Rivero L.Encyclopedia of database technologies and applications.2006

individual. It is necessary to “translate the vision and the strategy into goals and indicators, through a well-bal- anced set of perspectives” (Kaplan & Norton,1999). The indicators will give the necessary homogeneity to the perceptions of those who have to make decisions in relation to the reality given by the indicator as well as its interpretation (i.e., people’s subjective judgment).

Knowledge as Production

The occurrence of events, their measuring by means of indicators, and their processing provide elements of judgments, references, parameters, and interpretation of the reality on which decisions are made. In the dynamic of measuring and evaluating reality, we will “produce” new knowledge that will modify the evaluation as well as our behavior. A clear example is to study the environmental impact produced by tourist activity through intensity measures, and its extension and importance over the socioeconomical system (Coccossis & Parpairis, 1992).

Knowledge as State

According to its relevance, knowledge can be perceived in different ways: data, information, structured information, perceptions, judgments, and decisions (van Louizen, 1996). We are constantly changing the knowledge from one level to another until we get to the culmination of the knowledge, the decision. It is in this process that the precision and meaning of the information increases

At the same time, in the same process, we must reconcile material factors and goals with cultural and subjective factors (e.g., information, computing projects planning, information management with system of thoughts, learning systems, activity systems; Espinoza & Molina, 1999).

Knowledge Management in Tourism

Considering knowledge management as the process of managing immaterial assets explicitly, we immediately come to know that management in tourism (either private or public) is an especially fertile field. The aims of the service are mostly experiences, individual or group acquired knowledge, emotions, and personal transformations (Gunn, 1994). Likewise, the possibility of offering the service properly depends on the physical infrastructure and its usage as well as on the ability of interpreting expectations and needs, evaluating subjectivities and, getting ahead of them, understanding customs, likes and dislikes, preferences, and cultural and social guidelines (Tissen, Andriesen, & Lekanne,

Knowledge Management in Tourism

2000) and from the offer of services and hiring until arrival time, staying, leaving, and even the memories and the will to come back (Organización Mundial de Turismo, 1997).

The challenge is to make the tourist destination offer the guests the desired utility, getting as a reward a successful operation of the system. To achieve this, the prodigal nature or the large investments are not enough. There are other elements that should be considered, such as proper management concepts directed towards constant learning and innovation, service processes, orientation to the customers, and the proper application of the economic–financial resources (Inskeep, 1991; Wisniewski & Dickson, 2001).

Service Profit Chain

Among the diverse approaches and strategies to increase the quality of the provision facilities, the customers’ satisfaction, and, as a consequence, the increase in the incomes and the final profitability, it is useful to mention the “utility chain in services” (Heskett, Sasser, & Schlesinger, 1998).

The utility chain links different concepts that have an effect on the improvement of services, related to the ability of the ones whose mission is to carry them out. Almost all of them are related to knowledge, individual and shared knowledge of

a.structures

b.processes

c.needs

d.motivations for choice

e.service complementarity

f.demand complementarity

g.development of special packages for different segments

h.customers satisfaction

i.vision

j.personnel loyalty and satisfaction

k.investments in relevant aspects

These concepts can be appreciated in the “value equation” (see equation below).

Results + Process quality

Value equation = ——————————————————

Price + Cost of access to a service

The keys for its application would be in

a.understanding the client needs

b.responding or covering needs

Knowledge Management in Tourism

Figure 1. Service profit chain and balanced scorecard

•Knowledge

In information management, all the information is included at its different levels and support systems. In K people management, its equivalence in the complex dynamic of human behavior takes particular significance.

Both concepts should be considered within the frame of the structure in which they are developed or used. Therefore, we have three different elements at our disposal: information, people, and structure.

