Search engine marketing is not all gold: Insights from Twitter and SEOClerks

Exploring the Impact of Online and Offline Channel Advantages on Brand Relationship Performance: The Mediating Role of Consumer Perceived Value

As omnichannel shopping behavior becomes increasingly popular among consumers, how to leverage the respective advantages and synergies of online and offline channels to retain customers for a long time is an urgent issue for retailers to solve. The purpose of this study is to explore the key advantages of online and offline channels influencing the omnichannel shopping experience in the decision-making process, and investigate their impact on consumer perceived value and brand relationship performance, as well as the interaction effect of online channel advantages and offline channel advantages. This study identifies the key advantages of online channels (search convenience, customer-generated information richness, and social connection) and offline channels (direct product experience, sales-staff assistance, and servicescape aesthetics) through a qualitative study and relevant literature review. Then, the proposed research framework was tested using the structural model equation in AMOS and hierarchical regression techniques in SPSS utilizing data from 347 shoppers. The results show that all variables except customer-generated information richness have positive impact on consumer perceived value. Other than search convenience and customer-generated information richness, consumer perceived value mediates the effect of other variables on brand relationship performance. Additionally, the interaction effect of online and offline channel advantages positively impacts consumer perceived value.

Prediction of customer’s perception in social networks by integrating sentiment analysis and machine learning

Understanding the customer behavior and perception are important issues for motivating customer satisfaction in marketing analysis. Customer conversation with customer support services through social networks channel provides a wealth of information for understanding customer perception. Therefore, in this paper, a hybrid framework that integrated sentiment analysis and machine learning techniques is developed to analyze interactive conversations among customers and service providers in order to identify the change of polarity of such conversation. This framework aims to detect the conversation polarity switch as well as predict the sentiment of the end of the customer conversation with the service provider. This would help companies to improve customer satisfaction and enhance the customer engagement. The effectiveness of the proposed framework is measured by extracting a real dataset that expresses more than 5000 conversational threads between a customer service agent of an online retail service provider (AmazonHelp) and different customers using the retailer’s twitter public account for the duration of one month. Different classical and ensemble machine learning classifiers were applied, and the results showed that the decision trees outperformed all other techniques.

Search engine optimization (SEO) for digital marketers: exploring determinants of online search visibility for blood bank service

Purpose

Given that online search visibility is influenced by various determinants, and that influence may vary across industries, this study aims in investigating the major predictors of online search visibility in the context of blood banks.

Design/methodology/approach

To formalize the online visibility, the authors have found theoretical foundations in activity theory, while to quantify online visiblity the authors have used the search engine optimization (SEO) Index, ranking, and a number of visitors. The examined model includes ten hypotheses and was tested on data from 57 blood banks.

Findings

Results challenge shallow domain knowledge. The major predictors of online search visibility are Alternative Text Attribute (ALT) text, backlinks, robots, domain authority (DA) and bounce rate (BR). The issues are related to the number of backlinks, social score, and DA. Polarized utilization of SEO techniques is evident.

Practical implications

The methodology can be used to analyze the online search visibility of other industries or similar not-for-profit organizations. Findings in terms of individual predictors can be useful for marketers to better manage online search visibility.

Social implications

The acute blood donation problems may be to a certain degree level as the information flow between donors and blood banks will be facilitated.

Originality/value

This is the first study to analyze the blood bank context. The results provide invaluable inputs to marketers, managers, and policymakers.

Antecedents of E-Marketing of Agriculture Products in This Digital Era

Agriculture is the backbone of the Indian economy. The majority of the citizens of this country are dependent upon the agricultural supply chain for the livelihood. This study shows the role of the workforce in this digital era for the e-marketing of agriculture products. E-marketing platforms (i.e., search engine optimization, affiliate marketing, social media marketing, and e-mail marketing) help digital marketers to track and analyze the dynamic and complex buying behavior of consumers. Structural equation modelling is used to test the framework for the e-marketing of agriculture products. The developed model can enhance the capability of workforce in this digital era for developing an effective e-marketing strategy for agriculture products.

