MUCS: A model for ubiquitous commerce support

Electronic Commerce Research and Applications 10 (2011) 237–246

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Electronic Commerce Research and Applications journal homepage: www.elsevier.com/locate/ecra

MUCS: A model for ubiquitous commerce support Laerte K. Franco a, Joao H. Rosa a, Jorge L.V. Barbosa a,⇑, Cristiano A. Costa a, Adenauer C. Yamin b a b

Interdisciplinary Postgraduate Program in Applied Computer Science, University of the Sinos Valley (Unisinos), Sao Leopoldo, Rio Grande do Sul, 950, Unisinos Ave., 93.022-000, Brazil Postgraduate Program in Computing, Catholic University of Pelotas, Pelotas, Rio Grande do Sul, 412, Felix da Cunha Street, 96.010-000, Brazil

a r t i c l e

i n f o

Article history: Received 11 January 2010 Received in revised form 24 August 2010 Accepted 24 August 2010 Available online 18 September 2010 Keywords: Buyer and seller support Design science Experiments Prototype development Ubiquitous computing Ubiquitous commerce

a b s t r a c t The evolution of computing technology and the emergence of wireless networks have contributed to the miniaturization of mobile devices and their increase in power, providing services anywhere and anytime. In this context, new opportunities for technology support have arisen in different areas, for example, education, games and entertainment, automobile, and commerce. We propose a model for ubiquitous commerce support (MUCS). This model uses ubiquitous computing concepts to look for deal opportunities for users who act either as buyers or sellers. This paper also describes two everyday scenarios, in which the MUCS model could be applied, and explains the implemented prototype to be used in them. Finally, we present the results obtained from a practical experiment, which was performed with the participation of users who filled out an evaluation questionnaire. Ó 2010 Elsevier B.V. All rights reserved.

1. Introduction About two decades ago, Weiser (1991) introduced a new concept called ubiquitous computing. He predicted a world where computing devices would be present in objects, environments, and even human beings. These devices would interact naturally with users in a transparent manner, without being noticed. When Weiser’s seminal article was published, the computing technology needed to fulfill the ubiquitous computing vision was not available yet, which made his prediction somewhat futuristic. Nonetheless, since then, portable electronic equipments, including smartphones, and tablet and notebook PCs have become smaller and more powerful, providing access to interconnecting technologies based on wireless communications like Bluetooth, 3G, WiMax, and Wi-Fi. Those technologies have contributed to services provisioning anywhere and anytime. Furthermore, this evolution has brought new tools in different areas, such as education (Barbosa et al. 2008, Nino et al. 2007), automobiles (Li et al. 2005), games and entertainment (Franco et al. 2007, Segatto et al. 2008), and commerce (Galanxhi-Janaqi and Nah 2004, Gershman 2002, Lin et al. 2005, Roussos et al. 2003). Many authors have been using ubiquitous commerce (u-commerce) as a reference to employ ubiquitous computing technology in the commerce of products and services. For example, the work of Galanxhi-Janaqi and Nah (2004) suggests that ubiqui⇑ Corresponding author. Tel.: +55 (51) 3590 8161; fax: +55 (51) 3590 8162. E-mail addresses: [email protected] (L.K. Franco), joaohenrique89@ gmail.com (J.H. Rosa), [email protected] (J.L.V. Barbosa), [email protected] (C.A. Costa), [email protected] (A.C. Yamin). 1567-4223/$ - see front matter Ó 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.elerap.2010.08.006

tous commerce is a new paradigm that combines wireless networks, TV, voice, and silent commerce with e-commerce. According to Roussos et al. (2003), u-commerce is intimately related to e-commerce and m-commerce, employing the infrastructure and the expertise of both. Nonetheless, u-commerce is characterized by the electronic identification of physical products and the seamless provisioning of business and consumer services in ubiquitous computing infrastructures. Gershman (2002) identified the following prerequisites for the success of ubiquitous commerce strategies: (1) always be connected with the clients; (2) always be aware of the real context of the clients (where they are, what they are doing and what is available around them); and (3) always be proactive, identifying opportunities in real-time to meet client needs. In this article, we define u-commerce as the integration of ecommerce, by electronically identifying physical products, m-commerce, by allowing users to shop anywhere and anytime, and ubiquitous computing, by allowing users to shop intelligently and intuitively with the help of a smart environment. Environment is characterized by the sensors use to identify contexts, for example, location-based services (Hightower and Gaetano 2001), and mechanisms that continuously evaluate the environment, trying to identify business opportunities among users. In this paper, we introduce a model for ubiquitous commerce support (MUCS). The main goal of this work is to identify business opportunities among users in ubiquitous environments. The remainder of the article is organized as follows. Section 2 presents related works, focusing on describing models for u-commerce that are available today. Section 3 provides details on the MUCS model, presenting its architecture and each one of

