The micro-entrepreneurship sector, as the second-largest employment generator in rural areas, plays a crucial role in alleviating poverty. This study explores how social enterprises can assist rural micro-entrepreneurs in mobilizing and leveraging indigenous knowledge to align production with demand. We propose technoficing as a strategic approach to rural micro-entrepreneurship. We conducted a case study of CommunityLink, a social enterprise implementing ICT-enabled practices to facilitate the growth of rural micro-entrepreneurship in India. Our findings reveal knowledge disconnections impeding the development of rural micro-entrepreneurship. Importantly, we propose a four-phase approach of technoficing – basic, internal, external, and offline-online – that can effectively mobilize and leverage indigenous knowledge for rural micro-entrepreneurs with minimal resource investment. We synthesize our findings into a comprehensive process model. Our research contributes to the discourse on ICT for development and the emerging academic dialogue on decoloniality by highlighting the significance of empowering local institutions and practices through the strategic use of off-the-shelf technologies. Practitioners and policymakers can leverage our findings to propel indigenous efforts toward rural development and improve rural livelihoods.

With the increasing numbers of web services published on the Internet, there are many services existed that fulfill user's requirements equally well even after filtering by functional requirements, so how to rank the similar services based on QoS becomes an important issue. Due to the multi-dimensional attributes of QoS, it can be treated as a multiple criteria decision making (MCDM) problem. Since the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) is widely used in dealing with MCDM, we propose a novel ranking approach based on TOPSIS to solve the service ranking problem. Noticed that the weight of every dimension in alternatives will directly affect the ranking result in the TOPSIS method, determination of weight for each dimensional QoS attribute can be a key factor in service ranking. We use Shannon's Entropy theory to get the objective weight, which reveals the intrinsic impact from different QoS attributes to the overall service quality in actual. Then combine with subjective weight, to get the comprehensive weight, it can not only improve the objectivity of the weight-making process, but also better closer to the user's personalized needs. We have shown the applicability of the ranking approach by using a case study. It illustrates the Entropy-TOPSIS method for service ranking process, and the simulation results confirm the feasibility and validity of proposed method.