Presenting an interactive marketing model based on causal, contextual and intervening variables in the steel industry
Subject Areas : Agriculture Marketing and Commercialization
Hossein Vahidi Iry Sofla
1
,
Mahmoud Ahmadi Sharif
2
,
Mohammad Nasrolahnia
3
,
peyman Ghafari Ashtiani
4
1 - MA in Strategic Management, Faculty of Management, Damavand Branch, Islamic Azad University, Damavand, Iran
2 - هیئت علمی دانشگاه شهید رجایی
3 - استادیار گروه بازرگانی گرایش بازاریابی دانشکده مدیریت- دانشگاه آزاد واحد تهران جنوب
4 - Arak
Keywords: content-oriented interactive marketing, causal variables, contextual variables, intervening variables, consequences of the model, steel industry.,
Abstract :
The purpose of this research is Presenting an interactive marketing model based on causal, contextual and intervening variables in the steel industry based on the foundational data theory method. In this research, based on interview tools from experts and senior managers of the steel industry, many factors related to content-based marketing, and research variables have been identified and structures related to each variable have been presented using open, central and selective coding methods. In the next step, based on the analysis of the data obtained from the questionnaire, by confirmatory factor analysis method with PLS technique, the factor load and combined reliability for the research variables were calculated above 0.4 and 0.7, respectively, and as a result, the validity of each construct related to the factors And the implications of the research model were confirmed. Then, by the method of structural equations and estimation of the final model, causal factors, contextual factors, and intervening factors have been confirmed in the first to fourth positions of influence on content-based interactive marketing in the final model of the research. Finally, while presenting the research model with optimal overall goodness of fit, paying attention to the effects and consequences of the model including: internal and corporate consequences, competitive advantage, brand experience management, customer and market consequences are suggested.
Kumar Behera R. & Bala PK., & Rana NP. (2024). Assessing the intention to adopt computational intelligence in interactive marketing. Journal of Retailing and Consumer Services, 78(1): 103765-77.
Kuo YF. & Chen FL. (2023). The effect of interactivity of brands’ marketing activities on Facebook fan pages on continuous participation intentions: An S-O-R framework study. Journal of Retailing and Consumer Services, 74(1): 103446-55.
Paramita W. & Nhu HBC & Ngo LV. & Tran QHM. & Gregory G. (2021). Brand experience and consumers' social interactive engagement with brand page: An integrated-marketing perspective. Journal of Retailing and Consumer Services, 62(1): 102611-23.
Feizi K. & Mehrani H. & Vazifehdust H. & Sadeh E. (2024). Conceptualization and Design of a Digital Content Marketing Model Using Structural Equation Approach, Agriculture Marketing and Commercialization, 8(1): 51-69.
Brockmann EN. & Anthony WP. (2022). Tacit knowledge and strategic decision-making. Group & Organization Management, 27(4): 436-455.
Ramezani F. & Shokravi S. & Bagheri A. (2024). Investigating the Impact of Natural Resource Rent and Political Stability on the Environmental Degradation Index in Selected Developing Countries Using a Combined Data Approach, 8(1): 93-108.
Rezaei S. (2023). Content analysis of food industries marketing by focus on new trends. Agricultural Marketing and Commercialization Journal, 7(2): 128-143.
Seyfollahi N. & Movahed SM. (2022). Analysis of the effect of customer knowledge on the improvement of service quality in the hotel industry with the mediating role of customer relationship management. Journal: Public Management Research, 55(15): 155-178.
Emami K. & Kheiri B. & Momeni M. (2022). Identifying the antecedents of comprehensive marketing strategic planning in e-businesses. Business Reviews, 2(3): 54-69.
Mahmoudi E. & Serhangi K. & Mohammadzadeh Alamdari M. (2023). Examining the role of customer relationship management in the relationship between customer knowledge management and new product development (research sample: industrial companies operating in the plastic sector), publication: Organizational Knowledge Management, 6(3): 81-118.
Dadras L. & Feizi M. & Dadras T. (2015). Investigating the interactive relationship of political marketing dimensions and its weighted effect on the public participation of Ardabil citizens. International Conference on Iranian Islamic Economy, Management and Culture.