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its basic components. In Section 4, we show the development of a prototype and two experiments performed to evaluate the proposed model. Section 5 concludes and presents future trends for the area. 2. Related works Despite the fact that ubiquitous commerce is new, many models have been proposed. These models affect different areas, from the commerce of products in supermarkets to the control of parking lots. In this section, we will discuss the main characteristics of some models for u-commerce available today. For example, iGrocer, discussed by Shekar et al. (2003) was designed to be a smart assistant, aiding clients in the purchase process in supermarkets. iGrocer tracks and retains consumers’ nutritional profiles, suggesting the purchase of products or even warning about items that should be avoided. This is particularly practical for elderly people or those who need help to follow a specific diet. iGrocer also identifies the desired products, through barcode scanning, and gives the user information about the nutritional facts. In order to suggest product for the consumer purchase, iGrocer also combines the client’s nutritional profile with ESHA Research (2010), a database of nutritional facts. Another such application is MyGrocer, discussed by Roussos et al. (2003) and Roussos and Moussouri (2004). It is similar to iGrocer, and was designed to assist consumers in the food purchasing process. However, MyGrocer emphasizes the control of the stock of products in food-related businesses and households, using sensors on the shelves and in other locations. These sensors indicate the available quantity of a product, warning when something is missing or finishing. Roussos and Moussouri (2004) suggest the following possible scenarios for the use of MyGrocer:

 Restaurant booking. Suppose that there are two persons who are in different stores of the same shopping mall want to have lunch together. PAM checks the restaurants with cuisines of their preferences for table availability, notifying them of matching opportunities. At the end of the process, the clients choose one of the offers and book a table for lunch. The u-commerce concept may also be applied to services. For example, EPARK was proposed to assist clients and employees in the process of parking payment and management (Mazzari et al. 2007). Some of its functionalities are a parking map, booking a parking space, and extending the booking time for a parking space. Another example of a u-commerce application in the service market is the Ubiquitous Tourist Assistant System (UTAS) of Chiu and Leung (2005). UTAS was designed to satisfy tourists’ needs in situations where language is an obstacle. UTAS is capable of warning about delayed or canceled flights, obstructed roads, and restaurant or concert bookings, allowing the traveler’s trip plans to be handled though a mobile device. Although there are many proposals for u-commerce, MUCS is the only model that focuses on the generation of business opportunities for users as clients or suppliers. In addition, MUCS is generic, so it is able to work with to support commerce for products and services in different areas. 3. The MUCS model The MUCS model uses the ubiquitous computing infrastructure to identify business opportunities between clients and suppliers. The next subsections present a background on the model, its architecture, as well as the strategy used to identify, distribute, and deal with opportunities. 3.1. Background

 Supermarket. The client enters the supermarket and picks a supermarket trolley, which has a PDA with RFID readers to identify the products. The device recognizes the RFID tag of the client’s affinity card and loads his purchase list. After that, the system indicates the best route to find the products faster. When the client inserts or removes products from the trolley, the system updates the purchase list, and also updates the instock inventory of the supermarket. At the end, the trolley passes through an RFID reader located next to a cash register, which rescans all the items in the trolley, calculates the total cost of the products, shows that information on the display of the cash register, and prints out a receipt for the customer.  Customer’s home. At home, people can store food products in compartments that have RFID readers installed, like a refrigerator and a cupboard. Whenever an item is inserted or removed, the system can automatically update information about what’s in-stock. Lin et al. (2005) present a generic architecture called the Pervasive Activity Manager (PAM). Its aim is to assist clients, giving complementary information of products to help the decision on the purchase of an item. PAM uses services supplied by stores, shopping malls, or even other customers. Below, we show some possible scenarios suggested:  DVD rental. A client goes to a store in a shopping mall to rent a DVD. The client’s personal device identifies, by means of an RFID reader, that he is holding a DVD for more than thirty seconds. At this moment, PAM gathers data related to the movie, based on the consultation of services provided by the DVD rental store or by connecting to the Internet. PAM shows the information to the client and if he wants to rent the movie, he can complete the operation through the mobile device.

