Paper 3 for the Project

Expert Systems with Applications 37 (2010) 1503–1509

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Expert Systems with Applications

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The indicators of human capital for financial institutions

Kuang-Hsun Shih a, Yen-Tzu Liu b,1, Charlotte Jones c, Binshan Lin c,*

a Department of Banking and Finance, Chinese Culture University, Taipei, Taiwanb Department of Business Administration, National ChengChi University, No. 64, Sec. 2, ZhiNan Rd., WenShan District, Taipei 11605, Taiwan, ROCc College of Business Administration, Louisiana State University in Shreveport, USA

a r t i c l e i n f o

Keywords:Financial professions trainingFinancial institutionsHuman capitalIntellectual capitalExpert systems

0957-4174/$ - see front matter � 2009 Elsevier Ltd. Adoi:10.1016/j.eswa.2009.06.042

* Corresponding author. Tel.: +1 318 797 5025; faxE-mail address: blin@lsus.edu (B. Lin).

1 Tel.: +886 931 930308; fax: +886 2 2937 9611.

a b s t r a c t

This study examines indicators of measurement and relative weights on key competency in the financialprofession. In addition, the study functions as a reference for training in economic, academic, and finan-cial circles. The findings demonstrate that attitude is relatively the most important construct for profes-sional core competency in the financial professions. Industrial and academic circles suggest thatcontinuous learning is the most important indicator of measurement on key competency. According toindustrial circles, secondary indicators are work experience and emotional stability; academia suggeststhat secondary indicators are emotional stability and problem solving skills.

� 2009 Elsevier Ltd. All rights reserved.

1. Introduction

It is generally accepted that changes in the essence of work andthe workplace in a post-industrial economy are recreating thekinds of attitudes, skills, and knowledge needed for successfulemployment and work performance (Liebowitz, Agresti, & Djavan-shir, 2006; Versu-Jover, Gomez-Gras, & Llorens-Montes, 2008).Stasz (2001) thought these changes, due to such factors as technol-ogy, management innovations, and competition in the global mar-ketplace, raise questions for policymakers about the competenciesthat the graduated students will need in order to succeed in theworkplace and further education, especially immediately aftercompulsory schooling.

The manifest shift in competency requirements in a moderneconomy has significant implications on education and training.This is partly because of the widespread agreement that more edu-cation and training is good for a modern economy. A main questionfor policy makers is how to change education and training systemsto meet these demands most efficiently (Stasz, 2001). Many keycompetency indicators in financial professions are recognized byacademia and management of business circles (Peslak, 2008).However, rarely have studies indicated the relative importance ofthese indicators. Thus, the primary contribution of this study isto construct key competency indicators of the financial profession.This study attempts to identify the weights of factors upgradingcore competency by using the Decision Making Trial and Evalua-tion Laboratory (DEMATEL) and Analytic Hierarchy Process (AHP)systems to probe into the critical constructs and indicators as the

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basis for developing: (1) human capital for the financial industryof the future, and (2) criterion for industrial and academic circleson course planning and talent training.

2. Literature review

This study first collected and reviewed related literature as thetheoretical basis of the research framework. The first section de-fines competency in the financial profession; the second sectiondescribes the content of key competency and develops the indica-tors that determine the constructs of the research framework.

2.1. Definition of competency

Houtzagers (1999) pointed out that competence is closely con-nected with the attempts of companies to create an atmosphere fortheir employees to increase competitive advantage, innovation,and effectiveness. Beck (2003) stated that many researchers haveattempted to transform competency from companies to personneldevelopment. Prahalad and Hamel (1990) suggested that corecompetence provide a competitively unique and contributing va-lue to customers and cost. They distinguished the two by notingthat competencies relate to skills, knowledge, and technologicalknow-how, giving a definite advantage at specific points of the va-lue chain. In combination with the strategic processes, these com-petencies link the chain together from core capabilities.

