Understanding the Digital Marketing Environment with KPIs and Web Analytics

The synergetic impact of digital campaigns and economic incentives on environmental performance: the mediating role of household indoor and outdoor activities

The primary goal of this research is to look into the impact of digital campaigns for environmental and economic incentives on environmental performance, with indoor and outdoor activities of households taken as mediating variables. PLS-SEM was used to evaluate and quantify the novel and complex model to meet the study's goals. Furthermore, data were gathered from 1542 Pakistani households using convenient sampling techniques. The study's findings show that digital campaigns and economic incentives significantly increase household's participation in indoor and outdoor activities, which improves environmental performance. This study contributes to the literature on environmental performance by examining digital campaigns and economic incentives as resilient influencers. Furthermore, this study assists authorities in developing an effective and efficient policy that promotes environmental savaging information while providing economic incentives to encourage the activities. At the same time, it emphasizes environmental concerns and how they can be addressed.

The Effects of Logistics Websites’ Technical Factors on the Optimization of Digital Marketing Strategies and Corporate Brand Name

In a world overwhelmed with unstructured information, logistics companies increasingly depend on their websites to acquire new customers and maintain existing ones. Following this rationale, a series of technical elements may set the ground for differentiating one logistics website from another. Nevertheless, a suitable digital marketing strategy should be adopted in order to build competitive advantage. In this paper, the authors attempt to respond by implementing an innovative methodology building on web analytics and big data. The first phase of the research collects data for 180 days from 7 world-leading logistics companies. The second phase presents the statistical analysis of the gathered data, including regression, correlations, and descriptive statistics. Subsequently, Fuzzy Cognitive Mapping (FCM) was employed to illustrate the cause-and-effect links among the metrics in question. Finally, a predictive simulation model is developed to show the intercorrelation among the metrics studied as well as various optimization strategies. Research findings reveal a significant correlation between the logistics websites’ technical factors and the growth of the corporate brand name.

The Effectiveness of Centralized Payment Network Advertisements on Digital Branding during the COVID-19 Crisis

Crises are always challenging for banking systems. In the case of COVID-19, centralized payment networks and FinTech companies’ websites have been affected by user behavior globally. As a result, there is ample opportunity for marketing managers and professionals to focus on big data from FinTech websites. This can contribute to a better understanding of the variables impacting their brand name and how to manage risk during crisis periods. This research is divided into three stages. The first stage presents the web analytics and the data retrieved from the FinTech platforms. The second stage illustrates the statistical analysis and the fuzzy cognitive mapping (FCM) performed. In the final stage, an agent-based model is outlined in order to simulate and forecast a company’s brand name visibility and user behavior. The results of this study suggest that, during crises, centralized payment networks (CPNs) and FinTech companies with high organic traffic tend to convert new visitors to actual “customers”.

Avoiding digital marketing analytics myopia: revisiting the customer decision journey as a strategic marketing framework

The use of analytics data in digital marketing has had a profound impact on the way marketers create consumer relationships and how firms make decisions. However, the marketing analytics literature offers little guidance on how digital marketing analytics tools should be selected and leveraged in service to the firm’s overall strategy. Foundational marketing theory and research concerning the origin of consumer value and the primary importance of the consumer decision journey to strategy formation offer a pathway to evaluating digital marketing tools and analysis in a strategic and theoretically sound manner. This paper builds on seminal marketing thought to propose a conceptual framework that places use of digital marketing analytics tools and channels in the context of a firm’s marketing plan. The framework has diverse applications across many industries and platforms and can help markers avoid falling victim to digital marketing analytics myopia, even as evolving technologies and broader societal forces like the response to Covid-19 accelerate the digitalization of marketing.

The Effects of Cryptocurrency Trading Websites on Airlines’ Advertisement Campaigns

In future years, airline companies will be leaning more and more towards cryptocurrencies to implement their digital marketing strategies as leaders seek to gain an understanding of the factors affecting airlines’ visibility parameters. Cryptocurrency investment websites are currently experiencing rising demand, making them an appropriate site for paid advertisements. The above factors suggest the need for airlines to harvest cryptocurrency investment and platform users in their favour. To this end, it can be beneficial for airlines’ web promotions to link certain web analytics metrics to cryptocurrency trading site metrics. For research purposes, web analytics data were monitored and gathered for 2 consecutive years from 10 globally leading cryptocurrency trading companies and 10 airline websites. A three-stage model was adopted by the authors. In the first stage, statistical analysis was implemented using cryptocurrency and airline metrics, followed by fuzzy cognitive mapping and agent-based modelling stages. The findings of the study indicate that engagement with cryptocurrency trading websites has a positive impact on airline websites’ global ranking and visibility parameters. The outcomes of this research provide noteworthy digital marketing strategies which can be addressed by airline companies to increase their website visitors and optimise visibility parameters with the assistance of cryptocurrency trading websites.

