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commerce (Galanxhi-Janaqi and Nah 2004, Gershman 2002, Linet al. 2005, Roussos et al. 2003).

Many authors have been using ubiquitous commerce(u-commerce) as a reference to employ ubiquitous computingtechnology in the commerce of products and services. For example,the work of Galanxhi-Janaqi and Nah (2004) suggests that ubiqui-

characterized by the sensors use to identify contexts, for example,location-based services (Hightower and Gaetano 2001), and mech-anisms that continuously evaluate the environment, trying to iden-tify business opportunities among users. In this paper, weintroduce a model for ubiquitous commerce support (MUCS). Themain goal of this work is to identify business opportunities amongusers in ubiquitous environments.

The remainder of the article is organized as follows. Section 2presents related works, focusing on describing models foru-commerce that are available today. Section 3 provides detailson the MUCS model, presenting its architecture and each one of

Corresponding author. Tel.: +55 (51) 3590 8161; fax: +55 (51) 3590 8162.E-mail addresses: laerte.franco@gmail.com (L.K. Franco), joaohenrique89@

gmail.com (J.H. Rosa), jbarbosa@unisinos.br (J.L.V. Barbosa), caccac@gmail.com

Electronic Commerce Research and Applications 10 (2011) 237246

Contents lists availab

Electronic Commerce Res

.e(C.A. Costa), adenauer@ucpel.tche.br (A.C. Yamin).since then, portable electronic equipments, including smart-phones, and tablet and notebook PCs have become smaller andmore powerful, providing access to interconnecting technologiesbased on wireless communications like Bluetooth, 3G, WiMax,and Wi-Fi. Those technologies have contributed to services provi-sioning anywhere and anytime. Furthermore, this evolution hasbrought new tools in different areas, such as education (Barbosaet al. 2008, Nino et al. 2007), automobiles (Li et al. 2005), gamesand entertainment (Franco et al. 2007, Segatto et al. 2008), and

nected with the clients; (2) always be aware of the real contextof the clients (where they are, what they are doing and what isavailable around them); and (3) always be proactive, identifyingopportunities in real-time to meet client needs.

In this article, we dene u-commerce as the integration of e-commerce, by electronically identifying physical products, m-com-merce, by allowing users to shop anywhere and anytime, and ubiq-uitous computing, by allowing users to shop intelligently andintuitively with the help of a smart environment. Environment isUbiquitous commerce

1. Introduction

About two decades ago, Weiser (cept called ubiquitous computing.computing devices would be preseand even human beings. These devwith users in a transparent manner,Weisers seminal article was publishneeded to fulll the ubiquitous compyet, which made his prediction som1567-4223/$ - see front matter 2010 Elsevier B.V. Adoi:10.1016/j.elerap.2010.08.006introduced a new con-dicted a world whereobjects, environments,ould interact naturallyut being noticed. Whencomputing technologyision was not availablefuturistic. Nonetheless,

tous commerce is a new paradigm that combines wireless net-works, TV, voice, and silent commerce with e-commerce.According to Roussos et al. (2003), u-commerce is intimately re-lated to e-commerce and m-commerce, employing the infrastruc-ture and the expertise of both. Nonetheless, u-commerce ischaracterized by the electronic identication of physical productsand the seamless provisioning of business and consumer servicesin ubiquitous computing infrastructures.

Gershman (2002) identied the following prerequisites for thesuccess of ubiquitous commerce strategies: (1) always be con-ExperimentsPrototype development 2010 Elsevier B.V. All rights reserved.MUCS: A model for ubiquitous commerc

Laerte K. Franco a, Joao H. Rosa a, Jorge L.V. Barbosa a

a Interdisciplinary Postgraduate Program in Applied Computer Science, University of the Sb Postgraduate Program in Computing, Catholic University of Pelotas, Pelotas, Rio Grand

a r t i c l e i n f o

Article history:Received 11 January 2010Received in revised form 24 August 2010Accepted 24 August 2010Available online 18 September 2010

Keywords:Buyer and seller supportDesign science

a b s t r a c t

The evolution of computinminiaturization of mobile dIn this context, new oppoeducation, games and entcommerce support (MUCS).for users who act either asthe MUCS model could bewe present the results obtaof users who lled out an

journal homepage: wwwll rights reserved.upport

Cristiano A. Costa a, Adenauer C. Yamin b

Valley (Unisinos), Sao Leopoldo, Rio Grande do Sul, 950, Unisinos Ave., 93.022-000, BrazilSul, 412, Felix da Cunha Street, 96.010-000, Brazil

chnology and the emergence of wireless networks have contributed to theces and their increase in power, providing services anywhere and anytime.ities for technology support have arisen in different areas, for example,inment, automobile, and commerce. We propose a model for ubiquitouss model uses ubiquitous computing concepts to look for deal opportunitiesyers or sellers. This paper also describes two everyday scenarios, in whichlied, and explains the implemented prototype to be used in them. Finally,d from a practical experiment, which was performed with the participationluation questionnaire.

le at ScienceDirect

earch and Applications

l sevier .com/locate /ecra

Restaurant booking. Suppose that there are two persons who arein different stores of the same shopping mall want to havelunch together. PAM checks the restaurants with cuisines oftheir preferences for table availability, notifying them of match-ing opportunities. At the end of the process, the clients chooseone of the offers and book a table for lunch.

