Unraveling the Influence: Exploring the Role of User Generated Content Along the Customer Journey and Understanding Its Relevance for Research and Practice

The Impact of User-Generated Content On Product Innovation

Purpose– User-generated content (UGC), i.e. the feedback from consumers in the electronic market, including structured and unstructured types, has become increasingly important in improving online businesses. However, the ambiguity and heterogeneity, and even the conflict between the two types of UGC, require a better understanding from the perspective of human cognitive psychology. By using online feedback on hotel services, the purpose of this paper is to explore the effects of satisfaction level, opinion dispersion and cultural context background on the interrelationship between structured and unstructured UGC.Design/methodology/approach– Natural language processing techniques – specifically, topic classification and sentiment analysis on the sentence level – are adopted to retrieve consumer sentiment polarity on five attributes relative to itemized ratings. Canonical correlation analyses are conducted to empirically validate the interplay between structured and unstructured UGC among different populations segmented by the mean-variance approach.Findings– The variety of cognitions displayed by individuals affects the general significant interrelationship between structured and unstructured UGC. Extremely dissatisfied consumers or those with heterogeneous opinions tend to have a closer interconnection, and the interaction between valence and dispersion further strengthens or loosens the relationship. The satisfied or neutral consumers tend to show confounding sentiment signals in relation to the two different UGC. Chinese consumers behave differently from non-Chinese consumers, resulting in a relatively looser interplay.Practical implications– By identifying consistent opinion providers and promoting more valuable UGC, UGC platforms can raise the quality of information generated. Hotels will then be able to enhance their services through the strategic use of UGC by analyzing reviews with dispersed low-itemized rating and by addressing the differences exhibited by non-Chinese customers. This analytical method can also help to create richly structured sentiment information from unstructured UGC.Originality/value– This paper investigates the variety of cognitive behaviors in the process when UGC are contributed by online reviewers, focussing on the consistency between structured and unstructured UGC. The study helps researchers understanding emotion recognition and affective computing in social media analytics, which is achieved by exploring the variety of UGC information and its relationship to the contributors’ cognitions. The analytical framework adopted also improves the prior techniques.

Big data, particularly online reviews, has a major impact on customer experience. User-generated content (UGC) on social media sites significantly influences customer choice and enhances service providers' brand equity, revenue, and service innovations. The main objective of this study is to broaden the body of knowledge by analyzing research that have examined the impact of large data made accessible through UGC that helps to determine how the hospitality and tourism sectors should act in order to meet the expectations of their clients. All available research papers have been critically analyzed for future research. The finding of this paper is based on systematically reviewing 22 recent high-quality research papers using the keyword search method. The study begins by evaluating the importance of using big data and online reviews to meet consumer expectations through service, which serves to shape the hotel industry's decision-making process in relation to visitor happiness. Secondly, it also identifies the area for future research. The results indicate that the primary three components that influencing overall hospitality & tourism customer experience & satisfaction are service, room, and value evaluations. While negative emotions and the brand type have a detrimental impact on how satisfied guests are.

Content marketing is becoming an important trend in the tourism industry and is attracting the attention of many stakeholders. Previous studies have sporadically highlighted only some content marketing aspects, but none comprehensively described the topic. To fill this gap, this study adopts a comprehensive approach by combining two bibliometric co-citation and co-keyword analysis methods of 659 articles on content marketing in travel sectors. The co-citation results indicate that tourism content marketing research has been concentrated on the themes of (1) the impact of electronic word of mouth (eWOM) and word of mouth (WOM) on business performance; (2) the role of social media, user-generated content (UGC) and destination image formulation; (3) the impact of eWOM and UGC on the decision-making process; as well as (4) opportunities and challenges. Additionally, based on the co-keyword analysis, hot research topics are explored, including online review implementation; UGC implementation; communication and information search; customer behavior prediction model; the decision-making process; and issues related to user experience, quality, and management. Among these, UGC implementation is the most likely trend that researchers can develop in the future. In addition, the influence of other types of UGC (e.g., user-generated travel videos) is a promising avenue for future research. This study will help researchers understand the role and influence of tourism content marketing. Furthermore, tourism marketers can use content marketing to restore destination image and address last-minute booking issues after the COVID-19 pandemic.