Evaluating the Impact of Community Experience on Purchase Intention in Online Knowledge Community

Community experience has an important influence on the brand building of an online knowledge community. By enhancing the community experience of members, it can promote the building of an online knowledge community and increase users' purchase intention. Although existing research has explored the influence model of community experience, there is a dearth of research regarding the influence of community experience on purchase intention. To this end, this study uses the online knowledge community experience as a theoretical basis to construct a mediating model to examine the behavioral patterns of consumers using the online knowledge communities and to explore in detail the mechanisms of the different dimensions of the community experience on purchase intention. It was found that not only the three dimensions of community experience (i.e., information experience, entertainment experience, and interactive experience) had a significant effect on brand identity, but also brand identity had a significant effect on purchase intention. The study also confirmed that brand identity mediates the relationship between community experience and purchase intention. This study reveals the mediating mechanism of community experience on purchase intention and helps to effectively guide the innovative management practices of the online knowledge community.

Public Social Media Discussions on Agricultural Product Safety Incidents: Chinese African Swine Fever Debate on Weibo

Public concern over major agricultural product safety incidents, such as swine flu and avian flu, can intensify financial losses in the livestock and poultry industries. Crawler technology were applied to reviewed the Weibo social media discussions on the African Swine Fever (ASF) incident in China that was reported on 3 August 2018, and used content analysis and network analysis to specifically examine the online public opinion network dissemination characteristics of verified individual users, institutional users and ordinary users. It was found that: (1) attention paid to topics related to "epidemic," "treatment," "effect" and "prevent" decrease in turn, with the interest in "prevent" increasing significantly when human infections were possible; (2) verified individual users were most concerned about epidemic prevention and control and play a supervisory role, the greatest concern of institutional users and ordinary users were issues related to agricultural industry and agricultural products price fluctuations respectively; (3) among institutional users, media was the main opinion leader, and among non-institutional users, elites from all walks of life, especially the food safety personnel acted as opinion leaders. Based on these findings, some policy suggestions are given: determine the nature of the risk to human health of the safety incident, stabilizing prices of relevant agricultural products, and giving play to the role of information dissemination of relevant institutions.

Effect of manufacturer's flexible returns policy in a duopolistic competition

Purpose

Flexible return policies are offered by the manufacturers to encourage the retailers announcing a lenient returns scheme to their customers.

Design/methodology/approach

This study considers the distribution of durable products in a supply chain where the demand is sensitive to sales effort and retail price. Using a game theoretic framework, the paper presents an assessment of the strategic effect of flexible returns policy announced by the manufacturer under retail competition and highlights its implications on profitability.

Findings

Comparative analysis of monopolistic and duopolistic competition provides a better understanding about the repercussions and related facts on offering a flexible returns policy in these environments. It is profitable for the manufacturer to offer a flexible returns policy when there is retail competition than under monopolistic condition.

Practical implications

Practitioners view returns policy offered as an insurance given to the buyers and they infer it to be a better mechanism for doing business. Lenient returns policy promotes the sales by increasing the trust on the retailer and boosts up the perception of quality about the product by lowering the perceived risk for customers.

Originality/value

Effective product return strategies such as being lenient in terms of time, money, effort, scope and exchange can result in increased revenues, lower cost and improved profitability to the manufacturer and retailer, at the same time offering an enhanced level of customer service.

Analyzing the Stock Exchange Markets of EU Nations: A Case Study of Brexit Social Media Sentiment