While the above thoughts relate to organizational core compe-tencies, few scholars have transformed the idea of core competen-cies from the organization to the individual. Draganidis andMentzas (2006) proposed that competence is the construction ofskills, knowledge, abilities, and other characteristics that someone

1504 K.-H. Shih et al. / Expert Systems with Applications 37 (2010) 1503–1509

needs to perform a job effectively. However, some literature andscholars have begun to verify that attitude is a key factor that influ-ences competence. The literature also has suggested that compe-tence is related to the essential individual work-related factors,such as skills, knowledge, attitudes, beliefs, motives, and traits thatenable successful performance of a job (Blancero, Boroski, & Dyer,1996; Chen & Naquin, 2006; Shippmann, Ash, Battista, Carr, &Hesketh, 2000). Stasz (2001) provided empirical evidences fornew competencies required from various sources, including em-ployer surveys. Stasz, Ramsey, Eden, Melamid, and Kaganoff(1996) also indicated that employers are often more concernedabout attitudes than skills or knowledge.

2.2. Definition of competency indicators

Based on the previous studies, this research on the competen-cies in the financial profession consists of three indicators – atti-tudes, skills, and knowledge – that could assist in performing ajob effectively.

2.2.1. AttitudeOlson and Zanna (1993) indicated that attitude is the psycho-

logical tendency that is expressed by evaluating a particular entitywith some degree of favor or disfavor. Attitudes are formed basedon affective or emotional experiences and could be seen as cogni-tive structure representing past experiences (Eagly & Chaiken,2007; Olson & Zanna, 1993). Some researchers suggested that atti-tudes are most usefully viewed in terms of affective evaluations ofan object. Others researcher pointed out that attitude is composedof affects, cognitions, and behavioral intensions. Therefore, Georgeand Jones (1997) concluded that attitudes organize and summarizehow people feel and think about their jobs and organizations,which, in turn, can affect their subsequent experiences.

Stasz et al. (1996) found that employers seek employees with theright attitudes and dispositions toward work, such as motivation,reliability, willingness to learn, and willingness to take responsibil-ity. Vakola, Tsaousis, and Nikolaou (2004) indicated that conscien-tiousness describes people with self-discipline, responsibility,ambition, and competence. They also indicated that conscientious-ness is positively correlated to positive attitudes. Emotional stabil-ity was found to be an important predictor of job attitude (Staw,Bell, & Clausen, 1986), and negative emotional stability has beenlinked to job stress and strain (Brief, Burke, George, Robinson, &Webster, 1988; Lebreton, Binning, Adorno, & Melcher, 2004). Emo-tional instability refers to employees’ ability to withstand stress inthe job (Howard & Howard, 1995; Vakola et al., 2004).

Gibb (2008) stated that continuous learning is common to alloccupations and workplaces. Dehmel (2006) defined continuouslearning as all learning activities continued in order to improveknowledge, skills, and competencies within related jobs. Hartman,Bentley, Richards, and Krebs (2005) pointed out that employersprize job candidates who exhibit experiences in teamwork, inter-personal skills, motivation, and initiative. In addition, Frese, Kring,Soose, and Zempel (1996) found that modern employers needemployees who go beyond narrow task requirements and ap-proach work proactively by showing personal initiative. Personalinitiative can be defined as a behavior that individuals display thatshow active and self-starting approaches to work (Fay & Frese,2001; Frese, Fay, Hilburger, Leng, & Tag, 1997).

According to the above definitions, attitude influences a per-son’s actions through affective or emotional experiences and orga-nizes how people think and feel about their job. The indicators ofattitude are described below:

(1) Conscientiousness: a characteristic of a person with self-dis-cipline, responsibility, ambition, and competence.

(2) Continuous learning: the continuation of all learning activityin order to improve knowledge, skills, and competencieswithin related jobs.

(3) Personal initiative: a behavior in which a person takes anactive and self-starting approach to work.

(4) Emotional stability: an employees’ ability to withstandstress in the job.

2.2.2. SkillZaim (2007) defined skill as a means by which people are re-

quired to succeed in a particular job and accomplish their work.Buckingham and Vosburgh (2001) referred to skill as a specifictechnique or method. Skills can range from highly concrete andeasily identifiable tasks, such as filing documents alphabetically,to those that are less tangible and more abstract, such as managinga quality improvement project (Hoge, Tondora, & Marrelli, 2005;Lucia & Lepsinger, 1999).

