ladan hajianvari

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.

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.

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.

The current research was conducted with the aim of understanding the components of the emotional competencies of school principals.

The research was conducted with a qualitative approach and meta-composite method, 23 articles were selected for more detailed reviews and compared with the extraction of components related to managers’ emotional competencies & coding in the manner Open, central and selective. The validity of the findings was confirmed through descriptive and interpretive validity. In this regard, the analysis of documents has been carried out with high accuracy, and the opinions of experts have been used in the steps of the research.

Emotional competencies in 3 areas of ability (including the components of self-management, effective interpersonal communication, and constructive and collaborative interaction), characteristic (including the social capital, components of self-motivation, adaptive emotional readiness, and personal characteristics), and skill (including self-awareness, conflict management, and coaching and Mentoring), which included 110 subcomponents, were organized and presented.

Managers with emotional competence identify with the organization, are more committed to it, and make favorable business decisions through controlling their own emotions and managing the emotions of others.

 In today's complex and ever-changing world, the necessity to rethink research methods and the production of knowledge is more pressing than ever. Consequently, there is a need to innovate qualitative research approaches to address new challenges and generate fresh insights that yield different outcomes. Post-qualitative research highlights the importance of evolving methods that resist fixed classifications, thereby redefining the landscape of qualitative inquiry. This approach specifically addresses the limitations of traditional qualitative methods and fosters a space for critical and creative thinking. It not only delves deeper into social and cultural phenomena but also seeks to transcend the confines of existing research methodologies, moving toward a multidimensional and dynamic understanding of realities. While post-qualitative research opens up innovative pathways for inquiry, it also faces criticisms regarding its practical applicability and the potential for methodological chaos, raising important questions about balancing creativity with rigor in research endeavors. Thus, post-qualitative research serves not only as a tool for analyzing phenomena but also as an opportunity to reconsider how knowledge is produced. This study aims to provide insights into post-qualitative research by examining it through a multi-stage design that seeks to deepen understanding of this emerging approach and analyze its implications for research. Given that post-qualitative research remains relatively unexplored in the field of organizational studies, this method will be specifically investigated in the present study.

This study offers a comprehensive and coherent analysis of post-qualitative methodology, providing new insights into this emerging field. The research employed a descriptive and analytical approach, designed in several stages to facilitate a deeper understanding of this novel methodology. Initially, the study conducted a thorough review of key concepts and definitions related to post-qualitative research. To achieve this, it utilized reliable sources, scientific articles, and foundational texts in the field of post-qualitative research. This review not only covered essential concepts but also explored the historical and philosophical origins of post-qualitative inquiry to elucidate its theoretical and practical foundations. The analysis included the impact of various theories on the development of this methodology and identified significant historical turning points. Subsequently, the study provided a detailed examination of the key principles and various aspects of post-qualitative research. This section encompassed an analysis of data collection methods, data analysis techniques, and the challenges and limitations researchers may face when employing this approach. Additionally, a comparative analysis of the paradigms, ontologies, and subject matters of qualitative versus post-qualitative research was presented to highlight the distinctions between the two methodologies. Finally, the discussion and conclusion sections summarized the findings and included a critique of the methodology from the researchers' perspectives.

Despite the attention given to post-qualitative methodology, a clear, precise, and unified definition of post-qualitative research remains elusive. Researchers seldom provide explicit definitions of post-qualitative research because the question of “what” it entails implies a sense of permanence, while the term itself is inherently unstable and open-ended. Furthermore, some argue that post-qualitative research should not be defined at all, as labeling is a limiting, fixed, and closed practice. Instead, post-qualitative research embodies approaches that lack normative structures and clear boundaries. These studies are diverse and can originate at any time and place; however, their inherent uncertainty and vagueness can lead to confusion. In essence, post-qualitative research signifies a substantial shift from traditional qualitative research, emphasizing creativity, experimentation, and a departure from human-centered methods. It is grounded in post-structuralism and post-humanism and focuses on the fluidity of knowledge production rather than adhering to fixed methodologies.It is important to note that the post-qualitative approach does not entail a complete rejection of qualitative methods; rather, it represents a continuum that draws valuable insights from traditional qualitative research while expanding its focus to include both human and nonhuman factors. Below are some key characteristics of the post-qualitative research method 1. Post-Humanist Paradigm: In post-theoretical frameworks, all major epistemological and methodological concepts are deconstructed. 2. Flat Ontology: In flat (post-qualitative) ontology, subjects, objects, and language are always intertwined; humans do not exist separately but emerge in relation to one another.

