Chapter 7 Conclusion
7.2 Managerial Implications
國
立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y
97
from service science that focuses on studying, designing and implementation of service system with a set of systematic approaches.
7.2 Managerial Implications
Base on the destination image theory in tourism domain, our research offers an operationalized tools for companies to select partners while considering image building. We conclude that SMEs in tourism industry should cooperate with each other in order to compliment each others’ limited resources and co-develop new services that fit the proposed images. As identified in the hypothesis 4, the underlying efforts would make them be able to create market niche.
In particular, our hypothesis 1 justified that the proposed mechanism could improve the level of goal achievement on image building for businesses. However, hypotheses 1-A, 1-B and 3 also point out that the level of image diversity is a relevant factor that influences the opportunities of improving goal achievement and attractiveness on image building. Such facts imply it is beneficial to create a simulating environment of a destination (i.e., higher diversity context) where business have more chances to successfully build different image and improve attractiveness to benefit tourists by the efforts of alliances. We argue higher image diversity is worth to pursue because it also entails that tourists can have more diversified and comprehension experiences in a destination.
To pursue this end, we suggest two ways of actions should be taken. On one hand, businesses should be told that the images they proposed should be not similar to others. This would result in the reduction of opportunities in creating market niche and negative impact on image diversity of a destination. On the other hand, the
‧ 國
立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y
98
government can provide some incentives to attract diversified businesses to move to the lower image diversity destinations for the sake of satisfying various customer needs.
According to the results of testing hypothesis 2, our research offers evidence echoing the real world phenomenon that businesses already have unique features would not be necessarily to cooperate with partners. If these businesses cooperate with partners with common image attributes, they may suffer from the abatement of uniqueness level owing to the common images brought into the entire services.
Nevertheless, if this abatement on uniqueness can contribute to gain higher attractiveness in return, then it becomes a trade-off for businesses to decide. Our market niche assessment module provides both indicators (i.e., attractiveness and uniqueness) for businesses to reference. Hence, businesses need to consider all the possible results before they take their actions.
Finally, one point should be made clearer for businesses who intend to leverage metaphor-based partner recommendation mechanism. That is, the appropriate goal setting is relevant to create their market niche. More specifically, if the proposed goal is neither attractive nor unique to people, it is unlikely to form a new service with attractive or unique feature. By setting the appropriate goals, businesses are able to enjoy the benefits brought from the propose mechanism.
7.3 Limitations and Future Works
We note several limitations to our work that should be taken into account when leveraging our approach. First, sometimes it’s difficult for SME owners to come up with an appropriate or innovative goal setting to take the best advantages of the proposed mechanism. If this situation happens, the utility of the proposed approach might be might be reduced. Second, we are not able to gain practical usage data of the
‧ 國
立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y
99
proposed mechanism because the implementation of uVoyage platform would not be finished until mid June, 2010. It takes time to invite participants to use the platform and the evolvement of image model also needs time. Only when the image models of target entities (e.g., customer, business, destination) can actually represent the real images of them, it is more likely to generate meaningful results from metaphor-based partner recommendation mechanism.. We believe the way we used to justify the performance and utility of the proposed mechanism represents a considerable simplification of the real world context. A practical field experiment in which participants can gain more realistic experiences of using metaphor-based partner recommendation approach will certainly produce more reliable and meaningful results.
Third, partner selection for image building is only the beginning. The rest of activities, how to co-develop a new service, how to manage the operations and so on.., are even of decisive importance to successfully build images. These issues remain unaddressed in this study.
Finally, as the summary of above-mentioned limitations, the future research could move beyond the initial development of the proposed mechanism to gain more data from practical world and further examine the effect of image-driven service innovation. Also, the new mechanism could be developed to encourage SME owners to discover or come up with appropriate goals for using partner recommendation mechanism. Finally, the current study is only focusing on local/regional tourism development. Future research may extend the focus to across regions level or even national level.
‧ 國
立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y
100
7.4 Conclusion Remarks
In this paper, we address the problem of how to assist the tourism SME owners with a useful approach for managing partner selection to build the attractive and unique image. By leveraging the operant resources (i.e., skills, knowledge and technology) from others, SMEs are able to gain competitive advantages and eventually improve their sustainability and profitability. To this end, this paper identifies the interrelationships between computing metaphor, attractive and unique image building and alliance formation and then presents a novel and systematic approach to solve the problem.
We also offer a system scenario and experiments for this method to show its practicability. We believe the method can SME owners in managing the partner selection tasks for attractive and unique image building in a more efficient and effective way.