The u-commerce concept may also be applied to services. Forexample, EPARK was proposed to assist clients and employees inthe process of parking payment and management (Mazzari et al.

esearch and Applications 10 (2011) 237246its basic components. In Section 4, we show the development of aprototype and two experiments performed to evaluate the pro-posed model. Section 5 concludes and presents future trends forthe area.

2. Related works

Despite the fact that ubiquitous commerce is new, many mod-els have been proposed. These models affect different areas, fromthe commerce of products in supermarkets to the control of park-ing lots. In this section, we will discuss the main characteristics ofsome models for u-commerce available today.

For example, iGrocer, discussed by Shekar et al. (2003) was de-signed to be a smart assistant, aiding clients in the purchase pro-cess in supermarkets. iGrocer tracks and retains consumersnutritional proles, suggesting the purchase of products or evenwarning about items that should be avoided. This is particularlypractical for elderly people or those who need help to follow a spe-cic diet. iGrocer also identies the desired products, through bar-code scanning, and gives the user information about the nutritionalfacts. In order to suggest product for the consumer purchase,iGrocer also combines the clients nutritional prole with ESHAResearch (2010), a database of nutritional facts.

Another such application is MyGrocer, discussed by Roussoset al. (2003) and Roussos and Moussouri (2004). It is similar toiGrocer, and was designed to assist consumers in the food purchas-ing process. However, MyGrocer emphasizes the control of thestock of products in food-related businesses and households, usingsensors on the shelves and in other locations. These sensors indi-cate the available quantity of a product, warning when somethingis missing or nishing. Roussos and Moussouri (2004) suggest thefollowing possible scenarios for the use of MyGrocer:

Supermarket. The client enters the supermarket and picks asupermarket trolley, which has a PDA with RFID readers toidentify the products. The device recognizes the RFID tag ofthe clients afnity card and loads his purchase list. After that,the system indicates the best route to nd the products faster.When the client inserts or removes products from the trolley,the system updates the purchase list, and also updates the in-stock inventory of the supermarket. At the end, the trolleypasses through an RFID reader located next to a cash register,which rescans all the items in the trolley, calculates the totalcost of the products, shows that information on the display ofthe cash register, and prints out a receipt for the customer.

Customers home. At home, people can store food products incompartments thathaveRFID readers installed, like a refrigeratorand a cupboard. Whenever an item is inserted or removed, thesystem can automatically update information about whatsin-stock.

Lin et al. (2005) present a generic architecture called the Perva-sive Activity Manager (PAM). Its aim is to assist clients, giving com-plementary information of products to help the decision on thepurchase of an item. PAM uses services supplied by stores, shop-ping malls, or even other customers. Below, we show some possi-ble scenarios suggested:

DVD rental. A client goes to a store in a shopping mall to rent aDVD. The clients personal device identies, by means of anRFID reader, that he is holding a DVD for more than thirty sec-onds. At this moment, PAM gathers data related to the movie,based on the consultation of services provided by the DVD ren-

238 L.K. Franco et al. / Electronic Commerce Rtal store or by connecting to the Internet. PAM shows the infor-mation to the client and if he wants to rent the movie, he cancomplete the operation through the mobile device.2007). Some of its functionalities are a parking map, booking aparking space, and extending the booking time for a parking space.Another example of a u-commerce application in the service mar-ket is the Ubiquitous Tourist Assistant System (UTAS) of Chiu andLeung (2005). UTAS was designed to satisfy tourists needs in situ-ations where language is an obstacle. UTAS is capable of warningabout delayed or canceled ights, obstructed roads, and restaurantor concert bookings, allowing the travelers trip plans to be han-dled though a mobile device. Although there are many proposalsfor u-commerce, MUCS is the only model that focuses on the gen-eration of business opportunities for users as clients or suppliers.In addition, MUCS is generic, so it is able to work with to supportcommerce for products and services in different areas.

3. The MUCS model

The MUCS model uses the ubiquitous computing infrastructureto identify business opportunities between clients and suppliers.The next subsections present a background on the model, its archi-tecture, as well as the strategy used to identify, distribute, and dealwith opportunities.

3.1. Background

The MUCS model is based on the ve concepts. An Environmentcomprises all the physical area covered by the model, for example,a shopping mall, a home, a building or a city. A Context consists ofthe subdivisions of the environment. An environment is subdividedinto one or more Contexts, for example, stores in a mall. A Dealer isa person or a company that supports transaction-making, by offer-ing search services or by supplying other services or products. ADesire represents the services and products that a consumer wantsto acquire. For example, different consumer Desires may includebuying a camera, learning a language or having a certain type offood. An Offer indicates services and products that a Dealer wantsto supply, for instance, selling a computer or teaching music.