Stasz et al. (1996) found that employers and workers see theneed for skills, such as problem solving, communication, and theability to work in teams. Hollmann and Elliott (2006) surveyedemployers’ opinions on the characteristics of hired employeesand found that most employers required employees to have com-munication skills. Ulinski and O’Callaghan (2002) defined commu-nication skills as having the ability to listen, converse, followinstructions, and communicate with others. Rotundo and Sackett(2004) indicated that employers’ recognition of basic skills in-cluded language, problem solving, and interpersonal skills. Gibb(2008) stated that people provide insight into understanding howthe knowledge of a job is represented and contradicted throughlanguage; thus employees require language skills to understandtheir knowledge of a job. Lerman (2008) argued that required gen-eric skills of employees include communication, teamwork, andproblem solving. Prichard, Bizo, and Straford (2006) defined team-work as a skill to enhance work-group effectiveness by improvingteam member’s abilities in goal setting, problem solving, interper-sonal relations, and role clarification (Lee, 2008; Prichard et al.,2006). Adams and Wieman (2006) defined problem solving skillsas having the means to work on characterizing specific abilitiesneeded to solve problems.

Many scholars have different definitions of skill, concluding thatthe definition of skill is a special capability or manner of develop-ment that is not inherent and is displayed in the performance of ajob. The skill’s indicators of measurement are described as below:

(1) Communication skills: having the ability to listen, converse,follow instructions, and communicate with others.

(2) Teamwork skills: enhancing work-group effectiveness byimproving team members’ skills in goal setting, problemsolving, interpersonal relations, and role clarification.

(3) Problem solving skills: characterizing specific skills neededto solve problems.

(4) Language skills: providing insight into understanding howthe knowledge of a job is represented and contradictedthrough language.

2.2.3. KnowledgeMarrelli, Tondora, and Hoge (2005) defined knowledge as facts

about products, customers, organizational policies, and lessonslearned through experience. Knowledge is the fastest way to reacha decision maker, according to Buckingham and Vosburgh (2001).Stasz (2001) addressed knowledge as an abstract attribute that aperson could attain over years of schooling, thereby improvinghis labor market success. Mirabile (1997) suggested that knowl-edge refers to a body of information relevant to performing a job.

K.-H. Shih et al. / Expert Systems with Applications 37 (2010) 1503–1509 1505

Marrelli et al. (2005) indicated that knowledge is an awareness ofinformation or understanding about facts, rules, principles, guide-lines, concepts, theories, or processes needed to successfully per-form a task. Lucia and Lepsinger (1999) pointed out thatknowledge may be concrete, specific, and easily measurable, or itmay be more complex, abstract, and difficult to access.

Stasz (2001) indicated that credentials, including school de-grees, represent some measure of knowledge and are a primarymechanism by which individuals signal employers their potentialto perform their work. Stasz also found that most employers con-sider employees’ work experience and industry-based credentialsto be the basic requirements in hiring. Stasz further demonstratedthat ‘‘working knowledge,” which is the knowledge and skill de-rived from experience, is often more important than knowledgealone. Mane (1999) found that people with experiences are morelikely to be employed, paid a higher wage, and earn more thanpeople that pursued a purely academic curriculum. Theseemployees have professional knowledge in terms of the financialpressures and targets for recruitment (Burnett, 2006; Heijden,2001). Burnett (2006) defined professional knowledge as theknowledge needed to deliver the current curriculum – technical,finite, and originating from teachers, rather than owned or devel-oped alone.

Based on the above review, this paper defines knowledge asattributed to one who could obtain information from learningand experiences to successfully perform a job. The indicators ofknowledge are described below:

(1) Credentials (including school degrees): a primary mecha-nism by which individuals signal employers their potentialto perform their work.

(2) Professional knowledge: knowledge needed to deliver thecurrent curriculum – technical, finite, and originating fromteachers, rather than owned or developed by self.

