طراحی مدل استفاده از هوش مصنوعی برای تغییر فرایندهای واحد روابط عمومی سازمان با تاکید بر مدیریت دانش سازمانی

Purpose: Although new technologies, such as artificial intelligence, are transforming the public relations and communications profession, there is a scarcity of research on the impact of artificial intelligence in this field. In recent years, significant advancements in artificial intelligence technology have prompted organizations to seek solutions for enhancing the efficiency and effectiveness of their public relations unit. Additionally, knowledge management serves as a crucial element in improving processes and decision-making within this unit. Consequently, there is an urgent need to design models that comprehensively examine the application of artificial intelligence within the public relations departments of organizations. However, comprehensive and practical frameworks for assessing the integration of artificial intelligence into current public relations practices are still lacking. Many organizations continue to rely on traditional and inefficient methods for managing their communications, failing to harness the potential of artificial intelligence. Additionally, a lack of awareness and insufficient training regarding these technologies among public relations professionals has hindered their acceptance and implementation. Therefore, this article aims to develop a model for utilizing artificial intelligence to transform the procedures of public relations unit in organizations, with a particular emphasis on organizational knowledge management, based on grounded theory. In this context, the present study seeks to identify the causal conditions, intervening factors, phenomena, background conditions, strategies, and consequences associated with the implementation of artificial intelligence in public relations departments by examining the experiences of professionals and students engaged in related fields.
Methodology: The current applied research is based on an interpretive (constructivist) paradigm with a qualitative approach, specifically focusing on data-driven theory to develop a model for the application of artificial intelligence in the public relations units of organizations. The statistical population for this study included faculty members, experts, and students engaged in the fields of public relations, human resource management, artificial intelligence, and psychology. A purposive snowball sampling method was employed to select the research sample, continuing until theoretical data saturation was achieved. Data collection was conducted through semi-structured interviews with experts, sociologists, and selected students from the relevant fields, resulting in a total of 17 participants being interviewed, which marked the point of theoretical saturation. Each interview lasted between 60 and 65 minutes. Following each session, the interview content was transcribed from the recorded audio files and manually coded using the systematic approach developed by Corbin and Strauss. The interview transcripts were initially coded and categorized paragraph by paragraph, followed by a comprehensive coding and categorization process conducted in three stages. After coding all 17 interviews, the researchers reviewed the codes, eliminating duplicates and merging similar codes. Ultimately, the causal, contextual, intervening, strategic, and consequential conditions related to the central theme of utilizing artificial intelligence within public relations units were identified. To ensure the validity of the research, triangulation and external validation through a third-party reviewer were employed. The reliability of this qualitative study was further confirmed by recording participants' voices with an interview recorder, meticulous note-taking during interviews, and involving a second coder who achieved an agreement coefficient of 86%. Finally, after incorporating feedback from three experts overseeing the research process, necessary revisions were made, and the final research model was constructed based on the systematic approach of data-driven theory.
Findings: Based on the analysis and review of the interview transcripts, along with the coding and categorization process, a total of 101 open codes were identified, highlighting the causal, contextual, intervening, strategic, and consequential conditions related to the central theme of utilizing artificial intelligence in the public relations units of organizations. The motivations for employing artificial intelligence in these units were classified into four main categories: efficiency and productivity, enhancement of customer service, market research and analysis, and innovative aspects. Contextual factors were divided into two categories: technological conditions and organizational conditions. Intervening factors were categorized into two primary areas: market conditions and social/legal conditions. Strategies for implementing AI were identified across five categories: training and empowerment, culture building, fostering coordination, operational strategies, and promoting awareness and motivation. These strategies are anticipated to lead to improved communication, process optimization, enhanced credibility and transparency, reduced communication quality issues, and addressing technical and operational challenges, all of which play a crucial role in the effective utilization of artificial intelligence within public relations units. Ultimately, this research offers significant insights for managers regarding the transformative potential of AI in public relations practices. Through a rigorous qualitative methodology grounded in empirical data, it sheds light on the opportunities and challenges associated with integrating AI into public relations. As organizations navigate an increasingly complex communications landscape, adopting AI technologies is vital for enhancing efficiency, improving stakeholder engagement, and maintaining a competitive edge. By identifying key components and developing an applied model for optimizing public relations practices through AI, this study explores the causal, contextual, intervening, strategic, and consequential conditions for AI implementation in public relations departments and aims to provide practical solutions to facilitate digital transformation within organizations.
Research limitations: Despite yielding valuable findings, this research has several limitations. The research methodology is a primary limitation of the present study. Although data were collected through semi-structured interviews and adhered to scientific principles, the small sample size may limit the diversity and representativeness of the results, potentially impacting the overall conclusions. Future researchers could enhance the qualitative data obtained from interviews by incorporating quantitative data, thereby providing a broader perspective on the research problem. It is recommended that future studies explore the role of human creativity in conjunction with artificial intelligence in public relations. This approach could help identify the strengths of both domains and offer solutions for more effective collaboration between humans and machines. Based on the findings of this research, organizations are encouraged to implement continuous training programs for their employees focused on artificial intelligence and its applications in public relations. Such initiatives can help develop the necessary skills for the effective and efficient use of these technologies. Given the rapid pace of technological change, organizations must continuously analyze market trends to leverage the latest innovations. Implementing artificial intelligence-based tools within public relations departments can significantly benefit organizations by enhancing their ability to adapt and thrive in a dynamic environment.
Originality/value: Considering the rapid technological advancements and the necessity to enhance efficiency in organizational communications, this research offers valuable insights for managers on how artificial intelligence can transform public relations practices within organizations.