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1 INTRODUCTION

Shipping companies constitute a fundamental element

of the international maritime trade, an activity

reflecting more than 11.08 billion tons with a growth

up to 4.8 percent in 2021, despite the disruptive

impact of COVID-19 (United Nations Conference on

Trade And Development [UNCTAD], 2020). Within a

volatile and constantly changing macroeconomic

environment, with various factors occasionally

disrupting the global demand and supply, both

anthropogenic (i.e. trade wars, sanctions, fuel

economics) and not (i.e., the COVID-19 pandemic),

shipping companies need to constantly develop their

business strategies, deploy resources effectively and

efficiently, as well as monitor and improve their

performance in order to retain and better their

position in the market. Active involvement in a highly

cyclical and volatile industry (Stopford, 2009),

inevitably led contemporary shipping companies to

adopt a corporatist approach regarding various areas

of managerial interest, such as their business and

financing strategies (Melas, 2019).

However, despite the necessity for the

measurement of performance in the broader maritime

business context emphatically highlighted in existing

research, there exists a significant gap regarding the

investigation of several non-financial and non-

accounting performance dimensions, able to capture

the multi-dimensional nature of business performance

in the shipping industry as a whole, or in specific

shipping companies with accordingly differentiated

strategies.

This paper aims to fill in this research gap by

evaluating and analysing long-term performance

levels of the shipping industry as a whole, through

the use of Key Performance Indicators (KPI). More

specifically, it aims to evaluate the environmental,

health, safety management, HR management,

Maritime Sustainability and the Need for Global

Performance Indicators i

n Shipping: An Empirical

nvestigation Based on the Shipping KPI Standard by

BIMCO

.F. Darousos, M.Q. Mejia Jr, I. Panteladis & A. Pastra

World Maritime University,

Malmoe, Sweden

ABSTRACT: This paper aims to cover a gap in maritime literature by analysing the performance of the

international fleet through the BIMCO Shipping KPI System databases, and by highlighting the necessity for the

adoption of Global Performance Indicators to serve the needs of a sustainable maritime industry. The paper

investigates the complex interrelations of the various types of performance in shipping, consisting of 57,622

ships of all commercial types, operated from 26 countries, covering the envi

ronmental, health, safety

management, HR management, navigational safety, operational, security, and technical performance. Results

indicate that countries rank differently with regards to the aggregated performance of their respective shipping

companies, signifying different managerial approaches. This paper contributes to the discourse of maritime

governance, aiming to be of interest to all maritime stakeholders dealing with marine policies and institutional

arrangements for the management and regulation of international shipping.

http://www.transnav.eu

the

International Journal

on M

arine Navigation

and Safety of Sea Transportation

Volume 17

Number 4

December 2023

DOI: 10.12716/1001.17.04.

760

navigational safety, operational, security, and

technical performance of a sample indicative of the

global fleet. To address the need for performance

measurement in absolute terms and relative to the

industry average in a consistent way, allowing both

within-country and cross-country comparisons, the

research uses a unique international sample provided

by the Baltic and International Maritime Council

(BIMCO), consisting of performance indicators

reflecting a total of 57,622 ships of all commercial

types, operated from 26 countries, and providing a

truly international coverage of the above mentioned

performance types in the maritime industry. The

research aims to be useful to wide and narrow

shipping stakeholders, primarily maritime corporate

managers, and directors, as well as policy makers at

international, regional, and national level.

The remaining of the paper is organised as follows:

Section 2 presents the theoretical framework and the

specifics of Shipping KPI system; Section 3 presents

the Empirical Investigation of international shipping

performance based on the Shipping KPI System

databases and Section 4 provides a discussion of the

main findings. Finally, Section 5 discusses the

necessity of a Global Performance Indicator system

for the maritime industry and Section 6 concludes the

paper.

2 THEORETICAL FRAMEWORK

2.1 Performance Management

Performance measurement is a crucial management

function, allowing for efficient management and

materialization of key business strategies.

Traditionally, the performance measurement has been

mostly focused on financial and accounting measures

such as Return on Assets (ROA), Return on

Investment (ROI), Return on Capital (ROC), Return on

Sales (ROS), Return on Equity (ROE), Return on

Capital Employed (ROCE), and Return on Invested

Capital (ROIC) (Panayides, Gong and Lambertides,

2010). The sole evaluation of performance based on

financial and accounting data is nowadays considered

insufficient and much attention is shed upon multi-

dimensional performance indicators. There is

considerable evidence that in order to achieve a

representative reflection of its overall performance, an

organization should supplement financial with non-

financial performance evaluation methods, both

quantitatively and qualitatively (Narkunienė &

Ulbinaitė, 2018).