(3) Working experience: knowledge and skills derived fromexperience; often more important than knowledge alone.

Language skills

Teamwork skills

Problem solving skil

Competencies for financial professionals

Skills

Communication skill

Attitudes

Emotional stability

Conscientiousness

Knowledge

Credentials

Professional

Workin g experiences

Continuous learning

Indicators Construct

Personal initiative

Fig. 1. Conceptual frame

3. Research method

3.1. Process and framework

The purpose of this study was to investigate the factors of corecompetency in the financial profession. First, it reorganized the lit-erature related to core competency and constructed the criticalindicators and items. Second, it conducted semi-structural inter-views with managers of financial circles and scholars. These inter-views reinforced the standard processes with flexibility, collectionof systematic data, indirectly demonstrating the ‘‘reliability” ofdata. With regard to the planning of interview outlines, accordingto the research framework, this study aimed to identify the currentcore competencies of the financial profession and to determine ifthe indicators were necessary conditions of financial professions.The topics of the questionnaire included:

(1) What is core competency in the financial professions?(2) Are indicators of the research framework the necessary con-

ditions of current financial professions?

The interviews with experts and scholars further validated theframework of the study (see Fig. 1). Subsequently, this study foundthe representative items among the indicators in different catego-ries using DEMATEL, and it analyzed the weights using AHP. Fur-ther suggestions on specific and critical indicators for decision-makers were determined.

3.2. DEMATEL

DEMATEL designs a system structure with expert knowledge(Liou, Yen, & Tzeng, 2008; Tsai & Chou, 2008). This research firstconducts verification analysis with SEM and obtains a model fit-ness. Path coefficients, reaching statistical distinctiveness levels,serve as contents for DEMATEL analysis in order to establish highlyexact analysis levels and highly dependent research results.

Describes people as self-disciplined, responsible, ambitions, and competence

People provide insight into understanding how the knowledge of a job is represented and contradicted through language

Defined to enhance work-group effectiveness by improving team member’s skills in goal setting, problem solving, interpersonal relations, androle clarification

ls Meaning people that work on characterizing specific skills needs to solve problems

Defined as people have the ability to listen, converse, follow instruct ions, and communicate with others

s

Employees’ ability to with stand stress in the job

Including school degrees, represents some measure of knowledge and are a primary mechanism by which individuals signal

Knowledge and skill derive d from experience is often more important than knowledge

All learning activities were continued in order to improve knowledge, skills, and competences within related jobs

A behavior of individuals taking an active and self-starting approach to work

Definition of indicators

Presented as the knowledge needed to deliver the current curriculum, technical, finite, and is originated from teachers, rather than owned or developed by self

work of this study.

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With reference to studies by a number of authors (Gabus et al.,1972; Hung, Chou, & Tzeng, 2007; Lin & Wu, 2008; Liou et al.,2008; Seyed-Hosseini, Safaei, & Asgharpour, 2006; Wu, 2007; Wu& Lee, 2006; Zhou, Zhang, & Li, 2006), this research illustrates def-initions and steps of the DEMATEL method, as follows:

Step 1: The score of each pair of factors is made into n� nmatrix, where aij represents the influential relation of cri-terion i on criterion j. A ¼ ½aij�n�n. The purpose is toexplain the precise relation among pairwise factors.

Step 2: Normalizing the direct-relation matrix – Matrix A is nor-malized to generate direction relation matrix X ¼ ½xij�n�n

and 0 6 xij 6 1 while limk!1Xk ¼ ½0�

s ¼ 1max16i6n

Pnj¼1aij

ð1Þ

X ¼ s� A ð2Þ

Step 3: Attaining the total-relation matrix – with formula (3),one obtains the total-relation matrix T from the direct-relation matrix. The I below is a unit matrix.

T ¼ X þ X2 þ X3 þ � � � þ Xk ¼ XðI � XÞ�1 ð3Þ

Step 4: Producing a causal diagram – columns and lines of a totalrelation matrix T is summed, respectively. T ¼½tij�; i; j 2 f1;2; . . . ;ng. D and R represent lines, and aresummed in rows and columns.

