Research Paper
Management approaches in the field of smart
Mohammadakbar Sheikhzadeh; Mohammad Taghi Taghavifard; iman raeisivanani; Jahanyar Bamdadsoofi
Abstract
Crowdfunding is an effective way to achieve the non-profit goals of the charities which can be expanded using information and communication technology. In this regard, this study was conducted with the aim of providing an online donation intention model for collective financing of charitable institutions ...
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Crowdfunding is an effective way to achieve the non-profit goals of the charities which can be expanded using information and communication technology. In this regard, this study was conducted with the aim of providing an online donation intention model for collective financing of charitable institutions in Iran. The current research is an applied-developmental research in terms of its purpose, and it is considered a cross-sectional survey research from the data collection point of view. The statistical population in the qualitative section includes managers of charity institutions and university professors. Sampling was done using a purposeful method and theoretical saturation with 10 interviews. In the quantitative section, the views of 357 benefactors were used. The data collection tool is a semi-structured interview and a researcher-made questionnaire. To analyze the collected data, qualitative thematic analysis and partial least squares were used. The research findings showed that the technical infrastructure facilitating conditions, the quality and transparency of the website information, the security of the site and the application, and the preservation of privacy affect the perceived risk. Perceived risk affects the expectation of effort and the pleasure of helping others and leads to the expectation of performance and social influence. Finally, online donation intention is strengthened through trust.IntroductionToday, charity institutions in the country can expose to the various needs of the needy to the public using crowdfunding platforms, so that with the participation of people the necessary financial resources are provided and the problems of the needy and the deprived are solved. In this field, practical and scientific activities have also been carried out however one of the most important issues neglected from the researchers’ view point and activists in this field is the discussion of the desire or intention of online donation among benefactors. The best online donation platforms and the use of the latest technologies are not enough to attract public participation, and it is not easy to encourage and motivate people to donate online. This issue itself can be discussed from various aspects. On the one hand, the basic element of this method of financing is the maximum participation of people, and on the other hand, members of the society have little knowledge about the issue and do not have much desire to attend such calls.Therefore, in general, it can be said that the intention to donate online is a fundamental factor in the success of the efforts of the country's charities for crowdfunding. The basic question of this research is that: what is the online donation intention model for collective financing of charities in Iran?Literature ReviewCrowdfunding is an alternative way to finance a project with specific goals at a specific time through an online platform. The phenomenon of crowdfunding is emerging in the field of financing resources, goods and services in the modern digital field (Jiao and Yue, 2021). The current study focuses on the crowdfunding approach based on donations in Iranian charities. This method is mostly used in non-profit and philanthropic projects, and the participants do not expect any financial support, and are mostly used to finance charities, religious places, and civil institutions (Salido et al., 2021). In the crowdfunding method based on donation, the participants do not have any expectations for the provided financial support. The donor believes wholeheartedly in the correctness of his act and considers it to be socially beneficial. This model is usually used in financing civil institutions and charities (Van Thienbroek et al., 2023).MethodologyThis is an applied-developmental research. The population of participants in the qualitative section includes theoretical experts and experimental. Sampling was done with a purposeful method and theoretical saturation was achieved with 10 interviews. The sample size was estimated to be 357 people using Cochran's formula.The main tool for collecting research data is a semi-structured interview and a researcher-made questionnaire. And then, using ISM, the relationships between the main factors affecting the intention to donate online were determined by experts. The interview included 6 basic questions and was conducted in a semi-structured way. The research questionnaire includes 10 main constructs and 51 items with a five-point Likert scale. Thematic analysis was used to identify the categories of online donation intention for crowdfunding of charities in Iran. Data analysis was done in qualitative phase with MAXQDA software and in quantitative phase with Smart PLS software.ResultsThe results of the interviews were analyzed by the qualitative method of thematic analysis based on the six-step method of Etrid-Straling (2001). 830 open codes were identified in the first stage. After analysis and review, we reached 5 overarching themes, 10 organizing themes and 51 basic themes as below:Overarching themes: Social factors- Technical factors-Individual factors- Security factors- Consequence factors.Organizer themes: Performance expectation- Endeavour expectation- Social influence- Quality and transparency of website information- Technical infrastructure facilitating conditions- Trust- The joy of helping others- Perceived risk- Site and application security and privacy- Intention to donate online.DiscussionThe present study was conducted with the aim of presenting the online donation intention model for the collective financing of charitable organizations in Iran. Based on the results, it was determined that the facilitating conditions of the technical infrastructure, the quality and transparency of the website information, the security of the site and the application, and the protection of privacy affect the perceived risk. In the results of Zhang et al.'s (2023) and Shahidi and Kivani's (1401) studies, the technical infrastructure and website information components are also mentioned as important elements in the intention to donate online, and from this point of view, it is consistent with the results of the present study. It was also shown that the perceived risk affects the expectation of effort and the pleasure of helping others and leads to the expectation of performance and social influence. In the results of the studies of Hassanzadeh Sarostani et al. (2017) and Bass and Rushdi (2023), the importance of paying attention to the perceived risk is also mentioned, and from this point of view, it is consistent with the results of the present study. Finally, the results of the research showed that online donation intention is strengthened through trust. This importance has been confirmed in the results of Shahabi-Shojaei et al.'s study (1401).ConclusionThe present study was conducted with the aim of presenting the online donation intention model for the collective financing of charitable institutions in Iran. Based on the results, it was determined that the facilitating conditions of the technical infrastructure, the quality and transparency of the website information, the security of the site and the application, and the protection of privacy affect the perceived risk. It was also shown that the perceived risk affects the expectation of effort and the pleasure of helping others and leads to the expectation of performance and social influence. Finally, the results of the research showed that online donation intention is strengthened through trust.
Research Paper
Management approaches in the field of smart
vahid khashei varnamkhasti; Mahdi Ebrahimi; Shahram Khalil Nezhad; Fatemeh Motahari nejad
Abstract
.Today, digitization is considered as a requirement for all financial and monetary institutions and markets, and the banking industry is no exception. However, moving to digital banking is not an easy task for banks, and the digital revolution has become a major challenge for this industry. In order ...
