Improved decision aiding in human resource management

Improved decision aiding inhuman resource managementA case using constructivist multi-criteria

decision aidingSandra Rolim Ensslin

Programa de Pos-Graduac~ao em Contabilidade,Universidade Federal de Santa Catarina – UFSC, Florianopolis, Brazil

Leonardo EnsslinPrograma de Pos-Graduac~ao em Administrac~ao,

Universidade do Sul de Santa Catarina – UNISUL, Florianopolis, Brazil

Felipe BackPrograma de Pos-Graduac~ao em Engenharia de Produc~ao – UFSC,

Universidade Federal de Santa Catarina – UFSC, Florianopolis, Brazil, and

Rogerio Tadeu de Oliveira LacerdaPrograma de Pos-Graduac~ao em Administrac~ao – UNISUL,

Universidade do Sul de Santa Catarina – UNISUL, Florianopolis, Brazil

Abstract

Purpose – Identify the criteria/KPIs to support managers during human resource allocation based onknowledge demand, which serves as a decision support tool to help maintain organizationalcompetitiveness.Design/methodology/approach – Human resource allocation in a project management model,based on knowledge demand and using a multi-criteria decision aiding method as an interventioninstrument.Findings – Three major areas of concern were identified. In all, 76 KPIs to explain concernsassociated with the values of the manager, and develop cardinal and ordinal scales for each descriptorand integrate compensation rate. Further, he was allowed to implement and evaluate the currentperformance of the analyzed engineer, with 44 points on a cardinal scale, and provide a model withimproved actions that raised his assessment to 55,67.Originality/value – The Multi-Criteria Decision Aiding-Constructivist methodology (MCDA-C)emerges as a traditional MCDA method to support decision makers in the contexts where they have apartial understanding and wish to increase their knowledge of the consequences of their valuesand preferences. In addition, these managers will also need to utilize time management, as peopleissues in the place of other functions have been highlighted in numerous published articles overhow the management of human resource allocation can influence the competitive performances of anorganization.

Keywords Performance Management, Human Resource Management, HRM, MCDA-C

Paper type Case study

1. IntroductionManufacturing companies are increasingly adopting project management in theirdesign and development processes to help develop more sophisticated and customizedproducts. A key issue for the management of these companies is to ensure skilledindividuals are allocated as effectively as possible to cope with the demands ofcompeting projects. In this paper, we address this problem by using a constructivist

The current issue and full text archive of this journal is available atwww.emeraldinsight.com/1741-0401.htm

Received 24 April 2012Revised 13 December 2012

6 May 2012Accepted 28 May 2013

International Journal of Productivityand Performance Management

Vol. 62 No. 7, 2013pp. 735-757

r Emerald Group Publishing Limited1741-0401

DOI 10.1108/IJPPM-04-2012-0039

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Improveddecision aiding

multi-criteria approach to develop a decision support model for human resourcemanagement (HRM).

The study analyses a multinational company that manufactures home appliances.This firm is the market leader and has approximately 800 employees, out of a total of7,000, involved in project management. These professionals need a management modelthat allows them to meet the demand for customized products without increasing theresources available – a complex and challenging management issue.

First we need to understand the problem and the high importance of the approach;the next step is to identify any scientific knowledge that can be used to supportmanagerial functions. The constructivist approach develops managerial knowledgeand thus allows managers to expand their own knowledge and understanding aboutindividual decisions, goals, and objectives. Therefore, this approach has been identifiedas the most appropriate in this context when compared to the normative, descriptive,and prescriptive approaches.

To identify the relevant objectives for this analysis we need to define the goals to beachieved. In this context, the following research question emerged: How can MCDAbe used to construct a managerial decision support tool for human resourceallocation based on technical expertise? The objective of this study is, therefore,to develop a model to help human resource allocation based on technical expertise (i.e.knowledge demand).

2. Literature reviewThe theoretical framework is presented in three parts. In the first part we introduce theconcept and characteristics of HRM relevant to the study; the second part presentsthe research opportunities; and the third addresses the intervention instrument used inthis work, namely the MCDA-C.

2.1. Concepts and definitionsHRM contributes directly to achieving a firm’s strategic objectives (Baird andMeshoulam, 1988; Jackson and Schuler, 1999). Human resources practices generatevalue for organizations when individual actions are aligned to the development ofcritical resources or technical expertise (Wright et al., 2001). Managers also have toutilize most of their time managing people issues in place of other functions, withpeople managing skills being one of the more difficult skills (Dixon, 2011). Once humanresources are managed strategically, competitive results are more likely (Kiessling andHarvey, 2005). In terms of HRM, Hendriks et al. (1999) highlight the complexity inhuman resource allocation for heterogeneous activities, which usually involve multiplepurposes that are poorly defined and conflict with one another. Lado and Wilson (1994)emphasize, however, that a good strategic process of the allocation of human resourceshelps to develop a competitive advantage that can rarely be imitated by otherorganizations.

Globalization, including the increase of international business and growth inemerging markets such as China, India, Latin America, and Eastern Europe, iscontributing to the large increase in studies of performance management for both theacademic and business communities (De Waal et al., 2011). According to Huselid (1995),several articles have been published on how the management of human resourceallocation influences the competitive performance of individual companies.

Moreover, traditional resources used to achieve competitive advantage are dryingup and becoming less effective. In a context where the dissemination of knowledge,

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processes, and techniques occurs almost instantly, having a competitive differencepromotes widely perceptible results that are also difficult to reproduce. Thus, accordingto Schuler and MacMillan (1984), in the process of searching for new mechanismsto create competitive advantage, the management of human resource allocation isimportant.

We propose the use of MCDA-C due to the possibility of building knowledge inparticular contexts, such as complex and conflicting contexts where the managerneeds to expand their own understanding (Ensslin et al., 2010). It is noted thatresearchers such as Skinner (1986), Keeney (1992), Roy (2005, 1996, 1993), Landry(1995), Bana e Costa et al. (1999), Zimmermann (2000), Shenhar (2001), Stewart (2005),and Igarashi et al. (2008) have all drawn on these assumptions to develop their modelsin decision aiding.

This study presents a situation where the context is unique and the managerparticipates actively in the whole process of model building. This study highlights thestatus quo and the impact of the decision maker’s decisions on those aspects (KPIs),perceived as necessary and sufficient to manage the problem of resource allocation.

2.2. Perspectives on research about HRM and performance evaluationTheoretical knowledge required for the case study are dealt with in this section. TheProKnow-C process was used to conduct a systematic analysis of the literature(Marafon et al., 2012; da Rosa et al., 2012; Lacerda et al., 2012; Tasca et al., 2010).

The ProKnow-C method is designed to build a researcher’s knowledge on aparticular topic of interest and is composed of four macro-processes (as illustratedin Figure 1).

To accomplish the first macro-process, combinations of keywords related to the twoaxes of this research were adopted (as described in Table I). After this activity,the databases Scopus, Compendex Engineering, Wilson, Web of Science, and ScienceDirect were selected.

(i)Portfolio of relevant

articles selection

Source: Adapted from Lacerda et al. (2012)

(ii)Bibliometrices analysis

of the portfolio

(iii)Systemic analysis

(iv)Research question

and research objectivedefinition

Figure 1.The macro-processes of

ProKnow-C method.