Big Data Contribution in Desktop and Mobile Devices Comparison, Regarding Airlines’ Digital Brand Name Effect

Rising demand for optimized digital marketing strategies has led firms in a hunt to harvest every possible aspect indicating users’ experience and preference. People visit, regularly through the day, numerous websites using both desktop and mobile devices. For businesses to acknowledge device’s usage rates is extremely important. Thus, this research is focused on analyzing each device’s usage and their effect on airline firms’ digital brand name. In the first phase of the research, we gathered web data from 10 airline firms during an observation period of 180 days. We then proceeded in developing an exploratory model using Fuzzy Cognitive Mapping, as well as a predictive and simulation model using Agent-Based Modeling. We inferred that various factors of airlines’ digital brand name are affected by both desktop and mobile usage, with mobile usage having a slightly bigger impact on most of them, with gradually rising values. Desktop device usage also appeared to be quite significant, especially in traffic coming from referral sources. The paper’s contribution has been to provide a handful of time-accurate insights for marketeers, regarding airlines’ digital marketing strategies.

The Impact of Organic Traffic of Crowdsourcing Platforms on Airlines’ Website Traffic and User Engagement

With airline companies increasingly relying on crowdsourcing websites to deploy their digital marketing strategies, marketeers and strategists seek to acquire an understanding of the factors affecting airlines’ organic traffic and user engagement. Such an understanding is acquired through the consideration of variables that influence a company’s organic traffic and user engagement and their correlation to each other. A three-stage data-driven analysis is used to examine the correlation between the foregoing variables and to consider strategies that can be implemented to optimize organic traffic and user engagement. The first section gathers data from five airline companies’ websites and five crowdsourcing websites over an interval of 180 days. The second stage creates an exploratory diagnostic model, through Fuzzy Cognitive Mapping, to visually illustrate the cause-and-effect correlations between the examined metrics. Finally, a predictive micro-level agent-based model simulates optimization strategies that can be used to improve organic traffic and user engagement. The results of this study, reveal that crowdsourcing organic traffic increases airline websites’ user engagement through paid campaigns, while a limited correlation was found to exist between the average duration of a user to organic traffic. The results of this study provide tangible digital marketing strategies which can be used by airline companies to improve the influence of their digital marketing strategies on their users.

Harvesting Crowdsourcing Platforms’ Traffic in Favour of Air Forwarders’ Brand Name and Sustainability

In the modern digitalised era, the total number of businesses and organisations utilising crowdsourcing services has risen, leading to an increase of their website traffic. In this way, there is plenty of space for marketers and strategists to capitalise big data from both their own and the crowdsourcer’s websites. This can lead to a comprehension of factors affecting their brand name, sustainability (gross profit) and consequently visitor influence. The first of the three staged contexts, based on web data, includes the retrieval of web data analytics and metrics from five air forwarding and five crowdsourcing websites in 210 observation days. At stage two, we deployed a diagnostic-exploratory model, through Fuzzy Cognitive Mapping (FCM), and in the last stage, an Agent-Based Model is deployed for data prediction and simulation. We concluded that crowdsourcing referral traffic increases air forwarders’ top 3 keywords volume, and decreases social traffic and total keywords volume, which then boosts their global web rank and gross profit. The exact opposite results occur with crowdsourcing search traffic. To sum up, the contribution of this paper is to offer realistic and well-informed insights to marketers about SEO and SEM strategies for brand name and profit enhancement, based on harvesting crowdsourcing platform traffic.

Content Management Systems Performance and Compliance Assessment Based on a Data-Driven Search Engine Optimization Methodology

While digitalization of cultural organizations is in full swing and growth, it is common knowledge that websites can be used as a beacon to expand the awareness and consideration of their services on the Web. Nevertheless, recent research results indicate the managerial difficulties in deploying strategies for expanding the discoverability, visibility, and accessibility of these websites. In this paper, a three-stage data-driven Search Engine Optimization schema is proposed to assess the performance of Libraries, Archives, and Museums websites (LAMs), thus helping administrators expand their discoverability, visibility, and accessibility within the Web realm. To do so, the authors examine the performance of 341 related websites from all over the world based on three different factors, Content Curation, Speed, and Security. In the first stage, a statistically reliable and consistent assessment schema for evaluating the SEO performance of LAMs websites through the integration of more than 30 variables is presented. Subsequently, the second stage involves a descriptive data summarization for initial performance estimations of the examined websites in each factor is taking place. In the third stage, predictive regression models are developed to understand and compare the SEO performance of three different Content Management Systems, namely the Drupal, WordPress, and custom approaches, that LAMs websites have adopted. The results of this study constitute a solid stepping-stone both for practitioners and researchers to adopt and improve such methods that focus on end-users and boost organizational structures and culture that relied on data-driven approaches for expanding the visibility of LAMs services.

