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This paper presents a design approach for process-based knowledge management (PKM) systems that can support knowledge-intensive processes where effective task execution is highly reliant on the knowledge and expertise of participants executing the tasks. The proposed design approach includes design methods and kernel theories governing the design of PKM systems and can also be easily integrated with existing systems analysis and design techniques.

The design science research methodology is used to design and develop the artifact which includes the overall PKM design approach. Information systems design theory is used as a high-level framework to develop and structure the design approach. Relevant design methods and behavioral theories are reviewed to identify kernel theories that guide the design and development of PKM systems. The design approach consists of meta-requirements for PKM systems and design processes to achieve the meta-requirements. A feasibility study is conducted to evaluate the feasibility of the proposed approach.

The design approach presented in this paper can guide system analysts and system developers in the design of knowledge management systems for supporting knowledge-intensive processes. The paper also includes a comprehensive design theory for PKM systems consisting of meta-requirements and a synthesis of various kernel theories into actionable design procedures. The proposed procedures include knowledge requirements modeling, knowledge flows modeling and knowledge and process performance modeling procedures. The feasibility study indicates that the PKM approach can be more useful and effective than solely using unified modeling language (UML)-based systems analysis and design techniques for the design of PKM systems.

An implication to information systems design research is the feasibility of developing a specialized design approach that incorporates significant domain knowledge to solve complex information system design problems. An implication to practice is the significant potential to improve productivity and effectiveness of systems analysts and designers in developing PKM systems. A limitation is the small sample size of the feasibility study used to evaluate the ease of use and utility of the design approach.

The study makes a unique contribution by proposing a design approach that integrates business process and knowledge management considerations. The approach is particularly valuable because of the focus on integration with existing systems analysis and design techniques, thus allowing for easier adoption.

Peer-to-peer (P2P) accommodation sharing has become a significant part of the travel and lodging industry, allowing homeowners to engage in entrepreneurial activity via sharing of resources. However, there is limited understanding of how hosts can use listing descriptions to better match their offerings to different consumer segments. The purpose of this paper is to understand the use of listing descriptions by Airbnb hosts and the impact of such descriptions on sales performance.

In this paper, a deep learning-based sentence-level aspect mining approach is used to extract various aspects from host-provided listing descriptions. Then a regression-based approach is used to understand the impact of various aspects of listing descriptions on listing performance.

It was found that aspects for which listing descriptions are the sole source of information have the greatest influence on listing performance. The authors also find that the impact of an aspect on listing performance varies by listing type, and that there is a mismatch between the most included aspects by hosts in their listing descriptions and the most influential aspects that impact sales.

The impact of consumer reviews in the context of Airbnb has been extensively studied. A novel aspect of this study is the exploration of P2P accommodations from a supplier perspective, by understanding the use and impact of host-provided textual descriptions on sales. The findings of this study can help better market properties from a practice perspective and better understand consumer information consumption from a theoretical perspective. The authors also demonstrate a new approach for exploring social phenomena by performing quantitative analysis on textual data using deep-learning and regression-based techniques.