Application of Data-Driven Method in Identifying the Causal Factors Pattern of Internet of Things Usability in Libraries

Background and Objective: The research background indicates that only a limited number of models have been developed to identify the factors influencing IoT usability in libraries. However, to date, no significant study has been conducted to identify the causal factors affecting the implementation of IoT in libraries. IoT, through its intelligent capabilities, facilitates information management, service development, and library expansion. The objective of this study is to identify the causal factors of IoT usability in libraries and to present a model using the grounded theory method.
Methodology: This study was conducted using a qualitative approach. The statistical population consisted of 13 faculty members from the university departments of information science and knowledge. Purposive and convenience sampling techniques were employed to select participants. In other words, the target population was identified based on field expertise, relevant work experience, an understanding of the research problem, and a proven background in the subject matter. As the research progressed, the snowball (or chain referral) method was used to identify and recruit additional suitable participants. Interviews continued until theoretical saturation was reached, and the required data were collected through in-depth interviews. Each interview was recorded, transcribed promptly, and then converted into statements that formed the basis of the coding process. Data were analyzed using three levels of coding: open, axial, and selective. A paradigmatic model was also used to interpret the interviews. To ensure validity, the respondent validation method was employed, and reliability was assessed through the double-coding technique.
Findings: The interview transcripts were reviewed in multiple stages, and all interviews were converted into propositions. Through analysis of the data related to causal factors and components affecting IoT implementation in libraries—and following the identification of semantic relationships and conceptual coding—29 key categories were extracted. Subsequently, these categories were organized into eight broader categories: 1. Intelligence, 2. Communication and Interaction,
3. Automation, 4. Guidance and Training, 5. Expertise and Skills, 6. Infrastructure and Equipment,
7. Security, and 8. Motivation. The relationships between these categories were presented in a conceptual format. Further examination of the concepts and the identification of preliminary inter-category relationships led to the development of a conditional matrix model. This model provides a comprehensive framework for analyzing the conditions and factors related to the main phenomenon under study. In the resulting model, the central phenomenon is the applicability of IoT in libraries.
Conclusion: The research findings demonstrate that the data related to the causal factors of IoT applicability in libraries were analyzed through three stages of coding—open, axial, and selective—culminating in the development of the final research model. This process led to the identification of 29 categories across 8 overarching themes. By interpreting the relationships among these categories and analyzing the research findings, a final model for the applicability of IoT in libraries was designed and presented. To answer the research question and achieve the study’s goal, the causal factors identified in the theoretical model of IoT usability in libraries include: intelligence, communication and interaction, automation, guidance and training, expertise and skills, infrastructure and equipment, security, and motivation. Experts in the field of information science and knowledge believe that the implementation and integration of IoT in libraries can enhance library agility by leveraging these identified factors. According to the interviewees, these causal factors are essential and significant. The breadth of these factors in the theoretical model highlights the central importance and validation of the main phenomenon—IoT usability in libraries. These factors are foundational and have a direct, activating influence on the usability of IoT. The results indicate that the proposed model of causal factors can serve as a roadmap for libraries aiming to leverage IoT to offer innovative services. The findings of this research are useful and effective in enhancing library quality and facilitating greater interaction between libraries and their users.