An Innovative Approach for the Evaluation of the Web Page Impact Combining User Experience and Neural Network Score

The proposed paper introduces an innovative methodology useful to assign intelligent scores to web pages. The approach is based on the simultaneous use of User eXperience (UX), Artificial Neural Network (ANN), and Long Short-Term Memory (LSTM) algorithms, providing the web page scoring and taking into account outlier conditions to construct the training dataset. Specifically, the UX tool analyses different parameters addressing the score, such as navigation time, number of clicks, and mouse movements for page, finding possible outliers, the ANN are able to predict outliers, and the LSTM processes the web pages tags together with UX and user scores. The final web page score is assigned by the LSTM model corrected by the UX output and improved by the navigation user score. This final score is useful for the designer by suggesting the tags typologies structuring a new web page layout of a specific topic. By using the proposed methodology, the web designer is addressed to allocate contents in the web page layout. The work has been developed within a framework of an industry project oriented on the formulation of an innovative AI interface for web designers.

Do You See It Clearly? The Effect of Packaging and Label Format on Google Ads

The nature of e-commerce prevents the perception of the intrinsic and sensory attributes of wine. In the virtual environment, visual cues allow consumers to perceive the product, determine their attitude and form a preference. Users will choose one product or another based on the visual appeal of the advertisements they have seen. Wine marketers must consider the importance of the advertisement elements and attract the consumer’s attention. Optimizing the elements included in these messages can help capture consumers’ attention and achieve a higher click-through rate on the ads. The main objective of this work is to analyse the awareness that different advertisements achieve. Specifically, we use a 2 × 2 × 2 experimental design where we manipulate the packaging format (single bottle vs. pack), labelling (bottle without label vs. labelled bottle) for wine ads (white and red). To analyse attention, we used an eye-tracking methodology. The main results suggest that attention is captured more quickly with an individual bottle without a label than with a particular bottle with a label in Google ads. However, ads showing packs of bottles with labels get more attention than ads using packs of bottles without labels.

Driving Consumer Engagement through Diverse Calls to Action in Corporate Social Responsibility Messages on Social Media

Although a growing number of consumers acknowledge the importance of firms’ corporate social responsibility (CSR) activities, how companies can effectively communicate these initiatives to consumers is still a challenge. Although the rise of social media platforms has provided firms with opportunities for more immediate and interactive communication with consumers, recent academic studies related to social media have mostly focused on effective communication of promotional messages, and relatively little attention has been paid to prosocial messages. For better communication, social media branded messages have encouraged user participation (i.e., calls to action), but how diverse types of calls to action can generate consumer engagement remains unexplored. Using over 2000 branded posts from popular consumer product brands on Facebook and Twitter, this research explores diverse types of calls to action that drive consumers’ attitudinal (i.e., likes) and behavioral (i.e., shares) engagement with CSR-related messages on social media. The research findings suggest that the types of calls to action matter in the effectiveness of CSR messages. Specifically, CSR messages inviting consumers to brand-related programs or games generated a greater number of likes and shares. However, the overall engagement was lower when CSR messages encouraged further conversations, and rewarding consumers was also less effective. Finally, the results show that multiple calls to action within one message decrease engagement. Overall, this study contributes to the academic literature and management by providing new insights and actionable guidelines on how to encourage user participation when designing CSR messages to enhance consumers’ attitudinal and behavioral engagement within social media, thereby contributing to sustainable development while enhancing the effectiveness of marketing communications.

Digital inbound marketing: Measuring the economic performance of grocery e-commerce in Europe and the USA

This research investigates the cost-result relationship of the Inbound Marketing actions used by grocery e-commerce. The analysis is based on the application of the Dorfman and Steiner (1954) model for optimal advertising budget, which is adapted by the authors to digital marketing and verified with empirical statistical analysis. Considering 29 leading companies in six countries over a time horizon of six years, an analysis of the mix of SEO and SEM techniques aimed at the attraction and conversion of Internet users to the web pages of their companies is carried out. The results confirm that e-commerce is optimizing Digital Inbound Marketing in line with the established model. Differences are identified depending on the type of format (pure player versus brick and mortar) and at the country level (UK and USA versus others).

Using Data Sciences in Digital Marketing: Framework, methods, and performance metrics

In the last decade, the use of Data Sciences, which facilitate decision-making and extraction of actionable insights and knowledge from large datasets in the digital marketing environment, has remarkably increased. However, despite these advances, relevant evidence on the measures to improve the management of Data Sciences in digital marketing remains scarce. To bridge this gap in the literature, the present study aims to review (i) methods of analysis, (ii) uses, and (iii) performance metrics based on Data Sciences as used in digital marketing techniques and strategies. To this end, a comprehensive literature review of major scientific contributions made so far in this research area is undertaken. The results present a holistic overview of the main applications of Data Sciences to digital marketing and generate insights related to the creation of innovative Data Mining and knowledge discovery techniques. Important theoretical implications are discussed, and a list of topics is offered for further research in this field. The review concludes with formulating recommendations on the development of digital marketing strategies for businesses, marketers, and non-technical researchers and with an outline of directions of further research on innovative Data Mining and knowledge discovery applications.