Step 5: Obtaining the inner dependence matrix – D matrix repre-sents the sum of each row in total-relation matrix T. Theinfluencing values are from the influential relation of fac-tor i on factor j. Similarly, R matrix represents the sum ofeach column in total-relation matrix T. The influence val-ues are from factor i under factor j. When i ¼ j; ðDþ RÞmeans the influencing strength of the items. If (D� RÞis positive, factor I tends to affect other factors. On thecontrary, if ðD� RÞ is negative, factor i tends to affectother factors.

3.3. AHP

The Analytic Hierarchy Process (AHP) provides a comprehensiveframework for solving such problems and enables one to deal withthe intuitive, the rational, and the irrational at the same time whenmaking multi-criteria and multifactor decisions. Saaty (1986) ad-dressed the degree of consistency (or inconsistency) of the judg-ments as revealed at the end of the AHP process. Wind andSaaty, 1980 suggested simple pairwise comparison judgments todevelop priorities in each hierarchy and developed the tradeoff inthe course of structuring and analyzing a series of simple reciprocalpairwise comparison matrices.

Several researchers (Carlucci & Schiuma, 2007; Ngai & Chan,2005; Saaty, 1980) suggested the following hypotheses in AHP:

(1) systems or problems could be divided into several kinds ofcomponents for rating, which form the hierarchical structureof a network;

(2) in a hierarchical structure, the elements of each hierarchyare independent, and measurements could be based on someor all factors from the previous hierarchy;

(3) in measurements, an absolute scale could be transformedinto a ratio scale. for instance, the ratio between A1 andA2 is 5/1;

(4) after pairwise comparison, a reciprocal matrix reached aleading diagonal with symmetry and the positive reciprocalmatrix could be the solution;

(5) correlation satisfies transitivity, however, it was difficult tofulfill complete transitivity; thus, transitivity was not neces-sary, however, consistency could be tested to find the levelsof inconsistency;

(6) advantageous weights of the factors were acquired by theweighted method; and

(7) all factors in a hierarchical structure are related to the wholestructure of rating, regardless of the advantageous weights.

AHP includes two aspects: hierarchical construction and evalu-ation. AHP represents complicated problems by a simple hierarchi-cal structure, based upon evaluation of the factors by experts andscholars. Pairwise comparison is based on a scale evaluation toconstruct the factors and matrices. After acquiring the eigenvec-tors, the priority of each factor is compared; the study then exam-ines consistency of pairwise comparison matrix to validate itsreliability. In AHP, consistency is validated by a consistency index(CI) and consistency ratio (CR). CI = consistency of the first and sec-ond judgments, whereas CI > 0 refers to inconsistency. Prahaladand Hamel (1990) suggested that consistency was validated whenCR was less than or equal to 0.1.

4. Empirical analysis

Based on the literature review, this study first suggested theconstructs of core competency of financial professions: attitude,skill, and knowledge; the indicators include active, responsible,continuous learning, work stability, pressure resistance, communi-cation skills, team work skills, problem solving, language skills,professional licenses, financial knowledge, and experience. Thestudy conducted semi-structural interviews with five experts toconstruct an overall framework. DEMATEL analysis was conductedto validate the relations among the factors. Since significant corre-lation among the factors in the same construct may violate theindependence hypothesis of AHP, these factors were combined.With DEMATEL, the study analyzed the correlation among the fac-tors of three constructs. Since the indicators were based on the def-inition of the constructs, according to the literature, indicators ofthe constructs were based upon the hypothesis of independence.DEMATEL analysis did not analyze different constructs; it focusedonly on the correlation of the indicators in the same construct.