It is at this stage when the balanced scorecard functions as a knowledge management tool: the choice, diffusion, and application of the chosen strategy in the selected reference system. The data the BSC put together are useful to those people who are responsible for the application and control of the strategy. For example, the hotel reservation rate is useful to foresee the ticket sales to a certain tourist destination. In addition, the rate will be the result of the advertising and service strategies chosen and it will allow service providers to measure its efficiency (Miyake, 2002).

c.investing in key aspects, such as clients’ restrain

d.developing value packages for different markets

e.developing value conscience (Heskett et al.,1998; Ho & McKay,2002)

The service profit chain and its correspondence to the balanced scorecard is shown in Figure 1.

The service profit chain (Heskett et al., 1998) shows the relationship between different fundamental factors of tertiary activities such as tourism. This shows that the successful results in a certain administration come out as a result of the employees’ capability, loyalty, satisfaction and productivity; generating high value services due to quality and low costs. Hence, not only do clients’ satisfaction and loyalty rise, but so do growth and utilities.

Higher value is bounded to major commitment and people’s training (growing perspectives). As a result, there will be better performed processes (internal process perspective) with more effective results (customer perspective), and thus better financial results (financial perspective). There is an important correspondence between the utility chain and the balanced scorecard perspectives.

Balanced Scorecard as a Tool of

Knowledge Management

We can focus on knowledge in relation to two principal theories: information or people management (Probst, Raub, & Romhardt, 2000).

Need for Knowledge in Tourism

The basic knowledge in the sector are based on

a.physical assets

b.knowledge processes in tourist services (intangible assets)

c.strategies:

•choice

•diffusion

•application

This knowledge is detected, elaborated, and transmitted through ratios, outcome measures and performance drivers whose substantial value is to reflect the cause–effect relationship of the items they are linking (Baud-Bovy, 1998; McIntyre,1992).

Balanced Scorecard as a Means for Managing Knowledge

The balanced scorecard studies different aspects in the knowledge management of an organization and combines cultural, structural, and strategic aspects by causality relationships through

a.mission and vision

b.strategies

c.responsibilities

d.application

•modeling of tasks

•modeling of performance e. transformation

Balanced Scorecard Advantages with Regard to Knowledge Management

a.it considers intangible assets

b.it makes use of linked outcome measures and future performance drivers

c.it makes the mission and the strategy more comprehensive by means of action

d.it is a cyclical process

e.it considers intellectual factors: creativity, innovation

f.its main utility is communication

g.it is multidimensional

h.it allows selecting indicators and inductors

i.it transfers the strategic outcome to quantitative measures

j.it transforms the strategy into action

k.it quantifies qualitative variants

l.it links cause–effect relationships to strategic activities

m.it is prospective

n.it allows measuring, simulating and evaluating alternatives

o.it can be quickly modified (Kaplan & Norton, 2001; Malina & Selto, 2001; Neidorf, 2002)

The balanced scorecard (see Figure 2) outlines the tourist activity showing diverse cause–effect relationships.

Indicators

Outcome measures and performance drivers. The detection and selection of the indicators and inductors must be taken into account as diverse aspects to materialize the balanced scorecard.

a.cause–effect relationships

b.the possibility of obtaining, storing, and updating information

c.feasible control and processing

d.facility for diffusion and interpretation

Knowledge Management in Tourism

FUTURE TRENDS

Informatic Applications in Tourist

Knowledge Management

Data Warehouse, Data Mining, and Online

Analytical Processing (OLAP) Applications

and Use

Applications of these techniques (Fayyad, 1996, 1997) include

a.detection

b.selection of detection tools

c.integration of tools

d.application–organization

e.computerization and oversight

Data Warehouse as an Organization

Process of the BSC

Data warehouse is a collection of data aimed at the matter—integrated, not volatile, thematic, and histori- cal—organized to support a decision-making helping process. (Inmonn, 1996).

Useful Data Warehouse Characteristics for a BS

•Integrated: The data stored in the data warehouse are integrated in a structure; therefore, any existent inconsistency among the diverse operational systems must be eliminated. The information is often arranged into different detail levels for its adjustment to the users’ needs.

•Thematic: Only the data essential for the knowledge generation process is integrated from the operational environment. The data is organized by topics to facilitate their access and understanding to the final users. For example, customers’ data can be compiled in a unique data warehouse board. Consequently, the requests of information about customers will be easier to answer.

