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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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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

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

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.

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

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.

Based on the perspective of knowledge management capability, this paper aims to reveal the internal mechanism of the digital empowerment of mobile social platforms to improve the operational performance of Chinese traditional retail enterprises. Such improvements have crucial theoretical value and practical implications for Chinese traditional retail enterprises to achieve transformation and sustainable development.

This study applied the typical analysis method, selected China’s leading mobile social platform, WeChat, as a typical case, and observed and analyzed the public data of the traditional retail industry and social platforms and interviews with relevant enterprises. On this basis, this study used the inductive and deductive methods of qualitative research to conduct an in-depth analysis of the mechanism by which WeChat’s digital empowerment improves the operational performance of Chinese traditional retail enterprises. It also discussed the critical role and path knowledge management capabilities play in this mechanism.

This research demonstrated that mobile social platforms empower Chinese traditional retail enterprises to build diversified digital channels, enhance the knowledge acquisition capability of enterprises and thus improve their performance; empower Chinese traditional retail enterprises to build digital community networks, enhance the knowledge diffusion capability of enterprises and thus improve their performance; and empower Chinese traditional retail enterprises to integrate online and offline businesses, enhance the knowledge integration capability of enterprises and thus improve their performance.

This study clarifies the internal mechanism of how the digital empowerment of mobile social platforms can improve the performance of Chinese traditional retail enterprises. This mechanism implies that knowledge management capabilities (knowledge acquisition, diffusion and integration capability) are the underlying logic for Chinese traditional retail enterprises to achieve higher performance levels. This has important practical implications for managers of Chinese traditional retail enterprises to leverage the digital infrastructure of mobile social platforms to achieve the sustainable development of enterprises.

This study provides an in-depth analysis of how the traditional retail industry uses digital social platforms to improve operational performance from the perspective of knowledge management capabilities, which can further promote the theoretical research and practical development of digitalization and knowledge management. At the same time, this study explored the research on the operational performance of Chinese traditional retail enterprises from the perspective of knowledge management capabilities and expanded the research on knowledge management in related fields. The authors have initially sorted out the impact of knowledge management capabilities on the operational performance of Chinese traditional retail enterprises in the digital era. This will help better understand the role and function of knowledge management in strategic transformation and expand the application of knowledge management theory.

The effect of the transition toward digital technologies on today’s businesses (i.e. Industry 4.0 transition) is becoming increasingly relevant, and the number of studies that have examined this phenomenon has grown rapidly. However, systematizing the existing findings is still a challenge, from both a theoretical and a managerial point of view. In such a setting, the knowledge management (KM) discipline can provide guidance to address such a gap. Indeed, the implementation of fundamental digital technologies is reshaping how firms manage knowledge. Thus, this study aims to critically review the existing literature on Industry 4.0 from a KM perspective.

First, the authors defined a structuring framework to highlight the role of Industry 4.0 transition along with absorptive capacity (ACAP) processes (acquisition, assimilation, transformation and exploitation), while specifying what is being managed, that is data, information and/or (actual) knowledge, according to the data-information-knowledge (DIK) hierarchy. The authors then followed the systematic literature review methodology, which involves the use of explicit criteria to select publications to review and outline the stages a process has to follow to provide a transparent and replicable review and to analyze the existing literature according to the theoretical framework. This procedure yielded a final list of 150 papers.

By providing a clear picture of what scholars have studied so far on Industry 4.0 transition, in terms of KM, this literature review highlights that among all the studied digital technologies, the big data analytics technology is the one that has been explored the most in each phase of the ACAP process. A constructive body of research has also emerged in recent years around the role played by the internet of things, especially to explain the acquisition of data. On the other hand, some digital technologies, such as cyber security and smart manufacturing, have largely remained unaddressed. An explanation of the role of these technologies has been provided, from a KM perspective, together with the business implications.

This study is one of the first attempts to revise the literature on Industry 4.0 transition from a KM perspective, and it proposes a novel framework to read existing studies and on which to base new ones. Furthermore, the synthesis makes two main contributions. First, it provides a clear picture of the different digital technologies that support the four ACAP phases in relation to the DIK hierarchy. Accordingly, these results can emphasize what the literature has looked at so far, as well as which digital technologies have gained the most attention and their impacts in terms of KM. Second, the synthesis provides prescriptive considerations on the development of future research avenues, according to the proposed research framework.