فصلنامه مطالعات مدیریت کسب و کار هوشمند
پیاپی 47 (بهار 1403)

In recent years, the rapid growth of virtual space has made people devote more of their time in virtual space, especially to social networks, which can be attributed to the remarkable features of virtual space; including increasing the speed of information exchange, easy and free access to information and variety of knowledge topics. In this regard, the opinions recorded by users in virtual networks have grown day by day and have become very important, and extracting the opinions and feelings of users' opinions for more informed decision-making is of great help to businesses, on the other hand, virtual reality technology in the past few decades It has undergone technical changes and improved immersion and the feeling of remote presence; This technology is used in various fields such as education, tourism, health, sports, entertainment, architecture and construction, etc. The increasing progress of virtual reality technology has caused many businesses to operate in this field, but due to changes Continuous market and the need for timely information, companies should use differentiation and growth strategies, in this regard, they need to ask users' opinions and in line with that, try to grow and improve their business, considering that Users' comments are textual, and reading and summarizing them is time-consuming and difficult. Based on this, the aim of the current research was to categorize comments related to virtual reality technology using machine learning methods and a dictionary-based approach. Therefore, about one million tweets in the field of virtual reality technology were collected by the web crawler, and after data preprocessing, 480,432 samples remained in the data, then Dirichlet's hidden allocation topic modeling was implemented on the data. This modeling separated different topics by examining the distribution of words in tweets; The tweets whose distribution of words were similar were placed into a topic and the number of topics with the highest coherence score was selected, the number of topics 9 had higher coherence and the data were grouped into 9 topics, so once again the Dirichlet hidden allocation modeling was set to 9. The topic was done, with this the tweets were grouped into 9 different topics. To evaluate the model, considering that we had a probability distribution, the confusion criterion was used, the value of which was -9.44, and the coherence score was used for the degree of semantic similarity between words and the distinction between subjects, and the result was 0.47. The lower the confusion criterion and the higher the coherence score, the more efficient the model is. With the help of keyword weights obtained by Dirichlet hidden allocation modeling and examining at least 5 different tweets from each topic, 9 topics related to virtual reality technology were identified: "New Technology", "Creation and Make", "Technological Business", "Education", "Virtual Games", "Progress", "Gadget", "Metaverse", and "Indiegame", the topics were analyzed with the help of several graphs. We found that the number of neutral comments on topics such as "New Technology" and "Metaverse" is more than positive and negative comments, which indicates the lack of sufficient information or the lack of use of these technologies, and it is necessary for businesses in this field, to try more in this regard, in the same way, if we observe the graph of "Virtual Games" and "Technological Business", we can see that it changes almost with the same ratio in different years, in the sense that this The two graphs are related, in fact, businesses should keep in mind that the factors affecting these two issues are the same, but users pay more attention to the issue of "Virtual Games", as a result, if the creators of "Technological Business" Focus specifically on "Virtual Games", they will grow more due to the more attention of users, also the creators of games should consider that "Virtual Games" are a topic of more attention than "Indiegame". Is. In the subjects of "Education" and "Gadget", users lost their attention to these subjects in the field of virtual reality over time, in fact they showed their attention to other subjects, so it is better for businesses that operate in this field to take measures To advertise and attract users or change their user area if there is no growth.

Constant changes in the market and the need for timely information force companies to use differentiation and growth strategies appropriate to the needs of customers. (Sánchez, Folgado-Fernández, & Sánchez, 2022). Companies can check and analyze their customers' opinions through microblogging sites (Facebook, Twitter, etc.) and finally improve the desired products or services (Ahmad, Aftab, Bashir, & Hameed, 2018). Today, users express their opinions and feelings and review products in online social networks. Therefore, user comments and the analysis of these comments have become a valuable resource for businesses (Kim et al., 2015; Loureiro et al., 2019).
Virtual reality and augmented reality have undergone technical developments in the past few decades and have improved immersion and the feeling of remote presence. Several examples of applications of such techniques can be found in stores, the tourism industry, hotels, restaurants, etc. (Loureiro, Guerreiro, & Ali, 2020). Due to the constant changes in the market and the need for timely information, companies should use differentiation and growth strategies, nowadays, due to the rapid evolution of the Internet, instead of collecting their opinions through time-consuming and expensive methods such as questionnaires and interviews, etc., they express in the context of social networks, which is very useful for businesses in their development, and they can measure the feelings of customers towards products and services, and understand the needs of users, and finally make appropriate and appropriate decisions in the direction of adopt growth, but in order to use the produced content correctly, text mining and sentiment analysis techniques should be used, which has not been researched in Iran so far. Analysis of users' opinions and feelings about virtual reality technology can help businesses that operate in the field of metaverse, virtual game production, virtual education, virtual tourism, etc., to make better decisions and plans.

Social media generates a large amount of real-time social signals that can provide new insights into human behavior and emotions. People around the world are constantly engaged with social media. (Al-Samarraie, Sarsam, & Alzahrani, 2023).
On the other hand, the amount of data is increasing day by day. Almost all institutions, organizations and business industries store their data electronically. A huge amount of text is circulating on the Internet in the form of digital libraries, repositories, and other textual information such as blogs, social media networks, and emails (Sagayam, Srinivasan, & Roshni, 2012).
Topic modeling is one of the most powerful techniques in text mining for data mining, discovering hidden data and finding relationships between data and textual documents (Jelodar et al., 2017).
The technological advances of the last century have confronted societies with new realities that have indisputably improved daily life, making it more convenient and interesting. In recent decades, technology using virtual reality and wearable devices have had a significant impact in the fields of education, tourism, health, sports, entertainment, architecture and construction, etc. (Kosti et al., 2023).
Virtual reality is a technology that allows a user to interact with a computer-simulated environment, whether that environment is a simulation of the real world or an imaginary one. With virtual reality, we can experience the most frightening and overwhelming situations with safe play and a learning perspective (Mandal, 2013). Most people are curious about the possibilities and future of new technologies, considering the various applications it is supposed to offer such as virtual meetings, learning environments and many others, however, there are also concerns about potential negative effects. because real world signals can be transmitted in the virtual world. In this regard, people express their feelings in different social networks (Bhattacharyya et al., 2023).

