BI as a service: an attempt to understand the leading adoption factors

Marco, Marco de; Rossignoli, Cecilia; Ferrari, A.; Mola, Lapo; Zardini, Alessandro

doi:10.2991/icebi.2010.58

Cited by 2 publications

(1 citation statement)

References 25 publications

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“…Ma argued that computer-supporting collaborative learning and social creativity were key terms for designing work with critical reflection in examining consumers’ behaviors in social network services 31,32. Marco et al identified a misalignment observed in using social network services based on a comprehensive literature review on the effect of website designed tools 33. The IBM team suggested that it was a core competing strategy to maintain a visually impressive effective website in today’s marketplaces 34.…”

Section: Introductionmentioning

confidence: 99%

<p>Empirical analysis of the intelligent influence factors of social network services effectiveness in e-commerce based on human learning behaviors</p>

Zhang¹,

Xu²,

Liu³

2019

PRBM

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Background: It is crucial for companies to understanding users’ choices and learning behaviors, and the corresponding influencing factors and cognitive patterns regarding social network services to communicate with potential customers. Methods: In this study, a casual structural model was constructed and developed to model and characterize the relationships between problems to be resolved as antecedent variables and success factors as consequent variables with the intermediary variables based on human learning behaviors, whereas the concept of social network service was introduced to summarize the current issues of social network services and empirically factors affecting effectiveness of social network services. Discussion: This study highlighted the corporate need to examine the intelligent role and learning effectiveness of social network services when studying social creativity and intelligence in a social networking environment. Firstly, the framework and hypotheses of social network services were introduced to summarize the current issues of social network services and the main influencing factors affecting the working patterns of social network services. Subsequently, the empirically established model was further tested to explore the possible meaningful relationships among those variables used. Results: The study revealed that the social network services provider and the customer should improve their social creativity and community collaboration; these could be expanded and enhanced by increasing the social intelligence to raise the social network services’ effect and the customer’s externalization. Furthermore, social intelligence, community collaboration, and customer externalization were factors significantly influencing customers’ social creativity, while the customer externalization and community collaboration were the two important factors affecting social intelligence. Conclusion: The study implied that social network services providers should provide more and more intelligent and inspiring services for their customers.

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Section: Introductionmentioning

confidence: 99%

<p>Empirical analysis of the intelligent influence factors of social network services effectiveness in e-commerce based on human learning behaviors</p>

Zhang¹,

Xu²,

Liu³

2019

PRBM

View full text Add to dashboard Cite

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Business Intelligence as a Service

Spruit

Boer

2014

International Journal of Business Intelligence Research

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Demand for business intelligence (BI) applications continues to grow even at a time when demand for most information technology (IT) products is low, showing the importance of BI products for a modern organization. However, globalization changes the way organizations use BI, where geographic location and time independency is becoming more and more important. Gartner's hype-cycle on BI depicts the technology of BI as a Service as being almost on top of the hype cycle, indicating there are high expectations of this new technology. This research advances on existing literature on business intelligence and cloud computing from a development perspective by introducing the concept of business intelligence as a service (BIaaS). The most important deliverable is the BIaaS capability maturity model (CMM) that is introduced here. The BIaaS CMM explains the conceptual model of BIaaS by the creation of the first BIaaS capability model containing key capabilities of BIaaS. The capability model is further enhanced with maturity levels depicting the importance of each BIaaS capability, a maturity matrix suggesting a roadmap for BIaaS solution development, and a BIaaS assessment model introducing a tool for finding problem areas in existing BIaaS solutions. The developed BIaaS CMM ought to support (starting) BIaaS vendors to develop BIaaS solutions by providing an assessment tool for BIaaS solutions. The assessment outcome provides the current maturity of the BIaaS solution and also includes problem areas for solution improvement. The introduction of the CApability Maturity Positioning (CAMP) method for the development of a maturity matrix, which results in the BIaaS maturity model, is significantly different from conventional maturity modeling. To calculate the weight of each capability from the BIaaS capability model, a thorough product review of existing business intelligence and cloud computing products is performed. Analysis of the results and normalizing the outcome of that analysis together with the introduction of a calculation mapping, is input for the creation of the maturity matrix. The maturity matrix is the essential foundation for the developed business intelligence as a Service capability maturity model, which is the main deliverable of this research.

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BI as a service: an attempt to understand the leading adoption factors

Cited by 2 publications

References 25 publications

<p>Empirical analysis of the intelligent influence factors of social network services effectiveness in e-commerce based on human learning behaviors</p>

<p>Empirical analysis of the intelligent influence factors of social network services effectiveness in e-commerce based on human learning behaviors</p>

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