Ranjbaran B. & Rashid Kaboli M. & Sanyaei A. & Haddadian A. (2013). Analysis of the relationship between perceived value, perceived quality, customer satisfaction, and repurchase intention in chain stores in Tehran, Journal of Business Management, 11(4): 55-70.
Taheri Kia F. & Meschi M. (2011). the effect of retail communication efforts (direct mail, preferential treatment, tangible reward) on the attitude of consumer behavior of hyperstar stores in Tehran. Journal: Public Management Research, 62(16): 121-135.
Kumar V. & Rajan B. & Venkatesan R. & Lecinski J. (2019). Understanding the role of artificial intelligence in personalized engagement marketing. California Management Review, 61(4): 135-155.
Anjala SK. & Dwivedi YK. & Bindu N. & Satheesh Kumar K. (2021). A broad overview of interactive digital marketing: A bibliometric network analysis. Journal of Business Research, 131(1): 183-195.
Atmaja BS. & Susilo A. & Budi L. (2016). The effect of brand DNA on interactive marketing: Perspective of junior lecturers from the Faculty of Economics of a Catholic University. Procedia - Social and Behavioral Sciences, 224(1): 459-466.
Simon H. & Roth MS. & Madden TJ. & Hudson R. (2015). The effects of social media on emotions, brand relationship quality, and word of mouth: An empirical study of music festival attendees. Tourism Management, 23(2): 75-98.
Csikósová A. & Antošová M. & Čulková K. (2014). Strategy in direct and interactive marketing and integrated marketing communications. Procedia-Social and Behavioral Sciences, 116, 1615-1619.
Vahidi Iry Sofla et al; Presenting an interactive marketing model ….
Presenting an interactive marketing model based on causal, contextual and intervening variables in the steel industry
Hossein Vahidi Iry Sofla1, Mahmoud Ahmadi Sharif 2*, Mohammad Nasrolahnia 3, Peyman Ghafari Ashtiani 4
Received: 06 Jul 2024/ Revised: 07 Nov 2024/ Accepted: 30 Nov 2024/ Published: 31 Dec 2024
© Islamic Azad University (IAU) 2024
Abstract
The purpose of this research is presenting an interactive marketing model based on causal, contextual and intervening variables in the steel industry based on the foundational data theory method. In this research, based on interview tools from experts and senior managers of the steel industry, many factors related to content-based marketing, and research variables have been identified and structures related to each variable have been presented using open, central and selective coding methods. In the next step, based on the analysis of the data obtained from the questionnaire, by confirmatory factor analysis method with PLS technique, the factor load and combined reliability for the research variables were calculated above 0.4 and 0.7, respectively, and as a result, the validity of each construct related to the factors And the implications of the research model were confirmed. Then, by the method of structural equations and estimation of the final model, causal factors, contextual factors, and intervening factors have been confirmed in the first to fourth positions of influence on content-based interactive marketing in the final model of the research. Finally, while presenting the research model with optimal overall goodness of fit, paying attention to the effects and consequences of the model including: internal and corporate consequences, competitive advantage, brand experience management, customer and market consequences are suggested.
Keywords: Content-oriented Interactive Marketing, Contextual Variables, Consequences of the Model, Intervening Variables, Steel Industry.
[1] Business Management, Kish International Branch, Islamic Azad University, Kish, Iran.
[2] *Corresponding Author; Department of Business Management, Shahr-e-Qods Branch, Islamic Azad University, Tehran, Iran. Email: sharif58@gmail.com
[3] Department of Business Management, South Tehran Branch, Islamic Azad University, Tehran, Iran.
[4] Department of Business Management, Arak Branch, Islamic Azad University, Arak, Iran.
