Dynamic prediction of product competitive position: A multisource data-driven competitive analysis framework from a multi-competitor perspective

IF 11 1区 管理学 Q1 BUSINESS
Yanlai Li, Huiru Yu, Zifan Shen
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引用次数: 0

Abstract

Competitive analysis is critical in enhancing product quality and facilitating strategic adaptation, with user-generated content (UGC) offering new avenues to improve product competitiveness. Existing UGC-based SWOT analysis methods have made progress in minimizing subjective bias. However, they still lack in analysis scope, enhancement of dynamic forecasting, and improvement of factor prioritization. Given the rapid changes in the market environment, companies urgently require innovative approaches to secure a competitive advantage. From a multi-competitor perspective, this study introduces a multisource data-driven dynamic SWOT (D-SWOT) analysis framework, which broadens the analysis to include all potential market competitors, aiming to dynamically predict the focal product's position in a specific competitive environment and evaluate factor priorities. Initially, competitors are identified from multisource data through a competitor identification algorithm. Subsequently, attributes are identified, their importance is assessed using the LDA model, and their performance is evaluated through the bidirectional long-short-term memory model. This study aims to comprehend market dynamics and formulate effective strategies by employing a grey prediction model to forecast future attribute trends. Based on the core principles of SWOT analysis, rules for determining factors have been clarified, and a D-SWOT matrix has been constructed to predict changes in the product's market position. Finally, integrating attribute performance differences with their relative importance, a competitive priority index apt for competitive analysis has been developed to guide product enhancements and strategic realignments. Through a case study involving tablet products, the framework's substantial benefits in terms of competitive analysis have been validated.
产品竞争地位动态预测:多竞争者视角下的多源数据驱动的竞争分析框架
竞争分析对于提高产品质量和促进战略适应至关重要,用户生成内容(UGC)为提高产品竞争力提供了新的途径。现有的基于ugc的SWOT分析方法在最小化主观偏差方面取得了进展。但在分析范围、动态预测能力的增强、要素优先级的提高等方面还存在不足。鉴于市场环境的快速变化,企业迫切需要创新的方法来确保竞争优势。从多竞争对手的角度出发,本研究引入了一个多源数据驱动的动态SWOT (D-SWOT)分析框架,将分析范围扩大到所有潜在的市场竞争对手,旨在动态预测焦点产品在特定竞争环境中的地位,并评估因素优先级。首先,通过竞争对手识别算法从多源数据中识别竞争对手。随后,利用LDA模型对属性进行识别、重要性评估,并通过双向长短期记忆模型对属性的性能进行评估。本研究旨在运用灰色预测模型预测未来属性趋势,了解市场动态并制定有效策略。基于SWOT分析的核心原理,明确了决定因素的规律,构建了预测产品市场地位变化的D-SWOT矩阵。最后,结合属性绩效差异及其相对重要性,建立了一个适合于竞争分析的竞争优先度指数,以指导产品改进和战略调整。通过一个涉及平板产品的案例研究,该框架在竞争分析方面的实质性好处已经得到验证。
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来源期刊
CiteScore
20.40
自引率
14.40%
发文量
340
审稿时长
20 days
期刊介绍: The Journal of Retailing and Consumer Services is a prominent publication that serves as a platform for international and interdisciplinary research and discussions in the constantly evolving fields of retailing and services studies. With a specific emphasis on consumer behavior and policy and managerial decisions, the journal aims to foster contributions from academics encompassing diverse disciplines. The primary areas covered by the journal are: Retailing and the sale of goods The provision of consumer services, including transportation, tourism, and leisure.
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