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| Title: | 新興間接取貨模式之創新市場定位研究
Research on Innovative Market Positioning of Emerging Indirect Pickup Model |
| Authors: | 陳易群
Chen, I-Chun |
| Contributors: | 許牧彥
Hsu, Mu-Yen
陳易群
Chen, I-Chun |
| Keywords: | 取貨模式
破壞式創新
聯合分析
智能櫃
Pickup Model
Disruptive Innovation
Conjoint Analysis
Smart locker |
| Date: | 2021 |
| Issue Date: | 2021-10-01 10:14:04 (UTC+8) |
| Abstract: | 隨著電子商務迎來爆發式的成長，取貨服務成為不可或缺的一環。與實體購物可以馬上拿到貨品的特性不同，電子商務必須透過末端物流以及取貨服務來完成購物流程。由於過載的貨品數量使得取貨服務供不應求，除了原有的超商取貨服務外，新興取貨服務也加入市場。中華郵政所營運的智能櫃模式—i郵箱也是其中之一，但在超商取貨已經取得大部分的市場份額情況下，i郵箱的使用率遠遠落後超商取貨，因此本研究意圖找出可能使用i郵箱的族群以及偏好，找尋切入市場的機會，同時提供給業者改善服務的依據。
本研究使用層級貝氏選擇式聯合分析法來了解消費者對於取貨模式的偏好結構，首先整理出取貨服務的10項屬性，透過問卷調查方式釐清消費者重視的服務屬性和排序情形，接著按照聯合分析的架構設計正式問卷。採取網路便利抽樣蒐集有效問卷數量303份，藉由層級貝氏選擇式聯合分析了解消費者對於服務的偏好情形，根據結果探討在不同購買產品區隔下的偏好情形，並且利用集群分析了解各族群的偏好情形以及組成特性。
研究結果顯示，間接取貨的偏好情形為「運輸服務費用」、「貨品到貨時間」「取貨地點的便利性」、「身分驗證」、「服務類型」。分析結果區隔市場為：重視身分驗證的集群1命名為「謹慎取貨群」；重視價格的集群2命名為「斤斤計較群」；重視獨立作業的集群3命名為「自行操作群」。根據辨識破壞式創新的架構，目前市場上的偏好情形為大眾市場偏好價格便宜、快速的到貨時間以及取貨據點距離近等，而研究結果中的「自行操作群」為智能櫃服務主要可能使用者也符合其市場的定位，可以優先針對該族群進行服務，接者改善「謹慎取貨群」自助服務操作失當時不悅，讓潛在的客群能夠順利使用服務達到分離式侵蝕市場，最後智能櫃在主流屬性滿足消費者的需求時，便可一躍成為市場競爭者，本研究透過服務偏好與權重探詢出適當的服務內容，使得取貨服務廠商能夠訂定出良好的發展策略。
With the explosive growth of e-commerce, pick-up services have become an indispensable part. Unlike physical shopping, which can get the goods immediately, e-commerce must complete the shopping process through last mile logistics and pick-up services. Due to the quantity of overloaded goods, the demand for pick-up services exceeds demand. In addition to the original convenient store pick-up services, emerging pick-up services have also joined the market. The smart locker model operated by Chunghwa Post and named i box is also one of them. However, convenient store pick-up services has already achieved most of the market share. The use of i box service is far behind the convenient store pick-up service. Therefore, this research intends to find out the groups and preferences that may use the i box service, looking for opportunities to enter market, and provide tactics for the industry to improve services.
This study uses the hierarchical Bayesian choice-based conjoint (HB-CBC) analysis method to understand the consumer preference structure for the pick-up mode. First, the researcher sorts out the 10 attributes of the pick-up service, and then questionnaire was designed in accordance with the framework of the conjoint analysis. Valid questionnaires were collected by convenience sampling of 303. Through HB-CBC analysis to understand the consumer`s preference for services. According to the results, we discuss the division of different purchased products, and use cluster analysis to analyze the characteristics of each group.