Smart Digital Marketing Capabilities for Sustainable Property Development: A Case of Malaysia

Digital tools and marketing have been widely adopted in various industries throughout the world. These tools have enabled companies to obtain real-time customer insights and create and communicate value to customers more effectively. This study aims at understanding the principles and practices of sustainable digital marketing in the Malaysian property development industry by investigating the extent to which digital marketing has been adopted, the impediments to its adoption, and the strategies to improve digital capabilities for the local context. Digital marketing theories, practices, and models from other industries are adopted and applied to the local property development industry to lay the foundation for making it smart and sustainable. This paper proposes a marketing technology acceptance model (MTAM) for digital marketing strategy and capability development. The key factors used in the model are ease of use, perceived usefulness, perceived cost, higher return, efficiency, digital service quality, digital information quality, digital system quality, attitude towards use, and actual use. The model and hypothetical relationships of critical factors are tested using structural modeling, reliability, and validity techniques using a sample of 279 Malaysian property development sector representatives. A quantitative approach is adopted, using an online questionnaire tool to investigate the behavior of respondents on the current digital marketing practices and capabilities of Malaysian property development companies. The results show that the sample property development companies are driven by the benefit of easily obtaining real-time customer information for creating and communicating value to customers more effectively through the company brand. Further strategies, such as creating real-time interactions, creating key performance indicators to measure digital marketing, personalization, and encouraging innovation in digital marketing are most preferred by local professionals. An adoption framework is provided based on the reviewed models and results of the current study to help transform the Malaysian property development sector into a smart and sustainable property development sector by facilitating the adoption of digital technologies. The results, based on real-time data and pertinent strategies for improvement of the local property sector, are expected to pave the way for inducing sustainable digital marketing trends, enhancing capabilities, and uplifting the state of the property development sector in developing countries.

Big Data Analytics for Search Engine Optimization

In the Big Data era, search engine optimization deals with the encapsulation of datasets that are related to website performance in terms of architecture, content curation, and user behavior, with the purpose to convert them into actionable insights and improve visibility and findability on the Web. In this respect, big data analytics expands the opportunities for developing new methodological frameworks that are composed of valid, reliable, and consistent analytics that are practically useful to develop well-informed strategies for organic traffic optimization. In this paper, a novel methodology is implemented in order to increase organic search engine visits based on the impact of multiple SEO factors. In order to achieve this purpose, the authors examined 171 cultural heritage websites and their retrieved data analytics about their performance and user experience inside them. Massive amounts of Web-based collections are included and presented by cultural heritage organizations through their websites. Subsequently, users interact with these collections, producing behavioral analytics in a variety of different data types that come from multiple devices, with high velocity, in large volumes. Nevertheless, prior research efforts indicate that these massive cultural collections are difficult to browse while expressing low visibility and findability in the semantic Web era. Against this backdrop, this paper proposes the computational development of a search engine optimization (SEO) strategy that utilizes the generated big cultural data analytics and improves the visibility of cultural heritage websites. One step further, the statistical results of the study are integrated into a predictive model that is composed of two stages. First, a fuzzy cognitive mapping process is generated as an aggregated macro-level descriptive model. Secondly, a micro-level data-driven agent-based model follows up. The purpose of the model is to predict the most effective combinations of factors that achieve enhanced visibility and organic traffic on cultural heritage organizations’ websites. To this end, the study contributes to the knowledge expansion of researchers and practitioners in the big cultural analytics sector with the purpose to implement potential strategies for greater visibility and findability of cultural collections on the Web.

Smartphone Use and Academic Performance of University Students: A Mediation and Moderation Analysis

The purpose of the undertaken study is to examine the influence of smartphones on the performance of university students in Pakistan. This paper also investigates the functions of a smartphone as exogenous predictors such as smartphone applications, multimedia messaging service (MMS), short message service (SMS), warp-speed processing, and entertainment on the academic performance of a student. This paper also addresses the impact of electronic word of mouth (eWOM) and attitude as mediating variables between exogenous and endogenous variables. Finally, we incorporated technology and addiction as moderating variables between independent variables and the outcome variable to measure the influence of moderating variables. We have taken 684 responses from seven universities in Pakistan and employed the SEM-based multivariate approach for the analysis of the data. The findings of this paper demonstrate that smartphone functions have a significant influence on students’ academic performance, and moderating and mediating variables also have a significant influence on exogenous and endogenous variables. The practical implications have provided a guideline for university teachers, parents, and decision-makers of how a smartphone could be used to improve student academic performance inside and outside university campuses.

SEO inside Newsrooms: Reports from the Field

The journalism profession has changed dramatically in the digital age as the internet, and new technologies, in general, have created new working conditions in the media environment. Concurrently, journalists and media professionals need to be aware and possess a new set of skills connected to web technologies, as well as respond to new reading tendencies and information consumption habits. A number of studies have shown that search engines are an important source of the traffic to news websites around the world, identifying the significance of high rankings in search results. Journalists are writing to be read, and that means ensuring that their news content is found, also, by search engines. In this context, this paper represents an exploratory study on the use of search engine optimization (SEO) in news websites. A series of semi-structured, in-depth interviews with professionals at four Greek media organizations uncover trends and address issues, such as how SEO policy is operationalized and applied inside newsrooms, which are the most common optimization practices, as well as the impact on journalism and news content. Today, news publishers have embraced the use of SEO practices, something that is clear also from this study. However, the absence of a distinct SEO culture was evident in newsrooms under study. Finally, according to results, SEO strategy seems to depend on factors, such as ownership and market orientation, editorial priorities or organizational structures.

