Iranian Journal of Information Processing & Management
Year:2023 | Volume:38 | Issue:2|Papers Count:12

The ethical significance of information technology due to its increasing intertwining with social life has led associations and organizations such as ACM and I-EEE to develop ethical principles and codes for the IT profession and to update them from time to time. Although these principles and codes are helpful as general ethical guidance, in some complex situations they lose their effectiveness due to the conflict that may occur between the principles and consequently the codes. In this paper, we will attempt to suggest a normative model to help ethical decision-making in such situations. This model can help to respond to problems of allowing evil and appealing to evil in IT profession. In addition to explaining and responding to some important ethical dilemmas in the field of applied ethics, we will apply it in at least six ethically important themes in IT profession, including self-driving cars, privacy and security, cyber-attacks, ransomware, and the Internet of Things. In each we will show how the model can help the moral agent in making moral decisions. However, we will not claim that this model acts as a decision-making logic, since the process of reaching a moral decision is not algorithmic and many cognitive and non-cognitive factors play a role in it.

Identification of hot topics in research areas has always been of interest. Making smart decisions about what is needed to be studied is always a fundamental factor for researchers and can be challenging for them. The goal of this study is to identify hot topics and thematic trend analysis of articles indexed in Scopus database in the field of Knowledge and Information Science (KIS), between 2010 and 2019, by Text Mining techniques. The population consists of 50995 articles published in 249 journals indexed in Scopus database in the field of KIS from 2010 to 2019. To identify thematic clusters, algorithms of Latent Dirichlet Allocation (LDA) technique were used and the data were analyzed using libraries in Python software. To do this, by implementing the word weighting algorithm, using the TF-IDF method, and weighting all of the words and forming a text matrix, the topics in the documents and the coefficients for assigning each document to each topic (Theta) were determined. The output of the LDA algorithm led to the identification of the optimal number of 260 topics. Each topic was labeled based on the words with the highest weight assigned to each topic and with considering of experts’ opinions. Then, Topic clustering, keywords and topics identification were done. By performing calculations with 95% confidence, 63 topics were selected from 260 main topics. By calculating the average theta in years, 24 topics with a positive trend or slope (hot topic) and 39 topics with a negative trend or negative slope (cold topic) were determined. According to the results, measurement studies, e-management/ e-marketing, content retrieval, data analysis and e-skills, are considered as hot topics and training, archive, knowledge management, organization and librarians' health, were identified as cold topics in the field of KIS, in the period 2010 to 2019. The analysis of the findings shows that due to the interest of the most researchers in the last 10 years in using of emerging technologies, technology-based topics have attracted them more. In contrast, basic issues are less considered to be developed

The development of innovation in the service sector is a complex process that is affected by various factors, and the service sector due to its commercial nature must be updated in its innovative processes. Since knowledge innovation is required, knowledge extraction is essentially required to achieve goals and innovation in the industry. Appropriate and timely knowledge leads the industry to increase its productivity and level of innovation. This article is intended to provide a model for knowledge extraction in the service industry. For this purpose, the grounded theory model has been used, which is applied in terms of purpose and qualitative research in terms of collecting information. The population includes experts and managers in the service industry and the sample is due to the theoretical saturation of 14 people who were selected in a purposive sampling. The data collection tool, in the qualitative section, was a semi-structured interview. To confirm the accuracy of the data, the validity of the study was used by the research members. In this study, to calculate the reliability, the method of intercoder agreement has been used. The percentage of thematic agreement confirms encodings with 80% reliability. ATLAS. TI software was used for coding. After analyzing the data, a knowledge extraction model was extracted for the service industry. Based on this, 122 concepts and 18 sub-categories have been categorized using the Strauss and Corbin paradigm model. These factors were divided into 18 categories in the common model of grounded theory, which are: causal conditions (infrastructure and technological factors, structural factors, human resources, organizational culture, support of senior managers), contextual conditions (organizational strategic knowledge, management Organizational communication, support for intelligent knowledge extraction system), strategic (knowledge commercialization strategies, creativity strategies, innovative strategies), interventionist conditions (personal characteristics, financial factors, managerial factors), consequences (value creation, advantage) Competitive, globalization, growth, and maturity). Therefore, according to the results of this study, vital resources in the service industry can be identified to extract knowledge in a timely and appropriate manner, and the knowledge obtained from this extraction can be used to create a competitive advantage in domestic and foreign markets.

