the mapping of

empirical papers based on the three research issues and the unit of analysis. This is adapted

from Duncombe and Boateng (2009).

Table 3: Mapping of Research Issues according to the Level of Analysis

Studies focusing on

firm performance

Studies focusing on

labour performance

Studies focusing on

financial performance

Studies conducted at

the Meta

level(across countries

and on global levels)


the Macro level

(across industries in

one country)


the Meso level

(across firms and

organization within

one industry)


the Micro level

(individual firms and

organizations)

Bold: studies conducted on firms in the service industry (31 articles); Italics: studies

conducted on firms in the manufacturing sector (17 articles); Normal: Studies that cut across

both service and manufacturing sectors (4 articles).

Firm Performance

Most of the papers reviewed researched on the development and evaluation of models used in

the assessment of performance at the corporate level. Some of these papers are based on

theories that have gone through empirical studies to prove its effectiveness. Meanwhile, some

of these papers are based on conceptual models that are yet to go through empirical studies.

These papers cuts across all levels of analysis. For example, Articles [20] [23] [29] [33] and

26, 43,

44, 62, 64 21 32 44

9, 27, 28,

36, 39, 42,

48, 56, 65

45,

61

3, 4, 7, 10, 13,

14, 15, 16, 20,

23, 29, 33, 34,

35, 38, 51, 57,

59

1, 47, 66

19, 22, 53 46, 49

24, 31

2, 5, 54,

55, 67 37

12

[59] are a few of the articles that are providing insights into firm performance at the meso

level. Article [20] postulates, with empirical test, the ability to now measure productivity

without explicit input. This paper presents a more systematic theoretical background for the

use of DEA-WEI models. For papers [23] and [29] they all measure bank efficiency. Whereas

[23] uses a two-stage network framework, [29] incorporates Nash bargaining game (NGB)

theory in addition to the traditional DEA model used in [23]. Other insights have been

presented at different levels of analysis. Models to help the improvement of performance and

productivity in organisations have also been developed with the type of business as the focus.

Whereas [2] [9] [10] [13] and [26] are a few of those papers that focused on the service

industry, [43] [44] [28] [38] and [64] primarily looked for answers in the manufacturing

setting. Article [28] for example, shows how research and development affects productivity.

It is revealed in this paper that both production activity and research and development play

important roles in firm efficiency.

Financial Performance

Amado, Santos and Marques (2012)1 believe that, in a competitive environment, because of

the scarcity of resources, performance measurement and management assume a crucial role.

Financial performance therefore measures how well a firm can use assets to generate

revenues. This term is also used as a general measure of a firm's overall financial health over

a given period of time, and can be used to compare similar firms across the same industry or

to compare industries or sectors. There are many ways to measure financial performance, line

items such as revenue from operations, operating income or cash flow from operations can be

used, as well as total unit sales [49]. For example [49] focuses on the impact of product

variety on sales performance of retail outlets. Other papers focused on finding a relation

between ISO 9000 (quality) and firm’s financial performance [45]. Article [45] identified that

firms with high industry competiveness, high industry sales growth and low levels of

adoption of ISO 9000 obtain more benefit from the ISO 9000 adoption in terms of their

financial performance. This paper therefore answered the question “Under what factors is the

efficacy of ISO 9000 adoption stronger?” Meanwhile [44] focused on financial advantages

and performance accruing from international diversification of manufacturing companies.

They showed the extent to which managers could diversify their market to gain financial

advantages relative to constraints from the diversification prices.

Labour productivity

Labour productivity measures the efficiency of workers in the production of goods and

services in a firm, industry or the nation as a whole. It takes the ratio of a measure of output

to a measure of labour inputs. Schreyer (2005) opines that labour productivity does not

necessarily indicate the effort per worker or an individual. Measuring the performance of

labour can therefore be viewed from the angle of the individual worker and workers as teams.

A team’s productivity can be assessed and improved through team cohesion, team leader job

satisfaction and team competence and how product and service quality is affected by these

1 This is Paper [1] in Appendix A

13

team dimensions (Lee, Cheng, Yeung & Lai, 2011)2. Labour productivity can be measured on

the basis of gross output or based on value added. Moreover, multi-factor productivity can be

measured, where labour, together with other resources such as capital are used as input

measures for measuring productivity.