According to the main goal of the research, which is to classify comments related to virtual reality technology using machine learning methods and a dictionary-based approach, therefore, about one million tweets in the field of virtual reality technology were collected by the web crawler and After data preprocessing, 480,432 samples remained in the data, then Dirichlet hidden allocation thematic modeling was implemented on the data. By examining the distribution of words in tweets, this modeling tries to separate different topics by detecting the distribution of words; The tweets whose distribution of words are similar were put into a topic, and the number of topics with the highest score was selected, the number of topics 9 has higher coherence, and the data was grouped into 9 topics, so once again, Dirichlet hidden allocation modeling was applied 9 topics were done, whereby the tweets were grouped into 9 different topics. Considering that we have a probability distribution, the confusion criterion was used to evaluate the model. The lower the confusion criterion and the higher the coherence score, the more efficient the model is. With the help of keyword weights obtained by Dirichlet hidden allocation modeling and examining at least 5 different tweets from each topic, 9 topics related to virtual reality technology were identified: "New Technologies", "Creation and Make", "Technological Business", "Education", "Virtual Games", "Progress", "Gadget", "Metaverse" and "Indiegame" were named.

In this research, by examining topics in different years, we observed that the topic of "Progress" was the most popular topic among users from 2017 to the end of 2021, in early 2022, this topic gave way to "Metaverse", currently "Metaverse" is one of the most popular topics being discussed by users. Businesses in the field of virtual reality should strive for the attractiveness of "Metaverse" and attract users. Likewise, if we observe the "Virtual Games" and "Technological Business" graphs, we can see that they change with almost the same ratio in different years, meaning that these graphs are related to each other, in fact, business and keep in mind that the factors affecting these two issues are the same, but in the case of "Virtual Games" it has more effects, and if "Technological Businesses" specifically focus on virtual games, they will grow more due to the greater attention of users. had Similarly, "Indiegame" which have had a series of changes but in recent years have had a declining trend and then no change, now the creators of these games should check, and in general "Virtual Games" are a more interesting topic than "Indiegame". In the subjects of "Education" and "Gadget" it has been decreasing since the beginning of 2017, which shows that users lost their attention to these subjects in the field of virtual reality over time, in fact to other topics showed their attention, so it is better for businesses that are active in this field to take measures to advertise and attract users, or change their user field if there is no growth.

Smart technologies have brought changes in the supply chain. This study was conducted with the aim of investigating the impact of the Internet of Things on the intelligent management of the supply chain, which evaluates the relationships between variables and their impact and effectiveness with the fuzzy cognitive mapping method. The statistical population is academic experts and active experts in the drug distribution company in Bushehr province. After identifying the components from the background of the research, an interview was conducted. Then the questionnaire was presented to 10 experts and experts and it was analyzed in several stages, and finally, the main factors of the use of Internet of Things in the supply chain were determined in 9 categories of criteria and 41 sub-criteria. The criteria include: intelligent management of inventory and warehousing, intelligent management of operations, intelligent management of information, intelligent management of products, intelligent management of costs, intelligent management of corporate productivity, intelligent management of customers and drug suppliers, intelligent management of sales and marketing, and intelligent management of the environment.The results showed that intelligent information management was obtained as the most important indicator; Because it affects all indicators. intelligent management of customers, intelligent management of sales and marketing, and intelligent management of operations are the second most influential. Therefore, managers of the drug distribution industry should use Internet of Things technology to intelligently manage information in their organization, improve relationships with customers, improve operations and focus on the sales process, and optimize supply chain processes and profitability.

Artificial intelligence provides unique opportunities to improve the performance of various industries, including the automotive industry. The present study seeks to identify the applications and requirements of using artificial intelligence in new automotive products such as self-driving cars by obtaining opinions from managers and employees of domestic automotive companies through semi-structured interviews and thematic analysis. The interviewees included 11 managers and 17 employees, of which 15 had a bachelor's degree, 11 had a master's degree, and 2 had a doctorate degree. 21 codes were identified in the applications section and 26 codes were identified in the requirements section. After conducting 28 interviews, theoretical saturation was achieved. From the codes identified in the applications section, self-driving cars and voice assistants, shared transportation, and resource allocation, expert staff, and team formation can be mentioned in the requirements section. Considering the variety of artificial intelligence applications in new car products and according to the specified requirements according to the opinions of experts, the development of a suitable platform for hard and soft technologies in an integrated manner; And government support regarding the creation of legal infrastructure can improve the development path of the current technology. Of course, in order to create a context for the successful operation of artificial intelligence in the automotive industry, all the effects of its application from different cultural and social aspects should be considered with a systematic perspective.

Artificial intelligence has enormous potential to reduce the problems of automakers around the world. Nevertheless, reports show that between 2017 and 2019, the number of automobile manufacturers that consciously refrained from using artificial intelligence and related technologies such as machine learning and neural networks in the production and supply of new products such as connected and autonomous cars have done so; it has only increased from 26% to 39% (Gandhi et al., 2022).
The lack of attention to the complexities of artificial intelligence and the acceleration of the use of this technological tool have caused the failure of automobile manufacturers' plans to provide intelligent products (Fernandes et al., 2022). Despite the applications and benefits of artificial intelligence in automotive services, there are still many ambiguous aspects regarding the use cases and prerequisites that different researches have addressed from a specific perspective, and the lack of a framework consistency in this area is felt. For example, Gupta and colleagues (2021) argue in their research that cars equipped with artificial intelligence technology are not capable of evaluating and classifying their environment on their own.
The present study aims to identify applications and requirements related to the use of artificial intelligence in new automotive products, such as self-driving cars. Therefore, the results of this study can be useful to automobile manufacturers trying to revitalize the potential and improve their products in the field of using artificial intelligence.

In this regard, in order to achieve the objectives of the research, a fundamental question is posed:“What are the requirements and prerequisites for using artificial intelligence in the delivery of new products such as autonomous and connected cars"?

