Object Class or Association Class? Testing the User Effect on ...

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Object Class or Association Class? Testing the User Effect on Cardinality Interpretation

Geert Poels1 Frederik Gailly Ann Maes Roland Paemeleire

August 2005 2005/323

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Corresponding author - [email protected] All authors are members of the Management Informatics Research Unit, Department of Management Information, Operations Management and Technology Policy, Faculty of Economics and Business Administration, Ghent University, Hoveniersberg 24, 9000 Gent, Belgium. E-mail: {Geert.Poels, a.maes, Frederik.Gailly, Roland.Paemeleire}@UGent.be The authors wish to thank the reviewers of the BP-UML’05 workshop for their useful comments.

D/2005/7012/41

Object Class or Association Class? Testing the User Effect on Cardinality Interpretation Geert Poels, Frederik Gailly, Ann Maes, Roland Paemeleire Management Informatics Research Unit Faculty of Economics and Business Administration Ghent University - UGent Hoveniersberg 24, B-9000 Gent, Belgium {geert.poels, frederik.gailly, a.maes, roland.paemeleire}@UGent.be

Abstract. In UML class diagrams, a many-to-many relationship with attributes can be represented by an association class or by a connecting object class. It is unclear which modeling construct is preferred in particular modeling scenarios. Because of lack of theory, this paper investigates the issue empirically. An experiment was conducted that tested the effect of representational form chosen on the performance of model users at cardinality interpretation tasks. It was shown that, controlling for cardinality knowledge, business users can better interpret the information that a UML class diagram conveys about a many-to-many relationship with attributes if this relationship is represented as an association class. The implication for ‘best practices’ in UML modeling is that modelers should refrain from objectifying such relationships if the goal is an effective communication of domain semantics to users that are not modeling experts.

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1. Motivation In conceptual modeling practice, it is accepted that semantic relationships between objects can have attributes of their own [24]. The UML construct to specify such relationships is the association class, which is both an association and a class [14]. Although the representation by an association class seems a logical choice, widely-read literature on conceptual modeling (e.g. [6], [12]) recommends that also objects be used to represent semantic relationships. Relationship objectification means that the association class between two classes X and Y (Figure 1) is replaced by an object class, which is not an association, but has associations with X and Y (Figure 2).

a..b

X

c..d

Y

relationship attributes

Fig. 1. Association class represents a semantic relationship with attributes

X

1

c..d

relationship attributes

a..b

1

Y

Fig. 2. Objectifying a semantic relationship with attributes

Comparing both figures, it can be seen that objectification introduces a level of indirection in the relationship between X and Y. Another consequence is the changed positioning of the multiplicities. These multiplicities specify the allowable cardinalities of the modeled relationships (e.g. an object of class X is related to at least c and at most d objects of class Y). In a UML class diagram representing a structural model of the domain, the modeled cardinality constraints pertain to laws that hold for the semantic relationships between objects. A major function of such models is to communicate domain semantics [10]. It is therefore imperative that relationships and their laws be rightfully interpreted by model users, including business users that might have limited modeling experience and training in UML. The question addressed in this paper is whether the representation chosen for a relationship with attributes affects the ability of model users to understand the information conveyed by a UML class diagram. It might be that the indirection introduced through objectification makes it more difficult to interpret the modeled cardinalities as intended by the modeler. It might as well be that the pattern of multiplicity specifications is simpler after objectification. For class diagrams, there are no theoretically-grounded prescriptions to distinguish situations in which object classes should be used from situations in which association classes should be used [16]. Nevertheless, modeling practitioners expect clear guidance as to which modeling constructs should be used under particular circumstances

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[25]. With communication towards business users in mind, what is the best way to represent a relationship with attributes: object class or association class? This paper aims at finding an answer by investigating the issue empirically. Section 2 lists some advantages and disadvantages of objectification found in the literature, that are used to further refine the research question. Section 3 presents our research framework. The design, operation and data analysis of an exploratory experiment are presented in section 4. Section 5 concludes the paper.

