Consumer-Oriented Tech Mining: Integrating the Consumer Perspective into Organizational Technology Intelligence - The Case of Autonomous Driving

To avoid missing technological opportunities and to counteract risks, organizations have to scan and monitor developments in the external environment through a structured process of technology intelligence. Previous approaches in tech mining—the application of text mining for technology intelligence —have primarily focused on the elicitation of technical or legal information from web, patent, or research databases. However, knowledge of consumers’ needs, fears, and hopes is a prerequisite for the success of an emerging technology in the marketplace. Thus, we claim that technology intelligence needs to also consider consumers’ technology perceptions. Hence, we propose a novel and comprehensive approach to collect user-generated content from the web and apply text mining to derive consumer perceptions. In doing so, we align with an established tech-mining process. This paper illustrates our approach on the emerging technology of autonomous driving and provides an initial indication of concurrent validity

Destination image online analyzed through user generated content: a systematic literature review

Destination Image is a concept that has been studied for a long time in tourism research. The question of how a destination is perceived by tourists and potential new guests is an important insight, especially for local tourism managers, in order to evaluate the implemented strategies and to plan further tactics. Since the last two decades, due to a drastic digitalization, tourism research is now increasingly examining the Destination Image online. This creates new challenges in the selection of sources, methods, and in data collection. The aim of the present study was to systematically capture the approach to analyze the online Destination Image through User Generated Content using studies from the last ten years. Therefore, a Systematic Literature Review on primary research from academic databases was conducted. As a summary of the findings, a conceptual model was developed, based on the insights of the studies in the dataset, to contribute a guidance for the preparation phase of future online Destination Image research. In short, the main findings are: TripAdvisor.com is the main source for online Destination Image analysis. Researchers recommend using the help of software and programming languages to collect and analyzed the data. Equally to earlier Destination Image studies, the main methods applied in online Destination Image analysis are quantitative content analysis, qualitative content analysis and sentiment analysis. In combination with the examination of cognitive and affective factors, co-occurrence analysis, and correlation analysis. The present study has several limitations, which are: the loss of detail information due to reducing the studies to comparable key parameters, the absence of Anglo-American studies, due to the database selection as well as the lack of quality testing of the studies included.A Destination Image é um conceito que tem sido estudado há muito tempo na investigação turística. A questão de como o destino é visto pelos turistas e pelos potenciais novos hóspedes é uma perspectiva importante, especialmente para os gestores de turismo da região, a fim de avaliar as estratégias implementadas e de planear novas tácticas. Desde as últimas duas décadas, ocorreu uma digitalização drástica, a investigação turística adaptou-se a este fenómeno e está agora a estudar cada vez mais a imagem do destino online. Esta alteração criou novos desafios na selecção de fontes, métodos, e na recolha de dados. O objetivo do presente trabalho foi o de captar, de forma sistemática, as abordagens consideradas para analisar a imagem do destino online utilizando estudos dos últimos dez anos. Para este efeito, os estudos primários dos anos 2010-2020 das bases de dados académicos Web of Science, ProQuest e b-on, foram recolhidos utilizando palavras-chave de pesquisa pré-definidas. O grupo de artigos obtidos como resultado foram subsequentemente sujeitos a avaliação de eligibilidade, como recomendado por Moher et al. (2009). Isto significa que os estudos que não cumpriam os critérios pré-definidos foram excluídos. Os critérios de inclusão foram: O trabalho académico tinha de ser uma referência primária de uma revista científica, escrita em inglês e a amostra analisada tinha de ter uma origem associada à comunicação nas social media online. Posteriormente, os restantes 35 artigos foram transferidos para uma base de dados utilizando uma matriz de codificação. A matriz de codificação foi concebida para capturar os parâmetros-chave de cada estudo primário de uma forma padronizada e, portanto, comparável. Foi considerada informação geral, como o ano, localização e revista publicada, bem como informação temática específica, como o campo do turismo pesquisado e os meios analisados, juntamente com as categorias referentes à metodologia considerada, as ferramentas utilizadas e os resultados obtidos. A base de dados resultante foi então utilizada para obter declarações sobre a abordagem metodológica utilizada na análise da imagem de destinos online. Como resumo dos resultados, foi desenvolvido um modelo conceptual, baseado nos conhecimentos obtidos a partir do grupo de artigos, que constituiu o conjunto de dados para análise, para contribuir com um guião para a fase de preparação de uma futura investigação sobre imagem dos destinos online. Em resumo, as principais conclusões são: TripAdvisor.com é a principal fonte para a análise da imagem de destinos online. Os investigadores recomendam a utilização da ajuda de software e linguagens de programação para a recolha e análise dos dados. À semelhança de estudos anteriores de Destination Image, os principais métodos aplicados na análise imagem dos destinos online são a análise quantitativa do conteúdo, a análise qualitativa do conteúdo e a análise dos sentimentos. Em combinação com a análise dos fatores cognitivos e afectivos, análise de co-ocorrência, e análise de correlação. O presente estudo tem várias limitações. Que são: a perda de informação detalhada devido à redução dos estudos a parâmetros-chave comparáveis, a ausência de estudos anglo-americanos, devido à selecção do banco de dados, bem como a falta de testes de qualidade dos estudos incluídos.(TurExperience - Tourist experiences' impacts on the destination image: searching for new opportunities to the Algarve”)