Introduction
Nowadays, all industries, especially the steel products industry, are progressing at an increasing speed. This issue has led to increased competition to sell goods and services, diversification of brands, and challenges for the market and customers. Content-based interactive marketing plays a significant role in creating a long-term relationship with consumers. Buyers of steel products prefer reputable and well-known brands over less well-known ones. Unfamiliarity with the quality and advantages of steel products of unknown brands, aspects of quality, environmental protection, and the company's insufficient experience in branding and attracting customers can be considered some reasons for this. Thus, using content-based interactive marketing in this industry can strengthen the brand and thus, increase satisfaction and loyalty (Kumar Behera et al., 2024). Interactive marketing is different from traditional marketing. It establishes two-way interactions between the brand and the target audience. Interactive marketing emphasizes the two-way relationship between the customer and the brand. Content is created to communicate with the audience. It encourages future customers to buy and establishes communication with the customer as if only he has been addressed (Kuo & Chen, 2023). Thus, given what was stated, the lack using content-based interactive marketing methods in Iran’s steel industry has led to customers' lack of knowledge about the quality of the products of these industries, lack of identifying the needs of customers, reduced income, etc. The problems have led to a reduction in the customers of steel companies (Haft Almas Steel Holding in this study) compared to competitors using content matrix and new concepts of digital marketing since content-based interactive marketing is more popular compared to classic marketing. Hence, the primary purpose of this study is to present the content-based interactive marketing model in Haft Almas steel industries.
Theoretical foundations
Interactive marketing and content-based interactive marketing
The concept of interactive marketing was presented by Berry in 1983 for the first time in the field of service organizations. It has been defined as a strategy for attracting, maintaining, and improving relationships with customers. In a comprehensive definition of interactive marketing, Grunrus, defined it process of identifying, creating, maintaining, strengthening, and terminating the relationship with customers and other stakeholders for a mutual benefit, so that the goals of all groups in this relationship are met. Cutler also defines interactive marketing as creating, maintaining, and promoting strong relationships with customers and other stakeholders. They believe that marketing is increasingly moving away from individual transactions toward building relationships with customers and marketing networks (Paramita et al, 2021). Also, content-based marketing is using interactive marketing with an emphasis on the content emphasized in content marketing based on the content matrix. In other words, it is a roadmap that shows what kind of content your customer needs at each stage of the customer journey. This matrix helps your team focus on producing and publishing content that affects the customer journey (Feizi et al., 2024). The content matrix has four areas. They include entertaining content (it is one of the methods of creating awareness and maintaining the audience's interest), educational content (while including the answers to users' questions and educating them, it encourages customers to use your goods and services so their questions are answered), persuasive content (this type of content is suitable for some potential customers who need to consider all aspects before buying anything), and inspirational content (It affects the emotions of customers as it motivates them to take final action and buy) (Brockmann & Anthony, 2022). Content-based interactive marketing and customer knowledge management strategy in the steel industry. Interactive marketing in the steel industry, like other industries, can play a significant role in improving communication with customers, advertising, and sales of steel products (Ramezani et al., 2024). Accordingly, the advantages of this approach include direct communication with customers, personalization of customer experience, targeted advertising, inter-company communication, and improvement of customer experience in the supply chain. Given what was stated above, it can be stated that content-based interactive marketing in the steel industry helps businesses improve customer relationships, improve shopping experience, and increase sales (Rezaei, 2023). Thanks to digital technologies and customer data, steel companies can market more effectively and compete better in the market. Additionally, customer knowledge management is a crucial field in marketing and management science that deals with improving communication with customers, better understanding their needs and preferences, and improving customer experience. This strategy (approach) is a vital marketing tool that is effective in improving communication with customers, improving customer experience, and increasing sales (Seyfollahi & Movahed, 2022).
The position of causal, contextual, and intervening factors in content-based interactive marketing
Causal factors directly affect the axial category or phenomenon and cause the occurrence or development of the axial phenomenon (dependent variable). The causal factors include organizational factors, brand management, customer orientation, and brand strategy. Contextual factors in interactive marketing refer to elements that form the environment and surrounding conditions and affect marketing interactions and consumer behavior. They include resources, environmental uncertainty, capabilities, demographic factors, and environmental factors (Mahmoudi et al., 2023). Intervening variables include internal organizational factors, environmental factors, and brand factors. In other words, the intervening conditions moderate the intensity of the causal conditions on the content marketing strategy (Emami et al., 2022).
Research Background
In an article entitled “Investigating the role of customer relationship management in the relationship between customer knowledge management and new product development (research sample: industrial companies operating in the plastic sector), (Mahmoudi et al., 2023) found that companies should pay special attention to customer relationship management in activating knowledge management talent and new product development. In an article entitled “General Management Studies, Analysis of the Effect of Customer Knowledge on Improving the Service Quality of the Hotel Industry with the Mediating Role of Customer Relationship Management in the Hotel Industry”, (Seyfollahi & Movahed, 2022) reported that customer knowledge significantly and positively affects service quality in the hotel industry. Among the customer knowledge dimensions, the communication dimension was reported as the most effective dimension of service quality. Customer relationship management also played a mediating role in the relationship between customer knowledge and service quality in the hotel industry.
