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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.

The relationship between online reviews and purchase intention has been studied in previous research. However, there was little knowledge about the effect of information quality and the social presence of online reviews on purchase intention based on the stimulus-organism-response (S-O-R) framework. The purpose of this study is to explore the intrinsic relationship between the stimulus (perceived information quality and social presence) generated from online reviews and the response (purchase intention).

This study developed a research model by applying the S-O-R framework to test the proposed hypotheses. A combination of a web-based experiment and an online survey was employed to collect data. Hypotheses were empirically tested using Smart PLS.

The PLS analysis shows that both perceived information quality and the social presence of online reviews positively affect trust. Moreover, satisfaction with online reviews affects purchase intention, whereas trust has a positive impact on satisfaction, playing a mediating role between two stimuli and satisfaction. Besides, perceived information quality of positive online reviews is found to have a more significant impact on trust, satisfaction and purchase intention.

The results of this study are of great value for expanding both theoretical research and practical applications of online reviews in relation to purchase intention. This study with a new research model reveals the understanding of how the purchase intention will be motivated by online reviews. Meanwhile, the moderating effects and the mediating effects are also discussed.

Few academic studies specifically investigate how businesses can use social media to innovate customer loyalty programs. The purpose of this paper is to present an in-depth case study of the Shop Your Way (SYW) program, which is regarded as one of the most successful customer loyalty programs with social media.

This paper uses case study research as the methodology to uncover innovative features associated with the SYW customer loyalty program. The authors collected the data from SYW’s social media forums and tweets. The data set was analyzed using social media analytics tools including the R package and Lexicon.

Based on the research results, the authors summarize innovative social media features identified from SYW. The authors also provide insights and recommendations for businesses that are seeking to innovate their customer loyalty programs using social media technologies.

The results of this case study set a good example for businesses which want to innovate and improve their customer loyalty programs using social media technologies. This is the first in-depth case study on the SYW program, one of the most successful customer loyalty programs with social media. The results shed light on how social media can innovate customer loyalty programs in both theory and practice.

Few studies investigated the influence of heterogeneous knowledge and social networks on the innovation performance of new ventures in emerging economics. Particularly, as the focus of global economic development is paying more attention to emerging economics such as China, Indonesia and India, innovation research in these countries becomes more and more important. As such, this paper aims to propose a theoretical model and designed a survey based on the Theory of Inventive Problem Solving (TRIZ).

To investigate the influence of network heterogeneity and knowledge heterogeneity on the innovation performance of new ventures, the authors designed a structured questionnaire based on the TRIZ methodology for innovative problem solving to collect data from new ventures established in the past eight years in eastern China.

The empirical results verify the assumptions of our theoretical model: network heterogeneity has a positive impact on the innovation performance of new ventures; knowledge heterogeneity has a positive impact on the innovation performance of new ventures; network heterogeneity has a positive impact on knowledge heterogeneity.

The main contribution of this paper is that the proposed theoretical model determining the relationship of network heterogeneity and knowledge heterogeneity with the innovation performance of new ventures was empirically tested and verified. This study also empirically verified what types and what sources of social network and knowledge are critically linked to new venture innovation.

Case‐based reasoning (CBR), which has long been considered a branch of artificial intelligence, has recently gained great attention in the knowledge management circle because of its capability for storing and retrieving experience‐based knowledge by emulating human memory, and its ability to function similar to organizational memory to preserve organizational knowledge. While many successful CBR systems have been developed as knowledge repositories for preserving intellectual capital and for problem solving in business communities, both the concept and methodology of CBR are still novelties in education communities. While there are a plethora of projects and practical pursuits for CBR applications in business, literature relating to designing, developing and evaluating practical CBR educational applications is conspicuously scant. Based on the issues encountered and lessons learned in a CBR system development project, this paper attempts to provide some pragmatic guidelines for those who are interested in applying the CBR knowledge repository approach to supporting knowledge creation and diffusion in the education domain.

This paper illustrates and discusses a pedagogical model that views knowledge as a source for learning to occur. In this model, learning is viewed as a process that allows workers to advance from novices to experts through three levels of knowledge acquisition: know‐what, know‐how, and know‐why. Different types of knowledge require different learning methods and entail different learning activities. Built upon this model, a corporate e‐learning system entitled UNCLE (using notes for a case‐based learning environment) was developed and pilot tested. The pilot testing of UNCLE showed encouraging learning results and suggested that a well‐defined structure for learning components and processes is favorable in a corporate environment.

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.