نشریه اکتشاف و پردازش هوشمند دانش
پیاپی 7 (زمستان 1401)

In an increasingly uncertain environment, creativity is the right way to survive and remain competitive in domestic and international markets. In order to achieve this goal, managers should invest in new technologies and move towards integrated information systems. Therefore, the aim of the present study is to investigate the effect of human resources information systems on the ability of individual innovation by mediating the effect of organizational commitment. This study is applied in terms of purpose and survey-analytical in terms of method. The statistical population of this research is employees, senior managers, executive and middle managers in Torbat Heydarieh Oil Company. According to Morgan's table, for a community with a size of 384, the statistical sample size was determined to be 190 people. The data collection tool was a standard questionnaire that was distributed after checking its validity and reliability. Data analysis using S. P. S. S. Version number 26 and SmartPLS version number 3 were done. The results of the analyzes showed that human resource information systems have a positive and significant effect on individual innovation both directly and through the mediation of organizational commitment. In addition, the positive and significant effect of human resource information systems on organizational commitment and organizational commitment on individual innovation was confirmed.

In today's competitive age, customers have been the focus of companies' attention, and persuading them has been a competitive advantage. In meeting the needs of medical equipment, considering the expansion of private sector competition, organizational ethics is considered a competitive factor by affecting the emotions and feelings of customers. The present study in terms of purpose-based classification and applied in terms of descriptive-correlation research. The statistical population of the study was all customers and beneficiaries of the products and services of companies producing orthopedic and spinal implants. The sample size was determined based on Morgan's table and the subjects were selected by stratified method and in each stratum by simple random method The validity of the research instrument has been calculated by formal validation, content analysis and factor analysis and its reliability through Cronbach's alpha. Research Tools, Service Brand Dimensions Questionnaire, with a combination of Service Brand Judgment Models (SBV), Grace and Aks (2005) and Service Brand Dimensions (SBL), derived from the research of Grissogo and Gristalis (2014), Delgado Ballster Brand Confidence Questionnaire (BTS et al. (2003), the brand value questionnaire derived from the models of Yu et al. (2000) and Dagger et al. (2007) and the perceived image questionnaire derived from the Yang model (2007), by the five-choice Likert scale method. In order to analyze the data, structural equation modeling was used using Smart PLS software. The results showed that brand trust had an indirect indirect effect on brand value due to the dimensions of the service brand, but due to the dimensions of the service brand on the perceived image, it played the role of full mediation.

A self-adaptive system is capable of maintaining its quality requirements in the presence of dynamic environment changes.To develop a self-adaptive system,, engineers have to create self-adaptation logic that encodes when and how the system should adapt itself. However, developing self-adaptation logic may be difficult due to design time uncertainty; e.g., anticipating all potential environment changes at design time is in most cases infeasible. Online reinforcement learning addresses design time uncertainty by learning the effectiveness of adaptation actions through interactions with the system’s environment at run time, thereby automating the development of self-adaptation logic. Existing online reinforcement learning approaches for self-adaptive systems exhibit two shortcomings that limit the degree of automation: they require manually fine-tuning the exploration rate and may require manually quantizing environment states to foster scalability. We introduce an approach to automate the aforementioned manual activities by employing policy-based reinforcement learning as a fundamentally different type of reinforcement learning. We demonstrate the feasibility and applicability of our approach using self-adaptive system exemplar.We introduced and experimentally evaluated an online reinforcement learning approach to facilitate engineering of self-adaptive information systems. Our approach contributes to system engineering by increasing the degree of automation. our approach does neither require manually quantizing environment states nor manually having to determine suitable exploration parameters for the reinforcement learning algorithm to work.

the present study aims to identify and evaluate the indices of competition in the free trade market for products of saipa company .in the present study , the statistical population of this study includes experts and personnel working in saipa company and agency . there are 700 agencies , depending on the city and the number of agency sales , the number of personnel will be differen according to morgan table, the sample size is 130 and the questionnaire is distributed randomly among them . so that the shape and content structure of it is set as the five options of likert spectrum . to describe the data and observations , the cronbach 's alpha test for reliability of the questionnaire will be used . the validity of the questionnaire was confirmed by the professors and related experts ( content validity ) . in this regard , based on the literature of research , a model is designed to show the impact of competition factors on the free trade market and smart pls software . the results showed that in the present study , the competitiveness index of saipa company in the free trade market has a positive impact and all assumptions are correct.

One of the competitive advantages, which most enterprises have disregarded, is intangible capital. The objective of the present survey is to determine the relation between the intellectual capital, social capital and function of enterprises. The individuals of the sampling of this survey were 134 enterprises located in Semnan’s Industrial Town. The data were gathered with a help of a questionnaire whose validity and reliability were examined. The hypotheses were surveyed by structural equation modeling. According to the results received in this survey, intellectual capital has a direct, positive and significant effect on the employee performance. On the other hand, social capital had not significant impact on employee performance. The participants with the least questionnaire mark were choose for in-depth semi-structured interviews. Interviews with 18 number of managers analyzed by content method. The factors distinguished and categorized in three type, infractin, manager power and efficiency. On the other hand, social capital had not significant impact on employee performance. The participants with the least questionnaire mark were choose for in-depth semi-structured interviews. Interviews with 18 number of managers analyzed by content method. The factors distinguished and categorized in three type, infractin, manager power and efficiency. The factors distinguished and categorized in three type, infractin, manager power and efficiency.

Nowadays due to the more customer-oriented era and increasing competition and advancing technologies, direct marketing becoming one of the most commonly-used marketing approaches worldwide. Many businesses, such as banks, apply direct marketing methods to reach more positive responses from their customers in order to minimize the campaigning cost and maximize the return on investment. To achieve this goal, banking and finance have to determine the target customer group to promote the bank product and services for them. It means that they need to know their customer attitude and characteristic individually to predict their needs and their reactions to product promotion. A huge amount of customer data has been stored in bank databases. This data as a rich resource can be analyzed to determine the most appropriate product to offer to each customer through the most effective channel. Since manually analyzing this data is not effortlessly feasible, this task should be executed automatically. In this work, a predictive model which is appropriate for bank product offerings was designed and built, which firstly classifies the customers to decide if they are interested in the product offering, and then clusters them for product and channel suggestions. This article combines data mining models with practical problems of the banking industry, and establishes a bank predictive response model and customer targeting, through Random Forest, Naive Bayes, and Neural network machine learning algorithms to classify customers and proposes corresponding suggestions for bank marketing based on the best classification in terms of accuracy and sensitivity of the result. The extracted classification rules and patterns can effectively help banks to divide customer groups with K-means clustering and take targeted measures to improve marketing efficiency.