PREDICTING THE INTENTION OF USING A

PREDICTING THE INTENTION OF USING A TECHNOLOGY PRODUCT OR
SERVICE THROUGH A FUZZY CONTROLLER
Libertad Caicedo Acosta (District University Francisco José de Caldas, Bogotá, Colombia) –
licedoa@correo,udistrital.edu.co
Camilo A. Ospina Acosta (District University Francisco José de Caldas, Bogotá, Colombia) [email protected]
Jhon Francined Herrera Cubides (District University Francisco José de Caldas, Bogotá,
Colombia) – [email protected]
ABSTRACT
In the next article are shown the results of various works of investigation in which the main
objective is to calculate or obtain the Intention of Using a specific technology product or
service, based on the Technology Acceptance Model (TAM) proposed by Fred Davis. Taking
this model into account a Fuzzy Controller it is proposed as an alternative to the usual ways
and methods suggested for calculating the Intention of Use, allowing anyone with access to a
computer to estimate its value without needing the knowledge or the mathematical bases
required to calculate it. To conclude, a comparison among practical TAM implementations is
made, from its analysis it is noticeable that the Fuzzy Controller allows to obtain similar
results to the ones gotten by the previously compared implementations, using much less
resources and effort that the other methods.
KEYWORDS: Fuzzy Controller, Fuzzy Logic, Intention of Use, TAM, Technology Use.
Libertad Caicedo Acosta is a final year student from Computers and Science Engineering,
District University Francisco José de Caldas (Bogotá, Colombia). She is one of the founders
of the Python programming group at the same University, was an academic assistant for the
Computers and Science program in 2013 and for the Complexity Group since 2011. It belongs
to the research group GIIRA. E-mail: [email protected]
Camilo Andrés Ospina Acosta is a final year student from Computers and Science
Engineering, District University Francisco José de Caldas (Bogotá, Colombia). He is one of
the founders of the Python programming group at the same University, and was an academic
assistant for the Computers and Science program in 2013. It belongs to the research group
GIIRA. E-mail: [email protected]
Jhon Francined Herrera Cubides, Profesor Planta Tiempo Completo, Facultad de Ingeniería,
Universidad Distrital Francisco José de Caldas, Bogotá, Colombia. Ingeniero de Sistemas,
Corporación Universitaria del Meta (Villavicencio, Colombia). Especialista en Alta Gerencia,
Corporación Universitaria del Meta (Villavicencio, Colombia). Especialista en Construcción
de Software para Redes, Fundación Universitaria Autónoma de Colombia FUAC (Bogotá,
Colombia). Magister en Ingeniería de Sistemas y Computación, Universidad de los Andes
(Bogotá, Colombia) E-mail: [email protected]
RESUMEN
En el siguiente artículo se muestran los resultados de varios trabajos de investigación, en los
cuales el principal objetivo es calcular u obtener la intención de utilizar un servicio o producto
tecnológico específico, basado en el modelo de aceptación tecnológica (TAM) propuesto por
Fred Davis. Tomando en cuenta este modelo, se propone un controlador difuso como una
alternativa a los métodos usualmente sugeridos para el cálculo de la intención de uso,
permitiendo a cualquiera con acceso a un computador, estimar su valor sin necesitar el
conocimiento o las bases matemáticas para ello. Para concluir, se realiza una comparación
entre implementaciones prácticas del TAM, de lo cual se puede notar que el controlador
difuso permite obtener resultados similares a los de las implementaciones previamente
comparadas, usando muchos menos recursos y esfuerzo que otros métodos.
PALABRAS CLAVE: Controlador difuso, lógica difusa, Intención de uso, TAM, Uso
tecnológico.
1. INTRODUCTION
Across last decades or so, an important topic to technology manufactures, including software
and hardware, has been the capability to study the market so that the deployment of a product
or service will not be a failure or does not fit the profit expectations (Varela, Rivas, Chaparro,
2010). To decrease the chances of falling, companies started getting involved with Strategic
Planning, which allowed them to gain control over the needs and components of the business.
In this way, it emerges a model for Strategic Planning developed by Jhon Rockart called
Critical success factors (Bullen & Rockart, 1981), which allowed Enterprises to find which
elements where indeed necessary, or had more value so that the company could fulfill its
mission. Through the same period it jump out to notice that Information and Communications
Technologies were very important within the critical success factors (Davis, Bagozzi,
Warshaw, 1989; Davis, 1989; Renaud &Van, 2008).