In the shipping industry, this need for non-

financial performance measurement has also been

highlighted in past research (Chou and Liang, 2001;

Lagoudis, Lalwani and Naim, 2006; Panayides, Gong

and Lambertides, 2010). This paper aims to fill in this

gap in the literature, by presenting an international,

cross-country and cross-sector analysis of overall

shipping performance through the use of a suitable

standardised measurement system covering all the

non-financial and non-accounting types of shipping

business performance indicators covered by the

Shipping KPI System by BIMCO.

2.2 Key Performance Indicators (KPIs) in Shipping

According to Barr (2015, 2019), KPIs serve three

purposes: (a) the monitoring of important findings, (b)

the interpretation of the results, and (c) the

undertaking of action, if deemed necessary, and past

research provides evidence of their use in various

industries. In the shipping industry, the use of Key

Performance Indicators (KPIs) essentially represents a

byproduct of the required continuous improvement

processes. The latter are both due to mandatory

standards of quality, such as the International Safety

Management (ISM) code, and voluntary, such as the

ISO 9001 and ISO 14001, although self-regulatory

practices such as the Tanker Management Self-

Assessment (TMSA), derived from the Oil Companies

International Maritime Forum (OCIMF). Interestingly,

however, the existing literature on KPIs in the

shipping industry is relatively limited. Indicatively,

Konsta and Plomaritou (2012) have identified the

limited use of KPIs by Greek tanker companies,

despite them recognizing their value for performance

evaluation, while Banda et al. (2016) highlighted the

potential of KPIs to develop, monitor, control and

improve the safety of shipping operations, whereas

Nesheim and Fjørtoft (2019) used the Shipping KPI

System to PI Database to identify costs and benefits of

e-navigation solutions. Finally, Darousos et al. (2019)

identified the potential of a tailored KPI system as a

facilitator for good maritime governance, as a

common ‘language’ between regulating authorities

and market practitioners.

2.3 The emerging role of KPI for sustainability

The deployment of an efficient sustainability and

environmental, social and governance (ESG) strategy,

appropriately and organically integrated into the core

business strategy of a ship management company,

should begin by clearly demonstrating the way that it

permeates the corporate entity. The identification of

suitable KPIs, directly relevant to the respective

sustainability strategy, should be set after a thorough

identification of the material sustainability issues

which are relevant to both wide and narrow

stakeholders. Recent literature has focused on the

importance of KPI to address and measure various

dimensions of ESG performance, in a diverse range of

industrial segments. Indicatively, Yip and Yu (2023)

explored the ESG disclosure quality through KPIs in a

sample consisting of small and medium-sized

companies listed in the Hong Kong Stock Exchange,

interestingly identifying environment-related KPI as

the more underperforming. A study by Dragomir,

Parsons & Choi (2018) focused on the use of KPI as a

measurement tool for the evaluation of the economic

efficiency of shipping companies employing

multigender crews and implementing gendering

policies, suggesting a specific set of KPI and also

approaching social, financial, health & safety, and

training issues. An emerging wave of literature

explores the widespread impact of new technologies

and sustainability-ESG concerns upon the

international supply chain, and the necessity for the

development of suitable KPIs, able to measure this

impact and allow for performance information

exchange industry-wide (Patidar et al., 2022).

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2.4 The Shipping KPI System by BIMCO

The Shipping KPI System is a tool comprised by

shipping performance indexes (SPIs), Key

Performance Indicators (KPIs), and Performance

Indicators (PIs). Starting in 2011, the Shipping KPI

System – administered by InterManager since 2003 –

was superintended by the independent KPI

Association Ltd. In 2015, Denmark-based BIMCO

acquired the Shipping KPI System and along with the

IT support of SOFTImpact, a specialised maritime IT

service provider based in Cyprus, has been operating

and further developing it ever since. The System is a

benchmarking tool, meant to ameliorate the overall

non-financial performance of ship management

companies, as well as to provide efficient

communication regarding the ship operation to the

internal and external stakeholders (BIMCO, 2018).

The SPIs (high level indices) constitute the

aggregated expression of the various types of

performance and are calculated by the KPIs (mid-level

indices), which are in turn calculated via the PIs

(lowest level), as seen in Figure 1. The PI data are

directly measured and reported by the ship or the

ship management company. Then, a normalization

process takes place leading to the KPIs which are

scaled between 0-100, in a range between

unacceptable (0) and outstanding performance (100).

Thus, according to BIMCO, it is possible to compare

the performance of ships with different characteristics

or amount of data.

The SPIs are expressed as a weighted average of

relevant KPI ratings on a scale between 0 and 100.

Their objective is to allow the communication of

shipping performance information to external

stakeholders. Given that there is currently lack of a

commonly used, standardised system of

communication regarding the maritime industry, such

initiatives may actually serve the purposes of sector-

wide stakeholders by providing information on the

overall operation performance of the international

fleet. The types of performance expressed through the

SPIs are: (i) Environmental Performance; (ii) Health

and Safety Performance; (iii) HR Management

Performance; (iv) Navigational Safety Performance;

(v) Operational Performance; (vi) Security

Performance; (vii) Technical Performance; and (viii)

Port State Control Performance.

