This article was downloaded by: [University of Tasmania]On: 04 February 2014, At: 15:29Publisher: RoutledgeInforma Ltd Registered in England and Wales Registered Number: 1072954 Registeredoffice: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK

Maritime Policy & Management: Theflagship journal of internationalshipping and port researchPublication details, including instructions for authors andsubscription information:http://www.tandfonline.com/loi/tmpm20

Analysis of students’ perceptions ofseafaring career in China based onartificial neural network and geneticprogrammingJiangang Feia & Jianjun Lub

a Department of Maritime and Logistics Management, AustralianMaritime College, University of Tasmania, Launceston 7250,Australiab College of Economics and Management, China AgriculturalUniversity, Beijing 100083, P.R. ChinaPublished online: 31 Jan 2014.

To cite this article: Jiangang Fei & Jianjun Lu , Maritime Policy & Management (2014): Analysis ofstudents’ perceptions of seafaring career in China based on artificial neural network and geneticprogramming, Maritime Policy & Management: The flagship journal of international shipping andport research, DOI: 10.1080/03088839.2013.873545

To link to this article: http://dx.doi.org/10.1080/03088839.2013.873545

PLEASE SCROLL DOWN FOR ARTICLE

Taylor & Francis makes every effort to ensure the accuracy of all the information (the“Content”) contained in the publications on our platform. However, Taylor & Francis,our agents, and our licensors make no representations or warranties whatsoever as tothe accuracy, completeness, or suitability for any purpose of the Content. Any opinionsand views expressed in this publication are the opinions and views of the authors,and are not the views of or endorsed by Taylor & Francis. The accuracy of the Contentshould not be relied upon and should be independently verified with primary sourcesof information. Taylor and Francis shall not be liable for any losses, actions, claims,proceedings, demands, costs, expenses, damages, and other liabilities whatsoever orhowsoever caused arising directly or indirectly in connection with, in relation to or arisingout of the use of the Content.

This article may be used for research, teaching, and private study purposes. Anysubstantial or systematic reproduction, redistribution, reselling, loan, sub-licensing,systematic supply, or distribution in any form to anyone is expressly forbidden. Terms &Conditions of access and use can be found at http://www.tandfonline.com/page/terms-and-conditions

Dow

nloa

ded

by [

Uni

vers

ity o

f T

asm

ania

] at

15:

29 0

4 Fe

brua

ry 2

014

Analysis of students’ perceptions of seafaring careerin China based on artificial neural network and geneticprogramming

JIANGANG FEI†* and JIANJUN LU‡

†Department of Maritime and Logistics Management, Australian MaritimeCollege, University of Tasmania, Launceston 7250, Australia‡College of Economics and Management, China Agricultural University,Beijing 100083, P.R. China

Research indicates that the worldwide shortage of qualified seafarers has been due tothe rapidly increasing world merchant fleet on the one hand and the difficulty ofattracting and retaining people in the industry on the other. The paper argues that in thecontext of the Chinese shipping industry, the high “wastage” is one of the majorcontributors to the shortage. The aims of the research are to examine the motives ofstudents studying nautical courses; identify the key factors affecting nautical studentsto choose seafaring career, and to predict students’ career choice behavior.The research finds that (a) those who had a clear sense of their future career whenenrolled in the nautical studies tend to choose a seafaring career and remain active for alonger period of time; (b) high wage is the most important factor affecting students’decisions to choose seafaring as a career; (c) prolonged separation from family, jobopportunities ashore, and concern about children’s growth and education are amongthe top three factors for students not to choose a seafaring career or to quit it early; and(d) nautical students’ career choice behaviors can be predicted through the use ofartificial neural network (ANN) and genetic programming (GP) with five rules beinggenerated. This novel methodological approach, a combination of ANN and GP, hasbeen proven to be effective in analyzing complex variables and in generating rules.

1. IntroductionGlobal shortage of qualified seafarers has been an ongoing issue (Obando-Rojas, Gardner,and Naim 1999; Dinwoodie 2000; Leggate 2004; Theotokas and Progoulaki 2007). Despiteefforts to address the shortage issue (e.g. Thomas 2004; Wu, Lai, and Cheng 2006;Tsamourgelis 2009), the shortage problem still exists to a different extend in many maritimenations around the world. Research indicates that the worldwide shortage of qualifiedseafarers has been due to the rapidly increasing world merchant fleet on the one hand andthe difficulty of attracting and retaining people in the industry on the other. World merchantfleet increased from 28 754 vessels in 1996 to 45 662 in 2011, representing a total growth of59% in the 15 year period (UNCTAD 1997, 2011). Based on a net increase of 16 908 vesselsin this period and without considering retirement and wastage, a net input of 180 358officers/engineers is required on a 9 month on and 3 month off arrangement. The inputincreases to 270 528 if on a 6 month on and 6 month off arrangement (or the time ashoreequals to the time on ships).1 If retirement and wastage are taken into consideration, therequired net input will be much higher. Apart from the sheer numbers, there have been

*To whom correspondence should be addressed. E-mail: j.fei@amc.edu.au

© 2014 Taylor & Francis

Maritime Policy & Management, 2014http://dx.doi.org/10.1080/03088839.2013.873545

Dow

nloa

ded

by [

Uni

vers

ity o

f T

asm

ania

] at

15:

29 0

4 Fe

brua

ry 2

014

different concerns about the shortage across nations. For some nations, the major concern isthe low intake of new entrants (e.g. UK, Greece), while for others, the high wastage (e.g.China).

