社群商務決策支援機制之設計

Abstract

隨著社群網站的蓬勃發展，以其為基礎的商業用途應用程式也越來越多。然而目前所知多數相關研究以及應用程式開發，其目的多在於建立品牌形象以及支援客戶互動。與前述情況相比，對於購買決策相關應用則較少論及。事實上，許多消費者在購買商品時，會聽取朋友的意見與建議，以作為選擇最終購買商品的參考依據。本研究之目的在於以消費者之線上社群網路為基礎，透過社會心理學以及消費者購買行為決策流程，建立社群商務購買決策支援機制。 現實生活中，互動頻繁的朋友較可能是親密的朋友，但在線上社群網站中此種情況是否仍然如此，在進行決策機制設計前必須先加以驗證。透過蒐集本研究所使用之實驗平台上的各項互動，以及實際調查所得到之社會關係指標，利用社群網路分析之MRQAP 法對此推論進行驗證的結果，證實了此一關係的存在。此一關聯性被確認後，本研究接著針對三種常見的消費者購買決策情境，設計了不同的決策支援機制。 消費者進行購買決策時，通常會處於以下三種狀況其中之一。第一，消費者已經 找到數種符合需求的商品，需要在其中挑選一項作為最終購買商品。第二，消費者已經列出了某些評選商品的考量因素，但卻不知道有哪些商品符合所列條件。第三，消費者僅僅知道要購買某種商品，但卻不知道從何處開始著手。本研究針對以上情境，分別設計了相對應的決策支援機制。在第一種情境中，本研究設計了決策支援小組的篩選機制，以找出適當的參考團體。而改良過後的投票機制則被用來選出最終的建議購買產品。而在第二種情境中，決策支援小組以QOC表達方式，針對消費者所在乎之考量因素給予權重，而後形成最後建議。在形成最後建議的過程中，決策小組成員間彼此相互影響的程度也被納入考量。第三種情境裡，考量朋友之間的友誼會因時間產生變化，因此甄選決策小組的條件增加了時間因素。此外，決策小組發表的各項意見與建議，透過文字處理篩選出評選商品的考量因素，經由人工智慧的工具，做出最後的建議。除此之外，本研究也進行相關實驗以確認各機制之可行性。實驗結果確認本研究所提之機制，與其他決策方法比較後，能提供給消費者較佳的決策支援訊息。

With the vigorous development of the social networking sites, many application systems have been developing for the purpose of branding and consumer service. In contrast, researches on consumer purchase decision making is relatively rare. In fact, many consumers collect advices and suggestions from friends as a reference for final decision. In this study, purchase decision support mechanisms were designed to support the operation of social commerce for different scenarios. In the first scenario, the consumer has found several products that meet requirements. For the second scenario, the consumer knows only selection criteria about the item required. In the third scenario, the consumer just wants to buy something, but has no idea about how and what to buy. A screening mechanism was designed for first scenario to identify appropriate friends as support group, and an improved majority voting mechanism was proposed. For the second scenario, a personalized and socialized recommendation tool was designed. During the consensus-making process, the degree of mutual influence among the members of the decision group was also taken into account. For third scenario, the time factor was included in the decision group screening mechanism. By using part-of-speech processing technique the possible selection criteria were identified, and artificial intelligence methods were used to propose product reference list. In addition, the experimental results confirmed that the proposed mechanisms can provide better support when compared with other benchmark methods.