The research results show that the preferences for indirect pickup are “transportation service cost”, “goods arrival time”, “convenience of pickup location”, “identity verification”, and “service type”. The analysis results segment the market into three clusters. Cluster 1 is named “Prudent Pickup Group”; cluster 2 is named “Penny-pinching Group”; cluster 3 is named “Self-Operation Group”. According to the framework of identification disruptive innovation, the mass market’s preference for cheap prices, fast delivery times, and close pick-up locations are the current preferences in the market. The “Self-Operated Group” in the research results is the main possible users of smart locker services and the “Prudent Pickup Group” will use service if it improves timely assistance in order to achieve Detach-market low-end encroachment. Finally, when the smart locker service can satisfy the mainstream attribute It can become the market leader. This research hopes to design appropriate service content through service preferences and weights, so that pickup manufacturers can formulate good development strategies. |
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Wang, X., Yuen, K.F., Wong, Y., & Teo, C. (2018). An innovation diffusion perspective of e-consumers’ initial adoption of self-collection service via automated parcel station. The International Journal of Logistics Management, 29, 237-260.
Weltevreden, J. (2008). B2c e-commerce logistics: the rise of collection-and-delivery points in The Netherlands. International Journal of Retail & Distribution Management, 36, 638-660.
Bardi, E. J. (1973). Carrier Selection From One Mode. Transportation Journal, 13(1), 23-29.
Boyer, K. K., Prud`homme, A. M., & Chung, W. M. (2009). The Last Mile Challenge: Evaluating the Effects of Customer Density and Delivery Window Patterns. Journal of Business Logistics, 30(1), 185-+.
Boysen, N., Fedtke, S., & Schwerdfeger, S. (2021). Last-mile delivery concepts: a survey from an operational research perspective. OR Spectrum, 43(1), 1-58.
Chen, C., & Pan, S. (2015). Using the Crowd of Taxis to Last Mile Delivery in E-Commerce: a methodological research. SOHOMA..
Chen, C.-F., White, C., & Hsieh, Y.-E. (2020). The role of consumer participation readiness in automated parcel station usage intentions. Journal of Retailing and Consumer Services, 54, 102063.
Christensen, C. M. (1997). The Innovator’s Dilemma: When New Technologies Cause Great Firms to Fail. Harvard Business Review Press.
Cullinane, K., & Toy, N. (2000). Identifying influential attributes in freight route/mode choice decisions: a content analysis. Transportation Research Part E: Logistics and Transportation Review, 36(1), 41-53.
Cunningham, C. E., Deal, K., & Chen, Y. (2010). Adaptive Choice-Based Conjoint Analysis. The Patient: Patient-Centered Outcomes Research, 3(4), 257-273.
Danneels, E. (2004). Disruptive Technology Reconsidered: A Critique and Research Agenda. Journal of Product Innovation Management, 21, 246-258.
Desarbo, W. S., Ramaswamy, V., & Cohen, S. H. (1995). Market segmentation with choice-based conjoint analysis. Marketing Letters, 6(2), 137-147.
Govindarajan, V., & Kopalle, P. (2005). The Usefulness of Measuring Disruptiveness of Innovations Ex Post in Making Ex Ante Predictions*. Journal of Product Innovation Management, 23, 12-18.
Green, P., Krieger, A., & Wind, Y. (2001). Thirty Years of Conjoint Analysis: Reflections and Prospects. Interfaces, 31, S56-S73.
Green, P. E. (1974). On the Design of Choice Experiments Involving Multifactor Alternatives. Journal of Consumer Research, 1(2), 61-68.
Green, P. E., & Srinivasan, V. (1978). Conjoint Analysis in Consumer Research: Issues and Outlook. Journal of Consumer Research, 5(2), 103-123.
Green, P. E., & Srinivasan, V. (1990). Conjoint Analysis in Marketing: New Developments with Implications for Research and Practice. Journal of Marketing, 54(4), 3-19.
Hair, J. F., William C Black,Barry J Babin,Rolph E Anderson. (2009). Multivariate Data Analysis. Pearson Education.
Hill, A., Hays, J., & Naveh, E. (2000). A Model for Optimal Delivery Time Guarantees. Journal of Service Research - J SERV RES, 2, 254-264.
Holbrook, M. B., & Howard, J. A. (1977). Frequently purchased nondurable goods and services. Selected Aspects of Consumer Behavior, 1, 189-222.