According to BIMCO, the characteristics of the

performance indicators considered in the Shipping

KPI System need to be observable and quantifiable,

valid indicators of performance, robust against

manipulation, sensitive to change, transparent and

easy to understand, and compatible (BIMCO, 2018).

They all signify a useful tool for communication

among the crews and the companies, but also among

the shipping companies and the external stakeholders,

such as the international, regional, and national

formal and informal authorities.

Based on the above KPI system, the following

research objectives are set:

1. To identify the overall performance ranking of ship

management companies on a different national

basis;

2. To examine the relationship between aggregated

high SPI-level of performance on a different

national basis;

3. To examine the relationship between aggregated

mid KPI-level of performance on a different

national basis.

To obtain a representative set of data regarding

shipping performance related to the human element,

the sample was obtained from the BIMCO Shipping

KPI System databases.

Figure 1. The BIMCO Shipping KPI System (Darousos et al.,

2019; derived from BIMCO, 2018)

As discussed, BIMCO produces a variety of

information on various types of shipping

performance. Namely, the SPIs used in this paper,

including their constituent KPIs, and according to the

Shipping KPI System Version 3.0, are: (i)

Environmental Performance (SPI001); (ii) Health and

Safety Performance (SPI002); and (iii) HR

Management Performance (SPI003), (iv) Navigational

Safety Performance (SPI004), (v) Operational

Performance (SPI005), (vi) Security Performance

(SPI006), (vii) Technical Performance (SPI007), and

(viii) Port State Control Performance (SPI009). Table 1

below presents the Version 3.0 of the system, which

constitutes the basis of this analysis:

Table 1. Overview of Shipping KPI Version of BIMCO

Version 3.0

________________________________________________

SPI KPI PI

________________________________________________

SPI001 KPI028: Releases of Number of releases of substances to

Environ- substances the environment

mental Number of oil spills

Perfor- KPI001: Ballast water Number of ballast water

mance management management violations

violations

KPI007: Contained Number of contained spills of

spills liquid

KPI011: Number of environmental related

Environmental deficiencies

deficiencies Number of recorded external

inspections

KPI005: CO2 Emitted mass of CO2

efficiency Transport work

KPI021: NOx Emitted mass of NOx

efficiency Transport work

KPI030: SOx Emitted mass of SOx

efficiency Transport work

SPI002: KPI013: Fire and Number of fire incidents

Health Explosions Number of explosion incidents

and KPI017: Lost Time Number of fatalities due to work

Safety Injury Frequency injuries

Perfor- Number of lost workday cases

mance Number of permanent total

disabilities (PTD)

Number of permanent partial

disabilities

762

Total exposure hours

KPI015: Health and Number of health and safety

Safety deficiencies related deficiencies

Number of recorded external

inspections

KPI018: Lost Time Number of cases where a crew

Sickness Frequency member is sick for more than 24

hours

Number of fatalities due to sickness

Total exposure hours

KPI025: Passenger Number of passengers injured

Injury Ratio Passenger exposure hours

SPI003: KPI008: Crew Number of absconded crew

HR disciplinary Number of charges of criminal

Manage- frequency offences

ment Number of cases where drugs or

Perfor- alcohol is abused

mance Number of dismissals

Number of logged warnings

Total exposure hours

KPI009: Crew Number of seafarers not relieved

planning on time

Number of violation of rest hours

KPI016: HR Number of HR related deficiencies

deficiencies Number of recorded external

inspections

KPI003: Cadets Number of cadets under training

per ship with the DOC holder

Number of ships operated under

the DOC holder

KPI022: Officer Number of officer terminations

retention rate from whatever cause

Number of unavoidable officer

terminations

Number of beneficial officer

terminations

Number of officers employed

KPI023: Officers Number of officer experience

experience rate points

Number of officers onboard

KPI031: Training Number of officer trainee man days

days per officer Number of officer days onboard all

ships with the DOC holder

SPI004: KPI019: Number of navigational related

Naviga- Navigational deficiencies

tional deficiencies Number of recorded external

Safety inspections

Perfor- KPI020: Number of collisions

mance Navigational Number of allisions

incidents Number of groundings

SPI005: KPI002: Budget Last year’s running cost budget

Opera- performance Last year’s actual running costs and

tional accruals

Perfor- Last year’s AAE (Additional

mance Authorized Expenses)