Mixing User- and Publisher-Generated Content: Quantifying the Spillover Effect of User-Generated Content in a Hybrid Content Environment

This article aims to specify the performance implications of neutral user-generated content (UGC) on product sales by differentiating mixed-neutral UGC, which contains an equal amount of positive and negative claims, from indifferent-neutral UGC, which includes neither positive nor negative claims. The authors propose that positive and negative UGC only provide opportunities for consumers to process product-related information, whereas both mixed- and indifferent-neutral UGC affect consumers’ motivation and ability to process positive and negative UGC. The results of three studies using multiple measures (text and numerical UGC), contexts (automobiles, movies, and tablets), and methods (empirical and behavioral experiment) indicate contrasting premium and discount effects such that mixed-neutral UGC amplifies the effects of positive and negative UGC, whereas indifferent-neutral UGC attenuates them. Empirical evidence further indicates that ignoring mixed- or indifferent-neutral UGC leads to substantial under- or overestimates of the effects of positive and negative UGC. The effects of neutral UGC on product sales thus are not truly neutral, and the direction of the bias depends on both the type of UGC and the distribution of positive and negative UGC.

Background: Previous research on user-generated content in technical communication focused primarily on non-traditional forms of technical communication outside of traditional institutions and organizations. User-generated content from the forum StackOverflow provides rich knowledge and stories behind problems faced by web and software developers. This study explores how technical communicators engage in this knowledge-rich content specific to web and software developers. Our findings provide insights into how researchers, instructors, and practicing technical communicators might leverage user-generated forum content in their work. Literature review: Previous research examined how user-generated content is a form of technical writing and technical editing. Furthermore, some research in technical communication has examined how technical user forums present a type of user-generated content to which technical communicators might add value. However, no research on user forums has explicitly examined how technical communicators engage in user forums to gain subject-matter familiarity or expertise. This study seeks to explore how technical communication students engage in user forums to troubleshoot coding problems. Research questions: 1. Are technical communication students able to successfully solve unfamiliar coding problems using user-generated content posted on the StackOverflow website? Are they able to identify the conceptual knowledge needed to solve the coding problem? 2. When learning about new, unfamiliar technical content, how do technical communication students search for information and decide which forum content to engage with? 3. Do technical communication students make meaning and actively fill knowledge gaps when they engage in new, unfamiliar technical content on StackOverflow? 4. After searching and reading through user-generated forum content to troubleshoot a coding task, do technical communication students feel confident enough in what they learned to teach someone else? Results: Most participants were unable to solve any of the coding problems using StackOverflow. Those who did successfully troubleshoot the coding problems exhibited more active scanning when selecting a search thread and made meaning of thread content more closely. Conclusions: Actively engaging and making meaning of thread content reveals insights into the stories behind the thread. These stories provide important details and clues for gaining subject-matter familiarity, but users must actively engage in meaning-making to access the stories and fill knowledge gaps. Practitioners and instructors can leverage content on StackOverflow to better understand coding problems. StackOverflow threads, along with other user-generated forum content, also give instructors insight into technical audiences and can be leveraged to teach students how to use primary research to better understand audiences. Researchers can continue to study how novice users interact with user-generated content by investigating how confidence levels affect meaning-making.

This paper tries to show how the major features of the behavior of advertising can be explained by advertising's information function. For search qualities advertising provides direct information about the characteristics of a brand. For experience qualities the most important information conveyed by advertising is simply that the brand advertises. This contrast in advertising by these qualities leads to significant differences in its behavior. How does advertising provide information to the consumer? The producer in his advertising is not interested directly in providing information for consumers. He is interested in selling more of his product. Subject to a few constraints, the advertising message says anything the seller of a brand wishes. A mechanism is required to make the selling job of advertising generate information to the consumer. [Авторский текст]

PurposeThis study aims to investigate customers' personal innovativeness (PI) as an influencing factor of omni-channel customer experience throughout pre-purchase, purchase and post–purchase stages of the customer journey, and their subsequent influences on customers' reuse intention.Design/methodology/approachData were collected from customers who had experience with omni-channel shopping in South Korea through a questionnaire made with Naver Forms, both online and offline from 15 June to 15 July 2022. Out of the received responses, only valid and consistent questionnaires were considered for statistical analysis. In total, 272 valid samples were utilised for the final analysis. Analyses included reliability, validity, path, structural equation modelling and mediation effects, using SPSS and AMOS software.FindingsThe results revealed a significant influence of personal innovativeness on the omni-channel customer experience across all purchase stages. Personal innovativeness was found to influence the customer experience in the pre- and post–purchase stages, thus affecting reuse intention. However, it did not have the same effect in the purchase stage. The omni-channel experience customer experience also played an indirect mediating role in the relationship between personal innovativeness and reuse intention.Research limitations/implicationsFirst, personal innovativeness in the IT domain may be very prominent in studies examining innovative behaviours related to computing technology. Second, this study provides further understanding of customers' intentions to reuse omni-channel shopping. Third, the path analysis showed that personal innovativeness significantly affects customer experience at all pre-purchase, purchase and post–purchase stages of the customer journey. However, except for the purchase experience, both pre- and post–purchase experiences significantly impact customers' intention to reuse omni-channels and play a mediating role.Practical implicationsFirst, omni-channel retailers should launch new products, innovative promotional activities and explore new channels or new service modes to stimulate the need recognition of customers with high personal innovativeness. Second, omni-channel retailers should pay attention to the users' reviews of each channel because they play a key role in potential customers' purchase decisions. Third, offering customers a seamless shopping experience is essential as a marketing strategy for omni-channel retailing.Originality/valueThis study elucidates the causal relationship between personal characteristics and behaviour by dividing the omni-channel customer journey. In particular, personal innovativeness is identified as an important predictor of the intention to reuse omni-channels during the pre- and post–purchase stages. This suggests that omni-channel retailers need to strategically manage these stages to boost customers' reuse intention.