2. Objectifying Relationships: Advantages and Disadvantages The practice of relationship objectification is motivated by a number of empirical studies indicating that database designers find relationships more difficult to model than objects [24]. But also for other applications than database design, objectification is recommended. For instance, [7] presents a formally defined enterprise modeling method that objectifies all many-to-many relationships between objects into contract objects that are existence dependent on the objects participating in the relationships. Objectification reduces complexity by making the relationships between objects simple and easy to understand [20]. In particular when the maximum cardinality constraint is ‘many’ on both sides of the relationship (i.e. when the b and d in Figures 1 and 2 are ‘*’), objectification simplifies the many-to-many object connectivity (Figure 1) by replacing it with one-to-many links to a connecting object (Figure 2). Other researchers have argued against objectification. In [4], [24] it is shown that this practice violates an important rule of the Bunge-Wand-Weber (BWW) representation model, which is an ontological theory used to evaluate conceptual modeling grammars. According to the BWW model, objects should be used only to represent things. Furthermore, things can be linked only by mutual properties. A semantic relationship is a mutual property of two or more things, but is not a thing. Therefore, an object should not be used to represent a semantic relationship. Lack of ontological clarity leads to semantic ambiguities in models, which in turn affects the user understanding of the models; a prediction supported by theories of cognition [3]. On the other hand, objectification circumvents the direct representation of many-to-many relationships, which are more difficult to conceptualize than one-to-many relationships. It is not uncommon that the application of the BWW model contradicts widely used practices in conceptual modeling [19]. Therefore, ontology-based predictions should be tested empirically [10]. Based on the observations made in this section, the study presented next focuses on many-to-many relationships.

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3. Research Framework Our research framework (Figure 3) is derived from Cognitive Fit theory [23]. The task performance of model users is affected by cognitive fit, which is the match between the model representation and the task that the user has to perform using/on the model. The more suitable the representation is for the particular (type of) task executed by the particular (type of) user, the better the task performance is.

Task cardinality interpretation constant other characteristics controlled

Model Representation UML representation of many-to-many relationship with attributes - object class - association class independent variable

User Task Performance Cognitive Fit

-

correctness of cardinality interpretation dependent variable

other characteristics controlled

User business user constant cardinality knowledge covariate other characteristics controlled

Fig. 3. Research framework

The research question addressed is whether the representation of a many-to-many relationship with attributes in a UML class diagram has an impact on the correctness of cardinality interpretation by business users. The representation chosen is the main factor under investigation and is varied at two levels: object class or association class. Given the exploratory nature of the research, the direction of the effect is not stated. Consistent with the research question, the levels of the user and task factors are fixed at respectively business users and cardinality interpretation tasks. To investigate the research question in an experiment, other model representation, task, and user characteristics that may affect cognitive fit must be controlled to assure internal validity. Such characteristics include diagram layout [18], size

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[2], structural complexity [11], task complexity [1], domain knowledge [4], personal characteristics [9], modeling experience [15], and modeling language knowledge [13]. One user characteristic needs special attention. In [8] a significant impact of cardinality knowledge on user performance was found for tasks that involved cardinality interpretation and error identification. Low cardinality knowledge may affect someone’s ability to correctly interpret relationship cardinalities, regardless of the representation chosen. To assess the impact of such a confounding effect, cardinality knowledge is used as a covariate in the study.

4. An Experiment 4.1. Design The experiment employed two pairs of class diagrams representing two structural models from the auctioneering domain: a model for the sales/collection process and another model for the acquisition/payment process. Each model included one many-to-many relationship with attributes (hereafter called the focal relationship). For each model there was a diagram showing the focal relationship as an association symbol (i.e. a solid path that connects the related classes) with an attached association class used as a container for the relationship attributes (Figures 4 and 5). For each model there was another diagram representing the focal relationship as an object class. In that case the relationship attributes are contained within the object class, along with a primary key that is the concatenation of the primary keys of the related classes (Figures 6 and 7).2 These classes and the object class introduced for representing the relationship are connected by association paths, without attaching names to them. Order of process modeled and representation of the focal relationship were counterbalanced across participants (Table 1). The group 1 participants performed first an experimental task on the sales/collection process diagram with the focal relationship represented as an object class (Figure 6) and next an experimental task on the acquisition/payment process diagram with the focal relationship represented as an association class (Figure 5). The group 2 participants dealt first with the acquisition/payment process diagram with the focal relationship represented as an object class (Figure 7) and next with the sales/collection process diagram with the focal relationship represented as an association class (Figure 4). Counterbalancing reversed these orders for group 3 (i.e. reverse order of group 1) and group 4 (i.e. reverse order of group 2).

