banking industry

The ways in which consumers interact with businesses have changed significantly due to the rapid and numerous developments that have occurred in recent years. In addition to traditional sales centers, companies have begun to offer other interactive channels such as web and smartphone applications, social networks, digital stores and contact centers to their customers. In this context, a phenomenon called "Omni channel" has emerged, where customers can use not only one of the existing interaction channels, but all channels simultaneously. For this reason and in this regard, the present study was conducted with the aim of presenting a model for integrating Omni channels in the banking industry.

The approach of the article is qualitative and in terms of its purpose is applied-exploratory. In this way, data were collected through related texts, in-depth interviews and semi-structured interviews with 15 experts, and a thematic analysis strategy including coding was also used to achieve the model extracted from the article. The data were also analyzed using coding and Max Quad software.

Finally, the quality model of integration of collaborative channels was designed with three components (levels): 1- Dimensions of model integration quality, 2- Drivers of the model (facilitators), 3- Consequences of the model and in 12 dimensions and 50 indicators.

The purpose of this research is to present a paradigmatic model of financial performance in the banking industry with a focus on supporting small and medium-sized enterprises (SMEs). The present study is applied in terms of its objective and qualitative in terms of its method. The target population consisted of experts familiar with the research topic, including university professors in the fields of banking and entrepreneurship, as well as senior managers from Bank Mellat. The sampling was done theoretically, and interviews were conducted with 12 experts until saturation was achieved. The research approach was grounded theory, which involved open, axial, and selective coding through interviews with the experts. Data were collected through expert interviews. To assess the validity of the research, the methods of review during coding and examination by another informed individual were used to ensure the accuracy of the coding. In this study, the test-retest reliability method was employed to determine the reliability coefficient. Based on the research findings, it was observed that the financial performance of the banking industry in supporting small and medium-sized enterprises comprised 142 open codes, 18 categorical codes, and 6 final axial codes, including six main components: causal conditions (financing), central phenomenon (financial support), intervening condition (financial policy), contextual condition (infrastructure), strategies (financial strategies), and outcomes (sustainable growth). The presented paradigmatic model indicates that effective banking support for SMEs can contribute to the growth and sustainability of these businesses.

Organizations that employ professional and skilled personnel, particularly in management roles, possess a significant competitive advantage. This has resulted in heightened efforts to recruit qualified human resources in recent years. The objective of this research is to design a model for selecting competent professional managers in alignment with the organizational professionalism standards within the country's banking industry.

This research utilized the qualitative content analysis method to gather field data through semi-structured interviews with 12 experts from academia and the banking sector. The data analysis was facilitated using Atlas TI 8 software.

The findings revealed eight general categories, which encompass the semantic framework of organizational-oriented professional activism, the regulation of management requirements for professional roles, the functional aspects of interactive competencies, strategic and forward-looking competencies, the framework of individual-team-organizational competence-enhancing behaviors, the strategic ecosystem of human resources management actions, the promotion of positive employee behavior, and the enhancement of the image and organizational capital within the banking sector.

Attracting and employing skilled professional managers in the banking industry necessitates essential organizational measures. This includes fostering a culture of organizational professionalism, which will enable a clearer understanding of the strategies needed to effectively recruit and retain qualified and competent managers.

The present study was conducted with the aim of developing a paradigm model of innovation strategy with a digital transformation approach in the banking industry. The approach used in this research is qualitative and the objective is applied. The participants includes managers and experts in the banking industry and university professors in the field of innovation strategy and digital transformation. Purposive sampling was conducted using the snowball method and theoretical saturation was achieved after interviewing 14 experts. A semi-structured interview was used to collect data. The key components of the innovation strategy have been formulated using the grounded theory in three stages of open, axial and selective coding using Maxqda. The results showed that, according to the research paradigm model, legal requirements, change in customers’ needs and expectations, the rapid development of transformative technologies and new players in the financial industry have a significant impact on the innovation strategy with a digital transformation approach. Innovation strategy includes types of innovation, levels of innovation, carrying out innovation and evaluating the effectiveness of innovation. The digital innovation culture, collaboration capability, and digital talents management constituted contextual factors. On the other hand, the level of maturity of knowledge management, the absorptive and learning capacity of banks, business intelligence, organizational barriers and macro barriers play the role of an intervening condition. Finally, customers credit scoring with Artificial intelligence, open banking, updating of policies and laws in the financial field, digital governance and intelligent supervision, Intelligent process automation, Digital Experience Management of customers and employees, data and infrastructure management, intelligent risk management, strategic renewal and ambidextrous innovation in the banking industry leads to digital value creation for stakeholders and achieving organizational and extra-organizational consequences.