This current research focuses on the situation in China for three reasons: (a) the largesize of the Chinese merchant fleet and its rapid growth; (b) the phenomenal increase ofseafarers in the last 10 years in the nation’s fleet and China’s ambition to get more share inthe international labor market; and (c) the rapidly changing socioeconomic environment inChina that has been affecting people’s lifestyles and career choices in many significantways. In a similar way as outlined above, a brief introduction of the situation in Chinabetween 1996 and 2011 is given here. The Chinese national merchant fleet, including bothnational and open registries, increased from 1972 vessels in 1996 to 3651 vessels in 2011(UNCTAD 1997, 2011), representing a total growth of 85% in the 15 year period. Withoutconsidering retirement and wastage, the net increase required to operate the extra vesselsis 17 910 officers/engineers based on a 9 month on and 3 month off arrangement. Thisnumber becomes 26 864 on a 6 month on and 6 month off model. This means that anannual net input of 1200–1790 officers/engineers is required to catch up the growingnumber of merchant vessels without considering retirement and wastage. On the supplyside, while accurate numbers are not readily available given the fact that there are over130 maritime education and training institutions in China,2 the best estimate is thatapproximately 9000–12 000 new nautical graduates enter into the shipping industryevery year, based on the available information from China Maritime SafetyAdministration and enrolment information from these institutions. The number (9000–12 000) of new nautical graduates is over seven times more than the annual demand that isresulted from new vessels adding into the national fleet. The number of retirement isestimated at 3000–4000 a year.3 This leaves about half (4800–6210) of the new nauticalgraduates every year unaccounted for. One reasonable explanation is that apart from alarge number of retirements due to its very large national fleet, there must have been highwastage during graduates’ early seafaring careers.

It is clear that the shortage problem in the Chinese shipping industry is mainly a retentionproblem which differs from that of many traditional maritime nations in Europe. There is noshortage of recruitment, given the very large population base in China. However, asDinwoodie (2000) argues, there is no guarantee that the students who have enrolled onmaritime studies courses will actually enter into the shipping industry nor will they takeseafaring as a lifelong career. To understand the root factors of seafarer shortage in China, thecurrent research endeavors to answer the following three questions:

(a) Why did students choose nautical studies in the first place?(b) What are the factors affecting nautical students’ choice of seafaring career?(c) Can nautical students’ career choice behaviors be predicted?

2. Literature reviewLiterature on career choice frequently refers to “calling”, “a sense of purpose, that this isthe work one was meant to do” (Hall and Chandler 2005, 155) as one of the importantfactors affecting individual career choice behavior. In traditional maritime nations, seafar-ing was and still is a “calling” (Mack 2007) due to the long history and tradition ofsailing, the pride and promise associated with it, and the lifestyle it represents (Mack

2 J. Fei and J. Lu

Dow

nloa

ded

by [

Uni

vers

ity o

f T

asm

ania

] at

15:

29 0

4 Fe

brua

ry 2

014

2010). In newly emerging maritime nations, however, such a sense of calling is weakerand the choice of seafaring is more associated with socioeconomic factors. Research onseafaring career choice is limited and there is even less with a focus on nautical students.4

One exception is the research done by Guo, Liang, and Ye (2006). It reveals that students’intrinsic desire for seafaring is the most important factor for them to enter nautical studieswhile wage has no direct influence on their intention to work on board (Guo, Liang, andYe 2006). Another interesting finding is that objective variables such as students’ learningscheme and socioeconomic status turned out to be weak predictors of nautical students’intention to enter seafaring (Guo, Liang, and Ye 2006). It is not clear though what itemswere included in “socioeconomic status”. The finding that wage has no direct influence onstudents’ decision on seafaring career choice differs from some other research, forexample, Tsamourgelis (2009), where the relative wage level (between nationals andnonnationals) affects seafarer recruitment. In the context of China, seafaring has beenconsidered as a high-earning occupation. However, the wage gap between seafaring andother shore-based occupations has been closing up over the last 10 years, especially in theeast coast of China. As a result, the enrolment of nautical students has been shifting fromthe east coast to the inner and western parts of China, from large cities to small cities andeven the countryside. A final point from Guo, Liang, and Ye (2006) research is that factorssuch as wage are closely related to the overall socioeconomic development of a society.Thus, responses to the same question might be significantly different in other socio-economic contexts.

As stated by Dinwoodie (2000), studying nautical courses is one thing and being aseafarer is another. In fact, research (Dirks 1998) indicates that there has been a highdropout rate during the maritime education and training process. In EU countries, thedropout rate ranges between 5% and 15% at the lower band and between 65% and 75%at the higher end with an average of 22–32% across all EU countries (Dirks 1998). Oncein the industry, new entrants stop going to sea just after a few voyages or a couple ofyears. It is observed that the highest wastage comes from the short-term and medium-term stayers (Obando-Rojas, Gardner, and Naim 1999), that is, from cadetship to about5 years in the industry. The reasons for the high wastage are complex and various.However, research (e.g. Oldenburg et al. 2009; Sambracos and Tsiaparikou 2001;Thomas, Sampson, and Zhao 2003) indicates that family commitments, loneliness,poor living and working condition on ships, and opportunities ashore are among themost quoted reasons. Another often-quoted issue is job security (or insecurity).The employment rate of nautical graduates in China has been very high (above 95%),as compared with a national average of about 60% for graduates of other tertiary studies.Such a high employment record certainly is attractive to students when they choosenautical studies. However, in reality, seafaring job security is greatly affected by themacroeconomic environment. The seemingly iron bowl may not be secure when itcomes to severe economic downturns, such as the one we are currently experiencing,or other events such as “flagging out”, where jobs are lost (Tsamourgelis 2009; Mack2007; Inoue 2011). Job security will inevitably affect the income certainty, which isconsidered as critically important for a family. While nautical students may have notencountered any of these in a real sense, the awareness of, and unpreparedness for, theupcoming “difficulties” associated with seafaring might impact students’ decisions tochoose seafaring as a career. Such factors may become the reasons for the new entrantsto end their seafaring career prematurely.

