JOURNAL OF BUSINESS INTELLIGENCE MANAGEMENT STUDIES
Year:2021 | Volume:9 | Issue:37|Papers Count:10

Strategic planning plays a key role in the future success of organizations. Information technology as an emerging field in Iranian organizations requires strategic planning to succeed. In this study, after a systematic review of the IT strategy development methods and the evolution of these approaches, an indigenous approach is developed to develop the IT strategy of the distribution companies. This method is a quantitative-qualitative hybrid. During this five-phase study, the organization's cognition is based on the information architecture of processes, and then based on the business and IT alignment perspective, achieving optimal status in information technology infrastructure library as one of the business and information technology alignment methods with the IT governance approach, is followed. By matching the business processes of the electricity distribution and information technology infrastructure library, the status quo and the desired status in each process were determined based on the opinions of business experts. Accordingly, the processes of service continuity management, service catalog management and service portfolio management were the most distant from the desired situation, and for the other processes the same gap analysis was performed. Based on the distance analysis, appropriate strategies were provided for each of the information technology infrastructure library processes according to the best practices.

Mistake proofing is an approach whose main purpose is to make mistakes impossible. This technique can eliminate the possibility of errors by making changes in the process, or by appropriate culture, or by preventing them from making a mistake by sending the appropriate message. Using new tools or technologies to improve the quality expected of the customer. The use of the miscalculation will dramatically increase customer loyalty. Although it has been decades in the making, it has a limited track record in services as well as in information technology and especially in information systems. In this study, based on previous research, the main dimensions of the error-solving strategies are categorized. Then, using the questionnaire, the experts' opinions on prioritization of nonlinearity strategies are discussed. We also select the final criteria based on the nominal batch technique and then prioritize the factors that play an important role in making the information systems unreliable based on the best-worst or BWM weighting method. The results showed that proper planning of quality management programs, skilled staff, advanced facilities and equipment, design capability and finally the use of appropriate controllers as the most important factors of information systems malfunction can be considered as the top priority of service managers. Also, providing appropriate assumptions for information items and improved data model structures can reinforce technological and innovative approaches to information systems.

With the spread of Covid-19 disease, quarantine, and social isolation, people are increasingly posting their opinions about the coronavirus on social networks such as Twitter. However, no study has yet been reported to analyze online opinions of individuals in order to understand their feelings about the Covid-19 epidemic in Iran. This study analyzes the emotions in the opinions of the Iranian people on the social network Twitter during the Corona crisis. For this purpose, a deep neural network model is presented. As there is no labeled dataset of Covid-19 tweets, the proposed model is first trained on the Stanford University Sentiment140 dataset, which contains 1. 6 million tweets, and then used to classify the two classes of emotions contained in the collected corona-related tweets in Iran. The results show that the percentage of tweets with negative emotions is significantly higher than positive tweets. Also, the change in negative emotions of people in different months is proportional to the change in patient statistics.

Effecting fundamental changes in all organizational dimensions, digital transformation refers to the aggregation and integration of digital technologies in all areas of an organization. The design science methodology was used in this study to propose a maturity assessment model for digital transformation in industrial organizations. . For data collection in the qualitative phase, a systematic review of the literature was conducted to analyze relevant papers within the 2015–, 2019 period. As a result, 49 papers were selected. At the same time, the experts were interviewed. Axial and theoretical coding phases were then implemented through the grounded theory in MAXQDA 10 to classify data as four dimensions and 12 categories. Causal conditions, context conditions, intervening conditions, strategies, and consequences were then identified to design the paradigm model. In the quantitative phase, research questionnaires were used for data collection, and the structural equation modeling technique was employed for model testing in SmartPLS. According to the ISO 15504, the capability maturity model for digital transformation was designed at incomplete, initial, performed, managed, established, and optimized levels in order to make research practical, and a corresponding 48-item researcher-made questionnaire was then developed. The proposed model was analyzed in an industrial organization of the electronics sector to determine the organizational maturity level. the result showed that the organization is at the second level of maturity and transformation has begun in it. the focus of the organization has been on the technology aspect and it is necessary to develop organization aspects accordance with them.