What Do Customers Say About My Products? Benchmarking Machine Learning Models for Need Identification

Needmining is the process of extracting customer needs from user-generated content by classifying it as either informative or uninformative regarding need content. Contemporary studies achieve this by utilizing machine learning. However, models found in the literature cannot be compared to each other because they use private data for training and testing. This study benchmarks all previously suggested needmining models including CNN, SVM, RNN, and RoBERTa. To ensure an unbiased comparison, this study samples and annotates a dataset of customer reviews for products from 4 different categories from amazon. Henceforth, the dataset is publicly available and serves as a gold-set for future needmining benchmarks. RoBERTa outperformed other classifiers and seems to be best suited for needmining. The relevance of this study is reinforced by the fact that this benchmark creates a different hierarchy between models than otherwise suggested by comparing the results of previous studies

Designing a Methodology for Marketing Intelligence Systems – The Case of Brand Image Diagnostics

In situations of information overload and complexity, consumers consult their existing knowledge regarding brands as a guide in consumption decisions. This knowledge manifests as brand association networks (BANs) in consumers’ minds and reflects what the consumer thinks of when being confronted with a brand stimulus. BANs therefore characterize a brand’s image that determines consumers’ attitudes and behaviour. BANs serve as diagnostic instruments to explain a brand’s success or failure and to plan or control marketing activities. Traditionally, BANs are elicited directly from consumers utilizing survey-based instruments. However, in a dynamic and interactive environment, user-generated content (UGC) is increasingly relevant for a brand’s image and thus should be exploited for the elicitation of BANs. However, established elicitation instruments either follow another elicitation paradigm (i.e. surveys or interviews), or are unable to cope with volume, velocity, and variety of UGC as a big data source (e.g. content analysis). Hence, exploiting UGC for BAN elicitation requires the development of new, computer-supported instruments. Following a design science research approach, we contribute a novel methodology as our artefact to extract BANs from UGC using text-mining and net- work analysis. We evaluate our solution and demonstrate its utility for brand management on a study of automotive brands

USER-GENERATED CONTENT (UGC) ENCOUNTERED ENTERPRISE-GENERATED CONTENT (EGC): QUANTIFYING THE IMPACT OF EGC ON THE PROPAGATION OF NEGATIVE UGC

The impact of user-generated content (UGC), especially negative UGC on enterprises is well recognized. From the perspective of enterprises, different strategies of enterprise-generated content (EGC) have also been adapted to response to the unexpected UGC, but few studies have investigated the influence of such strategies on the UGC propagation. This research examines which strategy on the negative UGC propagation is optimal by proposing EGC-UGC interaction model. It aims to understand the interaction between UGC and EGC in the context of the social network. Using a simulation analysis method to measure the effect of such EGC factors as the first time of issuing EGC, EGC quantity and interactive frequency on the UGC propagation, the study finds that interactive frequency is the most key factor in defending against negative UGC propagation. This research further explores the effect of different strategy combination referring those three factors on the two types of negative UGC propagation based on deviation distance. The results present two optimal strategies for the two types of negative UGC propagation, respectively. Overall, these findings offer some unique implication for UGC management, information diffusion model of competitive information coexisting

Social media in marketing research : Theoretical bases, methodological aspects, and thematic focus

The widespread use of social media as a marketing tool during the last decade has been responsible for attracting a significant volume of academic research, which, however, can be described as highly fragmented to yield clear directions and insights. We systematically synthesize and critically evaluate extant knowledge of social media marketing extracted from 418 articles published during the period 2009–2021. In doing so, we use an organizing framework focusing on five key areas of social media marketing research, namely, social media as a promotion and selling outlet, social media as a communication and branding channel, social media as a monitoring and intelligence source, social media as a customer relationship management and value cocreation platform, and social media as a general marketing and strategic tool. Within each of these areas, we provide important theoretical, methodological, and thematic insights, as well as future research directions. We also offer useful managerial implications derived from the articles reviewed.© 2022 The Authors. Psychology & Marketing published by Wiley Periodicals LLC. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.fi=vertaisarvioitu|en=peerReviewed

The Trending Customer Needs (TCN) Dataset: A Benchmarking and Automated Evaluation Approach for New Product Development

In recent years, there have been many studies which summarize User Generated Content as lists of ranked keyphrases representing customer needs for the purposes of New Product Development. However, methods for the evaluation of keyphrase lists do not robustly assess solutions for these purposes. Therefore, in this paper we present the “Trending Customer Needs” (TCN) dataset of over 9000 top trending customer need keyphrases organized by month from 2007-2021 which spans 37 product categories in the area of Consumer Packaged Goods (e.g. toothpaste, eyeliner, beer etc.). TCN is a curated dataset for the benchmarking of supervised machine learning approaches in the prediction of customer needs using User Generated Content. We describe the process of curating TCN while ensuring its quality. Finally, we demonstrate its utility via a case study of Reddit discourse as a potential predictor for future customer needs in Consumer Packaged Goods