Fig. 1 presents an example of a shopping mall using the MUCSEnvironment representation. In this Environment, the physicalspaces of the stores and the food court are the Contexts. In theexample, they are represented by the dotted rectangles. Dealersare located within the Contexts, which are represented by circles.In the gure, we can identify ve Dealers in the food court, whichmay be restaurants or clients of the shopping mall.Fig. 1. Sketch of a MUCS environment.

3.2. MUCS model architecture

The MUCS architecture, presented in Fig. 2, is composed of eightcomponents. The rst is the Location System, which allows theidentication of the users current Context. The second is the Pro-le System that stores the users data. The third is the CategoryTree, which keeps a taxonomy of products and services. The fourthis the Reference System, which determines the users reputation,according to the previous transacted businesses. The fth is theService Manager, which operates as a layer of messages exchangebetween the interface component and the others. The sixth is theOpportunity Consultant, which is the main component of the mod-el. It operates as an analysis engine, generating business opportu-nities from the Proles and Locations of the Dealers. Finally, thereare two components used as interfaces with users, the Personal

L.K. Franco et al. / Electronic Commerce ReseaAssistant and the Website. The next subsections discuss each com-ponent of the MUCS architecture.

3.2.1. Location SystemThis converts the physical location of the Dealers (e.g., an anten-

nas power or GPS coordinates) to Contexts (e.g., a food court orRoom A). Following that, it supplies the Context information tothe Opportunity Consultant component. It is based on an architec-ture that supports different techniques to determine the location ofmobile equipment (Hightower and Gaetano 2001). The locationtechnique that is employed depends on the resources available inthe environment and on the mobile devices in use. Many proposalsemphasize GSM (Trkyilmaz et al. 2008) and GPS (Bulusan et al.2000) as efcient ways of outdoor location. Nonetheless, Bluetoothtechnology (Aalto et al. 2004, Bargh and Groote 2008) and the tri-angulation of wireless antennas (Brunato and Battiti 2005) arecommonly used for indoor location. In addition, studies have beenconducted that attempt to demonstrate the hybrid usage of thesetechnologies (Guillemette et al. 2008, Zou et al. 2008), thus allow-ing more precise locations and the optimization of energy con-sumption (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 Environmentserver physical location data, using the Location service of the Ser-vice Manager component; (2) these data are stored temporarily;(3) the Location System converts the data to a Context; and (4)the Dealers Context is stored to be used in the Opportunity Con-sultant module.

3.2.2. Prole SystemIn the last few years, many essays and surveys have covered the

organization of proles, and have proposed data meta-models tostandardize and organize information (Weibel 1995). For example,Fig. 2. The MUCS architecture.the PAPI standard (IEEE Learning Technology StandardizationCommittee 2001) and IMS LIP (IMS Global Learning Consortium,2010) focus on the description of learning proles, while theContent Standard for Digital Geospatial Meta-data (CSDGM)(Federal Geographic Data Committee 2010) is used to representgeographical data, and the Dublin Core (Dublin Core Meta-dataInitiative 2010) and the MARC 21 (Furrie 2003) aim at standardiz-ing general information of digital libraries.

Nevertheless, we did not nd any study that focused on describ-ing meta-data les for u-commerce. Instead of that, we foundmeta-models representing specic product lines. For example, (Rust and Bide 2000) and ONIX (Norm 2001), which repre-sent intellectual property products, such as books, songs, DVDs,and software. These models are used by companies such as Ama-zon.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 Dealers description is organized into a meta-mod-el for the Prole, which stores the information needed to generatebusiness opportunities. This meta-model is composed of six cate-gories, as shown in Table 1. The Dealers provide data for their Pro-les, using either the Personal Assistant component or the modelswebsite. The Prole is stored in the Dealers mobile devices andalso in the Environment server. Each time the Dealers log intothe MUCS environment, the model synchronizes the data in theserver with the data in the mobile devices. Therefore, the Dealerscan move among Environments without the need to register theirProles repeatedly.

The Prole meta-model supports the creation of groups of Deal-ers. A group shares the same Offers and Desires. When a Dealer ofthe group registers a Desire or an Offer, all the other Dealers havethe data registered in their Proles. Similarly, when the model cre-ates an Opportunity for a Dealer, the entire group receives thesame Opportunity.

3.2.3. Category TreeIn e-commerce, the heterogeneity of the description of products

is a serious obstacle to exchanging business information efciently(Yan et al. 2002). Similarly, in MUCS the structure of the informa-tion is a critical factor to identify business opportunities. We pro-pose the Category Tree component, which structures the data inthe format of a tree. Each node contains the following attributes:Father Category, referring to the category that a product or a ser-vice belongs to, for example, a computer belongs to the Informaticscategory; Name, containing the product or the service name;Description, containing the product or the service description; Syn-onyms, containing the synonyms of the name, for example, as syn-onyms of the word car, we could have automobile and vehicle.The aim of the Category Tree is to promote the generation of busi-ness opportunities, using synonyms to facilitate the match amongthe categories.