The importance of behavioral data to identify online fake reviews for tourism businesses: a systematic review

In the last several decades, electronic word of mouth (eWOM) has been widely used by consumers on different digital platforms to gather feedback about products and services from previous customer behavior. However, this useful information is getting blurred by fake reviews-i.e., reviews that were created artificially and are thus not representative of real customer opinions. The present study aims to thoroughly investigate the phenomenon of fake online reviews in the tourism sector on social networking and online reviews sites. To this end, we conducted a systematic review of the literature on fake reviews for tourism businesses. Our focus was on previous studies that addressed the following two main topics: (i) tourism (ii) fake reviews. Scientific databases were used to collect relevant literature. The search terms "tourism" and "fake reviews" were applied. The database of Web of Science produced a total of 124 articles and, after the application of different filters following the PRISMA 2009 Flow diagram, the process resulted in the selection of 17 studies. Our results demonstrate that (i) the analysis of fake reviews is interdisciplinary, ranging from Computer Science to Business and Management, (ii) the methods are based on algorithms and sentiment analysis, while other methodologies are rarely used; and (iii) the current and future state of fraudulent detection is based on emotional approaches, semantic analysis and new technologies such as Blockchain. This study also provides helpful strategies to counteract the ubiquity of fake reviews for tourism businesses.

How to Make E-Commerce More Successful by Use of Kano’s Model to Assess Customer Satisfaction in Terms of Sustainable Development

Personalization, mobility, deliveries on the same day, and perhaps artificial intelligence—all of these elements will shape e-commerce in the near future. It is necessary to consider what features and standards online shops will have to meet in order to achieve success and to adapt to the changing preferences and requirements of the customer and their awareness of the perception of the environment through the prism of, for example, sustainable development. This means there is a need to specify a set of attributes that will influence the decision to use the services of a given e-shop. Despite all efforts, many online shops fail because they do not meet the expectations of customers. At the same time, meeting customer expectations is a big challenge for newly emerging e-shops. There are many studies on sustainable development in e-commerce, but there is no specific methodology for e-shop design, especially in the aspect of sustainable development. The authors propose a methodology based on Kano’s model and customer satisfaction to explore customers’ stated needs and unstated desires and to divided them into different groups with different impacts on customer satisfaction. In this paper, a case study on the attributes of customer satisfaction for a newly opened e-shop with organic products, which is to operate in the countries of Central Europe and takes into account selected assumptions of sustainable development, was presented. The research took the form of an original (authorial), universal survey that can be used in other similar research. A total of 1069 correctly completed surveys were taken into account for the analysis. Respondents indicated 16 must-have features for the e-shop in order to make customers benefit from its services and 11 one-dimensional features that will determine the level of customer satisfaction. Among the must-have features, there were those related to sustainable development, which indicates the environmental awareness of potential customers. The obtained results were given to the management of the research e-shop and were included during the design of its operation. After one to two years of e-shop operation, the results will be verified.

A Holistic Framework for Forecasting Transformative AI

In this paper we describe a holistic AI forecasting framework which draws on a broad body of literature from disciplines such as forecasting, technological forecasting, futures studies and scenario planning. A review of this literature leads us to propose a new class of scenario planning techniques that we call scenario mapping techniques. These techniques include scenario network mapping, cognitive maps and fuzzy cognitive maps, as well as a new method we propose that we refer to as judgmental distillation mapping. This proposed technique is based on scenario mapping and judgmental forecasting techniques, and is intended to integrate a wide variety of forecasts into a technological map with probabilistic timelines. Judgmental distillation mapping is the centerpiece of the holistic forecasting framework in which it is used to inform a strategic planning process as well as for informing future iterations of the forecasting process. Together, the framework and new technique form a holistic rethinking of how we forecast AI. We also include a discussion of the strengths and weaknesses of the framework, its implications for practice and its implications on research priorities for AI forecasting researchers.

Effectiveness of Online Digital Media Advertising as A Strategic Tool for Building Brand Sustainability: Evidence from FMCGs and Services Sectors of Pakistan

The purpose of the undertaken study is to examine the effectiveness of online digital media advertising as a strategic tool for building brand sustainability. For this purpose, we investigate the impact of different channels of online media, such as email marketing, mobile phone marketing, search engine optimization and companies’ websites, and social media marketing for the effectiveness of online digital media advertising. Moreover, the researchers have introduced eight mediating variables and six moderating variables to examine the impact between exogenous variables and online digital media advertising (endogenous). The researchers have taken 910 responses through a modified questionnaire and employed quantitative research methods, such as the structural equation modeling, exploratory factor and confirmatory factor analyses, and Hayes process approach for examining the mediation and moderation amongst the variables. The results of direct impact have demonstrated that all of the channels of digital media advertising have a positive and significant influence on the effectiveness of online digital media that creates brand sustainability for fast moving consumer goods (FMCG) and services sectors of Pakistan. The outcomes of hypotheses pertaining to the mediating and moderating effects of different variables have shown the significant influence between exogenous variables and endogenous variable. The results of the undertaken study are beneficial for the existing literature that provides the basic foil for future research studies. Moreover, the results have significant industrial implications for the marketers and digital media managers that they can devise effective media and marketing strategies to maintain optimum market share and competitive advantages.