Analysis of students’ perceptions of seafaring career in China 3

Dow

nloa

ded

by [

Uni

vers

ity o

f T

asm

ania

] at

15:

29 0

4 Fe

brua

ry 2

014

3. MethodologyMethodologies used on seafarer-related topics in the last 15 years are mainly qualitative(e.g. Obando-Rojas, Gardner, and Naim 1999; Dinwoodie 2000; Thomas 2004; Mack 2007;Sambracos and Tsiaparikou 2001; Thomas, Sampson, and Zhao 2003; Li and Wonham1999; Ellis, Bloor, and Sampson 2010; Manuel 2011; Sampson, Gekara, and Bloor 2011),with some quantitative attempts including modeling (e.g. Tsamourgelis 2009; Guo, Liang,and Ye 2006; Glen 2003) and empirical research (e.g. Leggate 2004; Wu 2004; Oltedal andWadsworth 2010; Wu, Lai, and Cheng 2006; Theotokas and Progoulaki 2007; Silva,Stanton, and Stanton 2011; Fei 2011). Types of analysis include correlation (e.g.Theotokas and Progoulaki 2007; Silva, Stanton, and Stanton 2011; Fei 2011), regression(e.g. Oltedal and Wadsworth 2010; Silva, Stanton, and Stanton 2011), and factor analysis(e.g. Theotokas and Progoulaki 2007). While a comparison of logistic regression andartificial neural network (ANN) found no difference when they are used as predictivemodels (Dreiseitl and Ohno-Machado 2002), this research, however, adopts a novel analy-tical approach, a combination of ANN and genetic programming (GP) to identify the rootfactors contributing to the shortage and provide recommendations to tackle the problem.The advantage of this approach is its higher prediction rate than other methods (Tokinaga,Lu, and Ikeda 2005). It is acknowledged that the classification rule generation based on GPalone can be used to identify underlying problems for decision-making. However, due to thelarge number of variables involved, the original GP classification rules will contain a largenumber of statements described in arithmetic as well as logical expressions, making the rulegeneration process very complicated. The ANN serves as a pruning process to simplify rulegeneration. The use of GP enables us to extract rules from trained neural networks togenerate classification in spite of their black-box nature. This is important because the endresults will not only provide us the understanding of critical factors for retaining seafarersand keeping them active, but also the insight to intervene in certain events in order toimprove the output of our maritime education and training, therefore, effectively contribut-ing to the mitigation of the shortage problem.

3.1. The questionnaire surveyA questionnaire survey was conducted to the intended study sample focusing on the first-and third-year nautical students. The questionnaire consists of 17 questions in four parts.Part A is concerned with the initial intentions of students enrolled in nautical studies. PartB is about the students’ perceptions of the seafaring career. Questions in Part C aredesigned to collect information about the factors affecting decisions to quit seafaring. Andthe last part is to collect classification information including household income andoriginal place of residence. A total of 200 questionnaires were distributed betweenJanuary and March 2012 to the nautical students of the two largest maritime universitiesin China. One is state-funded and the other local government-funded. These two uni-versities account for a large percentage share of the nautical graduates with a bachelordegree and have been the major suppliers of deck officers and engineers for the nationalshipping industry. By March 2012, 149 completed questionnaires were received, repre-senting a response rate of 74.5%. The original questionnaire was designed in English. Toease the difficulty of respondents in answering English questionnaires, the originalEnglish version was then translated into Chinese using forward and backward translationprocess to ensure that the two versions are equivalent in terms of vocabulary, idiom, andconcept (Cavana, Delahaye, and Sekaran 2003).

4 J. Fei and J. Lu

Dow

nloa

ded

by [

Uni

vers

ity o

f T

asm

ania

] at

15:

29 0

4 Fe

brua

ry 2

014

3.2. ANNs and rule extractionANNs are mathematical representations inspired by the functioning of the human brain(Pao 1989). The multilayer neural network is typically composed of an input layer, one ormore hidden layers, and an output layer, each consisting of several neurons. Each neuronprocesses its input and generates one output value that is transmitted to the neurons in thesubsequent layer. All neurons and layers are arranged in a feed-forward manner, and nofeedback connections are allowed. The output of the neuron i is computed by processingthe weighted inputs and its bias that are explained as follows.

In the formula, wn;n�1i;j denotes the weight connecting the jth unit in layer n� 1 with the

ith unit in layer n. The value xn�1j is the output of jth unit in layer n� 1. Then, the input to

ith neuron in layer n is obtained by

uni ¼X

wn;n�1i;j xn�1

j (1)

The output of neurons is calculated using the transfer function emulating the thresholdlogic, which is called a sigmoid function, where hni is the threshold value for the neuron.Usually, the sigmoid function is used for the function f(x).

xni ¼ f ðuni � hni Þ (2)

f ðxÞ ¼ 1=ð1þ expða� xÞÞ (3)

The weight wn;n�1i;j and bias hni are the critical parameters of a neural network and need to

be estimated during a training process, which is usually based on gradient descent learningto minimize the error function over a set of training observations.

The signal which is back-propagated from ith neuron in output layer N is obtainedusing the difference between the output layer N and the prescribed observation dNi asfollows.

δNi ¼ ðdNi � xNi ÞgðuNi Þ; gðxÞ ¼ f ðxÞð1� f ðxÞÞ (4)

Similarly, the signal back-propagated from the ith neuron in layer n to neurons in layern� 1 is given as

δni ¼ df ðuni Þ=duX

k

δnþ1k wnþ1;n

k;i (5)

In summary, the update for the weight and the threshold value is given as follows wheret; t þ 1 are the time steps in the update and η; α are constants to confirm the convergence.