KPI010: Drydocking Agreed drydocking duration

planning Actual drydocking duration

performance Agreed drydocking budget

Actual drydocking costs

KPI004: Cargo Number of cargo related incidents

related incidents

KPI024: Operational Number of operational related

deficiencies deficiencies

Number of recorded external

inspections

KPI032: Ship Actual unavailability

availability Planned unavailability

KPI033: Vetting Number of observations during

deficiencies commercial inspections

Number of commercial inspections

SPI006: KPI029: Security Number of security related

Security deficiencies deficiencies

Perfor- Number of recorded external

mance inspections

SPI007: KPI006: Condition Number of conditions of class

Techni- of class

cal KPI012: Failure of Number of failures of critical

Perfor- critical equipment equipment and systems

mance and systems

SPI009: KPI027: Port state Number of PSC detentions

Port control detention Number of PSC inspections

State KPI026: Port state Number of PSC deficiencies

Control control deficiency Number of PSC inspections

Perfor- ratio

mance KPI014: Port state Number of PSC inspections

control performance resulting in zero deficiencies

Number of PSC inspections

________________________________________________

Source: Own elaboration, derived from BIMCO, 2018

Table 1, presents the performance indicators from

a total of 57,622 ships of all commercial types,

operated from 26 countries, shipping accounts,

providing an overview of the different performance

types of the maritime industry. It has to be noted that

the number of countries used as sample is smaller

than the actual total of countries with shipping

accounts registered in the Shipping KPI System, due

to the confidentiality policy of BIMCO.

Table 2. Overview of the research sample (countries,

number of registered ships per country, number of

corresponding registered accounts)

________________________________________________

Country Number of Number of

Registered Ships Registered Accounts

________________________________________________

Singapore 7975 27

Hong Kong 7885 9

Philippines 6737 6

Germany 4655 24

Greece 4262 43

Cyprus 4223 9

Japan 2986 14

United Kingdom 2521 14

Monaco 2078 4

China 1855 4

India 1750 10

Denmark 1567 11

Italy 1398 9

Netherlands 1177 11

Korea, Republic Of 1174 7

Norway 1060 14

Turkey 864 13

France 820 3

Belgium 777 3

United Arab Emirates 634 9

Sweden 252 3

Canada 244 4

Spain 239 3

Viet Nam 212 4

South Africa 192 4

Taiwan, Province 85 3

Of China

________________________________________________

Source: Own elaboration

3 EMPIRICAL INVESTIGATION OF GLOBAL

SHIPPING PERFORMANCE BASED ON THE

SHIPPING KPI SYSTEM DATABASES

Table 3 presents a liner regression analysis of the

aggregated SPI database. At this stage, the dependent

variable is the variance between the SPI indicators for

each country and the independent variable is the

mean of the SPI indicators for each country. The

coefficient of determination R2 is approximately 0.50,

so 50% of the variance in the values of the dependent

variable can be explained by the model and the use of

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the explanatory variables. The crucial point is the

statistical significance of the estimated coefficients of

the variables; indeed, the coefficients are statistically

significant for P<0.05; and the values are negative.

This signifies that when the mean of all SPI indicators

increases by one point, the variance between the

indicators decreases by 16,35. This is an indication

that countries which achieve a higher mean value for

all SPI indices also have a lower variance in the SPI

indices. In other words, higher average performance

across all indices, for each country, is also associated

with smaller dispersion of the indices. This is the first

important result of this study, signifying that

achieving high-level non-financial and non-

accounting performance as expressed through SPIs is

reflected upon all relevant types. Furthermore, that

higher standards of management, as expressed

through fleet performance, can be associated on a

specific national basis perhaps signifying a

combination of collective expertise and a well-

implemented regulatory framework.

Additionally, a dummy variable was used: D=1 for

each EU-27 country, and D=0 for all other countries of

the database, in order to investigate whether any

substantial differentiations can be observed regarding

the shipping performance of Europe-based companies

through the overall performance of their respective

fleets. This further supports the first conclusion, as the

fact that the coefficient of the dummy variable is

negative (-48.79) means that, irrespective of average

values, EU-27 countries present smaller variations

between SPI indicators compared to other countries.

The model is econometrically supported by the

fact that there is no issue related to heteroskedasticity

in these stratified data, as we can see from the Durbin-

Watson value of close to 2 (approximately 2.08). In

any case, the coefficients would be unbiased as White

heteroskedasticity-consistent standard errors &

covariance were used for the purposes of the below

analysis:

Table 3. The relationship between variance and mean value

of SPI per country

________________________________________________

Variable Coefficient Std. Error t-Statistic Prob.

of variables

________________________________________________

Constant 1562.376 405.0675 3.857076 0.0008

Average SPI -16.35594 4.457369 -3.669416 0.0013

per country

European -48.79891 19.76451 -2.469017 0.0214

Union

________________________________________________

R-squared 0.505063 Mean dependent var 108.7169

Adjusted 0.462025 S.D. dependent var 72.57239

R-squared

S.E. of 53.22949 Akaike info criterion 10.89527

regression

Sum squared 65167.71 Schwarz criterion 11.04043

resid

Log -138.6385 Hannan-Quinn criter. 10.93707

likelihood

F-statistic 11.73530 Durbin-Watson stat 2.089811

Prob 0.000307

(F-statistic)

________________________________________________

In the next part of the analysis, the variance for

each SPI was analysed separately based on country

performance for each indicator (dependent variable),

followed by the analysis of the average performance

(explanatory variable) for each indicator separately

from country performance.