Schreyer (2005) posits that among the inputs used to measure productivity, labour is the

simplest and has repeatedly been used. This is because of its readability, ease of measure and

its rippling influence on other single productivity measures such as material productivity,

capital productivity and energy productivity. The relevance of labour productivity to the total

firm efficiency requires a lot of investment into the selection, training, improvement,

motivation and supervision of the labour force to elicit the maximum output.

Three thematic areas were discovered during the review; optimum labour size, team

performance improvements, and finally learning, training and development. Article [31]

emphasized rightsizing to ensure increased productivity with fewer labour inputs. The

purpose of this exercise was to reduce slacks of tellers and typists which resulted in non-

optimization of human resources. The results of Article [31] demonstrate that the

effectiveness of the application in reducing and/or transferring staff, while maximizing

organizational output levels and implementing smooth down- sizing.

On the second theme, Article [19] looked at the five dimensions of transformational

leadership (Attribute, behavior, intellectual stimulation, inspirational motivation and

individual consideration) and how it impacts team performance. The measures of team

performance were: team cohesion, team-leader-job satisfaction, team competence, reliability

and responsiveness. Article [31] on the other hand, studied how behavioral rewards serve as

motivation for team performance to improve the quality of services in the retail industry.

Finally, Articles [1] and [32] stressed how performance management practices like learning,

training and development influence behavior of individual workers towards performance

improvements. The three clusters of operational behavior that were identified (understanding,

motivation and focus of improvement) had extensive impact on labour performance. Also

Article [24] considered how to use employee appraisal to make optimum staff decisions to

maximize their utilization.

Trends in research on performance measurement

Industrial trends in performance measurement

The aim here was to understand the industrial focus of these published articles. Generally,

industry may be a service or manufacturing one. OM has tools and techniques for addressing

each individually as well as together. It was therefore imperative to assess whether research

had been skewed towards a particular industry as compared to the other. Results showed that

whereas 33 articles focused only on the Service sector, 17 papers were mainly towards

manufacturing activities. Only 4 papers drew respondents from both industries.

2 This is Paper [19] at Appendix A

14

Table 4: Binomial Test of Proportions

Category N Observed

Prop.

Test

Prop.

Exact Sig.

(2-tailed)

Service 33 .66 .50 .033

Manufacturing 17 .34

503 1.00


Although the frequency of publication about services seemed higher, there was the need for

an inferential evidence to determine if there is any significant difference. The Binomial test

of Proportions, a nonparametric statistical test that tests the significance of the difference

between two proportions of occurrences, was therefore used. With a 5% level of significance

and an accompanied Sig. Value of less than 0.05, it was evident that there is significant

difference between the two proportions. It can be seen therefore that, research interest in

these three journals are mainly focussed on the Service sector.

For the service sector, Banking and Finance seem to be the prime focus of most of these

studies in the service sector. Out of the 33 articles written under the service sector, 36.4

percent were concerned with the performance measurement of Banks and other financial

institutions. This was followed by Education and Research firms (18.2%). Other services that

have seen research interest include; Health, Government, Retail, Utilities and Information

Technology and Telecom Industry. This is presented in Table 5.

Table 5: Types of Service businesses

Frequency Valid Percent

Education and Research Industry 6 18.2

Banking and Finance 12 36.4

Supermarkets and Retail Shops 2 6.1

Utilities 2 6.1

Government 3 9.1

Hospitals and Health Institutions 3 9.1

Tourism and Hospitality 1 3.0

IT & Telecom Industry 2 6.1

Airlines 1 3.0

Agriculture and Forestry 1 3.0

Total 33 100.0


3 Excludes the four Empirical papers that researches on both sectors. These are papers: [39] [42] [48] and [56]

Conceptual papers are not also added

15

Contextual trends in performance measurement

There is the need to also evaluate the trends in research from a contextual perspective. The

aim here is to understand the distribution of research in terms of continents and countries.

This will clearly show which continents will require more research. The results are presented

in Figure 2 and Table 6.

Figure 2: Distribution of articles (Continent)

Table 6: Distribution of papers (Country level)

Country Frequency Percent

United States 11 20.0

China 8 14.5

Spain 6 10.9

Taiwan 6 10.9

United Kingdom 4 7.3

Japan 3 5.5

Others4 17 30.9

Total 55 100.0


From Figure 2, it can be seen that, Asia and Europe are the primary focus of most researches

published in the three journals during the period under review. Whereas Asia represented

36.8 percent, Europe accounted for 33.3 percent of these articles. This was followed by North

America (24.6%) and Australia (5.3%). No research on performance measurement and

productivity during this period focussed on Africa or South America.

