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The purpose of this paper is to develop and empirically test a process model (comprising of seven dimensions), for identifying online customer engagement patterns leading to recommendation. These seven dimensions are communication, interaction, experience, satisfaction, continued involvement, bonding, and recommendation.

The authors used a non-participant form of netnography for analyzing 849 comments from Australian banks Facebook pages. High levels of inter-coder reliability strengthen the study’s empirical validity and ensure minimum researcher bias and maximum reliability and replicability.

The authors identified 22 unique pattern of customer engagement, out of which nine patterns resulted in recommendation/advocacy. Engagement pattern communication-interaction-recommendation was the fastest route to recommendation, observed in nine instances (or 2 percent). In comparison, C-I-E-S-CI-B-R was the longest route to recommendation observed in ninety-six instances (or 18 percent). Of the eight patterns that resulted in recommendation, five patterns (or 62.5 percent) showed bonding happening before recommendation.

The authors limited the data collection to Facebook pages of major banks in Australia. The authors did not assess customer demography and did not share the findings with the banks.

The findings will guide e-marketers on how to best engage with customers to enhance brand loyalty and continuously be in touch with their clients.

Most models are conceptual and assume that customers typically journey through all the stages in the model. The work is interesting because the empirical study found that customers travel in multiple different ways through this process. It is significant because it changes the way the authors understand patterns of online customer engagement.

Emerging markets – marketing and business strategy; social entrepreneurship; opportunity identification; frugal innovation.

MBA; marketing management; specialis ed courses such as entrepreneurship and international marketing. Data rich case, but analytical difficulty is only moderate.

Reboot Systems was conceived as a reverse engineering/refurbishing company for used computers when Rahul Chowdhury and Subbarao came in contact with Anand Tater who had started a small business in the used computer market. The team recognised the potential of the refurbished computer market in India, which was largely unorganised with penetration of personal computers pegged at less than 5 per cent. They identified the opportunity to address the digital divide, caused by lack of affordability and accessibility, by providing inexpensive “as good as new” used computers to those who aspired to own a computer. Additionally, in extending the life of used computers on a large-scale through “frugal innovation”, they hoped to reduce the extent of e-waste generated in the economy. This case provides a rich description of an emerging market characterised by market heterogeneity, social-political governance with poor policy measures, unorganised markets, chronic shortage of resources and inadequate infrastructure. Entrepreneurs hoping to address social issues must tackle these problems at the grass-root level and come up with improvised solutions that address the unique needs of the heterogeneous and resource constrained market. Some of Reboot Systems pressing challenges were in building a viable strategic approach to the market and ensuring scalability in a sustainable way.

An understanding of the characteristics of an emerging market from a macro (environmental) as well as micro (industry specific) perspective, an appreciation of opportunity identification and improvisation in emerging markets as well as differentiating “frugal” innovation from the idea of “Jugaad”, an understanding of the role of strategic vision and mission in accomplishing social and business objectives, an understanding of how to develop sustainability and competitive advantage from a social as well as business perspective.

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The authors’ goal is to investigate variations in the writing style of book reviews published on different social reading platforms and referring to books of different genres, which enables acquiring insights into communication strategies adopted by readers to share their reading experiences.

The authors propose a corpus-based study focused on the analysis of A Good Review, a novel corpus of online book reviews written in Italian, posted on Amazon and Goodreads, and covering six literary fiction genres. The authors rely on stylometric analysis to explore the linguistic properties and lexicon of reviews and the authors conducted automatic classification experiments using multiple approaches and feature configurations to predict either the review's platform or the literary genre.

The analysis of user-generated reviews demonstrates that language is a quite variable dimension across reading platforms, but not as much across book genres. The classification experiments revealed that features modelling the syntactic structure of the sentence are reliable proxies for discerning Amazon and Goodreads reviews, whereas lexical information showed a higher predictive role for automatically discriminating the genre.

The high availability of cultural products makes information services necessary to help users navigate these resources and acquire information from unstructured data. This study contributes to a better understanding of the linguistic characteristics of user-generated book reviews, which can support the development of linguistically-informed recommendation services. Additionally, the authors release a novel corpus of online book reviews meant to support the reproducibility and advancements of the research.