‧
Cognitive Science Society, Vancouver, Canada: 937–942.[2] Abeel, T., Y. Van de Peer, and Y. Saeys. 2009. “Java-ML: A machine learning library”. The Journal of Machine Learning Research 10: 931–934.
[3] Alan, R. H., Salvatore, T. M., Jinsoo, P., and Sudha, R. 2004. “Design science in information systems research”. MIS QUART, 28 (1), 75-105.
[4] Amid, A., Ghodsypour, S. H. and O’Brien, C. 2006. “Fuzzy multiobjective linear model for supplier selection in a supply chain”, International Journal of Production Economics(104:2), pp. 394–407.
[5] Baccianella, S., A. Esuli, and F. Sebastiani. 2010. “SentiWordNet 3.0: An enhanced lexical resource for sentiment analysis and opinion mining”. in Seventh conference on International Language Resources and Evaluation, Malta.
Retrieved May. 2010.
[6] Baumer, E. P., Tomlinson, B., Richland, L. E., and Hansen, J. 2009. “Fostering metaphorical creativity using computational metaphor identification,” in Proceeding of the seventh ACM conference on Creativity and cognition:
315–324.
[7] Bierly III, P. E., and Gallagher, S. 2007. “Explaining alliance partner selection:
fit, trust and strategic expediency”, Long Range Planning(40:2), pp. 134–153.
[8] Brouthers, K. D., Brouthers, L. E., and Wilkinson, T. J. 1995. “Strategic alliances:
Choose your partners,” Long Range Planning (28:3), pp. 2–25.
[9] Casakin, H. 2007. “Metaphor in design Problem Solving Implications for Creativity,” International Journal of Design (1:2),pp. 21-33.
[10] Camprub ı´, R., Guia, J., and Comas, J. 2008. “Destination networks and induced tourism image,” Tourism Review (63:2), pp. 47–58.
[11] Cracolici, M. F., and Nijkamp, P. 2009. “The attractiveness and competitiveness of tourist destinations: A study of Southern Italian regions”, Tourism Management(30:3), pp. 336–344.
[12] Chang, S.-L., Wang, R.-C. and Wang, S.-Y. 2006. “Applying fuzzy linguistic quantifier to select supply chain partners at different phases of product life cycle”, International Journal of Production Economics, International Journal of Production Economics(100:2), pp. 348-359, June 18, 2011.
[13] Dacin, M. T., Hitt, M. A., and Levitas, E. 1997. “Selecting partners for successful international alliances: examination of US and Korean firms,” Journal of World Business (32:1), pp. 3–16.
‧
[14] D'Harris, I. 2002. “A logical approach to the analysis of metaphors,” Logical and Computational Aspects of Model-Based Reasoningpp. 21.
[15] Ding, J. F., and Liang, G. S. 2005. “Using fuzzy MCDM to select partners of strategic alliances for liner shipping”, Information Sciences(173:1-3), pp.
197–225.
[16] Echtner, C., and Ritchie, B. 2003. “The meaning and measurement of destination image,” Journal of Tourism Studies (14:1), pp. 37–48.
[17] Esuli, A., and F. Sebastiani. 2006. “Sentiwordnet: A publicly available lexical resource for opinion mining”. in Proceedings of LREC , p. 417–422.
[18] Feng, B., Z. P Fan and J. Ma. 2010. “A method for partner selection of codevelopment alliances using individual and collaborative utilities”.
International Journal of Production Economics 124(1): 159–170.
[19] Fischer, M., J\ähn, H. and Teich, T. 2004. “Optimizing the selection of partners in production networks”, Robotics and Computer-Integrated Manufacturing(20:6), pp. 593–601.
[20] Geringer, J. M. 1991. “Strategic determinants of partner selection criteria in international joint ventures.,” Journal of International Business Studies (22:1), pp.
41-62.
[21] Hacklin, F., Marxt, C. and Fahrni, F. 2006. “Strategic venture partner selection for collaborative innovation in production systems: A decision support system-based approach”, International Journal of Production Economics(104:1), pp. 100–112.
[22] Hajidimitriou, Y. A, and A. C Georgiou. 2002. “A goal programming model for partner selection decisions in international joint ventures”. European Journal of Operational Research 138(3): 649–662.
[23] Hamel, G., Doz, Y. L., and Prahalad, C. K. 1989. “Collaborate with your competitors and win,” Harvard Business Review (67:1), pp. 133–139.