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.Today, digitization is considered as a requirement for all financial and monetary institutions and markets, and the banking industry is no exception. However, moving to digital banking is not an easy task for banks, and the digital revolution has become a major challenge for this industry. In order to keep their business stable, banks need to launch digital platforms and create robust ecosystems around them that have the ability to evolve and adapt to the challenges caused by the chaotic and unpredictable environment in which organizations operate and the increase in internal inefficiencies. Therefore, this research was conducted with the purpose of modeling the generative mechanisms of the evolution of Iran's digital banking ecosystem. For this purpose, the qualitative content analysis method has been used. A semi-structured interview has been used to collect information using the opinions of experts in the field of digital banking. The analysis of the conducted interviews led to the identification of 674 themes, 181 codes, 59 subcategories, and 20 categories. The results of the research show that there are external factors, including cyber-attacks, political and governance obstacles, the increasing progress of communication and information technology, changes in the competitive environment, and conflicting investment relationships in the financial and banking fields, and internal factors, including the absence of strong security infrastructure in the network and weak security, weakness in digital human capital, changing needs and demands of actors, weakness in the traditional business model, and the lack of a management perspective regarding the growth of digital banking and regulatory and legal requirements, are the stimuli for the co-evolution of the elements of the digital banking ecosystem, which occurs with the activation of reinforcing and transformative generative mechanisms. The consequences of the evolution of the digital banking ecosystem can be classified into three levels: users, owner, and society.IntroductionIn the digital revolution, with the change in customer behavior and expectations and the behavior of businesses, the structure of the competition and the strategic context of the business world has changed, and the banking industry is not an exception to this rule; therefore, digitalization is considered a requirement for the banking industry. However, moving to digital banking is not an easy task for banks, and the digital revolution has become a major challenge for this industry. The previous studies have revealed that, so far, some banks have not been successful in facing the fundamental changes of the digital age and have not been able to take fundamental actions to stabilize their businesses. To this end, the first step to realizing these changes in the banking industry is a complete reconsideration of customer relations and the method of providing value to meet customer needs, business models, platforms, and ecosystems. Yet it is not enough to set up a digital platform ecosystem and just rapidly multiply it, as keeping it stable is also highly important as well. In fact, digital platform ecosystems that can be sustained over the long term are very rare. Therefore, digital platform ecosystems must have the ability to evolve and adapt to the challenges caused by the chaotic and unpredictable environment that organizations operate in as well as the increase of internal inefficiencies. Thus, besides the need for a complete rethinking of the digital banking ecosystem, the manner in which its evolutionary process has been in Iran's banking industry is important. Thus far, neither of these issues has been investigated in Iran which signifies the necessity of applied and fundamental research. The current research tries to fill this gap in the country.Research Question(s)What are the elements of the digital banking platform ecosystem and which will evolve in the future?What are the generative mechanisms of digital banking platform ecosystem evolution?What are the stimulating factors and consequences of the evolution of the digital banking platform ecosystem?Literature ReviewNowadays, digitization has become a strategic priority for the banking industry, and the establishment of digital banking requires creating a strong ecosystem around the digital banking platform and developing it. However, so far, there has not been any systematic and comprehensive approach regarding the evolution of the digital platform ecosystem. Previous studies identify digital platform ecosystems as highly evolving socio-technical arrangements that require rapid development and adaptation to ensure their long-term sustainability. Due to the lack of a clear conceptualization of the evolution of digital platform ecosystems, Stykova (2019) integrated different perspectives and proposed a novel conceptualization of the evolution of digital platform ecosystems. According to his studies, the evolution of the digital platform ecosystem delineates continuous changes in the digital platform ecosystem in relation to its actors, architecture, and governance. Through these changes, the simultaneous development of platform structures, infrastructure, functionalities, and governance regime occurs. The endogenous and exogenous events and factors emerging during the evolution of the digital platform ecosystem challenge the configuration of the ecosystem and lead to changing it. Staykova defines two types of generative mechanisms for the evolution of the digital platform ecosystem: transformative and reinforcing.MethodologyIn this research, the qualitative content analysis method is used. A group of experts in the field of digital banking in Iran's banking industry who have deep insight and the necessary knowledge about the subject of the research participated as the statistical population of this research. The purposive sampling method was employed for sampling and continued until the theoretical saturation was reached. Therefore, in this research, 21 interviews were conducted, and due to the incompleteness of some of them, in the end, by analyzing 18 interviews, the research questions were answered, and theoretical saturation was achieved.ResultsThe analysis of the interviews led to the identification of 674 themes, 181 codes, 59 subcategories, and 20 categories. Based on the results of categorization in thematic analysis and review of the theoretical foundations and the related literature, the elements of the digital banking platform ecosystem, including ecosystem actors, platform architecture, and platform governance were identified. Moreover, External factors, such as cyber-attacks, political and governance obstacles, the increasing progress of communication and information technology, changes in the competitive environment, and conflicting investment relationships in the financial and banking fields, as well as internal factors, such as the absence of strong security infrastructure in the network and weak security, weakness in digital human capital, changing needs and demands of actors, weakness in the traditional business model, and lack of management perspective regarding the growth of digital banking and regulatory and legal requirements, are the co-evolutionary stimuli of digital banking platform ecosystem elements, which appear as a result of the activation of generative, reinforcing and transformative mechanisms. Finally, the implications of the evolution of the ecosystem of the digital banking platform can be classified into three levels. The benefits of the evolution of the ecosystem for users are increasing the speed of providing and receiving services, gaining usefulness, improved security, freedom in providing and receiving services, ease of access, and co-creation of value. The implications of evolution for the owner (organization) are the Orchestrator role of the owner, dynamic capability, market size, increased revenue, increased productivity, and business sustainability. Furthermore, the outcomes of the evolution of the ecosystem at the community level include social, environmental, and economic benefits, which are indicators of reaching the maturity stage in the life cycle of the digital banking platform ecosystem.
Research Paper
Management approaches in the field of smart
vahideh alipoor; Mohammadreza Saadi; atefeh mehri bazghaleh
Abstract
This study aimed to investigate the effect of brand interaction through social media on brand loyalty through brand equity. The research is a descriptive research of the correlational type in terms of practical purpose and based on the method. The statistical population of this research includes the ...
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This study aimed to investigate the effect of brand interaction through social media on brand loyalty through brand equity. The research is a descriptive research of the correlational type in terms of practical purpose and based on the method. The statistical population of this research includes the customers of three Iranian brands, My, Cinere and Callista. The size of the population is considered unlimited and the G-POWER tool was used to determine the sample size and the statistical sample size was calculated to be 199 people. A simple random sampling method was chosen for data collection and the data collection required in this research was done using a localized standard questionnaire. In order to confirm content validity, CVI and CVR and expert panel were used. In order to determine the reliability of the questionnaire, Cronbach's alpha coefficient, combined reliability and homogeneous reliability were used. Because the data do not follow normal modeling; Therefore, structural equation model and partial least squares method and PLS software were used for hypothesis testing and path analysis. The results of this study showed that customer interaction with the brand through social media can have a positive effect on brand loyalty. Also, brand equity was a positive mediating factor in the relationship between brand interaction and brand loyalty. Among the three cognitive, emotional and behavioral paths, the behavioral path with a path coefficient of 0.630 is the best path in influencing customer interaction with the brand on brand loyalty. IntroductionAccording to a Harvard Business Journal study, a 5% increase in customer loyalty can increase company profits by 25-95%. The importance of building and maintaining strong and long-term business relationships with customers is a great strength for brands (Rather, 2018). In recent years, customer interaction with the brand has become an important concept due to its promising impact on customer behavior (Rather, 2018; Rather & Sharma, 2017). By interacting with customers in virtual spaces and social networks, the brand can offer unique benefits.According to the statistics and figures announced by the Radio Communications Regulatory Organization, the number of internet users in Iran has reached more than 79 million people in 2021. This is a large number of people, each of whom may be customers of different brands and companies. Therefore, researching the impact of the customer's interaction with the brand, which usually takes place in the virtual space, on customer loyalty in Iran