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In [5] primary key attribute(s) of an object class are explicitly identified in the list compartment of the classifier by means of a {Primary Key} tag. Though not standard UML, this convention was followed in the course from which the study participants were drawn.

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Good In Auction

TakesOutOfAuction 1..1

description {Primary key} estimatedValue minimumBidPrice

ParticipatesIn Accept_Bid

1..*

0..1 acceptedBidId {Primary key} bidPriceOffered date

Question 5

0..*

Question 1 Question 4 Question 6

0..* 1..1

ParticipatesIn

Question 1 Question 3 Question 4

CollectedFor

PariticipatesIn

0..*

0..*

1..1

Increases Cash accountID {Primary key} balanc

ParticipatesIn

Cash_Receipt 1..1

0..*

name {Primary key} address

Question 2

amountPaid

Buyer

1..1

0..* collectionID {Primary key} date

Auctioneer_Agent 1..1

employeeID {Primary key} name

Fig. 4. Class diagram for Sales/Collection process, association class representation

ParticipatesIn

AddsToAuction Good In Auction

Accept For Auction 1..*

description {Primary key} estimatedValue minimumBidPrice

1..1

1..*

number {Primary key} date Question 2 Question 5

Owner

1..1

name {Primary key} address

0..* Question 6

1..*

1..1 ParticipatesIn Question 4

amountPaid Question 1 Question 3

PaymentFor 0..*

1..* Reduces Cash Disbursement 1..1

1..1

ParticipatesIn

Cash accountID {Primary key} balanc

PariticipatesIn

0..*

paymentID {Primary key} date

0..*

Auctioneer Agent 1..1

employeeID {Primary key} name

Fig. 5. Class diagram for Acquisition/Payment process, association class representation

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Good_In_Auction

TakesOutOfAuction 1..1

ParticipatesIn

Accept_Bid

1..*

0..1

acceptedBidId {Primary key} bidPriceOffered 0..* date Question 1 Question 3 1..1 Question 4

description {Primary key} estimatedValue minimumBidPrice

name {Primary key} address

1..1 ParticipatesIn

0..* Question 1 Question 4 Question 6

Buyer

1..1

Collected_For acceptedBidId {Primary key} collectionID {Primary key} amountPaid

Question 5

PariticipatesIn

Question 2 0..*

0..*

1..1 Increases Cash accountID {Primary key} balance

1..1

1..1

ParticipatesI 0..*

Cash_Receipt

0..*

collectionID {Primary key} date

Auctioneer_Agent 1..1

employeeID {Primary key} nam

Fig. 6. Class diagram for Sales/Collection process, object class representation

Good_In_Auction

ParticipatesIn

AddsToAuction 1..*

Accept_For_Auction

1..*

1..1

description {Primary key} estimatedValue minimumBidPrice

number {Primary key} date 1..1 Question 1 Question 3

Owner

1..1

name {Primary key} address

0..* Question 4

1..1 ParticipatesIn

1..* Payment_For number {Primary key} paymentID {Primary key} amountPaid

Question 2 Question 5

Question 6 PariticipatesIn

1..*

1..1 Cash accountID {Primary key} balance

Cash Disbursement 1..1

0..*

1..1

0..*

Reduces

ParticipatesI 0..*

paymentID {Primary key} date

Auctioneer_Agent 1..1

employeeID {Primary key} nam

Fig. 7. Class diagram for Acquisition/Payment process, object class representation

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Table 1. Experimental design Representational form Business process modeled Order Group