(Dadras et al., 2015), Investigated the interactive relationship between political marketing dimensions and its weighted effect on the public participation of Ardabil citizens. The results revealed a significant relationship between the dimensions of political marketing and the public participation of citizens. The results also revealed that among the political marketing dimensions, the revolutionary symbol dimension has the highest relative importance and the program tool dimension has the lowest relative importance in affecting the public participation of citizens.
In an article titled entitled “The relationship between perceived value, perceived quality, customer satisfaction, and customer repurchase intention”, (Ranjbaran et al., 2013) stated that perceived quality affects perceived value, customer satisfaction, and purchase intention in chain stores in Tehran province. They showed that perceived value affects customer satisfaction and repurchase intention, and customer satisfaction affects repurchase intention.
In an article entitled “The effect of retail communication efforts (direct mail, preferential treatment, and tangible reward) on the consumer behavior of hyper star stores in Tehran, (Taheri Kia & Meschi, 2011) stated that the tangible reward significantly affects the trust in the store, trust significantly affects the communication commitment and behavioral loyalty, and direct mail and preferential behavior significantly affect the trust in the store. In an article entitled “Assessing the intention to adopt computational intelligence in interactive marketing in e-commerce businesses in India” (Kumar Behera et al., 2019) stated that deep learning predicts e-customer behavior in a changing environment. This allows e-customers to compare features of similar products.
In an article entitled “The Effect of Interaction of Brands' Marketing Activities on Facebook Fan Pages on Continued Engagement Intentions: A Facebook S-O-R Framework Study”, (Kuo & Chen, 2023) argued that when the brand's marketing activities on its Facebook fan page are interactive, their perceived experience will be higher. Additionally, perceived experience positively affects the brand fan page attachment.
In an article entitled “An Overview of Interactive Digital Marketing: A Bibliometric Network Analysis of Libraries”, (Anjala et al., 2021) found that this study contributes to the field of interactive digital marketing as an international and interdisciplinary research field.
(Atmaja et al., 2016), investigated the impact of brand DNA on interactive marketing: the perspective of young professors from the Faculty of Economics of a Catholic University. The results revealed that "good" brand DNA determined good interactive marketing activity, which led to higher sustainability of X University in the next year.
In an article entitled "The effect of social media on emotions, brand communication quality, and word-of-mouth mouth advertising: an empirical study of music festival participants", (Simon et al., 2015) reported that one of the most used and effective of them is interactive marketing or word-of-mouth mouth. This marketing can be one of the least expensive and most productive methods that can achieve its goals with proper management and strategic planning. In an article entitled "Strategy in direct and interactive marketing and integrated marketing communications", (Csikósová et al., 2014) stated that direct and interactive marketing and integrated marketing communications professionals have tried to achieve a profitable balance between science and art as well as technique and creativity.
Method and Material
This study is applied-developmental regarding purpose. It is developmental due to enhancing the knowledge resulting from the presentation of a model with new and multidimensional variables. It is descriptive regarding the type of data and mixed and exploratory with data grounded approach regarding the method of implementation (due to the very limited background related to the research subject). Grounded theory is an inductive and exploratory research method that allows researchers in various subject areas to develop their theory instead of relying on existing theories. A paradigmatic model includes causal, contextual, intervening conditions, strategies, and outcomes. Thus, in this study, the causal, contextual, and intervening factors along with the customer knowledge management strategy were identified for the proposed interactive marketing model in the steel industry based on the opinions of 10 experts and senior managers of Haft Almas Steel industries. Then, the questionnaire data obtained from 269 samples among 900 customers of Haft Almas Holding products in Tehran (customers who purchased more than 50 billion Rials from this company from 2016 to the end of 2023) to analyze, estimate, and evaluate the structural equation model of the research.
Results
Qualitative data analysis
In this study, the qualitative results were analyzed based on the analysis of the data collected from the interviews using the grounded theory. Accordingly, the data were first analyzed at the micro-analysis level as open coding. Then, the extracted concepts from the micro level were set in the axial coding (Table 1). These categories are at a higher level of abstraction than the concepts of the previous stage. The researcher proceeded to the selective coding based on the subcategories, the primary category was created after extracting all the concepts used in the text of the data.