3.2.4. Reference SystemOne of the most common reasons that deals fail in commerce,

especially in e-commerce, is the users inability to rely upon a spe-cic Dealer (Leuch et al. 2006). This can inhibit the Dealer frommaking transactions. To minimize or eliminate this problem, wepropose to use a Reputation System Component (Dellarocas2001; Resnick 2002; Resnick et al. 2000). Such systems aim atassisting clients to decide whether the suppliers are reliable, with-out knowing them directly. In addition, these systems encouragegood behavior on the part of the suppliers, because they expectthat their customers consider past behavior as a basis for future

rch and Applications 10 (2011) 237246 239interactions (Resnick et al. 2000).The Reference System component operates as an adviser,

indicating which business opportunities could be created. This

esea240 L.K. Franco et al. / Electronic Commerce Rcomponent, like eBay (http://www.ebay.com), uses a mechanismthat calculates the average of the negative and the positive evalu-ations (Jsang et al. 2007). The Reference System is different fromthe mechanism used in eBay though, because it automatically con-siders the Degree of Tolerated Risk of the Dealer to decide on cre-ating an Opportunity or not. The Degree of Tolerated Risk is kept bythe Prole System in the Preferences category.

Every time a transaction occurs in MUCS, the involved Dealersgrade their counter-parties. Using this classication, the ReferenceSystem updates the Reference Grades of the Dealers. When themodel identies an Opportunity, it matches the Degree of Toler-ated Risk of the Dealer with the reference grade of the other Dealer.

Table 1The meta-model of prole of the MUCS model.

Category Description Elements

Identication Data to identify thedealer

Company registry or social securitynumber, name, telephone, e-mail,and groups

Desire Products and servicesthat the dealer wantsto acquire

Type of business, group, title,category, characteristics, terms ofpayment, and valid period

Offer Products or servicesthat the dealer wantsto supply

Type of business, group, title,category, characteristics, terms ofpayment, and valid period

Opportunity Business opportunitiesgenerated for thedealer

Desire identication, companyregistry or social security number,dealers data, context of theenvironment, percent of metcharacteristics, characteristics, andterms of payment

Business Business transactionsmade by the dealer

Desire identication, category,characteristics, type of business,company registry or social securitynumber, dealers data, context ofthe environment, terms ofpayment, dealers reference grade,dealers received grade, opiniondate, and opinion description

Preferences Preferences of thedealer

Terms of payment, desires andoffers by context, period to storethe opportunities, category ofinterest, degree of tolerated risk

Fig. 3. Workow of theThe Opportunity is ratied only if the Degree of Tolerated Risk islower or equal to the Reference Grade.

Although the Reference System decides on creating opportuni-ties automatically, it does not take into account the differentProles of the Dealers who made the classications. For example,a service considered good for a Dealer can be thought of as badfor another. In addition, the Reference System does not identifythe negative behaviors of the Dealer who uses the advantage ofan accumulated reputation to conduct dishonest or ill-intentionedtransactions. To address these questions, the Reference Systemcould be extended, becoming capable of using the collaborative l-tering (Zuo et al. 2007). Collaborative ltering supports the calcu-lation of reputation based on the classications made by Dealers

location process.

rch and Applications 10 (2011) 237246with similar Proles.

3.2.5. Service ManagerIt operates as a layer of communication between the interface

components and the other components, using a service-orientedarchitecture (SOA) (Sanchez-Nielsen et al. 2006). Each service be-longs to one of the following groups. The Environment is availablefor all Dealers anywhere in the Environment. The Context is avail-able only for Dealers who are within a specic Context. For in-stance, a service to book a meeting room in a trade fair could beprovided only for the Dealers who are within the mechanical sec-tor, which is the Context of the trade fair. The Dealer is availableonly for the counter-party of a Dealer in a negotiation, for example,a service to demonstrate products to clients. A Dealer within a Con-text is available only for the counter-parties of a Dealer who arewithin a specic Context. An example is a service to make restau-rant reservations that is available for clients located in the foodcourt.

The standard services of the MUCS model are organized into thefollowing categories:

Warnings. These are notications to Dealers regarding createdopportunities and proposals of payment. In addition, this cate-gory allows the Dealers to send messages to other traders inthe same Context.

Access. This provides authentication for Dealers. Location. This receives the physical location of Dealers and for-wards this data to the Location System component.

Dealing. This allows Dealers to submit proposals of payment andcounter-proposals, as well as to conclude the deal.

Prole. Prole enables Dealers to manage their proles and tosynchronize the data from the Environment server with theirmobile devices.