The MUCS model is based on the five concepts. An Environment comprises all the physical area covered by the model, for example, a shopping mall, a home, a building or a city. A Context consists of the subdivisions of the environment. An environment is subdivided into one or more Contexts, for example, stores in a mall. A Dealer is a person or a company that supports transaction-making, by offering search services or by supplying other services or products. A Desire represents the services and products that a consumer wants to acquire. For example, different consumer Desires may include buying a camera, learning a language or having a certain type of food. An Offer indicates services and products that a Dealer wants to supply, for instance, selling a computer or teaching music. Fig. 1 presents an example of a shopping mall using the MUCS Environment representation. In this Environment, the physical spaces of the stores and the food court are the Contexts. In the example, they are represented by the dotted rectangles. Dealers are located within the Contexts, which are represented by circles. In the figure, we can identify five Dealers in the food court, which may be restaurants or clients of the shopping mall.

Fig. 1. Sketch of a MUCS environment.

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3.2. MUCS model architecture The MUCS architecture, presented in Fig. 2, is composed of eight components. The first is the Location System, which allows the identification of the users’ current Context. The second is the Profile System that stores the users’ data. The third is the Category Tree, which keeps a taxonomy of products and services. The fourth is the Reference System, which determines the users’ reputation, according to the previous transacted businesses. The fifth is the Service Manager, which operates as a layer of messages exchange between the interface component and the others. The sixth is the Opportunity Consultant, which is the main component of the model. It operates as an analysis engine, generating business opportunities from the Profiles and Locations of the Dealers. Finally, there are two components used as interfaces with users, the Personal Assistant and the Website. The next subsections discuss each component of the MUCS architecture. 3.2.1. Location System This converts the physical location of the Dealers (e.g., an antenna’s power or GPS coordinates) to Contexts (e.g., a food court or Room A). Following that, it supplies the Context information to the Opportunity Consultant component. It is based on an architecture that supports different techniques to determine the location of mobile equipment (Hightower and Gaetano 2001). The location technique that is employed depends on the resources available in the environment and on the mobile devices in use. Many proposals emphasize GSM (Türkyilmaz et al. 2008) and GPS (Bulusan et al. 2000) as efficient ways of outdoor location. Nonetheless, Bluetooth technology (Aalto et al. 2004, Bargh and Groote 2008) and the triangulation of wireless antennas (Brunato and Battiti 2005) are commonly used for indoor location. In addition, studies have been conducted that attempt to demonstrate the hybrid usage of these technologies (Guillemette et al. 2008, Zou et al. 2008), thus allowing more precise locations and the optimization of energy consumption (Deblauwe et al. 2007). The stages of the location process, which are presented in Fig. 3, are as follows: (1) Personal Assistant supplies to the Environment server physical location data, using the Location service of the Service Manager component; (2) these data are stored temporarily; (3) the Location System converts the data to a Context; and (4) the Dealer’s Context is stored to be used in the Opportunity Consultant module. 3.2.2. Profile System In the last few years, many essays and surveys have covered the organization of profiles, and have proposed data meta-models to standardize and organize information (Weibel 1995). For example,

Fig. 2. The MUCS architecture.

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the PAPI standard (IEEE Learning Technology Standardization Committee 2001) and IMS LIP (IMS Global Learning Consortium, 2010) focus on the description of learning profiles, while the Content Standard for Digital Geospatial Meta-data (CSDGM) (Federal Geographic Data Committee 2010) is used to represent geographical data, and the Dublin Core (Dublin Core Meta-data Initiative 2010) and the MARC 21 (Furrie 2003) aim at standardizing general information of digital libraries. Nevertheless, we did not find any study that focused on describing meta-data files for u-commerce. Instead of that, we found meta-models representing specific product lines. For example, (Rust and Bide 2000) and ONIX (Norm 2001), which represent intellectual property products, such as books, songs, DVDs, and software. These models are used by companies such as Amazon.com (http://www.amazon.com), the National Book Network (http://www.nbnbooks.com), and McGraw-Hill (http://www. mcgrawhill.com) to organize their products. In MUCS, the Dealer’s description is organized into a meta-model for the Profile, which stores the information needed to generate business opportunities. This meta-model is composed of six categories, as shown in Table 1. The Dealers provide data for their Profiles, using either the Personal Assistant component or the model’s website. The Profile is stored in the Dealers’ mobile devices and also in the Environment server. Each time the Dealers log into the MUCS environment, the model synchronizes the data in the server with the data in the mobile devices. Therefore, the Dealers can move among Environments without the need to register their Profiles repeatedly. The Profile meta-model supports the creation of groups of Dealers. A group shares the same Offers and Desires. When a Dealer of the group registers a Desire or an Offer, all the other Dealers have the data registered in their Profiles. Similarly, when the model creates an Opportunity for a Dealer, the entire group receives the same Opportunity. 3.2.3. Category Tree In e-commerce, the heterogeneity of the description of products is a serious obstacle to exchanging business information efficiently (Yan et al. 2002). Similarly, in MUCS the structure of the information is a critical factor to identify business opportunities. We propose the Category Tree component, which structures the data in the format of a tree. Each node contains the following attributes: Father Category, referring to the category that a product or a service belongs to, for example, a computer belongs to the Informatics category; Name, containing the product or the service name; Description, containing the product or the service description; Synonyms, containing the synonyms of the name, for example, as synonyms of the word ‘‘car,” we could have automobile and vehicle. The aim of the Category Tree is to promote the generation of business opportunities, using synonyms to facilitate the match among the categories. 3.2.4. Reference System One of the most common reasons that deals fail in commerce, especially in e-commerce, is the user’s inability to rely upon a specific Dealer (Leuch et al. 2006). This can inhibit the Dealer from making transactions. To minimize or eliminate this problem, we propose to use a Reputation System Component (Dellarocas 2001; Resnick 2002; Resnick et al. 2000). Such systems aim at assisting clients to decide whether the suppliers are reliable, without knowing them directly. In addition, these systems encourage good behavior on the part of the suppliers, because they expect that their customers consider past behavior as a basis for future interactions (Resnick et al. 2000). The Reference System component operates as an adviser, indicating which business opportunities could be created. This

