فصلنامه مدیریت بازرگانی
سال چهاردهم شماره 54 (زمستان 1401)

Given the importance of digital content marketing, and the scattered knowledge in the body of literature, this study seeks to take a comprehensive look at the factors influencing the digital content marketing process in order to engage the consumer with the brand on social media. With the help of the Bibliometrix package of R and VOSviewer software, the science performance and the scientific network were recognized and mapped. Classifying five main clusters in the co-citation analysis, theories, contexts, and main constructs of the field were identified. The recent trends were identified using co-occurrence analysis.  Finally, the achieved results were presented within a conceptual framework. The most common antecedents and consequences of digital content marketing were presented together with the contexts.

Bibliometric systematic review has been used to identify and analyze thematic trends in digital content marketing and brand engagement scopes. In fact, bibliometric analysis is a specific scientific field that examines the evolution of knowledge, scientific quality, and the impact of resources in this field from an objective and quantitative perspective. The bibliometric method generally has two main parts: (1) performance analysis and (2) intellectual mapping of science. In the section on performance analysis, the actions of authors, countries, and scientific institutes as well as their contribution to scientific development in a specific field are examined, while knowledge maps are used to identify the intellectual structure of the field and examine time trends and current issues in each period.

In the performance analysis, the most-cited countries, authors, and articles were identified. The United States, China, and the United Kingdom were the most outstanding countries in this field. In the co-citation analysis, five main clusters were identified and the main constructs of each cluster were extracted along with important theories, contexts, and methods. With the help of these constructs, the antecedents and consequences of the digital content marketing process were presented. Co-occurrence analysis recognized new trends in this field to complete the framework.

Based on the ADO-TCM framework, five important antecedents of the digital content marketing process were introduced: social media characteristics, source characteristics, content characteristics, consumer characteristics, and online community characteristics. Consumer participation or non-participation is a behavioral decision that originates from the digital content marketing process, which ultimately leads to brand engagement as the output of the process. Finally, culture and artificial intelligence were introduced as important contexts to facilitate this process.

The purpose of this study was to explore the factors affecting users’ identification and selection of influencers on Instagram. Therefore, the study focused on the frequency and diversity of active influencers on Instagram as a social media platform. It sought to designate the key indicators and mental shortcuts employed by users to identify and select influencers before collaborating with them. Through empirical investigations, the study proposed a framework composed of effective factors.

This study is an applied, developmental research that relies on an exploratory mixed (qualitative-quantitative) method. In the qualitative part of the study, the literature on the topic and existing documents were systematically reviewed. Next, based on the Morgan table for determining sample size in an infinite population, 384 users of influencer marketing strategy on the Instagram platform were asked to complete copies of a designed questionnaire. Equation structural modeling (ESM) was used to analyze the data collected.

After the content obtained through the qualitative stage was analyzed, 22 initial categories were observed, which were classified into seven final categories considering their conceptual similarities. The final categories represented users’ mental shortcuts when they tried to identify and select influences on Instagram. Following an analysis of the relationships among the categories, they were divided into level partitions, and their driving power and dependence power diagram were drawn.

This study proposed a final model of the factors as users’ mental shortcuts in identifying and selecting influencers for financial support and collaboration purposes. The model fit also revealed causal-hierarchical relationships among the factors.

In the past, digital marketing and content creation in the B2B were less studied compared to the B2C field. According to the conducted small number of pieces of research, it is clear that the two concepts are completely different and that B2B digital marketing as a whole should be investigated in more depth. All B2B companies have the potential for social media marketing and digital content creation. Yet these B2B companies are not putting their efforts into that. Over the past few years, LinkedIn has been an important professional social media platform for B2B scholars to use in several ways and B2B researchers can be members of LinkedIn groups. B2B businesses need to market to the right audience, they need to make sure that they are placing their money and efforts into the right platforms. To increase their engagement and sales, it is important for these companies not to waste their time and money and to start making the right marketing decisions. It is essential for these B2B businesses to build their credibility online and create trust with their customers. Therefore, the purpose of this research is to identify the mental patterns of business-to-business interaction on LinkedIn by the Q method.

This paper is aimed to explore key philosophical underpinnings of fundamental research paradigms with reference to Positivism and Interpretivism (Q method). The statistical population of this study includes B2B business managers, active on LinkedIn. The discourse of the present research was collected from various sources (theoretical literature and semi-structured interviews with experts). 30 samples of Q phrases were selected after a survey of experts. The theme analysis technique was used to extract the factors in the interview section. In this section, the identified indicators were written on a card called Q, using the coding process. Then these cards were given to the participants. The data obtained from sorting the participants were analyzed by the Q factor analysis in SPSS software to identify the business interaction pattern. To measure validity of Q-method, the researcher needed to ask himself whether the collected statements had the required comprehensiveness to reflect different mentalities. Therefore, the validity of the content could be checked based on participants’ ratings of the phrase and its adjacent phrase. The test-retest method was used to calculate the reliability coefficient of Q sorting. The test-retest was re-run for 20% of the sample. The correlation coefficient was 73%, which indicated a high level of reliability.