Δwn;n�1i;j ðtÞ ¼ ηδni x

n�1j þ αΔwn;n�1

i;j ðt � 1Þ (6)

wn;n�1i;j ðt þ 1Þ ¼ wn;n�1

i;j ðtÞ þ Δwn;n�1i;j ðtÞ (7)

Δhni ðtÞ ¼ ηδni þ αΔhni ðt � 1Þ (8)

Analysis of students’ perceptions of seafaring career in China 5

Dow

nloa

ded

by [

Uni

vers

ity o

f T

asm

ania

] at

15:

29 0

4 Fe

brua

ry 2

014

hni ðt � 1Þ ¼ hni ðtÞ þ Δhni ðtÞ (9)

If the rule extraction after discretizing the signal is automatized, then it is expected thatrelevant rules can be easily obtained on the basis of routine works. The basic procedure ofANN is followed to discretize the input signal into binary representation. However, toreplace the complicated steps of reducing the rules in neural networks algorithm, the GPprocedure is introduced (Tokinaga, Lu, and Ikeda 2005). Once GP procedure is intro-duced, no more steps are required to examine the relation between the input signal and theactivation values in the hidden layers.

Figure 1 shows the overview of the system. The dashed lines represent the prunedweights and the solid lines correspond to the weights remaining in the system afterpruning the weights. The GP procedure for generating rules is summarized as follows.

Step 1 Discretize input variables: the input variables are discretized using thresholdvariables and are represented in binary forms (inputs).

Step 2 Train a neural network using the discretized input variables to meet theprespecified accuracy requirement.

Step 3 Remove the redundant connections in the network by pruning while maintainingits accuracy to obtain substantially important statements represented in binary forms.

Step 4 Generate rules based on the GP using the binary representation as the terminalvariables (statements) in the logical expression.

3.3. Production rules and the GPThe GP is an extension of the conventional genetic algorithm (GA), in which eachindividual in the population (pool of individuals) is a computer program composed ofthe arithmetic operations, standard mathematical expressions, and variables (Tokinaga,Lu, and Ikeda 2005; Lu and Tokinaga 2012; Yababa and Tokinaga 2002). There areseveral ways to represent the mathematical expressions in the GP, among which the prefixrepresentation is approved due to its simplicity for GP operations. The prefix representa-tion is equivalent to the tree representation, where the external points (leaves) of the treeare labeled with terminals (i.e. constants and variables), and the root of the tree is labeled

DiscretizedInputs

x1

x2

xn

PrunedWeights

PrunedWeights

Category

Category

Figure 1. Overview of system obtained by pruning weights.

6 J. Fei and J. Lu

Dow

nloa

ded

by [

Uni

vers

ity o

f T

asm

ania

] at

15:

29 0

4 Fe

brua

ry 2

014

with the primitive functions such as binomial operations +, –, ×, /, and the operationtaking the square root of the variable.

To ensure a valid representation of a tree after initialization, crossover operation andmutation, StackCount is used (Duffy and Sedlacek 2007). The StackCount is the numberof arguments it places on minus the number of arguments it takes off the stack. By usingStackCount, it can be made known the terminal of the sub-tree, which is the candidate forthe crossover operation. The basic rule is that any two loci on the two parent genomes canserve as crossover points, as long as the ongoing StackCount just before those points is thesame. The crossover operation creates new offspring by exchanging sub-trees betweentwo parents.

Before applying the genetic operation, it is essential to evaluate the ability of eachindividual (tree structure). The ability is called as the fitness, and is calculated based uponthe comparison between the predicted value of the individual and the observed value, thatis, the root mean square error (rmse) between xðtÞ and ~xðtÞ. The fitness Si of ith individualis defined as the inversion of rmse.

The following steps are performed iteratively until the termination criterion has beensatisfied.

Step 1 Generate an initial population of random composition of the functions and theterminals of the problem (constants and variables).

Step 2 Execute each program (evaluation of system equation) in the population andassign it a fitness value using the fitness measure. Then, sort the individualsaccording to the fitness Si.

Step 3 Create a new population of computer programs by applying the global andlocal mutation. The operations are applied to the individuals chosen with aprobability pi based on the fitness. The probability pi is defined for ith individualas follows.

pi ¼ ðSi � SminÞ=XN

i¼1

ðSi � SminÞ (10)

where Smin is the minimum value of Si, and N is the population size.Step 4 Create new individuals (offspring) from two existing ones by genetically

recombining randomly chosen parts of two existing individuals using the crossoveroperation applied at a randomly chosen crossover point (Figure 2).

Step 5 If the result designation is obtained by the GP (the maximum value of thefitness become larger than the prescribed value), then terminate the algorithm,otherwise go to Step 2.

4. Results and discussions4.1. Why did students choose nautical studies in the first place?To answer this research question, two questions are arranged in the questionnaire. The firstone is “why do you study nautical course?” A pie chart is provided (Figure 3) to illustratethe result. Among all students, only 41% had the idea of “may go for seafaring” when theywere enrolled in nautical courses; 48% “by accident”; 7% “no other better choices”; and 4%“was arranged to this course” through administrative process. The second question is “how

Analysis of students’ perceptions of seafaring career in China 7

Dow

nloa

ded

by [

Uni

vers

ity o

f T

asm

ania

] at

15:

29 0

4 Fe

brua

ry 2

014

well did you know about this course?” The result (Figure 4) shows that upon the enrolmentinto the nautical course, only 48% of students knew about the course (a little 39%; well 7%;and very well 2%), while 52% knew little (29%) or very little (23%).