Services. This locates services that are available in the Context.

The model also allows the Dealers to register their own servicesto meet the specic needs of deals. An example of a non-standardservice that could be added to the model is the m-payment service(Au and Kauffman 2008, Pousttchi 2008). This service allows elec-tronic payments to be made. A restaurant could register this ser-

cally present in the same environment. When the Dealers changesomething in their proles through the Website, the change be-comes effective in all the environments that they visited. There-fore, when Dealers return to a visited environment, the new datais synchronized with their mobile devices. Another functionalityof the Website is to allow Dealers to register their own services.

3.3. Identication and notication of the opportunities

The next subsections are dedicated to describing the process ofidentication of opportunities. We will give details on the methodof notication for the created opportunities, and the strategy used

L.K. Franco et al. / Electronic Commerce Research and Applications 10 (2011) 237246 241vice, conguring it to belong to the Context of the food court, forexample. Consequently, the m-payment service would be availablefor the clients of the restaurant within the food court.

3.2.6. Opportunity ConsultantThe Opportunity Consultant is the main component of the

MUCS model. This component is in charge of creating businessopportunities among Dealers. In the literature, the most similarmechanisms to create business opportunities are called recom-mender systems (Cumby et al. 2005, Sae-ueng et al. 2007, Wenget al. 2009). These systems are widely used in e-commerce web-sites such as Amazon and eBay to increase the chances of clientsnding the products that meet their needs (Schafer et al. 1999).The Opportunity Consultant uses the Proles of the Dealers andtheir physical Locations to create business opportunities. The iden-tication of the opportunities is based on a method of recommen-dation by attributes (Schafer 2001). This method uses theattributes of the Desire, Offer, Business, and Preferences categoriesin the proles meta-model. The attributes of the Dealers, whichare present in the same Context, are matched to create differenttypes of opportunities. The notications of the opportunities arecreated through the use of push technology, so they are exhibitedwithout the Dealers request (Schafer 2001).

3.2.7. Personal AssistantThe Personal Assistant component runs on the mobile devices of

the Dealers and interacts with the Environment server. The mainfunctionalities of the Personal Assistant are to: (1) identify theEnvironment in which the Dealer is and to request access by send-ing his identication; (2) collect data of physical Location and tosend it to the Environment server periodically; (3) store the Deal-ers prole in the mobile device; (4) synchronize the Prole presentin the mobile device with the data of the Environment server; re-ceive and show warnings; and (5) allow Dealers to manage theirProles.

3.2.8. WebsiteIn the same way as the Personal Assistant, it allows the Dealers

to manage their proles. However, they do not need to be physi-Fig. 4. Flow of the identicto promote the business.

3.3.1. Identication of opportunitiesThe process of opportunities creation starts whenever one of

the following events occurs: (1) a Dealer includes or edits thePreferences in the Prole System; (2) a Dealer includes or edits aDesire; (3) a Dealer includes or edits an Offer; (4) a Dealer con-cludes a Business; (5) a Dealer enters a Context.

When one of these events occurs, the Opportunity Consultantmatches the Proles of the Dealers within the same Context, tryingto identify business opportunities. The opportunities can be classi-ed into the following groups: (1) Buy & Sell opportunities are gen-erated from the Desires of a Dealer matched with the Offers ofanother Dealer. This is the most common form of business in whicha Dealer has an Offer and another Dealer has a Desire. (2) Knowl-edge Exchange is created based on the Desires of a Dealer matchedwith the Desires of another Dealer. This Opportunity identiescommon tastes between the Dealers, enabling them to exchangeknowledge on a particular subject. (3) Experience Exchange is pro-duced from the matching of Desires of a Dealer with Businessesmade by another Dealer. This Opportunity identies the possibilityof Dealers exchanging experiences that lead to a purchase or a sale.

When the Dealers register a Desire or an Offer, they explicitlystate which type of Opportunity must be created. Fig. 4 presentsthe main stages of the identication of opportunities:

The Opportunity Consultant identies the occurrence of one ofthe events that start the process of identication.

The Opportunity Consultant groups all Dealers present in thesame Context of the Dealer who started the identication pro-cess and consults the Reference System to verify which Dealershave the Degree of Tolerated Risk and the Reference Grade com-patible with the Dealer that started the process.

For clients who have compatible data, the Opportunity Consul-tant matches their Proles, and tries to identify the type ofOpportunity that they are looking for.

Identifying an Opportunity, the Opportunity Consultant sends anotication to the Dealers, using the Warnings service of theService Manager component.ation of opportunities.

3.3.2. Notication of the opportunitiesThe Opportunity Consultant module, after identifying an

Opportunity, sends notications to the involved Dealers. Thesenotications are sent depending on the type of Opportunitycreated:

Opportunity to Buy & Sell and Exchange Experience. Noticationis sent only to the Dealer who has a specic Desire. So only thisDealer can decide on whether a business transaction shouldoccur.