•Historical: Time is an implicit part of the information compiled in a data warehouse. In operational systems, data usually show the state of business activity at present. The information stored in a data warehouse can be used, among other purposes, to do trend analysis.

•Nonvolatile: The data of a data warehouse are meant to be read, no to be modified. Therefore, the information is permanent. Updating the data warehouse is the incorporation of the latest values the different variables it contains have taken without taking any kind of action on what already existed (Inmonn, Glassey, & Welch, 1997).

OLAP Tools for a Balanced Scorecard Information Analysis

We shall add the multidimensional information analysis by means of OLAP tools and consider OLAP systems as parts of the executive information systems (EIS), which are used to provide the strategic level with the necessary information for decision making (Codd, Codd, & Salley, 1993).

In an OLAP data model, the information is perceived as cubes that consist of descriptive categories (dimensions) and quantitative values (measures). The multidimensional data model makes it easier for the users to formulate complex queries, correct the mistakes in a report, change summed-up data for detailed data, and “filter” the data in meaningful subsets.

Applying Data Mining Tools in a

Balanced Scorecard

Data Mining Definition

Data mining is an analytical process that has been designed to explore large quantities of data and to search for consistent models and the systematic relationships among variables so as to validate the results of applying the discovered models to the new data subsets.

The process consists of three steps: exploration, construction or definition of the model, and validation/verification.

If the nature of the available data allows, we repeat it interactively until we identify a “strong” model. However, in business practice, the possibilities to validate the model in the analysis phase are usually limited. Consequently, the initial results often have the heuristic condition which might influence in the decision-making process.

There are three main working areas: knowledge engineering, classification, and problem solving. Each learning technique places itself in this three-dimen- sional space. No learning or pattern recognition technique is considered the best. An environment of knowledge databases discovery must bare these different types of techniques (hybrid environment, Indurkhya & Weiss, 1998).

Knowledge Management in Tourism

in the percentage of the average stay, we will get, in addition to this current indicator and its historical trajectory (i.e., applying temporal series), an indicator of the potential average stay for the following 6 months. The source of information of this indicator can be build up from (a) hotel and outing databases, and (b) sample interviews considering hotels and outings.

A temporal series represents the evolution of a magnitude in time. The factor of highest interest will be the correlation between different events along time. If such a correlation exists and can be modeled, predictions of the future behavior of the temporal series can be made.

The temporal series are build-up on a feed-forward architecture, such as multilayer perception (MLP) or radial basis functions (RBF). Alternatives include feedforward networks, with a fixed-entry window and recurrent networks with unique entries.

There are two types of models that can be applied to predict the next value in a temporal series:

a.single-step prediction, in which the inputs to the model are always known values taken from the temporal series, and

b.multi-step prediction, in which the result of the first prediction is fed back as a new entry in the network (see Figure 4).

Studying Techniques

There are two ways of studying temporal series:

a.finding out patterns to explain the past behavior of the temporal series, and

b.evaluating the effect of a fact that intervenes and changes the behavior of the temporal series.

Different Learning Algorithms Compared to Different Types of Tasks in the Application Inside a BSC

Any of the three techniques (i.e., knowledge engineering, classification, and problem solving) can be applied in a balanced scorecard to analyze and predict indicators or to take part in the indicators’ building process (see Figure 3).

Structure and Application to the Proposed Indicators

Neuronal Networks Application in the Prediction of an Indicator. If we take a resulting indicator as the increase

Knowledge Management in Tourism

Figure 4. Sequence of steps in the prediction

Cycle Development: Preproject →

Data Gathering → Data Preparation →

Design → Training and Testing →

Implementation and Maintenance

Between the design, and training and testing stages there is an interactive stage that corresponds to optimization.

Preparation of Data

The data employed in the application consisted of the following five fields registered (or calculated) in the T instant:

K

Time Series Windows

Time series windows allow the making of two kinds of predictions: single-step prediction and multi-step prediction.