[24] Hampton, R. D., Guy, B. S., and Sinkula, J. M. 1987. “Consumer Images of Financial Institutions,” Journal of Professional Services Marketing (2:3), pp.
83–100.
[25] Hill, R. C., and Levenhagen, M. 1995. “Metaphors and mental models:
Sensemaking and sensegiving in innovative and entrepreneurial activities,”
Journal of Management (21:6), pp. 1057.
[26] Holmberg, S. R., and Cummings, J. L. 2009. “Building Successful Strategic Alliances:: Strategic Process and Analytical Tool for Selecting Partner Industries and Firms”, Long range planning(42:2), pp. 164–193.
[27] Inkpen, A. C., and Ross, J. 2001. “Why do some strategic alliances persist beyond their useful life? ”, California Management Review(44:1), pp. 132–148.
‧
multidimensional scaling of free response data,” Journal of Retailing (52:4), pp.61–70.
[29] Jung, H. 2010. “A fuzzy AHP-GP approach for integrated production-planning considering manufacturing partners”. Expert Systems With Applications.
[30] Jones, M. A. 1992. “Generating a specific class of metaphors,” in Proceedings of the 30th annual meeting on Association for Computational Linguistics: 323.
[31] Kobayashi, S. 1981. “The aim and method of the color image scale,” Color Research & Application (6:2), pp. 93-107.
[32] Kohonen, T. 1990. “The self-organizing map,” Proceedings of the IEEE (78:9), pp. 1464–1480.
[33] Kolb, P. 2008. “DISCO: A Multilingual Database of Distributionally Similar Words”.in Tagungsband der 9. Konferenz zur Verarbeitung nat\ürlicher Sprache–KONVENS 2008.
[34] Kolb, P. 2009. “Experiments on the difference between semantic similarity and relatedness” in Proceedings of the ordic Conference on Computational Linguistics (ODALIDA), p. 81–88.
[35] Kotler, P., Asplund, C., Rein, I., and Haider, D. 1999. “Marketing Places Europe:
attracting investments, industries, residents and visitors to European Cities, Communities, Regions and Nations,” Financial Times Prentice-Hall, Harlow.
[36] Lakoff, G., and Johnson, M. 1980. Metaphors we live by, Chicago London.
[37] Lakoff, G. 1987. Women, fire, and dangerous things: What categories reveal about the mind, University of Chicago press Chicago.
[38] Lin, Z., Yang, H., and Arya, B. 2009. “Alliance partners and firm performance:
resource complementarity and status association,” Strategic Management Journal (30:9), pp. 921–940.
[39] Lubart, T. I., and Getz, I. 1997. “Emotion, metaphor, and the creative process,”
Creativity Research Journal (10:4), pp. 285–301.
[40] MacInnis, D. J., and Price, L. L. 1987. “The role of imagery in information processing: Review and extensions,” Journal of Consumer Research (13:4), pp.
473–491.
[41] Mackay, K. J., and Fesenmaier, D. R. 1997. “Pictorial element of destination in image formation,” Annals of Tourism Research (24:3), pp. 537–565.
[42] Mason, Z. J. 2004. “CorMet: a computational, corpus-based conventional metaphor extraction system,” Computational Linguistics (30:1), pp. 23–44.
[43] Mat, N. A., Cheung, Y., Melbourne, V., and Scheepers, H. 2009. “Partner Selection: Criteria for Successful Collaborative Network.”
[44] Martin, J. H. 1990. A computational model of metaphor interpretation, Academic
‧
Press Professional, Inc. San Diego, CA, USA.
[45] McGlone, M. S., and Manfredi, D. A. 2001. “Topic-vehicle interaction in metaphor comprehension,” Memory & cognition (29:8), pp. 1209.
[46] Medcof, J. W. 1997. “Why too many alliances end in divorce,” Long Range Planning (30:5), pp. 718–732.
[47] Morgan, G. 2006. Images of organization, Sage Publications.
[48] Nijdam, N. A. 2005. Mapping emotion to color. The Netherlands: University of Twente.
[49] OECD, 2008. Tourism in OECD countries 2008: trends and policies. Paris, Organisation for Economic Co-operation and Development.
[50] Ou, L., Luo, M. R., Woodcock, A., and Wright, A. 2004. “A study of colour emotion and colour preference. part i: colour emotions for single colours,” Color Research & Application (29:3), pp. 232-240.