Selective coding based on the results of open coding and axial coding is the primary stage of theorizing. Accordingly, it systematically relates the central category to other categories, presents those relationships in the framework of a narrative, and corrects the categories that need further improvement and development. Given the interactive marketing with knowledge management approach as a central category, the result of selective coding from the interview can be presented in the following (Table 1).
Table 1. Structures of axial category, strategies, and the outcomes of the research model based on selective coding.
Concepts | Subcategories | Primary categories |
Organizational infrastructure | Organizational factors |
Causal factors |
Attitude toward sales | ||
Competitive approach | ||
Pricing approach | ||
The price of products | Brand management | |
Quality of products | ||
Brand differentiation | ||
Obtaining a competitive advantage of the brand | ||
Improving customer experience | Customer orientation | |
Predicting needs | ||
Gaining customer knowledge | ||
Increasing customer satisfaction | ||
Personalization of services | ||
Increasing customer confidence | ||
Increasing the growth of the penetration rate | Brand strategy | |
Ability to implement a strategy with planning | ||
Formulation of branding strategy | ||
physical resources | Resources |
Underlying factors |
Human resources | ||
financial and income resources | ||
Organizational resources | ||
Marketing resources | ||
legal resources | ||
Environmental dynamics | Environmental uncertainty | |
Environmental frequencies | ||
Environmental complications | ||
Manageability | Capabilities | |
Interactive feature | ||
Marketing capabilities | ||
Programmable | ||
Demographic variables | Demographic factors | |
Geographic variables | ||
Behavioral variables | ||
The difference between the working environment and other industries | Environmental | |
The need for high knowledge and expertise in human resources | ||
Importance of the industry at the country level | ||
Expansion of social networks | ||
Entertaining Content | content-based interactive marketing |
Axial category |
Educational content | ||
Persuasive Content | ||
Inspirational content | ||
| internal organizational factors |
Intervening factors |
Organizational structure | ||
Organizational culture | ||
Organizational infrastructure | ||
Brand awareness and association | Brand factors
| |
Word-of-mouth mouth advertising | ||
Economic environment |
Environmental factors
| |
Competitive environment | ||
Political-legal and socio-cultural environment | ||
Economic improvement |
Internal and corporate outcomes |
Outcomes |
Situational improvement | ||
Improving human resources | ||
Value creation and the acquisition of legitimacy | ||
Organizational interactive synergy | ||
Valuable service | competitive advantage | |
Capable human resources | ||
Creating a situation | ||
Exclusive financial resources | ||
Encouraging interaction about the brand experience | Brand Experience Management | |
Sharing a personal brand experience | ||
Using customers' knowledge in reinforcement channels | ||
Improving brand personality | ||
Improving the subjective experience of the brand | ||
Customer satisfaction | Outcomes for customers and the market | |
Customer loyalty | ||
Customer and market share development | ||
Achieving comprehensive communication goals | Outcomes for customers and the market | |
Communication control and management | ||
Speed, accuracy, and communication |
Source: research findings
Finally, the abstract model of the study can be presented based on (Figure 1) to check its accuracy and validity in the next step with the help of the confirmatory factor method and the estimation of the structural equations of the research.
Figure 1. Abstract research model (the output of qualitative data analysis based on the grounded theory)
Inferential analysis (confirmatory factor analysis method)
Before the confirmatory factor analysis of the causal factors, it is necessary to determine whether the sample size of the study is appropriate for the confirmatory factor analysis of the causal factors by calculating the sampling adequacy coefficient. It was measured in the form of a part of the questionnaire questions for the variable of causal factors. (Table 2) shows the results of the KMO-Bartlett test. The KMO value of more than 0.5 indicates the sampling adequacy and the confidence level of zero (0.000) for the Bartlett test also indicates the appropriateness of the factor model as the KMO test value was 0.900 in this study. This value indicates the degree of appropriateness of data related to the structure of causal factors for the implementation of factor analysis.