Opportunity for Knowledge Interchange. Notication is sent totwo Dealers, because both have the same Desires.

The notications are sent to the mobile devices of the Dealers.The Personal Assistant simply shows a graphical interface contain-ing 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 prole of theDealer, so that he can analyze it in the future. Nonetheless, if

4. The MUCS prototype and the experiments

This section presents the MUCS prototype and the two experi-ments related to it that we conducted. The rst experiment simu-lated two possible scenarios of ubiquitous commerce with theassistance of users. The second experiment aimed at conrmingthe acceptance of the MUCS prototype.

4.1. The MUCS prototype

Fig. 6 shows the oor plan in which the prototype was evalu-ated. The MUCS environment, which is represented by the dottedsquare, is composed of nine rooms which act as Contexts in whichfour Cisco Aironet 1100 wireless antennas were installed.

The Personal Assistant was developed in the C# programminglanguage, using the .NET Compact Framework. The OpenNETCFopen source library was used for supporting wireless networksand the .NET System.Xml library was employed to store the Prolesin the mobile devices. The Personal Assistant was installed on anHTC 4351 smartphone (Fig. 7a), the iPAQ 4700 (Fig. 7b), and on aTablet PC tc1100. All of this equipment uses the Windows Mobile

242 L.K. Franco et al. / Electronic Commerce Research and Applications 10 (2011) 237246the Dealer decides on carrying on the business later, the Oppor-tunity may not be available anymore, because the Desire or theOffer of the counter-party expired or was removed.

Dispose. This choice deletes the Opportunity. Visualize. This alternative shows a graphical interface describingthe Opportunity in detail. (See Fig. 5b.) In the interface, theDealers can access the services provided by the counter-partyand some services that belong to the Environment or the Con-text. In addition, the Dealer can negotiate the terms of paymentwith the counter-party, using the Negotiate option, as describedin the next section.

3.3.3. Promoting businessThe MUCS model promotes business transactions, enabling the

Dealers to make proposals for payment. The Dealing service ofthe Service Manager component is used to allow the Dealer tosubmit proposals and counter-proposals for Buy & Sell Opportu-nities. To formulate a proposal, a Dealer must specify a suggestedprice and the terms of payment. After receiving the proposal, thecounter-party can accept the proposal or make a counter-pro-posal. This dealing cycle may repeat until the Business isconcluded.(a) Interface of the NotificationFig. 5. Graphical interfacesoperating system and supports Wi-Fi networks. The Personal Assis-tant captures the antennas power, using the OpenNETCF library,and forwards this data to the Location System. The Location Systemuses 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 Prole System wereimplemented using the Firebird relational database (FirebirdProject 2010). The Opportunity Consultant and the Reference Sys-tem components were implemented in C# as Windows services,and both were installed on different servers. The Service Managerwas developed using standard Web service technology (W3CWorking Group 2004). We chose to use Web service technologybecause of its capabilities to support different platforms and pro-gramming languages.

4.2. Experiment 1: possible application scenarios of the MUCS model

The scientic community has been using scenarios to validatecontext-aware environments, based on Dey et al.s (2001) approach,(b) Interface of the Opportunityof the opportunities.

of th

eseaFig. 6. Environment

L.K. Franco et al. / Electronic Commerce Rand ubiquitous environments, according to Satyanarayanan(2001). Following this strategy, we created two scenarios in whichthe model could be applied. The experiments were conducted withthe assistance of Computing Engineering students at the Universityof the Sinos Valley. The students received a scenario of simulationand an HP iPAQ 4700 with the prototype of the Personal Assistantinstalled. Both scenarios were simulated using the environmentshown in Fig. 6. We next describe these scenarios.

4.2.1. Scenario 1: start of business at an exposition fairIn this scenario, Dealer A, who has a consulting business, regis-

ters the Offer Consulting about the implementation of ERP with afocus on mobility, and moves around a trade fair. Dealer B, who isin the mechanical sector of the trade fair, registers the Desire Con-sulting about implementation of ERP, focusing on mobility also.

When Dealer A enters the mechanical sector, the model identi-es an Opportunity between him and Dealer B. MUCS then notiesDealer B about the created Opportunity because he has a similarDesire. Dealer B is interested in the Opportunity and next decidesto negotiate the terms of payment. Thus, he sends a proposal sug-gesting a lower price. Dealer A receives the proposal and respondswith a message saying that he agrees, but they must negotiatesome terms regarding the service. Dealer A subsequently suggeststo Dealer B to book a meeting.

(a) HTC 431 Fig. 7. Personal assistant exece MUCS evaluation.

rch and Applications 10 (2011) 237246 243Dealer B books a meeting room, using a service provided bythe exposition fair environment. Dealer A receives the invita-tion of the meeting and conrms that he will participate.The service books the meeting room and noties the Dealersabout the time, location, and participants. Table 2 shows thesimulation of the scenario with the development of the actionsover time.