[51] Pesa¨maa, O., O¨ rtqvist, D., and Hair, J. F. 2007. “It is all about trust and loyalty:
partner selection mechanisms in tourism networks,” World Journal of Tourism Small Business Management (1:2), pp. 12–18.
[52] Reid, L. J., Smith, S. L., and McCloskey, R. 2008. “The effectiveness of regional marketing alliances: A case study of the Atlantic Canada Tourism Partnership 2000–2006,” Tourism Management (29:3), pp. 581–593.
[53] Samsonova, E. V., Kok, J. N., and IJzerman, A. P. 2006. “TreeSOM: Cluster analysis in the self-organizing map,” Neural Networks (19:6-7), pp. 935–949.
[54] Shah, R. H., and Swaminathan, V. 2008. “Factors influencing partner selection in strategic alliances: the moderating role of alliance context,” Strategic Management Journal (29:5), pp. 471.
[55] Sheth, J. N., Sisodia, R. S. and Sharma, A. 2000. “The antecedents and consequences of customer-centric marketing”, Journal of the Academy of Marketing Science(28:1), pp. 55.
[56] Slack, J. M. 1980. “Metaphor comprehension: a special mode of language processing?,” in Proceedings of the 18th annual meeting on Association for Computational Linguistics: 23–24.
[57] Smeral, E. 1998. “The impact of globalization on small and medium enterprises:
new challenges for tourism policies in European countries,” Tourism Management (19:4), pp. 371–380.
[58] Stell, R., and Fisk, R. P. 1986. “Services images: A synthesis of image creation and management,” Creativity in services marketing: What's new, what works, what's developing. Chicago: American Marketing Associationpp. 113–117.
[59] Suk, H., and Irtel, H. 2010. “Emotional response to color across media,” Color Research & Application (35:1), pp. 64-77.
‧
Nordic Tourism research Symposium, Vassa, Finland.[61] Vanolo, A. 2004. “Internationalization in the Helsinki Metropolitan Area: Images, Visions and Metaphors” . European Planning Studies (16:2), 229–252.
[62] Vargo, S. L. and Lusch, R. F. 2008. “Service-Dominant Logic: Continuing the Evolution,” Journal of the Academy of Marketing Science (36:1), 1-10.
[63] Veale, T., and Hao, Y. 2007. “Comprehending and generating apt metaphors: a web-driven, case-based approach to figurative language.” Proceedings of the National Conference on Artificial Intelligence: 1471.
[64] Wang, T. C., and Chen, Y. H. 2007. “Applying consistent fuzzy preference relations to partnership selection”, Omega(35:4), pp. 384–388.
[65] Wang, H. H., and Liao, W. J. 2009. “Applications of Metaphor Theory to Product Design.” IASDR 2009, Seoul.
[66] Weick, C. W. 2003. “Out of context: Using metaphor to encourage creative thinking in strategic management courses,” Journal of Management Education (27:3), pp. 323.
[67] Weiner, E. J. 1984. “A knowledge representation approach to understanding metaphors,” Computational Linguistics (10:1), pp. 1–14.
[68] Woodside, A. G., and Lysonski, S. 1989. “A general model of traveler destination choice,” Journal of Travel Research (27:4), pp. 8–14.
[69] Wu, W. Y., Shih, H. A. and Chan, H. C. 2009. “The analytic network process for partner selection criteria in strategic alliances”, Expert Systems with Applications(36:3), pp. 4646–4653.
[70] Xin, J. H., Cheng, K., Chong, T., Tetsuya Sato, Taeko Nakamura, Kanij Kajiwara, and Hiroshi Hoshino. 1998. “Quantifying colour emotion - what has been achieved,” Research Journal of Textile and Apparel (2:1), pp. 46-54.
[71] Ye, F. 2010. “An extended TOPSIS method with interval-valued intuitionistic fuzzy numbers for virtual enterprise partner selection”, Expert Systems with Applications (37:10), pp. 7050–7055.
[72] Yeh, W. C., and Chuang, M. C. 2010. “Using multi-objective genetic algorithm for partner selection in green supply chain problems”, Expert Systems With Applications.
[73] Yu¨ ksel, A., and Akgu¨ l, O. 2007. “Postcards as affective image makers: An idle agent in destination marketing,” Tourism Management (28:3), pp. 714–725.
[74] Zhou, C. L., Yang, Y., and Huang, X. X. 2007. “Computational mechanisms for metaphor in languages: a survey,” Journal of Computer Science and Technology (22:2), pp. 308–319.