Due to the unique national examination and admission system in China and the largestudent population, the competition for entering into tertiary education is fierce. Formany highschool students in China, it is a luxury to think about what course (or career) they want to do inthe future because the utmost important objective is to get the highest scores possible in orderto be in a better position to choose their preferred universities or courses. It is not surprisingthat many students entered a university and enrolled in a course “by accident” and without anyidea of what the course was all about and what they wanted to do in the future. Researchreveals that the presence of career calling is strongly correlated with career decidedness andself-clarity, suggesting that students who have a career calling are likely to be more mature intheir career development process and have a good sense of their interest and abilities, thus arecomfortable in making career decisions, and are firm in the specific jobs or areas of work theywould like to enter (Yababa and Tokinaga 2002). The absence of career calling (or ratherunawareness of career choice) among the nautical students will lead to indecisiveness inmaking career choices in the later stages. In addition, nautical students who do not have a clearsense of career calling are markedly less clear about their interests and abilities.

Figure 2. Overview of GP for production rules.

by accident

48%

no other

better

choices 7%

I was

arranged to

this course

4%

I may go for

seafaring

41%

Figure 3. Why do you study nautical course?

8 J. Fei and J. Lu

Dow

nloa

ded

by [

Uni

vers

ity o

f T

asm

ania

] at

15:

29 0

4 Fe

brua

ry 2

014

4.2. What are the factors which affect nautical students’ choice of seafaring career?Two sections of statement are designed to investigate how the choice of seafaring careeris affected. Students were asked to indicate the extent to which each factor affects theircareer choice. In the first section, students were asked how much the listed factors affecttheir decision to choose a seafaring career. The results are presented in Figure 5. Highwage is ranked as the most important factor (mean: 8.309) for nautical students tochoose a seafaring career, while the idea of travelling the world for free and job securityare ranked second and third (mean: 6.55 and 6.174). The other four factors, nationaleconomic development, when I cannot find other jobs, long holidays, and influence ofmy family members or friends, have moderate effects (mean ranging from 5.691 to5.839) when students make their decision to choose seafaring as a career. The finding ofhigh wage being the most important factor is different from the findings of Guo, Liang,and Ye (2006), where there is no direct influence on students’ intention to work on boardships. However, wage as the most important factor is consistent with the work ofTsamourgelis (2009), where the relative wage level of nationals and nonnationals affectsthe level of seafarer recruitment. In China, a seafarer’s wage is still considerably higher

Very

little

23%

A

little

39%

Little

29%

Very

well

2%

well

7%

Figure 4. How well did you know about this course?

Seafarers have high wage

Seafarers can travel around the world for free

Seafarers have high job security

National economic development

When I cannot find other jobs

Seafarers have long holidays

Influence of my family members or friends

0 1 2 3 4

8.309

6.55

6.174

5.839

5.812

5.732

5.691

5 6 7 8 9

Figure 5. Factors affecting nautical students’ decision to choose seafaring career.

Analysis of students’ perceptions of seafaring career in China 9

Dow

nloa

ded

by [

Uni

vers

ity o

f T

asm

ania

] at

15:

29 0

4 Fe

brua

ry 2

014

than the average wage of other shore-based vocations, especially when comparing withthose in the central, northwestern, and southwestern regions. Such a wage gap has beenreducing in the last 10 years, particularly in the east coastal area. It is anticipated thatwhen wage is no longer an advantage, the retention issue in the Chinese shippingindustry might become a recruitment issue, similar to that of developed maritimenations. As for the idea of travelling overseas and seeing the world, the tighteningand extensive security measures, such as the International Ship and Port Security Code(ISPS), have withdrawn the seafarers’ constitutional right to go ashore while they are inports (Hall and Chandler 2005). The situation is compounded with the fact that modernships, especially containerships, have reduced port turnaround time dramatically leavingno time for seafarers to go ashore to “see the world”. The awareness of nautical studentsof such changes in the real world might negatively affect their tendency to choose aseafaring career.

In the other section, students were asked, if they do not choose seafaring, or one daythey decide to stop seafaring, to what extent the listed factors affect their decisions. Asindicated in Figure 6, prolonged separation from family, good job opportunities ashore,children’s growth and education are ranked the top three factors with high mean scoresof 8.6, 8.5, and 8.2, respectively. Life on board is also a major concern with loneliness,boringness, and safety receiving mean scores of 7.6, 7.4, and 7.1, respectively. Personalhealth concern receives a mean score of 7.5, while job insecurity and low social statusare ranked the two lowest with mean scores of 6.8 and 6.2. Early research (e.g.Oldenburg et al. 2009; Thomas, Sampson, and Zhao 2003) on seafaring lives indicatesthat separation from family, which results in loneliness, boringness, and concern aboutthe growth and education of the next generation, is the major cause of stress forseafarers, which has a significant influence on seafarers’ decision to reduce activeseafaring lives. A job ashore with wage comparable to that of a seafarers’ would bevery attractive to those who want to make a career change to shore and spend more timewith their families. As explained earlier, the economic development in the last threedecades in China has seen significant wage increases, in particular in the east coastalregion where the wage gap between sea and shore has been closing up. Unlesscommitted to a seafaring career, such onshore opportunities would easily become themotivation for people to reduce their seafaring life.

Prolonged separation from family

There are good job opportunities ashore

Concern about children’s growth and education

Life on ships is too lonely

Concern about my health

Life on ships is too boring

Do not feel safe on ships

Seafaring jobs are insecure

Seafarers have low social status

0 1 2 3 4 5 6 7

6.2

6.8

7.1

7.4

7.5

7.6

8.2

8.5

8.6

8 9 10

Figure 6. Factors affecting nautical students’ decision not to choose a seafaring career or tostop seafaring.