Object class Sales/ Collection first second 1 3

Acquisition/ Payment first second 2 4

Association class Sales/ Collection first second 4 2

Acquisition/ Payment first second 3 1

This within-subjects experimental design was chosen to control differences in user characteristics. It necessitated, however, the use of two models. The models are conceptually very similar. The focal relationship fulfills in both models the role of relating a transfer of goods with a transfer of money. Only the perspective is different: an outwards transfer of goods and inwards transfer of money for the sales/collection process (Figure 4 and 6) versus an inwards transfer of goods and outwards transfer of money for the acquisition/payment process (Figures 5 and 7). Apart from the representation of the focal relationship, the other diagram elements are identical within each pair of diagrams. Also, there are no remarkable differences in size, structural complexity or aesthetics across the four diagrams.3 The minimum and maximum cardinality constraints specified for the focal relationship express important business policies that govern the business process modeled. The experimental task was directed towards the interpretation of these cardinalities. For each diagram there were six ”yes or no” questions requiring participants to interpret the information about the focal relationship conveyed by the diagram and check this interpretation against a given scenario (described in the question). Discrepancy checking is a key aspect of model validation [13]. Furthermore this task is representative for the way that business users interact with conceptual models. The same questions were used for the two diagrams representing a same model (Tables 2 and 3). Also across the two models, the questions used were conceptually similar. Participants were told that they had to answer these questions using the diagrams they received, even if they believed the diagrams were incomplete or invalid. The cardinalities that must be interpreted (in terms of business policies) to correctly answer the questions are annotated in Figures 4 to 7. The number of questions correctly answered was used as a measure of a participant’s correctness of cardinality interpretation.

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The diagrams with the object class representation (Figures 6 and 7) are slightly bigger in the sense that they show an additional diagram node. However, the questions used for the comprehension task (cf. infra) focused the attention on the semantic relationship between the transfer of goods and the transfer of money. The total size of the diagrams is not relevant for the type of comprehension tested.

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Table 2. Interpretation task for class diagrams of Sales/Collection process Questions for Sales/Collection process 1. Must, according to the diagram, a buyer pay the full amount of the bid price offered immediately upon acceptance of his/her bid ? 2. Is the diagram correct if a buyer is not allowed to pay for more than one accepted bid at a time ? 3. Can, according to the diagram, a buyer who’s bid has been accepted, pay the bid price offered in parts ? 4. Is the diagram correct if some days after acceptance of their bid, buyers must pay the full amount of the bid price offered ? 5. Can, according to the diagram, payment be received from a buyer, even before his/her bid is accepted ? 6. Is the diagram correct if a buyer must pay part of the bid price offered, immediately upon acceptance ? Table 3. Interpretation task for class diagrams of Acquisition/Payment process Questions for Acquisition/Payment process 1. Is the diagram correct if an owner is paid only after some of his/her goods have been sold at the auction ? 2. Must, according to the diagram, every payment made to an owner, relate to at least one accepted offer of goods ? 3. Must, according to the diagram, some amount be paid to an owner, immediately upon acceptance of his/her offer of goods to auction ? 4. Is the diagram correct if more than one payment made to an owner, relates to the same accepted offer of goods? 5. Is the diagram correct if payments cannot be made unless they relate to an offer of goods that is accepted for the auction? 6. Must, according to the diagram, every payment made to an owner, relate to at most one accepted offer of goods ?

Forty-three graduate students took part in the experiment. As business students majoring in accounting, they were intimately familiar with transaction-oriented business processes and accounting policies. In the weeks before the experiment a thirty hours course on Accounting Information Systems was given. About half the time was spent on conceptual modeling of business processes. During classes the students were forced into the roles of (future) system users and system auditors that had to read business process documentation (such as structural models) in order to check processes as modeled against reality (as for instance described in textual scenarios). The course exercises on conceptual modeling focused especially on the interpretation of modeled business policies, considering cardinality constraints on relationships as a kind of internal accounting controls. Students also learned how to interpret business policies that cross-over relationships. The notation used for structural models was that of the UML class diagram. The teaching approach taken was to consider UML as a communication vehicle for domain knowledge, meaning that, as future business professionals, the students must be able to comprehend conceptual models in UML notation (at least class diagrams and activity diagrams). A questionnaire administered in the first course session indicated that none of the students had taken prior UML training and only a couple of students were familiar with entity-relationship concepts, though most of them acquired elementary relational database (MS Access) knowledge. As the participating students possessed up to four years of working experience, they approximate a representative sample of the target population of business users.

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The students had studied sales/collection and acquisition/payment processes before, but not for an auctioneer. Therefore participants’ domain knowledge was assessed as low. In [17] it is shown that when participants are domain experts, they tend to interpret conceptual models using their domain knowledge, instead of using the information conveyed by the models. The cardinality knowledge of the students was assessed using a procedure similar to [8]. At the beginning of the experiment each participant had to answer four “true or false” questions that tested the ability to identify the correct narrative corresponding to a cardinality notation. Following [17], diagram fragments were shown with symbolic labels (e.g. “A”, “B”, …) attached to the diagram elements, instead of meaningful names. Hence any potential impact of domain knowledge was avoided. The number of questions correctly answered was used as a measure of a participant’s cardinality knowledge.