4.2.2. Scenario 2: ordering foodIn 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 theWebsite of the model. This Desire becomes available in all the envi-ronments that the Dealer visited in the past.

Dealer A goes to a shopping mall. When he enters the foodcourt, MUCS starts the process of Opportunity creation. The modelidenties an Opportunity with a restaurant and noties Dealer A.He visualizes the Opportunity and decides to place an order, usinga service provided by the restaurant. This service informs Dealer Athat he can pay either using a credit card or by making a direct de-posit. Dealer A decides to use a credit card and concludes the pay-ment. After eight minutes, Dealer A receives the ordered food.Table 3 shows the simulation of the scenario with the developmentof the actions over time.

(b) iPAQ 4700uting on mobile devices.

Act

EntGoeRegthe

Regwit

eseaTable 2The actions of the exposition fair scenario over time.

Time Character

15:35 Dealers A and BDealer B

15:37 Dealer A

Dealer B

244 L.K. Franco et al. / Electronic Commerce R4.3. Experiment 2: acceptance evaluation of the model

This experiment aimed at evaluating user acceptance of themodel. The assessment involved volunteers who used the modeland lled out a questionnaire. The sample used was composed oftwenty Dealers from the professors and students of the Universityof the Sinos Valley. Each Dealer received an HP iPAQ 4700, with thePersonal Assistant installed. In addition, the volunteers receivedthe basic instructions to conduct the test. The experiment was per-formed in the following stages:

Stage 1. Registry of the Stores. We registered stores that sellclothing, shoes, and IT equipment in three Contexts of the sim-ulation Environment. Each store represented a Dealer. Subse-

15:38 Dealer A Goe15:38 Opportunity Consultant Iden

Opportunity Consultant OppThis

Opportunity Consultant Opp15:39 Dealer B Visu

Dealer B Sub15:40 Dealer A Rec

Dealer A Sen15:41 Dealer B Rec15:42 Dealer A Con

Dealers A and B Rec

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

Time Character Action

Restaurant Registers the Offer Fe Category = Food Ingredient = Pasta Ingredient = Fettuc Price = $19.00

Dealer A Dealer A Registers thecharacteristics: Category = Food Ingredient = Pasta Price 6 $21.00

12:03 Dealer A Enters the EnvironmenLogs into the MUCS m

12:05 Dealer A Enters the food court12:05 Opportunity Consultant Identies Dealer As en

Opportunity Consultant Opportunity Consultanopportunity meets 100

Opportunity Consultant Opportunity Consultan12:06 Dealer A Visualizes the opportu12:07 Dealer A Pays for the order, usi12:08 Dealer A Receives a message sa12:16 Dealer A Receives the ordered fion

er the exposition fair environment. Log into the MUCS modelss to the mechanical sectoristers the Offer Consulting about implantation of ERP with focus on mobility, inProle System component, with the following information:Category = Service ConsultingCharacteristic = ERPCharacteristic = ManagementCharacteristic = Mobilityisters the Desire Consulting about implantation of ERP, focusing on mobilityh the following information:

rch and Applications 10 (2011) 237246quently, we registered many Offers of the products to thestores, considering data from real shops on the Internet. Allthe stores had approximately 100 Offers.

Stage 2. Businesses with the Stores. The volunteers logged in theprototype and registered Desires related to the stores. Afterthat, they moved freely around the Environment, entering theContexts of the stores. When the Dealers entered these Con-texts, the MUCS model started the process of Opportunity iden-tication and notied them about the Opportunities that werecreated.

Stage 3. Business among the Volunteers. The volunteers regis-tered complementary Offers and Desires and moved aroundthe Environment. When the Dealers performed one of theactions that starts the process of Opportunity identication,

Category = Service ConsultingCharacteristic = ManagementCharacteristic = ERPCharacteristic = Mobilitys to the mechanical sectorties Dealer As entry in Context, starts process of identifying Opportunitiesortunity Consultant identies opportunity to Buy & Sell with Dealers A and B.Opportunity matches 100% with data provided by Dealer Bortunity Consultant noties Dealer B about Opportunity createdalizes the Opportunitymits a proposal to pay Dealer Aeives the proposal to be paid by Dealer Bds a message to Dealer Beives Dealer As messages and reserves a meeting roomrms the meeting room reservationeives meeting conrmation, containing the location, time and participants

ttuccine Alfredo with the following characteristics:Menu

cine

Desire Fettuccine Alfredo, using the Website of the model, with the following

Menu

t of the shopping mallodel using the Personal Assistant

try in the context and starts the process of opportunities identicationt identies an opportunity to Buy & Sell between Dealer A and a restaurant. This% of the characteristics indicated by Dealer A, including the terms of paymentt noties Dealer A about the created opportunitynity and decides on placing an order, using a service provided by the restaurantng the service provided by the restaurantying that the order will be ready in 8 minood

the MUCS model looked for Opportunities. It generated Oppor-tunities for the Dealers with complementary interests and noti-ed them. Visualizing the Opportunities, the Dealers were ableto negotiate the terms of payment, and send messages to thecounter-party of the Business.