Time Series Plot

Time series plot shows the results of an application as a two-dimensional graphic as a function of time. It shows the temporal comparison between the actual and predicted outputs and the actual outputs with inputs.

a.An increase in the percentage of the average stay (result indicator)

b.Quantity of necessary consultancies to define the trip

INDICATORS GROUPING TO GENERATE ANOTHER GUIDING INDICATOR

ages

d.Quantity of innovations in the Web site

e.Information availability in service centers

The last four items are guiding indicators.

Design

Selection of neuronal processing tools, model structure, and initial conditions.

a.Data input tool

b.Time series window

c.Time series plot

Data Input Tool

The data input tool allows the specifying of the variables to be included in the input window and also the ones that will go in the target window (prediction).

level and Kohonen network.

The generated guiding indicator is “type of customer,” with the following characteristics:

a.A varied passengers’ origin

b.Percentage of reiteration of visits

c.Percentage of provenances

d.Segments diversity

e.Number of visitors

f.Total expenditure per tourist

Kohonen Auto-Organized Maps

These are based on evidences found at brain level. They associate entry vectors with output patterns and are able to make cluster analysis (i.e., to project a high-dimen- sioned space onto a smaller one).

The learning process is nonsupervised competitive. The neurons compete with each other to carry out a

given task. When there is just one input, only one neuron activates (i.e., the winning neuron).

•Goal: “Cluster” the data introduced to the network.

SOFM Architecture: Connections

Each entry memory i is connected to each of the output neurons of j by means of weight (Wij). Each output neuron has a weight vector associated to it (Wij) as a reference vector, which represents the average vector of this category. Among the neurons of the output layer there are implicit lateral connections of excitement or inhibition (del Brío, 1997; see Figure 5).

Learning Stage

This stage aims to establish, by means of the presentation of a training pattern set, the different categories that will be employed during the working stage to classify new input patterns. A set of patterns is presented repeatedly in the network, until the different reference vectors are tuned to one or more input patterns. If the input space is divided into groups, each neuron will specialize in one of them, and the essential operation of the network will be interpreted as a “cluster analysis” (see Figure 6).

Working Stage

In the output layer, each neuron estimates the similarity between the input vector (Xp) and its own weight vector (Wij). Simulating a competitive process, the winning neuron will be the one whose weight vector resembles the input vector. The activated output neuron represents the class the input belongs to. When the input is a similar pattern, the same neuron, or a neighbor neuron, is activated.

Application of the Fuzzy Decision Theory as a Financial Indicator of the Future Investment in the Area

Fuzzy logic deals with imprecise information (such as the acceptance of a tourist package, the proper customers’ attention, or an uncomfortable means of transport to the city) in terms of fuzzy sets (Lazzari, Machado, & Pérez, 1998). These fuzzy sets are turned into rules to define actions, such as “if the quality of a service is not good, the amount of tourists will decrease 50% in the following 6 months.”

Control systems based on fuzzy logic combine input variables (which are defined in terms of fuzzy sets) by means of rules that produce one or several output values (Kasabov,1996). The fuzzy set theory comes from the classic set theory and adds a belonging function to the set, which is defined as a real number between 0 and 1.

The concept of fuzzy sets or subsets is introduced in association with a determined linguistic value, defined by a word, adjective, or linguistic label A.

For each fuzzy set or subset there is a belonging or inclusive function ƒa(t) that indicates the degree in which the t variable is included in the concept that label A represents (Hilera & Martínez, 1995). For example, the linguistic value customers’ attention may represent the acceptation degree in percentage to the attention of a specific tourist place expressed through interviews. Then, we can define three fuzzy sets, each one identified

Figure 6. Bidimensional map sample

Reference vectors

W ijij

Vectors de la

clase

Classes of tourists

with a label (Bad, Regular, or Good) and with a belonging or inclusive function.

{ƒBad (t), ƒRegular (t),ƒ Good (t)}.