Originating from Facebook, LinkedIn, Twitter, Instagram, YouTube, and many other networking sites, the social media shared by users and the associated metadata are collectively known as user generated content (UGC). To analyze UGC and glean insight about user behavior, robust techniques are needed to tackle the huge amount of real-time, multimedia, and multilingual data. Researchers must also know how to assess the social aspects of UGC, such as user relations and influential users. Mining User Generated Content is the first focused effort to compile state-of-the-art research and address future directions of UGC. It explains how to collect, index, and analyze UGC to uncover social trends and user habits. Divided into four parts, the book focuses on the mining and applications of UGC. The first part presents an introduction to this new and exciting topic. Covering the mining of UGC of different medium types, the second part discusses the social annotation of UGC, social network graph construction and community mining, mining of UGC to assist in music retrieval, and the popular but difficult topic of UGC sentiment analysis. The third part describes the mining and searching of various types of UGC, including knowledge extraction, search techniques for UGC content, and a specific study on the analysis and annotation of Japanese blogs. The fourth part on applications explores the use of UGC to support question-answering, information summarization, and recommendations.

The linguistic analyses on easily accessible, user-generated social media content offers great opportunities to identify individual characteristics such as their cognitive styles. In this paper, We explore the potential to use social media content to identify individuals' cognitive styles. We first employed crowdsourcing to collect Twitter users' cognitive styles using standard psychometric instruments. Then, we extracted the linguistic features of their social media postings. Leveraging these features, we build prediction models that provide estimates of cognitive styles through statistical regression and classification. We find that user generated content in social media provide useful information for characterizing people's cognitive styles. The models' performance indicates that the cognitive styles automatically inferred from social media are good proxies for the ground truth, and hence provides a promising and scalable way to automatically identify a large number of people's cognitive styles without reaching them individually.

In the field of personalized recommendation, user-generated content (UGC) such as videos, images, and product comments are becoming increasingly important, since they implicitly represent the preferences of users. The vectorized representation of a commodity with multisource and heterogeneous UGC is the key for sufficiently mining the preference information to make a recommendation. Existing studies have mostly focused on using one type of UGC, e.g., images, to enrich the representation of a commodity, ignoring other contents. When more UGC are fused, complicated models with heavy computation cost are often designed. Motivated by this, we proposed a low-computational-power model for vectorizing multisource and recommendation UGC to achieve accurate commodity representations. In our method, video description keyframes, commodities’ attribute text, and user comments were selected as the model’s input. A multi-model fusion framework including feature extraction, vectorization, fusion, and classification based on MobileNet and multilayer perceptrons was developed. In this UGC fusion framework, feature correlations between images and product comments were extracted to design the loss function to improve the precision of vectorized representation. The proposed algorithm was applied to an actual representation of a commodity described by UGC, and the effectiveness of the proposed algorithm was demonstrated by the classification accuracy of the commodity represented.