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A systematic search using combinations of the keywords returned 5,132articles published since 2001. After a screening process, described in Figure 2,11 relevant articles were selected (Athanassopoulos and Gounaris, 2001; Bititciet al., 2001; Chen and Lee, 2007; Golec and Kahya, 2007; Huang et al., 2011; Kahya,2009; Laitinen, 2002; Lee et al., 2009; Medlin and Green, 2009; Moon et al., 2010; Trejoet al., 2002).

The selection criteria included the number of citations and the relevance of a paper’stitle, abstract and full text.

The next step in the ProKnow-C method is to perform a bibliometric analysis ofthe representative sample of articles. The bottom of Figure 2 lists the major journals,articles, authors, and keywords identified in the bibliometric analysis of the sample.

After the disclosure of the bibliometric attributes described above, the ProKnow-Cmethod prescribes the systemic analysis of the sample content. The systemic analysis

Axis 1: HRM Boolean operator Axis 2: Performance evaluation

(a) “Human resource management” And (e) “Performance assessment”(b) “Job performance” (f ) “Performance evaluation(c) “Employee” (g) “Performance measurement”(d) “Resource allocation” (h) “Performance appraisal”

Source: The Authors (2012)

Table I.Combinations ofkeywords related to thetwo axes of this research

5,132 • All papers returned with keywords combination

• Elimination of redundancies

446• Alignment by titles of the papers

21• Scientific recognition through the number of citations

18• Alignment by abstracts of the papers

11• Full alignment with the theme of the research and availability of full text

SELECTION OF RELEVANT PAPERS

Relevant papers11 articlesBIBLIOMETRICAN ALYSIS


Outstanding journals

(i) International Journal ofProject Management(ii) European Journal ofOperational Research

Outstanding papers

(i) A dynamic performancemeasurement system: evidencefrom small Finnish technologycompanies(ii) Strategy management throughquantitative modelling ofperformance measurementsystems

Outstanding authors

(i) Bititci, U.S.(ii) Carrie, A.S(iii) Kahya, E.

Outstandingkeywords

(i) Human resourcemanagement(ii) Resource allocation

Filters

3,194

Figure 2.Screening process to selectrelevant papers on thetopics of HRM andperformance evaluation

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constitutes a means to highlight the theoretical constructs adopted, and these elementshave three goals:

(1) highlight opportunities for research on the topic and, therefore, explain thetheoretical contribution of this paper;

(2) justifying the use of MCDA-C as a research tool; and

(3) demonstrate alignment between the case study and the theoretical constructidentified.

To systematically analyze the selected articles, it was necessary to define the lensthrough which the researcher would analyse the contents. The systemic analysisprocess aims to highlight outstanding issues and gaps of knowledge found in thesample compatible with the worldview adopted by the researchers.

In this paper, the worldview was that performance evaluation is a process todevelop knowledge for a decision maker that is relevant to the specific context that heor she intends to evaluate. This is conducted through activities that identify, organize,and measure ordinally and cardinally the key performance factors, which allowthe decision maker to understand the consequences of actions (Lacerda et al., 2012;Marafon et al., 2012; Ensslin et al., 2010; da Rosa et al., 2012).

By adopting this worldview, the lenses listed in Table II were extracted from theconcept of performance evaluation. They were then used to analyze the content ofthe selected articles.

The first lens of analysis, singularity, seeks to understand whether the performancemeasurement models present in the sample recognize the uniqueness of the decisioncontext and the actors. In the selected sample, it was found that only two of the 11articles (Golec and Kahya, 2007; Moon et al., 2010) defined and operationalized thecriteria from the perspective of an actor (here named decision maker; i.e. the personwho has the authority and responsibility to change the current situation). The otherpapers dealt with the issue of performance evaluation in a generic way.

From the singularity lens, a research opportunity emerged to structure anevaluation model for the solution of singular problems, recognizing the uniqueness ofthe actors and the organizational context.

The second lens concentrated analysis on how selected articles identified the usedcriteria to evaluate HR management. It also expanded on how decision makers areinvolved in this activity and if articles recognize the limited knowledge of managers inthe studied contexts. With respect to this lens, it was noted that two of the 11 articles(Golec and Kahya, 2007; Moon et al., 2010) took into account the need to expand theknowledge of the decision maker throughout the process of identifying and operationalizingthe criteria.

With this observation, a research opportunity emerges to present a method whichfocusses on the generation of knowledge in decision making with the aim to identifywhat is relevant to his or her specific context.

The third lens in the systemic analysis had the goal of identifying the scales used inthe selected articles. In this analysis, it was found that six of the 11 articles (Laitinen,2002; Kahya, 2009; Medlin and Green, 2009; Trejo et al., 2002; Golec and Kahya, 2007;Moon et al., 2010) used the Likert scale in their evaluation models. The Likert scale iswidely used as it is a quick and easy application, but it fails to meet an importantproperty for the improvement of context, given its ambiguity regarding clarification ofwhat is needed for improvements to be made. Besides this limitation of its use in people

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2)Table II.The systemic analysisLens used to analyze thecontents of the selectedarticles, theoreticalconstructs andrelationshiop betweenthe researchopportunities and howMCDA-C attends them

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management, the Likert scale only allows statistical operations, such as count,frequency, mode, and median (Hart et al., 2003; Setijono and Dahlgaard, 2007; Wang,2009; Yan et al., 2001).

From this analysis, a research opportunity emerges to present a methodology forevaluating performance and to make use of ordinal scales for the identification ofwhat is needed to improve in each criterion. This will also present a transformationprocess from ordinal to cardinal scales and allow the use of all statistical operations,such as averages.

The fourth lens focussed on seeking how the articles performed and integratedamong scales. From this viewpoint, it was found that eight articles (Lee et al., 2009;Huang et al., 2011; Laitinen, 2002; Kahya, 2009; Golec and Kahya, 2007; Moon et al.,2010; Chen and Lee, 2007; Bititci et al., 2001) presented an integration process usingcardinal integration. The other articles did not address this property.

Despite this observation that eight articles integrated all criteria on a globalscale, no single article recognized the need to use reference levels in each local scale todetermine the constant level of integration. Without reference levels, the process incurs“the most common critical mistake” (Keeney, 1992, pp. 146-147) and the problem ofrank reversal order is an important limitation of the AHP method (Bititci et al., 2001).

From the lens of integration of criteria another research opportunity emerges:presenting a methodology that integrates all the criteria of the model and takes intoaccount the reference levels in each ordinal scale.

The fifth lens of the analysis will diagnose the current context, as well asgenerate actions for improvement. This particular analysis showed that all the articlescontained a form of diagnosis of the current state, and that nine articles (Lee et al.,2009; Huang et al., 2011; Laitinen, 2002; Kahya, 2009; Medlin and Green, 2009; Golecand Kahya, 2007; Moon et al., 2010; Chen and Lee, 2007; Bititci et al., 2001) presented anumeric diagnosis and the others only presented a descriptive diagnosis.

From these papers, eight articles (Lee et al., 2009; Huang et al., 2011; Laitinen, 2002;Kahya, 2009; Golec and Kahya, 2007; Moon et al., 2010; Chen and Lee, 2007; Bititci et al.,2001) presented the processes for ranking priority actions in order for improvement tooccur. These observations show a maturing process of managing people, conducted bythe scientific community, and take into account the selected sample.