Holdorf, S., & Haasis, H. (2014). Last mile delivery concepts in E-Commerce an empirical approach. The 8th International Conference on Software, Knowledge, Information Management and Applications (SKIMA 2014), 1-6.
Holton, R. H. (1958). The Distinction between Convenience Goods, Shopping Goods, and Specialty Goods. Journal of Marketing, 23(1), 53-56.
Jia, J., Fischer, G. W., & Dyer, J. S. (1998). Attribute weighting methods and decision quality in the presence of response error: a simulation study. Journal of Behavioral Decision Making, 11(2), 85-105.
Kull, T., Boyer, K.K., & Calantone, R. (2007). Last‐mile supply chain efficiency: an analysis of learning curves in online ordering. International Journal of Operations & Production Management, 27, 409-434.
Leung, K. H., Choy, K. L., Siu, P. K. Y., Ho, G. T. S., Lam, H. Y., & Lee, C. K. M. (2018). A B2C e-commerce intelligent system for re-engineering the e-order fulfilment process. Expert Systems with Applications, 91, 386-401.
Li, F., Fan, Z., Cao, B., & Li, X. (2020). Logistics Service Mode Selection for Last Mile Delivery: An Analysis Method Considering Customer Utility and Delivery Service Cost. Sustainability, 13, 284.
Lian, L., Zhang, S., Wang, Z., Liu, K., & Cao, L. (2015). Customers’ Mode Choice Behaviors of Express Service Based on Latent Class Analysis and Logit Model. Mathematical Problems in Engineering, 2015, 1-8.
Lin, J.-S. C., & Hsieh, P.-L. (2011). Assessing the Self-service Technology Encounters: Development and Validation of SSTQUAL Scale. Journal of Retailing, 87(2), 194-206.
McGinnis, M. A. (1990). The Relative Importance of Cost and Service in Freight Transportation Choice: Before and After Deregulation. Transportation Journal, 30(1), 12-19.
Moore, W. L. (2004). A cross-validity comparison of rating-based and choice-based conjoint analysis models. International Journal of Research in Marketing, 21(3), 299-312.
Murphy, P. E., & Enis, B. M. (1986). Classifying Products Strategically. Journal of Marketing, 50(3), 24-42.
Olsson, J., Hellström, D., & Pålsson, H. (2019). Framework of Last Mile Logistics Research: A Systematic Review of the Literature. Sustainability, 11(24), 7131.
Johnson, R. and B. Orme (2003). Getting the Most from CBC, Sawtooth Software Research Paper Series, Sawtooth Software Inc., Sequim.
Schmidt, G., & Druehl, C. (2008). When Is Disruptive Innovation Disruptive? Journal of Product Innovation Management, 25, 347-369.
Sharma, A., Grewal, D., & Levy, M. (1995). The customer satisfaction/logistics interface. Journal of Business Logistics, 16(2), 1.
Thirumalai, S., & Sinha, K. K. (2005). Customer satisfaction with order fulfillment in retail supply chains: implications of product type in electronic B2C transactions. Journal of Operations Management, 23(3), 291-303.
United Nations, Department of Economic and Social Affairs, Population Division (2019). World urbanization prospects: The 2018 revision (ST/ESA/SER.A/420). New York: United Nations.
Vakulenko, Y., Hellström, D., & Hjort, K. (2018). What`s in the parcel locker? Exploring customer value in e-commerce last mile delivery. Journal of Business Research, 88, 421-427.
Vakulenko, Y., Shams, P., Hellström, D., & Hjort, K. (2019). Service innovation in e-commerce last mile delivery: Mapping the e-customer journey. Journal of Business Research, 101, 461-468.
Wang, X., Yuen, K.F., Wong, Y., & Teo, C. (2018). An innovation diffusion perspective of e-consumers’ initial adoption of self-collection service via automated parcel station. The International Journal of Logistics Management, 29, 237-260.
Weltevreden, J. (2008). B2c e-commerce logistics: the rise of collection-and-delivery points in The Netherlands. International Journal of Retail & Distribution Management, 36, 638-660. |
| Description: | 碩士
國立政治大學
科技管理與智慧財產研究所
108364101 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0108364101 |
| Data Type: | thesis |
| DOI: | 10.6814/NCCU202101576 |
| Appears in Collections: | [科技管理與智慧財產研究所] 學位論文
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