4.2. Operation When the students entered the room where the experiment took place, they were randomly assigned to the four experimental groups. Participants started with the cardinality knowledge test. Next they were given a first diagram and the interpretation questions corresponding to the process modeled in the diagram. After finishing the first interpretation task, participants were given their second diagram along with the corresponding questions. No time limit was put upon the experiment; students could take whatever time they thought was necessary. The students were motivated to perform well in the experiment as it was part of their final exam. However, the experiment made up only one quarter of the exam and on the totality of the exam there was a time constraint.

4.3. Data analysis Thirty-seven participants managed to give correct answers to all questions for testing cardinality knowledge. Three students made one mistake; three others made two mistakes. Given the limited variability in the scores, cardinality knowledge could not be included as a covariate in the study, as originally planned. Instead, cardinality knowledge was controlled by excluding from the analysis the data of participants with a less than perfect score. Descriptive statistics are shown in Table 4. For each representational form the mean number of questions correctly answered (i.e. a real value in the interval [0..6]) and its standard deviation are shown, per group as well as for all groups together. The mean score for correctness of interpretation was always higher for the association class representation. The paired differences in correctness of cardinality interpretation scores between the treatments of the within-subjects factor (i.e. representational form) were normally distributed. Therefore a paired samples t-test was applied to test whether the representation of the focal

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relationship in the diagrams had an impact on the correctness of cardinality interpretation by the participants. The test results indicate that the mean correctness of cardinality interpretation score (for all groups, n = 37) was significantly higher for the association class representation (t = –5.18, p < .001). Table 4. Experimental data – descriptive statistics & results of hypothesis test (controlled for cardinality knowledge)

Experimental groups (n = group size after controlling for cardinality knowledge) Group 1 (n = 11) First: Sales/Collection, object class representation Second: Acquisition/Payment, association class representation Group 2 (n = 9) First: Acquisition/Payment, object class representation Second: Sales/Collection, association class representation Group 3 (n = 9) First: Acquisition/Payment, association class representation Second: Sales/Collection, object class representation Group 4 (n = 8) First: Sales/Collection, association class representation Second: Acquisition/Payment, object class representation All groups (n = 37)

Correctness of interpretation Mean score (Standard deviation) Object class Association representation class representation 3.82 4.09 (0.87) (0.83) 3.56 (1.24)

5.11 (0.78)

3.33 (0.50)

4.56 (0.88)

3.63 (0.92)

4.63 (0.74)

3.59a (0.90)

4.57a (0.87)

Results of hypothesis test: a significantly different, p < .001 (two-tailed)

Also tested were confounding effects that might be caused by the order in which the diagrams were shown to the participants or the business process modeled. As the normality requirement was satisfied, paired samples t-tests were used. The results show that there was no order effect (t = 0.218, p = .829). Neither was the correctness of cardinality interpretation different for the two business processes modeled (t = 0.880, p = .384). The latter posttest also confirms that characteristics related to the interpretation task (e.g. task complexity) were effectively controlled in the experiment, i.e. the sales/collection process questions and acquisition/payment questions were equally difficult.

5. Discussion The results of the experiment indicate that, controlling for cardinality knowledge, business users can better interpret the information that a UML class diagram conveys about a many-to-many relationship with attributes if this relationship is represented as an association class instead of an object class. The implication for establishing ‘best practices’ in UML modeling is that modelers should refrain from objectifying such relationships if the goal is an effective communication of domain semantics to business users, who are not UML or modeling experts.

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A weakness of the study is the use of only a limited number of class diagrams. In replication studies the use of more diagrams from different business domains should be considered to increase the validity of the study results. The main limitation of the study concerns its external validity, which needs to be further investigated. Without further research it is difficult to assess whether the findings of this study can be generalized outside its specific task and user setting. The experiment was conducted in a very homogeneous environment, primarily defined by the course from which the participants were drawn. Future studies should consider more heterogeneous environments to address the possible impact of environmental setup on the results. Perhaps model users with more UML training and modeling experience react differently to the task and treatments presented? Perhaps objectification of many-tomany relationships results in a conceptual data model that is easier to implement in a relational database?4 Notwithstanding these questions, the use of models for communication between system analysts and developers has been investigated much more than their use for communication between analysts and users [22]. This study contributes towards a better understanding of UML as a communication instrument towards business users of conceptual models. In this exploratory study, user and task factors were controlled to increase internal validity. In future research, the interaction between the different factors that determine cognitive fit must be further investigated. In our own future work we will focus on the impact of representation familiarity on the task performance of conceptual model users. It is plausible that users experience less interpretation problems with a representational form simply because it is more familiar to them (e.g. because of learning). Therefore, it is better to explicitly consider the potentially confounding effect of representation familiarity by including this variable in the research model.