The applications of artificial intelligence in automotive products can be divided into two categories: personal applications and social applications. Personal applications refer to products designed with two elements of security and convenience for users in mind. These applications include cruise control, automatic parking, voice assistant, alert systems, and route suggestion systems, all of which manifest in self-driving cars (Paliotto et al., 2022). Social applications refer to products whose effects include all members of society. For example, self-driving cars and cars equipped with artificial intelligence will reduce urban congestion or reduce the need for parking. These cars also play an effective role in transporting disabled and vulnerable people. Other social applications include the role of these cars in reducing environmental pollution and shared transportation (Zhang et al).
Regarding the requirements and prerequisites for the use of artificial intelligence in modern automotive products, various researches have been carried out, among which we will cite only a few examples below:- Barzegar and Elham (2019), using a descriptive-analytical approach, the criminal liability of the user of self-driving cars in accidents was discussed.
- Demlehner et al. (2021) conducted a study to identify 20 applications of artificial intelligence in the production of intelligent and autonomous cars and to examine these applications from the two dimensions of business value and realizability.
- Othman (2022) studied the requirements for the use of artificial intelligence in automotive products, such as cruise control, warning systems and self-driving cars, and studied its consequences from the point of view security, the economy and society, etc.

This research is”an applied research”in terms of purpose and a descriptive survey in terms of data collection. The information collection method is a survey and semi-structured interview with experts. The experts include two categories of managers and senior employees from the research and development department of interior automakers who have more than five years of work experience and are familiar with artificial intelligence. In order to collect samples, semi-structured interviews were conducted with the target people in person or in person using the snowball method.
The method of data analysis in this research is thematic analysis; so, after implementing the text of the interviews and analyzing and coding it with the thematic analysis method, 21 codes were identified in the applications section and 26 codes were identified in the requirements section. After carrying out 28 interviews, theoretical saturation was reached. From the codes identified in the applications section we can refer to self-driving cars, voice assistant, and in the requirements section we can refer to resource allocation, specialized personnel.

The main goal of this research was to identify the applications and requirements related to the use of artificial intelligence in new car products, such as self-driving cars. According to the review and analysis of the interviews with the thematic analysis method, the research results were determined into two groups:In the first group, applications of artificial intelligence in new products of automobile manufacturers were identified, such as self-driving cars, cruise control and warning systems, among which, according to the interviews, self-driving cars were the most important. Therefore, in this research, emphasis was placed on identifying key applications, which were separated into two dimensions: personal and social applications; In this regard, a total of 21 applications were identified.
In the second group, the requirements and prerequisites of artificial intelligence were classified, and due to the dispersion of results in previous research, a great effort was made to integrate the requirements. In this regard, the requirements of artificial intelligence are divided into six general categories, which are: 1- road infrastructure, 2- technical infrastructure and equipment, 3- knowledge, 4- users, 5- the role of managers, 6- culture, Rules. Therefore, as far as possible, in this category, fundamental requirements such as society, individual, technology and knowledge have been taken into account.
In short, taking into account the diversity of applications of artificial intelligence in modern automotive products, it can be concluded that, according to the established requirements and opinions of experts, the development of a suitable and integrated platform of hard technologies and soft law requires serious support from the government and attention to the creation of legal infrastructure. Therefore, we suggest that policy makers and managers of the automobile industry, in order to facilitate the technological development and optimal use, and successful application of artificial intelligence in the automobile industry, should all first systematize their point of view, and pay particular attention to the necessary infrastructure and consider different dimensions such as technical, cultural, social, etc.

The convergence of information technology, media and communication has changed consumer behavior in terms of searching, obtaining, processing and responding to company information or services. A company's ability to plan, implement and manage digital marketing to increase its competitiveness in the eyes of consumers is called digital marketing capability. The purpose of this research is to design a model for improving marketing capabilities by emphasizing the indicators of using digital marketing in industrial companies. This research is a type of mixed research with a qualitative and quantitative approach, which is a survey study in terms of its purpose, application, and in terms of data collection. The statistical population of the research was managers and experts in the field of digital marketing of industrial companies and university professors who were selected using the snowball sampling method. In the qualitative part, the data collection tool was an interview, and in the quantitative part, a questionnaire was used to identify the categories, and a semi-structured interview was used, and a questionnaire was used to validate the model. In the qualitative part of the data analysis method, the Grounded theory approach was based on the Strauss and Corbin method, which was compiled using MAXQDA software and using the coding method, and in the quantitative part, the analysis method was based on Kendall's correlation test.

The availability of digital technologies for a growing number of companies offers new opportunities in terms of market and consumer research and analysis, as well as communicating with customers throughout the consumer life cycle and building brand awareness and loyalty. On the other hand, changes in consumer preferences and lifestyles, including the increase in time spent by consumers worldwide on digital media and their expectation of a highly personalized approach, make manufacturers' shift to digital tools a necessary condition for survival.
 Digital marketing strategies have been studied, however, research focused on the understanding and application of digital marketing usage indicators in digital marketing has not been analyzed and the novelty of the current study is that despite the exponential development of digital technologies and its emerging application in Unlike marketing, none of the previous studies have addressed the indicators of using digital marketing.
 The purpose of this study is to identify the factors influencing the improvement of digital marketing capability and to analyze a company's digital marketing usage index (DMUI) and to plan strategies derived from these indicators, as well as to identify the motivating, contextual and intervening factors to improve the digital marketing capability of industrial companies.

The term digital marketing refers to almost all marketing activities that take place online. It is a collective term that includes all digital communication and advertising channels that businesses can use to communicate with existing and potential customers (Alexander, 2017)
 A company's ability to plan, implement and manage digital marketing is known as its digital marketing capability. It refers to a company's ability to use the Internet and other information technologies to facilitate deep customer interactions. Through these interactions, customers have access to the company's resources and information, and the company learns more about its customers. The processes, structures and skills that a company needs to succeed in the digital age are also defined as digital marketing capabilities (Chaffey, 2016).
 Digital transformation is a process of change that leverages technology and digital capabilities to create added value through business models, operational processes and customer experiences (Markanian, 2020). Therefore, digital transformation aims to improve entities by making significant changes in their characteristics through a combination of It is from information technology, computing, communication and connection (Viyal, 2019). Innovation Ecosystem Readiness is a measure of ecosystem readiness to accept innovation. Ecosystem interactions affect the adoption rate of organizational innovations (Wang, 2020).
Adoption of digital marketing: shows the extent of use of digital marketing technology in the organization. Companies that are able to use digital marketing technology effectively tend to have higher levels of digital marketing capabilities (Wang, 2020).