The coefficient is negative (-3.56) and statistically

significant, meaning that when the average

performance expressed through a SPI indicator

increases (based on all/among countries'

performance), the variance for that indicator also

decreases. That is, if all countries show good average

performance on a selective performance type,

meaning that the average value of the indicator

(average performance) is high, then their variance is

also lower as the differences in performance between

countries for that indicator are relatively smaller.

For this testing, no dummy variable for the EU-27

countries was used because the average value of each

SPI indicator is formulated by the performance of all

countries of the database. This is also the case for the

variances in each performance category, which are

due to the differences in the according SPI’s of all

countries.

Table 4. White heteroskedasticity-consistent standard errors

& covariance

________________________________________________

Variable Coefficient Std. Error t-Statistic Prob.

________________________________________________

C 350.5086 46.41695 7.551307 0.0003

AVERAGE_ -3.562008 0.442299 -8.053392 0.0002

INDEXES_

SPI

________________________________________________

R-squared 0.681725 Mean dependent var 38.42636

Adjusted 0.628679 S.D. dependent var 37.47137

R-squared

S.E. of 22.83360 Akaike info criterion 9.306661

regression

Sum squared 3128.239 Schwarz criterion 9.326522

resid

Log -35.22664 Hannan-Quinn criter. 9.172711

likelihood

F-statistic 12.85161 Durbin-Watson stat 1.614223

Prob 0.011574

(F-statistic)

________________________________________________

In the next part of the analysis, the variance for

each SPI was analysed separately based on country

performance for each indicator (dependent variable),

followed by the analysis of the average performance

(explanatory variable) for each indicator separately

from country performance.

The coefficient is negative (-3.56) and statistically

significant, meaning that when the average

performance expressed through a SPI indicator

increases (based on all/among countries'

performance), the variance for that indicator also

decreases. That is, if all countries show good average

performance on a selective performance type,

meaning that the average value of the indicator

(average performance) is high, then their variance is

also lower as the differences in performance between

countries for that indicator are relatively smaller.

For this testing, no dummy variable for the EU-27

countries was used because the average value of each

SPI indicator is formulated by the performance of all

countries of the database. This is also the case for the

variances in each performance category, which are

due to the differences in the according SPI’s of all

countries.

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Table 5. SPI White heteroskedasticity-consistent standard

errors & covariance

________________________________________________

Variable Coefficient Std. Error t-Statistic Prob.

________________________________________________

C 350.5086 46.41695 7.551307 0.0003

AVERAGE_ -3.562008 0.442299 -8.053392 0.0002

INDEXES_

SPI

________________________________________________

R-squared 0.681725 Mean dependent var 38.42636

Adjusted 0.628679 S.D. dependent var 37.47137

R-squared

S.E. of 22.83360 Akaike info criterion 9.306661

regression

Sum squared 3128.239 Schwarz criterion 9.326522

resid

Log -35.22664 Hannan-Quinn criter. 9.172711

likelihood

F-statistic 12.85161 Durbin-Watson stat 1.614223

Prob 0.011574

(F-statistic)

________________________________________________

Due to the relatively smaller number of

observations, a dummy variable was not set for each

SPI indicator. Such a dummy variable would be set as

D-1, reflecting the number of SPI indicators minus 1

dummy variable; thus, the regression constant would

incorporate the variance of the last dummy variable.

Therefore, the coefficients of the other dummy

variables would reflect the variation of the variance of

the other SPI indicators, irrespective of the average

value of each indicator achieved by countries.

However, due to the larger number of observations,

the KPI analysis following below also incorporated

the corresponding dummy variables.

Table 6. KPI White heteroskedasticity-consistent standard

errors & covariance analysis

________________________________________________

Variable Coefficient Std. Error t-Statistic Prob.