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In [24] it is noted that the design and implementation perspective of relationships as links between objects is different from the conceptual modeling view of a domain (i.e. relationships as mutual properties of things). This argument is further supported in [21] specifically for binary UML associations.

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[3] Burton-Jones, A., Meso, P., 2002. How good are these UML diagrams. An empirical test of the Wand and Weber good decomposition model. In: 23rd International Conference on Information Systems. Barcelona, Spain. 101-114. [4] Burton-Jones, A., Weber, R., 1999. Understanding relationships with attributes in entityrelationship diagrams. In: Proceedings of the 20th International Conference on Information Systems. Charlotte, NC, USA. 214-228. [5] Connolly, T., Begg, C., 2002. Database Systems: A Practical Approach to Design, Implementation, and Management, 3rd Ed. Addison-Wesley. [6] Date, C.J., 1995. An Introduction to Database Systems, 6th Ed. Addison-Wesley. [7] Dedene, G., Snoeck, M., 1995. Formal deadlock elimination in an object-oriented conceptual schema. Data & Knowledge Engineering 15(1), 1-30. [8] Dunn, C.L., Gerard, G.J., Grabski, S.V., 2003. Visual Attention Overload: Representation Effects on Cardinality Error Identification. In: Proceedings of the 24th International Conference on Information Systems. Seattle, WA, USA. 47-58. [9] Dunn, C.L., Grabski, S.V., 2001. An Investigation of Localization as an Element of Cognitive Fit in Accounting Model Representations. Decision Sciences 32(1), 55-94. [10] Gemino, A., Wand, Y., 2003. Evaluating modeling techniques based on models of learning. Communications of the ACM 46(10), 79-84. [11] Genero, M., Poels, G., Piattini, M., 2002. Defining and Validating Measures for Conceptual Data Model Quality. Lecture Notes in Computer Science 2548, 147-153. [12] Halpin, T., 1996. Conceptual Schema and Relational Database Design: A Fact Oriented Approach. 2nd Ed. Prentice-Hall. [13] Kim, Y.-G., March, S.T., 1995. Comparing Data Modelling Formalisms. Communications of the ACM 38(6), 103-115. [14] OMG, 2004. UML 2.0 Superstructure Specification, Revised Final Adopted Specification (October 8, 2004). Object Management Group. http://www.omg.org. [15] Parsons, J., 2003. Effects of Local Versus Global Schema Diagrams on Verification and Communication in Conceptual Data Modelling. Journal of Management Information Systems 19(3), 155-183. [16] Parsons, J., Cole, L., 2004. An Experimental Examination of Property Precedence in Conceptual Modelling. In: Proceedings of the 1st Asia-Pacific Conference on Conceptual Modeling. Dunedin, New Zealand.

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[17] Parsons, J., Cole, L., 2005. What Do the Pictures Mean? Guidelines for Experimental Evaluation of Representation Fidelity in Diagrammatic Conceptual Modeling Techniques. Data & Knowledge Engineering, accepted for publication. [18] Schütte, R., Rotthowe, T., 1998. The Guidelines of Modeling – An Approach to Enhance the Quality in Information Models. Lecture Notes in Computer Science 1507, 240-254. [19] Shanks, G., Tansley, E., Weber, R., 2003. Using ontology to validate conceptual models. Communications of the ACM 46(10), 85-89. [20] Snoeck, M., Dedene, G., 1998. Existence Dependency: The key to semantic integrity between structural and behavioural aspects of object types. IEEE Transactions on Software Engineering 24(4), 231-253. [21] Stevens, P., 2002. On the interpretation of binary associations in the Unified Modeling Language. Software and Systems Modeling 1(1), 68-79. [22] Topi, H., Ramesh, V., 2002. Human Factors Research on Data Modeling: A Review of Prior Research, An Extended Framework and Future Research Directions. Journal of Database Management 13(2), 3-19. [23] Vessey, I., 1991. Cognitive fit: A theory-based analysis of the graph versus tables literature. Decision Sciences 22(2), 219-240. [24] Wand, Y., Storey, V.C., Weber, R., 1999. An Ontological Analysis of the Relationship Construct in Conceptual Modeling. ACM Transactions on Database Systems 24(4), 494-528. [25] Wand, Y., Weber, R., 2002. Information Systems and Conceptual Modeling – A Research Agenda. Information Systems Research 13(4), 363-376.