Stock exchange analysis is regarded as a stochastic and demanding real-world setting in which fluctuations in stock prices are influenced by a wide range of aspects and events. In recent years, there has been a great deal of interest in social media-based data analytics for analyzing stock exchange markets. This is due to the fact that the sentiments around major global events like Brexit or COVID-19 significantly affect business decisions and investor perceptions, as well as transactional trading statistics and index values. Hence, in this research, we examined a case study from the Brexit event to assess the influence that feelings on the subject have had on the stock markets of European Union (EU) nations. Brexit has implications for Britain and other countries under the umbrella of the European Union (EU). However, a common point of debate is the EU’s contribution preferences and benefit imbalance. For this reason, the Brexit event and its impact on stock markets for major contributors and countries with minimum donations need to be evaluated accurately. As a result, to achieve accurate analysis of the stock exchanges of different EU nations from two different viewpoints, i.e., the major contributors and countries contributing least, in response to the Brexit event, we suggest an optimal deep learning and machine learning model that incorporates social media sentiment analysis regarding Brexit to perform stock market prediction. More precisely, the machine learning-based models include support vector machines (SVM) and linear regression (LR), while convolutional neural networks (CNNs) are used as a deep learning model. In addition, this method incorporates around 1.82 million tweets regarding the major contributors and countries contributing least to the EU budget. The findings show that sentiment analysis of Brexit events using a deep learning model delivers better results in comparison with machine learning models, in terms of root mean square values (RMSE). The outcomes of stock exchange analysis for the least contributing nations in relation to the Brexit event can aid them in making stock market judgments that will eventually benefit their country and improve their poor economies. Likewise, the results of stock exchange analysis for major contributing nations can assist in lowering the possibility of loss in relation to investments, as well as helping them to make effective decisions.

Evaluation Expediency of Eco-Friendly Advertising Formats for Different Generation Based on Spanish Advertising Experts

The advertising industry is also responsible for promoting a sustainable future for our planet. Besides launching messages of environmental respect, it is essential to choose and use advertising tools that will leave the lightest footprint in the environment. While environmental issues are indeed relevant, in any way, the need remains to spread the word about the products/services and make rational decisions that will maximize the reach of potential consumers. In other words, support measures are needed to reach the target market more effectively. Based on the above considerations, the article presents the research results of evaluating the expediency of eco-friendly advertising formats to different generational cohorts in the Spanish media scene. The MCDM method defined the structure of the research and was used to summarize the results of the expert study. This method’s choice is based on motive related to the purpose of the evaluation and the applicability of the research results in practice. Research results are helpful guidelines for decision-makers of advertising agencies or their clients in the campaign planning process. They show that generational differences are a determining factor for reaching the target market.

How digital marketing evolved over time: A bibliometric analysis on scopus database

Nowadays, a large number of customers are spending their time on social and digital media for a variety of purposes ranging from information searching to the final purchase of products. Responding to this shift, marketers are spending a significant part of the advertising budget on digital marketing. Therefore, the purpose of this study is to review articles on digital marketing to identify top themes, determine the current status of research in digital marketing and indicate how influential works have shaped it. This research has reviewed 925 papers published between 2000 and 2019 in Scopus by applying bibliometrics analysis. These results show that on average 2.18 authors have contributed to every single paper on digital marketing and the collaboration index is 2.71. The top contributing countries in the digital marketing field are USA, India and UK. The study also identifies three dominant clusters in digital marketing research, e.g., 1) strategic planning with digital marketing 2) mobile marketing with apps development and 3) dealing with demographic profiles of customers.

Adoption of Social Media Marketing for Sustainable Business Growth of SMEs in Emerging Economies: The Moderating Role of Leadership Support

Social media marketing (SMM) plays an important role in business communication, marketing, operations, and other activities. There is a growing interest among researchers, academicians, and practitioners to understand the role of SMM in business sustainability in small and medium enterprises (SMEs) in an emerging economy, like India. Few studies have attempted to understand this role. Thus, the aim of this study is to examine the impact of adopting social media marketing for sustainable business growth of SMEs in an emerging economy. The study also investigates the moderating role of SME leadership support on the relationship between SMM usage and sustainable business growth of SMEs. After reviewing the existing literature and technology adoption model, a theoretical model is developed, which is then validated using a structural equation modeling technique to analyze 304 samples of Indian SMEs that use different social media marketing applications in their enterprises. This study confirmed that SMM tools significantly and positively improve the sustainable growth of SMEs in an emerging economy. Additionally, the study also found that SME leadership team plays a vital role in supporting actual usage of SMM tools that accelerate sustainable business growth of SMEs.

