Digital Transformation and Administration Innovation

This study aimed to design and evaluate an advanced Long Short-Term Memory (LSTM) deep learning model to accurately detect fraudulent claims in supplementary health insurance by leveraging sequential data patterns and domain-specific engineered features. An applied research design was used to build a robust fraud detection framework. A dataset of 20,000 health insurance claims was obtained from a supplementary insurance provider, containing both legitimate and fraudulent cases. Raw relational tables — including policy, insured individuals, claims, disease registry, and branch information — were merged into a single structured dataset using SQL. Rigorous data preprocessing was performed: irrelevant variables were removed, highly correlated features were eliminated through correlation analysis, dimensionality reduction was applied via Principal Component Analysis (PCA), and extreme outliers were excluded using the interquartile range (IQR) method. All numerical features were standardized, and class imbalance was addressed by weighting fraudulent cases during training. The processed data were reshaped into sequences suitable for LSTM input and divided into training and testing sets. An LSTM model with 32 hidden units and a Sigmoid output layer was trained using the Adam optimizer and binary cross-entropy loss, with performance validated through k-fold cross-validation. The LSTM model achieved outstanding predictive performance, with an overall accuracy of 100% on the test dataset. Both fraudulent and non-fraudulent claims reached perfect precision, recall, and F1-scores. Macro and weighted averages also recorded 1.00 across all metrics, indicating the model’s ability to detect rare fraudulent events without sacrificing specificity. By combining advanced deep learning with systematic data preparation and domain-informed feature engineering, the proposed LSTM framework effectively identified complex fraud patterns in supplementary health insurance. This approach offers a scalable and reliable solution to strengthen fraud risk management and reduce financial losses in the insurance sector.

This study aimed to design and empirically test a gamification model based on customer engagement indicators to enhance customer loyalty and improve business performance in small and medium-sized online businesses. A mixed-methods research design was employed, combining qualitative and quantitative approaches. In the qualitative phase, semi-structured interviews were conducted with 12 experts in digital marketing, user experience design, and online business management to identify key gamification components and mechanisms. Data were analyzed using thematic analysis with open, axial, and selective coding. In the quantitative phase, a researcher-developed questionnaire, informed by the qualitative findings and theoretical literature, was distributed to 384 active customers of Iranian online businesses who interacted with gamified elements through e-commerce platforms. Convenience sampling was used, and data were analyzed using SmartPLS 3.2 for confirmatory factor analysis, structural equation modeling (SEM), and model fit assessment. Inferential analysis showed that gamification had a significant positive effect on customer engagement (β = 0.72, t = 12.58, p < 0.001) and that customer engagement significantly influenced customer loyalty (β = 0.69, t = 11.76, p < 0.001). Gamification also had a direct positive effect on loyalty (β = 0.34, t = 6.32, p < 0.001), with a substantial total effect (β = 0.83, t = 15.89, p < 0.001). R² values indicated that gamification explained 52% of the variance in engagement and, with engagement, 61% of the variance in loyalty. The Q² value (0.440) demonstrated strong predictive relevance, and model fit indices (SRMR = 0.068; GoF = 0.470) confirmed good model adequacy. Gamification is an effective strategy for increasing customer engagement and loyalty in small and medium-sized online businesses, with engagement serving as a partial mediator in the gamification–loyalty relationship. These findings support the integration of gamification into SME digital marketing strategies to strengthen customer relationships and enhance performance outcomes.

This study was conducted with the objective of designing a model for institutionalizing dynamic knowledge management with a sustainable development approach in the Oil Industries Commissioning and Operation Company (OICO). In the qualitative phase of the research, and with the aim of developing the model based on the grounded theory methodology, a group of experts—including academic faculty members, senior managers of the Oil Industries Commissioning and Operation Company, and experienced consultants in the oil, gas, and petrochemical sectors—were selected as the statistical population and participated in in-depth interviews. In this phase, theoretical sampling was applied, and the process continued until theoretical saturation was reached; in total, 11 interviews were conducted. In the quantitative phase, senior, middle, and operational managers of the Oil Industries Commissioning and Operation Company were considered the target population. Out of this population, 108 individuals were selected as the statistical sample using the simple random sampling method. In the qualitative phase of the research, the primary tool for data collection consisted of deep and semi-structured interviews with the experts. In the quantitative phase, the main data collection instrument was a closed-ended researcher-made questionnaire comprising 33 items, which was developed based on the initial conceptual model. Ultimately, the findings of this study led to the development of a model for institutionalizing dynamic knowledge management with a sustainable development approach in the Oil Industries Commissioning and Operation Company, and the hypothesized relationships within the model were tested and confirmed across a broader organizational sample.

Political behavior in governmental organizations refers to a set of informal actions and interactions that employees and managers engage in beyond their formal job descriptions to gain, maintain, and increase influence, power, and access to scarce resources. The primary objective of the present study is to provide a model of political behavior in governmental organizations using the meta-synthesis method. This study employed a qualitative and development-oriented research design. The seven-step method of Sandelowski and Barroso (2007) was used to examine the model of political behavior in governmental organizations through meta-synthesis. The statistical population included scientific articles, academic theses, and books. A total of 157 sources were reviewed, and ultimately 60 sources were selected and analyzed. Through a purposive meta-synthesis approach, the components related to political behavior were identified. The reliability of the components was confirmed using the Kappa coefficient. SPSS version 19 was used for statistical analysis. The findings of the study revealed that political behavior in governmental organizations includes three categories: black political behavior (33 components), white political behavior (37 components), and gray political behavior (20 components). Understanding political behavior enables managers to predict and manage such actions instead of directly confronting and eliminating them, which is often impractical.