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04/280 T. FASEUR, M. GEUENS, Different Positive Feelings Leading to Different Ad Evaluations: The Case of Cosiness, Excitement and Romance, November 2004, 17 p. 04/281 B. BAESENS, T. VAN GESTEL, M. STEPANOVA, D. VAN DEN POEL, Neural Network Survival Analysis for Personal Loan Data, November 2004, 23 p. (Forthcoming in Journal of the Operational Research Society) 04/282 B. LARIVIÈRE, D. VAN DEN POEL, Predicting Customer Retention and Profitability by Using Random Forests and Regression Forests Techniques, December 2004, 30 p. (Forthcoming in Expert Systems with Applications, 2005). 05/283 R. I. LUTTENS, E. OOGHE, Is it fair to “make work pay”?, January 2005, 28 p. 05/284 N. A. DENTCHEV, Integrating Corporate Social Responsibility In Business Models, January 2005, 29 p. 05/285 K. FARRANT, G. PEERSMAN, Is the exchange rate a shock absorber or a source of shocks? New empirical evidence, January 2005, 26 p. (forthcoming Journal of Money Credit and Banking, 2005) 05/286 G. PEERSMAN, The relative importance of symmetric and asymmetric shocks and the determination of the exchange rate, January 2005, 24 p. 05/287 C. BEUSELINCK, M. DELOOF, S. MANIGART, Private Equity Investments and Disclosure Policy, January 2005, 44 p. 05/288 G. PEERSMAN, R. STRAUB, Technology Shocks and Robust Sign Restrictions in a Euro Area SVAR, January 2005, 29 p. 05/289 H.T.J. SMIT, W.A. VAN DEN BERG, W. DE MAESENEIRE, Acquisitions as a real options bidding game, January 2005, 52 p. 05/290 H.T.J. SMIT, W. DE MAESENEIRE, The role of investor capabilities in public-to-private transactions, January 2005, 41 p. 05/291 A. VEREECKE, S. MUYLLE, Performance Improvement Through Supply Chain Collaboration: Conventional Wisdom Versus Empirical Findings, February 2005, 33 p. 05/292 A. PRINZIE, D. VAN DEN POEL, Incorporating sequential information into traditional classification models by using an element/position- sensitive SAM, February 2005, 32 p. (Forthcoming in Decision Support Systems). 05/293 D. DEBELS, M. VANHOUCKE, A Decomposition-Based Heuristic For The Resource-Constrained Project Scheduling Problem, February 2005, 25 p. 05/294 D. DEBELS, M. VANHOUCKE, A Bi-Population Based Genetic Algorithm for the Resource-Constrained Project Scheduling Problem, February 2005, 10 p. 05/295 W. VANDEKERCKHOVE, N.A. DENTCHEV Network perspective on stakeholder management: Facilitating entrepreneurs in the discovery of opportunities, February 2005, 31 p. 05/296 N. MORAY, B. CLARYSSE, Institutional Origin and Resource Endowments to Science-Based Entrepreneurial Firms: A European Exploration, February 2005, 39 p. 05/297 B. CLARYSSE, M. KNOCKAERT, A. LOCKETT, How do Early Stage High Technology Investors Select Their Investments?, March 2005, 38 p. 05/298 A. PRINZIE, D. VAN DEN POEL, Constrained optimization of data-mining problems to improve model performance: A direct-marketing application, March 2005, 32 p. (forthcoming in Expert Systems with Applications, 2005). 05/299 B. LARIVIÈRE, D. VAN DEN POEL, Investigating the post-complaint period by means of survival analysis, March 2005, 25 p.