Based on this facts, Fred Davis developed it´s model for Technology Acceptance (Davis,
1986), which in its classical version involved two variables to calculate the Intention of Use,
allowing establishing the real use of a technology product or service. This Model, has been
used through all of these years in an acceptable manner, although it presents several
disadvantages.
In this way, within an academic investigation context, through the District University
Francisco José de Caldas’s GIIRA group, it is proposed a project that seeks solving the
Technology Acceptance Model disadvantages, analyzing and identifying its phases to
automate a very important process. The bounds of the project are linked to the Classical
Technology Acceptance Model in which only two variables (in a specific range) are taken into
account for the model, as well as the existence of enough practical implementations to
validate the proposed solution. It is important to clarify that project scope is projected to
design, implement and validate a Fuzzy Controller for predicting the intention of using a
technology product or service.
2. PREDICTING USER INTENTIONS
As previously told, enterprises began to get interested in developing Information systems that
were effective, and therefore the creation of various techniques, models and strategies were
needed. In this way the Theory of Planned Behavior appeared as an alternative to other
acceptance models, this theory involved three (3) factors for being able to determine the
intention of use, including some social variables that affect it. Although this theory is really
similar to the technology acceptance model, they differ in the degree of generality and the
complexity to apply them, being the Theory of Planned Behavior more difficult to apply
across various user contexts, requiring pilot studies to identify relevant outcomes and control
variables for each context in which is used (Mathieson, 1991).
In this way this article will focus in the technology acceptance model, for being a tool more
practical to implement in several cases. Now a days exist a lot researches and works about the
technology acceptance model, which in the words of some, may have saturated the usefulness
of the model, requiring some new ways of calculating the intention of use that integrate the
good thing from the model and eliminate its deficiencies, which is exactly what this article
seeks (Chuttur, 2009).
3. TECHNOLOGY ACCEPTANCE MODEL
The Technology Acceptance Model (TAM), proposed and developed by Fred Davis in 1989,
arises from the need of being able of predicting the Intention of Use of a technology product
or service, by establishing a set of relevant analysis variables for accomplishing this. The
model input variables were chosen based on the adoption of the attitude psychological
principles proposed by Fishbein and Ajzen’s (Ajzen & Fishbein, 1977).
On a large scale TAM comprehends three (3) fundamental aspects: it take into account a
variable amount of factors that affect the perceived usefulness and ease of use, it gives a priori
criteria of the acceptance degree of a product or service to produce and it validates the real
acceptance degree of it. From these, three (3) phases can be associated to each aspect
previously mentioned:
1. Identification and calculation of variables: On this phase are established which variables
are going to dictate the usefulness and ease of use, calculated in a range from zero (0) to
one (1) through surveys and statistical methods.
2. Prediction of the Intention of Use: Through the calculated values on the first phase, a
mathematical model that takes into account all variables relations and their corresponding
weights is implemented, to predict the Intention of Use.
3. Results Validation: This is an optional phase that is performed once the final users are
making use of the product or service, and its final purpose is to establish the effectiveness
of the prediction made on phase two (2).
On figure 1 is represented the TAM, which will be later used to perform a comparison
between practical implementations of the model against the proposed Fuzzy Controller.
Figure 1. TAM. Source: authors.
From the analysis of various TAM implementations the following deficiencies were found:
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It doesn't exist a unique methodology that establishes the correct form of
implementing the three (3) phases, making subjective the obtained results from the
model (Venkatesh, Davis, Davis, Morris, 2003; Washaw et al, 1989).
It requires a mathematical knowledge in the implementation of all of it phases (López
& López, 2006).
It requires a complete knowing of the model to be able to implement it (Leyton, 2013).
It is difficult to use to the majority of the people, and it requires experts to implement
it (Leyton, 2013).
The results interpretation is difficult to comprehend without understanding the model
and the mathematical bases (Leyton, 2013).
The model implementation difficult getting agile and timely results (Leyton, 2013).
The model implementation have high costs in terms of time and money for a regular
person or a small company (Leyton, 2013).
To solve all of above problems a Fuzzy Controller was design based on the same inputs
and outputs proposed by TAM. This model is different in many ways from the one
proposed by Fred Davis, but mainly because it is based on several rules that allow the
controller to establish the Intention of Use.
