Effect of implementation of improved methods of the life cycle stages organisation to the online community management

This paper presents the current problem of investigation of the effect of implementation of improved methods of the life cycle stages organisation to online community management. The online community life cycle is the sum of the stages of organisation and development of online community. The current approaches of scientific researches of social processes within the WWW are analysed. The types of life cycles are distinguished and implemented in the online community management. The algorithm of life cycle stages of the online community is designed. The total quality of the life cycle stages execution and quality of the life cycle stages execution of Lviv Polytechnic online community from 2013 till 2016. Currently, the appropriate study and development of community management hardware and software provoke the most interest because online communities are a widespread and popular phenomenon, and the existing management software for them is imperfect and not complex.

💡 Research Summary

The paper addresses the problem of improving online community management by structuring the life‑cycle stages of a community and evaluating the impact of this structuring on a real‑world case. After a brief literature review that classifies prior work into three streams—consumer‑demand satisfaction, communication systems, and community formation/management—the authors propose a unified life‑cycle model that can be applied to four generic types of online communities: software‑based, website‑based, investment‑project‑based, and product‑marketing‑based.

The core of the contribution is an algorithm consisting of nine sequential stages: Planning, Analysis, Designing, Development, Testing, Implementation, Exploitation, Comprehensive Verification, and Successful Checking. Depending on the outcome of the verification, the algorithm branches into decision points for urgent re‑engineering, conservation, or liquidation, thus creating a feedback loop that supports continuous improvement or orderly shutdown. Figure 1 in the paper visualizes this flow.

To test the model, the authors applied it to the Lviv Polytechnic online community over four years (2013‑2016). They introduced a “total quality” metric (denoted Q) intended to aggregate the execution quality of each stage, but the exact formula is omitted, leaving the metric’s definition ambiguous. Nevertheless, the paper presents two graphs (Figures 2 and 3) that display the evolution of Q and the per‑stage quality percentages across the four years. The data suggest that early stages (Planning, Analysis, Designing) started with relatively low scores, while later stages (Development, Testing, Implementation, Exploitation, Verification) improved steadily, reaching above 80 % overall quality by 2016.

In the conclusion, the authors argue that existing community‑management software is fragmented and lacks comprehensive functionality. They claim that their life‑cycle framework, combined with a software system they have developed, can enhance long‑term community performance, reduce management costs, and provide a basis for predictive modeling of new communities. They also call for further development of mathematical and computational tools to address current shortcomings.

Critical appraisal reveals several weaknesses. First, the “total quality” metric is not mathematically defined, making the reported results non‑reproducible. Second, the empirical study is limited to a single academic community, preventing generalization to other domains such as corporate or public‑sector communities. Third, the evaluation relies solely on internal stage‑completion percentages; external performance indicators such as user satisfaction, activity levels, retention rates, or ROI are absent. Fourth, the literature review omits recent advances in social‑media analytics, machine‑learning‑driven user behavior prediction, and large‑scale platform studies, which limits the paper’s relevance to current research trends.

Overall, the paper contributes a structured life‑cycle perspective that could serve as a checklist for community managers, and it demonstrates a practical application through a multi‑year case study. However, to achieve scholarly impact and practical utility, future work should (a) formalize the quality metrics with transparent formulas, (b) conduct comparative studies across diverse community types, (c) integrate external success metrics, and (d) align the framework with contemporary data‑driven community analytics. Such enhancements would strengthen the methodological rigor and broaden the applicability of the proposed approach.