This research is a type of mixed exploratory research with a qualitative and quantitative approach, which is practical in terms of its goal. The method of data collection is, in the qualitative part, interviews, review of library documents, articles, and in the quantitative part, a questionnaire (survey). The statistical population of the research was managers and experts in the field of digital marketing of industrial companies and university professors who were selected using the snowball sampling method. In the qualitative part, the data collection tool was an interview, and in the quantitative part, a questionnaire was used to identify the categories, and a semi-structured interview was used, and a questionnaire was used to validate the model. In the qualitative part of the data analysis method, the grounded theory approach was based on the Strauss and Corbin method, which was compiled using MAXQDA software and using the coding method, and in the quantitative part, the analysis method was based on Kendall's correlation test.

 In this research, in order to meaningfully interpret the effective factors in improving digital marketing capabilities, personal views and personal experiences of experts, senior marketing managers in the digital field of industrial companies and university professors have been examined. Data collection was done through in-depth and semi-structured interviews with 18 people from the mentioned statistical community. It should be noted that the interview with the 13th person led to theoretical saturation and after that almost all the information and data were repeated, but for more certainty and the possibility of obtaining new data, we continued the interview until the 18th person. The interviews started in a semi-structured way by asking questions about the effective factors in improving the digital marketing capability, and the subsequent questions were designed based on the answers of the interviewees during the interview session, although certain frameworks were considered before the interview. The interview lasted approximately 40 minutes to an hour. The method of sampling in this research is judgmental (theoretical) and the interviewees were selected randomly during the research.

The results of the research showed that management factors in industrial companies can influence the promotion of digital marketing capability. The knowledge and expertise of the manager about the up-to-date science of marketing, the manager's belief in customer orientation, good thinking and risk-taking, creativity, management's confidence in the existence of expert human resources, financial and time resources for electronic marketing, management's enthusiastic desire to use existing and up-to-date technologies, use And having successful and related experiences in this field and ensuring the intention and decision of the management to invest in the development of digital marketing, can be considered as very important factors in the field of management. The company's strategies in terms of being customer-oriented, having clear visions for digital marketing and using communication and information technologies are very important for development in this field.
 Although a company's digital marketing capabilities can be achieved through one of the channels of digital marketing adoption, digital transformation, or innovation ecosystem readiness, digital marketing is about more than technology adoption. It is also about strategies for integrating technology into business processes. Digital transformation is the main driver of increasing digital marketing capabilities. Companies can enhance the role of managerial innovation, organizational readiness and perceived usefulness to improve their innovation ecosystem readiness. In addition, businesses must master changing and re-engineering new business models to accomplish digital transformation. Finally, in addition to implementing digital marketing through websites, social media, mobile marketing, and content marketing, the company should emphasize the importance of digital analytics, digital CRM, digital advertising, and display advertising.
Although a company's digital marketing capabilities can be achieved through one of the channels of digital marketing adoption, digital transformation, or innovation ecosystem readiness, digital marketing is about more than technology adoption. It is also about strategies for integrating technology into business processes. Digital transformation is the main driver of increasing digital marketing capabilities. Companies can enhance the role of managerial innovation, organizational readiness and perceived usefulness to improve their innovation ecosystem readiness. In addition, businesses must master changing and re-engineering new business models to accomplish digital transformation. Finally, in addition to implementing digital marketing through websites, social media, mobile marketing, and content marketing, the company should emphasize the importance of digital analytics, digital CRM, digital advertising, and display advertising.

In the digital age, blockchain technology is recognized as an operational innovation that is rapidly joining the field of supply chain and humanitarian logistics. Hence, blockchain technology has the potential to fundamentally change the field of humanitarian aid, but still relatively little research has been published aimed at improving understanding of the various barriers to blockchain adoption in humanitarian logistics. The aim of this research is to provide an integrated framework for evaluating the barriers to blockchain adoption in the field of humanitarian logistics. To assess the barriers, integrated approach has been applied in three phases. In the first phase of this approach, based on the literature, 10 barriers to the adoption of blockchain in humanitarian logistics are identified and evaluated using the FMEA method. In the second phase, using the opinions of experts, the weights of the three factors are calculated. Then, in the third phase and according to the outputs of the previous phases, obstacles are prioritized using the proposed Z-ARAS method. In addition to assigning different weights to the three factors considering uncertainty and reliability in barriers is also considered in this approach through the theory of Z numbers. The proposed approach of current study was implemented in the evaluation of blockchain adoption barriers in humanitarian logistics. According to the results, the most critical barriers concern with integrating issues, risk of cyber-attacks, and technology risks. The results shown the capability and superiority of the proposed approach compared to other traditional methods such as FMEA and Fuzzy ARAS.

In the context of the Fourth Industrial Revolution, advanced technologies are reshaping production and business models across various industries, offering new opportunities for enhanced competitiveness but also introducing challenges in terms of adoption and optimization (Wong et al., 2020; Khan et al., 2021). Notably, the convergence of advanced technology and humanitarian logistics is crucial, especially in addressing natural and man-made disasters (Ar et al., 2020; Dubey et al., 2020). This necessitates effective management and the combination of humanitarian logistics with blockchain technology, although this integration comes with multifaceted challenges (Baharmand et al., 2021).
To address these challenges, we explore the Failure Modes and Effects Analysis (FMEA) method as a systematic approach to identify and assess barriers and risks. Traditional FMEA approaches rely on subjective evaluations, which introduce uncertainty into the results. In this context, our research aims to introduce an innovative approach that addresses these limitations by integrating the ARAS method and Z-numbers theory. This approach allows for more reliable prioritization of barriers related to blockchain technology adoption in humanitarian logistics, enhancing the robustness and effectiveness of decision-making processes. In this extended abstract, we present our method and compare its outcomes with traditional approaches to prioritize barriers and risks in blockchain technology adoption within humanitarian logistics. Also, the barriers to blockchain technology adoption in humanitarian logistics and how to prioritize these barriers are among the main research questions.