Table 2. Bartlett's test result of causal factors variable
Coefficient of Sampling Adequacy (KMO) | 0.900 | |
Coefficient of Sampling Adequacy (KMO) | Chi-square | 2763.129 |
df | 253 | |
Significant numbers | 0.000 | |
Source: research findings
First-order confirmatory factor analysis was used to examine the validity of causal factors. Causal factors have 4 subscales of organizational factors, brand management, customer orientation, and brand strategy with 17 questions. The results showed that the obtained factor load value is more than 0.4, the significance level is less than 0.05, and the t-value is greater than the absolute value of 1.96. Thus, it can be stated that the validity of the structures of all questions (variables related to causal factors) is confirmed (Figure 2). Also, based on the results shown in (Table 3), the combined reliability of all these factors was confirmed due to the high value of this index (0.7), and Cronbach's alpha.
Table 3. Confirmatory factor analysis results for causal factors
Variable | AVE≥0.5 | Composite reliability higher than 0.7 | Cronbach's alpha higher than 0.7 |
Organizational factors | 0.57 | 0.83 | 0.81 |
Brand management | 0.61 | 0.79 | 0.72 |
Customer orientation | 0.57 | 0.81 | 0.77 |
Brand strategy | 0.53 | 0.82 | 0.76 |
Source: research findings 95% confidence level
Figure 2. Measurement model of causal factors in the state of standard coefficients (factor loading)
Examining the validity of contextual factors, intervening factors, content-based interactive marketing strategies, and the model results, the value of the obtained factor load is more than 0.4 and is at the significance level of less than 0.05.
Its t-value is also higher than the absolute value of 1.96. Thus, it can be stated that the validity of the structures of all the questions related to these factors, strategies, and the outcomes of the abstract research model is confirmed. Also, according to the results of composite reliability tests and Cronbach's alpha, all these factors were confirmed with a high value of 0.7. Thus, based on the confirmatory factor analysis method, the abstract model of the study was approved (Figure 3).
Figure 3. Measurement model of causal factors in the state of t-values (significance).
Estimating and testing the structural equation model of research
The conceptual model of the research was tested using the structural equation modeling technique using the partial least squares method. Smart-PLS software was used. Then, the research model was presented in the state of significance or its value and in the state of standardized coefficients. (Figure 4) and (Figure 5) show the model fit.
(Figure 4) shows the model in the significant state with a level of 5%. If the value is higher than the absolute value of 1.96, it means that the statistical relationship is determined at a level of significance and confidence of at least 95%. The obtained t-values show that some of the above relationships are more than 1.96, and thus, these relationships were confirmed at the 95% confidence level. Based on (Figure 4), R2 values show the model's appropriate fit. Criteria for assessing the goodness of fit (GOF criterion): The value of the GOF criterion is related to the general part of the structural equation models and is calculated based on the following formula:
The average sign is the average of the shared values of each structure and Ave R2 is the endogenous structures of the model. The result of the above formula shows that the above formula value is 0.259. Thus, based on the three values of 0.01, 0.25, and 0.36, which are introduced as weak, medium, and strong values for GOF, and obtaining the value of 0.259, the overall model's performance is at a moderate level. Also, the coefficient of determination index or R2 is a measure used to connect the measurement part and the structural part of structural equation modeling and it shows the effect of an exogenous variable on an endogenous variable. Based on (Figure 4), R2 values show the model's appropriate fit.
Figure 4. Structural equation model in the general state in the path coefficients part
(Figure 5) also shows the experimental model in the state of standard coefficients, which are standards for comparing the intensity of variables and their impact on each other. The range of the standard path coefficient is from zero to one.
Figure 5. Structural equation model in the general state in the significant coefficients part
Discussion and Conclusion
The results can be presented as follows:
1-First-order confirmatory factor analysis was used to examine the validity of causal factors. Causal factors have 4 subscales of organizational factors, brand management, customer orientation, and brand strategy with 17 questions. The results revealed that due to the high values of the obtained factor load, the validity of the structures of all the questions (causal factors) is confirmed. Also, the composite reliability of all these factors was confirmed due to the high value of this index (0.7) and Cronbach's alpha. Thus, all the calculated causal factors can be included in the final model of this study.
2-First-order confirmatory factor analysis was used to examine the validity of contextual factors. This scale has 5 subscales of resources, environmental uncertainty, capabilities, demographic, and environmental factors with 20 questions. The results showed that due to the high values of factor load, the validity of the structures of all questions is confirmed. Also, the composite reliability of all these factors was confirmed due to the high value of this index (0.7) Cronbach's alpha. Thus, all the contextual factors can be included in the final model of the study.