A reflection on post-qualitative research indicates that this approach facilitates a more nuanced and multidimensional analysis of realities, fostering the creation of new and meaningful knowledge. In light of the increasing complexity of societies and the necessity for a deeper understanding of social phenomena, post-qualitative methodology has emerged as an effective tool for re-evaluating traditional concepts and methods in qualitative research. This approach not only enhances the depth and breadth of reality analysis but also supports the generation of innovative insights. One of the defining characteristics of post-qualitative methodology is its emphasis on uncertainty and ambiguity throughout the research process. This perspective encourages researchers to transcend normative frameworks and rigid boundaries, allowing for the examination of phenomena from diverse angles. Post-qualitative methodology serves as a reminder that knowledge is a collective endeavor shaped by various social, cultural, and historical contexts. This is particularly significant in organizational and social studies, where it can aid in better understanding the intricate relationships between humans and their environments. However, the adoption and implementation of post-qualitative methodology are not without challenges. One notable challenge is the sense of uncertainty and confusion arising from the absence of clear normative structures. Researchers must confront these challenges and develop strategies to navigate ambiguities in order to fully leverage the advantages of this approach. Given the wide array of methods and perspectives available within this field, it is anticipated that researchers will achieve deeper and more comprehensive analyses of human phenomena through the application of post-qualitative methodologies. Thus, engaging with post-qualitative methodology is not only essential but also has the potential to cultivate a new discourse that enhances our understanding of complex human realities.

The aim of this study is to investigate the factors that affect the acceptance of artificial intelligence among faculty members, as well as their level of artificial intelligence literacy.

The current applied research is grounded in the philosophy of pragmatism, utilizing a mixed-method (sequential explanatory approach). The statistical population for the quantitative aspect consisted of 1220 faculty members at Farhangian University campuses during the academic year 2023-2024. Through Cochran's formula, 315 individuals were selected via random cluster sampling. For the qualitative component, 15 experts were chosen using snowball sampling. Quantitative data were analyzed using SPSS, while thematic analysis was applied to the qualitative data.

Factors such as behavioral intention, perceived ease of use, perceived usefulness, job relevance, social norms, facilitating conditions, and attitude towards AI have been identified as influencing artificial intelligence acceptance. The results revealed that the artificial intelligence literacy levels of faculty members were suboptimal. The second-order factor analysis of the structure showed significant fit indices, validating the statistical analysis.

Enhancing AI literacy of professors in higher education is crucial in addressing resistance to AI adoption. Therefore, increased support from relevant authorities and educational leaders is essential to assist professors in developing their artificial intelligence skills and literacy.

Generation Z and Alpha consist of students who, regardless of their legal working age, have not waited to graduate from school. They are actively involved in various capital markets such as Forex, cryptocurrencies, and other new businesses. These include influencer marketing, digital network marketing, internet advertising, etc. Despite the growing interest of students in new businesses and the capital market, and recognizing the importance of exploring this trend, there is a remarkable lack of research in this area. For this reason, the current study aims to identify the factors influencing students' tendency towards new businesses and the capital market.