The primary objective of this research is to identify the characteristics of the banking industry to enable the modeling of the industry's development by considering parallel markets and macroeconomic variables.

This study was conducted using a qualitative approach and employed the grounded theory technique. Data were collected through semi-structured interviews. In this regard, eight interviews were conducted with banking and financial experts and managers relevant to the research topic. Subsequently, based on Strauss and Corbin’s systematic grounded theory approach (Strauss & Corbin, 2008), a native model for the development of the country's banking industry was presented through three main coding phases: open coding, axial coding, and selective coding.

The results of the interview coding indicate that the development of the banking industry comprises three main components: banking system stability, modern banking, and comprehensive banking. The causal factors are primarily categorized into two groups: internal factors and external factors. Internal factors mainly emphasize risks, structural issues, digital banking, and the national currency's value. In contrast, external factors pertain to interactions with foreign banks, including sanctions and international policy reforms.

Originality / Scientific Contribution: 

According to the findings of this study, banking industry professionals should consider the factors identified in this research, as they have been recognized by experts and industry practitioners as influential in the development of the country's banking industry.

The aim of the present study is to identify the criteria affecting the financial performance of the banking industry in supporting small and medium enterprises (SMEs) with an emphasis on entrepreneurship. This research is applied in nature and qualitative in method. The statistical population comprised experts familiar with the research topic, including university professors in the fields of banking and entrepreneurship, as well as senior managers from Bank Mellat. The sampling was conducted theoretically, resulting in interviews with 12 experts until saturation was reached. The research approach was grounded theory, which involved interviews with experts and three types of coding: open, axial, and selective. Data collection was carried out through interviews with experts. To rank and categorize the factors, determine the types of variables, and examine the relationships between the model variables, as well as to assess the intensity of these relationships and identify the influence and dependency of the criteria, a combined ISM-DEMATEL method was employed. The results indicated that the main categories of the model include validation, infrastructure, financial policy, investment, risk, and transparency. Furthermore, based on the research findings, the first level includes risk management; the second level includes investment and financial transparency; the third level includes infrastructure; the fourth level includes validation; and the fifth level includes financial policies. The findings reveal that financial transparency, investment, and validation are connective factors, while infrastructure and risk are dependent factors, and financial policies are influential factors.

The increasing use of information technology in financial and banking services continues, and the challenge of better managing information technology projects to maximize their economic benefits remains unresolved. Therefore, this study aimed to identify and prioritize the criteria for selecting the optimal portfolio of information technology projects in Iran’s banking industry using both quantitative and qualitative methods. For this purpose, the research sample included experts (university professors and senior managers in the information technology management sector of the banking industry), selected through the snowball sampling method (n=12). In addition, for data analysis, first, the final criteria of the model were determined using the fuzzy Delphi technique, and then the criteria were prioritized using the DEMATEL technique. The results of the fuzzy Delphi technique indicated that the criteria for selecting the portfolio of information technology projects in Iran’s banking industry consist of 5 main criteria (organizational, environmental, economic, technical, and risk-related) and 18 sub-criteria. The results of the DEMATEL technique further revealed that among the main criteria, the risk criterion, and among the sub-criteria, low security risk, hold the highest importance.