Blockchain technology is gaining traction in supply chains due to its diverse applications and unique advantages. As supply chains face increasing disruptions, blockchain technology adoption can address challenges and enhance performance (Akhavan & Philsoophian, 2022; Hald & Kinra, 2019). Blockchain structures data into interconnected blocks, ensuring the security and transparency of transactions (Akhavan & Namvar, 2021; Azizi et al., 2021). Blockchain technology is appealing for supply chains due to four main characteristics: encouraging data sharing, minimizing fraudulent transactions, ensuring data immutability, and providing asset security (Babich & Hilary, 2020; Cole, Stevenson, & Aitken, 2019; Rahimi, Akhavan, Philsofian, & Darabi, 2022).
Research on blockchain applications in humanitarian logistics primarily focuses on motivations, such as improved collaboration, transparency, trust, cost reduction, intermediary removal, and shared participation (Baharmand, Maghsoudi, et al., 2021; Seyedsayamdost & Vanderwal, 2020). However, more research is needed in this area (Sahebi, Masoomi, & Ghorbani, 2020). Existing studies have identified barriers to blockchain adoption in humanitarian supply chains, including financial constraints, senior management support, organizational readiness, technological complexity, infrastructure, technology compatibility, and regulatory issues (Baharmand & Comes, 2019).
Multi-criteria decision-making methods (MCDM) have been used to improve FMEA's performance (Ghoushchi et al., 2021; Ghoushchi et al., 2022). These approaches often combine FMEA with methods like GRA, BWM, TOPSIS, and AHP in various fuzzy environments. Such integrated methods have been proposed for barrer identification in the context of blockchain adoption (Li, Li, Sun, & Wang, 2018; Lo & Liou, 2018; Kolios, Umofia, & Shafiee, 2017; Carpitella, Certa, Izquierdo, & La Fata, 2018; Sayyadi Tooranloo & Ayatollah, 2017). Additionally, unified methods like MOORA have been applied to address specific challenges in different contexts (Jafarzadeh Ghoushchi, Memarpour Ghiaci, et al., 2022).
The literature indicates a gap in research on blockchain applications in humanitarian logistics, as most studies focus on business supply chains. Using insights from business supply chains to inform decisions in humanitarian logistics can be misleading, given their fundamental differences (Baharmand, Saeed, Comes, & Lauras, 2021). Consequently, this study aims to address these gaps by proposing an extended FMEA approach based on MCDM methods to identify and prioritize barriers to blockchain adoption in humanitarian logistics, using Z-numbers theory.

The proposed approach of this research is presented, utilizing FMEA and Z-ARAS methods for barrier assessment. The proposed approach consists of three phases. In the first phase, barriers are identified, and the values of the criteria are scored by the FMEA team using linguistic variables from Z-number theory. In the second phase, considering the differences in the importance of criteria, the weight of each criterion is determined based on expert opinions as triangular fuzzy numbers. In the third phase, based on the results of the first and second phases, barrier prioritization is performed while taking into account the criterion weights, using the Z-ARAS method. Unlike the conventional fuzzy ARAS method, the Z-ARAS method can consider uncertainty and reliability for each criterion concerning the options. In this method, after determining the decision matrix, which comprises fuzzy numbers and reliability values (Z-numbers), these values are transformed into triangular fuzzy numbers, and then the Z-ARAS method is executed.

Humanitarian logistics is a relatively new area of research. The impact of humanitarian logistics is crucial, as it saves lives and improves conditions. Research has shown that effective humanitarian logistics is a key driver for the performance of humanitarian organizations. Currently, there exists a significant gap in humanitarian logistics research, particularly in developing countries, between theoretical research and practical implementation.
The adoption of blockchain technology will play a pivotal role in the future development of humanitarian logistics. Therefore, the identification and prioritization of barriers to adopting blockchain technology in humanitarian logistics have gained increasing importance. In this study, an enhanced approach to FMEA is proposed using the Z-ARAS method. Based on the results obtained, "Integration Issues," "Cybersecurity Risks," and "Technology Risks" have been chosen as critical barriers to blockchain technology adoption in humanitarian logistics and are given priority for mitigation and resource allocation. The use of this enhanced approach has addressed some of the limitations of the conventional FMEA method, such as not providing a complete ranking of options. While the developed FMEA approach using the Z-ARAS method is a promising and reliable method, it has limitations. This model may be complex for decision-makers, and it is expected that software tools will be developed to assist decision-makers using this enhanced approach. Additionally, the interaction and impact of barriers were not discussed in this study. Future work can analyze the interplay between barriers to identify critical barriers. Furthermore, researchers can consider multi-criteria decision-making methods like PIPRECIA, SWARA, BWM, and others to determine the importance and weights of criteria. Developing the FMEA method using multi-criteria decision-making methods such as MARCOS, EDAS, CoCoSo, and others for ranking barriers in uncertain environments, including pythagorean, q-rung, and spherical fuzzy scenarios, is also suggested for future studies. Regardless of the issue used for implementing the proposed approach in this research, this approach can be applied to identify and analyze risks and failure modes in various scenarios.

Nowadays, the development of science and technology parks and improving their performance depends on cooperation with industry and university and communication with the environment and related centers. Hence, it is important to identify cooperation network and networking indicators in science and technology parks. The purpose of this research is to identify the indicators of networking in science and technology parks. The method of the current research is qualitative and in it three methods of metacomposition, fuzzy Delphi and Dimetal were used. A search was made in Persian and English databases and 10 related studies were identified and analyzed. In order to verify the networking indicators extracted from the theoretical literature, 13 experts and managers of Pardis Technology Park were surveyed and the indicators were confirmed by the experts using the fuzzy Delphi method. In order to draw the causal model of the relationships between the indicators, DEMATEL method was used. The data was analyzed using Excel software. The results showed that networking in science and technology parks has 15 indicators, such as improving the level of products, information, increasing market share, goals and creating value. According to experts, the market share increase index is the first priority and organizational learning is the last. Drawing the causal model of networking showed that indicators such as management, organizational learning, information and knowledge are effective indicators. Indicators such as new product development, market opportunity creation, relationships and opportunity exploitation are also effective indicators in the networking of science and technology parks.