However, as noted under the lens of measurement, most articles use the Likert scale.This scale hampers the manager and his or her staff in understanding what needsto be done to continuously improve the compromised aspects in a given context. Thisis caused by the ambiguity provided by the psychometric Likert scale. Therefore, aresearch opportunity emerges to present a method that performs a cardinal diagnosisof the situation, enabling prioritization explicitly and unambiguously.

Table II shows the theoretical constructs drawn from a sample of 11 relevant andwell-cited articles on the topic of performance evaluation and HRM, the researchopportunities identified and, therefore, the theoretical contribution of this paper.

The next section will present the methodology and its correlation with thetheoretical constructs.

2.3. MCDA-CThe process of choosing a scientific research methodology should be aligned with thenature of the problem to be solved (Mel~ao and Pidd, 2000). Research that addressesdecision problems, such as Bana e Costa (1992), categorized these into two groups:the problematics of structuring and the problematics of evaluation.

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The problematics of structuring are designed to provide tools for understandingthe problem and can unfold in rationalist or constructivist approaches (Roy, 1993).The distinction between these two groups is apparent through the limits of objectivity(Landry, 1995), where the constructivist approach focusses on the decision maker’sknowledge, while the rationalist approach focusses on physical properties to identifywhat is important to a particular decision.

In addition, there are problematics of the evaluation of actions. Its operationalization isgiven by methods that make it possible for the evaluation of actions from the preferencesof a decision maker. For Roy (1993), these problematics can be classified into four types:choosing the best action, the sorting of actions, screening, and describing the actions.

Table III presents this taxonomy and it relation to the main methods of problematicdecision aiding.

This research aims to create knowledge in decision making through activities thatidentify, organize, measure, and integrate aspects that are necessary and sufficient forHRM. The goal for this research was structuring the problem from the constructivistperspective, where the MCDA-C is suitable and aligned to the problem.

Keeney (1992), Bana e Costa (1993, 1999), Landry (1995), Roy (1996), and Ensslin(2000, 2010) consolidated the use of MCDA-C as a scientific instrument over the pasttwo decades, although its origins can be found some 200 years ago:

. Roy (1996) and Landry (1995) – limits of objectivity for decision aiding processes;

. Skinner (1986) and Keeney (1992) – attributes (objectives, criteria) are specific tothe decision maker in each context; and

. Bana e Costa (1993, 1999) – MCDA convictions.

MCDA-C emerges as a traditional MCDA method to support decision makers in thecontexts in which they have partial understanding and wish to increase theirknowledge to better comprehend the consequences of their values and preferences.This feature links to the theoretical construct C1 of Table II.

Furthermore, the MCDA-C method differs from the traditional MCDA method byhaving an initial phase of knowledge development known as the Structuring Phase.This feature links to the theoretical construct C2 of Table II.

MCDA restricts decision support to two steps, formulation and evaluation, accordingto a defined group of objectives (decision maker with little or no participation), and thusit seeks to select the best alternative (optimal solution) from among the alternativespreviously established (see Keeney, 1992; Roy and Bouyssou, 1993; Roy, 1996; Goodwinand Wright, 1998).

Since MCDA-C is a branch of traditional MCDA, it has a step structure that allowsfor decision support in the following environments: conflicting, uncertain, and complex(Ensslin et al., 2010).

Structuring Racionalist MCDA, AHP, MAUT, MAVT e SMARTRacionalist MCDA-C

Evaluation Screening ELECTRE-TRISorting ELECTRE-II, III e IVSelection ELECTRE-I e ISDescribing Soft Systems Methodology


Table III.Problematics of decisionaiding and main methodsrelated

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Roy (2005, 1996, 1994) grouped MCDA researchers into three groups: rationalist,axiomatic or prescriptivist, and constructivist. MCDA-C is a constructivist approachthat focusses on the process that seeks to scientifically expand the knowledge ofdecision makers, and to help them understand the impact of their decisions basedon their own criteria (that are aligned with their values). To achieve these purposes,MCDA-C is organized into three sequential phases: structuring, evaluation, andrecommendation, as shown in Figure 3.

3. Methodological frameworkThe research is exploratory, applied, and carried out as a case study. It has theobjective of broadening the knowledge of the Chief Technology Officer (CTO) bycreating a HRM model based on knowledge demand in a global organization thatdevelops home appliances. The data were gathered through non-structured interviewswith the CTO and the Technology and General Manager (TGM) of the organization.Bibliographical research with an exploratory character was used to constructthe theoretical framework and to broaden the understanding of the context understudy, as well as to develop the adopted intervention instrument.

The approach to the research problem shows qualitative and quantitativecharacteristics. The qualitative side aims to deepen knowledge about the context byidentifying criteria and building ordinal scales. Whereas the quantitative side usesmathematical models to convert these ordinal scales into cardinal scales, to identifythe compensation rates that serve to integrate the criteria of the model and allow globalperformance evaluation (Ensslin and Vianna, 2008). This qualitative and quantitativeresearch forms part of constructivist models, such as in the MCDA-C case, byconsidering that initial knowledge is qualitative and then quantitative once measuredmathematically (Ensslin et al., 2010). This calls for the use of performance evaluation

MCDA-C

1. CONTEXTUALIZATION

2. VIEWPOINT FAMILY

3. CONSTRUCTION OF DESCRIPTORS

4. INDEPENDENCE ANALYSIS

5. CONSTRUCTION OF VALUE FUNCTIONS ANDIDENTIFICATION OF CONVERSION RATES

6. IDENTIFICATION OF IMPACT PROFILE OFALTERNATIVES

7.SENSITIVITY ANALYSIS

8. FORMULATION OF RECOMMENDATIONS

Source: Lacerda et al. (2011a, b)

StructuringPhase

EvaluationPhase

RecommendationPhase

Figure 3.The MCDA-C decision

aiding phases

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tools that allow for a broader understanding of the KPIs to be chosen and represent thedecision maker’s values in a HRM setting.

4. MCDA-C case study: HRM model based on knowledge demandFor complex situations with multiple variables, conflicts of interest among stakeholdersand relevant consequences on the final results, we recommended the use of MCDA-C,and follow the steps proposed in Figure 3. The case study was developed over six months,consuming 528 facilitator hours and 48 meetings through decision and the compilationof results.

4.1. Step 1: contextualization (soft approach to structuring)The research introduction incorporated the summary and the context of the problem.The facilitator, in conjunction with the decision maker, labeled the problem to explainthe decision maker’s concerns succinctly and objectively. Therefore, following the stepsof the MCDA-C the actors were identified according to Table IV.

This step links to the first theoretical construct of Table II.

4.2. Step 2: viewpoint familyStep 2 aimed to obtain all the possible primary assessment elements (PAEs) thatexplain the initial aspects, references, desires, goals, and constraints of the problemjudged as relevant by the decision maker. With facilitator support, the decision makeridentified 80 PAEs.

MCDA-C recommends that information from PAEs is expanded by turning theminto concepts. However, these concepts must represent the direction of the decisionmaker’s preference and its opposite psychological polar to motivate him or her toexpress the direction of preference (Eden et al., 1992).

Thus, in the second stage the decision maker was asked to talk about each PAE andexplain the purpose underlying it. He answered questions such as: What is the best andworst possible performance? What is considered a good and bad performance?What is the current performance (status quo)? What is the intensity of eachperformance? (The verb used in this final question reflects the intensity during theconstruction of the concept).