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Fig. 3. Workflow of the location process.

Table 1 The meta-model of profile of the MUCS model. Category

Description

Elements

Identification

Data to identify the dealer

Desire

Products and services that the dealer wants to acquire Products or services that the dealer wants to supply Business opportunities generated for the dealer

Company registry or social security number, name, telephone, e-mail, and groups Type of business, group, title, category, characteristics, terms of payment, and valid period Type of business, group, title, category, characteristics, terms of payment, and valid period Desire identification, company registry or social security number, dealer’s data, context of the environment, percent of met characteristics, characteristics, and terms of payment Desire identification, category, characteristics, type of business, company registry or social security number, dealer’s data, context of the environment, terms of payment, dealer’s reference grade, dealer’s received grade, opinion date, and opinion description Terms of payment, desires and offers by context, period to store the opportunities, category of interest, degree of tolerated risk

Offer

Opportunity

Business

Business transactions made by the dealer

Preferences

Preferences of the dealer

component, like eBay (http://www.ebay.com), uses a mechanism that calculates the average of the negative and the positive evaluations (JØsang et al. 2007). The Reference System is different from the mechanism used in eBay though, because it automatically considers the Degree of Tolerated Risk of the Dealer to decide on creating an Opportunity or not. The Degree of Tolerated Risk is kept by the Profile System in the Preferences category. Every time a transaction occurs in MUCS, the involved Dealers grade their counter-parties. Using this classification, the Reference System updates the Reference Grades of the Dealers. When the model identifies an Opportunity, it matches the Degree of Tolerated Risk of the Dealer with the reference grade of the other Dealer.

The Opportunity is ratified only if the Degree of Tolerated Risk is lower or equal to the Reference Grade. Although the Reference System decides on creating opportunities automatically, it does not take into account the different Profiles of the Dealers who made the classifications. For example, a service considered good for a Dealer can be thought of as bad for another. In addition, the Reference System does not identify the negative behaviors of the Dealer who uses the advantage of an accumulated reputation to conduct dishonest or ill-intentioned transactions. To address these questions, the Reference System could be extended, becoming capable of using the collaborative filtering (Zuo et al. 2007). Collaborative filtering supports the calculation of reputation based on the classifications made by Dealers with similar Profiles. 3.2.5. Service Manager It operates as a layer of communication between the interface components and the other components, using a service-oriented architecture (SOA) (Sanchez-Nielsen et al. 2006). Each service belongs to one of the following groups. The Environment is available for all Dealers anywhere in the Environment. The Context is available only for Dealers who are within a specific Context. For instance, a service to book a meeting room in a trade fair could be provided only for the Dealers who are within the mechanical sector, which is the Context of the trade fair. The Dealer is available only for the counter-party of a Dealer in a negotiation, for example, a service to demonstrate products to clients. A Dealer within a Context is available only for the counter-parties of a Dealer who are within a specific Context. An example is a service to make restaurant reservations that is available for clients located in the food court. The standard services of the MUCS model are organized into the following categories:  Warnings. These are notifications to Dealers regarding created opportunities and proposals of payment. In addition, this category allows the Dealers to send messages to other traders in the same Context.  Access. This provides authentication for Dealers.  Location. This receives the physical location of Dealers and forwards this data to the Location System component.