Negative Impact of Social Network Services Based on Stressor-Stress-Outcome: The Role of Experience of Privacy Violations

Social network service (SNS) information has benefited many individuals. However, as such information has increased exponentially, the number of SNS users has increased dramatically and negative effects of SNSs on users have emerged. Many SNS users experience negative psychological conditions such as fatigue, burnout, and stress. Thus, in this study, we investigated the SNS and user characteristics that affect SNS fatigue, living disorder, and reduced SNS use intention. We developed a research model to examine the impact of two SNS characteristics (irrelevant information overload and open reachability) and two user characteristics (engagement and maintaining self-reputation) on SNS fatigue. We also examined the role of the experience of privacy violations in the relationship between living disorder and reduced SNS use intention. We collected data from 579 SNS users and created a partial least squares structural equation model to test the hypotheses. The results of the analysis showed that three factors, other than open reachability, positively affected SNS fatigue. Furthermore, we found that SNS fatigue significantly affected living disorder and reduced SNS use intention, and that experience of privacy violations significantly affected the relationship between living disorder and reduced SNS use intention. These results expand our understanding of SNS fatigue and users’ negative behaviors.

Session-Based Recommender System for Sustainable Digital Marketing

Many companies operate e-commerce websites to sell fashion products. Some customers want to buy products with intention of sustainability and therefore the companies need to suggest appropriate fashion products to those customers. Recommender systems are key applications in these sustainable digital marketing strategies and high performance is the most necessary factor. This research aims to improve recommendation systems’ performance by considering item session and attribute session information. We suggest the Item Session-Based Recommender (ISBR) and the Attribute Session-Based Recommenders (ASBRs) that use item and attribute session data independently, and then we suggest the Feature-Weighted Session-Based Recommenders (FWSBRs) that combine multiple ASBRs with various feature weighting schemes. Our experimental results show that FWSBR with chi-square feature weighting scheme outperforms ISBR, ASBRs, and Collaborative Filtering Recommender (CFR). In addition, it is notable that FWSBRs overcome the cold-start item problem, one significant limitation of CFR and ISBR, without losing performance.

A Text Mining Approach for Sustainable Performance in the Film Industry

Many previous studies have shown that the volume or valence of electronic word of mouth (eWOM) has a sustainable and significant impact on box office performance. Traditional studies used quantitative data, such as ratings, to measure eWOM. However, recent studies analyzed unstructured data, such as comments, through web-based text analysis. Based on recent research trends, we analyzed not only quantitative data, like ratings, but also text data, like reviews, and we performed a sentiment analysis using a text mining technique. Studies have also examined the effect of cultural differences on the decision-making processes of individuals and organizations. We applied Hofstede’s cultural theory to eWOM and analyzed the moderating effect of cultural differences on eWOM influence. We selected 338 films released between 2006 and 2015 from the BoxOfficeMojo database. We collected ratings and reviews, box office revenues, and other basic information from the Internet Movie Database (IMDb). We also analyzed the effects of cultural differences, such as power distance, individualism, uncertainty avoidance, and masculinity, on box office performance. We found that user comments have a greater impact on film sales than user ratings, and movie stars and co-production contribute to box office success. We also conclude that cultural and geographical differences moderate the sentiment elasticity of eWOM.

Digital Marketing Actions That Achieve a Better Attraction and Loyalty of Users: An Analytical Study

Currently, the digital economy contributes decisively to an increase in competitiveness, especially as a digital transformation involves migrating to new technological models where digital marketing is a key part of growth and user loyalty strategies. Internet and Digital Marketing have become important factors in campaigns, which attract and retain Internet users. This study aims to identify the main ways in which users can be gained and retained by using Digital Marketing. The Delphi method with in-depth interviews was the methodology used in this study. The results of the research show the most important actions for achieving user recruitment and loyalty with Digital Marketing from the opinions of consulted experts. The limitations of this study are those related to the number of experts included in the study, and the number of research papers consulted in the literature review. The literature review and the results of this research are used to propose new solid research with a consolidated critical methodology. This research deals with a new approach that will optimize web technologies for the evolution of user trends, and therefore, will be of academic and professional use for marketing managers and web solution developers. The conclusions of the investigation show the key factors, discarding others that do not affect the optimization of conversions in B2C businesses such as the duration of the session and the rebound percentage. Likewise, the results of the research identify the specific actions that must be carried out to attract and retain users in B2C companies that use the Digital Marketing ecosystem on the Internet. The requirements for companies that wish to implement a model to optimize conversions using the current digital economy are also shown.