Based on the above process, we created 80 concepts (shown in Table V as concepts11 to 23). Note that the ellipsis (y) is read as “instead of ”; in other words, the presentpole “is preferable to” or “instead of ”, which corresponds to the psychological opposite.Table II illustrates the five concepts built from five of the 80 PAEs identified.

Actor Description Responsibility

StakeholdersDecision maker Chief Technology Officer Make decision/validationFacilitator Product Engineer Conduct the entire processActives stakeholders People involved on the project Direct contribution to process

Technology Manager Direct contribution processGeneral Manager Direct contribution process

Agents Employees and customers Indirect contribution process

Source: The Authors (2012)Table IV.Actors

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Based on knowledge acquired at this point (contextualization, PAEs, and concepts) andendorsed by the decision maker, the facilitator was encouraged to group the conceptsinto areas of concern. These accounted for the contextual aspects associated with thestrategic objectives of the HRM model based on knowledge demand. Therefore, allconcepts created were placed under each area of concern in order to group the initialconcepts that reflected the values and properties of the decision maker (Bana e Costaand Ensslin, 1999; Ensslin et al., 2000, 2010).

These activities link to the second theoretical construct of Table II.

4.3. Step 3: construction of descriptors4.3.1. Means-end maps. The MCDA-C method considers the process of expandingknowledge and identifies hierarchical relationships between concepts and influence.Thus, it can be used as a tool to achieve means-end maps (Bana e Costa and Ensslin,1999; Ensslin et al., 2000, 2010). This process aims to obtain relevant information fromthe decision maker for each identified concept. Some key questions considered were:How can the end concept be obtained? Why is the end concept important? (Ensslinet al., 2010) In the cognitive map, the clusters of concepts must be identified becausethey represent the map in an exhaustive process. In this way, each cluster in the means-end map has an equivalent point of view in the hierarchical structure of value. Thismakes it possible to transfer knowledge from the means-end map to the hierarchicalstructure of value. Based on the knowledge acquired, Figure 4 demonstrates theprocess for FPV2 (fundamental point of view)-Engineering and all means-endsrelations created. The same process was used for the other eight FPVs.

It is also important that the initial clusters are homogeneous, understandable,concise, manageable, essential, isolable, measurable, non-redundant, and operating(Keeney, 1992; Ensslin et al., 2001; Roy, 2005; Ensslin et al., 2010). Clusters must bedismembered until they meet the above properties; only then may they become part ofthe hierarchical structure of value and thus become a FPV.

The next MCDA-C step proposes the construction of the hierarchical structure ofvalue. This graphical representation aims to expand current knowledge, absorbing thewhole structure of influential relationships developed to organize those aspects toexplain the values of the decision maker in the context (Keeney, 1992). Figure 5 showsthis representation to the FPV level for the nine identified FPVs. The same processwas conducted for the elementary points of view (EPVs) level (Figure 7 shows thehierarchical structure of value for the EPV process).

The means-end maps activities relate to the second theoretical construct of Table II.

Id PAE/concept

11 Ensure that all involved in the project are engaged y Having no formal documentation ofwhat was discussed

12 Must-integrate individuals within and outside the company y not include the necessaryknowledge

13 Must coordinate activities as expected by the leadership y not fulfilling activities for lackof experience and skill

14 Experience in supervision y Adopt practices differing demands by the company and otherbenchmarking

15 Have technical expertise in supervision y Commit project management

Table V.Primary assessment

elements and concepts

745


Sour

ce: T

he A

utho

rs (2

012)

Figure 4.Means-end maprelationship – clusters:conduction and process

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4.3.2. Hierarchical value structure. After the hierarchical value structure wasestablished, we returned to the means-end maps and repeated the process ofidentifying clusters. But now it takes place within each of the existing clustersresulting from the sub-clusters in the hierarchical value structure. These sub-clustersare EPVs in the expanded hierarchical value structure. This FPV decomposition mustbe followed to obtain EPVs that represent the context and that can be measured in ahomogeneous and unambiguous way.

4.3.3. Descriptors (KPIs). The next stage of the MCDA-C method suggests theconstruction of ordinal scales to measure the points of view, and participation of thedecision maker is crucial. He must work interactively with the facilitator, looking atthe lowest sub-cluster to obtain an understanding associated with it in order to identifythe property used to express his or her own values. Thus, each ordinal scale wascreated to best represent his or her judgment of values.

During this meeting, the decision maker was asked about the reference levels(anchors). Bana e Costa and Ensslin (1999), Ensslin et al. (2000, 2010), and Roy (2005) alldenominate two levels: good, which establishes the lower boundary of the consideredmarket performance to excellence; and neutral, which is the limit between theconsidered market performance and jeopardizing performance. However, performancebetween good and neutral is called market performance.

Once the structuring phase had been concluded, we had a qualitative understandingof the context. Following the MCDA-C stage, the next step is the expansion of thatknowledge by incorporating more information to allow for the transformation ofqualitative knowledge (ordinal scale) into a quantitative model (cardinal scale); knownas the evaluation phase.

This step attends the third theoretical construct of Table II.

4.4. Evaluation phaseThe structuring phase built a qualitative model to reflect the aspects deemednecessary and sufficient for the decision maker to evaluate. This process allowed forthe construction of a model with ordinal scales using numerical symbols for theirrepresentation. However, according to Ensslin et al. (2001), Barzilai (2001), and Azevedo(2001), these numbers are only alpha-numeric symbols that are not part of the setof real numbers. Therefore, any function that uses mathematics or statistics would notbe able to make use of these symbols.

4.4.1. Step 4: independence analysis. The MCDA-C methodology uses thecompensatory model and requires that the criteria measured be preferentiallyindependent. A criterion is considered preferentially independent of other criteria whenthe difference in attractiveness between the levels of reference remains stable whenany alternative impacts at different levels of performance in other criteria.

HUMAN RESOURCE MANAGEMENT MODELBASED ON KNOWLEDGE DEMAND

Organization Conversion Reproduction

FPV 2-Engineering

FPV 1-Management

FPV 6-Preliminary

Assessment

FPV 5 -Materialization

FPV 4 -TechnicalSolutions

FPV 9 -Final

Assessment

FPV 8 -Reproducibility

FPV 7 -Detailing

FPV 3-Allocation


Figure 5.Hierarchical value

structure

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In the constructivist approach, the independence occurs preferentially, i.e. it is not astatistical independence but the perception of the decision maker.

A variation of attractiveness in this analysis is that it identifies the criteria analyzedas dependent and these cannot be attributed constant to the criteria set out in isolation.In the case of dependence, we must create a new scale of measurement that representsthe criteria dependent on one single performance indicator.

In this research, all indicators and their reference levels were presented to the decisionmaker. The decision maker then noted that these were preferentially independent criteria,i.e. the facilitator (from the decision maker’s perspective) might assign integrationconstants for the proposed indicators, as showed in the following sections.

4.4.2. Step 5: construction of value functions and identification of conversion rates.The following sections transform ordinal scales into cardinal scales and integrate themin a global cardinal scale.

4.4.2.1. Construction of values functions. For this stage, the cardinal scales werebuilt using information on the difference of attractiveness between ordinal scale levelsand Macbeth-M software, resulting in cardinal scales that would meet the valuejudgments of the decision maker: value functions.