4. CONTROLLER DESIGN
The proposal of using a controller, instead of other possible tools, emerges from the fact that
the problem have various characteristics that can be well represented through the Fuzzy
Logic; it can represent a set of rules; it manages fuzzy ranges that explicitly specify the
human language subjectivity; and provides a more understandable way of representing the
problem (Chuen, 1990). Also, the idea of making a Fuzzy Controller to replace phase II of the
model, is to change some qualitative values proposed by TAM to a quantitative range of
results, which require less effort to be calculated. On this way a Fuzzy Controller with two (2)
inputs and one (1) output is proposed to calculate the Intention of Use as shown on figure 2.
Figure 2. Fuzzy Controller. Source: authors.
To design the controller, the following rules were defined taking into account the inputs and
outputs propose by Fred Davis and the results obtained from various TAM implementations,
as can be seen on figure 3:
Figure 3. Controller's Rules. Source: authors.
On figure 4 are shown the corresponding membership functions for all inputs and outputs of
the Fuzzy Controller, which vary in a range from 0 to 1, where the Low and High functions
are of Trapezoidal-Shaped Type and the Med function is a Triangular-Shaped type.
Figure 4. Membership Functions. Source: authors
From designing the Fuzzy Controller is obtained as result the rules surface at figure 5, which
represents in a graphical way the previous mentioned rules.
Figure 5. Rules Surface. Source: authors.
5. PRACTICAL TAM IMPLEMENTATIONS
Through the analysis of various TAM implementations and investigation works from experts
(Villarejo, 2007; Tavera & Arias, 1990; Ramírez & Alfaro, 2012; Davis, 1991; Venkatesh &
Davis, 2000; Rejón, Liebana, Martínez, 2011) the Table 1 is created, showing the results of
calculating the Intention of Use employing the Ease of Use and Usefulness as the only
parameters to do so.
Implementation of TAM
Usefulness
Ease of Use
Intention of Use
0,3550
0,2280
0,3380
0,5720
0,3280
0,5390
0,1700
0,3000
0,3400
0,6500
0,1300
0,2100
0,4600
0,4500
0,4400
0,5500
0,1700
0,5200
0,6400
0,3500
0,6900
Table 1. Analysis of TAM Implementations. Source: authors
The previous table shows the results from works of various kinds, which used different TAM
implementations that took into account diverse external variables, as well as managed
different budgets and implementation times. Through the results shown on this table is later
made a comparison to test the effectiveness of the proposed Fuzzy Controller.
6. RESULTS COMPARISON
After creating the controller, which has suffered several changes to give the best possible
result so far, a comparison between the obtained results of the practical implementations and
the Fuzzy Controller is made, taking into account a literature review of the model
background. Thereby Table 2 represents both results with their respective difference.
Intention of Use
TAM IMP.
Fuzzy Controller
Difference
0,3380
0,3424
0,0044
0,5390
0,4930
0,0460
0,3400
0,2880
0,0520
0,2100
0,3349
0,1249
0,4400
0,4386
0,0014
0,5200
0,4348
0,0852
0,6900
0,4998
0,1902
Table 2. Results Comparison. Source: authors.
From the results obtained from the Fuzzy Controller is already noticeable that various of the
TAM deficiencies have been reduced, leading to simplifying the use the model and reduction
of costs in terms of time and money, improving the ability to get agile and timely results as
well as the ability of comprehend the results, all of this without having to implement any
mathematical methods and still getting similar results with much less effort.
7. CONCLUSIONS
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The implementation of a Fuzzy Controller reduces the human interaction for
calculating the ''Intention of Use'' and therefore reduces the chances of making
mistakes on this calculations.
It is noticeable that although it is a much simpler process to implement, the obtained
results does not change much which the ones given by humans applying the TAM
Model.
It avoids requiring knowledge of mathematics, statistics and the complete structure of
the model to implement phase II of TAM.
The Fuzzy Controller allows reduce the dependency from humans to calculate the
''Intention of Use'' and reduces costs almost to cero to anyone who want to implement
it.
For future investigations a set of different optimization algorithms can be
implemented to get a much more precise controller, as well as a bigger amount of
rules and input variables can be added to improve the prediction.
A software solution is recommended, through the use of Fuzzy Logic libraries,
allowing to get a portable implementation.
Finally yet importantly, breaks with the qualitative problem of different people having
different implementations of the model for a same problem.
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