________________________________________________

C 589.1517 150.8509 3.905522 0.0005

AVERAGE_ -5.516156 1.606399 -3.433865 0.0017

INDEXES_

KPI

________________________________________________

R-squared 0.440598 Mean dependent var 127.1210

Adjusted 0.422553 S.D. dependent var 180.9519

R-squared

S.E. of 137.5053 Akaike info criterion 12.74389

regression

Sum squared 586138.7 Schwarz criterion 12.83459

resid

Log -208.2742 Hannan-Quinn criter. 12.77441

likelihood

F-statistic 24.41631 Durbin-Watson stat 1.774169

Prob 0.000025

(F-statistic)

________________________________________________

In the subsequent regression analysis of the KPIs

which addressed the variance between all KPIs for

each country as the dependent variable and the mean

between all KPIs for each country as the independent

variable, the results reconfirmed the previous

findings. Indeed, countries which collectively achieve

higher average performance across all KPIs, present

substantially smaller inter-indicator performance

variations. This leads to the conclusion that in

comparison, the differences in performance, measured

by each KPI, decrease as the average performance

across all KPIs for each country increases.

As for the EU-27 dummy variable, European states

present smaller variation across all KPIs when

compared to non-European countries, irrespective of

the average value of all KPIs. This result is

highlighted by the statistically significant coefficient

of 0.1 p-value.

Table 6. KPI Regression analysis with EU-27 Variable

________________________________________________

Variable Coefficient Std. Error t-Statistic Prob.

________________________________________________

C 5177.207 494.9564 10.45993 0.0000

AVERAGE_ -54.48752 5.904463 -9.228192 0.0000

COUNTRIES_

KPI

DUMMYEU -58.03503 33.99452 -1.707188 0.0998

________________________________________________

R-squared 0.779940 Mean dependent var 576.3351

Adjusted 0.760804 S.D. dependent var 185.2358

R-squared

S.E. of 90.59453 Akaike info criterion 11.95883

regression

Sum squared 188769.5 Schwarz criterion 12.10400

resid

Log -152.4648 Hannan-Quinn criter. 12.00063

likelihood

F-statistic 40.75837 Durbin-Watson stat 2.035745

Prob 0.000000

(F-statistic)

________________________________________________

Regarding the regression analysis of the variance

for each KPI as it is formed by the respective

performance of all countries for each indicator

(dependent variable) against the average performance

achieved by the countries in each KPI (explanatory

variable), the coefficient is also negative and

statistically significant (P<0.01 or 1%). This is a final

confirmation that when the average performance

increases (from the performance of all countries in

each KPI) the variance is smaller (i.e. the differences in

performance between countries for that indicator).

Table 7. White heteroskedasticity-consistent standard errors

& covariance

________________________________________________

Variable Coefficient Std. Error t-Statistic Prob.

________________________________________________

Constant 385.2174 129.2635 2.980094 0.0065

AVERAGE_ -4.330393 1.532568 -2.825579 0.0094

INDEXES_

KPI

DUMMYSPI001 56.26377 29.80685 1.887612 0.0712

DUMMYSPI002 96.94605 37.84314 2.561787 0.0171

DUMMYSPI003 254.6813 70.68217 3.603190 0.0014

DUMMYSPI004 41.18462 30.81005 1.336727 0.1938

DUMMYSPI005 95.68496 73.44351 1.302838 0.2050

DUMMYSPI006 44.81663 32.51158 1.378482 0.1808

DUMMYSPI007 44.83244 25.00463 1.792966 0.0856

________________________________________________

R-squared 0.636292 Mean dependent var 127.1210

Adjusted 0.515056 S.D. dependent var 180.9519

R-squared

S.E. of 126.0111 Akaike info criterion 12.73762

regression

Sum squared 381091.1 Schwarz criterion 13.14576

resid

Log -201.1707 Hannan-Quinn criter. 12.87494

likelihood

F-statistic 5.248382 Durbin-Watson stat 1.675846

Prob 0.000718

(F-statistic)

________________________________________________

The final regression analysis, presented in Table 7

below, was also performed using dummy variables,

keeping the same explanatory variable of average

performance between all countries in the database,

per KP

I. That is, a value of 1 was set for each KPI

constituting a specific SPI. We observe that the

indicators with the largest variance, regardless of the

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average performance of all countries on the specific

KPIs, and based on the statistical significance of the

coefficients, are SPI001 (Environmental Performance),

SPI002 (Health and Safety Performance), SPI003 (HR

Management Performance) and SPI007 (Technical

Performance). SPI003 in particular shows a large

variation, as performance varies significantly across

countries on this indicator.

4 DISCUSSION

The above analysis of the BIMCO databases revealed

some useful results regarding the management of the

international fleet and its multidimensional

performance at aggregated level. The analysis of SPIs

and KPIs provided strong evidence that efficient

management of the fleet and the potential cultivation

of good managerial practices can be reflected in all

types of non-financial and non-accounting

performance in the shipping industry. Furthermore, it

leaves room for reasonable extension of this

conclusion at national and even regional level. The

fact that data from Europe-based shipping companies

points towards a collectively robust shipping

performance with no particular variance between the

various performance metrics can be attributed to the

extensive regulatory framework of the EU, which in

close step (and often supplementing) the international

IMO framework, sets the pace for more efficient and

holistic shipping management. Furthermore, the close

link between the ship-owning and ship management

functions, which have been historically inseparable

until the late 20th century (Stopford, 2009) and which

is still strong in the case of european coastal nations as

a result of their socioeconomical, geographical, and

historical characteristics, may be one of the factors

behind this.