4 Others include: Australia, Portugal, Canada, New Zealand, Greece, Bangladesh, Netherlands, France

Thailand, Singapore, India, Sweden and Nicaragua

36

.8

24

.6

5.3

33

.3

A S I A N O R T H A M E R I C A A U S T R A L I A E U R O P E

PER

CEN

TAG

E O

F A

RTI

CLE

S

CONTINENT

16

For individual country level, United States of America accounted for 20 percent of published

articles on performance measurement. This was closely followed by China, Spain and

Taiwan. The most striking observation is that, only 6 countries account for more than 69

percent of all published articles. Among these 6 only one (China) is a developing country

(IMF, 2012). This therefore shows that, publications in these three journals focuses on a few

countries, and does not adequately cater for the research needs of developing countries.

Method trends in performance measurement

Classification of the articles was also based on the research methods that were used. The

research methods were based on three major indicators namely: the Research approaches

used; the time dimensions of the research; the sources of data. Given in Table 7 below are the

frequencies of articles for the various methods used in categorising the articles.

Table 7: Methods used in the papers

Frequency Percentage

Research approach used

Quantitative 50 87.7

Qualitative 1 1.8

Mixed 6 10.5

Total 57 100

Time dimension

Cross-sectional 32 59.3

Longitudinal 22 40.7

Total 54 100

Data Source

Primary 21 36.8

Secondary 36 63.2

Total 57 100


Research approaches used for the classification were based on whether they were qualitative,

quantitative or mixed methods. Over 85 percent of the articles gathered were analysed using

quantitative methods distantly followed by articles that contained both qualitative and

quantitative techniques numbering up to six ([10][22][37][44][62][67]). There was only one

study [26] that made use of a purely qualitative approach to analysing the data that were

presented. Cross sectional and longitudinal studies received fairly equal number of articles

presented within the said period of analysis. With respect to the data sources, data mostly

used were derived from secondary sources mostly from databases as compared to the primary

data sources. Secondary sources accounted for over 60 percent of articles written.

Table 8 showed the results of chi-square tests of goodness of fit conducted to test whether the

differences in each category of the various analysis approaches were substantial.

17

Table 8: Results of goodness of fit test

Research

approach

Time

dimension Data Source

Chi-Square test statistic 76.526a 1.852b 3.947c

Degree of Freedom 2 1 1

P value .000 0.174 0.047

Evidently, chi-square goodness of fit results showed differences in proportions of articles

grouped under research approach and data source. In other words, more quantitative research

are being conducted in performance measurement with data mostly historical or gathered

from database rather than being collected primary sources. Articles classified on the basis of

time dimension do not have much difference within their categories. That is to say that there

is almost an equal number of articles published based on longitudinal and cross sectional

studies.

As indicated in Table 8, majority of the studies were based on quantitative methods. As such,

there were varying statistical methods used in analysing the data gathered with each method

of analysis serving as a desired method to satisfy the objectives at hand.

Table 9: Statistical Methods Used

Frequency Percent

Data Envelopement Analysis (DEA) 23 31.5

Regression/Correlation 11 15.1

Descriptive Statistics 7 9.6

Structural Equation Modelling 5 6.8

Path Analysis 2 2.7

Malmquist Productivity Index Analysis 2 2.7

Mann Whitney U-Test 2 2.7

Hierarchical linear Modelling 2 2.7

Others5 19 26.0

Total 73 100.0


In efficiency and productivity measurement, DEA models are often used in the analysis of

data in establishing benchmarks for other decision making units. As evident in table 8, the

concept of DEA were used mostly in the articles published. Notable of them was [51] who

employed DEA in studying the profit efficiency of banks. In addition to DEA, other similar

non-parametric statistical techniques employed in the analysis of data included the Malmquist

Productivity Index Analysis [58], the Mann Whitney U-Test or the Sign Rank Test as

employed by [48] to test for impacts of the adoption of Six Sigma in firm performance.