The current applied research employs a mixed-method, specifically a sequential exploratory type. The research is based on the philosophy of pragmatism. In the qualitative section, semistructured interviews were conducted with 27 experts in the field and students actively engaged in these areas. They were selected through targeted snowball sampling, and after reaching data saturation, the interviews were concluded and themes were analyzed. Based on the insights from the qualitative phase, a researcher-developed questionnaire with 25 items was formulated for data collection in the quantitative section. The quantitative portion of the research focused on a population of 18,357 male and female high school students in Zanjan city during the academic year 2023-2024. A sample of 376 individuals was selected using proportional stratified random sampling based on Cochran's formula. The data from the quantitative part were analyzed using SPSS software version 27 and exploratory factor analysis. Subsequently, a final research model was developed using path analysis.

Subsequently, in consultation with experts, appropriate names were assigned to the variables, leading to the identification of factors shaping students' preferences for new businesses and the capital market. The factors influencing students' interest in new businesses and the capital market encompass individual, economic, demographic, social, technical-technological, and family-related factors.

These findings emphasize the multifaceted and diverse nature of the influencing factors, highlighting the necessity for collaboration among economic, cultural, and educational planners to address this phenomenon effectively and in a logical manner. Considering the focus of this research on the factors affecting students' tendency towards new businesses and the capital market, it is suggested that future researchers test the model of this research in a new sample (validation sample) to assess the quality and validity of the model.

Education has gained increasing significance from various perspectives, particularly in terms of economics. It can be argued that education constitutes the largest portion of public expenditure worldwide. Few individuals, having examined the direct and indirect social and private benefits of education—including both public and higher education—regard investment in education as useless or of little utility. However, there remains a gap in research regarding the roles of governments and the private sector in education, especially concerning Islamic perspectives. The aim of this qualitative research is to analyze the economic role of the government in education with an emphasis on Islamic viewpoints. In this regard, semi-structured interviews were conducted with 15 education economics experts who were purposefully selected using snowball sampling until data saturation was achieved. The content of interviews was analyzed to elicit themes that can be used in a SWOT matrix. The findings indicate that government involvement in education presents strengths, weaknesses, opportunities, and threats, emphasizing the importance of collaborative financing by both public and private sectors. With such cooperation, governments and markets can address current shortcomings and contribute to enhancing the quality of education.

Blended learning, as a combination of face-to-face learning and technology-based learning, has many advantages; however, there are still gaps in research related to this issue. Therefore, the aim of the current study was to compare the effects of traditional and blended learning on knowledge, self-efficacy, and perception of nursing students about web-based learning environment at the undergraduate level. This research design was quasi-experimental, based on pre-post surveys, in which 20 second-year nursing students from Azad University of Zanjan, who had chosen the health assessment course. The data were analyzed using descriptive and inferential statistics using SPSS 27.0.1. There were no significant differences in the scores of the first to third tests between the blended and traditional teaching groups (p > 0.05); however, the blended learning group achieved better scores in assignments (p < 0.05). No significant differences in self-efficacy scores was observed between the groups in pretest and posttest. The students' perception of the web-based learning environment was also positive. Based on this, blended learning can be employed to create a flexible learning space. The findings of this research emphasize the advantages of blended learning as an instructional strategy in nursing.

The positive effect of social networking, particularly social networking sites (SNSs), on improving the process of learning has been acknowledged by many recent types of research. The relationship between features and characteristics of SNSs and the development of students' social networking was of interest to past researchers. As social networking is primarily perceived as intelligent thought and action in both real and virtual environments, there seems to be a need for a qualitative exploration of the influential factors of students' social networking. The study has been conducted using the case study method to look at the identified factors retrieved from previous research. A semi-structured in-depth interview was used to investigate the viewpoints and experiences of socially proactive and successful students at Iranian universities. Findings explain students' social networking due to three factors categorized as central, causal, and contextual. The personal learning system has a critical position among the various factors affecting students' social networking. Therefore, despite the facilitating role of social networking in promoting the learning process, students' social networking would be useless without utilizing a personal learning system. We can see a dynamic and interactive cycle of learning and social networking in the university context. The research has been founded on critical consideration of previously studied factors affecting social networking that were mainly limited to online technologies according to qualitative exploration. As a result of this research, different learning and social networking levels regarding diverse meaning, function, and complexity were identified.