U V OYAGE S ERVICE P LATFORM A RCHITECTURE

uVoyage service platform is a centric platform which can apply for innovating regional tourism. When viewing tourism as a service ecosystem for a tourism destination, all tourism SMEs should have service value propositions, value delivery mechanisms, provide value-in-use for customers (Vargo and Lusch, 2004) and can create attractive value propositions based on their advantages. Thus, their dilemma includes market visibility (due to inadequate marketing competence) and ineffective service delivery (due to poor human and financial resource restricting development choices). Consequently, services offered by tourism SMEs cannot be discovered smoothly by tourists, resulting in a lack of positive service experiences and generating

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an unsustainable tourism ecosystem. Weak SMEs at a tourism destination result in decreased employment and income potential. A tourism ecosystem then worsens in terms of both growth and productivity. Therefore, increasing the effectiveness tourism SMEs in creating value, growing markets, and improving service delivery channels are important to enhance SME tourism sustainability for destinations and achieving the objectives of tourism innovation, growth, and productivity.

The uVoyage platform has six modules (Figure 6.3)¹. The modules for image modeling and image mixing are related to the first two tiers of uVoyage conceptual framework, which are responsible for sensing dynamic environments and customers’

needs using color image scales. The sheltering service management module realizes sheltering operations for SMEs. The other two modules, the SME alliance service formation module and the alliance feasibility management module, are designed to correspond to the common assets in Voyage service platform. The aim of SME alliance service formation module based on metaphor theory is to facilitate business cooperation and value sharing. SME cooperation strategies are achieved through the six modules of uVoyage, ranging from facilitating business value creation to value sharing in an ecosystem.

Both tourists and regional tourism SMEs are target users in uVoyage service platform; in other words, tourists search for services associated with desired images, whereas regional tourism SMEs cooperate to offer the services in specific images.

Data for tourist preferences, regional SMEs and environments (e.g., search output from Google and tourist feedback) are used as the primary input for the image modeling module, which constructs images of a destination, businesses, and tourists.

1 For the details of uVoyage models, please see appendix A and B.

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For tourists using a complex search term (i.e., a sentence or phrase instead of a noun or adjective only), the image modeling module decomposes the search term into details and delivers blended images as the outcome. Additionally, the image mixing module processes interaction among tourists, tourism SMEs, and destinations and supports processes in the uVoyage B2B and B2C service modules.

To fulfill the goals of tourists using uVoyage, groups of services in different regions should match tourists’ images. Therefore, regional tourism SMEs can cooperate and combine their services to meet the changing needs of tourists or create new services via the alliance service formation modules. When cooperative intention increases, images from services offered by different SMEs or for different destinations will be generated by the image mixing module. Each cooperation suggestion generated from the alliance service formation module is evaluated by the alliance feasibility measurement module to assess the possibility of cooperation success. The destination service matching module then recommends services to tourists by matching tourist images with tourist needs and SME services. The sheltering service management module fulfills electronic cooperation management and marketing functions, such that tourism SMEs can deliver services with increased effectiveness.

Last, feedback from tourists about SME service experiences affect environmental data (i.e., discussions on the Web or a blog) and tourist impressions.

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Figure 6.3 uVoyage Service Platform Architecture

The following subsections discuss additional details of each module, except for the sheltering service management module, which was elucidated when discussing electronic marketing and cooperation management functions. For the details of image modeling, image mixing, destination service matching and SME alliance service formation model, Appendix A and B provide further information and related references.

6.2.1 Image Modeling Module

In this work, a representation of images is composed of a set of psychological words defined in the Color Image Scale (Kobayashi 1992), in which each image attribute is represented by a psychological word and has several properties (e.g., RGB values). The image modeling module transforms words/data into image matrices using this color image scale (given that an image of a destination n or SME can be

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destination and regional SME data are used to construct image matrices. The image modeling module transforms those psychological words into image matrixes modeled using RGB values and their intensity values (count of particular psychological words divided by the total number of psychological words in percentages).

For the tourist image construction process, this work classifies different tourist types—Organized Mass Tourist, Independent Mass Tourist, Explorer, and Drifter (Cohen, 1972)—using a simple questionnaire. Each tourist has short-term and long-term images. Long-term images represent tourist behavior in the proposed platform (i.e., destination browsing history, travel experiences, feedback, and interaction with other tourists). Conversely, short-term images identify tourist expectations when planning to travel. These two tourist images are applied for service matching in the destination service matching module.

Additionally, tourism SME images are initiated from SME settings initially.

