- 學位論文
在物聯網中探討適地性行動商務對零售業應用精準定位行銷與行為意圖研究
Exploring Location-Based Mobile Commerce in IoT Applying Precision Positioning Marketing and Behavioral Intention Study in Retail
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摘要
由於智慧聯網技術逐漸成熟，傳送速率增加，在人們對智慧型手機的高度依賴下，結合無線感測設備的資料搜集、管理與分析，掌握顧客在賣場活動相關資訊，企業可將電子商務延伸至具有個人化的行動商務，並達到有效的行動廣告，及精準定位行銷。除此之外，隨著資訊技術不斷演進，以及IPv6技術的日漸成熟，一個以未來網路的新概念應用於具有感測、網路以及運算功能進而發展成智慧物件(Smart Object)，使物件與物件能進行資料傳遞，形成新興的網路環境，稱之為物聯網(Internet of Things；IoT)。 在物聯網複雜的環境中，所提供的信息量是非常龐大，以及使用者相對的也會增加，在如此大量的使用者之下，物聯網電子商務中的行銷策略就顯得較為重要。且為了因應環境與產業的變化，企業在進行電子商務的行銷策略也隨之改變。對企業而言，在不確定的市場環境中，顧客已成為企業達成營利目標的關鍵資源與獲利來源。使得現今企業於進行產品銷售時，必須以顧客的需求為首要條件來進行行銷手法。 因此，本研究所提出的以物聯網為基礎的行動商務模型(IoT-based Mobile Commerce model；IoTMC)，實現將物聯網的技術應用至行動商務中。透過本研究所提出的IoTMC讓用戶可以依時間、位置及情境，在適地性服務充分接收整合性的信息，提供一個更有效率的購物體驗。尤其，在IoTMC的情境感知模組中，可依據消費者的購物行為軌跡，結合資料分析消費者的購物型態與行為，做到賣場人流分析與優化賣場配置，並調整商品策略，提供更符合消費者需求。 除此之外，在本研究中透過資訊科技行為意圖研究，更可以瞭解消費者對商店APP的接受度與使用意願。本研究以科技接受模式為架構，外部變項加入誘因理論觀點，針對消費者受到各種外部刺激誘因，是否透過知覺有用性與知覺易用性間接影響使用行為。研究中，採用SPSS 22版及Smart PLS 2.0版進行資料分析。由資料分析結果顯示，本研究各構面具有良好的信度水準，且各構面擁有高度的內部一致性及良好的建構效度。在模式驗證結果發現，外部變項會透過知覺有用性間接影響行為意圖。但外部變項僅互動誘因對知覺易用性產生顯著性，卻未透過知覺易用性而間接影響到行為意圖。
並列摘要
As intelligent networking technology gradually comes to maturity and as its transmission speed increases, people are now heavily dependent on smartphones. Moreover, in combination with data collection, management, and analysis through wireless sensing equipment to obtain customer information regarding store activities, businesses can now extend e-commerce to personalized mobile commerce and can deploy effective mobile advertising and precise positioning marketing. Furthermore, as IT evolves and as IPv6 technology eventually matures, data transmission can be carried out between smart objects by using sensing networks, networking, and computing functions. This concept which is emerging into a new network environment is known as the Internet of Things (IoT). In the complex environment of IoT, the amount of information available is enormous and the number of users also increases at a blistering pace. With a huge number of users, e-commerce marketing strategies in the IoT become extremely important and must be altered accordingly in response to changes in the environment and industry. For enterprises in an uncertain market environment, customers are the key source of profit and resource for reaching their profit targets. In other words, satisfying customer needs is the key in marketing strategy. Hence, the IoT-based Mobile Commerce Model (IoTMC) proposed by this study for the application of IoT technology to mobile commerce allows users to receive integrated information according to time, location, and context using location-based service, and provides them with a more effective shopping experience. In particular, the context-aware module of IoTMC can use consumers’ shopping behavior and patterns to conduct data analysis. This makes it possible to implement store traffic analysis and optimization of store configuration, as well as the adjustment of product strategy to provide more services that match consumer needs. In addition, IT behavior and intention research was used in this study to understand consumers’ acceptance and willingness in using store apps. In this study, technology acceptance model was used as a framework, and incentive theory viewpoints were used for some external variables. The model was able to shed light on whether consumers’ usage behavior was affected by various external stimuli through the indirect influence on perceived usefulness and perceived ease of use. In this study, SPSS version 22 and Smart PLS version 2.0 were used for data analysis. The results of the data analysis show that all facets of this study had good reliability levels, internal consistency, and construct validity. The model validation results show that external variables indirectly influence behavioral intention through perceived usefulness. However, among the external variables, only interactive incentives exerted significant influence on perceived ease of use, although it did not indirectly affect behavioral intention via perceived ease of use.
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