5 Balanced Score Card, Game Theory, Sharpe Market Model, Analytical Network Process, Bayesian Frontier

Model, Super Efficiency Test, Hicks Moortesen TFP Index, Kousemanen Weakly Disposable Technology,

Mixed Mode Modeling, T-Test, Saidin Test, Median Test, Stochastic Simulation, Balanced Score Card, Process

Mapping, Cause and Effect, Mean Absolute Percentage Error (MAPE)

18

Parametric techniques such as regression and structural equation models were used

extensively to test for relationships between certain variables that relate to productivity ([43]

[42]). Seven of the articles did not use any rigorous statistical methods in the data analysis

but rather resorted to the use of descriptive statistics. They were involved in applying

frequencies and percentages to describe the data, making inferences based on the information

provided.

RESEARCH GAPS

This section identifies the research gaps related to issue, conceptual approaches, context and

methods. This section clearly presents the areas in performance measurement that requires

further research and concludes with suggestions for further research.

Gaps in research issue

This paper studied performance measurement and productivity as one of the major topic areas

of study in operations management. The specific issues considered included firm

performance, labour productivity and financial performance. From the study, it can be

deduced that financial performance and labour performance seem to have the least research

interest relative to firm performance. However, these are important dimensions of

performance that should be researched into. This is because labour and financial goals are at

the heart of any smooth operational functioning.

Even with the few studies that has been done on financial performance, most of them are

narrowed on individual institutions and industries. For example, [44] identified the

advantages of international diversification of products. However, this is based on a single

industry (Automobile industry) and as such problem of over generalization of results could be

alarming. Similarly, there is little crossed country assessment of issues. Article [14] for

example, only focuses on the Chinese banking industry. Cross-cultural evaluations may

provide the effects of international competition and collaboration on firm performance,

especially in a time where trade is becoming more liberalized. There is therefore the need to

consider productivity from a multi-setting perspective.

Gaps in conceptual approaches

It was noted that there were more empirical studies as compared to literature review and

conceptual studies. Empirical studies accounted for over half of the articles reviewed during

the study period. However, there were a number of interesting conceptual papers that had no

or little real life application. Article [6], for example, provide some guidance to practitioners

about the types of simplified models that are used for uncertain decision making. Article [12],

on its part, introduces the option contract mechanism into relief material supply chain

management.

Also, [50] designed control strategies on railway networks to guarantee service quality and

minimize energy consumption using mathematical programming and simulation procedures.

12 other papers [8][16][17][18][25][41][52][57][60][63][68] and [69] all presented new

19

insights in performance measurement. However, these studies lack adequate empirical

grounding.

Gaps in context of study

Asia, Europe and North America were seen as the primary areas where most research was

targeted. These three accounted for more than 90 percent of reviewed works. No article in

these three journals during the review period had used an African or South American country

as the study area. This does not necessarily mean that there are no studies concerning

performance measurement relating to Africa, as various authors like Ohene-Asare (2009) and

Figueira, Nellis and Parker (2006) have all assessed efficiency of Banks in various parts of

Africa. However, it seems that most research relating to Africa may lack the necessary rigour

to be published in such top ranking journals.

At the individual country level, six countries accounted for over 69 percent of published

works. Out of these six, there was only one developing country- China. Similarly, out of all

the 19 countries that had been research subjects, only 5 (China, Bangladesh, Thailand, India

and Nicaragua) were developing economies (see IMF, 2012). This shows a significantly low

focus on developing economies. There is the need for more developing economy oriented

studies.

Gaps in research methods used

An assessment of the research approaches used showed an over dependence on purely

quantitative techniques in research. Although six articles combined both quantitative and

qualitative techniques, only one work published used a purely qualitative approach [26].

There was no empirical study that used a purely qualitative technique.

For the statistical methods used, although there were generally a wide variety of techniques

employed in assessing performance, there seem to be an overdependence on DEA (a

nonparametric technique). This accounted for about 31.5 percent of all articles reviewed.

Researchers can apply more of other tests like Malmquist Productivity Index, Hicks

Moortesen TFP Index and Saidin Test for further studies. The use of qualitative techniques

must be encouraged in productivity measurement.

Gaps with Industrial focus

Understanding the extent of research from the industrial focus perspective was an integral

aspect of the study. It was evident that Service had received the majority of focus during the

study period. This was confirmed by the binomial test of proportions conducted.

