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Article
Publication date: 11 February 2019

Wu He, Weidong Zhang, Xin Tian, Ran Tao and Vasudeva Akula

Customer knowledge from social media can become an important organizational asset. The purpose of this paper is to identify useful customer knowledge including knowledge…

Abstract

Purpose

Customer knowledge from social media can become an important organizational asset. The purpose of this paper is to identify useful customer knowledge including knowledge for customer, knowledge about customers and knowledge from customers from social media data and facilitate social media-based customer knowledge management.

Design/methodology/approach

The authors conducted a case study to analyze people’s online discussion on Twitter regarding laptop brands and manufacturers. After collecting relevant tweets using Twitter search APIs, the authors applied statistical analysis, text mining and sentiment analysis techniques to analyze the social media data set and visualize relevant insights and patterns in order to identify customer knowledge.

Findings

The paper identifies useful insights and knowledge from customers and knowledge about customers from social media data. Furthermore, the paper shows how the authors can use knowledge from customers and knowledge about customers to help companies develop knowledge for customers.

Originality/value

This is an original social media analytics study that discusses how to transform large-scale social media data into useful customer knowledge including knowledge for customer, knowledge about customers and knowledge from customers.

Details

Journal of Enterprise Information Management, vol. 32 no. 1
Type: Research Article
ISSN: 1741-0398

Keywords

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Article
Publication date: 3 April 2017

Wu He, Feng-Kwei Wang and Vasudeva Akula

This paper aims to propose a knowledge management (KM) framework for leveraging big social media data to help interested organizations integrate Big Data technology…

Abstract

Purpose

This paper aims to propose a knowledge management (KM) framework for leveraging big social media data to help interested organizations integrate Big Data technology, social media and KM systems to store, share and leverage their social media data. Specifically, this research focuses on extracting valuable knowledge on social media by contextually comparing social media knowledge among competitors.

Design/methodology/approach

A case study was conducted to analyze nearly one million Twitter messages associated with five large companies in the retail industry (Costco, Walmart, Kmart, Kohl’s and The Home Depot) to extract and generate new knowledge and to derive business decisions from big social media data.

Findings

This case study confirms that this proposed framework is sensible and useful in terms of integrating Big Data technology, social media and KM in a cohesive way to design a KM system and its process. Extracted knowledge is presented visually in a variety of ways to discover business intelligence.

Originality/value

Practical guidance for integrating Big Data, social media and KM is scarce. This proposed framework is a pioneering effort in using Big Data technologies to extract valuable knowledge on social media and discover business intelligence by contextually comparing social media knowledge among competitors.

Details

Journal of Knowledge Management, vol. 21 no. 2
Type: Research Article
ISSN: 1367-3270

Keywords

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Article
Publication date: 13 November 2017

Wu He, Xin Tian, Ran Tao, Weidong Zhang, Gongjun Yan and Vasudeva Akula

Online customer reviews could shed light into their experience, opinions, feelings, and concerns. To gain valuable knowledge about customers, it becomes increasingly…

Abstract

Purpose

Online customer reviews could shed light into their experience, opinions, feelings, and concerns. To gain valuable knowledge about customers, it becomes increasingly important for businesses to collect, monitor, analyze, summarize, and visualize online customer reviews posted on social media platforms such as online forums. However, analyzing social media data is challenging due to the vast increase of social media data. The purpose of this paper is to present an approach of using natural language preprocessing, text mining and sentiment analysis techniques to analyze online customer reviews related to various hotels through a case study.

Design/methodology/approach

This paper presents a tested approach of using natural language preprocessing, text mining, and sentiment analysis techniques to analyze online textual content. The value of the proposed approach was demonstrated through a case study using online hotel reviews.

Findings

The study found that the overall review star rating correlates pretty well with the sentiment scores for both the title and the full content of the online customer review. The case study also revealed that both extremely satisfied and extremely dissatisfied hotel customers share a common interest in the five categories: food, location, rooms, service, and staff.

Originality/value

This study analyzed the online reviews from English-speaking hotel customers in China to understand their preferred hotel attributes, main concerns or demands. This study also provides a feasible approach and a case study as an example to help enterprises more effectively apply social media analytics in practice.

Details

Online Information Review, vol. 41 no. 7
Type: Research Article
ISSN: 1468-4527

Keywords

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Article
Publication date: 19 October 2015

Wu He, Jiancheng Shen, Xin Tian, Yaohang Li, Vasudeva Akula, Gongjun Yan and Ran Tao

Social media analytics uses data mining platforms, tools and analytics techniques to collect, monitor and analyze massive amounts of social media data to extract useful…

Abstract

Purpose

Social media analytics uses data mining platforms, tools and analytics techniques to collect, monitor and analyze massive amounts of social media data to extract useful patterns, gain insight into market requirements and enhance business intelligence. The purpose of this paper is to propose a framework for social media competitive intelligence to enhance business value and market intelligence.

Design/methodology/approach

The authors conducted a case study to collect and analyze a data set with nearly half million tweets related to two largest retail chains in the world: Walmart and Costco in the past three months during December 1, 2014-February 28, 2015.

Findings

The results of the case study revealed the value of analyzing social media mentions and conducting sentiment analysis and comparison on individual product level. In addition to analyzing the social media data-at-rest, the proposed framework and the case study results also indicate that there is a strong need for creating a social media data application that can conduct real-time social media competitive intelligence for social media data-in-motion.

Originality/value

So far there is little research to guide businesses for social media competitive intelligence. This paper proposes a novel framework for social media competitive intelligence to illustrate how organizations can leverage social media analytics to enhance business value through a case study.

Details

Industrial Management & Data Systems, vol. 115 no. 9
Type: Research Article
ISSN: 0263-5577

Keywords

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Article
Publication date: 14 August 2017

This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies.

Abstract

Purpose

This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies.

Design/methodology/approach

This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context.

Findings

Effective knowledge management (KM) is becoming even more important as firms access data from an increasing number of sources. The massive amounts of data generated on social media platforms such as Facebook and Twitter can reveal priceless insights into customer attitudes and preferences. Practitioners can utilize Big Data analytics to categorize data and gain valuable knowledge of consumer sentiments toward their firm and competitor organizations. Creating a knowledge management system (KMS) that integrates KM, social media, and Big Data technology better positions businesses to extract, store, and utilize knowledge more effectively.

Practical implications

The paper provides strategic insights and practical thinking that have influenced some of the world’s leading organizations.

Originality/value

The briefing saves busy executives and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.

Details

Strategic Direction, vol. 33 no. 8
Type: Research Article
ISSN: 0258-0543

Keywords

Content available

Abstract

Details

Online Information Review, vol. 41 no. 7
Type: Research Article
ISSN: 1468-4527

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