The Macbeth method determines the construction of each value function through asemantic judgment matrix. The decision maker is asked to speak about all pairsof combinations at a descriptor level and inform us of his preferred intensity.The M-Macbeth software uses an ordinal scale with seven levels of attractivenessfor the judgment: null, very weak, weak, moderate, strong, very strong, and extreme.Once the facilitator has filled in the matrix, the software uses linear programmingmodels (Bana e Costa et al., 2005) to calculate the solution space that meets the judgmentsof the decision maker’s preferences. This proposes a scale that represents the valuefunction of the descriptor. The decision maker tests and adjusts the scale to legitimate it.Each value function is normalized to reference levels to make comparable value functionsand to develop a global model. This function is performed by assigning the value “0”to the neutral reference level and “100” to the good reference level. For the case study,we present the transformation of the descriptor (ordinal scale) of EPV – experiencein supervision in its respective value function. The processing is shown in Figure 6.

Once the construction of the value functions has been completed, the decision makercan check the local impact of the actions at each level (operational view) to establish thecardinal measurement. This information expands the knowledge and possibilities ofthe analysis of the decision maker, but it still does not allow for comparisons betweenalternative impacts (profile impact) at tactical and strategic levels. Therefore, it wasnecessary to create the conversion rates for all EPVs as presented below.


Figure 6.Transformation processof a descriptor “experiencein supervision”, using theMACBETH software

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This step links to the third theoretical construct of Table II.4.4.2.2. Identification of conversion rates (compensation rate). The unique aggregate

of synthesis in the proposed model in MCDA-C requires constant compensation rates.This property is guaranteed by testing their independence from the cardinal-preferredreference levels. Therefore, before the compensation process begins, it is necessary torun the independence preferred cardinal test between all pairs of value functions forthe range levels between neutral and good. After this has been completed we candetermine the compensation rate. We used a comparison method in Macbeth to obtainthe rates described above.The construction process of the compensation rates was carried out in three steps:identification of alternatives, the ordering of alternatives, and construction of thesemantic judgment matrix of the attractiveness differences of the alternatives. We usedthe Roberts matrix to establish the alternatives and organize them before makingvalue judgments. The principle of this matrix is to score alternatives and to sort themin descending numerical order.

After the alternatives have been created and ordered, the process is repeated in thesame way using the M-Macbeth software, which results in compensation or replacementrates. The process was repeated in all the hierarchical value structures to allow for thedisclosure of the value judgments and preferences of the decision maker, to measurethe knowledge of the candidates for the realization of a project. This evaluation allowsthe manager to make the best human resource allocation based on knowledge demand, aswell as highlight their strengths and improvement areas. Figure 7 shows the hierarchicalvalue structure containing the compensation rates for the FPV2-Engineering and otherEPVs, according to the reference levels and intensity of preference of the decision maker.

This step links to the fourth theoretical construct of Table II.4.4.3. Step 6: Identification of impact profile of alternatives (global evaluation). After

the model has been created according to MCDA-C, it becomes possible to evaluate theimpact of the alternatives (candidates) to the research problem – human resourceallocation in a project management model, based on knowledge demand – to constructknowledge of the status quo. The equation that represents the local value (partialglobal model) for action “a” is calculated in the equation below:

VFPVkðaÞ ¼

Xnk

i¼1

wi;k�V i;kðaÞ: ð1Þ

VFPVk(a) is the global value for action “a” to the FPVk; Vi,k(a) the partial value of action“a” to the criterion i, i¼ 1,y, n; a the action to be evaluated; wi,k the substitution ratesto the criterion i, i¼ 1,y, n; nk the number of criteria to the FPVk; k: FPV number.

The global value is represented by the equation (complete model) and measures thechosen alternative “a” (in this study the engineer has three years’ experience) that sumnine FPVs constructed, as shown in Equation (2), and replace the values in the genericequation for local values (FPV) in Equation (1):

VGlobalðaÞ ¼ w1 � VFPV1ðaÞ þ w2 � VFPV2

ðaÞ þ w3 � VFPV3ðaÞ þ w4 � VFPV4

ðaÞþ w5 � VFPV5



ðaÞð2Þ

VGlobal(a) is the global score (impact profile) of the model.This paper is restricted to FPV-engineering because of the didactic and volume of

information. Equation (3) presents a detailed equation of FPV2-engineering. The base

749


alternative for analysis (status quo) was an engineer with three years’ experience,evaluated into FPV2-engineering to generate a score in FPV, as shown in Equation (3):

VFPV2ðaÞ ¼ 0:55½0:2�Vintegration þ 0:35ð0:39�Vpmi þ 0:61�Vsupervision experienceÞ

þ 0:29�Vexecution þ 0:16�Vdelegation� þ 0:45½0; 37�Vpoints of approval

þ 0:45ð0:58�V3Dmodel-software þ 0:42�Vexperience with systemsÞþ 0:18�Vmanufacturing�

ð3Þ

The operational process illustration was for FPV2-engineering, but to obtain the valuefor each FPV the same process was run for all FPVs in order to achieve a global score.The equation is completed for each point of view, from the lowest to the highest level,in the form of the hierarchical structure of the corresponding value. After the

ProcessConduction

Newcomponents

Points ofapproval

Degree of skill inmodeling (3D

software)

% Of service of thecompany’s standard

check list in themiddle of the last

projects

Number of systems alreadyused (ECR, visions, SAP,classification and creation)

Manufacturing

Number of manufacturingprocesses and knowledge

that contributes to the project(ex: extrusion)

3D Model-Software

Experience withSystems

Solid, drawing,surface andsheetmetal

Solid, drawing andsurface or sheetmetal

Solid and drawing

Solid and drawing orsheetmetal

Drawing

Nothing

5 or more

4

3

2

1

0

100%

95 to 99%

90 to 94%

50 to 89%

10 to 49%

1 to 9%

0

7 or more

5 to 6

3 to 4

2

1

0

HUMAN RESOURCE MANAGEMENT MODEL BASEDON KNOWLEDGE DEMAND


FPV 2 -Engineering

FPV 1 -Management

FPV 3 -Allocation

NEUTRAL

level

GOODlevel

Descriptor(KPI)

EPV

EPV

EPV

FPV

JEOPARDIZE

MARKET

EXCELLENCE

Legend:


Engineer 3 years experience (Alternative 1)

Newly hired engineer (Alternative 2)

D20 D21 D22 D23

Good

Neutral

Global ScoreFPV 2 –Engineering

V FPV2

Alternative 2: –37

Alternative 1: 44

37%45% 18%

58% 42%

125

100

80

40

0

–50

–80

100

70

40

0

–40

–80

122

100

67

33

0

–77

137

100

62

25

0

–25

a V(a) a V(a) a V(a) a V(a)

Figure 7.Status quo engineer threeyears experience� newlyhired engineer

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construction of the model, the decision maker can make decisions, understand theirimpact (locally and globally), and evaluate suitable alternatives. He can also easilyidentify improvements to increase alternative performance.

Global evaluation of the current situation (status quo) can start from a basealternative (initially) for assessment. For the case studied, the CTO (decision maker)counted on a group of engineers with varied skills and work experiences. He decidedto use two alternatives: alternative 1 (status quo): and engineer with three years’experience, and alternative 2: a newly hired engineer. This would allow the decisionmaker to check the impact of the worst case scenario according to his plan. Oncealternative 2 was considered (market or excellence result), the decision maker couldchallenge the new engineer and, at the same time, attend to his required job functions,develop talent, manage the team, and save money. This test will allow for a diagnosis(score) of the same profile and make improvements, if considered necessary.