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 Dealing. This allows Dealers to submit proposals of payment and counter-proposals, as well as to conclude the deal.  Profile. Profile enables Dealers to manage their profiles and to synchronize the data from the Environment server with their mobile devices.  Services. This locates services that are available in the Context. The model also allows the Dealers to register their own services to meet the specific needs of deals. An example of a non-standard service that could be added to the model is the m-payment service (Au and Kauffman 2008, Pousttchi 2008). This service allows electronic payments to be made. A restaurant could register this service, configuring it to belong to the Context of the food court, for example. Consequently, the m-payment service would be available for the clients of the restaurant within the food court. 3.2.6. Opportunity Consultant The Opportunity Consultant is the main component of the MUCS model. This component is in charge of creating business opportunities among Dealers. In the literature, the most similar mechanisms to create business opportunities are called recommender systems (Cumby et al. 2005, Sae-ueng et al. 2007, Weng et al. 2009). These systems are widely used in e-commerce websites such as Amazon and eBay to increase the chances of clients finding the products that meet their needs (Schafer et al. 1999). The Opportunity Consultant uses the Profiles of the Dealers and their physical Locations to create business opportunities. The identification of the opportunities is based on a method of recommendation by attributes (Schafer 2001). This method uses the attributes of the Desire, Offer, Business, and Preferences categories in the profile’s meta-model. The attributes of the Dealers, which are present in the same Context, are matched to create different types of opportunities. The notifications of the opportunities are created through the use of push technology, so they are exhibited without the Dealer’s request (Schafer 2001). 3.2.7. Personal Assistant The Personal Assistant component runs on the mobile devices of the Dealers and interacts with the Environment server. The main functionalities of the Personal Assistant are to: (1) identify the Environment in which the Dealer is and to request access by sending his identification; (2) collect data of physical Location and to send it to the Environment server periodically; (3) store the Dealer’s profile in the mobile device; (4) synchronize the Profile present in the mobile device with the data of the Environment server; receive and show warnings; and (5) allow Dealers to manage their Profiles. 3.2.8. Website In the same way as the Personal Assistant, it allows the Dealers to manage their profiles. However, they do not need to be physi-

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cally present in the same environment. When the Dealers change something in their profiles through the Website, the change becomes effective in all the environments that they visited. Therefore, when Dealers return to a visited environment, the new data is synchronized with their mobile devices. Another functionality of the Website is to allow Dealers to register their own services. 3.3. Identification and notification of the opportunities The next subsections are dedicated to describing the process of identification of opportunities. We will give details on the method of notification for the created opportunities, and the strategy used to promote the business. 3.3.1. Identification of opportunities The process of opportunities creation starts whenever one of the following events occurs: (1) a Dealer includes or edits the Preferences in the Profile System; (2) a Dealer includes or edits a Desire; (3) a Dealer includes or edits an Offer; (4) a Dealer concludes a Business; (5) a Dealer enters a Context. When one of these events occurs, the Opportunity Consultant matches the Profiles of the Dealers within the same Context, trying to identify business opportunities. The opportunities can be classified into the following groups: (1) Buy & Sell opportunities are generated from the Desires of a Dealer matched with the Offers of another Dealer. This is the most common form of business in which a Dealer has an Offer and another Dealer has a Desire. (2) Knowledge Exchange is created based on the Desires of a Dealer matched with the Desires of another Dealer. This Opportunity identifies common tastes between the Dealers, enabling them to exchange knowledge on a particular subject. (3) Experience Exchange is produced from the matching of Desires of a Dealer with Businesses made by another Dealer. This Opportunity identifies the possibility of Dealers exchanging experiences that lead to a purchase or a sale. When the Dealers register a Desire or an Offer, they explicitly state which type of Opportunity must be created. Fig. 4 presents the main stages of the identification of opportunities:  The Opportunity Consultant identifies the occurrence of one of the events that start the process of identification.  The Opportunity Consultant groups all Dealers present in the same Context of the Dealer who started the identification process and consults the Reference System to verify which Dealers have the Degree of Tolerated Risk and the Reference Grade compatible with the Dealer that started the process.  For clients who have compatible data, the Opportunity Consultant matches their Profiles, and tries to identify the type of Opportunity that they are looking for.  Identifying an Opportunity, the Opportunity Consultant sends a notification to the Dealers, using the Warnings service of the Service Manager component.

Fig. 4. Flow of the identification of opportunities.