The researchers determined the Q expressions, after conducting semi-structured interviews and library studies. These expressions were approved by experts. A total of 30 Q expressions were extracted. Accordingly, three mental models were identified from the perspective of the participants. The first mentality was called the professional content mentality. The second one was the lead mentality, and the third was called the tracking mentality.

The results showed three mental models of B2B business interaction on LinkedIn: "Professional content", "Tracking" and "Lead". The mentality of professional content refers to the production of suitable content for a company on LinkedIn. Lead mentality refers to activities that lead to creating leads to create interactions and communication between companies. The third mentality, called tracking, refers to a set of activities that place the company in a position to have better and more access to the relevant audience. The constant participation of consumers in the social media space requires companies to develop and maintain a strong media presence. This research could accurately identify the influential components in each mentality to provide a clearer view of better interaction on LinkedIn and to help managers adopt practical strategies based on customer needs. According to the obtained results, B2B companies can use social media and platforms to facilitate their relationships with customers, identify their company and brand just like a living entity and introduce the products, services and value they provide to the customer.

Finding innovative solutions to satisfy consumers has always been a concern of businesses. Accordingly, this study seeks to investigate the effect of gamification on consumer shopping behavior using the artificial neural network, in the tourist offices of the Iranian western city of Kermanshah.

This study is developmental and applicable research. The semi-experimental method (pre-test/post-test) was used with experimental and control groups. The statistical population of this study included the customers of Kermanshah tourist offices with one registered trip in the first quarter of the Iranian calendar year of 1400 (2020-2021). Each of the control and experimental groups consisted of 20 participants. Kim’s consumer behavior standard questionnaire was used to gather the required data. SPSS software was used to analyze descriptive statistics and MATLAB software was used to design an artificial neural network. The artificial neural network was constructed using the radial basis functions. The used data set was divided into 70 percent for training and 30 percent for testing.

The results of descriptive statistics showed that nine participants in both control and experimental groups were women and 11 were men. In the experimental group, eight participants were single and 12 were married. Seven participants in the control group were single and 13 were married. The average ages of the experimental group and control group were 41.8 years and 39.6 years, respectively. The average number of made trips in the year 1400 for the experimental group, and control group were 3.6 and 3.55, respectively. The achieved results also showed that the average scores of all three dimensions of willingness to attend, word of mouth and purchase intention were increased in the experimental group after applying gamification. These values were increased from 6.5 to 13.15, 8.95 to 17.6, and 7.55 to 12.9, respectively. While in the control group, the average scores in all dimensions remained almost constant. The effect of gamification on changing consumer buying behavior was approved by these results. Furthermore, the behavior of a typical person in experimental or control groups (with or without gamification) was predicted using RBF artificial neural network. The obtained results from the designed neural network showed that all of the predictions using the proposed artificial neural network were predicted correctly. Also, the network was able to classify all of the outputs correctly based on the defined inputs. The network had an acceptable error rate.

Based on the obtained results from the artificial neural network, prediction of customer behavior was done correctly. It should be noted that the gamification activities such as completing the puzzle which was tested in this study should be used accurately in businesses. This result will be useful for businesses, marketers, sales managers, market management analysts, and researchers interested in this field.

Today, people face different decision-making criteria when purchasing products and services. One of these criteria is using the reviews of the previous purchasers of products and services. A large volume of reviews is seen as a challenge for these people. The present study aimed to create a model to analyze users’ sentiments and to classify their reviews to solve the mentioned challenge.

The present study investigated the buyers’ reviews of mobile phones purchased on the Digikala Website from 2015 to 2016. To analyze the sentiments, and to classify the reviews, deep learning-based algorithms, and convolutional networks, subtypes of deep networks, were suggested. Prior to preprocessing and homogenizing the data, the study used a pre-trained Fastext model to convert the words into integer vectors and deliver them as inputs to the proposed deep network.

To train the selected model, the training algorithm was carried out on it 90 times. To validate the performance of the selected model, confusion matrix, accuracy, recall, F1-score, and precision rate criteria were used.

The present study used the deep networks approach, convolutional networks, and bidirectional long short-term memory to classify the buyers’ reviews of the mobile phone from the website above at 93% accuracy, and after 90 training periods.

The present research seeks to design a model of corporate social responsibility (CSR) and help with the development of the Iranian banking industry.