The global crisis caused by the spread of the coronavirus has created new management scenarios in various economic and social sectors. In this study, by introducing smart water meters and examining their effectiveness in the process of remote reading of water consumption of subscribers, we seek to use the benefits of information technology solutions in the water industry to reduce or eliminate the risks of future crises. To do so and for the first time in the country by implementing of data obtained from nationwide projects as well as using paired t test, we will examine changes in water use before and after the implementation of smart household water meters and during the outbreak of the virus, according to which the positive impact of the deployment of such smart measuring equipment in the face of the water shortage crisis caused by the deployment of the virus has been confirmed.

News and rumors about the prevalence of corona virus on social media have a significant impact on people. The aim of this study is to examine the topics discussed by people about corona disease in social media from the beginning of corona prevalence to the present day. The research data were collected from people’, s comments in posts related to Corona News on Instagram and analyzed using the method of text mining and clustering. Based on the results of the research, the topics of discussion of the citizens were divided into 10 clusters, which are: Lack of sanitary equipment, lack of attention to quarantine, news and rumors, mental condition, information about symptoms, prevention, control and treatment, government and public actions, lack of personal hygiene, death rate in patients and burial, closure of educational activities And economic problems. Then they were compared with the issues in December and January, when some issues such as access to vaccines, hourly traffic restrictions and the mutated virus were added to the concerns of the people, and some of them were addressed by government measures.

While this study identifies the most important key indicators that influence the banking industry, it also attempts to provide a forecast for the Iranian banking industry in the future. Scenario planning and cross-impact matrix are used in this study. Among all the identified factors, 29 key factors influencing the future of the industry were selected through a fuzzy analytical hierarchical process and then the impact of each of these factors was determined through analysis of the cross-impact matrix. The cross-impact balance was then used to write scenarios. Accordingly, of all combined scenarios, the most likely strong scenarios were clustered into five general categories using K-mode clustering. Finally, four scenarios were identified, including optimism for the bank, banking industry development, inflationary conditions and sanctions. It was therefore possible to define action plans for each of the scenarios.

Social media data is one of the most effective and accurate indicators of public sentiment, so that analyzing this information can provide researchers with interesting results from users' sentiment about characters, subjects, products, and services. In this study, while reviewing users' opinion on Twitter about the various features of two competing mobile phone products on the market, Apple's Iphone X and Samsung's Galaxy S9, we examine their sentiment based on the gender of consumers of these two products. This study is performed using the relation-based method in the feature extraction step and Lexicon-Based in the polarity of opinions step. The results of this study show that the popularity of different product features varies between male and female users, and based on these results, business owners can produce products that focus on people's gender or design smart advertising plan according to their interests. These measures ultimately lead to increased business profitability and customer satisfaction.

Today, we see the great impact of social networks on the purchase of many products in various industries. One of the industries that has been affected by social networks in recent years is the clothing industry. Therefore, this study seeks to identify the factors affecting the purchase of clothing in social networks and to develop a model of consumer shopping behavior in social networks for the clothing industry. In this research, a combined method has been used. In the qualitative section, through interviews with clothing sellers active in social networks, texts were prepared and coded, 46 concepts were identified, which were classified into four main categories and 16 sub-categories and presented in the form of a prototype. In the quantitative part, based on the initial model, a questionnaire was developed and distributed to 385 clothing buyers in networks, and the final model of consumer purchasing behavior for purchasing clothing through social networks was presented. The results showed that individual factors, company-related factors and social and cultural factors directly and also with the mediating role of trust, had a significant effect on the consumer's decision to buy clothing, followed by a significant effect on loyalty, repurchase and Advise others to buy through social media.

Nowadays, Bitcoin is one of the most important cryptocurrencies that has the largest volume of exchanges in the cryptocurrency market and between businesses. The feature of the possibility of online payments between individuals and businesses directly and without referring to the financial institution has made the price of these cryptocurrencies important for businesses and traders and the basis for decision making. Therefore, the issue of price predictability is an important issue that can be affected by search volume. The purpose of this research is studying and investigating the relationship between the volume of Internet searches and its effect on the price of these cryptocurrencies. In addition, another goal of this article is to introduce Google Trends (GT) as a tool for accessing big data for business researches. The required data was extracted from Google Trends in the period 2016 to 2021. The volume of data was 5742 and the whole statistical population was used. The research method is descriptive-exploratory with the aim of explaining the relationship between "Google search volume index" and "bitcoin price". Data were analyzed using Spearman correlation test. Findings indicate a strong and very strong relationship between the studied indicators, which is explained.