JOURNAL OF INTELLIGENT STRATEGIC MANAGEMENT
Year:2025 | Volume:4 | Issue:1|Papers Count:6

The term "organizational training and learning" is a blend of important principles of the working life of any organization, and since the most important aspect of this term is the development and growth of work skills and specializations, the establishment and continuation of the training process in today's organizations plays a significant role in the specialization of those organizations and in fact, their leadership. Therefore, it should always be noted that effective and valuable training is related to long-term results and effects and prepares the organization and its individuals to accept the difficult and heavy responsibilities of the future. Regardless of the size of the organization, from large enterprises, small and medium-sized businesses to family businesses and startups, all use companies active in the field of business education services to constantly improve their level of learning and management development. Value in use includes important concepts such as experience (empathetic feelings of interaction for enjoyment), personalization and positive two-way interaction between the provider and recipient of business education services. Value in use of business education services evolves positively and negatively during the process of creating customer value. Value in use goes beyond providing excellent quality of services to service recipients. Given the importance of this topic, the present study was conducted on the topic of " Designing Value-In-Use Management Model in Business to Business Education Services Markets". Initially, the existing literature was reviewed from doctoral dissertations and reference books and from foreign articles. Using the literature, indicators were extracted, which were the introduction to creating an analytical model. Given that the author, while using Existing models have not been based on the use of known models; therefore, the content analysis method, which is a qualitative method, was used to produce the model. In the next stage, a questionnaire was used to measure the opinions of experts. In order to measure the validity of the model, a confirmatory factor analysis test was used to measure and confirm the relationship between the components of the model.

Objective: Smart information technologies are an important strategic asset for organizations that can be used to improve organizational performance and strategic competition. However, the adoption and dissemination of smart information technologies are complex processes that are influenced by several factors. The purpose of this research is to provide a model to investigate the role of information technology importer strategy on the communication chain of intelligent information technology and organizational learning.Methodology: The method of this research is applied in terms of purpose and survey in terms of data collection. The tool of data collection in this research was a questionnaire. For this purpose, 450 questionnaires were distributed among the employees of Zob Ahan Isfahan Company, of which 420 were returned. Structural equation modeling approach based on partial least squares has been used for data analysis.Findings: The findings showed that the strategy of introducing information technology directly leads to the strengthening of organizational learning and enables organizations to adapt to environmental changes and emerging technologies. However, the moderating role of media literacy in this process was not significant, which may be due to other factors such as the high level of media literacy of users or the greater influence of organizational and cultural variables.Conclusion: The results show that the adoption of intelligent information technology as a key factor in improving organizational performance is influenced by variables such as perceived usefulness and perceived ease of use. User acceptance as a fundamental variable plays an important role in the success of IT strategies.

This study investigates the impact of fin tech companies on the financial stability of banks in an emerging market context. Utilizing a sample of 26 banks over the period 2012-2022, the findings suggest that the growth and development of fin tech companies positively influence bank stability. This positive relationship is robust across various sub-sample analyses, considering factors such as bank size, ownership structure, and corporate governance. These findings contribute to a growing body of literature on the evolving relationship between traditional banking and fin tech. Further research is warranted to explore the specific mechanisms through which fin tech innovation enhances bank stability and to assess the potential for systemic risks arising from the rapid growth of the fin tech sector.

The aim of this research is to develop an intelligent strategic decision-making model based on customers' emotional and behavioral interactions in social networks. In this regard, the effect of the variable "empathy" as an emotional indicator on "purchase intention" has been examined and the mediating role of the variables "customer participatory behavior", "customer citizenship behavior" and "brand value" has been carefully analyzed. Also, the moderating role of "extra-social relationships" as an influential factor on the occurrence of participatory and citizenship behaviors has been evaluated. The statistical population of the research includes digital goods customers in social networks, which were considered unlimited and, using the Cochran formula, the sample size of 384 people was determined and collected using the available method. The research method is applied in terms of purpose and descriptive-survey in terms of data collection method. Data were collected through a questionnaire with a five-point Likert scale and analyzed using SPSS.V24 and Smart PLS.V3 software in two parts of descriptive and inferential statistics. The results of the study show that the variables of empathy on brand value with the mediating role of customer participation behavior (0.147), empathy on brand value with the mediating role of citizenship behavior (0.225), empathy on purchase intention with the mediating role of citizenship behavior (0.298), empathy on purchase intention with the mediating role of customer participation behavior (0.311), empathy on customer participation behavior with the moderating role of extra-social relationships (0.264), empathy on citizenship behavior with the moderating role of extra-social relationships (0.093), customer participation behavior with the mediating role of brand value (0.120), and citizenship behavior with the mediating role of brand value (0.251) have a significant effect among digital goods customers on social networks.

To effectively leverage artificial intelligence (AI), organizations need a comprehensive understanding of their AI maturity level. Assessing AI maturity can serve as the first step in developing a digital transformation roadmap and AI strategies. This study aims to present a comprehensive framework for evaluating organizational AI maturity. The research methodology employed is a systematic literature review. Through this process, after screening and assessing the quality of scientific articles and consulting reports, 31 selected documents were analyzed. The findings indicate that key dimensions of AI maturity include strategy and leadership, organization and culture, data, technology and infrastructure, operations, decision-making, ethics and regulations, security, and privacy. Additionally, it was found that existing maturity models primarily focus on technical and managerial aspects, with less attention given to social, legal, and ethical dimensions. By conducting a comparative analysis of various AI maturity models, this study proposes a comprehensive framework for assessing organizational readiness in adopting and developing AI, which can serve as a guideline for policymakers and organizational managers.

This research presents an approach based on customer lifetime value (CLV) and artificial neural networks (ANN) to classify bank corporate customers. CLV in banking means how much financial value each customer creates for the bank. The research is of an applied and quasi-experimental type and the statistical population includes 127,672 corporate IDs in the Tejarat Bank Corporate Customer System. Data are collected by scanning customer files over a six-month period. Sampling is by enumeration. K-means algorithms and artificial neural networks are used to cluster customers. Simulations showed that the artificial neural network algorithm provides more accurate results than the K-means algorithm. This approach can effectively classify customers into three clusters. The clusters were reviewed based on the opinions of banking experts; in order to conduct a deeper analysis. Based on the data of Tejarat Bank's corporate customers, these categories were analyzed in terms of CLV. Marketing and sales strategies were developed for each customer cluster. The approach proposed in this research can help banks improve their customer segmentation process and ultimately increase profitability and customer retention.