Paulo Albuquerque (“ Evaluating Promotional Activities in an Online Two-Sided Market of User-Generated Content ”) is an assistant professor of marketing at the Simon Graduate School of Business, University of Rochester. He holds a Ph.D. in management from the UCLA Anderson School of Management. He is currently interested in competition and consumer behavior in online markets, new product diffusion across markets, and spatial competition models. He was named a 2011 MSI Young Scholar, and his articles have appeared in Marketing Science, the Journal of Marketing Research, and Management Science. Udi Chatow (“ Evaluating Promotional Activities in an Online Two-Sided Market of User-Generated Content ”) is a program and research manager at Hewlett-Packard (HP) Labs and a lead on MagCloud.com incubation, which he cofounded. He earned bachelor's and master's degrees in physics and medical physics from Tel Aviv University and an EMBA from Kellogg/Tel Aviv University in their international program. Since joining HP Labs in July 2005, he has led and supported several Web-to-print services and incubations; he previously spent 17 years at HP-Indigo, where he held various research and development positions such as research scientist, project manager, section manager, and director. He has over 30 patents awarded and is active in the information systems and technology organization and in nonimpact printing conferences. Kay-Yut Chen (“ Evaluating Promotional Activities in an Online Two-Sided Market of User-Generated Content ”) is a principal scientist at Hewlett-Packard (HP) Labs. He started behavioral economics research at HP Labs, a first in a corporation, after he received his Ph.D. from Caltech in 1994. He has pioneered the application of behavior economics to business issues in areas such as supply chain contracting and human-based forecasting, and his work has been featured in many popular publications such as Scientific American, Newsweek, the Wall Street Journal, and the Financial Times. He is the author of the book The Secrets of the Moneylab: How Behavioral Economics Can Improve Your Business, published by Portfolio in October 2010. Theodoros Evgeniou (“ Content Contributor Management and Network Effects in a UGC Environment ”) is an associate professor of decision sciences and technology management at INSEAD, Fontainebleau. His current research interests include preference measurement methods and market research, social networks, machine learning, and data analytics for marketing. He has published more than 30 top academic journal and conference papers. Moshe Fresko (“ Mine Your Own Business: Market-Structure Surveillance Through Text Mining ”) is a consulting expert on the topics of text mining, data mining, natural language programming, and machine learning. He holds a B.A. and an M.A. in computer engineering from Boğaziçi University, Istanbul, Turkey, and he received his Ph.D. in computer science from Bar-Ilan University, Israel. Between 2001 and 2010, he worked as a researcher and lecturer at Bar Ilan's Computer Science department, studying text mining, data mining, natural language programming, and machine learning, as well as teaching several programming-related courses; between 2007 and 2008, he worked as a visiting researcher and lecturer at the School of Business Administration at the Hebrew University of Jerusalem. He was active in the founding and progress of two text-mining related start-up companies. Ronen Feldman (“ Mine Your Own Business: Market-Structure Surveillance Through Text Mining ”) currently serves as the Head of the Internet Studies Department at the School of Business Administration of the Hebrew University of Jerusalem. He received his Ph.D. in computer science from Cornell University and his B.Sc. in math, physics, and computer science from the Hebrew University of Jerusalem. In 1997, he founded ClearForest, a Boston-based business intelligence company later acquired by Reuters. He coined the term “text mining” in 1995 and wrote the textbook The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data (Cambridge University Press, 2007); he has given over 30 tutorials on text mining and information extraction and has written numerous scholarly papers on these topics. Anindya Ghose (“ Designing Ranking Systems for Hotels on Travel Search Engines by Mining User-Generated and Crowdsourced Content ”) is an associate professor in the Department of Information, Operations, and Management Sciences at the Stern School of Business of New York University. He received his Ph.D. from Carnegie Mellon University. His expertise is in analyzing how the massive amount of data generated by technological advances such as the Internet and mobile phones can influence marketing and advertising decisions, and his recent research interests include social media, mobile Internet, crowdfunding, Internet marketing, and digital advertising. He has received multiple best paper awards at premier conferences and journals, is a 2011 MSI Young Scholar, and is also a recipient of a National Science Foundation CAREER Award. David Godes (“ Sequential and Temporal Dynamics of Online Opinion ”) is an associate professor in the Marketing Department at the Robert H. Smith School of Business, University of Maryland. He received a B.S. in economics from the University of Pennsylvania and an S.M. and Ph.D. in management science from the Massachusetts Institute of Technology. His research interests include word-of-mouth communication, social networks, media competition, and sales management. His work has appeared in Marketing Science, Management Science, Quantitative Marketing and Economics, and the Harvard Business Review. Jacob Goldenberg (“ Mine Your Own Business: Market-Structure Surveillance Through Text Mining ”) is a professor of marketing at the School of Business Administration at the Hebrew University of Jerusalem and a visiting professor at the Columbia Business School. His research focuses on creativity, new product development, diffusion of innovation, complexity in market dynamics social networks effects, and social media. He has published papers in the Journal of Marketing, the Journal of Marketing Research, Management Science, Marketing Science, Nature Physics, and Science; in addition, he is an author of two books by the Cambridge University Press and one by the Chicago Press. His scientific work has been covered by the New York Times, the Wall Street Journal, the Boston Globe, the BBC News Harold Tribune, the Economist, and Wired Magazine. Rajdeep Grewal (“ User-Generated Open Source Products: Founder's Social Capital and Time to Product Release ”) is the Irving & Irene Bard Professor of Marketing at the Smeal College of Business at the Pennsylvania State University and is also the Associate Research Director of the Institute for the Study of Business Markets at the Smeal College of Business. He received his Ph.D. from the University of Cincinnati in 1998. His research focuses on empirical modeling of strategic marketing issues and has appeared in the top field journals. He has received several awards for his research, including a doctoral dissertation award from the Procter & Gamble Market Innovation Research Fund, an honorable mention award at the prestigious MSI/Journal of Marketing competition on “Linking Marketing to Financial Performance and Firm Value,” the 2003 Young Contributor Award from the Society of Consumer Psychology for his article in the Journal of Consumer Psychology, and the AMA Marketing Strategy SIG Early Career Award in 2007. Panagiotis G. Ipeirotis (“ Designing Ranking Systems for Hotels on Travel Search Engines by Mining User-Generated and Crowdsourced Content ”) is an associate professor in the Department of Information, Operations, and Management Sciences at the Stern School of Business of New York University. He received his Ph.D. degree in computer science from Columbia University in 2004, with distinction. His recent research interests focus on crowdsourcing and on mining user-generated content on the Internet. He has received three best paper awards (International Conference on Data Engineering 2005, ACM Special Interest Group on Management of Data 2006, and International World Wide Web Conference 2011), two best paper runner-up awards (Joint Conference on Digital Libraries 2002 and ACM Knowledge Discovery and Data Mining Conference 2008), and is also a recipient of a CAREER Award from the National Science Foundation. Zainab Jamal (“ Evaluating Promotional Activities in an Online Two-Sided Market of User-Generated Content ”) is a research scientist at Hewlett-Packard Labs. She holds a Ph.D. in marketing science from the University of California, Los Angeles. Her area of focus is in developing econometric and statistical models to understand and predict customer response behavior; this area feeds into the broader research stream of enabling businesses to optimize their marketing operations through analytical technologies in the backdrop of major paradigm shifts in the landscape such as personalized marketing. She brings deep industry experience to her research expertise, having worked in different roles in brand management and product development after receiving her master's in economics (Delhi School of Economics) and an MBA (Indian Institute of Management, Ahmedabad). Gerald C. Kane (“ Network Characteristics and the Value of Collaborative User-Generated Content ”) is an assistant professor of information systems at Boston College's Carroll School of Management. He received his Ph.D. from the Goizueta Business School of Emory University and his MBA in computer information systems from Georgia State University. His research interests include