The outcome would be a distribution, as shown in Figure 7.

An economic–financial indicator to apply to the scorecard may be the amount of acquired packages for a specific tourist program. We will apply the fuzzy decision theory mechanisms to deduce different decisions about the acquisition of those packages and the future profits linked to each option (e.g., for each travel agent), according to the ways of searching information, the attitudes towards risk, the decision criteria, the total uncertainty, or others.

For these purposes, the concept of fuzzy logic may be applied, estimating the number of package tours to be sold, who will make decisions about the amount of reservations periodically booked in a specific excursion program. These decisions will have an initial capital investment intended to establish booking cost for each package to estimate the profitability.

sion, and quality, whose scope broadens the deterministic and quantitative approaches. Within this frame, the methods of proximal reasoning (in which we tend to include as an essential element the typical uncertainty that the social sciences constitute), together with computational algorithms, is a promising path in search of knowledge, which is a human concern for all people at all times.

REFERENCES

Baud-Bovy, M. (1998). Tourism and recreation: Handbook of planning and design. Oxford, UK: Architectural Press.

Bonsón, E. (1999). Tecnologías inteligentes para la gestión empresaria. Madrid, Spain: RA-MA Editorial.

Coccossis, H., & Parpairis, A. (1992). Tourism and the environment. Some observations on the concept of carrying capacity. Dordrecht, The Netherlands: Kluwer Academic Press.

Codd, F., Codd, B., & Salley, C. (1993). Providing OLAP to user-analysts. Codd.

CONCLUSION

Management has powerful formal tools at its disposal to optimize decisions. The computational decision systems (e.g., management information systems, decision support systems, executive information systems) are samples of the growing trend to eliminate empiricism, to go deeply into the analysis, to release from duty those who decide on routines, thus allowing them to adopt approaches of greater conceptual amplitude. Among these approaches are the ideals of understanding, vi-

del Brío, M. S. A. (1997). Redes neuronales y sistemas borrosos, introducción teórica y práctica. Madrid, Spain: RA-MA Editorial.

Espinoza E. M., & Molina S. C. (1999). Cambio organizacional: Sistemas de información y emociones.

Gestión y Estrategia, 15.

Fayyad, U., & Simoudis, E. (1997). Data mining and knowledge discovery in data bases. Cambridge, MA: MIT Press.

Fayyad, U. M., & Piatetsky-Shapiro, G. (Eds.). (1996).

Advances in knowledge and data mining. Cambridge, MA: MIT Press.

Grunewald, L. (Ed.). (2001). Plan de desarrollo integral de la actividad turística y recreativa del municipio de tandil. Buenos Aires, Argentina: Universidad del Salvador.

Gunn, C. (1994). Tourism planning. London: Taylor & Francis.

Heskett, J., Sasser, E., & Schlesinger, L. (1998). The service profit chain. Buesos Aires, Argentina: Gestion.

Hilera, J. R., & Martínez, V. J. (1995). Redes neuronales artificiales, fundamentos, modelos y aplicaciones.

Madrid, Spain: RA-MA Editorial.

Ho, S.-J., & McKay, R. (2002). Balanced scorecard: Two perspectives. The CPA Journal, 72(3), 21-25.

Indurkhya, N., & Weiss, S. (1998). Predictive data mining: A practical guide. San Francisco: Morgan Kaufmann.

Inmon, W. H. (1996). Building the data warehouse.

New York: Wiley.

Inmonn, W. H., Glassey, K. L., & Welch, D. (1997).

Managing the data warehouse. New York: Wiley.

Inskeep, E. (1991). Tourism planning: An integral and sustainable development approach. New York: Van Nostrand Reinhold.

Kaplan, R., & Norton, D. (1999). The balanced scorecard: Translating strategy into action. Cambridge, MA: Harvard Business School Press.

Kaplan, R., & Norton, P. (2001). Transforming the balanced scorecard from performance measurements to strategic management. Accounting Horizons, 15(1), 87.

Kasabov, N. K. (1996). Foundations of neural networks, fuzzy systems and knowledge engineering.