Equally interesting is the established variance

SPI001 (Environmental Performance), SPI002 (Health

and Safety Performance), SPI003 (HR Management

Performance) and SPI007 (Technical Performance),

overarched by the large variation of the HR

Management performance. The latter, considering

issues relevant to the health and safety of the crews, is

particularly important as relevant occupational

problems may potentially influence and situational

awareness of the crew ultimately leading to maritime

accidents. As Theotokas (2018) argues, in the greatly

intensified working conditions of contemporary

maritime industry, where crews are substantially

confined in the social environment of ships -their

working and living environment being absolute

synonymous, for the duration of their engagement-

for longer periods of time, shipping companies need

to meaningfully intervene. Be it through a strategy

based on CSR or other means, shipping companies

should try surpassing the minimum regulatory

requirements and tend to the needs of the crews, in

order to ameliorate their everyday life, thus reducing

the possibility for a failure due to the onboard human

element.

Regarding the use of KPIs either as a means of

inter-industrial communication tool, the results of this

research highlighted the potential of an appropriate

KPI system to serve as a foundation towards the

establishment of a shipping Global Performance

Indicator (GPI) system.

As has been argued before, “…in every situation

requiring cross-sectional cooperation, the need for a

common system of reference, a common “language”,

is required by institutional and market stakeholders.”

(Darousos, Mejia and Visvikis, 2019). In a recent

collective work, entitled “The Power of Global

Performance Indicators” (GPI), Kelley and Simmons

explore the role of GPI which they define as “…a

named collection of rank-ordered data that purports

to represent the past or projected performance of

different units”, highlighting the importance of

various numerical indices for the ranking of state

performance, focusing on standards with the

following characteristics:

− Public and easily available.

− Regular and published on a predictable schedule.

− Purposive, explicitly normative, policy focused

− Deployed to influence state-level outcomes.

− Comparative of the performance of multiple states

within a region or more broadly. (Kelley and

Simmons, 2019).

Various existing indicators, including the United

Nation’s Human Development Index and the UN

Gend

er Equality Index, the World Bank’s Ease of

Doing Business (EDB) Index, the Millennium

Development Goals (MDG), the Financial Action Task

Force (FATF) database, and the Aid Transparency

(AT) Index, serve as examples of what could

successfully constitute a successful GRI. The

importance of GRI not only as a way of international

performance communication, but as a means of

transferring social knowledge and applying social

pressure within emerging forms of influence and

governance, has been therefore established in

previous studies. (Kelley and Simmons, 2019).

The need for similar initiatives for the needs of the

broader maritime industry are obvious and already

expressed through existing databases, such as the

Paris MoU database publishing the port state control

results and the according detention lists, leading to

“White, Grey and Black (WGB) list”, presentιng a

wide range from flags of high to poor performance.

Similar databases exist, emphasizing on

environmental performance, such as the

Environmental Shipping Index (ESI), measuring air

emissions of NOx and SOx with the aim of reducing

them.

Considering the characteristics of GPI and their

social and self-regulatory dynamics far exceeding

simple benchmarking purposes, but rather

constituting a pathway towards wide and narrow

stakeholder cooperation, transparency, participation,

all important aspects of good governance, the need for

a similar multidimensional instrument for the

maritime industry seems to be of paramount

importance. The Fourth Industrial Revolution (4IR)

with its various advances in Big Data, automation and

digital interconnection already alters the global

transportation sector (World Maritime University,

2019) and reshape the industry.

The continuously expanding and evolving

maritime regulatory framework, mostly driven by the

International Maritime Organization (IMO) and the

International Labour Organization (ILO), already

766

includes an environment allowing for the nurturing of

sustainable development in the sector. From the

Agenda 21 (UNCED, 1992) highlighting the major role

of the ocean, sea, and coastal areas to support human

life to addressing sustainability in the maritime

industry as part of the UN Agenda 2030, multiple

efforts at all levels of the international power structure

underline the effort for protecting the fundamental

pylons of sustainability, present in the first

conceptualization of this concept through the

Brundtland Report (1987): Economy, Society,

Environment. Issues relevant to the environment,

safety and security of the vessel and the cargos, as

well as the human element, its working and living

conditions, health, safety, welfare and appropriate

training and certifications, are already regulated

primarily through various conventions, i.e. the

International Convention for the Safety of Life at Sea

(SOLAS) (IMO, 1974); the International Convention

for the Prevention of Pollution from Ships (1973), as

modified by the Protocol of 1978 relating thereto and

by the Protocol of 1997 (MARPOL) (IMO, 2011), the

Maritime Labour Convention (MLC), (ILO, 2006) and

the International Convention on Standards of

Training, Certification, and Watchkeeping for

Seafarers (STCW) (1978).