10 J. Fei and J. Lu

Dow

nloa

ded

by [

Uni

vers

ity o

f T

asm

ania

] at

15:

29 0

4 Fe

brua

ry 2

014

4.3. Can nautical students’ career choice behaviors be predicted?The neural network rule extraction based on the GP method was applied to derive thelinguistic rules to predict the seafaring career. As explained in the “Questionnaire Survey”section, there are 17 variables to predict the seafaring career. The number of variables isrelatively large to derive a concise prediction rule in a linguistic manner. As a result, aprincipal component analysis (PCA) was applied to the 17 variables, leading to ninevariables for prediction. In the preprocessing by the neural network to discretize the inputvariables, the options in each question of the survey instrument were used as threshold valuefor each of the financial ratios (variables). After discretization, a procedure was conducted toprune the weight of the neural networks. Then, binary prediction is applied for learningusing back-propagation to derive meaningful propositions included in the production rules.

Table 1 shows input variables that were ultimately used for classifications. The nine inputvariables were discretized and represented in binary forms as input variables, which werethen trained in the neural network to get weight and rules. There were redundant connec-tions in the network, which were removed after the pruning process to obtain substantiallyimportant statements represented in binary forms. The statements represented in binaryforms (rules) were input into GP automatically using the binary statements as the terminalvariables in the logical expression. By genetic operation, GP generated production rules.

Table 2 summarizes the explanatory (linguistic) rules to predict the seafaring career.Five rules are obtained with four rules having prediction accuracy rate of 100% and onewith 80%. Rule one reveals that when a student has a clear motive of studying nauticalstudies and a good understanding of the course, and knows the course through familymembers or friends, and will choose seafaring as a career upon graduation, it can bepredicted that such a student will work on board ships for 5–10 years. Furthermore, if thestudent is originally from the countryside of the southwestern region with annual familyincome less than USD 3500, the active seafaring life of the student is expected to be over10 years (Rule two). For students who are originally from the countryside of the centralregion with annual family income less than USD 3500, the predicted seafaring life ofthese students ranges from 5 to 10 years (Rule three). Rule four indicates that if a studentis originally from a capital city of the eastern region with annual family income of USD3500–8500, the predicted seafaring life is less than 5 years. If a student was enrolled innautical study by accident, knew little about the course and seafaring career and is unsureabout whether a seafaring career will be chosen upon graduation, the predicted seafaringlife of the student is less than 5 years (Rule five).

Table 1. The nine variables of original questions.

Question Item description

1 Why do you study nautical course2 How well did you know about this course before you started your study3 How did you know about this course4 How well do you know about the seafaring career5 Will you choose the seafaring career after you graduate from the course6 If you choose the seafaring career, how long will you work on board ships14 Which area are you originally from (province)15 Are you from (countryside; small and medium cities; capital cities)16 Your annual family income

Analysis of students’ perceptions of seafaring career in China 11

Dow

nloa

ded

by [

Uni

vers

ity o

f T

asm

ania

] at

15:

29 0

4 Fe

brua

ry 2

014

There are four important findings in relation to the prediction of nautical students’career choice behavior. First, a clear motive to pursue nautical studies is helpful inchoosing a seafaring career upon graduation and to stay active for a prolonged time.This is a confirmation of “calling” (Hall and Chandler 2005; Mack 2007) as an importantfactor affecting individual career choice behavior. In contrast, those who were enrolled innautical courses by accident or as a last option will be less likely to choose a seafaringcareer as a long-term plan. Second, a basic understanding of nautical courses and aseafaring career is a good indication of students’ proactive role to prepare themselvesfor their future career. Such a positive attitude would lead to a determined career choicewith confidence that is reflected by the factor that these students also have positiveanswers when asked if they would choose seafaring as a career (5a). Third, the involve-ment of family members or friends in recommending, or providing information about,nautical studies has a positive influence on students’ career choice. In some cases, thesefamily members/friends are from a seafaring background where seafaring is gradually

Table 2. Rules and prediction accuracy rate.

Rule Coding Item description Career choice behavior

One 100% 1a I study nautical course because I may go for seafaring 6C: I will work on boardships for 5–10 years.2c I knew a little about this course before I started my

study3d I knew the course through my family members/

friends5a I will choose the seafaring career after my graduation

Two 100% 1a I study nautical course because I may go for seafaring 6D: I will work on boardships for over 10 years2c I knew a little about this course before I started my

study3d I knew the course through my family members/

friends5a I will choose the seafaring career after my graduation

14g I am originally from southwestern region (includingprovinces: Sichuan, Chongqing, Guizhou, Yunnan,Xizhang)

15a I am from countryside16a My family income is less than USD 3500 a year

Three 100% 14c I am originally from central region (includingprovinces: Henan, Hubei, Hunan)

6C: I will work on boardships for 5–10 years.

15a I am from countryside16a My family income is less than USD 3500 a year

Four 100% 14d I am originally from eastern region (includingprovinces: Jiangsu, Zhejiang, Anhui, Jiangxi,Shangdong, Shanghai, Fujian)

6B: I will work on boardships for 1–5 years

15c I am from capital cities16b My family income is about USD 3500–8500) a year

Five 80% 1d I study nautical course by accident 6B: I will work on boardships for 1–5 years2d I knew little about this course before I started my

study3e How did you know about this course (Answer: I

knew little)4c I know little about the seafaring career5b I will maybe choose the seafaring career after my

graduation

12 J. Fei and J. Lu

Dow

nloa

ded

by [

Uni

vers

ity o

f T

asm

ania

] at

15:

29 0

4 Fe

brua

ry 2

014

becoming a “tradition”, as observed in may traditional maritime nations, although thehistory of seafaring in the Chinese society is relatively short. Such “tradition” shall not beunderestimated because it involves personal passion and emotion attached to it andseafaring is no longer seen as merely a career, but a promise to be honored and a customto be observed. Finally, the socioeconomic status background of nautical students plays animportant part in predicting their career choice behavior. For those from less-developedregions with low family income, for example, those from the countryside of southwesternand central regions of China, seafaring is a good career to choose and to keep. The factorthat the shipping industry is located in the large cities of the east coast area is veryattractive, especially to those from the countryside. Moving into cities and becoming a“legitimate resident” of a large city has been considered as a golden opportunity to changethe socioeconomic status bound to the origin of birthplace. The opposite is also true.When students are from large capital cities of the eastern region with better familyincome, the chance of choosing seafaring as a career becomes much less, simply becausethe incentive is much weaker than the former group. This finding is also different fromthat of Guo, Liang, and Ye (2006), where socioeconomic status is only a weaker predictorof nautical students’ intention to enter seafaring. The obvious reason is that the socio-economic difference across the entire population in Taiwan is much less severe than inmainland China.