Figure 7 shows graphically and numerically the local (operational) and global(strategic) impacts of the alternatives with a focus on the four descriptors. In this way,it is easy to note that the engineer evaluated, even with his experience of the company,that knowledge demands were required for the project under review at a market level(total value 44). Once alternative 2 had been analyzed, the possibility of allocating anewly hired engineer (less than one year’s work experience in the company) wouldjeopardize performance (total value �37). Thus, the decision maker estimated thesuccess of a project depending on his decision in relation to the available alternatives.For this study he chose alternative 2.

This step attends the last theoretical construct of Table II because the MCDA-Cprovides tools to transform the qualitative model to a quantitative model and, in turn,show the current overall situation from the decision maker’s perspective.

4.4.4. Step 7: sensitivity analysis. The model allows for the development of a sensitivityanalysis on the impact of alternatives on the scales, and on the attractiveness differencein the cardinal scales as well as on the compensation rates (Lacerda et al., 2011a). Thesensitivity analysis will explain what happens to the overall evaluation of the currentsituation if a certain set of actions are funded by the decision maker.

Another way of conducting sensitivity analysis is to verify how the actions arerobust in the face of the model change. For example, if a criterion has its compensationrate increased will this change modify the order of alternatives? How much cansuch changes in compensation rates be completed without changing the priority ofprevious actions?

In summary, the sensitivity analysis is useful when: the decision maker wants todevelop scenarios about the consequences from certain sets of actions that can beperformed; and, when the decision maker wants to know the consequences of any changein the compensation rate in the priorities presented by the model before the change.

4.5. Recommendation phaseThe MCDA-C method valorizes the recommendation phase because of the potential toeasily develop opportunities to improve the performance of the alternatives.

4.5.1. Step 8: formulation of recommendations, improvement actions andopportunities within existing resources. The opportunities for managerial improvementsare evident after the model building. It is easy to visualize the current profile (status quo)and the impact of each improvement action on the global score, as well as allowingfurther analysis, such as prioritization, according to the judgment of the decisionmaker. Since the only reasonable alternative was the engineer with three years’

751


experience (alternative 1) – due to the impossibility of training or hiringanother professional – the decision maker decided to improve alternative 1 to increasethe chances of success.

To select those EPVs to be evaluated and propose improvements in the PVF2

(engineering – status quo 44) based on the above assumptions and cost limitations, thefacilitator and decision maker elaborated on an action plan. This plan consideredthe improvement of four descriptors (D20, D21, D22, and D23), increasing one level andthereby generating the improvement actions (b1, b2, b3, and b4), as shown in Figure 8.The action plan was developed based on the process improvement actions thatdepended only on the efforts of the people involved and without relevant investment.Therefore, an action plan was legitimized by the decision maker. He then created aspecific tracking project for these improvements to guarantee 100 percent of theactions were implemented for FPV2-engineering. As a result of these actions, the globalscore of the FPV2 increased 11.68 points from the base score of 44 (status quo), to 55.68after the recommendation phase.

ProcessConduction

Newcomponents

Points ofapproval

Degree of skill inmodeling (3D

software)

% Of service of thecompany’s standard

check list in themiddle of the last

projects

Number of systems alreadyused (ECR, visions, SAP,classification and creation)

Manufacturing

Number of manufacturingprocesses and knowledge

that contributes to the project(ex: extrusion)

3D Model-Software

Experience withSystems

Solid, drawing,surface andsheetmetal

Solid, drawing andsurface or sheetmetal

Solid and drawing

Solid and drawing orsheetmetal

Drawing

Nothing

5 or more

4

3

2

1

0

100%

95 to 99%

90 to 94%

50 to 89%

10 to 49%

1 to 9%

0

7 or more

5 to 6

3 to 4

2

1

0

HUMAN RESOURCE MANAGEMENT MODEL BASED ON KNOWLEDGE DEMAND


FPV 2 -Engineering

FPV 1 -Management

FPV 3 -Allocation

NEUTRALlevel

GOODlevel

Descriptor(KPI)

EPV

EPV

EPV

FPV

JEOPARDIZE

MARKET

EXCELLENCE

Legend: Engineer 3 years experience after action plan (Alternative 1)

Engineer 3 years experience (Alternative 2)


D20 D21 D22 D23

Global ScoreFPV 2–Engineering

V FPV2

Alternative 2: 44

Alternative 1: 55

37% 45% 18%

58% 42%

125

100

80

40

0

–50

–80

100

70

40

0

–40

–80

122

100

67

33

0

–77

137

100

62

25

0

–25

β1 β 2β 3

β4

Good

Neutral

a V(a)a V(a)a V(a)a V(a)

Figure 8.Engineer three yearsexperience afterrecommendation phase –EPV process

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IJPPM62,7

This step relates to the last theoretical construct of Table II because the MCDA-Cuses knowledge supplied by the ordinal scales to generate actions for improvement,and then uses the cardinal knowledge to present the attractiveness of each action fromthe decision maker’s perspective.

5. Conclusions and recommendations for further researchThe final product of this study was human resource allocation in a projectmanagement model based on knowledge demand, which allowed for expandingknowledge and understanding of the CTO (decision maker). Because of the complexityand conflicting interests of stakeholders, the MCDA-C method was selected as anintervention instrument to identify the objectives, evaluate their impact, and aidmanagers enduring managerial difficulties.

The process identified 80 PAEs that were transformed into concepts anddescriptors, which generated an expansion of knowledge and understanding of theproblem by the decision maker and, consequently, others involved.

The decision maker actively participated in all steps of the process to legitimizethem. Moreover, the decision maker could use the model as a management tool toimprove opportunities with the clear understanding of the impact on both the local andthe global scores of each action.

The theoretical contribution of this paper is based on theoretical constructs builtfrom research opportunities observed from 11 relevant and well-cited papers aboutperformance evaluation and HRM. These constructs were built using ProKnow-C,a systematic way to build the researcher’s knowledge about a scientific topic.

At the beginning, the human resources manager considered the method both slow(after his focus was to manage all of the project and people) and hard (due to his activeparticipation and deep involvement during the process). However, the benefits ofmeasuring the indicators are were not only qualitative; after showing him the graphicmodel and the possible improvement opportunities, he promptly changed his mind andprovided favorable support. Table II presents the theoretical constructs built from thisstudy’s research and highlights how MCDA-C attends to them, as observed fromthe case study section.

The MCDA-C methodology has demonstrated its usefulness in the process ofdecision aiding in other contexts such as project management (Lacerda et al., 2011b),R&D management (Marafon et al., 2012), healthcare technology management(De Moraes et al., 2010), and presented in a HRM context in this study.

However, it is recommended that the method be used in other specific peoplemanagement problems, such as professional selection, reward programmes,professional career planning, and promotions. The adequacy of the method needs tobe observed and improvements made to this decision aiding methodology.

References

Athanassopoulos, A. and Gounaris, C. (2001), “Assessing the technical and allocative efficiencyof hospital operations in Greece and its resource allocation implications”, EuropeanJournal of Operational Research, Vol. 133 No. 2, pp. 416-431.