However, despite most elements of shipping

business -crucial for the sustainability of the wider

sector- being thoroughly regulated as described

above, there is almost complete lack of a standardized

and internationally, industry-wide applied system for

benchmarking (and thus, comparing) the performance

of market actors and member states, wide and narrow

stakeholders, a multi-dimensional GPI tailored for the

needs of the maritime industry.

A maritime GPI not only addressing

environmental and technical dimensions, such as

already existing initiatives, but also aspects relevant to

the human element for example, would support the

exercise of good maritime governance as an

indispensable element of sustainability. Enabling the

homogeneous measurement and comparison of

performance internationally could possibly lead to

targeted sectoral improvements within an

environment of cooperation and participation

between regulators and authorities, as well as market

practitioners, bridging two often opposing forces.

Effective maritime governance should be addressed as

a problem of collective action, with a set of policies

able to reduce conflicts between individual interests

and global efficiency (Ostrom, 2009). For example,

maritime environmental policy has been “…informed

by a command-and-control approach to regulation”

(Furger, 1997), with the self-interested stakeholders

seeking competitive advantage at the expense of the

public interest (Sugden, 1991; Roe, 2013). Several past

studies recognise that the overall issues and problems

in the maritime policy implementation is generated by

lack of effective governance, and not by the

regulations themselves. Bloor et al. (2006) argue that

at the root of the problem lie several governance

issues rather than regulatory failings. Gekara (2010)

provides evidence about lacking jurisdictional and

governance integrity in the maritime sector, while

Benett (2000) states that the problems related to

maritime governance and shipping companies are

due to lack of responsibility and enforcement.

A holistic approach would greatly facilitate the

development of a comprehensive and inclusive

maritime policy, but not one imposed by hierarchical

leading authorities in a top-bottom linear approach

and process. Good governance is not only

synonymous with efficiency in achieving goals but,

far more than that, by interactions from the top of the

governance model to the bottom. A more open and

democratic approach is calling for, and being

characterised by, the decentralization of power; that

is, according to Sørensen and Torfing (2005), a

gradual, yet ongoing, process of debating how

political institutions exercise their power by

governing top-down through enforceable laws and

bureaucratic regulations. The establishment of a

shipping GPI allowing for the homogeneous exchange

of performance information between all maritime

stakeholders could be the next step in this

evolutionary process.

5 CONCLUSION

The objectives of this paper were to (i) explore, for the

first time, the overall performance of the international

fleet based on market-generated data through the

Shipping KPI System of BIMCO and (b) investigate

the potential of a suitable KPI standard to bridge, for

the first time, a research gap in the non-financial and

non-accounting performance measurement in the

maritime industry towards the adoption of a Global

Performance Indicator initiative.

To reach its first objective, the research focused on

potential empirical correlations between the various

types of maritime performance. The analysis showed

that indeed, different sub-types of performance seem

to correlate through the scope of shipping

management companies. Overall high scores in mid

and high-level performance can be associated,

signifying that managerial efforts and a robust

regulatory framework can lead to an overall. As a

result, countries can rank regards to the performance

of their respective shipping companies as evidence

suggests correlation between health and safety

performance, and navigational, environmental, and

safety performance of the international fleet.

In this paper by using the unique sample of

BIMCO Shipping KPI System, the authors focused on

the correlation between several categories of

performance, for the first time, and attempted an

according ranking based on the aggregated

performance of national business clusters since 2011.

According to BIMCO, the KPIs need to be observable

and quantifiable; valid indicators of performance;

robust against manipulation; sensitive to change;

transparent. and easy to understand (BIMCO, 2018).

Those elements are directly relevant to some of the

prerequisites for the development of a GRI; which in

turn allows for the suggestion, given its potential, of a

similar, suitable KPI standard, aiming for the

expression of non-accounting and non-financial

performance score of the global fleet.

By attempting an analytical overview of shipping

performance globally, and by identifying structural

relationships between several of its multidimensional

constituting elements, a primary indication is

767

demonstrated that highlights the reality that market-

generated appliances, such as benchmarking tools,

may serve far greater purposes in the case of industry-

wide adoption.

As potential future research, the connection

between health and safety, and navigational safety

performance, should be further investigated. Focused

investigation of performance indicators of selective

shipping companies at micro-level would be

suggested, in order to conduct a deeper investigation

of the conditions influencing their human element

performance. Furthermore, the examination of

instruments similar to the Shipping KPI System,

would be suggested regarding their potential for

cross-industry standardized exchange of information.

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