5. ConclusionsWhile the global shipping industry has been experiencing a shortage of qualified seafarers,the reasons for the shortage are different across nations. For some, the shortage problem iscaused by the difficulty of the shipping industry to attract students to nautical studies andconsequently to recruit them to the shipping industry. For others, the shortage is mainly aretention problem. In the context of the Chinese shipping industry, the number ofadmissions for nautical studies around the country has increased significantly over thelast 10 years, reaching an output of 9000–12 000 nautical graduates every year. Theresearch finds that only 8% of nautical students will choose to stay over 10 years on boardships, 46% of students will stay on board ships for 5–10 years. Forty-two percent ofstudents will work on board ships for only 1–5 years while 4% will not choose seafaringat all. This finding is consistent with the estimate that has been put forward earlier in thispaper. The pyramid shape with a very large input base and a small output top is the maincontributor of the shortage problem in the Chinese shipping industry.

The lack of career calling or absence of a desire to study a nautical course hasimplications on students’ career choice behaviors. The socioeconomic status of studentsalso plays an important role in deciding the length of future seafaring career. This findingis different from earlier research, for example, Guo, Liang, and Ye (2006), which indicatesthat prediction of career choice behaviors differs according to a range of other factors, forinstance, the overall social and economic structure of a nation. The practical implicationfor the shipping industry is that appropriate advertising campaigns should be carried toprovide potential candidates and their parents with necessary information on education,training, and career opportunities. For maritime education and training institutions, it isnecessary to apply scientific selection techniques to improve the admission process. Assuggested by Obando-Rojas, Gardner, and Naim (1999), to effectively reduce wastage,recruits should be selected to meet minimum physical and psychological standards, and

Analysis of students’ perceptions of seafaring career in China 13

Dow

nloa

ded

by [

Uni

vers

ity o

f T

asm

ania

] at

15:

29 0

4 Fe

brua

ry 2

014

that potential new entrants should be provided with an accurate picture of the careerstructure and employment process as a means to avoid recruiting unsuitable candidates.The research shows that nautical students’ career choice behaviors can be predicted byapplying appropriate techniques. If the selection criteria are designed to be able to channelthose who are most likely to drop out of the seafaring career prematurely into othersuitable courses through a voluntary process, then it is possible to reduce the wastage ratein the later stages, thus considerably improving the effectiveness of maritime educationand training and mitigating the shortage problem.

Finally, the use of ANN and GP in this research has been proven to be effective inpredicting the seafaring choice behaviors. This methodology has advantage in simplifyingthe process of analyzing a large number of variables in finding rules in a complex setting.A major limitation of this research is its relatively small sample size. An increase of thesample/variable ratio would strengthen the validity of the findings. Further studies mayinclude a cross-border (e.g. three nations) research using one survey instrument toenhance our understanding of seafarer shortage in different contexts. In addition, acomparison of results using different methodologies on one data set (e.g. regression andANN) would be beneficial to the literature.

Notes1. Assuming the manning level of officers/engineers is eight per vessel (captain, three deck

officers, chief engineer and three other engineers). Under 3:1 arrangement, 10–11 (10.667 tobe precise) officers/engineers are required to maintain normal operation of one vessel whileunder 1:1 arrangement, the total number of officers/engineers to operate one vessel becomes 16.

2. This includes major maritime universities such as Dalian and Shanghai Maritime Universities,Jimei University, etc., mariners colleges such as Qingdao Ocean Shipping Mariners College,and other “ocean-related” universities that provide maritime courses such as Dalian OceanUniversity, and many more local education and training institutions.

3. There was an observed retirement peak during 2000–2008. Approximately 11 000 seafarersretired every year, among which about 30% were officers/engineers. There has also been anincreasing number of officers/engineers participating in the international seafaring labor market.

4. A search of literature in the Maritime Policy & Management for the period 1995–2011 found 28papers being related to seafarers.

ReferencesCavana, R. Y., B. L. Delahaye, and U. Sekaran. 2003. Research Methods for Business Students. 3rd

ed. Harlow: Person Education.Dinwoodie, J. 2000. “The Perceived Importance of Employment Considerations in the Decisions of

Students to Enrol on Undergraduate Courses in Maritime Business in Britain.” Maritime Policy& Management 27 (1): 17–30.

Dirks, J. 1998. Improving the Employment Opportunities for EU Seafarers: An Investigation toIdentify Seafarer Training and Education Priorities. Brussels: Federation of Transport Workers’Unions in the European Union (FST) and European Community Shipowners’ Association(ECSA).

Dreiseitl, S., and L. Ohno-Machado. 2002. “Logistic Regression and Artificial Neural NetworkClassification Models: A Methodology Review.” Journal of Biomedical Informatics 35 (5–6):352–359.

Duffy, R. D., and W. E. Sedlacek. 2007. “The Presence of and Search for a Calling: Connections toCareer Development.” Journal of Vocational Behaviour 70: 590–601.

14 J. Fei and J. Lu

Dow

nloa

ded

by [

Uni

vers

ity o

f T

asm

ania

] at

15:

29 0

4 Fe

brua

ry 2

014

Ellis, N., M. Bloor, and H. Sampson. 2010. “Patterns of Seafarer Injuries.” Maritime Policy &Management 37 (2): 121–128.