seems to be very necessary. Considering the importance of customer loyalty to the brand and the need for brands to promote effective communication with customers, this study investigated the impact of customer's interaction with the brand on customer loyalty. This study attempts to answer questions such as: "Can the customer's interaction with the brand lead to an increase in the customer's loyalty to the brand?" and "What type of interaction with the brand has the greatest influence on the customer's loyalty?" Literature review2.1. customer interaction with the brand via social mediaSocial media focuses on collaboration, conversation and exchange between users. Social networks are online hosts that allow members to create their own private profiles and interact with each other (Tuten & Solomon, 2017). Companies are increasingly looking to engage customers and interact with their brands. The concept of customer interaction with the brand encompasses three dimensions, namely the cognitive, emotional and behavioral dimensions, making it a multidimensional concept for study. Customer interaction with the brand emphasizes the relationship between the customer and the brand (Islam et al., 2019). Interaction with the brand may be more influential than perceived value and service quality, which are known to be important factors for brand loyalty (So et al., 2016).2.2. customer interaction with the brand via social media and brand equityCurrently, social media plays an important role in creating brand equity through interaction with customers. Therefore, customers' interaction with brands through social networks is a symbol of their greater commitment to brands, and this issue increasingly contributing to brand equity (Choedon & Lee et al., 2020). enhances the brand, which in turn creates strong, favorable and unique connections with the brand and influences their purchasing decisions and creates value (Elsharnouby et al., 2021), which means the creation of brand equity (Schivinski & Dabrowski, 2015). MethodologyAs the current research focuses on observable facts, it is part of the positivist paradigm. Due to its specific application in the field of brand management, it is considered practical research. As far as the method of data collection is concerned, it is a descriptive and survey-type study whose temporal scope lies in the area of cross-sectional research. The statistical population of this study includes all customers of Iranian cosmetics and health brands. For this purpose, the followers of the social pages of three famous Iranian brands in the field of cosmetics and health products such as My, Cinere and Callista were surveyed. Since the number of customers of these three famous Iranian brands is more than one hundred thousand people, the size of the community is considered unlimited. The G-POWER tool was used to determine the sample size. According to Cohen, reducing the alpha error leads to an increase in the generalizability and accuracy of the research results. By lowering the alpha error to a lower level, the likelihood that the researcher will incorrectly reject their hypothesis and commit a type 1 error is reduced. In addition, by increasing the power of the test, the second type error is reduced and the precision of the research results is increased (Cohen-Tannoudji et al., 1998). For this purpose, the sample size was set at an error level of 0.1 and a test power of 0.85 and with a number of 7 variables, 199 subjects.A simple random sampling method was chosen for data collection. The data collection required for this study was done using a localized standard questionnaire consisting of two parts: demographic questions and specific questions in the area of research variables. ResultsThe results obtained show that the cognitive, emotional and behavioral interactions with the brand have a positive and significant influence on the brand equity, with path coefficients of 0.330, 0.410 and 0.751, respectively. brand equity also has a positive and significant influence on customer cognitive, emotional and behavioral loyalty, with path coefficients of 0.894, 0.870 and 0.839, respectively. The Sobel test was used for the significance of the mediating effect of one variable on the relationship between two other variables. Three hypotheses - H7 cognitive path, H8 emotional path and H9 behavioral path - measure the mediating role of brand equity in the relationship between the dimensions of customer interaction and customer loyalty. The cognitive path with a path coefficient of 0.295 and a z- value of 6.183, the emotional path with a path coefficient of 0.356 and a z- value of 7.407 and the behavioral path with a path coefficient of 0.630 and a z-value of 11.18 were confirmed. DiscussionThe present study was conducted with the aim of determining the impact of customers' interaction with the brand via social media on brand loyalty, considering the mediating role of brand equity and the priority of the three cognitive, emotional and behavioral pathways in these impacts. Specifically, the impact of the dimensions of customer interaction with the brand, including cognitive interaction, emotional interaction, and behavioral interaction, on the dimensions of brand loyalty, including cognitive loyalty, emotional loyalty, and behavioral loyalty, was examined under the mediating role of brand equity. ConclusionAccording to the path coefficient, the behavioral path with a path coefficient of 0.630 is the best path to influence the customer's interaction with the brand on brand loyalty. Therefore, the said brands should communicate less emotionally on social media and be more precise so that the content presented on social media creates a sense of fit and congruence between the customer's image and the brand and a positive image of the said companies' brand remains in the customer's mind even when they compare it with other brands. Therefore, the marketing managers of the mentioned brands are recommended to share the latest and trending content and organize and engage in activities that are considered trendy and show the popular, modern and successful personality of social media users so that they can support the customers' need for self-expression.Keywords: Customer Engagement, Customer Loyalty, Brand Equity, Social Media, Beauty Brands
Research Paper
Data, information and knowledge management in the field of smart business
Nahid Entezarian; Mohammad Mehraeen
Abstract
New technologies in the field of Industry 4.0 enable companies to enhance their business processes and customize products and services through the generation of new knowledge. The creation and sharing of this new knowledge depends on both the optimal use of Industry 4.0 technologies and interactions ...
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New technologies in the field of Industry 4.0 enable companies to enhance their business processes and customize products and services through the generation of new knowledge. The creation and sharing of this new knowledge depends on both the optimal use of Industry 4.0 technologies and interactions along the value chain. However, achieving business benefits is highly dependent on human resources and their digital skills and competencies. Therefore, companies approaching the Industry 4.0 paradigm should consider these new technologies as tools that facilitate the creation and sharing of new knowledge. They should pay attention to the digital skills and competencies required to manage this technological transformation and enhance internal competencies. The purpose of this research is to combine the results and findings obtained from qualitative studies, providing new insights from previous research. In this study, a meta-composite approach was used to investigate qualitative case studies, examining the relationship between knowledge management and Industry 4.0 capabilities in organizations. The results show that knowledge management capabilities in the field of Industry 4.0 are examined in two dimensions: business models and organizational innovation. This research also emphasizes that in order to address organizational challenges, knowledge management strategies and the maturity level of Industry 4.0 technologies within organizations must be understood.IntroductionIndustry 4.0, driven by digital technologies such as smart sensors, IoT, cloud computing, big data, and AI, holds significant importance in the realm of organizational knowledge management. It enables convenient access to vast repositories of data that can be meticulously scrutinized to drive improvements in processes. Moreover, Industry 4.0 seamlessly merges the physical and virtual domains, thereby enhancing both production processes and resulting products (Wilkesmann, 2018). This study endeavors to propose a model that seamlessly integrates knowledge management and Industry 4.0 to gain a competitive advantage. The researchers will utilize the Meta-synthesis method to identify capabilities and develop a new framework, thus contributing to a deeper understanding in this field.Literature ReviewThe theoretical foundations are categorized into two components: Industry 4.0 and knowledge management.2.1. Industry 4.0Industry 4.0 emerged in 2011 as the fourth industrial revolution, focusing on fully automated and intelligent production systems. It involves the integration of production systems through real-time information exchange and flexible production. The internet and related technologies play a crucial role in connecting physical objects, machines, and processes across organizations (Ghobakhloo, 2018). Industry 4.0 relies on data-driven decision-making and recognizes the value of real-time data utilization. It disrupts traditional competition and impacts various aspects of organizational strategy, business models, innovation, supply chains, production processes, and stakeholder relationships (Pozzi et al., 2023).2.2. Knowledge management strategies and approaches in Industry4.0Knowledge is essential for decision-making in implementing Industry 4.0 technologies. Industry 4.0 significantly influences knowledge management within organizations. These technologies facilitate knowledge management by enhancing existing knowledge and generating new knowledge. Knowledge sharing and storage are key components of knowledge management in the context of Industry 4.0 (Salvadorinho & Teixeira, 2021). The cost-effective and high-performance nature of Industry 4.0 technologies makes them suitable for storing and sharing knowledge. Industry 4.0 technologies enhance value creation through knowledge sharing within organizations and enable organizational innovation and competitive advantage maximization through knowledge management (Gupta et al., 2022).MethodologyThis research proposes Meta-synthesis as a suitable method for effectively combining the various factors involved in knowledge management capabilities and Industry 4.0 technologies within organizations. Meta-synthesis serves as a valuable instrument in formulating a comprehensive theory by systematically amalgamating these elements. The selection of the Hoon model (Hoon, 2013) for this research is based on its comprehensive and innovative nature in comparison to other Meta-synthesis models. It is characterized as an exploratory and inductive research design that integrates