The main object of this study is analyzing the components and indicators of the scholarly Publication System in the scholarly Publication databases in terms of access, communication, control, infrastructure, language, materials (information resources), support, technology, economics, evaluation, education, ethics and their characteristics. The research community has been identified by content analyzing and 73 databases were extracted, based on the frequency and approval of experts the sample limited to 12 scholarly publication databases. These include the ArXiv, DOAJ, Elsevier, Springer, Google Scholar, PubMed, Nature, Web of Science, Scopus, National Institutes of Health (NIH), SPARC (Scholarly Publishing and Academic Resource Coalition), and Amazon databases. The checklist designed to study these databases is taken from the scholarly Publication System dimensions. Research findings show that all components have been considered in publication databases and the difference between them returned in scholarly Publication indicators. however, according to the type, age and general policy of the databases, some indicators of components, Like education, economics, and information resources has not received enough attention, in all databases, some components such as technology, support and control and their indicators have an equal importance.

The aim of this study was to explain the application of text corpus tagging method in Sense disambiguation from specialized homographs and increasing the retrieval F-Measure of scientific texts containing such homographs. This is an experimental study. Specialized homographs were identified by direct observation and morphological analysis of the word. The research sample consisted of 442 scientific articles of two groups of experimental group and control group. The control group had 221 full-text articles without tags and the experimental group had same 221 tagged articles, which were tested in the information retrieval system to measure the effectiveness of tagging in word sense disambiguation from specialized homographs. The level of significance of the Wilcoxon signed-rank test showed that the F-Measure of retrieval results of specialized homographs after using the tagged specialized text corpus in the information retrieval system is significantly different than before. Examination of negative and positive rankings showed that the F-Measure of the results after using the tagged specialized text corpus has increased significantly and has reached its maximum level of 1. The findings of the present study showed that there is not necessarily an inverse relationship between recall and precision, and the two can reach their maximum level of 1. The better efficiency of the retrieval system using this approach is due to the empowerment of the retrieval system in distinguishing between specialized homographs and identifying their semantic roles by using semantic tags as training data that were considered in the test and training set. Embedding the training set in the structure of the retrieval system provides additional information to serve the retrieval system to distinguish between the various meanings of specialized homographs. This tool is one of the elements that causes the optimal quality of retrieval and leads the information retrieval system from word-driven retrieval to content-driven retrieval when retrieving texts containing specialized homographs.

Context-aware systems serve the user by providing information that aligns with the preferences and context of the user. This study aims to recognize the contexts related to the entities of electronic theses and dissertations. Research is of the qualitative type that has used the documentary research method to find concepts, make coding, and classify contexts. The contribution of this research in increasing knowledge is to recognize the contexts of the users, electronic theses, and dissertation "ETD" and the system entities. The findings showed that the contexts of users are classified into 10 categories: user identity, interests and preferences, activities and history, social communication, computer context, user locations, user times, user access level, user status, and physical environment. The contexts of ETD have 8 categories: ETD identity, access level, audiences, activities and events, ETD content, time, place, and physical format of ETD. System contexts were grouped into 8 categories: system characteristics, user interaction capabilities, indexing capability, systems communication, user’s data storage, computing context, activity, and location. Conducting this research gives developers and decision-makers of ETD systems knowledge of contexts to redesign the system by considering the capabilities of context-aware systems to respond to users' needs, recommend documents, retrieve user-oriented documents, and develop system compatibility procedures. In the present study, we have tried to recognize all the contexts in an ETD system, and not only the user contexts but also the ETD contexts and the system contexts have been considered. context-aware ETD developers need some of the recognized contexts based on the type of context-aware services