Feedback from tourists and destination images influence SME images over time.

Therefore, an image matrix of an SME is the combination of an SME’s self-positioning, environmental factors, and feedback from customers. Conversely, destination images are constructed by emotional words from regional tourism SMEs and tourists. These data were collected from Internet at the very beginning (i.e., a Google search) and from the proposed our platform. To comply with destination image theory, destination images for a large region can be decomposed into sub-region images. Destination images are scaled up into holistic images and scaled down into detailed sub-images and attribute images.

6.2.2 Image Mixing Module

In this work, we assume tourists, SMEs, and the environment influence each

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other. Therefore, images change with interactions among tourists, SMEs and the environments. For instance, when a tourist with a red image chooses a destination and an SME with a yellow image, the image color gradually becomes orange. This also occurs when red and yellow SMEs cooperate. The interactions among tourists, SMEs, and a destination are appropriately incorporated to weigh different influences. The aim of the image mixing module is to compute mixed images after interactions to support the uVoyage B2B/B2C service modules.

This work designs two image mixing processes for different aims. Both processes obtain RGB values from images and convert them into the CIE XYZ color space. The center of gravity law is applied to calculate the mixing result and fuzzy logic is applied to lookup corresponding words (i.e., dazzling + cheerful = bright).

The first process computes mixed images more precisely than before by selecting image elements (psychological words) from both image with high intensities. These selected elements are then combined and normalized into a new image. The second process produces surprising results for SME alliances. Elements within both images are categorized into five groups—Evaluative, Sensitive, Emotional, Dynamic, and Scale (Kobayashi, 1981). Mixing is performed within each image to produce five new image elements as additional attributes for combined images; this leads to the creation of very surprising results for SME cooperation.

Events that influence an entire ecosystem always occur. In the tourism ecosystem, the number of destination attributes may increase because special events occur. For instance, when a destination organizes a new festival or is chosen as a film location, its images may change. However, such changes can be dramatic or slow. The color mixing module also adopts these changes by applying text mining technology and

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Web 2.0 content analysis.

6.2.3 SME Alliance Service Formation Module

The SME alliance service formation module is mainly for tourism SMEs seeking attractive and unique cooperation using image matrices (which are obtained from the image modeling module and image mixing module). Metaphor theory can be applied to drive the creation of innovative services based on SME cooperation. Given an SME with its own goal associated with a specific image to build that can be represented as a metaphorical sentence, the SME alliance service formation module first analyzes gaps between the actual state and goal state. The goal statement is decomposed into essential lexical units and the tenor and vehicle parts can then be identified. A case-based approach is used to identify the properties of each lexical unit from Internet search results (e.g., Google). Relevant meanings following filtering are retained as the adjectival form and are compared with image matrices from SMEs.

Gap images are then identified as a set of their complements.

Second, the SME alliance service formation module searches for partner candidates based on identified gap images. Each gap image is viewed as a “common supertype,” which represents the parts common to both the tenor and vehicle. Since the tenor (SME) and supertype sets are obtained, other vehicles (other SMEs) are needed to make the metaphor reasonable. Restated, when vehicles contain image elements resembling those in the superset, the vehicles (i.e., partner compositions) may be suited to cooperation. However, partner compositions generated at this time can only be utilized for business needs without tourist considerations. Third attractiveness analysis and uniqueness analysis are applied to examine tourist desirability and the degree to which each candidate differs.

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During attractiveness analysis, the predicted image configuration of each partner composition is calculated by the image mixing process. When combined images closely resemble the needs of additional tourists on the platform, this composition is classified as having high potential attractiveness. Conversely, uniqueness analysis identifies the differences between images with and without selected partners. If the configuration set of new image elements is the same with that for the current set of image elements, the calculated uniqueness index will be lower than that of the configuration set of new image elements that are partially the same as or totally different from the current set of image elements. We assume partner compositions with high attractiveness and uniqueness will have excellent market potential. That is, cooperation with this partner composition may have additional market niches to serve.

Finally, the partner composition list is used by the alliance feasibility measurement module for advanced assessments of alliance feasibility, which can be referenced by tourism SMEs when selecting partners.

6.2.4 Alliance Feasibility Measurement Module

This module evaluates feasibility, which is potential cooperation sustainability with selected partner compositions for SME alliance service formation module reference. Based on SDL, businesses only provide a value proposition to customers and values are realized through value-in-use. Alliances or cooperation among businesses that collaborate for specific products or services also provide integrated value propositions. The value of value proposition cooperation or alliance is delivered based on value-in-use resulting from customer service experiences. This means that customers determine the value of a value proposition through service experience (Sandstrom et al., 2008) and co-create value with businesses via a feedback process.