This is a mixed methods study and combines elements of both quantitative and qualitative research. The model of corporate social responsibility was designed using the qualitative approach and the model was tested by the quantitative approach afterward. In the qualitative section, the data was collected by conducting semi-structured interviews with 15 academics and experts from Iran’s public and private banks. In the quantitative part and through random cluster sampling, districts 1 and 4 of Tehran were initially selected. Next, a questionnaire was distributed randomly based on the designed pattern among 248 managers, administrators, and employees of different branches of Bank Mellat, in districts 1 and 4 of Tehran. The interviews were analyzed using real and theoretical coding and the Glaser Grounded Theory. In the quantitative part, Structural Equation Modeling (SEM) was applied and SPSS and AMOS software were used.

The findings of the qualitative part led to the design of a model of corporate social responsibility in the banking industry based on the views of academic experts and the banking system. After confirming the validity and reliability of the model, the hypotheses of the quantitative part were developed. Testing the hypotheses indicated the validity of the model.

Data analysis showed that corporate social responsibility in the banking industry has five dimensions: economic, political-legal, cultural-ethical-social, humanitarian, and technological. Also, the results of the quantitative part indicated that these five dimensions are of significance in Bank Mellat.

This study aimed to design a customer participation model in social media marketing of clinics providing beauty services for skin and hair.

The current research has a qualitative approach and was applied in terms of objective and was carried out systematically with the grounded theory method. Sampling in this research is theoretical sampling. The data was collected and coded through the interview tool and with the help of MAXQDA software. The statistical population includes specialists and marketing experts active in the field of social media and skin and hair beauty services. The sampling method of this research is targeted and snowball sampling was used, and 17 people were selected using this sampling method. This research used the interview tool to collect people's views.

Causal conditions (such as management factors, and professional factors related to beauty services), underlying conditions (such as cultural, legal, and religious factors), strategies (such as social media-related actions and customers), and outcomes (such as customer loyalty, participation in creating new content and products, and promotion of customer's emotional commitment) affect customer participation in social media marketing of clinics providing beauty services for skin and hair.

The current limitation of social media marketing in Iran has been affected by various conditions, which is far from the ideal situation. The use of some strategies provides the possibility of achieving the desired situation and can have favorable results to a large extent.

Considering the high rate of failure among startups and the atmosphere of the Iranian startup ecosystem, as well as the economic-political crisis in recent years, lots of entrepreneurs starting their business face failure or serious crisis. Thus, investigating the dimensions of entrepreneurs' resilience and its nature among Iranian entrepreneurs is vital. Hence, the objective of this research is to identify the dimensions of the personal resilience of startup founders.

This research has a descriptive-exploratory orientation, inductive approach, and phenomenological strategy. Conducting in-depth interviews with 13 Iranian startup founders and carrying out thematic analysis, five main themes, 16 subsidiary themes, and 454 codes were extracted.

Based on the achieved results, the formation of personal resilience among entrepreneurs consists of five main themes: (1) Entrepreneur's characteristics: including personal characteristics and psychological capabilities, values and beliefs, entrepreneurial motivation, and skills and readiness for facing business adversities, (2) External support: including psychologically empowering factors and providing needed facilities by different sources of support for instance from a spouse, family, friends, co-founders, employees, investors, mentors, therapist, customers, and society, (3) Characteristics of failure: including costs of failure and causes of failure, (4) The process of forming resilience: including initial actions for improving mood, initial reactions, mental approaches and actions, person-related proceedings and work-related proceedings, and (5) After-failure growth: including psychological growth, attitudinal growth, work-related behavioral growth, and non-work-related behavioral growth.

This study presented a model of resilience formation from failure to shaping resilience, to after-failure growth. This model clarifies the dimensions of entrepreneurs' personal resilience in more detail by putting dimensions in three relatively distinct time intervals. It also discussed several factors in entrepreneurs' personal resilience in more detail, adding to the existing literature. The dual role of self-confidence is notable in this regard. Self-confidence and efficacy can strengthen one's resilience; however, if not realistic, such self-confidence can amplify the negative consequences of failure, as entrepreneurs cannot act as expected by themselves in the face of adversity. Another dual factor in personal resiliency is the bond between an entrepreneur and a business, which is usually referred to as parental love. Although this passion and love for the business advance them to persist in the business, it may blind the entrepreneur's unbiased analysis of the problems and challenges both in the internal and external environment, which may lead them to more business crises. The current study also emphasizes the high similarity between entrepreneurs' characteristics that strengthen one's resiliency, and the reported growth as a result of resilient coping with failure, in different aspects such as psychological capabilities, values and beliefs, attitudes, and behavioral improvements. It suggests that facing adversity and showing resilience toward failure, is, by itself, an important source of even more resilience. Finally, based on the findings, applicable suggestions on increasing the personal resiliency of entrepreneurs are presented, including the development of psychological capabilities, skills, and work/family/friends network and their means; learning mental and behavioral approaches toward business failure such as acceptance, mourning over the loss of failure, and avoiding denial; cultural attempts toward a better atmosphere in Iranian startup ecosystem by investors and other players in Iran's startup ecosystem such as looking at using psychotherapy counseling as a personal development attempt; and supports and infrastructures needed to improve resilience.