 

 

In today’s world, the rapid development of information technology has led to the creation of vast amounts of data. Approximately 2.5 quintillion bytes of data are generated daily, and this figure is expected to rise with the advent of the Internet of Things (IoT) (Seranmadevi & Kumar, 2019). The availability of reliable data is crucial for producing and utilizing information that enables quick and accurate business decisions. The rapid technological advancements and their widespread distribution globally have created opportunities to gain a competitive advantage through new methods based on data and artificial intelligence (AI) in marketing management. This study aims to enhance the marketing process by providing a systematic and precise approach toidentifying research gaps, thereby connecting practical and research actions in the field of marketing and AI. Consequently, the primary objective of this research is to develop a marketing plan based on AI using a hybrid approach and strategy.Artificial intelligence finds applications in various business contexts. Experts and academics consider AI to be the future of our society. With technological advancements, the world has become a network of interconnected systems (Jeric et al., 2019). The implementation of technology has led to investments in AI to analyze big data and create market intelligence (Stone et al., 2020).A marketing plan primarily consists of the marketing mix, which is a combination of marketing elements. The marketing mix comprises controllable marketing tools that a company uses to implement its marketing strategy, designed to satisfy consumers (Jerk & Mazurk, 2019). It is divided into four general groups known as the 4Ps. Subsequently, the 4Cs plan was introduced to emphasize the importance of the customer’s position and the formation of effective marketing communications (Tangyananakit & Samasat, 2022).Shirmohammadi and Mohammadi (2023) conducted a study titled “Using Artificial Intelligence and Internet of Things Based on Big Data to Create Knowledge and Rational Decision Making in B2B Marketing to Improve the Performance of Smart Travel Service Offices.” Their research demonstrated that AI and IoT positively and significantly impact the creation of customer and user knowledge. Additionally, the creation of customer knowledge and user knowledge of foreign markets positively andsignificantly affects rational marketing decision-making.Shah Nazari and Sharifi (2023) conducted a study titled “Artificial Intelligence-Based Systems for Consumption in Structural Marketing: Historical Reminder, Current and Future Views.” Their research highlighted the development of conscious systems and special power points to support decision-making situations for companies, especially those with a strategic identity, where good strategic data is essential.Shaik (2023) conducted a study titled “The Effect of Artificial Intelligence on Marketing,” which demonstrated that artificial intelligence significantly impacts marketing. Nalbant and Aydin (2023) conducted research titled “Development and Transformation in Digital Marketing and Artificial Intelligence Branding: The Dynamics of Digital Technologies in the Metaverse World,” showing that artificial intelligence is effective in digital marketing. Torres et al. (2022) conducted a study titled “Artificial Intelligence and Its Impact on Brand Loyalty: Relationships and Combinations with Satisfaction and Brand Name.” They used Fuzzy Qualitative Comparative Analysis (FS/QCA) along with Structural Equation Modeling (SEM). These methods reveal subtle differences that help understand the effects of different value dimensions. While SEM results emphasize the mediating role of brand love, FS/QCA results show that brand love is a major condition for brand loyalty. Different paths can be sufficient to produce beneficial results. These findings enhance our understanding of AI performance and can guide practitioners in using game experiences to influence consumer behavior.

The primary objective of this research is to identify the antecedents, dimensions, and consequences of an AI-based marketing program. It is developmental in nature and, since the data were collected without direction and manipulation, it is classified as non-experimental (descriptive) research. This study was conducted within the pragmatism paradigm using a mixed-method approach, incorporating both quantitative and qualitative methods. The bibliometric analysis method and the four-step approach proposed by Costa et al. (2017) were used to review the systematic research background. In the qualitative approach, metacomposition and the seven-step method of Sandlowski and Barroso (2007) were employed.Bibliometric Analysis As stated, this study utilized a four-step method proposed by Costa et al. (2017) for bibliometric analysis, which includes: Selection of bibliometric databases. Defining keywords (search strategy). Refining the initial results (entry and exit criteria).  Data analysis plan.

Data were collected from the Scopus database and saved in CSV format for further analysis. A total of 115 documents related to this particular issue have been published up to the mentioned date. The authors have used 789 keywords for the mentioned period. The chart illustrates the number of articles published over different years. As shown, only one article was published in 1985 in the field of artificial intelligence and marketing. From 2010 onwards, this issue gradually gained attention from researchers, and the trend of publishing articles has been on the rise from 2010 to 2022. In 2021, 50 articles were published, and in 2022, 21 articles have been published to date.