qualitative case studies to extend the findings of the original studies. Hoon's proposed Metasynthesis entails eight specific steps, which are briefly outlined below:Step 1 involves designing and framing the research question related to knowledge management capabilities in Industry 4.0. Step 2 includes searching for articles using specific keywords and selecting relevant research. Step 3 involves screening and selecting suitable texts based on inclusion criteria. Step 4 entails extracting and coding evidence from selected studies. Step 5 analyzes individual studies using a causal network technique. Step 6 synthesizes findings on an across-study level. Step 7 involves building theory from meta-synthesis.Results and DiscussionThe convergence of Industry 4.0 and knowledge management within organizational frameworks serves to amplify the influence of knowledge management on the performance of organizational innovation (Tortorella et al., 2022). This study furnishes valuable perspectives for formulating an adoption strategy and prioritizing tasks in the integration of Industry 4.0. It underscores the significance of knowledge dissemination in expediting the assimilation of Industry 4.0 and recommends a focus on cultivating affiliations with strategic counterparts. The development of internal capabilities and competencies stands as pivotal for meaningful engagement in knowledge dissemination for Industry 4.0. Effective knowledge exchange among organizations can offset the dearth of internal resources and knowledge during the adoption process. This study accentuates the cost-effectiveness of knowledge sharing as an alternative to external consultants. In sum, it furnishes invaluable insights for managers seeking to augment organizational innovation, fortify stakeholder associations, and attain a competitive edge in the landscape of Industry 4.0.ConclusionThe Meta-synthesis approach used in this study has limitations, including a smaller sample size of only 8 studies, which raises concerns about the generalizability of the findings. The reliance on a limited number of keywords for searching and identifying studies is another limitation. However, the study's analysis revealed similarities among the chosen articles, and the selection process followed the criteria set by Hoon (2013). The Meta-synthesis protocol allows for the development of causal networks, meta-causal network, and case comparison table, showing a wider context of knowledge management and Industry 4.0 capabilities in organizations. Future studies should encompass a wider scope, as organizations in the Industry 4.0 environment need to share and manage knowledge both internally and externally. The Meta causal network developed in this study can be used as a foundation for developing strategies that generate value and foster a competitive advantage in the realm of Industry 4.0.Keywords: Knowledge Management, Industry 4.0, Meta-Synthesis, Case Study.Figure 1. Meta-causal network of selected analyzed studies (research findings)
Research Paper
Management approaches in the field of smart
Kamran Feizi; Hormoz Mehrani; Hossein Vazifehdoost; Ehsan Sadeh
Abstract
The purpose of this research is to identify the main components and dimensions of digital content marketing according to the conditions of Iran and to present a model using a qualitative method based on the Grounded Theory approach. Data were collected through semi-structured in-depth interviews with ...
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The purpose of this research is to identify the main components and dimensions of digital content marketing according to the conditions of Iran and to present a model using a qualitative method based on the Grounded Theory approach. Data were collected through semi-structured in-depth interviews with experts. These experts included university professors in the field of digital content marketing and managers and activists of advertising and digital marketing agencies. Using targeted sampling and after conducting 15 in-depth interviews, theoretical saturation was achieved after the implementation and analysis of interviews and open, axial, and selective coding. The results showed that digital content marketing is realized by these conditions. Causal conditions (Internet development, ease of use, popularity of cyberspace, ease of communication with customers, virtual network features, The appeal of Digital Content Marketing, changing customer behavior, changing current structures), contextual conditions (personal characteristics, social characteristics), intervening factors (technical limitations, increasing competitors, lack of access, unawareness, severity of the spread of Covid-19), central category (Digital Content Marketing), strategies (make it enjoyable, increasing participation, providing various services to users, using various methods to increasing audience, exchange of information, infrastructure improvement, audience-friendly style, customer orientation), and consequences (market expansion, increasing profitability, groundwork for brand creation). The results indicate that the research model is acceptable level. IntroductionThe emergence of digital networks and the wide spread of technology dramatically changed human life compared to the latest technological tools of the previous decades (Torkestani et al, 2020), so that nowadays customers have significantly changed their behavior in line with the technology and economic environment of the world (Rostami et al, 2022). Modern advertising is more cost-effective and inexpensive than traditional advertising (Abdulle, 2022). Therefore, to attract customers, trust must be gained first, and customers should feel satisfied with our brand before purchasing a product or service. These features can be found in digital content marketing (Mozaffari et al, 2023). Inflation and the prevailing economic recession have created difficult conditions for individuals and businesses (Naseri, 2017). To overcome these difficult conditions, a native model must be explained in the field of digital content marketing.Research Question (s)The main research question is as follows: What is the conceptual model for digital content marketing? What are the main components and dimensions of this model? Literature ReviewTorkestani et al. (2022) discussed the possibility of personalizing content for each audience, the emotions hidden in the content and the date of publication of the content, the way the author is a member and his identity in the online community. Tahmasebpour et al. (2022) considered digital marketing elements to include six components: technical features of digital tools, relative advantage, cost to customers, management of items and processes, promotion, service quality, and information quality. Rostami et al. (2022) also prioritized the factors affecting marketing in digital businesses in their research and did not provide a model that included all aspects of this issue. (Naseri, 2017) also only looked at content features in four categories, content inherent elements, form elements, content distribution media elements and effectiveness measurement elements. (Naseri, 2017) examined only the characteristics of the content in four categories; intrinsic content elements, form elements, content distribution media elements, and effectiveness measurement elements. (Lou & Xie, 2020) also emphasized in their research on shaping brand experience and customer loyalty of. Boban et al. (2020) only investigated the relationship between content entertainment and informational social influence and between self-expression and normative social influence and electronic word-of-mouth communication. Furr. (2019) considered digital content marketing as a branding tool for colleges and universities. Taiminen & Ranaweera, (2019) focused on support, engagement, and interaction in brand communication and perceptions. MethodologyIn terms of purpose, this research is fundamental and applied, and in terms of nature, it is in the category of qualitative research. In terms of approach, the Grounded Theory and paradigm model of Strauss and Corbin have been used as a research plan, which is based on the identification of causal conditions, contextual conditions, central category, intervening conditions, strategies and consequences, and describing the relationships between them. A semi-structured in-depth interview was conducted with 15 experts who were selected in a non-probable and purposeful manner. Sampling was performed until theoretical saturation using the snowball method. The research community includes digital experts (academic and non-academic). This group includes university professors in the field of digital content marketing, as well as activists and specialists in the field of digital content production (institutions and agencies of digital marketing and advertising). They have at least five years of scientific, research, and executive experience in DCM and at least a master's degree or doctorate in the fields of marketing management, information technology management, and other related fields. In the analysis of the data obtained from the interviews, after the full implementation of the text of the interviews, MaxQuda 2020 was used. Immediately after each interview, the interview text was extracted and typed, entered into the software, and open, axial, and selective coding procedures were performed. ResultsThe information obtained from the interviews resulted in the extraction of 135 concepts (open coding), which were placed in the form of 26 subcategories (axial coding) and 5 main categories (selective coding), as follows: Internet development, ease of use, popularity of cyberspace, ease of communication with customers, virtual network features, the appeal of Digital Content Marketing, changing customer behavior, changing current structures, technical limitations, increasing competitors, lack of access, unawareness, severity of the spread of Covid-19, personal characteristics, social characteristics, make it enjoyable, increasing participation, providing various services to users, using various methods for increasing the audience, exchange of information, infrastructure improvement, audience-friendly style, customer orientation, market expansion, increasing profitability, groundwork for brand creation.The paradigm model is presented in figure 1.Figure.1. Paradigm model of the research ConclusionThis article aims to design a digital content marketing model. Making it enjoyable in this research includes gamification in social networks, visualizing content according to people's tastes, using humorous content, and entertaining content. The obtained results showed that making it enjoyable is an effective strategy for digital content marketing. In this research, the following were discovered to increase participation in digital content: using celebrities, participating in online campaigns, holding contests, and building trust and commitment in customers. The results also show that increasing participation is an important strategy in digital content marketing. The services that can be paid in the production of digital content to attract the audience are as follows: offering discounts, bonuses to users, free training, and support. The obtained results also show that providing diverse services