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3.3.2. Notification of the opportunities The Opportunity Consultant module, after identifying an Opportunity, sends notifications to the involved Dealers. These notifications are sent depending on the type of Opportunity created:  Opportunity to Buy & Sell and Exchange Experience. Notification is sent only to the Dealer who has a specific Desire. So only this Dealer can decide on whether a business transaction should occur.  Opportunity for Knowledge Interchange. Notification is sent to two Dealers, because both have the same Desires. The notifications are sent to the mobile devices of the Dealers. The Personal Assistant simply shows a graphical interface containing a brief description of the Opportunity. (See Fig. 5a.) In addition, the Dealers have the following alternatives:  Store. This option stores the Opportunity in the profile of the Dealer, so that he can analyze it in the future. Nonetheless, if the Dealer decides on carrying on the business later, the Opportunity may not be available anymore, because the Desire or the Offer of the counter-party expired or was removed.  Dispose. This choice deletes the Opportunity.  Visualize. This alternative shows a graphical interface describing the Opportunity in detail. (See Fig. 5b.) In the interface, the Dealers can access the services provided by the counter-party and some services that belong to the Environment or the Context. In addition, the Dealer can negotiate the terms of payment with the counter-party, using the Negotiate option, as described in the next section. 3.3.3. Promoting business The MUCS model promotes business transactions, enabling the Dealers to make proposals for payment. The Dealing service of the Service Manager component is used to allow the Dealer to submit proposals and counter-proposals for Buy & Sell Opportunities. To formulate a proposal, a Dealer must specify a suggested price and the terms of payment. After receiving the proposal, the counter-party can accept the proposal or make a counter-proposal. This dealing cycle may repeat until the Business is concluded.

(a) Interface of the Notification

4. The MUCS prototype and the experiments This section presents the MUCS prototype and the two experiments related to it that we conducted. The first experiment simulated two possible scenarios of ubiquitous commerce with the assistance of users. The second experiment aimed at confirming the acceptance of the MUCS prototype. 4.1. The MUCS prototype Fig. 6 shows the floor plan in which the prototype was evaluated. The MUCS environment, which is represented by the dotted square, is composed of nine rooms which act as Contexts in which four Cisco Aironet 1100 wireless antennas were installed. The Personal Assistant was developed in the C# programming language, using the .NET Compact Framework. The OpenNETCF open source library was used for supporting wireless networks and the .NET System.Xml library was employed to store the Profiles in the mobile devices. The Personal Assistant was installed on an HTC 4351 smartphone (Fig. 7a), the iPAQ 4700 (Fig. 7b), and on a Tablet PC tc1100. All of this equipment uses the Windows Mobile operating system and supports Wi-Fi networks. The Personal Assistant captures the antennas’ power, using the OpenNETCF library, and forwards this data to the Location System. The Location System uses this information to calculate the current Context of the Dealer, that is, in one of the nine rooms shown in Fig. 6. A neural network, developed with the JNNS (Java Neural Network Simulator 2010), converts the data of physical location into a Context. The Category Tree component and the Profile System were implemented using the Firebird relational database (Firebird Project 2010). The Opportunity Consultant and the Reference System components were implemented in C# as Windows services, and both were installed on different servers. The Service Manager was developed using standard Web service technology (W3C Working Group 2004). We chose to use Web service technology because of its capabilities to support different platforms and programming languages. 4.2. Experiment 1: possible application scenarios of the MUCS model The scientific community has been using scenarios to validate context-aware environments, based on Dey et al.’s (2001) approach,

(b) Interface of the Opportunity

Fig. 5. Graphical interfaces of the opportunities.

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Fig. 6. Environment of the MUCS evaluation.

(a) HTC 431

(b) iPAQ 4700

Fig. 7. Personal assistant executing on mobile devices.

and ubiquitous environments, according to Satyanarayanan (2001). Following this strategy, we created two scenarios in which the model could be applied. The experiments were conducted with the assistance of Computing Engineering students at the University of the Sinos Valley. The students received a scenario of simulation and an HP iPAQ 4700 with the prototype of the Personal Assistant installed. Both scenarios were simulated using the environment shown in Fig. 6. We next describe these scenarios. 4.2.1. Scenario 1: start of business at an exposition fair In this scenario, Dealer A, who has a consulting business, registers the Offer ‘‘Consulting about the implementation of ERP with a focus on mobility,” and moves around a trade fair. Dealer B, who is in the mechanical sector of the trade fair, registers the Desire ‘‘Consulting about implementation of ERP, focusing on mobility” also. When Dealer A enters the mechanical sector, the model identifies an Opportunity between him and Dealer B. MUCS then notifies Dealer B about the created Opportunity because he has a similar Desire. Dealer B is interested in the Opportunity and next decides to negotiate the terms of payment. Thus, he sends a proposal suggesting a lower price. Dealer A receives the proposal and responds with a message saying that he agrees, but they must negotiate some terms regarding the service. Dealer A subsequently suggests to Dealer B to book a meeting.