Three experts in industrial circles and three from academiafilled out the questionnaires of DEMATEL. Thus, six samples wereanalyzed, and the scoring was based on the means. The scoring in-cluded 0 points (no effect), 1 point (low effect), 2 points (mediumeffect), and 3 points (high effect). The participants evaluated theinfluences of the indicators in attitude, skill, and knowledge. Thescoring upon arithmetic mean was the direct correlation matrixof DEMATEL. After constructing the matrix, a total correlation ma-trix was calculated by Eqs. (1)–(3). The total correlation matrix isshown in Tables 1–3. The experts determined a threshold valueas the criterion for the influence level. When the figure was higherthan the threshold value, the experts suggested that the factor inthe row would significantly influence the factor of the line. Thus,the former was the lead variable of the latter. Dependent variablesare discussed with independent variables since there was a signif-icant correlation.

In terms of attitude, personal initiative and conscientiousnesssignificantly influence emotional stability. Thus, personal initiativeand conscientiousness were eliminated, and emotional stabilitywas retained as an indicator. In terms of skill, communication skillsand teamwork skills would significantly influence problem solvingskills. Thus, communication skills and teamwork skills were elim-inated, whereas problem solving skills was retained as an indica-tor. In terms of knowledge, credentials would significantly

Table 1Total-relation matrix of attitude.

Personal initiative Continuous learning Conscientiousness Emotional stability

Personal initiative 1.3399 1.3265 1.2197 1.5034Continuous learning 1.2897 0.8571 0.8770 1.2381Conscientiousness 1.6176 1.3265 0.9419 1.5034Emotional stability 1.0428 0.7959 0.6596 0.7687

Table 2Total-relation matrix of skill.

Communication skill Teamwork skill Problem solving skill Language skill

Communication skill 0.4878 0.8780 1.0732 0Teamwork skill 0.5366 0.3659 0.7805 0Problem solving skill 0.4390 0.3902 0.3659 0Language skill 0.6677 0.4268 0.7439 0

Table 3Total-relation matrix of knowledge.

Credential Professional knowledge Working experience

Credential 0.3273 1.0909 0.9273Professional knowledge 0.3091 0.3636 0.7091Working experience 0.3636 0.5455 0.3636

K.-H. Shih et al. / Expert Systems with Applications 37 (2010) 1503–1509 1507

influence professional knowledge. Thus, credential was eliminatedas an indicator, and professional knowledge was retained. Thecause-and-effect relations among the indicators of different con-structs are shown in Fig. 2.

According to Fig. 2, in terms of attitude, personal initiative andconscientiousness were included in emotional stability. Emotionalstability and continuous learning were treated as two independentindicators for further weight analysis in AHP. In terms of skills,problem solving skills and language skills were treated as twoindependent indicators. In terms of knowledge, professionalknowledge and working experience were retained.

After validating the relations among the indicators by DEMA-TEL, the indicators were combined with cause-and-effect relationsand the independent ones were retained for further weight com-parison in AHP. There were 15 questionnaires distributed to busi-nesses and 30 to academic institutes. The analytical results ofDEMATEL indicated that there were two indicators, respectively,on attitude, skill, and knowledge. Thus consistency of scoringwas considered. Competencies included three sub-constructs, atti-tude, skill, and knowledge. Thus, the CI and CR of 30 questionnaireswere calculated. The analytical result showed that CI and CR wereless than the standard 0.1. After confirming that all questionnairesmet the basic hypotheses, the study calculated the weights of thethree constructs and the related indicators by AHP, as shown inTable 4.

Teamwoskill

Personal initiative

Conscientiousness

Emotional stability

Continuous learning

Attitude

Fig. 2. Cause-and-effect d

According to Table 4, both businesses and academic institutessuggested that attitude was the most important construct with re-spect to the training on core competency. In attitude, continuouslearning was the more important indicator. For businesses, thetop three indicators of core competency were continuous learning,working experience, and emotional stability. Academic institutessuggested that continuous learning, emotional stability, and prob-lem solving skills were the most important. Thus, this study iden-tified the important views of the experts on the indicators of corecompetency that could function as criteria for the practical trainingof financial personnel.

5. Conclusions and recommendations

Traditionally speaking, researchers using AHP tend to find theitems of the constructs by deduction or literature reviews. How-ever, few of these researchers demonstrate the independence ofthe items. This study applied DEMATEL upon the experts’ opinionsto validate the correlation among the items in the same constructsand then combined the factors with significant relations. Thus, thehypothesis of independence of AHP can be partially supported, as itsolves the problems of excess CI or CR in the factors and increasesthe precision and feasibility of analysis.