to users is an influential factor in digital content marketing strategies. Various methods of increasing the audience in this research include: providing outstanding and strange information, producing educational content, producing therapeutic content, producing sports content, using new ideas to produce content, producing fresh and new content, teaching new methods of producing content, increasing quality, extensive advertising, social network viral marketing, and content creation based on frequent words. The results show that the use of methods to increase the audience is the most effective strategy. Information exchange includes modeling external pages, modeling successful pages, participation in online campaigns, and content exchange. The obtained results show that information exchange has a significant impact on digital content marketing strategies. Improving the infrastructure in the field of digital content includes increasing the speed of the Internet, website design, and the production of powerful internal applications, which the results show is one of the most important strategies. Audience-friendly style also includes creating differentiation, special and unique style, being up-to-date; using one of the special effects is the mastery of body language. The results show that an audience-friendly style is one of the most effective strategies in digital content marketing. Customer orientation also includes content production based on society's problems, compliance with professional ethics, monitoring customer needs, finding customers' tastes, and honesty in content production. The obtained results show that customer orientation is an important strategy in digital content marketing.Keywords: Digital Content Marketing, Online Marketing, Brand Awareness, Grounded Theory. The purpose of this research is to identify the main components and dimensions of digital content marketing according to the conditions of Iran and to present a model using a qualitative method based on the Grounded Theory approach. Data were collected through semi-structured in-depth interviews with experts. These experts included university professors in the field of digital content marketing and managers and activists of advertising and digital marketing agencies. Using targeted sampling and after conducting 15 in-depth interviews, theoretical saturation was achieved after the implementation and analysis of interviews and open, axial, and selective coding. The results showed that digital content marketing is realized by these conditions. Causal conditions (Internet development, ease of use, popularity of cyberspace, ease of communication with customers, virtual network features, The appeal of Digital Content Marketing, changing customer behavior, changing current structures), contextual conditions (personal characteristics, social characteristics), intervening factors (technical limitations, increasing competitors, lack of access, unawareness, severity of the spread of Covid-19), central category (Digital Content Marketing), strategies (make it enjoyable, increasing participation, providing various services to users, using various methods to increasing audience, exchange of information, infrastructure improvement, audience-friendly style, customer orientation), and consequences (market expansion, increasing profitability, groundwork for brand creation). The results indicate that the research model is acceptable level. IntroductionThe emergence of digital networks and the wide spread of technology dramatically changed human life compared to the latest technological tools of the previous decades (Torkestani et al, 2020), so that nowadays customers have significantly changed their behavior in line with the technology and economic environment of the world (Rostami et al, 2022). Modern advertising is more cost-effective and inexpensive than traditional advertising (Abdulle, 2022). Therefore, to attract customers, trust must be gained first, and customers should feel satisfied with our brand before purchasing a product or service. These features can be found in digital content marketing (Mozaffari et al, 2023). Inflation and the prevailing economic recession have created difficult conditions for individuals and businesses (Naseri, 2017). To overcome these difficult conditions, a native model must be explained in the field of digital content marketing.Research Question (s)The main research question is as follows: What is the conceptual model for digital content marketing? What are the main components and dimensions of this model? Literature ReviewTorkestani et al. (2022) discussed the possibility of personalizing content for each audience, the emotions hidden in the content and the date of publication of the content, the way the author is a member and his identity in the online community. Tahmasebpour et al. (2022) considered digital marketing elements to include six components: technical features of digital tools, relative advantage, cost to customers, management of items and processes, promotion, service quality, and information quality. Rostami et al. (2022) also prioritized the factors affecting marketing in digital businesses in their research and did not provide a model that included all aspects of this issue. (Naseri, 2017) also only looked at content features in four categories, content inherent elements, form elements, content distribution media elements and effectiveness measurement elements. (Naseri, 2017) examined only the characteristics of the content in four categories; intrinsic content elements, form elements, content distribution media elements, and effectiveness measurement elements. (Lou & Xie, 2020) also emphasized in their research on shaping brand experience and customer loyalty of. Boban et al. (2020) only investigated the relationship between content entertainment and informational social influence and between self-expression and normative social influence and electronic word-of-mouth communication. Furr. (2019) considered digital content marketing as a branding tool for colleges and universities. Taiminen & Ranaweera, (2019) focused on support, engagement, and interaction in brand communication and perceptions. MethodologyIn terms of purpose, this research is fundamental and applied, and in terms of nature, it is in the category of qualitative research. In terms of approach, the Grounded Theory and paradigm model of Strauss and Corbin have been used as a research plan, which is based on the identification of causal conditions, contextual conditions, central category, intervening conditions, strategies and consequences, and describing the relationships between them. A semi-structured in-depth interview was conducted with 15 experts who were selected in a non-probable and purposeful manner. Sampling was performed until theoretical saturation using the snowball method. The research community includes digital experts (academic and non-academic). This group includes university professors in the field of digital content marketing, as well as activists and specialists in the field of digital content production (institutions and agencies of digital marketing and advertising). They have at least five years of scientific, research, and executive experience in DCM and at least a master's degree or doctorate in the fields of marketing management, information technology management, and other related fields. In the analysis of the data obtained from the interviews, after the full implementation of the text of the interviews, MaxQuda 2020 was used. Immediately after each interview, the interview text was extracted and typed, entered into the software, and open, axial, and selective coding procedures were performed. ResultsThe information obtained from the interviews resulted in the extraction of 135 concepts (open coding), which were placed in the form of 26 subcategories (axial coding) and 5 main categories (selective coding), as follows: Internet development, ease of use, popularity of cyberspace, ease of communication with customers, virtual network features, the appeal of Digital Content Marketing, changing customer behavior, changing current structures, technical limitations, increasing competitors, lack of access, unawareness, severity of the spread of Covid-19, personal characteristics, social characteristics, make it enjoyable, increasing participation, providing various services to users, using various methods for increasing the audience, exchange of information, infrastructure improvement, audience-friendly style, customer orientation, market expansion, increasing profitability, groundwork for brand creation.The paradigm model is presented in figure 1.Figure.1. Paradigm model of the research ConclusionThis article aims to design a digital content marketing model. Making it enjoyable in this research includes gamification in social networks, visualizing content according to people's tastes, using humorous content, and entertaining content. The obtained results showed that making it enjoyable is an effective strategy for digital content marketing. In this research, the following were discovered to increase participation in digital content: using celebrities, participating in online campaigns, holding contests, and building trust and commitment in customers. The results also show that increasing participation is an important strategy in digital content marketing. The services that can be paid in the production of digital content to attract the audience are as follows: offering discounts, bonuses to users, free training, and support. The obtained results also show that providing diverse services to users is an influential factor in digital content marketing strategies. Various methods of increasing the audience in this research include: providing outstanding and strange information, producing educational content, producing therapeutic content, producing sports content, using new ideas to produce content, producing fresh and new content, teaching new methods of producing content, increasing quality, extensive advertising, social network viral marketing, and content creation based on frequent words. The results show that the use of methods to increase the audience is the most effective strategy. Information exchange includes modeling external pages, modeling successful pages, participation in online campaigns, and content exchange. The obtained results show that information exchange has a significant impact on digital content marketing strategies. Improving the infrastructure in the field of digital content includes increasing the speed of the Internet, website design, and the production of powerful internal applications, which the results show is one of the most important strategies. Audience-friendly style also includes creating differentiation, special and unique style, being up-to-date; using one of the special effects is the mastery of body language. The results show that an audience-friendly style is one of the most effective strategies in digital content marketing. Customer orientation also includes content production based on society's problems, compliance with professional ethics, monitoring customer needs, finding customers' tastes, and honesty in content production. The obtained results show that customer orientation is an important strategy in digital content marketing.