Dealer B books a meeting room, using a service provided by the exposition fair environment. Dealer A receives the invitation of the meeting and confirms that he will participate. The service books the meeting room and notifies the Dealers about the time, location, and participants. Table 2 shows the simulation of the scenario with the development of the actions over time. 4.2.2. Scenario 2: ordering food In this scenario, Dealer A has just one hour to have lunch. He cannot afford to spend time in a queue. So, still in his work place, the Dealer registers the Desire ‘‘Fettuccine Alfredo”, using the Website of the model. This Desire becomes available in all the environments that the Dealer visited in the past. Dealer A goes to a shopping mall. When he enters the food court, MUCS starts the process of Opportunity creation. The model identifies an Opportunity with a restaurant and notifies Dealer A. He visualizes the Opportunity and decides to place an order, using a service provided by the restaurant. This service informs Dealer A that he can pay either using a credit card or by making a direct deposit. Dealer A decides to use a credit card and concludes the payment. After eight minutes, Dealer A receives the ordered food. Table 3 shows the simulation of the scenario with the development of the actions over time.

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Table 2 The actions of the exposition fair scenario over time. Time

Character

Action

15:35

Dealers A and B Dealer B Dealer A

Enter the exposition fair environment. Log into the MUCS models Goes to the mechanical sector Registers the Offer ‘‘Consulting about implantation of ERP with focus on mobility”, in the Profile System component, with the following information:  Category = Service – Consulting  Characteristic = ERP  Characteristic = Management  Characteristic = Mobility Registers the Desire ‘‘Consulting about implantation of ERP, focusing on mobility” with the following information:  Category = Service – Consulting  Characteristic = Management  Characteristic = ERP  Characteristic = Mobility Goes to the mechanical sector Identifies Dealer A’s entry in Context, starts process of identifying Opportunities Opportunity Consultant identifies opportunity to Buy & Sell with Dealers A and B. This Opportunity matches 100% with data provided by Dealer B Opportunity Consultant notifies Dealer B about Opportunity created Visualizes the Opportunity Submits a proposal to pay Dealer A Receives the proposal to be paid by Dealer B Sends a message to Dealer B Receives Dealer A’s messages and reserves a meeting room Confirms the meeting room reservation Receives meeting confirmation, containing the location, time and participants

15:37

Dealer B

15:38 15:38

Dealer A Opportunity Consultant Opportunity Consultant Opportunity Consultant Dealer B Dealer B Dealer A Dealer A Dealer B Dealer A Dealers A and B

15:39 15:40 15:41 15:42

Table 3 The actions of the ordering food scenario over time. Time

Character

Action

–

Restaurant

Registers the Offer ‘‘Fettuccine Alfredo” with the following characteristics:  Category = Food – Menu  Ingredient = Pasta  Ingredient = Fettuccine  Price = $19.00 Dealer A Registers the Desire ‘‘Fettuccine Alfredo”, using the Website of the model, with the following characteristics:  Category = Food – Menu  Ingredient = Pasta  Price 6 $21.00 Enters the Environment of the shopping mall Logs into the MUCS model using the Personal Assistant Enters the food court Identifies Dealer A’s entry in the context and starts the process of opportunities identification Opportunity Consultant identifies an opportunity to Buy & Sell between Dealer A and a restaurant. This opportunity meets 100% of the characteristics indicated by Dealer A, including the terms of payment Opportunity Consultant notifies Dealer A about the created opportunity Visualizes the opportunity and decides on placing an order, using a service provided by the restaurant Pays for the order, using the service provided by the restaurant Receives a message saying that the order will be ready in 8 min Receives the ordered food

Dealer A

12:03

Dealer A

12:05 12:05

Dealer A Opportunity Consultant Opportunity Consultant

12:06 12:07 12:08 12:16

Opportunity Consultant Dealer A Dealer A Dealer A Dealer A

4.3. Experiment 2: acceptance evaluation of the model This experiment aimed at evaluating user acceptance of the model. The assessment involved volunteers who used the model and filled out a questionnaire. The sample used was composed of twenty Dealers from the professors and students of the University of the Sinos Valley. Each Dealer received an HP iPAQ 4700, with the Personal Assistant installed. In addition, the volunteers received the basic instructions to conduct the test. The experiment was performed in the following stages:  Stage 1. Registry of the Stores. We registered stores that sell clothing, shoes, and IT equipment in three Contexts of the simulation Environment. Each store represented a Dealer. Subse-

quently, we registered many Offers of the products to the stores, considering data from real shops on the Internet. All the stores had approximately 100 Offers.  Stage 2. Businesses with the Stores. The volunteers logged in the prototype and registered Desires related to the stores. After that, they moved freely around the Environment, entering the Contexts of the stores. When the Dealers entered these Contexts, the MUCS model started the process of Opportunity identification and notified them about the Opportunities that were created.  Stage 3. Business among the Volunteers. The volunteers registered complementary Offers and Desires and moved around the Environment. When the Dealers performed one of the actions that starts the process of Opportunity identification,