The present study aimed to identify the components of digital leadership in the universities of Mazandaran Province. The research was applied in terms of purpose and qualitative with an exploratory mixed design in terms of data nature. Accordingly, in the qualitative phase, based on grounded theory, a descriptive–survey method was used to identify the components of digital leadership in the universities of Mazandaran Province. The statistical population consisted of all experts in the scientific community as well as academic specialists, managers, and planners of universities in Mazandaran Province. The sampling method was theoretical purposive and aligned with the principle of theoretical saturation; thus, 14 individuals were selected as the sample. Data collection in the qualitative part was performed through semi-structured interviews. The validity and reliability of the research were confirmed. Based on the qualitative findings, six key components of digital leadership within the context of higher education were identified. These components were extracted using three-stage coding (open, axial, and selective) and the analysis of more than 60 initial concepts. They represent the main dimensions of the role of academic leaders in the process of digital transformation. Thematic analysis of the qualitative data revealed eleven main components related to innovative organizational culture in the universities of Mazandaran Province. Each component reflects the internal and external mechanisms that influence the institutionalization of innovation in the academic environment. These findings indicate that an innovative organizational culture is not merely the product of macro-level policymaking or managerial decisions but rather the outcome of the dynamic interaction between structural, human, value-based, and technological factors.

The purpose of this study was to develop a qualitative model explaining how neuromarketing influences customer purchase decision-making in the context of FMCG industry. This research employed a qualitative phenomenological design with an interpretive approach. Data were collected through in-depth semi-structured interviews with 13 experts in psychology, neuroscience, and marketing, selected via purposive snowball sampling from a pool of approximately 50 specialists located in District 6 of Tehran. Interviews were recorded, transcribed, and verified by participants. Data analysis followed grounded theory principles and the coding framework of King and Horrocks (2010), moving from descriptive coding to interpretive coding and thematic integration. Credibility and trustworthiness were ensured through prolonged engagement, triangulation, peer debriefing, and member checking, applying the four criteria of Guba and Lincoln—credibility, transferability, dependability, and confirmability. The analysis yielded 146 initial codes that were categorized into contextual, causal, strategic, and intervening conditions, with outcomes integrated under a central category of neuromarketing. Contextual conditions emphasized content structuring and emotional engagement, while causal conditions highlighted the role of sensory stimuli such as packaging, color, and music. Strategies included storytelling, multisensory experiences, and visual displays, whereas intervening conditions revealed cultural differences, conflicting information, and brand switching behaviors as mediators of consumer response. Outcomes indicated increased purchase intention, brand trust, emotional stimulation, and consumer satisfaction. Collectively, the findings illustrate how subconscious cognitive and emotional mechanisms shape purchasing behavior in supermarket settings. Neuromarketing provides a powerful framework for understanding and influencing consumer behavior by integrating sensory, emotional, and cognitive dimensions into marketing strategies. This study highlights the importance of tailoring neuromarketing interventions to cultural and contextual factors while emphasizing trust and ethical responsibility in fostering sustainable consumer–brand relationships.

The present article was conducted with the aim of identifying the factors and components of environmentally ethical technology development. In terms of purpose, the research is applied; in terms of data, it is qualitative; and in terms of implementation method, it was conducted using the systematic grounded theory approach. The statistical population consisted of key informants in the field of research within the Tehran Province Department of Environment, and interviews were conducted with 11 individuals using the snowball sampling method until theoretical saturation was achieved. Data were collected through both library research (documents and records) and field research (semi-structured interviews). To assess the validity of the research instrument, three triangulation methods were used: methodological triangulation (71%), investigator triangulation (77%), and participant triangulation (83%), indicating that the instrument had acceptable validity. Data analysis was performed using theoretical coding (based on the paradigm model of grounded theory). The coding results showed that environmentally ethical technology development consisted of 133 open codes (indicators), 26 axial codes (components), and 6 selective codes (dimensions).

In the era of hybrid and cognitive warfare, the national security of states is profoundly influenced by narrative construction, psychological operations, and the management of public perception. Despite utilizing media capacities during the Sacred Defense and regional crises, the Islamic Republic of Iran lacks an institutionalized and indigenous model for combat information dissemination—one that can play a deterrent and coordinating role in confronting perceptual threats and misinformation. This study, aiming to design and explain a conceptual model for combat information dissemination, examines the media mechanisms effective in enhancing defensive soft power, psychological resilience, and legitimization of military actions within the framework of the national security of the Islamic Republic of Iran. The main research question is: What components, conditions, and consequences does the optimal model of combat information dissemination for cognitive deterrence and strengthening national security of the Islamic Republic of Iran include? The research method is qualitative, based on grounded theory, and relies on the analysis of data collected from 34 semi-structured interviews with media experts, military commanders, and security analysts using MaxQDA software. The findings indicate that effective combat information dissemination depends on six key components: real-time information management, strategic narrative construction, on-the-ground presence of military journalists, pre-emptive psychological operations, intelligent utilization of digital media, and institutional coordination between media and military organizations. Moreover, causal, contextual, and intervening conditions influencing this model, along with its strategies and consequences, were identified, and the final conceptual model was designed. Implementation of this model can lead to the reinforcement of cognitive deterrence, preservation of the psychological cohesion of forces, counteraction against enemy psychological operations, and enhancement of media–military synergy during crises.