System Interaction Theory: Describing Interactions between Work Systems

Interactions between systems are a necessity, a source of opportunity, and a source of difficulty and complication in building, implementing, and maintaining IT-reliant systems in organizations. This paper presents system interaction theory (SINT), a theory for analysis that covers almost all intentional and unintentional interactions between work systems that may be sociotechnical or totally automated. SINT is a broadly applicable theory that encompasses interactions between the types of systems that are central to the IS discipline. To minimize redundancy, this paper summarizes SINT immediately after introducing the research goal and, thereby, provides a context for the many distinctions and references that follow. A discussion of SINT’s domain and scope explains why SINT views interacting entities as work systems rather than as tasks, components, or software modules. The literature review positions SINT in relation to topics under headings that range from general systems theory and computer science to human computer interaction and organization science. Topics in SINT include relevant characteristics of systems and system interactions, purposes and/or causes of system interactions, system interaction patterns, direct effects of system interactions, responses to direct effects, and outcomes related to system interactions. The paper discusses a variety of potential contributions to theory, practice, and research

Harnessing the power of the general public for crowdsourced business intelligence: a survey

International audienceCrowdsourced business intelligence (CrowdBI), which leverages the crowdsourced user-generated data to extract useful knowledge about business and create marketing intelligence to excel in the business environment, has become a surging research topic in recent years. Compared with the traditional business intelligence that is based on the firm-owned data and survey data, CrowdBI faces numerous unique issues, such as customer behavior analysis, brand tracking, and product improvement, demand forecasting and trend analysis, competitive intelligence, business popularity analysis and site recommendation, and urban commercial analysis. This paper first characterizes the concept model and unique features and presents a generic framework for CrowdBI. It also investigates novel application areas as well as the key challenges and techniques of CrowdBI. Furthermore, we make discussions about the future research directions of CrowdBI

Life Cycle Engineering 4.0: A Proposal to Conceive Manufacturing Systems for Industry 4.0 Centred on the Human Factor (DfHFinI4.0)

Engineering 4.0 environments are characterised by the digitisation, virtualisation, and connectivity of products, processes, and facilities composed of reconfigurable and adaptive socio-technical cyber-physical manufacturing systems (SCMS), in which Operator 4.0 works in real time in VUCA (volatile, uncertain, complex and ambiguous) contexts and markets. This situation gives rise to the interest in developing a framework for the conception of SCMS that allows the integration of the human factor, management, training, and development of the competencies of Operator 4.0 as fundamental aspects of the aforementioned system. The present paper is focused on answering how to conceive the adaptive manufacturing systems of Industry 4.0 through the operation, growth, and development of human talent in VUCA contexts. With this objective, exploratory research is carried, out whose contribution is specified in a framework called Design for the Human Factor in Industry 4.0 (DfHFinI4.0). From among the conceptual frameworks employed therein, the connectivist paradigm, Ashby's law of requisite variety and Vigotsky's activity theory are taken into consideration, in order to enable the affective-cognitive and timeless integration of the human factor within the SCMS. DfHFinI4.0 can be integrated into the life cycle engineering of the enterprise reference architectures, thereby obtaining manufacturing systems for Industry 4.0 focused on the human factor. The suggested framework is illustrated as a case study for the Purdue Enterprise Reference Architecture (PERA) methodology, which transforms it into PERA 4.0