Existing studies, such as those by Avari (2018), Decamp (2020), Antons and Breidbach (2018), Chang et al. (2009), Chang et al. (2016), Liebman et al. (2019), and Gu et al. (2019), indicate that marketers can use mechanical artificial intelligence for standardization to prepare marketing plans. Intellectual artificial intelligence has a high potential for creating advertising content and personalization. For instance, authors of advertising content, websites, and social networks can facilitate the production of advertisements or post content using artificial intelligence. AI technologies enable the personalization and optimization of customer profiles in different locations and times. Content analysis can assist advertisers in creating more effective content.Based on the obtained results, the following practical suggestions can be made:Using AI for customer interaction (e.g., conversational bots to gather data related to customers’ moods and emotions).Employing AI to create music and write short stories to make advertisements more creative and memorable for customers.Utilizing mechanical AI to automate adjustments and price changes.Implementing automation by mechanical AI in the advertising media planning department.Using AI writers to produce content.Future research should consider conducting studies using experimental and longitudinal methods, as well as mixed methods. It is also suggested that future researchers address the limitation of excluding non-English language studies

The role of sustainable marketing as a central phenomenon in competitive industries including the banking industry is undeniable. The purpose of this research was to design a sustainable marketing model with an emphasis on sustainable development in Iran's banking industry. The current study is fundamental in terms of purpose and qualitative. In the qualitative part, a semi-structured interview was used to collect data, and the Foundation's data theorizing method was used to theorize about the topic. The statistical population consisted of 20 marketing professors and heads of branches in the National Bank. This research obtained 140 primary codes, 27 core categories, and 10 macro categories after conducting interviews. The sampling method in this section was the snowball approach. The data were analyzed with three types of open, central, and selective coding, and relationships between interviews were extracted with MAXQDA20 software. In the quantitative part, a questionnaire was used to collect data, and structural equation modeling was used to validate the model. The statistical population in the quantitative section consisted of 384 customers of Melli Bank. The cluster sampling method was simple. First, Bank Melli branches were divided into 6 clusters and questionnaires were distributed among customers in each cluster based on a random approach. Based on the results of the research, it was determined that individual psychological factors, sociocultural factors, and demographic factors affect sustainable marketing. Sustainable marketing, a complex competitive environment, and motivational factors affect sustainable development and sustainable development has been effective in improving financial performance, employee performance, and marketing performance in Melli Bank.

The purpose of this study is to design a model for the implementation of new fintechs in the banking industry (peer-to-peer lending). The research method in terms of nature is applied, the research method and method is qualitative, the research approach is inductive, the paradigm governing the research, the interpretive, the research strategy, the grounded theory, and the source of data collection includes the study of theoretical foundations and interviews. The statistical population of the study was divided into two groups including experts in the banking industry and companies providing financial technology services, and finally 20 people announced their presence using the judgmental sampling method and in the form of a snowball for cooperation and their opinions were collected through semi-structured interviews until reaching theoretical saturation. Data collection from several information sources, permanent comparison method in data analysis and prevention of initial assumptions in conclusions through feedback were used to estimate validity and internal consistency method (Cronbach's alpha coefficient = 0.91) was used to measure reliability. The data analysis method was performed by performing three stages of coding including open, axial and selective coding. The results showed that among the extracted codes, causal conditions including: organizational learning, The core conditions include: communication capital, The governing platform includes: partner banks, and environmental conditions, intervening conditions including: organizational factors, etc. Strategies include: policy-making, And the consequences include: knowledge-based policy-making.

The present study aims to design and provide a well-founded and reliable framework for content marketing in the banking industry of Iran, with the objective of enhancing the effectiveness of this approach and contributing to increased market share and profitability of banks. 

This mixed-method research was conducted in two phases: qualitative and quantitative. In the qualitative phase, grounded theory was used to design the model. A purposive sampling method was employed, selecting a group of academic experts and banking industry specialists, and in-depth interviews were conducted until theoretical saturation, which was reached after 15 interviews. The collected data were analyzed systematically and continuously in three stages: open coding, axial coding, and selective coding. In the quantitative phase, the designed model was validated using confirmatory factor analysis based on a covariance approach, and path analysis was used to finalize the model. 