Nowadays, the development of science and technology parks and improving their performance depends on cooperation with industry and universities and communication with the environment and related centers. Hence, it is important to identify cooperation network and networking indicators in science and technology parks. The ultimate mission of technology parks is to be able to coordinate the results obtained from academic research with the needs of the industry and thus fill the gap between the industry and the university, and this will ultimately lead to the commercialization of knowledge. One of the major influential factors in changing the approach of science and technology parks and creating new structures and mechanisms is the birth of new concepts such as networking in the field of business. The purpose of business networking is to increase competition, cooperation and organizational expansion. Considering the importance of these centers and the impact of networking on their performance, it is essential to identify the indicators of networking in science and technology parks. So far, many researchers have investigated the relationship between science and technology parks and other actors in the innovation ecosystem, but few researchers have focused only on the indicators of park networking. In this regard, this research aims to identify the factors influencing the networking of science and technology parks and to evaluate the cause-and-effect relationships between these factors by using the method of a systematic review of previous studies (super combination) and a survey of experts. This question should answer what are the indicators of networking in science and technology parks.

Paztto and Burin's research (2022) indicates that management control systems are effective in inter-organizational cooperation and identification of companies. This system promotes collaborative behaviors among companies related to science and technology parks. Networking and inter-organizational partnership ultimately lead to knowledge and information sharing, increasing flexibility, improving problem-solving strategies and limiting the use of power. The research of Glitova et al. (2022) showed that for cooperation and networking between industry, university and the public sector, attention should be paid to indicators such as knowledge creation by universities, research and development centers and businesses, technology transfer, creation of new businesses, industrial clusters, Business support services, customization, building the necessary infrastructure and equipment, and legal requirements at the local level are required. The research of Khan-Mirzaei et al. (2021) showed that networking and emphasizing cooperation and communication between science and technology parks and growth centers can lead to gaining a competitive advantage for the national economy. Communication with universities and research and development centers, cooperation with companies that have a similar field of work, access to the information flow and access to the information needed in the market, or in other words, the market situation, are among the factors that create a cooperation network between Science and technology, industry, university parks are important. In confirmation of this issue, Cadorin et al. (2019) stated that talent resources and the government play an important role in promoting cooperation between science and technology parks and universities. Managers of science and technology parks should strengthen their relationship with local universities and the student community (as sources of talent) and pay attention to their relations with government representatives to receive the necessary support for the development of the park.

The method of the current research is qualitative and in it, three methods of Meta-synthesis, Fuzzy Delphi and DEMATEL were used. A search was conducted in Persian and English databases and 10 related studies were identified and analyzed. To verify the networking indicators extracted from the theoretical literature, 13 experts and managers of Pardis Technology Park were surveyed and the indicators were confirmed by the experts using the Fuzzy Delphi method. To draw the causal model of the relationships between the indicators, DEMATEL method was used. The data was analyzed using Excel software.

In this research, a set of 62 codes and 15 indicators was obtained by extracting concepts effective on park networking from previous qualitative research. The main indicators include improving the level of products, and information, increasing market share, goals (park goals, socio-economic and environmental goals), creating value, exploiting the opportunities available in the park, optimizing resources, and developing new products, Knowledge includes the knowledge of the market-partners and co-creation of knowledge, the international and commercial performance of the park, creating opportunities through the market, management, the need for resources and operational resources, creating and developing relationships and organizational learning. According to experts, the market share increase index is the priority and organizational learning is the last. The indicators of relationships, value creation, resources, market opportunities, goals, management, knowledge, exploiting opportunities, resource optimization, performance, upgrading products, information and new product development are ranked second to fourteenth respectively. Indicators of management, organizational learning, information, knowledge, goals, resources, and upgrading of products are effective indicators. New product development, creating market opportunities, and relationships, exploiting opportunities, optimizing resources, creating value, and increasing market share and performance are also influential indicators in the networking of science and technology parks.

The review of the subject literature showed that paying attention to the indicators obtained in this research can lead to networking in science and technology parks. For example, the implementation of the indicators of improving the level of products, increasing market share, park goals, creating value, exploiting opportunities, knowledge, creating market opportunities, relations between actors, organizational learning and technical and human resources in Nihu Technology Park and Nankang Software Park in Taipei City. Networked. Researchers have pointed out various actors in the cooperation network of science and technology parks. The review of the texts in the meta-synthesis stage showed that each of the sources identified one to three actors based on their purpose. What was tried to be considered in this research was the gathering and consensus of all actors and their placement in the form of networking indicators such as increasing market share, resources and management. Among the new findings of this research, we can mention the type of causal relationships that are established between the indicators of networking in science and technology parks. Most researchers have not paid attention to these relationships and have focused more on the relationship between the park and variables such as innovation, performance, development, etc. However, the identification of networking behavior and the type of communication between the elements of this ecosystem can lead to the improvement of performance and optimization of activities and actions, and in this research, we tried to consider more and more comprehensive indicators in the cooperation network. be placed Finally, the purpose of the formation and development of science and technology parks is to increase the capacity of innovation and the growth of the knowledge-based economy through knowledge management (creation, sharing and access to knowledge and technology) among the members of the cooperation network of parks and to develop and commercialize the product, it becomes possible by them.

The reduction of profit margins and the disappearance of past competitive advantages have pushed companies in Petrochemical industries toward innovation by utilizing digital capabilities. This necessitates the establishment of a strategic alignment between digital capabilities and innovation strategies and decisions. This research aims to examine the dimensions of alignment between digital capability variables and innovation strategies and create a framework for its assessment. Initially, by reviewing the background of studies, a framework for assessing each of the variables was developed. Subsequently, a questionnaire for confirmatory structural analysis of the identified concepts and dimensions was formulated. This questionnaire was completed by 99 experts in innovation management, digital technologies in the industry, and academia. As a result, it was determined that to assess the level of alignment between digital capabilities and innovation strategies, creating digital value and digital innovation processes for innovation strategies, digital innovation infrastructure and digital innovation capabilities for digital capabilities, and complementarity, balance, and coordination for alignment were considered as assessment dimensions of the variables.