In recent years, organizations in the public and private sectors have been faced with large volumes of structured and unstructured data that require a big data governance framework. Big data governance, by using environment monitoring and data collection, data storage and data analysis, provides the information needed by organizational decision makers. Establishing a big data governance framework enables organizations in the public and private sectors to make better decisions based on evidence and insight. Therefore, the purpose of this study is to investigate the mentality of experts in the field of establishing big data governance and for this purpose, Q methodology has been used. The statistical population of the present study included experts of government organizations and based on purposive sampling, 38 experts were selected for the study. In the present study, the discourse atmosphere included authentic domestic and foreign books and articles and semi-structured interviews. The number of propositions that were identified in the discourse atmosphere included 52 propositions, and by modifying and removing duplicate propositions, 48 final propositions were identified. Then, each of the Q propositions was numbered and the experts were asked to sort the number of each Q proposition in the Q diagram. Exploratory factor analysis and correlation matrix used to analyze the resulting data from the discourse atmosphere. Cronbach's alpha test, KMO index and Bartlett test used to evaluate the validity and reliability of the research method. The results of the present study showed three types of mentality that the total amount of variance explained was equal to 80. 81%. The percentage of explained variance was 30. 59% for the first type, 26. 31% for the second type, and 23. 90% for the third type. Evaluation of propositions related to the mentality of expert’s shows that most experts emphasize the results of establishing big data governance, They emphasize such things as facilitating knowledge flow, efficient and effective decision making, innovative performance, strengthening teamwork, and strategic planning and analysis. Experts, on the other hand, focus on drivers such as information technology, data control and oversight, structural mechanisms, democratization capacity, and legal capacity. In general, to take advantage of big data governance in public organizations and reduce the data gap, there must be coordination and consistency between the propositions and the results of establishing big data governance, and this research can serve as a stepping stone to this.

The supply chain for perishable products, especially agricultural goods, has always been one of the most important and challenging management issues at different times. Because, at all stages of the agricultural production process, unsafe and unsanitary factors may endanger the health of agricultural products. In addition, one of the key problems of the agricultural supply chain is the high volume of products wasted throughout the whole supply chain. For example, in Iran, as a developing country, about 30% of all agricultural products are wasted annually. The high volume of agricultural waste is especially important concerning wheat products as a political and strategic product. The results of some studies show that a large amount of wheat waste in developing countries is due to the widespread use of traditional methods in the storage process. Therefore, the application of emerging technologies such as the Internet of Things (IoT) can be an effective solution to this kind of problem. However, there are shortcomings in the IoT deployment in the supply chain, especially in the logistics sector, and researchers need to cover theoretical gaps in this area through modeling and optimization. Therefore, the present study intends to emphasize this important research issue for the first time in Iran using the Action Design Research approach. The most important findings of this research include the conceptual data model, the logical model of the database, and the physical data model for the IoT deployment in the storage section of the wheat supply chain, which has been designed and validated with the participation of industry and software engineering experts. The models designed in this research can be useful for the implementation of IoT technology in wheat storage centers and food factories. The findings of this study can provide a good guideline for officials and decision-makers to deploying IoT in the field of wheat storage.

The accumulated volume of customer information due to the growth and development of information technology and the creation of databases has led companies that want to provide better services to their customers to benefit from new tools for customer relationship. One of these tools and methods is data mining techniques that can play an important and key role in customer relationship management. The purpose of this study is to analyze customer value with a combined data mining approach based on the WRFM model. So 64858 samples from customer database in the period 2019-2020 have been selected by available purposive sampling method. The weight of WRFM attributes has been determined by surveying 3 experts of the company using a hierarchical analysis process. Based on the initial variables of the research and the variables obtained from the attributes of the WRFM model, the purchase value of customers has been analyzed. SPSS Modeller and SPSS software were used to analyze the data. The results show that the K-Means clustering method has a better performance in customer segmentation than the TwoStep clustering and the Cohonen neural network methods. Finally, based on the criteria of purity percentage, repetition, error rate and Normalized Mutual Information (NMI (index, six clusters with NMI (0. 631) were selected from different K-Means clustering. This study introduces the WRFM model for customer value analysis. The weight of the attributes of this model is based on a survey of experts and using a hierarchical analysis process based on the degree of incompatibility (0. 052) obtained from the hierarchical analysis method (0. 15), (0. 29) and (0. 56), respectively, have been determined that these values ​​indicate the greater importance of the monetary value index than the other two indices, Finally, these six clusters were divided into 4 general categories using naming market segments methods in research (Chang and Tsai 2004, Babaian and Sarfarazi 2019): key and special customers, golden potential customers, missing uncertain customers and new uncertain customers. According to the research model, the company should focus more on its specific and key customers, ie customers who are in the first, third and fifth clusters, ie loyal customers who have higher than average values in the two attributes of monetary value and frequency and recently they have had purchases with a high value of Rials that the company should consider effective marketing strategies for this group of customers due to its limited resources in order to lead to more profitability for the company while maintaining customer relationship management.