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To improve a value proposition], a business can adjust its value proposition based on customer feedback. To propose value in a service system, all roles are resource integrators. Businesses integrate the necessary resource for a service provision for customers. Resource can be gathered internally outside suppliers or partners in a value network. Existing relationships with partners help businesses obtain the necessary resource. Businesses utilize the unique capabilities to transform operand and operant resource into products or services to realize a value proposition.

Resource, capabilities, and relationships are discussed to determine whether a partnership has good cooperation. The resource, including both operand and operant resource, are used to deliver a value proposition and transform it into customer value after value-in-use. Enhanced resource utilization for an SME means that additional resource is transformed. Regardless of the amount of value created by both kinds of resource, an SME with high utilization has many opportunities to deliver its resource for itself or other SMEs. Restated, additional customers or partners are willing to utilize both kinds of resource. Therefore, an SME resource with high resource utilization is a good resource supplier in a value creation process in the tourism service system.

After an SME makes its value proposition (e.g., a trip plan) using its capabilities (e.g., knowledge in the region, and organizational competence in different services), tourist satisfaction resulting from the service experience reflect the SME’s capabilities.

During cooperation, an SME integrates its resource and those of its partners and coverts these resource (i.e., rooms, services, and food) into a value proposition. In addition to tourist satisfaction, an SME’s resource and its partner’s resource utilization of operand and operant resource during cooperation prove the capability of a business

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to convert resource into value. The last feature for a business in evaluation of its relationships with other businesses represents the ability of a business to find excellent partners and then improve the overall value proposition via cooperation.

Therefore, if an SME gains more tourist satisfaction from cooperation than by itself, we assert that this SME can provide more value via cooperation. That is, value output from resource usage is enhanced. From the perspective of its partners, if value enhancement can benefit all firms cooperating, partner satisfaction will be positive and the effect of utilizing a partner’s resource via cooperation will be effective.

In the uVoyage platform, alliance feasibility can be treated as a score. The feasibility score of an alliance comprises the scores of SMEs involved. Table 6.1 proposes a method to calculate the alliance feasibility score. First, resource utilization of each SME and service satisfaction associated with a specific cooperation image is determined for partners and customers over time. Each aspect has two or three sub-indicators for computation.

Tourism SME has a score for each aspect; this score is the outcome of comparison with other SMEs at the same destination. For example, if a tourism SME has a higher resource utilization rate than other SMEs at the same destination and average resource utilization when cooperating, it receives resource a high score for resource utilization.

Notably, SMEs may be assigned different roles in different alliances. When a tourism SME acts only as an enabler and is not the main contact window in the alliance or cooperative relationship for tourists, the tourism SME shares it value mainly on recourses and relationships part. The enabler role emphasizes resource and relationships. In contrast, when tourism SME is an initiator and the main contact window for tourists, all three scores are considered for the alliance feasibility

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measurement. At the end, an alliance composed of tourism SMEs with different roles can obtain its alliance feasibility as the sum of member scores. Evaluation results will be sent back to the SME alliance service formation module for selection of partners for cooperation.

Table 6.1 Alliance Feasibility Measurement Index

Aspect Meaning Index

Resource Asset utilization Average own resource utilization

Average own resource utilization in co-operations

Capabilities Value conversion Average customer satisfaction under specific image

Average resource utilization in co-operation in a specific image

(both own and partners)

Co-operation hit rate and actual transaction happened

Relationships Value enhancement Average partner satisfaction

Average partners’ resource utilization in co-operations

Average customers’ satisfaction in co-operations in compared with average own customer satisfaction

6.2.5 Destination Service Matching Module

The aim of this module is to match tourist expectations and tourism services available on the platform based on images from tourists, SMEs, and a destination. To meet tourist expectations, the desired images from tourists and recommended services at a destination must be as close as possible. The modeled colors with high intensity in tourist images (both long-term and short-term images) are selected and compared first with destination images. Based on the concept of color harmony (similar colors

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on the color wheel) (Cohen-Or et al., 2006) and the color image scale, image similarity between these two sets of images is computed. If similarity is acceptable, the module takes images of SME services from the SME alliance formation module at this destination and adds them to pre-recommendation service sets. With these pre-recommendation service sets, tourists can be engaged to a filtering based on functional preferences, such as time and budget, for final recommendation outputs.

Feedback about tourist choices also influences future SME alliance formation at a destination.