Research Paper
Data, information and knowledge management in the field of smart business
Mohammad Kazemi; Mohammad Ali Keramati; Mehrzad Minooie
Abstract
The effort of this article is to solve one of the main problems in the field of banking, which is closely related to the field of information technology. The combination of the management discussion of this issue with the field of information technology will be one of the important topics in the field ...
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The effort of this article is to solve one of the main problems in the field of banking, which is closely related to the field of information technology. The combination of the management discussion of this issue with the field of information technology will be one of the important topics in the field of information technology management. The main purpose of this article is the clustering of bank customers.At first, all customer characteristics were extracted from the bank's database, which was randomly extracted for 900,000 customers and will be provided as input to the proposed method of this article. All the characteristics of these customers were extracted and 10 characteristics (except four characteristics of the LRFM method) were listed using the opinions of experts. The proposed method should be able to choose among these 10 features for customer clustering that results in more resolution in clustering. This makes more suitable features to be placed next to the four features of LRFM and improve the performance of LRFM. Due to the high number of variations in this problem, it is not possible to do it manually and the proposed method tries to provide a separate pattern for clustering for the customers of each bank by examining different situations. Also, the problem of choosing the right value for the number of clusters in the K-means method is solved by the method proposed in this article. The results show that it is better than the basic RFM and LRFM methods.IntroductionToday, the Achilles heel of all customer-oriented businesses is customer satisfaction and providing services tailored to each customer's situation. This issue has gone so far that regardless of customer satisfaction, any organization will face failure (Otto et al., 2019). One of the main current challenges for customer-oriented organizations is understanding the differences and ranking customers in order to optimally allocate resources. This issue is very important in managing the correct relationship with the customer. Banks are one of the main customer-oriented institutions in the country (Morzdashti et al., 2022). The bank does not do any proper clustering to know its customers and plan future goals. More precisely, it does not have information about the total number of customers and their distribution. Because of this, more time and money is wasted. As far as the research of this article has followed; The clustering that currently exists for customers does not have the necessary dynamics and people are clustered based on some characteristics such as transaction amounts, occupation or other general characteristics. LRFM model is a method used to cluster customers in customer relationship management. In this model, customers are clustered based on four characteristics of customer relationship, novelty of exchange, number of times of exchange and monetary value exchanged. In fact, the customer relationship length has been added to the RFM model and created the LRFM model. Because, the RFM model was not able to identify loyal customers (Moslehi et al., 2013).In the proposed model of this article, an attempt will be made to provide a dynamic method for using variables with the LRFM method to provide the possibility of implementing different clusters depending on the time of use. This issue will lead to more compliance of the proposed clustering method with reality.Research Question(s)What methodology is used to follow the process of presenting the proposed model?What features can be placed next to the LRFM model to provide appropriate results?What methodology is used to follow the process of presenting the proposed model?What features can be placed next to the LRFM model to provide appropriate results?What will be the structure of particle swarm algorithm?What similarity measure or clustering method would be suitable for customers?How can the LRFM model be improved by the particle swarm algorithm and the creation of different clusters based on the K-means method? Literature ReviewShrahi and Ali Qoli have implemented a clustering method for the customers of one of Sepeh Bank branches in Tehran (Shrahi and Ali Qoli, 2015). This model is based on K-means clustering algorithm. In this method, an attempt has been made to identify sixty companies loyal to the bank from among all legal customers. However, the K-means algorithm has some problems (Bagatini et al., 2019, Santini, 2016):Determine the optimal value for the number of clusters.The initial points that are chosen randomly at the beginning of the algorithm have a great impact on the final result.The order of data entry and their review is effective in the final result.Ayoubi has tried to cluster bank customers using Kohonen neural networks (Ayoubi, 2016). In this method, the training of a neural network is done using the training data, and after that it is possible to cluster the new customer.Yousefizad and Sorayai have also used the RFM model to cluster customers in order to design a model for providing services to customers, which consists of two stages (Yosefizad and Sorayai, 2017).suggested method:In this section, the proposed method of the article is described in full detail. MethodologyIn this part, how to improve the LRFM method using the combination of particle swarm algorithm and K-means method is described. All the steps of particle swarm algorithm are followed and its functions and parameters are specified. The steps of the proposed method will be as follows:Initialization: The schematic of the initial population matrix will be as shown in Figure (2). This matrix consists of two parts. The first part has one element that tries to suggest the number of clusters using the K-means method, and the second part will have 10 binary elements.Calculating the fitness of each particle: Using the fitness function, the fitness level is determined for each particle present in the population. This fitness level is based on clustering using the K-means method. The appropriateness of the clustering done is measured based on the intraclass variance criterion, which corresponds to the image of the fitness of each particle (Ahmar et al., 2018).Update of particle values: Using two parameters, local optimum (LBEST) and global optimum (GBEST), the values present in the particles can be updated. By LBEST, we mean the best value that the I-th particle has reached so far (the best-fit value for the I-th particle). Also, GBEST means the value that has the best fit until T iterations. These two values are used to update the values of other particles. ConclusionThis article tries to provide a dynamic method for clustering bank customers in order to improve their service. The LRFM method has four important features in the field of banking, but its problem is lack of dynamics. More precisely, it is possible that other characteristics such as financial, occupational, or daily transaction characteristics can be added to the four LRFM characteristics and improve the performance of this method. Among all the features that can be placed next to the four features of LRFM; Depending on the customer's data, the appropriate features should be selected. This choice is the responsibility of the particle swarm algorithm. This algorithm tries to put appropriate features along with the four LRFM features depending on the data conditions and customer information to get a better result in clustering. Also, because this algorithm methodK-means helps in finding the number of clusters.It is also possible to replace the particle swarm with other meta-heuristic methods and compare its results with the results in the article.