Today, the advantages of the past in the petrochemical industry are diminishing, and the competitive landscape is changing. It can be noted that one of the main challenges encompassing the petrochemical industry today is enhancing competitiveness and reducing operational costs, which require innovation in the use of new technologies (O. V. Zhdaneev, V. Korenev, and A. S. Lyadov, 2020).
Most organizations in this industry use structures and organizational procedures that are not well-suited for utilizing innovative capabilities, including digital capabilities (Alexey Shinkevich, Naira Barsegyan, Vladimir Petrov, and Tatyana Klimenko, 2021). On the other hand, organizations are striving to create complementarity between their different capabilities to strengthen potential innovation capacity (Rogier van de Wetering, Patrick Mikalef, 2017).
Therefore, one of the crucial questions for companies in the petrochemical industry can be how to assess the alignment between digital capabilities and innovation strategy. Consequently, the goal of this research is to identify appropriate dimensions and components for assessing the alignment of digital capabilities and innovation strategy in the petrochemical industry. To achieve this, the relevant concepts related to the main variables are identified and examined, and based on this, the dimensions and components under these variables will be confirmed through a validation process to create an assessment tool.

In the examination of digital capabilities in the petrochemical industry, it can be noted that new processes and patterns are emerging due to adaptation to new technologies, (Amankwah-Amoah, J., Khan, Z., Wood, G., & Knight, G., 2021). Studies conducted on dynamic capabilities (Loureiro, R., Ferreira, J. J., & Simoes, J., 2021) claim that the proper combination of resources and capabilities allows organizations to gain a competitive advantage and improve their performance. (Torres, R., Sidorova, A., & Jones, M. C, 2018). From automating data movement to leveraging processes, all of these have a significant impact on creating added value and generating income (Oztemel, 2018). Based on this, to assess the digital capability variable, one can consider the effective use of digital innovation resources, the management of digital innovation networks, the capacity for absorbing and accepting digital innovation, predicting trends and technologies, managing digital innovation risks, access, transparency, and information security, advanced analysis, and artificial intelligence, as primary components.
Pisano introduces three key questions as the pillars of innovation strategy: The first question is how the organization's innovation creates value for potential customers. The second is how the company gains a share of the value it creates due to its innovation. The third question returns to the type of innovations that enable the company to create and gain value, and what resources each innovation requires (Pisano, 2015). The role and position of digital technologies in addressing these key questions seem crucial. Since digital technologies have significantly influenced technical and social changes for individuals and societies, including organizations, they have caused products, services, processes, and business models to have a more substantial impact (Ciriello RF, Richter A, Schwabe G, 2018).
The concept of alignment implies the existing collaboration between different organizational units based on environmental needs. Organizations with greater alignment perform better in various performance standards, and an aligned organization has internalized directions (Labovitz, G. H., & Rosansky, V., 1997). Growth and profitability are ultimately the results of alignment between employees, customers, strategies, and processes (Labovitz, G. H., & Rosansky, V., 1997). It is necessary for organizations to prepare for changes by creating structures and processes that can easily be adjusted and realigned (Galbraith, 2002). Alignment should exist at all levels of the organization (individuals, projects, systems, and the company). In recent studies, digital platforms and the ecosystem around the company have been added to the scope (Coltman, T., P. Tallon, R. Sharma, and M. Queiroz, 2015).

This research was conducted with an applied approach using quantitative methods and confirmatory factor analysis. The main question in this study relates to the components and dimensions of assessing the alignment between two variables: digital capability and innovation strategy. Therefore, it was necessary to identify and categorize concepts, indicators, and main dimensions of each of the three variables (alignment, digital capability, and innovation strategy) based on previous studies, and this formed the basis for analysis in the confirmatory factor analysis. Based on the identified concepts and indicators for the variables, a questionnaire was developed. A total of 120 individuals were identified. A purposive sampling method was used to collect their opinions, and questionnaires were distributed. In the end, 110 responses were received, of which 99 were usable. The reliability of the questionnaire was calculated for each of the variables, and all of them had values above 0.7 (as reported in the findings). Then, using the smart PLS software and the confirmatory method, the sub-structures of each of the variables were modeled.

Based on a review of the literature and relevant concepts and topics related to the research question, a comprehensive understanding was developed. Previous alignment models in organizations have mostly focused on information technology and high-level business strategies.
Regarding the assessment of the innovation strategy variable, it's important to note that, given the decreasing profit margins and the increasing operational costs of companies, a shift toward value-oriented strategies (economic, social, etc.) is becoming more prominent. The realization of value can be achieved through customizing products, improving industrial processes, automating decision-making, and increasing the speed of decision-making in innovation. On the other hand, digital technology has brought fundamental changes to innovation management processes, requiring companies to be attentive to new tools and approaches when formulating innovation strategies. Artificial intelligence aids in identifying new opportunities, while big data analysis helps organizations make decisions based on their past records and experiences.
Furthermore, as companies in the petrochemical industry need to create digital capabilities for success in the field of digital innovation, some of these capabilities will be focused on changing historical business routines. In this context, businesses strive to continuously evaluate the returns on their digital projects and optimize resource allocation. Additionally, the enhancement of digital literacy, thinking, and human capital competencies, often referred to as digital talent, is essential.
In the context of digital capability and innovation strategy, there are three main dimensions. The first is coordination. If the path to digital innovation is pursued in a fragmented and uncoordinated manner within the organization, it is unlikely to enhance organizational performance and alignment. Therefore, organizational goals and needs in the digital innovation and digital capability domains should be coordinated, and the organization should be able to establish new processes to create dynamism in the problem-solution and digital innovation processes. Moreover, stronger attention and balancing are required, as unbalanced attention to digital capability or innovation strategy can disrupt alignment and equilibrium between organizational capabilities. This indicates the importance of flexibility and transparency regarding resource allocation. The illustration of model is showed in figure 1.

This research has been done with the aim of developing Iran's banking scenarios with an emphasis on big data. The current research is practical in terms of orientation and exploratory in terms of the goal. It is also mixed in terms of its philosophical, pragmatic and methodological foundations. To carry out the research in the first stage, 20 key drivers of the research were extracted through literature review and interviews with banking and technology experts. After screening with the fuzzy Delphi method, 8 factors were removed and the rest were evaluated with the Marcus decision making technique. The findings of the research show that the two factors of "technology regulation" and "technology transfer costs" were chosen as key uncertainties for developing research scenarios. Based on these two key uncertainties, four scenarios were developed based on interviews with the focus group with the titles of comprehensive banking, static banking, searching banking, wandering banking. In the comprehensive banking scenario, everything is in its optimal state; Technology transfer costs have decreased and regulators are supportive of the technologies. According to the findings of the research, considering drivers, key uncertainties and alternative scenarios by managers and decision makers can improve the performance and increase the competitive advantage of banks.