Azevedo, J. (2001), Aplicac~ao da Metodologia Multicriterio de Apoio a Decis~ao na Selec~ao deCentros de Usinagem para uma Central de Usinagem, Dissertac~ao (Mestrado emEngenharia de Produc~ao)-Universidade Federal de Santa Catarina, Florianopolis[Links].

Baird, L. and Meshoulam, I. (1988), “Managing two fits of strategic human resourcemanagement”, Academy of Management Review, pp. 116-128.

753


Bana e Costa, C.A. (1993), “Tres conviccoes fundamentais na pratica do apoio a decis~ao”, PesquisaOperacional, Vol. 13 No. 1, pp. 9-20.

Bana e Costa, C.A. and Ensslin, L. (1999), “Decision support systems in action: integratedapplication in a multi-criteria decision aid process”, European Journal of OperationalResearch, Vol. 113, pp. 315-335.

Bana e Costa, C.A.B., De Corte, J.M. and Vansnick, J.C. (2005), “On the mathematical foundationsof Macbeth”, in Greco, J.F. and Ehrgott, S.M. (Eds), Multi-Criteria Decision Analysis: Stateof the Art Survey, Springer Verlag, Boston, MA, Dordrecht and London, pp. 15-24.

Bana e Costa, C.A., Ensslin, L., Correa, E.C. and Vansnick, J.C. (1999), “Decision support systemsin action: integrated application in a multicriteria decision aid process”, European Journalof Operational Research, Vol. 113 No. 2, pp. 315-335.

Barzilai, J. (2001), “On the foundations of measurement”, paper presented at the Systems, Man,and Cybernetics, 2001 IEEE International Conference.

Bititci, U.S., Suwignjo, P. and Carrie, A.S. (2001), “Strategy management through quantitativemodelling of performance measurement systems”, International Journal of ProductionEconomics, Vol. 69 No. 1, pp. 15-22.

Chen, S.H. and Lee, H.T. (2007), “Performance evaluation model for project managers usingmanagerial practices”, International Journal of Project Management, Vol. 25 No. 6, pp. 543-551.

da Rosa, F.S., Ensslin, S.R., Ensslin, L. and Lunkes, R.J. (2012), “Environmental disclosuremanagement: a constructivist case”, Management Decision, Vol. 50 pp. 1117-1136.

de Moraes, L., Garcia, R., Ensslin, L., da Conceic~ao, M.J. and de Carvalho, S.M. (2010), “The multi-criteria analysis for construction of benchmarkers to support the clinical engineeringin the healthcare technology management”, European Journal of Operational Research,Vol. 200 No. 2, pp. 607-615.

de Waal, A., Goedegebuure, R. and Geradts, P. (2011), “The impact of performance managementon the results of a non-profit organization”, International Journal of Productivity andPerformance Management, Vol. 60 No. 8, pp. 778-796.

Dixon, G. (2011), “Service learning and integrated, collaborative project management”, ProjectManagement Journal, Vol. 42 No. 1, pp. 42-58.

Eden, C., Ackermann, F. and Cropper, S. (1992), “The analysis of cause maps”, Journal ofManagement Studies, Vol. 29 No. 3, pp. 309-324.

Ensslin, L., Dutra, A. and Ensslin, S.R. (2000), “MCDA: a constructivist approach to themanagement of human resources at a governmental agency”, International Transactions inOperational Research, Vol. 7 No. 1, pp. 79-100.

Ensslin, L., Giffhorn, E., Ensslin, S.R., Petri, S.M. and Vianna, W.B. (2010), Avaliac~ao dodesempenho de empresas terceirizadas com o uso da metodologia multicriterio de apoio adecis~ao-construtivista, SciELO.

Ensslin, L., Montibeller, G.N. and Noronha, S.M. (2001), Apoio a decis~ao: metodologias paraestruturac~ao de problemas e avaliac~ao multicriterio de alternatives, Insular, Florianopolis.

Ensslin, L. and Vianna, W.B. (2008), “O design na pesquisa quali-quantitativa em engenharia deproduc~ao–questoes epistemologicas”, Revista Produc~ao Online, Vol. 8 No. 1, pp. 1-16.

Golec, A. and Kahya, E. (2007), “A fuzzy model for competency-based employee evaluation andselection”, Computers & Industrial Engineering, Vol. 52 No. 1, pp. 143-161.

Goodwin, P. and Wright, G. (1998), Decision Analysis for Management Judgment, John Wiley &Sons, Chichester.

Hart, S., Jan Hultink, E., Tzokas, N. and Commandeur, H.R. (2003), “Industrial companies’evaluation criteria in new product development gates”, Journal of Product InnovationManagement, Vol. 20 No. 1, pp. 22-36.

754

IJPPM62,7

Hendriks, M., Voeten, B. and Kroep, L. (1999), “Human resource allocation in a multi-project R&Denvironment: resource capacity allocation and project portfolio planning in practice”,International Journal of Project Management, Vol. 17 No. 3, pp. 181-188.

Huang, Y.F., Hsu, K.H., Chen, P.S. and Dong, S.H. (2011), “Discussing performance index ofhuman resource valuation with AHP-occupational safety section in T company in Taiwanas the case study”, Information Technology Journal, Vol. 10 No. 3, pp. 549-556.

Huselid, M.A. (1995), “The impact of human resource management practices on turnover,productivity, and corporate financial performance”, Academy of Management Journal,Vol. 38 No. 3, pp. 635-672.

Igarashi, D.C.C., Ensslin, S.R., Ensslin, L. and Paladini, E.P. (2008), “A qualidade do ensino sob ovi”, s da avaliac~ao de um programa de pos-graduac~ao em contabilidade: proposta deestruturac~ao de um modelo hıbrido. Revista de Gest~ao USP, Vol. 43 No. 2, pp. 117-137.

Jackson, S.E. and Schuler, R.S. (1995), “Understanding human resource management in thecontext of organizations and their environments”, Strategic Human ResourceManagement, Vol. 46 pp. 237-264.

Kahya, E. (2009), “The effects of job performance on effectiveness”, International Journal ofIndustrial Ergonomics, Vol. 39 No. 1, pp. 96-104.

Keeney, R.L. (1992), Value-Focused Thinking: A Path to Creative Decision Making, HarvardUniversity Press.

Kiessling, T. and Harvey, M. (2005), “Strategic global human resource management researchin the twenty-first century: an endorsement of the mixed-method research methodology”,The International Journal of Human Resource Management, Vol. 16 No. 1, pp. 22-45.

Lacerda, R.T.O., Ensslin, L. and Ensslin, S.R. (2011a), “A performance measurement frameworkin portfolio management: a constructivist case”, Management Decision, Vol. 49 No. 4,pp. 648-668.

Lacerda, R.T.O., Ensslin, L. and Ensslin, S.R. (2011b), “CONTRIBUIcOES A GESTAOESTRATEGICA DE ORGANIZAcOES QUANDO ANALISADOS NA VISAO DE SEUDESEMPENHO”, GESTAO. Org-Revista Eletronica de Gest~ao Organizacional, Vol. 9 No. 2,pp. 1-15.

Lacerda, R.T.O., Ensslin, L. and Ensslin, S.R. (2012), “A bibliometric analysis of strategy andperformance measurement”, GESTAO & PRODUcAO, Vol. 19 pp. 59-78.

Lado, A.A. and Wilson, M.C. (1994), “Human resource systems and sustained competitiveadvantage: a competency-based perspective”, Academy of Management Review, Vol. 19No. 4, pp. 699-727.