Research Paper
Management approaches in the field of smart
Hosein Rahimi kolour; Rahim Mohammad khani
Abstract
The digital world provides many opportunities for marketers to reach customers. However, in the fast-paced world, finding new and innovative ways to advertise and sell products and services is very important. Due to the advancement of artificial intelligence and its development in the field of advertising ...
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The digital world provides many opportunities for marketers to reach customers. However, in the fast-paced world, finding new and innovative ways to advertise and sell products and services is very important. Due to the advancement of artificial intelligence and its development in the field of advertising and sales, professionals now have the tools to completely redefine the current understanding of branding, marketing, advertising and sales. The growing popularity of the Internet and the increased use of mobile devices are generating massive amounts of consumer data that feed artificial intelligence-based systems. This research is a type of mixed research with a qualitative and quantitative approach, which is a survey descriptive 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 and IT in the field of advertising and sales, who were selected using the snowball sampling method. In the qualitative part, the tools for collecting information were library and articles review, interviews, and in the quantitative part, questionnaires. In the qualitative part of the data analysis method, using the theme analysis that was compiled with MAXQDA software and using the coding method, and in the quantitative part, the analysis method was based on Kendall's correlation test. According to the results of the research, 7 main themes, 22 sub-themes and 44 codes were discovered, which included the consequences of using artificial intelligence and machine learning in advertising and sales. The findings of the research can have important results for marketers and activists in the field of advertising and sales. Among the consequences of the application of artificial intelligence and machine learning, we can mention things such as understanding, recognizing and revealing consumer needs and desires, classifying target advertisements, intelligent evolution of commercial advertisements, innovation in sales, development of sales channels, and optimization of the fields of using artificial intelligence in advertising agencies Keywords:: artificial intelligence, machine learning, big data, advertising and salesIntroductionMost of the research on the use of artificial intelligence and machine learning in advertising and sales has been done in the last four years. The gap between AI research, the application of AI and machine learning in advertising and sales is still significant. Theoretical findings still need to be supported by real tools and software solutions. In the academic context, most researchers either focus on describing one or two of the newest solutions available on the market or mention very generalized application areas and focus on AI as a phenomenon and the main object of study. There is little research on the results of the general implementation of artificial intelligence in advertising and sales and the results of the implementation of specific artificial intelligence tools. Studies have been conducted on the applications and challenges of the application of artificial intelligence and machine learning in marketing, international marketing and marketing strategies. The innovation of the current study is that despite the exponential development of artificial intelligence and related technologies, its emerging application in various production environments, none of the previous studies have addressed the consequences and results of the application of artificial intelligence and machine learning in a qualitative manner in advertising and sales; Therefore, to cover the issues raised above, we intend to answer the following research question. What areas of artificial intelligence and machine learning are used in advertising and sales? What are the existing solutions based on artificial intelligence and related technologies such as machine learning in the field of advertising and sales development and optimization? Literature ReviewArtificial intelligence is a computer science technology that teaches computers to understand and imitate human communication and behavior. Today, around the world, artificial intelligence has become a hot topic in many sciences and public discussions in society; Because it seems to expand and challenge human cognitive capacity. It is obvious that artificial intelligence will become an integral part of every business organization worldwide in the long run. One of the definitions of artificial intelligence is to teach computers to learn, reason and adapt (Bardo Eritav et al., 2020). Artificial intelligence is supposed to simulate human intelligence in order to support or even expand human abilities (Ote, 2019). In other definitions, the possession of machines with rational and human thinking and action has been emphasized (Berry Hill et al., 2019; Zahouri et al. Moghadam, 2020). Machine learning (ML) is a process that uses observations or data, such as direct experience or instruction, to recognize patterns in data without human intervention, allowing you to make better decisions in the future. The goal of ML is to enable computers to learn automatically "on their own," without human intervention or assistance, so that systems can adjust their actions accordingly. Today, most AI applications use ML in marketing activities, from personalizing product offers to helping discover the most successful advertising channels, estimating churn rates or customer lifetime value, and creating superior customer groups (Tiwari et al., 2021; Shissel et al., 2020). Compared to traditional advertising production, artificial intelligence technology has increased the effectiveness of advertising production and marketing, and has made brand marketing more humane, accurate and effective, and has improved the effectiveness of advertising communications and information call rates. Advertising production using artificial intelligence technology can categorize, combine information sources, quickly generate new ideas, and implement intelligent marketing (Deng et al., 2019). MethodologyThis research is a type of mixed research with a qualitative and quantitative approach, which is a survey descriptive study in terms of its purpose, application, and in terms of data collection. The tools of data collection in the qualitative part of the library review were articles and semi-structured interviews with 18 managers and experts in the field of digital marketing and IT in the field of advertising and sales, who were selected using the snowball sampling method. The method of data analysis in the qualitative section, using theme analysis, which was compiled with MAXQDA software and using the coding method. In the quantitative part, purposeful sampling with 35 digital marketing experts and information gathering through a questionnaire, the analysis method was based on Kendall's correlation test. ResultsAccording to the results of the research, 7 main themes, 22 sub-themes and 44 codes were discovered, which included the consequences of using artificial intelligence and machine learning in advertising and sales. The findings of the research can have important results for marketers and activists in the field of advertising and sales. Among the consequences of the application of artificial intelligence and machine learning, we can mention things such as understanding, recognizing and revealing consumer needs and desires, classifying target advertisements, intelligent evolution of commercial advertisements, innovation in sales, development of sales channels and optimization of the fields of using artificial intelligence in advertising agencies. Discussion and ConclusionThe rapid development of modern technology, especially artificial intelligence, has led to the creation of powerful solutions to take advertising and sales to a whole new level. With the increased use of social media and the Internet, the amount of data available on customer behavior and customer communication is immense. Although research on the use of artificial intelligence and related technologies is still limited due to the novelty of the topic, this paper reviews existing research on innovation, the use of social media with AI, machine learning, and big data capabilities to provide opportunities to increase advertising effectiveness and Sales have been linked. Using artificial intelligence, it is possible to gain a clearer view of consumer behavior on social media that leads to brand preferences. Artificial intelligence-based systems that work in digital marketing environments focus on machine learning and big data techniques and use data-driven marketing strategies to guide and collect customer knowledge data and evaluate activity performance; Therefore, by using systems based on artificial intelligence and machine learning, facilitate decision-making processes, understanding user behavior and responses, innovation strategies, sales forecasting, understanding social network strategies, customer orientation and optimization of activities and strategic advertising planning in digital environments.Advertising and sales systems based on artificial intelligence can add value to the business, as well as turn the application of artificial intelligence and machine learning in advertising and sales into a sustainable strategy that can guide the steps a company takes to succeed in its marketing strategies, such as content analysis and optimization. social; performance analysis and media selection; Budget analysis and optimization; Identifying and evaluating target groups; Predicting reactions; monitoring the competition, it needs to realize; Therefore, the application and new uses of advertising and sales system based on artificial intelligence seem necessary for companies
Research Paper
Data science, intelligence and future analysis
Manijeh Ramsheh; mohammad hasan maleki; narges sarlak; monireh falahat bangdeh
Abstract
Fintech and its entrepreneurial opportunities have the ability to play an effective role in the development of the financial industry. Therefore, it is necessary to make a correct and effective policy in this area to identify its probable future. This study is exploratory in terms of purpose and practical ...