Cambridge, MA: MIT Press.

Lazzari, L. L., Machado, E. A. M., & Pérez, R. H. (1998).

Teoría de la decisión fuzzy. Buenos Aires, Argentina: Ediciones Macchi.

Lopez, G. M. (1999). El cambio y la cultura organizacional en el diseño de un sistema de información de Gestión. Gestión y Estrategia, 15.

Malina, M. A., & Selto, F. (2001). Communicating and controlling strategy: An empirical study of the effective-

Knowledge Management in Tourism

ness of the balanced scorecard. Journal of Management Accounting Research, 44, 47.

Marakas, G. (1999). Decision support systems. Upper Saddle River, NJ: Prentice Hall.

McIntyre, G. (1992). Desarrollo turístico sostenible. Guía para los planificadores locales. Madrid, Spain: Organización Mundial de Turismo.

Miyake, D. (2002). Beyond the numbers: After years of evolution, balanced scorecard applications now integrate strategy and management for competitive advantage. Intelligent Enterprise, 15(12), 24-27.

Neidorf, R. (2002, September/October). Knowledge management: Changing cultures changing attitudes. Online, 26(5), 60-63.

Olvé, N., Roy, J., & Wetter, M. (1999). Performance drivers: A practical guide to using the balanced scorecard. New York: Wiley.

Organización Mundial de Turismo. (1997). Guía práctica para el desarrollo y uso de indicadores de turismo sostenible. Madrid, Spain: Author.

Probst, G., Raub, S., & Romhardt, K. (2000). Managing knowledge. Chichester, UK: Wiley

Simon, R. (1995, September/October). Los sistemas de control como instrumento de la renovación estratégica.

Harvard Deusto Business Review.

Tissen, R., Andriesen, D., & Lekanne Deprez, F. (2000).

The knowledge dividend. New York: Prentice Hall.

Van Lohuizen, C. W. W. (1996). Knowledge: Creation, diffusion, utilization. Knowledge Management and Policy Making, 8(1).

Wisniewski, M., & Dickson, A. (2001). Measuring performance in Dumfries and Galloway constabulary with the balanced scorecard. Journal of the Operation Research Society, 52(10), 1057.

KEY TERMS

Balanced Scorecard Collaborative: A strategic management system that measures, by means of quantitative relations of different selected variables, the behavior of the organization, taking into account the settled aims established in different perspectives (e.g., increase, internal processes, customers, finances). The analysis is

Knowledge Management in Tourism

based on the cause–effect relations between the variables and ratios that represent them.

Data Mining: The process of the discovery of patterns, profiles, and significant trends through data analysis, making use of specific techniques such as neuronal networks and genetic and learning algorithms.

Decision Support System: Systems designed, built, and used to support the decision-making process. Its components are

a.the data management system

b.the model management system

c.the knowledge engine

d.the user interface

e.the user or users

Fuzzy Logic: A procedure for analyzing approximate reasoning, which uses its imprecision and settles borderline cases with the concept of precision. Human reasoning is imprecise, and the ability to make reasonable decisions in such a clear environment of uncertainty is a major aspect that depends on the possibilities to obtain an approximate answer to some questions based on the acquired knowledge, which is normally inexact and not always reliable.

Indicators: Generally quantitative expressions that

relate variables to defined criteria. They can be statis- K tics, numbers, values, reasons, or other ways of representing information.

Knowledge Management: An organizational process that consists chiefly of the following stages:

a.creating and generating knowledge

b.organizing and attaching value to that knowledge

c.transforming and transferring knowledge

d.storing knowledge

e.reusing knowledge

Neuronal Networks: Computing devices designed in such a way that they simulate nervous systems, with a great number of calculation elements that carry out no linear analogous functions. In computing, they can be used for forecasts, classification, detection of connections, or groupings.

Online Analytical Processing (OLAP):.Systems of information for decision making in which the information is seen as cubes that consist of the combination of descriptive categories and qualitative values.