The Knowledge Mapping of Mobile Commerce Research: A Visual Analysis Based on I-Model

With the advance of 5G communication technologies and Internet+ strategy, mobile commerce has experienced rapid growth and needs urgent attention from researchers. It is the aim of this article to analyze the literature on mobile commerce to address the following question: With the wide application of artificial intelligence and big data, what are the latest technology, models and problems in the background of the new era that researchers and practitioners need to understand in order to grasp the research frontier in this field quickly? Therefore, to achieve these objectives, this paper reviews 1130 m-commerce articles with 25,502 associated references from the SCI-EXPANDED, SSCI, CPCI-S, CPCI-SSH database and develops a framework of m-commerce value by analyzing the most influential authors, institutions, countries, journals and keywords in m-commerce. We apply three types of knowledge mapping to our study—cluster view, timezone view and timeline view. Frequency statistics, clustering coefficient as well as centrality calculation are employed to analyze by CiteSpace. We use the strength of citation bursts to analyze keywords and put result into the I-Modelwhich provide an important framework for classifying m-commerce activities and theories. In this study, we explore the knowledge structure, development and the future trend of mobile commerce for researchers. We identify the main technology and models to improve customer satisfaction and adoption behavior in the background of the new era which provide decision support for practitioners. Compared with the existing literature reviews of mobile commerce, we make a set of knowledge maps to show the future trend of mobile commerce and analyze visual results based on I-model. It is the first study to present the major clusters to reveal their associated intellectual bases and research fronts.

Text Mining for Big Data Analysis in Financial Sector: A Literature Review

Big data technologies have a strong impact on different industries, starting from the last decade, which continues nowadays, with the tendency to become omnipresent. The financial sector, as most of the other sectors, concentrated their operating activities mostly on structured data investigation. However, with the support of big data technologies, information stored in diverse sources of semi-structured and unstructured data could be harvested. Recent research and practice indicate that such information can be interesting for the decision-making process. Questions about how and to what extent research on data mining in the financial sector has developed and which tools are used for these purposes remains largely unexplored. This study aims to answer three research questions: (i) What is the intellectual core of the field? (ii) Which techniques are used in the financial sector for textual mining, especially in the era of the Internet, big data, and social media? (iii) Which data sources are the most often used for text mining in the financial sector, and for which purposes? In order to answer these questions, a qualitative analysis of literature is carried out using a systematic literature review, citation and co-citation analysis.

Online Store Locator: An Essential Resource for Retailers in the 21st Century

Most retailers use their websites and social media to increase their visibility, while potential customers get information about these retailers using the Internet on electronic devices. Many papers have previously studied online marketing strategies used by retailers, but little attention has been paid to determine how these companies provide information through the Internet about the location and characteristics of their stores. This paper aims to obtain evidence about the inclusion of interactive web maps on retailers’ websites to provide information about the location of their stores. With this purpose, the store locator interactive tools of specialty retailers’ websites included in the report “Global Powers of Retailing 2015” are studied in detail using different procedures, such as frequency analysis and word clouds. From the results obtained, it was concluded that most of these firms use interactive maps to provide information about their offline stores, but today some of them still use non-interactive (static) maps or text format to present this information. Moreover, some differences were observed among the search filters used in the store locator services, according to the retailer’s specialty. These results provided insight into the important role of online store locator tools on retailers’ websites.

Detecting Indicators for Startup Business Success: Sentiment Analysis Using Text Data Mining

The main aim of this study is to identify the key factors in User Generated Content (UGC) on the Twitter social network for the creation of successful startups, as well as to identify factors for sustainable startups and business models. New technologies were used in the proposed research methodology to identify the key factors for the success of startup projects. First, a Latent Dirichlet Allocation (LDA) model was used, which is a state-of-the-art thematic modeling tool that works in Python and determines the database topic by analyzing tweets for the #Startups hashtag on Twitter (n = 35.401 tweets). Secondly, a Sentiment Analysis was performed with a Supervised Vector Machine (SVM) algorithm that works with Machine Learning in Python. This was applied to the LDA results to divide the identified startup topics into negative, positive, and neutral sentiments. Thirdly, a Textual Analysis was carried out on the topics in each sentiment with Text Data Mining techniques using Nvivo software. This research has detected that the topics with positive feelings for the identification of key factors for the startup business success are startup tools, technology-based startup, the attitude of the founders, and the startup methodology development. The negative topics are the frameworks and programming languages, type of job offers, and the business angels’ requirements. The identified neutral topics are the development of the business plan, the type of startup project, and the incubator’s and startup’s geolocation. The limitations of the investigation are the number of tweets in the analyzed sample and the limited time horizon. Future lines of research could improve the methodology used to determine key factors for the creation of successful startups and could also study sustainable issues.