During the open coding stage, 188 concepts were extracted, which were grouped into 41 categories through axial coding. Finally, 10 main categories were identified: understanding the business and planning, establishing a content management and strategy unit, defining content marketing goals, understanding the audience, identifying audience needs, generating content ideas and topics, managing content production, managing content dissemination, managing customer expectations and experiences, and gathering feedback for process improvement. The final model was developed based on these categories and validated in the quantitative phase. 

This study presented a comprehensive and clear model for content marketing in the banking industry, outlining specific stages and processes for implementation. The model has the potential to enhance the effectiveness of content marketing strategies and contribute to the improvement of market share and profitability for banks.

Digital transformation and the integration of digital technologies in all areas of an organization, including in the bank organization, cause many changes and transformations and can create a competitive advantage for the bank organization and industry. Therefore, the purpose of this study was to overview on digital transformation maturity measuring models and the necessity of a model for the banking industry.

This research in terms of purpose was practical and in terms of implementation method was qualitative from type of overview. The research population was the articles related to the research field between 2015 until 2023 years, which among the 64 related articles, 39 articles were selected as a sample with the purposeful sampling method. The data collection tool included taking notes of the articles, which the validity was confirmed by triangulation method and the reliability was calculated by the agreement coefficient method between two coders at 0.88. In this study, different digital transformation maturity measuring models were examined.

The findings of this research showed that were identified twelve models for digital transformation maturity measuring and the necessity of its implementation for the banking industry, which the salient and distinctive features of each were extracted.

According to the findings of the current research, banking industry specialists and planners to improve this industry can benefit from digital transformation maturity measuring models.

In the current era the banking industry is considered one of the most important industries of the country and banks need to take advantage of the dynamic capabilities of Ambidextrousinnovation to survive in competitive and dynamic market era.this research has been conducted with the aim of the effect of knowledgesharing on ambidextrousinnovation with the mediating role of intellectualcapital and moderating of openinnovation The current research is of quantitative type fromthe point of view ofthe objective, itis of applied type and the data collection method is of descriptivesurvey type.statistical population of this research was the stateprivate and QarzulHasneh banks of Golestan province,which were referred to the central office of all of them 26 banks at the organizational level and questionnaires were distributed among 5 employees in each bank and a total of 130 questionnaires were collected form of 26 banks.In general the structural equation modeling method was used to analyze the hypotheses;The results of the research showed that the effect of knowledgesharing on ambidextrousinnovation,the effect of knowledgesharing on intellectualcapital,the effect of intellectualcapital Ambidextrousinnovation and the effect of knowledgesharing through the mediating role of intellectualcapital on ambidextrousinnovation are confirmed.However the effect of knowledgesharing through moderation of openinnovation on ambidextrousinnovation is not confirmed.

Based on macroeconomic indicators, a significant portion of the world's gross domestic product comes from the services sector. The $2.7 trillion share of services in large economies and its increasing growth since 2011, according to UN accounts, highlight the undeniable importance of the services sector. In line with this perspective, previous researchers have shown that lean-based approaches can facilitate the efficient and development-oriented growth of the services sector. Producers' move towards lean-based strategies in services has been reported to exceed 30%, a significant factor in maintaining competitiveness and gaining a competitive advantage in the service industry. The concept of lean first emerged in the industry with a focus on eliminating waste from production and refining it. However, this concept has entered the theoretical literature of organizations on the one hand, and on the other hand, it has appeared in the public domain, such as public administration and governance. Therefore, concepts such as lean government and lean services are outcomes of this concept. The present study aims to identify and validate a predictive model of lean services in the context of lean government in the banking industry.

In terms of paradigm, this research is interpretive during the model discovery phase and confirmatory during the model testing phase, and it is generally pragmatic. The research sample for the qualitative phase consists of experts in the academic field, including university professors, faculty members, and banking industry managers. A total of 15 experts were interviewed to collect qualitative data. The research questionnaire was developed based on exploratory factor analysis to collect quantitative data. In the quantitative section, using G*Power sample size determination software with an alpha level of 0.01 and a power test of 85%, 190 individuals were selected as the final sample for the research. The research approach is mixed, and the strategy employed is thematic analysis. Clark's method (2006) was used to analyze the data with MAXQDA software. The path analysis of the discovered model was also examined using variance-based structural equation modeling algorithms with SPSS version 24 and SmartPLS version 3. 