Furthermore, within the Service sector, Banking and Finance seemed to be the prime focus of

research in these top journals. This notwithstanding, health, education and retail (commerce)

had seen some levels of research as well. Other research can do more to evaluate productivity

with the manufacturing sector as the prime focus.

20

FUTURE RESEARCH DIRECTIONS

This study has presented insights into the research publications on Performance

Measurement, issued in three journals between 2011 and 2013 highlighting the areas that

require further research. In all, 69 papers were reviewed. It should be noted that inferences

made in this paper are limited to the three journals reviewed within the review period.

The review explicitly indicates that:

a. Extensive research has been directed to the measurement and evaluation of

productivity and performance of firms;

b. Most of the research undertaken went through empirical testing [31];

c. Extensive amount of research have been directed towards developed economies; and

d. Productivity measurements are mostly directed towards the service industry.

Following from the gaps identified in the previous section, the review identified that:

a. There is insufficient research directed towards labour and financial performance

relative to studies on firm performance [19].

b. The manufacturing sector is deficient in number of research it has seen, as there are a

lot of works concentrating on the service sector.

c. There seem to be inadequate research interest on matters relating to developing

economies. No paper published within the period studied subjects in Africa.

d. There is concentration on studying a single firm or country. Few papers assesses

performance by considering cross-cultural situations [14] [44]. This makes such

studies narrowly focused and hence generalization highly difficult.

e. There are a number of conceptual studies that are yet to undergo empirical testing to

validate the models and frameworks that have been propounded [6] [17].

f. There are few papers that make analysis from qualitative perspective, ignoring those

aspects of performance that cannot be explicitly quantified. Meanwhile, these are very

significant facets about productivity [22] [10].

It appears that, extensive research has been carried out in firm performance as compared to

financial and labour performance which seem to lack enough research coverage. There is

therefore the need for more research on financial and labour performance to be used by both

managers and researchers in the academia. In addition, there seem to be a narrow focus either

on one industry or country which calls for further research on a cross cultural and industry

assessments with more focus on manufacturing firms.

An evaluation of conceptual issues revealed substantial number of conceptual studies which

require empirical grounding. Article [6] and [17], which are all conceptual, encourage further

empirical applications of its constructs. A look at the contexts revealed no research carried on

Africa. Although there are researches on operational issues in Africa, the focus may not be on

performance measurement or it may lack the rigour for publication in the top journals.

African and other researchers in the developing countries are therefore called to focus more

21

on performance measurement as well as advancing their research for global recognition and

significance.

Finally, for the research methods, there is seen to be preponderance of researches based on

mathematical or quantitative understanding of the phenomena. However, since performance,

especially labour productivity, has human implications, there is the need for more qualitative

understanding about the human factors affect performance. Articles [10] and [22] for

example, underscore the need to incorporate qualitative aspects in performance measurement.

Further quantitative research can use relatively underused statistical tests like the Malmquist

Productivity and the Saidin test.

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Appendix A: List of Articles Reviewed

[1] Amado, A.F.C., Santos, S.P., & Marques, P.M. (2012). Integrating the Data

Envelopment Analysis and the Balanced Scorecard approaches for enhanced

performance assessment. Omega International Journal of Management Science,

40(3), 390-403.

[2] BanaeCosta, C.A., & Oliveira, M.D. (2012). A multicriteria decision analysis model

for faculty evaluation. Omega International Journal of Management Science, 40(4),

424-436.

[3] Barros, C.R., Managei, S., & Matousek, R. (2012). The technical efficiency of the

Japanese banks: Non-radial directional performance measurement with undesirable

output. Omega International Journal of Management Science, 40(1), 1-8.

[4] Grigoroudis, E., Orfanoudaki, E., & Zopoundis, C. (2012). Strategic performance

measurement in a healthcare organisation: A multiple criteria approach based on

balanced scorecard. Omega International Journal of Management Science, 40(1),

104-119.

[5] Kao, C., & Pao, H.L. (2012). Predicting project approvals: A case of grants from the

National Science Council of Taiwan. Omega International Journal of Management

Science, 40(1), 89-95.

[6] Durbach, I.A., & Stewart, J.S. (2012). A comparison of simplified value function

approaches for treating uncertainty in multi-criteria decision analysis. Omega

International Journal of Management Science, 40(5), 611-618.