Recognizing causal elements and causal relations in the text is among the challenging issues in natural language processing (NLP), specifically in low-resource languages such as Persian. In this research, we prepare a causality human-annotated corpus for the Persian language. This corpus consists of 4446 sentences and 5128 causal relations. Three labels of Cause, Effect, and Causal mark are specified to each relation, if possible. We used this corpus to train a system for detecting causal elements’ boundaries. Also, we present a causality detection benchmark for three machine-learning methods and two deep learning systems based on this corpus. Performance evaluations indicate that our best total result is obtained through the CRF classifier, which provides an F-measure of 0. 76. In addition, the best accuracy (91. 4%) is obtained through the BiLSTM-CRF deep learning method

The aim of this study was to investigate the image retrieval from selected search engines according to the written and semantic features of Persian language and determine their relevance using recall and precision formulas and to identify the most efficient search engine in retrieving images in Persian and by survey-analytical method. It was done using direct observation technique. After reviewing related researches, search keywords list was formed in the form of a checklist based on the written and semantic features of Persian language. Each of these keywords in the studied search engines, including two general search engines Google and Bing and Duckduckgo semantic search engine, which are among the most used search engines and have also provided the ability to search for images in Persian, search and the number of relevant and unrelated retrieved results were recorded. Then, the recall and precision of search results in each search engine were calculated and the relevance of images based on these features in each of the studied search engines was investigated. A variety of descriptive statistical techniques were applied to analyze the data along with Kolmogorov-Smirnov, Shapiro-Wilk, Kruskal-Wallis and Friedman tests. Findings demonstrated that Google, Bing and Duckduckgo search engines do not pay enough attention to the written and semantic features of Persian language and many of these features are ignored while searching and retrieving images. In the present study, Google search engine had a higher recall and precision than the other two search engines, and despite the claim of semantic search engines to provide better and more relevant information than other search engines, Duckduckgo search engine did not show good performance in retrieving images related to the written and semantic part of Persian language. There is also a significant difference between the recall and the precision of the three studied search engines.

Despite the novelty in methodologies, User behavior study based on brain activity during information-seeking stages has become popular among information science researchers. This paper reviews scientific publications in which information-seeking behavior has been studied along with recorded brain activity to shed light on research status, challenges, and suggestions for future studies. Based on Kitchenham & Charters (2007) framework, a complete web search was performed in English and Persian scientific databases, and 22 publications in English were found as the final result, from 2007 to 2020. Review results demonstrate that exploring the user status (10 papers) and brain wave activity during information-seeking episodes (12 papers) were the most dominant subjective approaches in the field of user behavior studies. Cognitive load was found as an effective cognitive component on user status. With eye movement measurement and brain waves frequency study, 3 factors were found effective on cognitive load level generated during information searching and processing: searching media type, information representation, and text reading style. Brain wave activity and pupil dilation analysis were the most important measures in user status during search stages, and alpha and theta band waves were demonstrated as an index for cognitive load measurement during the information searching process. A correlation among eye data, search behavior, task complexity based on user experience, and cognitive style – as another effective factor on user status-led to results in different information searching behavior demonstrations. Also, 3 main stages were analyzed in the information-seeking process, based on brain wave activity: information exploring and query formulation, query reformulation and selection, relevance judgment, and decision making. Results showed a difference between brain activity areas, and differences in pupil dilation change level and alpha/beta frequency level during different search episodes. For future research, some suggestions were offered based on reviews. Finding relations between correlations among cognitive styles, task features, and domain knowledge during information searching process, personalized information retrieval improvement, more collaboration between information science and neurocognitive specialists, research in more user affective status like aggression and fatigue during the search process, using more economic methods and portable devices aiming to reduce research costs and expenses, facilitating larger sample studies and designing standard tasks were considered as a suggestion. Finally, some challenges were found based on reviewed studies. Some concepts like relevance feedback in information retrieval need more investigation. Also, it is necessary to investigate and explore user affections during the search process with multiple approaches.