3-First-order confirmatory factor analysis was used to examine the validity of intervening factors. This scale has 3 subscales of intra-organizational factors, brand factors, and environmental factors with 10 questions. The results showed that due to the high values of factor load obtained, the validity of the structures of all questions is confirmed. Also, the composite reliability of all these factors was confirmed due to the high value of this index (0.7) Cronbach's alpha. Therefore, all the intervening factors can be included in the final model of this study.
4- First-order confirmatory factor analysis was used to examine the validity of the outcomes. This scale has 5 subscales of internal and corporate outcomes, competitive advantage, brand experience management, customer and market outcomes, and operational and interactive outcomes with 20 questions. The validity of all questions in the outcomes is confirmed. Also, the composite reliability of all these factors was confirmed due to the high value of this index (0.7) and Cronbach's alpha. (Table 3) shows the results of the research model.
5- The conceptual model of the study was tested using the structural equation modeling technique using the partial least squares method. Due to the average value of 0.259, the overall model's fit was approved. Based on the structural equation model, in the general state, in the part of the path of coefficients, causal factors, contextual factors, and strategies with significant impact coefficients of 0.37, 249, and 0.178 are ranked first to third, respectively. In this regard, the intervening factors are ranked eighth with an impact coefficient of 0.145.
6- Finally, given what was stated, it is possible to confirm the content-based interactive marketing model by considering causal, contextual, and intervening factors for Iran's steel industry (generalizable from Haft Almas Steel Industries Holding) along with its outcomes.
Recommendations based on results
Based on the results of the study, the following recommendations are presented:
1- Due to the first rank of the causal factors regarding significant and higher path coefficients in the final content-based interactive marketing model in Iran's steel industry, more attention should be paid to these factors: organizational factors (organizational infrastructures, attitude toward sales, competitive approach, and pricing approach), brand management (price of products, quality of products, differentiating the brand, and gaining competitive advantage for the brand), customer orientation (improving customer experience, predicting needs, acquiring customer knowledge, increasing customer satisfaction, personalizing services, and increasing customer confidence), and brand strategy (increasing the growth of the penetration rate, the capability to implement the back strategy by planning, and developing the branding strategy).
2- Due to the second rank of contextual factors regarding significant path coefficients in the final content-based interactive marketing model in Iran's steel industry, more attention should be paid to these factors: resources (physical resources, human resources, financial and income resources, organizational resources, marketing resources, and legal resources), environmental uncertainty (environmental dynamism, environmental frequency, and environmental complexity), capabilities (manageability, interactive capability, marketing capability, and programmatic capability), demographic factors (demographic variables, geographic, and behavioral variables), and environmental (difference of work environment with other industries, need for high knowledge and expertise of human resources, industry importance at the national level, and expansion of social networks).
3- Due to the third rank but the effective role of intervening factors in content-based interactive marketing in this study and considering the significant path coefficients in the final content-based interactive marketing model in Iran's steel industry, more attention should be paid to these factors: internal organizational factors (strategies organizational, organizational structure, organizational culture, and organizational infrastructure), brand factors (brand awareness and association and word-of-mouth advertising), and environmental factors (economic environment, competitive environment, political-legal environment, and socio-cultural environment).
4- Due to the confirmation of the goodness of fit of the final model, it is recommended to pay attention to the model's outcomes including internal and corporate outcomes (economic improvement, situational improvement, human resources improvement, value creation, gaining legitimacy, and synergy of organizational interactions), competitive advantage (valuable services, capable human resources, creating a situation, and exclusive financial resources), brand experience management (encouraging interaction regarding brand experience, sharing personal brand experience, using customer knowledge in reinforcement channels, improving brand personality, and improving subjective brand experience), customer and market outcomes (customer satisfaction, customer loyalty, and development of customer and market share), and operational and interactive outcomes (realization of comprehensive communication goals, control, and management of communication speed, accuracy, and communication speech).
Recommendations for future studies
1-In addition to paying attention to content-based interactive marketing, this study can be analyzed and evaluated given the concept of gamification, proposed in 2002, and can evoke and simulate game conditions in real marketing platforms.