Stage 4. Questionnaire. The volunteers lled out the question-naire related to their experiences with using the MUCS model.The responses followed a ve-point Likert (1932) scale, span-ning 1 for completely disagree to 5 for completely agree.A 3 means that the respondence is indifferent and has no par-ticular opinion. In addition, the Dealers were able to commenton the MUCS model. These comments were used to comple-ment the evaluation. The questionnaire was developed basedon the concepts of the technology acceptance model (TAM).This model was proposed by Davis (1989), and applied andexpanded by Yoon and Kim (2007) in their study on the accep-tance of wireless networks, among others. TAM considers thefollowing items as main inuences for the acceptance of anew technology: user friendliness, or the degree to which usersbelieve that the technology will reduce their effort; and per-ceived utility, which is the degree to which people believe thatuse of the technology will improve their performance.

Table 4 shows the questionnaire that we presented to the Deal-ers. Questions 1 to 4 represent user friendliness from TAM and

tem like that. The only problem is the low penetration of devicescapable of running this type of application. Since the PersonalAssistant prototype was developed only for the Windows Mobileplatform, this commentary must be considered. Trying to increasethe models coverage, we developed the capability for communica-tion between the Personal Assistant and the Environment serverusing standard Web service technology. This technology enablesthe Personal Assistant component to be implemented in any plat-form that supports Web service technology, such as Symbian,iPhone OS, or Android.

Similarity, the Location System component requires that the

L.K. Franco et al. / Electronic Commerce Research and Applications 10 (2011) 237246 245Questions 58 characterize TAMs perceived utility. The resultsshow that 70% of the Dealers agreed with the statements regardingthe perceived ease of use of the MUCS model, based on Questions14. This indicates that, according to most of the respondents, theroutine use of this model seems to reduce effort to identify busi-ness Opportunities. Fig. 8a depicts these results.

In the results obtained from the Questions 58, 79% of the Deal-ers agreed with the statements regarding the utility of the MUCSmodel. This indicates that the model likely will be helpful day byday and will improve the identication of commercial Opportuni-ties. Fig. 8b depicts these ndings.

The volunteers made comments on the model. The followingcomment was especially notable: I see great perspectives in a sys-

Table 4Questionnaire contents.

# Provide your opinion on the following statements

1 The Personal Assistant of the MUCs model is easy to understand2 The Personal Assistant of the MUCs model is easy to use3 The Personal Assistant of the MUCs model does not require too much

effort to set up new Offers and Desires4 The Opportunities were presented in a plain and clear form5 The Opportunities that were created are accordance with my Desires6 The MUCS models use stimulates interaction with other dealers7 The MUCS model enables the identication of opportunities in ubiquitous

computing environments8 The MUCS will be commercially helpful(a) Perceived Ease of Use Fig. 8. Perceived ease of usmobile devices have a feature to capture wireless signals. However,any other strategy for location, using technologies that are cur-rently available, will require some special capabilities in mobiledevices, such as RFID readers, GPS, or bar-code scanners.

5. Conclusion

We presented the MUCS model for ubiquitous commerce sup-port. MUCS is intended to support the creation of business oppor-tunities among Dealers playing the role either of buyer or seller.We performed two experiments. The rst simulated two possiblescenarios of u-commerce and the second aimed at attesting theMUCS acceptance. The main conclusions of this work are the fol-lowing: (1) mobility allows to create business opportunitiesaccording to the Dealers Context; (2) the precise informationabout Location stimulates the use of mobile devices for creatingbusinesses opportunities; (3) the proposed model contains the ba-sic components to support the creation of opportunities usingubiquitous computing; (4) the prototype and the experiment haveattested the feasibility of the proposal.

MUCS is an initial proposal and requires improvements, espe-cially a well-dened model of business, to become commerciallyfeasible. Future work will be carried out to extend the model. Weintend to employ an ontology, which would replace the currentCategory Tree component. This ontology will be used for classify-ing products and services in different commercial areas. Anotherfuture effort is to extend of the Opportunity Consultant, so that itbecomes capable of predicting the Dealers Desire. Finally, we wantto analyze the model performance with a bigger number of users.

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MUCS: A model for ubiquitous commerce supportIntroductionRelated worksThe MUCS modelBackgroundMUCS model architectureLocation SystemProfile SystemCategory TreeReference SystemService ManagerOpportunity ConsultantPersonal AssistantWebsite

Identification and notification of the opportunitiesIdentification of opportunitiesNotification of the opportunitiesPromoting business

The MUCS prototype and the experimentsThe MUCS prototypeExperiment 1: possible application scenarios of the MUCS modelScenario 1: start of business at an exposition fairScenario 2: ordering food

Experiment 2: acceptance evaluation of the model

ConclusionReferences