Understanding #WorldEnvironmentDay User Opinions in Twitter: A Topic-Based Sentiment Analysis Approach

The main objective of this exploratory study is to identify the social, economic, environmental and cultural factors related to the sustainable care of both environment and public health that most concern Twitter users. With 336 million active users as of 2018, Twitter is a social network that is increasingly used in research to get information and to understand public opinion as exemplified by Twitter users. In order to identify the factors related to the sustainable care of environment and public health, we have downloaded n = 5873 tweets that used the hashtag #WorldEnvironmentDay on the respective day. As the next step, sentiment analysis with an algorithm developed in Python and trained with data mining was applied to the sample of tweets to group them according to the expressed feelings. Thereafter, a textual analysis was used to group the tweets according to the Sustainable Development Goals (SDGs), identifying the key factors about environment and public health that most concern Twitter users. To this end, we used the qualitative analysis software NVivo Pro 12. The results of the analysis enabled us to establish the key factors that most concern users about the environment and public health such as climate change, global warming, extreme weather, water pollution, deforestation, climate risks, acid rain or massive industrialization. The conclusions of the present study can be useful to companies and institutions that have initiatives related to the environment and they also facilitate decision-making regarding the environment in non-profit organizations. Our findings will also serve the United Nations that will thoroughly review the 17 SDGs at the High-level Political Forum in 2019.

Understanding User Behavioral Intention to Adopt a Search Engine that Promotes Sustainable Water Management

An increase in users’ online searches, the social concern for an efficient management of resources such as water, and the appearance of more and more digital platforms for sustainable purposes to conduct online searches lead us to reflect more on the users’ behavioral intention with respect to search engines that support sustainable projects like water management projects. Another issue to consider is the factors that determine the adoption of such search engines. In the present study, we aim to identify the factors that determine the intention to adopt a search engine, such as Lilo, that favors sustainable water management. To this end, a model based on the Theory of Planned Behavior (TPB) is proposed. The methodology used is the Structural Equation Modeling (SEM) analysis with the Analysis of Moment Structures (AMOS). The results demonstrate that individuals who intend to use a search engine are influenced by hedonic motivations, which drive their feeling of contentment with the search. Similarly, the success of search engines is found to be closely related to the ability a search engine grants to its users to generate a social or environmental impact, rather than users’ trust in what they do or in their results. However, according to our results, habit is also an important factor that has both a direct and an indirect impact on users’ behavioral intention to adopt different search engines.

Social Media and Customer-Based Brand Equity: An Empirical Investigation in Retail Industry

As customer-brand engagement progressively shifts to digital domains, understanding social media effects in branding has become a vital issue. Social media effectiveness is especially important for the US retail sector due to intense competition among retailers for consumer attention and engagement on digital channels. Yet, the research on the effectiveness of social media in the retail industry remains sparse. Thus, the purpose of this paper is to investigate how social media affects US retailers’ customer-based brand equity (CBBE) which is an important indicator of brand success. Using a dataset of 15,717 retailer-day observations, the authors empirically test the dynamics between owned and earned social media and CBBE using panel vector autoregression (PVAR). The authors find strong impacts of owned and earned social media on CBBE across the board. However, they find that owned social media harms CBBE of retailers dealing in hedonic and high involvement products. Whereas owned social media helps general retailers in building CBBE, it reduces CBBE of specialty retailers.

Do Online Comments Affect Environmental Management? Identifying Factors Related to Environmental Management and Sustainability of Hotels

The main aim of this study was to identify the key indicators related to environmental management and sustainability of hotels as perceived by travelers during their trips. The methodology used was a sentiment analysis with an algorithm developed in Python trained with data mining and machine learning, with the MonkeyLearn library in the hotel industry sector under the eWOM model (e-Word of Mouth). The results with negative, positive and neutral feelings were submitted to a textual analysis with the qualitative analysis software Nvivo Pro 12. The sample consisted of the 25 best hotels in Switzerland according to Traveler’s Choice from TripAdvisor ranking 2018 that draws from more than 500,000 reviews. For data extraction, we connected to the TripAdvisor API, obtaining a sample of n = 8331 reviews of the hotels that made up the ranking. The results of the study highlight the key factors related to environmental management detected by travelers during their stay in hotels and can be meaningfully used by managers or hotel managers to improve their services and enhance the value provided by their policies of sustainability and respect for the environment. The limitations of the present study relate to the size of the sample and the number of hotels included in the present analysis.

Attitudes Expressed in Online Comments about Environmental Factors in the Tourism Sector: An Exploratory Study

The object of this exploratory study is to identify the positive, neutral and negative environment factors that affect users who visit Spanish hotels in order to help the hotel managers decide how to improve the quality of the services provided. To carry out the research a Sentiment Analysis was initially performed, grouping the sample of tweets (n = 14459) according to the feelings shown and then a textual analysis was used to identify the key environment factors in these feelings using the qualitative analysis software Nvivo (QSR International, Melbourne, Australia). The results of the exploratory study present the key environment factors that affect the users experience when visiting hotels in Spain, such as actions that support local traditions and products, the maintenance of rural areas respecting the local environment and nature, or respecting air quality in the areas where hotels have facilities and offer services. The conclusions of the research can help hotels improve their services and the impact on the environment, as well as improving the visitors experience based on the positive, neutral and negative environment factors which the visitors themselves identified.