La publicidad en buscadores de las plataformas españolas de compraventa de trabajos académicos: análisis del tráfico, costes y palabras clave

Los portales de compraventa de trabajos académicos se han convertido en una suerte de colaboradores necesarios en las prácticas deshonestas del alumnado universitario a nivel mundial y en los últimos años su presencia se ha incrementado notablemente. Nuestro estudio, basado en los datos extraídos del programa SEMrush, analiza el tráfico, la publicidad online y las palabras clave empleadas por 36 empresas españolas dedicadas a la venta de trabajos académicos a través de Internet. Los resultados obtenidos ponen de manifiesto el incremento de visitas que reciben estos sitios web durante el último año, a la vez que ponen el acento en la mayor tasa de tráfico recibido a través de la publicidad online frente al tráfico orgánico. Nuestro aporte concluye con una serie de recomendaciones generales que entendemos pueden ayudar a encarar el problema.

Big data precision marketing and consumer behavior analysis based on fuzzy clustering and PCA model

Precision marketing is faced with multiple levels of problems, such as pollution of the data environment and unscientific algorithms, which need to be sorted out urgently. Based on neural network technology, this paper constructs a neural network-based precision marketing model and focuses on data mining to study user churn prediction and user value enhancement, which are the two most important factors affecting marketing revenue. Moreover, this paper conducts an empirical test of the product strategy and market strategy adopted by big data precision marketing. According to the characteristics of the user population and the application scenarios of the product, this paper puts the corresponding precision marketing methods in a targeted manner and analyzes the performance of the model through experimental research. The research results show that precision marketing methods based on big data information platforms need to be more detailed and more comprehensive. At the same time, precision marketing methods need to correspond to the sensitive information characteristics of target users and consider the background and current situation of actual market execution to effectively play it role.

Marketing customer response scoring model based on machine learning data analysis

In marketing, problems such as the increase in customer data, the increase in the difficulty of data extraction and access, the lack of reliability and accuracy of data analysis, the slow efficiency of data processing, and the inability to effectively transform massive amounts of data into valuable information have become increasingly prominent. In order to study the effect of customer response, based on machine learning algorithms, this paper constructs a marketing customer response scoring model based on machine learning data analysis. In the context of supplier customer relationship management, this article analyzes the supplier’s precision marketing status and existing problems and uses its own development and management characteristics to improve marketing strategies. Moreover, this article uses a combination of database and statistical modeling and analysis to try to establish a customer response scoring model suitable for supplier precision marketing. In addition, this article conducts research and analysis with examples. From the research results, it can be seen that the performance of the model constructed in this article is good.

How do network attributes impact information virality in social networks?

Purpose

Social media platforms play a key role in information propagation and there is a need to study the same. This study aims to explore the impact of the number of close communities (represented by cliques), the size of these close communities and its impact on information virality.

Design/methodology/approach

This study identified 6,786 users from over 11 million tweets for analysis using sentiment mining and network science methods. Inferential analysis has also been established by introducing multiple regression analysis and path analysis.

Findings

Sentiments of content did not have a significant impact on the information virality. However, there exists a stagewise development relationship between communities of close friends, user reputation and information propagation through virality.

Research limitations/implications

This paper contributes to the theory by introducing a stagewise progression model for influencers to manage and develop their social networks.

Originality/value

There is a gap in the existing literature on the role of the number and size of cliques on information propagation and virality. This study attempts to address this gap.

Influence of political leaders on sustainable development goals – insights from twitter

Purpose

The importance and criticality of sustainable development goals is witnessed by 195 member countries. For its full-fledged adoption and implementation, it needs to be understood by masses and political leaders are critical agents those engage diverse communities through social media such as twitter. Therefore, in this study focuses on how political leaders can influence the sustainable development goals through Twitter.