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the MUCS model looked for Opportunities. It generated Opportunities for the Dealers with complementary interests and notified them. Visualizing the Opportunities, the Dealers were able to negotiate the terms of payment, and send messages to the counter-party of the Business.  Stage 4. Questionnaire. The volunteers filled out the questionnaire related to their experiences with using the MUCS model. The responses followed a five-point Likert (1932) scale, spanning ‘‘1” for ‘‘completely disagree” to ‘‘5” for ‘‘completely agree.” A ‘‘3” means that the respondence is indifferent and has no particular opinion. In addition, the Dealers were able to comment on the MUCS model. These comments were used to complement the evaluation. The questionnaire was developed based on the concepts of the technology acceptance model (TAM). This model was proposed by Davis (1989), and applied and expanded by Yoon and Kim (2007) in their study on the acceptance of wireless networks, among others. TAM considers the following items as main influences for the acceptance of a new technology: user friendliness, or the degree to which users believe that the technology will reduce their effort; and perceived utility, which is the degree to which people believe that use of the technology will improve their performance. Table 4 shows the questionnaire that we presented to the Dealers. Questions 1 to 4 represent user friendliness from TAM and Questions 5–8 characterize TAM’s perceived utility. The results show that 70% of the Dealers agreed with the statements regarding the perceived ease of use of the MUCS model, based on Questions 1–4. This indicates that, according to most of the respondents, the routine use of this model seems to reduce effort to identify business Opportunities. Fig. 8a depicts these results. In the results obtained from the Questions 5–8, 79% of the Dealers agreed with the statements regarding the utility of the MUCS model. This indicates that the model likely will be helpful day by day and will improve the identification of commercial Opportunities. Fig. 8b depicts these findings. The volunteers made comments on the model. The following comment was especially notable: ‘‘I see great perspectives in a sysTable 4 Questionnaire contents. Provide your opinion on the following statements

1 2 3

The Personal Assistant of the MUCs model is easy to understand The Personal Assistant of the MUCs model is easy to use The Personal Assistant of the MUCs model does not require too much effort to set up new Offers and Desires The Opportunities were presented in a plain and clear form The Opportunities that were created are accordance with my Desires The MUCS model’s use stimulates interaction with other dealers The MUCS model enables the identification of opportunities in ubiquitous computing environments The MUCS will be commercially helpful

8

tem like that. The only problem is the low penetration of devices capable of running this type of application.” Since the Personal Assistant prototype was developed only for the Windows Mobile platform, this commentary must be considered. Trying to increase the model’s coverage, we developed the capability for communication between the Personal Assistant and the Environment server using standard Web service technology. This technology enables the Personal Assistant component to be implemented in any platform that supports Web service technology, such as Symbian, iPhone OS, or Android. Similarity, the Location System component requires that the mobile devices have a feature to capture wireless signals. However, any other strategy for location, using technologies that are currently available, will require some special capabilities in mobile devices, such as RFID readers, GPS, or bar-code scanners. 5. Conclusion We presented the MUCS model for ubiquitous commerce support. MUCS is intended to support the creation of business opportunities among Dealers playing the role either of buyer or seller. We performed two experiments. The first simulated two possible scenarios of u-commerce and the second aimed at attesting the MUCS acceptance. The main conclusions of this work are the following: (1) mobility allows to create business opportunities according to the Dealer’s Context; (2) the precise information about Location stimulates the use of mobile devices for creating businesses opportunities; (3) the proposed model contains the basic components to support the creation of opportunities using ubiquitous computing; (4) the prototype and the experiment have attested the feasibility of the proposal. MUCS is an initial proposal and requires improvements, especially a well-defined model of business, to become commercially feasible. Future work will be carried out to extend the model. We intend to employ an ontology, which would replace the current Category Tree component. This ontology will be used for classifying products and services in different commercial areas. Another future effort is to extend of the Opportunity Consultant, so that it becomes capable of predicting the Dealers’ Desire. Finally, we want to analyze the model performance with a bigger number of users. References

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