This study included three stages. In the first stage, the indicatorswere selected, according to experts’ opinions, in order to increase

rk Communication skill

Problem solving skill

Language skill

Skill Knowledge

Credential

Professional knowledge

Working experience

iagram of DEMATEL.

Table 4AHP weighted analysis.

Industry circle Academia

Construct Percent Indicators Percent Overall percent Rank Construct Percent Indicators Percent Overall percent Rank

Attitude 54.6 Emotional stability 32.8 17.91 3 Attitude 63.2 Emotional stability 49.6 31.35 2Cont. learning 67.2 36.69 1 Cont. learning 50.4 31.85 1

Skill 11.2 Problem solving 82.7 9.26 5 Skill 19.9 Problem solving 68.6 13.65 3Language 17.3 1.94 6 Language 31.4 6.25 6

Knowledge 34.2 Prof. knowledge 35.3 12.07 4 Knowledge 16.9 Prof. knowledge 47.3 7.99 5Work experience 64.7 22.13 2 Work experience 52.7 8.91 4

1508 K.-H. Shih et al. / Expert Systems with Applications 37 (2010) 1503–1509

the expert and content validity of the questionnaire. In the secondstage, the study constructed a correlation among the indicatorsusing DEMATEL. Significant relations among the indicators in thesame construct would violate the hypothesis of independence ofAHP, so these factors needed to be combined. In the third stage,the study probed into the weights using AHP.

The results demonstrated that, in the first hierarchy, the expertsand scholars suggested that attitude is the most important con-struct in core competencies of the financial profession. Businessesindicated that continuous learning, working experience, and emo-tional stability were the three indicators having higher weights.Academia indicates that continuous learning, emotional stability,and problem solving skills were the top three indicators. Accordingto analytical results, it was concluded that the business experts feltthat working experience is relatively important.

When businesses recruit employees, they expect them to havemore work experience related to the specific industry (Earl &Bright, 2003; Seyed-Hosseini et al., 2006). On the contrary, insti-tutes value training in problem solving skills. Thus, when settinga training program or selecting employees, businesses target onthe problem solving skills of candidates.

Stasz (2001) suggested that positive problem solving skills as-sist firms in the application of limited resources. Taylor and McNe-mar (1955) indicated that problem solving skill can reinforceteamwork. When employees have problem solving skills, they tendto solve problems with positive thinking, based upon their confi-dence. This, in turn, upgrades the overall work efficiency of theorganizations. In addition, according to the weighted analytical re-sults of AHP, industrial and academic circles both value continuouslearning. The international financial environment changes signifi-cantly, and Wilson and Madsen (2008) suggested that continuouslearning of the talents in the organizations would be their mostcritical competency.

In recent years, some scholars have tried to determine howfirms or organizations reinforce the talents’ continuous learningthat can be achieved by: (1) scheduling adequate time for learning;(2) providing just-in-time training; (3) involving employees intraining design; (4) using active training formats; (5) establishinga safe environment for learning, encouraging positive measure-ments; (6) ensuring new skills are promptly applied on the job;and (7) recognizing performance improvements (Mallon & Walton,2005; Spitzer, 1995). Thus, in the future, when businesses and aca-demic institutes train talents, they should focus on continuouslearning.

Academic institutes can strengthen continuous learning by (1)establishing a safe environment for learning; (2) involving employ-ees in training designs; (3) ensuring new skills are promptly ap-plied on the job; and (4) recognizing performance improvementsas motivations.

According to this analysis, businesses and academic institutesshould recognize the factors of core competency in the financialprofession and strengthen core competencies by designing relatedcourses and systems. In the future, students who intend to pursue

careers in financial fields or talents of cross-disciplinary fields cancontinuously learn the knowledge and skills related to financialfields and develop related working and internship experiences toreinforce their problem solving skills and potential.

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