Laitinen, E.K. (2002), “A dynamic performance measurement system: evidence from small Finnishtechnology companies”, Scandinavian Journal of Management, Vol. 18 No. 1, pp. 65-99.

Landry, M. (1995), “A note on the concept of ‘problem’ ”, Organization Studies, Vol. 16 No. 2,pp. 315-343.

Lee, H., Park, Y. and Choi, H. (2009), “Comparative evaluation of performance of national R&Dprograms with heterogeneous objectives: a DEA approach”, European Journal ofOperational Research, Vol. 196 No. 3, pp. 847-855.

Marafon, A.D., Ensslin, L., Lacerda, R.T.O. and Ensslin, S.R. (2012), “The implications of R&Dmanagement for organisational effectiveness: a literature review”, TechnologyManagement Conference (ITMC), 2012 IEEE International, IEEE, pp. 202-211.

Medlin, B. and Green, K.W. Jr (2009), “Enhancing performance through goal setting,engagement, and optimism”, Industrial Management & Data Systems, Vol. 109 No. 7,pp. 943-956.

Mel~ao, N. and Pidd, M. (2000), “A conceptual framework for understanding business processesand business process modelling”, Information Systems, Vol. 10 No. 2, pp. 105-129.

755


Moon, C., Lee, J. and Lim, S. (2010), “A performance appraisal and promotion ranking systembased on fuzzy logic: an implementation case in military organizations”, Applied SoftComputing, Vol. 10 No. 2, pp. 512-519.

Roy, B. (1993), “Decision science or decision-aid science?”, European Journal of OperationalResearch, Vol. 66 No. 2, pp. 184-203.

Roy, B. (1994), “On operational research and decision aid”, European Journal of OperationalResearch, Vol. 73, pp. 23-26.

Roy, B. (1996), Multi-Criteria Methodology for Decision Aiding, Vol. 12, Springer.

Roy, B. (2005), “Paradigms and challenges”, Multi-Criteria Decision Analysis: State of the ArtSurvey, Springer, New York, NY, pp. 3-24.

Roy, B. and Bouyssou, D. (1993), “Decision-aid: an elementary introduction with emphasis onmultiple criteria”, Information Science and Technology, Vol. 2, pp. 109-123.

Schuler, R.S. and MacMillan, I.C. (1984), “Gaining competitive advantage through humanresource management practices”, Human Resource Management, Vol. 23 No. 3,pp. 241-255.

Setijono, D. and Dahlgaard, J.J. (2007), “Customer value as a key performance indicator (KPI)and a key improvement indicator (KII)”, Measuring Business Excellence, Vol. 11 No. 2,pp. 44-61.

Shenhar, A.J. (2001), “One size does not fit all projects: exploring classical contingency domains”,Management Science, Vol. 47 No. 3, pp. 394-414.

Skinner, W. (1986), “The productivity paradox”, Management Review, Vol. 75 No. 9,pp. 41-45.

Stewart, T. (2005), “Dealing with uncertainties in MCDA”, Multiple Criteria Decision Analysis:State of the Art Surveys, Springer, New York, NY, pp. 445-466.

Tasca, J.E., Ensslin, L., Ensslin, S.R. and Alves, M.B.M. (2010), “An approach for selecting atheoretical framework for the evaluation of training programs”, Journal of EuropeanIndustrial Training, Vol. 34 No. 7, pp. 631-655.

Trejo, D., Patil, S., Anderson, S. and Cervantes, E. (2002), “Framework for competency andcapability assessment for resource allocation”, Journal of Management in Engineering,Vol. 18 No. 1, pp. 44-49.

Wang, W.-P. (2009), “Evaluating new product development performance by fuzzy linguisticcomputing”, Expert Systems with Applications, Vol. 36 No. 6, pp. 9759-9766.

Wright, P.M., Dunford, B.B. and Snell, S.A. (2001), “Human resources and the resource based viewof the firm”, Journal of Management, Vol. 27 No. 6, pp. 701-721.

Yan, W., Chen, C.-H. and Khoo, L.P. (2001), “A radial basis function neural network multiculturalfactors evaluation engine for product concept development”, Expert Systems, Vol. 18pp. 219-232.

Zimmermann, H.J. (2000), “An application-oriented view of modeling uncertainty”, EuropeanJournal of Operational Research, Vol. 122 No. 2, pp. 190-198.

Further Reading

Azevedo, R.C., Ensslin, L., Lacerda, R.D.O., Franca, L.A., Gonzalez, C.J.I., Jungles, A.E. and Ensslin,S.R. (2011), “Avaliac~ao de desempenho do processo de orcamento: estudo de caso em umaobra de construc~ao civil”, Ambient. constr.(Online), Porto Alegre, Vol. 11 No. 1.

Della Bruna, E., Ensslin, L. and Ensslin, S.R. (2011), “Supply chain performance evaluation: acase study in a company of equipment for refrigeration”, Technology ManagementConference (ITMC), 2011 IEEE International, IEEE, pp. 969-978.

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Lacerda, R.T.O. (2012), “Strategic decision aiding methodology for continuous generationof competitive advantages from the organizational resources”, thesis presented for thedegree of Doctor of Production Engineering – Federal University of Santa Catarina,Florianopolis.

About the authors

Sandra Rolim Ensslin has degree in accounting science by Catholic University of Pelotas, masterin Production Engineering by Federal University of Santa Catarina (UFSC) and doctor inProduction Engineering by UFSC (2003). She is Program Coordinator of Graduate Studies inAccounting and assistant professor at the UFSC. She has experience in Accounting andProduction Engineering, working actually in the following topics: methodology of multicriteriadecision aiding construtivist, organizational performance evaluation, intellectual capital, intangibleassets and accounting research.

Leonardo Ensslin has a post-doctoral position in Multicriteria Decision Aiding at LancasterUniversity (2000) and has a PhD in Industrial Systems from the University of SouthernCalifornia (1974). Leonardo Ensslin is professor and coordinator of organizational intelligencein the Department of Systems Engineering and Production at the Federal University of SantaCatarina (UFSC) on undergraduate and post-graduate courses. His degree in mechanicalengineering was awarded by UFRGS and he has a masters in Production Engineering fromUFSC. He is a consultant and lecturer in analysis and performance evaluation, organizationalimprovement systems, innovation and decision aiding processes.

Felipe Back is currently a master student in Business Intelligence (Production Engineeringdepartment) from the Federal University of Santa Catarina and he graduated in ProductionEngineering. Back has spent many years working in a global home appliances company asProject leader (PMO). Felipe Back is the corresponding author and can be contacted at:[email protected]

Rogerio Tadeu de Oliveira Lacerda is Doctor in Production Engineering (2012) from theFederal University of Santa Catarina, has a master’s degree in Production Engineering fromthe Federal University of Santa Catarina (2009), Degree in Business Administration fromUniversidade Metropolitana de Santos (1997) and post graduate degree in InformationEngineering by FASP (1999). Rogerio Lacerda is professor in the Post-graduate program inBusiness Administration from the University of Southern Santa Catarina (UNISUL). His currentresearch interest is decision aiding, performance measurement, strategic management andoperations management. Also researchs on the topics of project management, portfolio andbusiness processes. He has experience in Administration, with emphasis in Project Managementand is PMP certified by PMI professional. OPM3 participated in the project – ProjectManagement Institute.

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