Design/methodology/approach

This study examines the social media conversations of political leaders on Twitter. Social media analytics methods such as sentiment mining, topic modelling and content analysis-based methods have been used.

Findings

The findings indicate that most political leaders are primarily discussing the sustainable development goals (SDGs) “partnership for goals” and “peace, justice and strong institutions”. Many other goals such as “clean water and sanitation”, “life below water”, “zero hunger”, “no poverty” and “educational quality” are not being focused on.

Research limitations/implications

This study offers implications in terms of collective decision making and the role of policy makers towards the goals of promoting SDGs. The authors highlight how political leaders need to involve key stakeholders in this journey.

Originality/value

This study scores and provides a cohort-specific prioritization of the leadership within these countries with regard to SDGs, which could be beneficial to the society.

Exploring engagement among mobile app developers – Insights from mining big data in user generated content

Purpose

The Web 3.0 has been hugely enabled by smartphones and new generation mobile applications. With the growing adoption of smartphones, the use of mobile applications has grown exponentially and so has the development of mobile applications. This study is an attempt to understand the issues and challenges faced in the mobile applications domain using discussions made on Twitter based on mining of user generated content.

Design/methodology/approach

The study uses 89,908 unique tweets to understand the nature of the discussions. These tweets are analyzed using descriptive, content and network analysis. Further using transaction cost economics, the findings are reviewed to develop practice insights about the ecosystem.

Findings

Findings indicate that the discussions are mostly skewed toward a positive polarity and positive user experiences. The tweeters are predominantly application developers who are interacting more with marketers and less with individual users.

Research limitations/implications

Most of these applications are for individual use (B2C) and not for enterprise usage. There are very few individual users who contribute to these discussions. The predominant users are application reviewers or bloggers of review websites who use the recently developed applications and discuss their thoughts on the same.

Practical implications

The results may be useful in varied domains which are planning to expand their reach to a larger audience using mobile applications and for marketers who primarily focus on promotional content.

Social implications

The domain of mobile applications on social media is still restricted to promotions and digital marketing and may solely be used for the purpose of link building by application developers. As such, the discussions could provide inputs towards mobile phone manufacturers and ecosystem providers on what are the real issues these communities are facing while developing these applications.

Originality/value

The study uses mixed research methodology for mining experiences in the domain of mobile application developers using social media analytics and transaction cost economics. The discussion on the findings provides inputs for policy-making and possible intervention areas.

What Affects Usage Satisfaction in Mobile Payments? Modelling User Generated Content to Develop the "Digital Service Usage Satisfaction Model"

Mobile payment services have become increasingly important in daily lives in India due to multiple planned and unplanned events. The objective of this study is to identify the determinants of usage satisfaction of mobile payments which could enhance service adoption. The "Digital Service Usage Satisfaction Model" has been proposed and validated by combining technology adoption and service science literature. First the data was extracted from Twitter based on hashtags and keywords. Then using sentiment mining and topic modelling the large volumes of text were analysed. Then network science was also used for identifying clusters among associated topics. Then, using content analysis methodology, a theoretical model was developed based on literature. Finally using multiple regression analysis, we validated the proposed model. The study establishes that cost, usefulness, trust, social influence, credibility, information privacy and responsiveness factors are more important to increase the usage satisfaction of mobile payments services. Also methodologically, this is an endeavour to validate a new approach which uses social media data for developing a inferential theoretical model.

Does SEO Matter for Startups? Identifying Insights from UGC Twitter Communities

In the present study, we analyzed User Generated Content (UGC) to measure the importance of Search Engine Optimization (SEO) for startups. For this purpose, we used several clustering algorithms to identify user communities on Twitter. The dataset contained a total of 67,126 tweets. A three-step UGC analysis process was applied to the data. First, a Latent Dirichlet allocation (LDA) was developed to divide the UGC-sample into topics. Next, a sentiment analysis (SA) with machine-learning was applied to divide the sample of topics into negative, positive, and neutral feelings. Finally, a textual analysis (TA) process with data mining techniques was used to extract indicators related to the SEO technique optimization in startups. The results helped us identify UGC communities in Twitter about SEO for startups and the main optimization indicators according to the feelings expressed in tweets. Our results also demonstrated that Black Hack SEO is not the most relevant strategy of positioning of digital marketing for startups and that, although this strategy is used by the startups, it is predominantly negatively perceived by SEO UGC communities.