[7] Kong, W.H., & Fu, T.T. (2012). Assessing the performance of business colleges in

Taiwan using data envelopment analysis and student based value-added performance

indicators. Omega International Journal of Management Science, 40(5), 541-549.

[8] Li, Y., Chen, Y., Liang, L., & Xie, J. (2012). DEA models for extended two-stage

network structures. Omega International Journal of Management Science, 40(5), 611-

618.

[9] Nicolau, J.L. (2012). The effect of winning the 2010 FIFA World Cup on the tourism

market value: The Spanish case. Omega International Journal of Management

Science, 40(5), 503-510.

23

[10] Verdecho, M.J., Alfaro-Saiz, J.J., Rodriguez-Rodriguez, R., & Ortiz-Bas, A. (2012).

A multi-criteria approach for managing inter-enterprise collaborative relationships.

Omega International Journal of Management Science, 40(5), 611-618.

[11] Li, X., Li, Y., & Cai, X. (2012). A note on the random yield from the perspective of

the supply chain. Omega International Journal of Management Science, 40(5), 601-

610.

[12] Liang, L., Wang, X., & Gao, J. (2012). An option contract pricing model of relief

material supply chain. Omega International Journal of Management Science, 40(5),

594-600.

[13] Assaf, G.A., Barros, C., & Sellers-Rubio, R. (2011). Efficiency determinants in retail

stores: A Bayesian framework. Omega International Journal of Management Science,

39(3), 283-292

[14] Avikiran, N.K. (2011). Association of DEA super-efficiency estimates with financial

ratios: Investigating the case for Chinese banks. Omega International Journal of

Management Science, 39(1), 323-334.

[15] Epure, M., Kerstens, K., & Prior, D. (2011). Technology-based total factor

productivity and benchmarking: New proposals and an application. Omega


[16] Hinojosa, M.A., & Marmol, A.M. (2011). Axial solutions for multiple objective linear

problems.An application to target setting in DEA models with preferences. Omega


[17] Hsieh, Y-J. (2011). Demand switching criteria for multiple products: An inventory

cost analysis. Omega International Journal of Management Science, 39(2), 130-137.

[18] Kuosmanen, T., & Matin, R.K. (2011). Duality of weakly disposable technology.


[19] Lee, P.K.C., Cheng, E., Yeung, A.C.L., & Lai, K-h. (2011). An empirical study of

transformational leadership, team performance and service quality in retail banks.


[20] Liu, W.B., Zhang, D.Q., Menag, W., Li, X.X., & Xu, F. (2011). A study of DEA

models without explicit inputs. Omega International Journal of Management Science,

39(5), 472-480.

[21] Moais, P., & Camanho, A.S. (2011). Evaluation of performance of European cities

with the aim to promote quality of life improvements. Omega International Journal of


[22] Nicholls, M.G., & Cargaill, B.J. (2011). Establishing best practice university research

funding strategies using mixed-mode modelling. Omega International Journal of


[23] Akther, S., Fukuyama, H., & Weber, W.L. (2013). Estimating two-stage network

Slacks-based inefficiency: An application to Bangladesh Banking. Omega the

International Journal of Operations Management, 41(1), 88–96.

[24] Asmild, M., Bogetoft, P., & Hougaard, J. (2013). Rationalizing inefficiency: Staff

utilization in branches of a large Canadian bank. Omega the International Journal of

Operations Management, 41(1), 80–87.

[25] Chen, Y., Du, J., & Huo, J. (2013). Super-efficiency based on a modified directional

distance function. Omega the International Journal of Operations Management,

41(2), 621– 625.

[26] Paradi, J.C., & Zhu, H. (2013). A survey on bank branch efficiency and performance

research with data envelopment analysis. Omega the International Journal of


24

[27] Samoilenko, S., & Osei-Bryson, K.M. (2013). Using Data Envelopment Analysis

(DEA) for monitoring efficiency-based performance of productivity–driven

organizations: Design and implementation of a decision support system. Omega the


[28] Wang, C.H., Lu, Y.H., Huang, C.W., & Lee, J.Y. (2013). R&D, productivity, and

market value: An empirical study from high-technology firms. Omega the


[29] Yang, X., & Morita, H. (2013). Efficiency improvement from multiple perspectives:

An application to Japanese banking industry. Omega the International Journal of


[30] Li, Y.J., Yang, M., Chen, Y., Dai, Q., & Liang, L. (2013). Allocating a fixed cost

based on data envelopment analysis and satisfaction degree. Omega the International

Journal of Operations Management, 41(1), 55–60.

