Purpose

Recently, reverse logistics (RL) has become more prominent due to growing environmental concerns, social responsibility, competitive advantages and high efficiency by customers because of expansion of product selection and shorter product life cycle. However, effective implementation of RL results in some direct advantages, the most important of which is winning customer satisfaction that is vital to a firm's success. Therefore, paying attention to customer feedback in supply chain (SC) and logistics processes has recently increased, so manufacturers have decided to transform their RL into customer-centric RL. Hence, this paper aims to identify the features of a mobile phone which affect consumers’ purchasing behavior and to analyze the causality and prominence relations among them that can help decision-makers, policy planners and managers of organizations to develop a framework for customer-centric RL. These features are studied based on analysis of product review sites. This paper's special focus is on social media (SM) data (Twitter) in an attempt to help the decision-making process in RL through a big data analysis approach.

Design/methodology/approach

This paper deals with identifying mobile phone features that affect consumer's mobile phone purchasing decisions. Using the DEMATEL approach and using experts' insights, a cause and effect relationship diagram was generated through which the effect of features was analyzed.

Findings

Eighteen features were categorized in terms of cause and effect, and the interrelationships of features were also analyzed. The threshold value is calculated as 0.023, and the values lower than that were eliminated to obtain the digraph. F6 (camera), F13 (price) and F5 (chip) are the most prominent features based on their prominent score. It was also found that the F5 (chip) has the highest driving power (1.228) and acts as a causal feature to influence other features.

Originality/value

The focus of this article is on SM data (Twitter), so that experts can understand the interaction between mobile phone features that affect consumer's decision on mobile phone purchasing by using the results. This study investigates the degree of influence of features on each other and categorizes the features into cause and effect groups. This study is also intended to help organizational decision-makers move toward a reverse customer SC.

中文翻译：

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迈向以客户为中心的手机逆向物流：使用 DEMATEL 方法和社交媒体数据

目的

最近，由于产品选择范围的扩大和产品生命周期的缩短，客户对环境的关注、社会责任、竞争优势和高效率，逆向物流 (RL) 变得更加突出。然而，有效实施 RL 会带来一些直接优势，其中最重要的是赢得客户满意度，这对公司的成功至关重要。因此，最近在供应链 (SC) 和物流流程中越来越关注客户反馈，因此制造商决定将他们的 RL 转变为以客户为中心的 RL。因此，本文旨在识别影响消费者购买行为的手机特征，并分析它们之间的因果关系和显着关系，以帮助决策者，组织的政策规划者和管理人员为以客户为中心的 RL 开发框架。这些功能是基于对产品评论网站的分析进行研究的。本文特别关注社交媒体 (SM) 数据 (Twitter)，试图通过大数据分析方法帮助 RL 中的决策过程。

设计/方法/方法

本文涉及识别影响消费者手机购买决策的手机功能。使用DEMATEL方法，利用专家的见解，生成因果关系图，通过该图分析特征的影响。

发现

根据因果关系对十八种特征进行了分类，并分析了特征之间的相互关系。阈值计算为0.023，将低于该值的值剔除得到有向图。F6（相机）、F13（价格）和F5（芯片）是基于其突出得分的最突出的功能。还发现F5（芯片）具有最高的驱动力（1.228），并作为影响其他特征的因果特征。

原创性/价值

这篇文章的重点是SM数据（推特），让专家可以通过结果了解影响消费者手机购买决策的手机特征之间的相互作用。本研究调查特征之间的相互影响程度，并将特征分为因果组。本研究还旨在帮助组织决策者转向反向客户 SC。