Social Media and IOT Wearables in Developing Marketing Strategies. Do SMEs Differ From Large Enterprises?

The fast development of technologies shapes the way companies address and understand their customers’ needs, including the more and more pressing call for sustainability. If, by now, many organizations use the advantages of social media in their marketing strategies, newer technologies, such as Internet of things (IoT) wearables, are not fully used to their whole potential. Thus, we conducted two research studies—a qualitative one in the form of a focus group where eight different companies’ representatives took part, followed by a quantitative one in the form of an online questionnaire, where 84 (Small and Medium Sized Enterprises) SMEs and Large Enterprises answered. The main purpose of our research was to investigate companies’ attitudes and practices about using social media and IoT wearable technologies in developing organizational marketing strategies. The results indicate that, though there are some differences in the perception and use of social media and IoT wearables for developing marketing strategies, these differences are not marked between SMEs and Large Enterprises, but rather between micro enterprises and other companies with higher numbers of employees. Additionally, there are some differences noticed between companies operating in regional, national, or international markets.

Experience

Governance of misinformation is a serious concern in social media platforms. Based on experiences gathered from different case studies, we offer insights for the policymakers on managing misinformation in social media. These platforms are widely used for not just communication but also content consumption. Managing misinformation is thus a challenge for policymakers and the platforms. This article explores the factors of rapid propagation of misinformation based on our experiences in the domain. An average of about 1.5 million tweets were analysed in each of the three different cases surrounding misinformation. The findings indicate that the tweet emotion and polarity plays a significant role in determining whether the shared content is authentic or not. A deeper exploration highlights that a higher element of surprise combined with other emotions is present in such tweets. Further, the tweets that show case-neutral content often lack the possibilities of virality when it comes to misinformation. The second case explores whether the misinformation is being propagated intentionally by means of the identified fake profiles or it is done by authentic users, which can also be either intentional, for gaining attention, or unintentional, under the assumption that the information is correct. Last, network attributes, including topological analysis, community, and centrality analysis, also catalyze the propagation of misinformation. Policymakers can utilize these findings in this experience study for the governance of misinformation. Tracking and disruption in any one of the identified drivers could act as a control mechanism to manage misinformation propagation.

Predicting Information Diffusion on Twitter a Deep Learning Neural Network Model Using Custom Weighted Word Features

Researchers have been experimenting with various drivers of the diffusion rate like sentiment analysis which only considers the presence of certain words in a tweet. We theorize that the diffusion of particular content on Twitter can be driven by a sequence of nouns, adjectives, adverbs forming a sentence. We exhibit that the proposed approach is coherent with the intrinsic disposition of tweets to a common choice of words while constructing a sentence to express an opinion or sentiment. Through this paper, we propose a Custom Weighted Word Embedding (CWWE) to study the degree of diffusion of content (retweet on Twitter). Our framework first extracts the words, create a matrix of these words using the sequences in the tweet text. To this sequence matrix we further multiply custom weights basis the presence index in a sentence wherein higher weights are given if the impactful class of tokens/words like nouns, adjectives are used at the beginning of the sentence than at last. We then try to predict the possibility of diffusion of information using Long-Short Term Memory Deep Neural Network architecture, which in turn is further optimized on the accuracy and training execution time by a Convolutional Neural Network architecture. The results of the proposed CWWE are compared to a pre-trained glove word embedding. For experimentation, we created a corpus of size 230,000 tweets posted by more than 45,000 users in 6 months. Research experimentations reveal that using the proposed framework of Custom Weighted Word Embedding (CWWE) from the tweet there is a significant improvement in the overall accuracy of Deep Learning framework model in predicting information diffusion through tweets.