Fei, J. 2011. “An Empirical Study of the Role of Information Technology in EffectiveKnowledge Transfer in the Shipping Industry.” Maritime Policy & Management 38 (4):347–367.

Glen, D. 2003. “Regression Modelling of the Employment Durations of UK Seafaring Officers in1999.” Maritime Policy & Management 30 (2): 141–149.

Guo, J., G. Liang, and K. Ye. 2006. “An Influence Model in Seafaring Choice for TaiwanNavigation Students.” Maritime Policy & Management 33 (4): 403–421.

Hall, D. T., and D. E. Chandler. 2005. “Psychological Success: When the Career is a Calling.”Journal of Organisational Behaviour 26: 155–176.

Inoue, K. 2011. “Maritime Community and Its Human Resource Mobility.” The Journal ofNavigation 64: 633–643.

Leggate, H. 2004. “The Future Shortage of Seafarers: Will it Become a Reality?” Maritime Policy &Management 31 (1): 3–13.

Li, K. X., and J. Wonham. 1999. “Who Mans the World Fleet? A Follow-up to the BIMCO/ISFManpower Survey.” Maritime Policy & Management 26 (3): 295–303.

Lu, J., and S. Tokinaga. 2012. “Analysis of Cluster Formations on Planer Cells Based on GeneticProgramming.” Computational and Mathematical Organization Theory. doi:10.1007/s10588-012-9112-3.

Mack, K. 2007. “When Seafaring is (or was) a Calling: Norwegian Seafarers’ Career Experiences.”Maritime Policy & Management 34 (4): 347–358.

Mack, K. S. 2010. “A Forgotten History, the Impact of Globalisation on Norwegian Seafarers’Shipboard Organisational Lives.” Journal of Management History 16 (2): 253–269.

Manuel, M. E. 2011. “Potential Sociological Impacts of Unfair Treatment of Seafarers.” MaritimePolicy & Management 38 (1): 39–49.

Obando-Rojas, B., B. Gardner, and M. Naim. 1999. “A System Dynamic Analysis of OfficerManpower in the Merchant Marine.” Maritime Policy & Management 26 (1): 39–60.

Oldenburg, M., H. Jensen, U. Latza, and X. Baur. 2009. “Seafaring Stressors Aboard Merchant andPassenger Ships.” International Journal of Public Health 54: 96–105.

Oltedal, H., and E. Wadsworth. 2010. “Risk Perception in the Norwegian Shipping Industry andIdentification of Influencing Factors.” Maritime Policy & Management 37 (6): 601–623.

Pao, Y. H. 1989. Adaptive Pattern Recognition and Neural Networks. Reading, MA: Addison-Wesley.

Sambracos, E., and J. Tsiaparikou. 2001. “Sea-Going Labour and Greek Owned Fleet: A MajorAspect of Fleet Competitiveness.” Maritime Policy & Management 28 (1): 55–69.

Sampson, H., V. Gekara, and M. Bloor. 2011. “Water-Tight or Sinking? A Consideration of theStandards of the Contemporary Assessment Practices Underpinning Seafarer LicenceExaminations and Their Implications for Employers.” Maritime Policy & Management 38 (1):81–92.

Silva, R., P. Stanton, and J. Stanton. 2011. “Determinants of India Sub-Continent Officer-SeafarerRetention in the Shipping Industry.” Maritime Policy & Management 38 (6): 633–644.

Theotokas, I., and M. Progoulaki. 2007. “Cultural Diversity, Manning Strategies and ManagementPractices in Greek Shipping.” Maritime Policy & Management 34 (4): 383–403.

Thomas, M. 2004. “‘Get Yourself a Proper Job Girlie!’: Recruitment, Retention and WomenSeafarers.” Maritime Policy & Management 31 (4): 309–318.

Thomas, M., H. Sampson, and M. Zhao. 2003. “Finding a Balance: Companies, Seafarers andFamily Life.” Maritime Policy & Management 30 (1): 59–76.

Tokinaga, S., J. J. Lu, and Y. Ikeda. 2005. “Neural Network Rule Extraction by Using the GeneticProgramming and Its Applications to Explanatory Classifications.” IEICE Transactions onFundamentals of Electronics Communications and Computer Sciences E88A (10): 2627–2635.

Tsamourgelis, I. 2009. “Selective Replacement of National by Non-National Seafarers in OECDCountries and the Employment Function in the Maritime Sector.” Maritime Policy &Management 36 (5): 457–468.

UNCTAD. 1997. Review of Maritime Transport 1997. Geneva: United Nations Publication.UNCTAD. 2011. Review of Maritime Transport 2011. Geneva: United Nations Publication.

Analysis of students’ perceptions of seafaring career in China 15

Dow

nloa

ded

by [

Uni

vers

ity o

f T

asm

ania

] at

15:

29 0

4 Fe

brua

ry 2

014

Wu, B. 2004. “Participation in the Global Labour Market: Experience and Responses of ChineseSeafarers.” Maritime Policy & Management 31 (1): 69–82.

Wu, B., K. Lai, and T. C. E. Cheng. 2006. “Emergence of ‘New Professionalism’Among Chinese Seafarers: Empirical Evidence and Policy.” Maritime Policy & Management33 (1): 35–48.

Yababa, M., and S. Tokinaga. 2002. “Applying the Genetic Programming to Modelling of DiffusionProcesses by Using the CNN and Its Applications to the Synchronisation.” IEICE Transactionson Fundamentals of Electronics Communications and Computer Sciences (Japanese Edition)J85–A (5): 548–559.

16 J. Fei and J. Lu

Dow

nloa

ded

by [

Uni

vers

ity o

f T

asm

ania

] at

15:

29 0

4 Fe

brua

ry 2

014