[31] Yu, M.M., Chern, C.C., & Hsiao, B. (2013). Human resource rightsizing using

centralized data envelopment analysis: Evidence from Taiwan’s Airports. Omega the

International Journal of Operations Management, 41(1), 119-130.

[32] DeLeeuw, S., & van den Berg, J.P. (2011). Improving operational performance by

influencing shopfloor behaviour via performance management practices. Journal of

Operations Management, 29(3), 224-235.

[33] Inman, A.R., Sale, S.R., & Green, W.K. (2011). Agile manufacturing: Relation to JIT,

operational performance and firm performance. Journal of Operations Management,

29(4), 343-355.

[34] Queenan, C.C., Angst, M.C., & Devaraj, S. (2011). Doctors’ orders-If they’re

electronics, do they improve patient satisfaction? A complements/substitutes

perspective. Journal of Operations Management, 29(7), 639-649.

[35] McDermott, M.C., & Stock, N.G. (2011). Focus as emphasis: Conceptual and

performance implications for hospitals. Journal of Operations Management, 29(6),

616-626.

[36] Terjesen, S., Patel, C.P., & Covin, G.J. (2011). Alliance diversity, environmental

context and the value of manufacturing capabilities among new high technology

ventures. Journal of Operations Management, 29(1), 105-115.

[37] Wu, Y., Loch, C., & Ahmad, G. (2011). Status and relationships in social dilemmas of

teams. Journal of Operations Management, 29(7), 650-662.

[38] Wong, Y.C., Boon-itt, S., & Wong, Y.W.C. (2011). The contingency effects of

environmental uncertainty on the relationship between supply chain integration and

operational performance. Journal of Operations Management, 29(6), 604-615.

[39] Goodale, C.J., Kuratko, F.D., Hornsby, S.J., & Covin, G.J. (2011). Operations

management and corporate entrepreneurship: The moderating effect of operations

control on the antecedents of corporate entrepreneurial activity in relation to

innovation performance. Journal of Operations Management, 29(1), 116-127.

[40] Sahoo, K.B., & Tone, K. (2013). Non-parametric measurement of economies of scale

and scope in non-competitive environment with price uncertainty. Omega


[41] Edelstein, B., & Paradi, C.J. (2013). Ensuring units invariant slack selection in radial

data envelopment analysis models, and incorporating slacks into an overall efficiency

score. Omega International Journal of Management Science, 41(1), 31-40.

[42] Devaraj, S., Vaidyanathan, G., & Nath, A.M. (2012). Effect of purchase volume

flexibility and purchase mix flexibility on e-procurement performance: An analysis of

two perspectives. Journal of Operations Management, 30(7), 509-520.

25

[43] Stouthuysen, K., Slabbinck, H., & Roodhooft, F. (2012). Controls, service type and

perceived supplier performance in inter-firm service exchanges. Journal of


[44] Lampel, J., & Giachetti, C. (2013). International diversification of manufacturing

operations: Performance Implications and moderating forces. Journal of Operations

Management, 31(4), 213-227.

[45] Lo, C.K., Wiengarten, F., Humphreys, P., Yeung, A.C., & Cheng, T. (2013). The

impact of contextual factors on the efficacy of ISO 9000 adoption. Journal of


[46] Oh, L.B., Teo, H.H., & Sambamurth, V. (2012). The effects of retail channel

integration through the use of information technologies on firm performance. Journal

of Operations Management, 30(5), 368-381.

[47] Sawhney, R. (2013). Implementing labor flexibility: A missing link between acquired

labor flexibility and plant performance. Journal of Operations Management, 30(2),

98-108.

[48] Shafer, S.M., & Moeller, S.B. (2012). The effects of Six Sigma on corporate

performance: An empirical investigation. Journal of Operations Management, 30(8),

521-532.

[49] Wan, X., Evers, P.T., & Dresner, M.E (2012). Too much of a good thing: The impact

of product variety on operations and sales performance. Journal of Operations

Management, 30(4), 316-324.

[50] Yang, L., Li, K., Gao, Z., & Li, X. (2012). Optimizing trains movement on a railway

network. Omega International Journal of Management Science, 40(5), 619-633.