Rethinking summarization and storytelling for modern social multimedia

Traditional summarization initiatives have been focused on specific types of documents such as articles, reviews, videos, image feeds, or tweets, a practice which may result in pigeonholing the summarization task in the context of modern, content-rich multimedia collections. Consequently, much of the research to date has revolved around mostly toy problems in narrow domains and working on single-source media types. We argue that summarization and story generation systems need to re-focus the problem space in order to meet the information needs in the age of user-generated content in different formats and languages. Here we create a framework for flexible multimedia storytelling. Narratives, stories, and summaries carry a set of challenges in big data and dynamic multi-source media that give rise to new research in spatial-temporal representation, viewpoint generation, and explanatio

The effectiveness of content marketing activities in Facebook and Instagram : generating leads and improving sales for a B2C experiential service company

Nowadays a majority of society is using social media, which increased the number of social network sites (SNS) users. Business to consumer (B2C) companies have started to use SNS in order to gain from its close connectivity to customers and to attract new customers. Becoming of most self-interest to master the art of online marketing through SNS for B2C companies. The aim of this thesis is to study the effectiveness of visual user-generated content (UGC) versus own branded content for sales conversions on Facebook. Additionally, the study compares the effectiveness of own branded visual content vs UGC in generating leads on Instagram and Facebook. By analyzing the effectiveness of content marketing activities on SNS, we are contributing to the discussion on strategic marketing decisions for B2C enterprises. Having an interaction effect between the type of social network platform and type of content that is not statistically significant different, we can truthfully reject hypothesis one. The difference between conversion rates indicates that UGC inspires more confidence, achieving higher conversions rates over the five days the campaign occurred. Although the sample size was restricted which can affect the power of detecting meaningful difference and not allowing a statement on significance.Atualmente, a maior parte da nossa sociedade usa redes sociais, o que aumentou o número de usuários de redes sociais. Muitas empresas (B2C) começaram a usar redes sociais para contribuir ao bem do negocio. Tornando-se do maior interesse saber dominar a arte do marketing online através de redes sociais, estas plataformas sendo Facebook e Instagram. O objetivo desta tese é estudar a eficácia do conteúdo visual gerado por usuários (UGC) versus o conteúdo de marca própria para conversões de vendas no Facebook. Além disso, o estudo compara a eficácia do conteúdo visual de marca própria versus o de usuários (UGC) na geração de leads no Instagram e no Facebook. Ao analisar a eficácia das atividades de marketing com o conteúdo das redes sociais contribuindo para a discussão sobre decisões estratégicas de marketing para as empresas (B2C). Resultados revelam um efeito de interação entre o tipo de rede social e o tipo de conteúdo que não é estatisticamente significativo, rejeitando a hipótese 1. A diferença entre as quotas de conversão indica que o conteúdo visual gerado por usuários (UGC) inspira mais confiança, alcançando taxas de conversão mais altas nos cinco dias em que a campanha ocorreu. Embora o tamanho da amostra tenha sido restrito, o que pode afetar o poder de detetar diferenças significativas e não permitindo uma declaração com significância

#TikTokMadeMeBuyIt A content analysis of TikTok to understand why product-related user-generated content goes viral.

Purpose: The main goal of this master thesis is to understand why product-related user-generated content (UGC) on social media goes viral by looking at the content-, messenger-, and product characteristics. The social media of interest for the purposes of our research is TikTok. It is important because TikTok is a relatively new social media, and gaining a more profound knowledge of it would be beneficial for both individuals, digital marketers, and brands to increase their reach on TikTok. Problem statement: Why product-related UGC goes viral on TikTok, and what characteristics do the videos hold? Design/methodology/approach: A mixed-method is used, which includes a qualitative and quantitative analysis. The dataset was made using a qualitative content analysis of 500 videos from TikTok. For the quantitative analysis, an ANOVA analysis in SPSS is performed to conclude whether or not the hypotheses can be supported. Findings: Six out of ten hypotheses were confirmed. Two were rejected due to inconsistencies in the sample (skewed numbers). The main findings demonstrated that several characteristics are crucial for creating a viral video. Hypotheses in all three groups, content-, messenger-, and product characteristics, were supported. This demonstrates that numerous attributes in a video are critical for the virality on TikTok. It was found that when creating a TikTok video with the purpose of going viral, the video should be positive and include high-arousal emotions like amusement and curiosity. Also, the product displayed in the video should be unique or unusual, and solve a problem. Lastly, the creator should be entertaining or a good storyteller, and a high number of followers is helpful. Practical implications: Marketers can use this thesis to create improved marketing strategies on TikTok, as well as on other social media. Additionally, gaining a better understanding of how existing and potential consumers react to material online can be helpful to improve and adjust existing social media marketing activities. It is essential for brands who use social media to market and sell their products. Keywords: TikTok, User-generated content, social media, virality, Berger’s STEPP