2- This study was conducted based on the tools of artificial intelligence and business intelligence, along with other research variables, in the process of intelligent and content-based interactive marketing.
References
_ Kumar Behera R. & Bala PK., & Rana NP. (2024). Assessing the intention to adopt computational intelligence in interactive marketing. Journal of Retailing and Consumer Services, 78(1): 103765-77.
_ Kuo YF. & Chen FL. (2023). The effect of interactivity of brands’ marketing activities on Facebook fan pages on continuous participation intentions: An S-O-R framework study. Journal of Retailing and Consumer Services, 74(1): 103446-55.
_ Paramita W. & Nhu HBC & Ngo LV. & Tran QHM. & Gregory G. (2021). Brand experience and consumers' social interactive engagement with brand page: An integrated-marketing perspective. Journal of Retailing and Consumer Services, 62(1): 102611-23.
_ Feizi K. & Mehrani H. & Vazifehdust H. & Sadeh E. (2024). Conceptualization and Design of a Digital Content Marketing Model Using Structural Equation Approach, Agriculture Marketing and Commercialization, 8(1): 51-69.
_ Brockmann EN. & Anthony WP. (2022). Tacit knowledge and strategic decision-making. Group & Organization Management, 27(4): 436-455.
_ Ramezani F. & Shokravi S. & Bagheri A. (2024). Investigating the Impact of Natural Resource Rent and Political Stability on the Environmental Degradation Index in Selected Developing Countries Using a Combined Data Approach, 8(1): 93-108.
_ Rezaei S. (2023). Content analysis of food industries marketing by focus on new trends. Agricultural Marketing and Commercialization Journal, 7(2): 128-143.
_ Seyfollahi N. & Movahed SM. (2022). Analysis of the effect of customer knowledge on the improvement of service quality in the hotel industry with the mediating role of customer relationship management. Journal: Public Management Research, 55(15): 155-178.
_ Emami K. & Kheiri B. & Momeni M. (2022). Identifying the antecedents of comprehensive marketing strategic planning in e-businesses. Business Reviews, 2(3): 54-69.
_ Mahmoudi E. & Serhangi K. & Mohammadzadeh Alamdari M. (2023). Examining the role of customer relationship management in the relationship between customer knowledge management and new product development (research sample: industrial companies operating in the plastic sector), publication: Organizational Knowledge Management, 6(3): 81-118.
_ Dadras L. & Feizi M. & Dadras T. (2015). Investigating the interactive relationship of political marketing dimensions and its weighted effect on the public participation of Ardabil citizens. International Conference on Iranian Islamic Economy, Management and Culture.
_ Ranjbaran B. & Rashid Kaboli M. & Sanyaei A. & Haddadian A. (2013). Analysis of the relationship between perceived value, perceived quality, customer satisfaction, and repurchase intention in chain stores in Tehran, Journal of Business Management, 11(4): 55-70.
_ Taheri KIA F. & Meschi M. (2011). The effect of retail communication efforts (direct mail, preferential treatment, tangible reward) on the attitude of consumer behavior of hyperstar stores in Tehran. Journal: Public Management Research, 62(16): 121-135.
_ Kumar V. & Rajan B. & Venkatesan R. & Lecinski J. (2019). Understanding the role of artificial intelligence in personalized engagement marketing. California Management Review, 61(4): 135-155.
_ Anjala SK. & Dwivedi YK. & Bindu N. & Satheesh Kumar K. (2021). A broad overview of interactive digital marketing: A bibliometric network analysis. Journal of Business Research, 131(1): 183-195.
_ Atmaja BS. & Susilo A. & Budi L. (2016). The effect of brand DNA on interactive marketing: Perspective of junior lecturers from the Faculty of Economics of a Catholic University. Procedia - Social and Behavioral Sciences, 224(1): 459-466.
_ Simon H. & Roth MS. & Madden TJ. & Hudson R. (2015). The effects of social media on emotions, brand relationship quality, and word of mouth: An empirical study of music festival attendees. Tourism Management, 23(2): 75-98.
_ Csikósová A. & Antošová M. & Čulková K. (2014). Strategy in direct and interactive marketing and integrated marketing communications. Procedia-Social and Behavioral Sciences, 116, 1615-1619.