Focusing on four types of user-generated content (UGC) on Instagram for travel planning, this study examined the impacts of UGC format, distribution channel, and engagement on travelers’ feelings of social presence, trustworthiness, and perceived enjoyment. These factors were then used to test an extended Technology Acceptance Model (TAM) on trip planning. Results showed response-to-marketer-action UGC hosted on official Instagram channel was an effective way to increase perceived enjoyment of travel-related UGC in a low engagement situation. Social presence, perceived trustworthiness, and perceived enjoyment were included as three external variables in an extended TAM for UGC-based travel planning. Compared to previous TAM studies, perceived ease of use and enjoyment, instead of perceived usefulness, were strong predictors of attitude and intention for UGC-based travel planning. Seeking fun with less effort is a main reason to process UGC information on Instagram for travel planning.

User-generated content (UGC) is generally understood as an expression of opinion in many forms (e.g., complaints, online customer reviews, posts, testimonials) and data types (e.g., text, image, audio, video, or a combination thereof) that has been created and made available by users of websites, platforms, and apps on the Internet. In the digital age, huge amounts of UGC are available. Since UGC often reflects evaluations of brands, products, services, and technologies, many consumers rely on UGC to support and secure their purchasing and/or usage decisions. But UGC also has significant value for marketing managers. UGC allows them to easily gain insights into consumer attitudes, preferences, and behaviors. In this article, we review the literature on UGC-based decision support from this managerial perspective and look closely at relevant methods. In particular, we discuss how to collect and analyze various types of UGC from websites, platforms, and apps. Traditional data analysis and machine learning based on feature extraction methods as well as discriminative and generative deep learning methods are discussed. Selected use cases across various marketing management decision areas (such as customer/market selection, brand management, product/service quality management, new product/service development) are summarized. We provide researchers and practitioners with a comprehensive understanding of the current state of UGC data collection and analysis and help them to leverage this powerful resource effectively. Moreover, we shed light on potential applications in managerial decision support and identify research questions for further exploration.