The results of the data analysis show that 32 indicators and 7 variables in the context of 6 theories were identified and validated as predictors of lean services for the realization of lean government in the Iranian banking industry. Therefore, factors such as lean improvement strategy, lean cultural transformation, development of lean capabilities, lean management methods, lean work methods, and lean contextual factors can facilitate the realization of lean services.

The banking industry is one of the driving forces of the economy and the engine of every society. Therefore, paying attention to the banking sector and its growth and development is an inevitable necessity. In particular, in the field of banking services, this is important due to the nature of financial and monetary services. Economic liberalization, globalization, and increasing competitiveness in financial services have forced banks to enhance and expand their services to maintain a competitive position. In the present study, the predictive theories of lean services with a lean government approach in the Iranian banking industry were identified through exploratory research, based on the natural conditions of Iranian society. Also, a conceptual model of predictors and a model for the application of lean services to the Iranian banking industry were introduced.

This study is a cross-sectional survey research in terms of the applied-developmental goal, in terms of the method and time frame of data collection, and in terms of the method of data analysis, it is a mixed research. The data collection tools were semi-structured interviews and researcher-made questionnaires. In order to identify the dimensions and components of the design of the optimal network policy system for the integration of Iranian banks, thematic analysis method was used, the structural-interpretive modeling method was used to determine the relationships of the elements, and the partial least squares method was used for validation. 442 codes were identified using the qualitative theme analysis method in the open coding stage. Finally, through axial coding, 5 overarching categories, 8 organizing categories and 51 basic themes were obtained. Based on the results, it was determined that banking environmental assessment, internal organization assessment, bank stakeholder assessment and upstream documents have an impact on network policy and integration policy. Network and integration policies affect bank integration, and bank integration leads to financial and non-financial performance of banks.

Purpose By combining psychology and marketing, researchers invented a new method called neuromarketing, in which indirect and sustainable influence on the customer's mind is emphasized. The aim of the current research is to design a suitable neuromarketing model with a consumer behavior approach in the banking industry.

The current research is practical in terms of the nature of the research; The manner and method of research, qualitative; research approach, inductive; The ruling paradigm of research is interpretive; The research strategy, foundational data theory and source of data collection include the study of theoretical foundations and interviews. The statistical population of the research consists of 2 groups, managers of public and private banks, academic faculty professors in the field of management and marketing of public and independent universities, who, using the non-probability purposeful snowball sampling method, the opinions of 20 of them during semi-structured interviews until reaching The theoretical saturation limit was collected. Acceptability, transferability and verifiability were used to estimate validity and internal consistency method (Cronbach's alpha coefficient = 0.91) was used to measure reliability. The method of data analysis was done by performing three stages of coding including open, central and selective coding.

The research findings showed that 245 final codes were extracted from the open coding stage. In the central coding stage, there are 6 sub-categories and 62 codes in causal conditions, 5 sub-categories and 41 codes in contextual conditions; Intervening factors number of 6 subcategories and 35 codes; Strategies, 9 subcategories and 58 codes, and outcomes, 10 subcategories and 63 codes were identified.

In this research, the neuromarketing model has been designed with the approach of consumer behavior in the banking industry, which is expected to be used by bank managers.

Digital transformation requires banks to move towards exploring new opportunities in addition to exploiting existing facilities. Therefore, banks seek to create organizational ambidexterity by forming alliances with organizations from different industries. The purpose of this article is to answer what type of alliance banks can form with different actors to lead to organizational ambidexterity. For this purpose, the initial conceptual model was formed by synthesizing the ambidexterity, the industrial similarity and the tie strength theories. Then, the code co-occurrence matrix was extracted from the content analysis of 115 empirical articles of alliance in the banking industry. The co-occurrence matrix was used to customize the conceptual model for banking industry in order to propose the type of alliance of the bank with other actors. The conceptual model shows that any type of alliance, based on industrial similarity of the actors with the banking industry, realizes some factors of exploration and exploitation in the form of trade-offs. Therefore, the decision to determine the appropriate type of alliance should be made according to the priority of exploration and exploitation factors based on industrial similarity of actors.