This study aims to uncover and develop explicit knowledge of existing smart communities (SCs) to guide services and business solutions for enterprises and serve community users in a well-thought-out manner. These sagacious frameworks will assist in analyzing trends and reaching out to pre-existing setups with different degrees of expertise.
A systematic overview is provided in this paper to unify insights and competencies toward building SCs; a hybrid analytical approach is used consisting of machine learning and bibliometric analysis. Scopus and Web of Science (WoS) are the primary databases for this purpose.
SCs implement cutting-edge technologies to enhance mobility, elevating information and communication technology (ICT) skills and data awareness while improving business processes and efficiency. This system of SC is an evolution of the conventional method. It provides a foundation for intelligent community services based on individual users and technologies such as the Internet of Things (IoT), artificial intelligence, cloud computing and big data. Manufacturing-based, service-based, retail-based, resource management and infrastructure-based SCs exist in the literature.
The paper summarizes a conceptual framework of SCs based on existing works around SCs. To the best of the authors’ knowledge, this is the first systematic literature review that uses a hybrid approach of topic modeling and bibliometric analysis to understand SCs better.
This study aims to fill the gap in collaboration culture improvement by designing an information-sharing system as an enabler to support the forming dimensions of the collaboration process in previous studies. The authors propose the appropriate system to eliminate the collaboration culture gaps between the related functional units based on in-process interaction learning in a business process outsourcing company.
This study employed action research (AR) based on soft systems methodology (SSM) with a system thinking approach, in which the authors and process actors design the agreed information-sharing system by involving the process actors in identifying the initial problem situation as well as validating the conceptual model through discussions and designing the expected system.
This study discovers that SSM-based AR is a suited method for designing a system that supports the formation of collaboration culture among actors in the digital service creation process by learning user perceptions and expectations in order to obtain their commitment to empower the proposed system.
In addition to providing a system to minimize the lack of collaboration culture, this study contributes to the academic literature by offering a new way of planning and designing in a system development methodology using soft systems approaches to understand user perceptions, expressing user interaction in a conceptual model and validating it and defining agreed activities to obtain the best design according to user expectations.
Artificial intelligence (AI) is the most progressive commodity among current information system applications. In-house development and sales of beneficial products are difficult for many software development and service companies (SDSCs). SDSCs have some implicit concerns about implementing AI software development due to the complexity of AI technology; they require an evaluation framework to avoid development failure. To fill the void, this study identified the factors influencing SDSCs when developing AI software development.
Based on complex adaptive systems theory, three aspects were developed as the main factors of hierarchy, namely, employees' capabilities, environmental resources and team capabilities. Fuzzy analytic hierarchy process (FAHP) was used to assess the SDSCs' attitude. Based on SDSCs, attitudes toward implementing AI software projects were collected to calculate the hierarchy of factors.
The outcome of FAHP is used as understanding the key factors of SDSCs for selecting an AI software project, toward the improvement of overall project planning. Employees' stress resistance was considered as a priority for the project, although professional AI skills and resources were also important.
This study suggested three variables developed using complex adaptive systems. This study contributes to a better understanding of the critical aspects of developing AI software projects in SDSCs. The study's findings have practical and academic implications for SDSCs and subsequent academic development, broadening the scope of AI software development research.
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