Financial innovations has been challenged the banking sector and can improve it. They cover a variety of financial businesses such as online lending, asset management platforms, trading management, mobile payment platforms, etc. All these services generate a large amount of data every day (Hasan et al, 2020: 1). Analyzing this volume of data is difficult, giving rise to the concept of "big data" (Munawar et al, 2020: 2). Big data as one of the important fields of future technology has attracted the attention of various industries (Raguseo & Vitari, 2018: 5206). In general, big data refers to a large volume of structured or unstructured data that is generated and stored at a high speed (Dicuonzo et al, 2019: 41). Big data has found its position in the banking industry; Because of the useful data they have stored in recent years (Rakhman et al, 2019: 1632). Recent applications of big data in banking have been for improving customer relationship management, marketing, optimizing strategic management and human resources (Parmar, 2018: 33; Hassani et al, 2018: 2). Therefore, it can be said that nowadays big data plays a major role in providing financial and banking services, and the realization of its potential benefits in banking is more from technical aspects and affects the organizational structure of banking and mobilizes a large number of different actors (Diniz et al, 2018: 151- 152). With changes in customer expectations and increased competition, the banking industry is no longer able to ignore technological innovations in the banking sector. Due to the numerous applications and benefits of big data in various industries, including the banking industry, and it's becoming more widespread in the future, this technology is becoming a prominent research topic (Phan & Tran, 2022: 6.)

What are the plausible scenarios for banking in Iran with an emphasis on big data?

Many studies conducted in the field of banking and big data deal with the role of big data in improving the performance of the banking industry (for instance: Shakya & Smys, 2021; Gonsalves & Jadhav, 2020; Hung et al, 2020; Parmar, 2018). Also, another part of the studies conducted with a future research approach in the banking sector without focusing on innovative financial technologies and specifically big data (for instance: Baumgartner & Peter, 2022; Eskandari et al,2020). The focus on innovative banking and financial technologies with a Futures Studies approach has been weak (for instance: Maja & Letaba, 2022; Murinde et al, 2022; Hajiheydari et al, 2021; Broby, 2021; Harris & Wonglimpiyarat, 2019). And the role of big data in the Futures Studies of the banking industry has been seen to be very limited due to the relatively large amount of data available in banks and its effect on performance and gaining a competitive advantage (for instance: Valero et al, 2020). Therefore, despite the studies conducted in the field of banking and big data, some of these researches have paid attention to the present time, and the researches conducted in the future of the banking industry have also been without focusing on the role of big data. Now, the most important theoretical gap in research is the lack of studies on the future of banking in Iran with an emphasis on big data.

The current research is pragmatism due to the use of qualitative and quantitative methods from the perspective of philosophical foundations. It is also exploratory in terms of purpose due to the identification of drivers and practical in terms of direction due to the application of the results in the analysis of the future of banking in Iran. In the current research, two methods of literature review and interviews with experts are used to identify drivers, both of which are qualitative methods. According to Popper, the interview tool is based on the expert dimension. The literature review is evidence-based and uses articles and scientific texts to identify factors. Fuzzy Delphi, which is semi-quantitative and evidence-based, is used to screen and determine key drivers that require great accuracy. Then, to determine the key uncertainties, the MARCOS technique is used based on the importance and uncertainty indicators of the Global Business Network (GBN) approach, which is a quantitative and evidence-based technique. Finally, interviews with focus groups are used to write the scenario, which is a qualitative method based on the expert dimension. The theoretical community of the research includes academic experts and managers of the banking sector and are aware of new banking and financial technologies (Fintechs) and specifically big data. The selection of the participants is based on their knowledge and nobility of the research topic and the importance of their presence in the research, and finally 15 people were selected by purposeful sampling using the snowball method. Experts have at least 10 years of relevant work experience and a master's degree.

This research has clarified the situation of this area by identifying the shaping factors and drivers of the future of banking in Iran. Two factors of "technology regulation" and "technology transfer costs" were chosen as key uncertainties for developing research scenarios. Based on these two key uncertainties, four scenarios were developed based on interviews with the focus group with the titles of comprehensive banking, static banking, searching banking, wandering banking. In the comprehensive banking scenario, everything is in its optimal state; Technology transfer costs have decreased and regulators are supportive of the technologies. Considering drivers, key uncertainties and alternative scenarios by managers and decision makers can improve the performance and increase the competitive advantage of banks.

The purpose of this research is to investigate the effective factors in predicting the academic performance of undergraduate students in the classification of four classes. To achieve this goal, the study follows the CRISP data mining method. The data set was extracted from the NAD educational system for the bachelor's degree in Shahed University for the entry of the years 2011 to 2021. 1468 records were used in data mining. First, the effective features on students' academic performance were extracted. Modeling was done using Rapidminer9.9 tool. To improve classification performance and satisfactory prediction accuracy, we use a combination of principal component analysis combined with machine learning algorithms and feature selection techniques and optimization algorithms. The performance of the prediction models is verified using 10-fold cross-validation. The results showed that the decision tree algorithm is the best algorithm in predicting students' performance with an accuracy of 84.71%. This algorithm correctly predicted the graduation of 77.88% of excellent students, 85.26% of good students, 84.69% of medium students, and 85.96% of weak students based on the final GPA.

Considering that the users are the main focus of immersive journalism, any study in this field without understanding and recognizing them is incomplete. The quality of the VR news experience depends on many parameters, the most important of which are related to the cognitive and behavioral science of the users, apart from the technological factors that are prerequisites for making VR. In this regard, through interviews with experts in journalism and cognitive sciences, this research identified the categories that influence the depth of user’s immersion based on the Grounded Theory methodology and finally presented a conceptual model. The phenomenon of the model is “user involvement”. This category is affected by contextual factors such as "user’s demographic characteristics" and "type of news", as well as the intervening factors of "trauma" and "preventing factors of using virtual reality". In addition, the three categories of "cognition", "narrative" and "crafting pieces" provided the causal conditions that are the basis for the immersion in the news narrative. Finally, "focusing on user’s cognitive factors" in creating VR pieces is the interaction strategy that brought two consequences of "increasing immersion" and "changing norms and behaviors".