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Fintech and its entrepreneurial opportunities have the ability to play an effective role in the development of the financial industry. Therefore, it is necessary to make a correct and effective policy in this area to identify its probable future. This study is exploratory in terms of purpose and practical in terms of orientation. Interviews and questionnaires were used to collect data. 28 drivers were extracted by reviewing the background and interviewing experts. In order to screen the propellants, expert questionnaire and fuzzy Delphi method were used. Then the propellants screened were ranked through the priority assessment questionnaire and the developed COPRAS technique. The two drivers of the development of smart contracts in the financial industry and the tendency of financial institutions towards open innovation had the highest priority and were used to write research scenarios. Based on these two drivers, 4 scenarios of the era of pristine opportunities, the era of conservative managers, the era of dilapidated infrastructure and the ice age were developed. Then, using the MABAC technique, the scenario of the age of dilapidated infrastructures was selected as the possible scenario of the research. Research proposals were proposed based on priority drivers and possible scenarios. Government support, providing sufficient funds in order to create the necessary infrastructure for the development of smart contracts by banks or establishing a cooperative relationship between banks and financial institutions, fair legislation, development of regtechs, creating compatibility between current systems with new technologies were the most important practical proposals of the study.IntroductionThe financial industry includes a set of institutions and organizations that allocate credit and equip resources. The development of economic activities requires investment, and investment also requires the provision of financial resources, which is an important task for the financial industry. Therefore, the development of the financial industry is the driver of economic development. Therefore, the increasing need for new technologies to improve performance and increase efficiency in the financial industry is strongly felt. One of the working models affecting the financial industry is fintech (Qaemi & et al., 2017). Fintech is a field that uses innovative technologies to provide all services of the financial industry with greater speed and transparency and lower cost while maintaining security and quality (Zavolokina et al., 2016). According to reports, global fintech investments have grown from $9 billion in 2010 to $25 billion in 2016. Fintech's market share this year was not even 1%, but it is expected to increase to 35% by 2023 (Koshesh Kordsholi & et al., 2021). In Iran, one of the priorities of the sixth development plan is the issue of financing and expansion of financial instruments, which the financial and banking actors have encouraged to support fintech startups in order to realize innovation and expand their services (Qaemi & et al., 2017). But the fact is that fintech in Iran is behind the rest of the world. In iran, due to several problems, including legal challenges, technology problems, financing, etc., fintech businesses are not growing and there are many entrepreneurial opportunities in this field that have not been addressed. Thus, the current research seeks to answer the following questions:What are the key drivers affecting the future of fintech entrepreneurial opportunities in Iran?What is the degree of priority of the key drivers affecting the future of fintech entrepreneurial opportunities in Iran?What are the plausible future scenarios of fintech entrepreneurial opportunities in Iran?What is the possible future scenario of fintech entrepreneurial opportunities in Iran?Literature ReviewFintech can be considered as any innovative idea that improves financial services processes by providing technological solutions according to different business situations) Suryono & et al., 2020). Fintech startups are looking for new approaches to business models, improving customer experience and new approaches that lead to service changes and are trying to enter financial systems and challenge traditional financial institutions (Gomber et al., 2018). If the context and the possibility of growth and application of entrepreneurial opportunities hidden in the field of fintech, which are in their maturity stage, are provided; It will follow the increasing economic progress of the countries.Uncertainty about the future of organizations prompts managers to look for new tools and methods to determine future situations and create the future. Future research is a systematic way to look at the long-term future in any field and draw it, the main purpose of which is to know the new structures, mechanisms, opportunities and processes and to determine the sectors that have more efficiency. Understanding and applying futurist theories and methods enables individuals and groups to more usefully anticipate the future and shape it to a greater extent based on their preferences (Dator et al., 2019).MethodologyIn this research, four quantitative methods, fuzzy Delphi, entropy, developed COPRAS and MABAC technique were used. Also, to develop believable research scenarios, the qualitative method of the consultation workshop was used. The theoretical community of this research is 10 members of the academic staff of the university, fintech experts, managers of fintech businesses, experts of fintech associations and senior experts of the central bank in the regulatory field. The sampling method of the present study is judgmental and based on the expertise of individuals in the field of fintech. The steps of the current research are: 1) background review and interviews with experts to identify drivers affecting the future of fintech entrepreneurial opportunities in Iran; 2) Screening research drivers with the fuzzy Delphi technique; 3) prioritizing the final drives with the developed COPRAS method; 4) Compilation of plausible future scenarios of Iran's fintech entrepreneurship opportunities using a consultation workshop (participation of 7 experts); 5) Selecting a possible research scenario using the MABAC technique.ResultsAt first, 28 drivers were extracted through reviewing financial technology-oriented backgrounds and interviewing experts. Then, with the application of expert questionnaire and fuzzy Delphi technique, 15 drivers were removed from the calculations and 13 drivers were selected to extract the effect model of drivers. Based on the output of COPRAS technique, the drivers for the development of smart contracts in the financial industry are the tendency of financial institutions towards open innovation, the variety of financing methods and the attitude of the regulator towards fintechs, respectively, they have the most importance in terms of influencing the future of fintech entrepreneurial opportunities. The two drivers of smart contract development and the trend of financial institutions towards open innovation were used to map research scenarios. Considering that for each driver, two opposite situations can be set, four scenarios were developed, which are: the era of pristine opportunities, the era of conservative managers, the era of dilapidated infrastructure, and the ice age. In the following, MABAC technique was used to select the possible research scenario. The ranking of the research scenarios in terms of 3 selected indicators is such that the scenario of the age of dilapidated infrastructures is the most likely research scenario. The ice age scenarios, the conservative managers era scenario, and the pristine opportunities era scenario were ranked next.ConclusionThe trend of financial institutions towards open innovation in the scenario of the age of worn out infrastructure shows that the development of digital technologies has gradually created interest in the managers of this industry and has improved their attitude towards themselves. But the lack of development of smart contracts in the financial industry in this future has several reasons. Among the reasons for this lack of development, we can mention the lack of necessary infrastructure for the development of information technology, which is mainly due to the lack of support from the government and the relevant ministry and their lack of attention to the importance of learning information technology. In addition, banks should also provide the necessary funds in order to create the necessary infrastructure for the development of smart contracts, and if there is a heavy cost, establish a cooperative relationship between the banks and financial institutions of the country along with government support to reduce the cost and implement it in a tangible way. Another important discussion in this field is that fair regulation is necessary for the spread of smart contracts. Strengthening regtechs through science and technology parks and growth centers can also help. Another important reason for not developing smart contracts is the incompatibility of current systems with new technologies, which prompts managers of financial institutions to change the system and make them compatible. Lack of sufficient training for financial industry activists can also be another factor for this lack of development.Keywords: Future Study, Driver, Scenario Planning, Entrepreneurial Opportunities, Fintech