[51] Juo, J.C., Fu, T.T., & Yu, M.M. (2012). Non-oriented slack-based decompositions of

profit change with an application to Taiwanese banking. Omega International Journal

of Management Science, 40(5), 550-561.

[52] Gonzalez-Pachon, P.J., & Romeo, C. (2011). The design of socially optimal decision

in a consensus scenario. Omega International Journal of Management Science, 39(2),

179 – 185.

[53] Lee, P.K.C., Cheng, T.C.E., Yeung, A.C.L., & Lai, K.H. (2011). An empirical study

of transformational leadership, team performance and service quality in retail banks.

Omega International Journal of Management Science, 39(6), 690 – 701

[54] Paradi, J.C., Royalt, S., & Zhu, H. (2011). Two stage evaluation of bank branch

efficiency using data envelopment analysis. Omega International Journal of

Management Science, 39(1), 99 -109.

[55] Partovi, Y.F. (2011). Corporate philanthropic selection using data envelopment

analysis. Omega International Journal of Management Science, 39(5), 522-527.

[56] Premachandra, I.M., Chen, Y., & Watson, J. (2011). DEA as a tool for predicting

corporate failure and success: A case of bankruptcy assessment. Omega International

Journal of Management Science, 39(6), 620 – 626.

[57] Ramon, N., Ruiz, J.L., & Sirvent, I. (2011). Reducing differences between profiles of

weights: A “peer-restricted cross-efficiency evaluation”. Omega International Journal

of Management Science, 39(6), 634 -641.

[58] Simon, J., Simon, C., & Arias, A. (2011). Changes in productivity of Spanish

University Libraries. Omega International Journal of Management Science, 39(5),

578 -588.

[59] Udhayakumar, A., Charles, V., & Kumar, M. (2011). Stochastic simulation based

genetic algorithm for chance constrained data envelopment analysis problems. Omega

International Journal of Management Science, 39(4), 387- 397.

26

[60] Wang, Y.M., & Chin, K.S. (2011). The use of OWA operator weights for cross-

efficiency aggregation. Omega International Journal of Management Science, 39(5),

493-503.

[61] Zhong, W., Yuan, W., Li, S.X., & Huang, Z. (2011). The performance evaluation of

Regional R&D investments in China: An application of DEA based on the first

official China Economic Census data. Omega International Journal of Management

Science, 39(4), 447 – 455.

[62] Adrian, E., Mondragon, C., & Lalwani, C. (2011). Measures for auditing performance

and integration in closed-loop supply chains. Supply Chain Management: An

International Journal, 16(1), 43–56.

[63] Bai, C., Sarkis, J., Wei, X., & Koh, A.L. (2012). Evaluating Ecological Sustainable

Performance Measures Supply Chain Management. Supply Chain Management: An


[64] Soosay, C., Fearne, A., & Dent, B. (2012). Sustainable Value Chain Analysis – A

Case Study of Oxford Landing From “Vine to Dine”. Supply Chain Management: An


[65] Bjorklund, M., Martinsen, U., & Abrahamsson, M. (2012). Performance

Measurements in the Greening of Supply Chains. Supply Chain Management: An


[66] Ruben, R., & Zuniga, G. (2011). How Standards Compete: Comparative Impact of

Coffee Certification Schemes In Northern Nicaragua. Supply Chain Management: An


[67] Atilgan, C., & Mccullen, P. (2011). Improving Supply Chain Performance through

Auditing: A Change Management Perspective. Supply Chain Management: An


[68] Gandomi, A., & Zolfaghari, S. (2013). Profitability of Loyalty Reward Programs: An

Analytical Investigation. Omega the International Journal of Operations

Management, 41(4), 797–807.

[69] Fang, H., Lee, H., Hwang, S., & Chung, C. (2013). A Slacks-Based Measure of

Super-Efficiency in Data Envelopment Analysis: An Alternative Approach. Omega

the International Journal of Operations Management, 41(4), 731–734.

27

Appendix B: The Steps in a Systematic Review

Adopted from Khan, Kunz, Kleijnen and Antes (2003)

Step 1• Framing questions for a review

Step 2• Identifying relevant work

Step 3• Assessing the quality of studies

Step 4• Summarizing the evidence

Step 5• Interpreting the findings

Planning the Review

Conducting the

Review

Reporting the

Review

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