FACULTEIT ECONOMIE EN BEDRIJFSKUNDE HOVENIERSBERG 24 9000 GENT

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05/300 D. VANDAELE, P. GEMMEL, Impact of Support Services on End-users’ Satisfaction, March 2005, 28 p. 05/301 T. DEMUYNCK, L. LAUWERS, Nash rationalizability of collective choice over lotteries, April 2005, 16 p. 05/302 R. BRACKE, M. DE CLERCQ, Implementing Extended Producer Responsibility in Flemish Waste Policy: Evaluation of the Introduction of the Duty of Acceptance, April 2005, 24 p. 05/303 Ph. DE MOOR, I. DE BEELDE, Environmental Auditing and the Role of the Accountancy Profession: A Literature Review, April 2005, 34 p. 05/304 G. SARENS, I. DE BEELDE, Internal Auditor’s Perception about their Role in Risk Management Comparison between Belgian and US Companies, April 2005, 25 p. 05/305 Ph. VAN CAUWENBERGHE, I. DE BEELDE, On the IASB Comprehensive Income Project, Neutrality of Display and the Case for Two EPS Numbers, May 2005, 29 p. 05/306 P. EVERAERT, G. SARENS, Outsourcing bij Vlaamse Ondernemingen: een Exploratief Onderzoek, May 2005, 36 p. 05/307 S. CLAEYS, G. LANINE, K. SCHOORS, Bank Supervision Russian Style: Rules vs Enforcement and Tacit Objectives, May 2005, 60 p. 05/308 A. SCHOLLAERT, D. VAN DE GAER, Boycotts, power politics or trust building: how to prevent conflict?, June 2005, 34 p. 05/309 B. MERLEVEDE, K. SCHOORS, How to Catch Foreign Fish? FDI and Privatisation in EU Accession Countries, June 2005, 32 p. 05/310 A. HEIRMAN, B. CLARYSSE, The Imprinting Effect of Initial Resources and Market Strategy on the Early Growth Path of Start-Ups, June 2005, 38 p. 05/311 A. MAES, G. POELS, J. GAILLY, R. PAEMELEIRE, Measuring User Beliefs and Attitudes towards Conceptual Models: A Factor and Structural Equation Model, June 2005, 25 p. 05/312 S. VANDEVOORDE, M. VANHOUCKE, A Comparison of Different Project Duration Forecasting Methods using Earned Value Metrics, June 2005, 18 p. 05/313 K. BAEYENS, T. VANACKER, S. MANIGART, Venture capitalists’ selection process: the case of biotechnololy proposals, June 2005, 23 p. 05/314 M. VANHOUCKE, D. DEBELS, The discrete time/cost trade-off problem under various assumptions. Exact and heuristic procedures, June 2005, 32 p. 05/315 G. POELS, A. MAES, F. GAILLY, R. PAEMELEIRE, User Attitudes towards Pattern-Based Enterprise Information Models: A Replicated Experiment with REA Diagrams, July 2005, 32 p. 05/316 B. MAENHOUT, M. VANHOUCKE, An Electromagnetic Meta-Heuristic for the Nurse Scheduling Problem, July 2005, 34 p. 05/317 M. VANHOUCKE, S. VANDEVOORDE, A simulation and evaluation of earned value metrics to forecast the project duration, July 2005, 30 p. 05/318 I. VERMEIR, M. GEUENS, Need for Closure and Youngsters’ Leisure Time Preferences, July 2005, 25 p.

FACULTEIT ECONOMIE EN BEDRIJFSKUNDE HOVENIERSBERG 24 9000 GENT

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05/319 D. VANTOMME, M. GEUENS, S. DEWITTE, How to Portray Men and Women in Advertisements? Explicit and Implicit Evaluations of Ads Depicting Different Gender Roles, July 2005, 34 p. 05/320 B. WEIJTERS, M. GEUENS, Evaluation of age-related labels by senior citizens, July 2005, 27 p. 05/321 S. STEENHAUT, P. VANKENHOVE, An Empirical Investigation of the Relationships among a Consumer’s Personal Values, Ethical Ideology and Ethical Beliefs, July 2005, 36 p. 05/322 V. VANSTEEGER, The current state of accounting harmonization: impediments to and benefits from harmonization, August 2005, 30 p. 05/323 G. POELS, F. GAILLY, A. MAES, R. PAEMELEIRE, Object Class or Association Class? Testing the User Effect on Cardinality Interpretation, August 2005, 14 p. 05/324 G. BUCKINX, G. VERSTRAETEN, D. VAN DEN POEL, Predicting Customer Loyalty Using The Internal Transactional Database, August 2005, 33 p.