Smart grids are robust, self-healing networks that allow bidirectional propagation of energy and information within the utility grid. This introduces a new type of energy user who consumes, produces, stores and shares energy with other grid users. Such a user is called a “prosumer.” Prosumers’ participation in the smart grid is critical for the sustainability and long-term efficiency of the energy sharing process. Thus, prosumer management has attracted increasing attention among researchers in recent years. This paper systematically examines the literature on prosumer community based smart grid by reviewing relevant literature published from 2009 to 2018 in reputed energy and technology journals. We specifically focus on two dimensions namely prosumer community groups and prosumer relationships. Based on the evaluated literature, we present eight propositions and thoroughly describe several future research directions.

Smart Grid (SG) achieves bidirectional energy and information flow between the energy user and the utility grid, allowing energy users not only to consume energy, but also to generate the energy and share with the utility grid or with other energy consumers. This type of energy user is called the "prosumer". The sustainability of the SG energy sharing process depends on its participating prosumers. Hence the prosumer management schemes are crucial within the energy sharing field. However, the existing literature on SG energy sharing has shown little attention on prosumer participation and management. The contribution of this paper is twofold. First we critically analyze the prosumer management schemes used by existing approaches and identify the open research issues. Second, we introduce a novel concept to manage the prosumers in the form of goaloriented virtual prosumer-communities and we discuss the aspects of prosumer-community formation, growth and overallmanagement. The main significance of this approach is that the prosumer-communities facilitate the prosumers with similar interest to join together and increase the quantity of energy to be auctioned to the SG and accordingly increase the bargaining power in the energy market. In addition, the prosumercommunities can attain more sustainable energy sharing process in long-term.

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Purpose 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. Design/methodology/approach 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. Findings 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. Research limitations/implications 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. Practical implications 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. Originality/value 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.

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The unstructured data impacts 95% of the organizations and costs them millions of dollars annually. If managed well, it can significantly improve business productivity. The traditional information extraction techniques are limited in their functionality, but AI-based techniques can provide a better solution. A thorough investigation of AI-based techniques for automatic information extraction from unstructured documents is missing in the literature. The purpose of this Systematic Literature Review (SLR) is to recognize, and analyze research on the techniques used for automatic information extraction from unstructured documents and to provide directions for future research. The SLR guidelines proposed by Kitchenham and Charters were adhered to conduct a literature search on various databases between 2010 and 2020. We found that: 1. The existing information extraction techniques are template-based or rule-based, 2. The existing methods lack the capability to tackle complex document layouts in real-time situations such as invoices and purchase orders, 3.The datasets available publicly are task-specific and of low quality. Hence, there is a need to develop a new dataset that reflects real-world problems. Our SLR discovered that AI-based approaches have a strong potential to extract useful information from unstructured documents automatically. However, they face certain challenges in processing multiple layouts of the unstructured documents. Our SLR brings out conceptualization of a framework for construction of high-quality unstructured documents dataset with strong data validation techniques for automated information extraction. Our SLR also reveals a need for a close association between the businesses and researchers to handle various challenges of the unstructured data analysis.